revert overwrites

Merge pull request #619 from Azure/release_update/Release-153
2025-12-20 01:27:06 -05:00 · 2019-10-15 12:33:31 -07:00 · 2019-10-15 12:27:56 -07:00 · 2019-10-14 15:39:40 -07:00 · 2019-10-14 22:30:58 +00:00 · 2019-10-06 11:44:35 -07:00
439 changed files with 147205 additions and 20797 deletions
--- a/00.configuration.ipynb
+++ b/00.configuration.ipynb
@@ -1,236 +0,0 @@
 {
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
    "\n",
    "Licensed under the MIT License."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 00. Installation and configuration\n",
    "This notebook configures your library of notebooks to connect to an Azure Machine Learning Workspace.  In this case, a library contains all of the notebooks in the current folder and any nested folders.  You can configure this notebook to use an existing workspace or create a new workspace.\n",
    "\n",
    "## What is an Azure ML Workspace and why do I need one?\n",
    "\n",
    "An AML Workspace is an Azure resource that organizes and coordinates the actions of many other Azure resources to assist in executing and sharing machine learning workflows.  In particular, an AML Workspace coordinates storage, databases, and compute resources providing added functionality for machine learning experimentation, operationalization, and the monitoring of operationalized models."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prerequisites"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Access Azure Subscription\n",
    "\n",
    "In order to create an AML Workspace, first you need access to an Azure Subscription.  You can [create your own](https://azure.microsoft.com/en-us/free/) or get your existing subscription information from the [Azure portal](https://portal.azure.com).\n",
    "\n",
    "### 2. If you're running on your own local environment, install Azure ML SDK and other libraries\n",
    "\n",
    "If you are running in your own environment, follow [SDK installation instructions](https://docs.microsoft.com/azure/machine-learning/service/how-to-configure-environment).  If you are running in Azure Notebooks or another Microsoft managed environment, the SDK is already installed.\n",
    "\n",
    "Also install following libraries to your environment. Many of the example notebooks depend on them\n",
    "\n",
    "```\n",
    "(myenv) $ conda install -y matplotlib tqdm scikit-learn\n",
    "```\n",
    "\n",
    "Once installation is complete, check the Azure ML SDK version:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "install"
    ]
   },
   "outputs": [],
   "source": [
    "import azureml.core\n",
    "\n",
    "print(\"SDK Version:\", azureml.core.VERSION)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Make sure your subscription is registered to use ACI\n",
    "Azure Machine Learning makes use of Azure Container Instance (ACI). You need to ensure your subscription has been registered to use ACI in order be able to deploy a dev/test web service.  If you have run through the quickstart experience you have already performed this step.  Otherwise you will need to use the [Azure CLI](https://docs.microsoft.com/en-us/cli/azure/install-azure-cli?view=azure-cli-latest) and execute the following commands.\n",
    "\n",
    "```shell\n",
    "# check to see if ACI is already registered\n",
    "(myenv) $ az provider show -n Microsoft.ContainerInstance -o table\n",
    "\n",
    "# if ACI is not registered, run this command.\n",
    "# note you need to be the subscription owner in order to execute this command successfully.\n",
    "(myenv) $ az provider register -n Microsoft.ContainerInstance\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set up your Azure Machine Learning workspace\n",
    "\n",
    "### Option 1: You have workspace already\n",
    "If you ran the Azure Machine Learning [quickstart](https://docs.microsoft.com/en-us/azure/machine-learning/service/quickstart-get-started) in Azure Notebooks, you already have a configured workspace!  You can go to your Azure Machine Learning Getting Started library, view *config.json* file, and copy-paste the values for subscription ID, resource group and workspace name below.\n",
    "\n",
    "If you have a workspace created another way, [these instructions](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-environment#create-workspace-configuration-file) describe how to get your subscription and workspace information.\n",
    "\n",
    "If this cell succeeds, you're done configuring this library!  Otherwise continue to follow the instructions in the rest of the notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.core import Workspace\n",
    "\n",
    "subscription_id ='<subscription-id>'\n",
    "resource_group ='<resource-group>'\n",
    "workspace_name = '<workspace-name>'\n",
    "\n",
    "try:\n",
    "   ws = Workspace(subscription_id = subscription_id, resource_group = resource_group, workspace_name = workspace_name)\n",
    "   ws.write_config()\n",
    "   print('Workspace configuration succeeded. You are all set!')\n",
    "except:\n",
    "   print('Workspace not found. Run the cells below.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Option 2: You don't have workspace yet\n",
    "\n",
    "\n",
    "#### Requirements\n",
    "\n",
    "Inside your Azure subscription, you will need access to a _resource group_, which organizes Azure resources and provides a default region for the resources in a group.  You can see what resource groups to which you have access, or create a new one in the [Azure portal](https://portal.azure.com).  If you don't have a resource group, the create workspace command will create one for you using the name you provide.\n",
    "\n",
    "To create or access an Azure ML Workspace, you will need to import the AML library and the following information:\n",
    "* A name for your workspace\n",
    "* Your subscription id\n",
    "* The resource group name\n",
    "\n",
    "**Note**: As with other Azure services, there are limits on certain resources (for eg. BatchAI cluster size) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Supported Azure Regions\n",
    "Specify a region where your workspace will be located from the list of [Azure Machine Learning regions](https://linktoregions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "workspace_region = \"eastus2\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "subscription_id = os.environ.get(\"SUBSCRIPTION_ID\", subscription_id)\n",
    "resource_group = os.environ.get(\"RESOURCE_GROUP\", resource_group)\n",
    "workspace_name = os.environ.get(\"WORKSPACE_NAME\", workspace_name)\n",
    "workspace_region = os.environ.get(\"WORKSPACE_REGION\", workspace_region)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create the workspace\n",
    "This cell will create an AML workspace for you in a subscription provided you have the correct permissions.\n",
    "\n",
    "This will fail when:\n",
    "1. You do not have permission to create a workspace in the resource group\n",
    "2. You do not have permission to create a resource group if it's non-existing.\n",
    "2. You are not a subscription owner or contributor and no Azure ML workspaces have ever been created in this subscription\n",
    "\n",
    "If workspace creation fails, please work with your IT admin to provide you with the appropriate permissions or to provision the required resources."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "create workspace"
    ]
   },
   "outputs": [],
   "source": [
    "# import the Workspace class and check the azureml SDK version\n",
    "from azureml.core import Workspace\n",
    "\n",
    "ws = Workspace.create(name = workspace_name,\n",
    "                      subscription_id = subscription_id,\n",
    "                      resource_group = resource_group, \n",
    "                      location = workspace_region,\n",
    "                      create_resource_group = True,\n",
    "                      exist_ok = True)\n",
    "ws.get_details()\n",
    "ws.write_config()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Success!\n",
    "Great, you are ready to move on to the rest of the sample notebooks."
   ]
  }
 ],
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--- a/01.getting-started/01.train-within-notebook/01.train-within-notebook.ipynb
+++ b/01.getting-started/01.train-within-notebook/01.train-within-notebook.ipynb
@@ -1,812 +0,0 @@
 {
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
    "\n",
    "Licensed under the MIT License."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 01. Train in the Notebook & Deploy Model to ACI\n",
    "\n",
    "* Load workspace\n",
    "* Train a simple regression model directly in the Notebook python kernel\n",
    "* Record run history\n",
    "* Find the best model in run history and download it.\n",
    "* Deploy the model as an Azure Container Instance (ACI)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prerequisites\n",
    "1. Make sure you go through the [00. Installation and Configuration](00.configuration.ipynb) Notebook first if you haven't. \n",
    "\n",
    "2. Install following pre-requisite libraries to your conda environment and restart notebook.\n",
    "```shell\n",
    "(myenv) $ conda install -y matplotlib tqdm scikit-learn\n",
    "```\n",
    "\n",
    "3. Check that ACI is registered for your Azure Subscription.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!az provider show -n Microsoft.ContainerInstance -o table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If ACI is not registered, run following command to register it. Note that you have to be a subscription owner, or this command will fail."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!az provider register -n Microsoft.ContainerInstance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Validate Azure ML SDK installation and get version number for debugging purposes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "install"
    ]
   },
   "outputs": [],
   "source": [
    "from azureml.core import Experiment, Run, Workspace\n",
    "import azureml.core\n",
    "\n",
    "# Check core SDK version number\n",
    "print(\"SDK version:\", azureml.core.VERSION)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initialize Workspace\n",
    "\n",
    "Initialize a workspace object from persisted configuration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "create workspace"
    ]
   },
   "outputs": [],
   "source": [
    "ws = Workspace.from_config()\n",
    "print('Workspace name: ' + ws.name, \n",
    "      'Azure region: ' + ws.location, \n",
    "      'Subscription id: ' + ws.subscription_id, \n",
    "      'Resource group: ' + ws.resource_group, sep='\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set experiment name\n",
    "Choose a name for experiment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "experiment_name = 'train-in-notebook'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Start a training run in local Notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load diabetes dataset, a well-known small dataset that comes with scikit-learn\n",
    "from sklearn.datasets import load_diabetes\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.externals import joblib\n",
    "\n",
    "X, y = load_diabetes(return_X_y = True)\n",
    "columns = ['age', 'gender', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
    "data = {\n",
    "    \"train\":{\"X\": X_train, \"y\": y_train},        \n",
    "    \"test\":{\"X\": X_test, \"y\": y_test}\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train a simple Ridge model\n",
    "Train a very simple Ridge regression model in scikit-learn, and save it as a pickle file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "reg = Ridge(alpha = 0.03)\n",
    "reg.fit(X=data['train']['X'], y=data['train']['y'])\n",
    "preds = reg.predict(data['test']['X'])\n",
    "print('Mean Squared Error is', mean_squared_error(data['test']['y'], preds))\n",
    "joblib.dump(value=reg, filename='model.pkl');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add experiment tracking\n",
    "Now, let's add Azure ML experiment logging, and upload persisted model into run record as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "local run",
     "outputs upload"
    ]
   },
   "outputs": [],
   "source": [
    "experiment = Experiment(workspace=ws, name=experiment_name)\n",
    "run = experiment.start_logging()\n",
    "\n",
    "run.tag(\"Description\",\"My first run!\")\n",
    "run.log('alpha', 0.03)\n",
    "reg = Ridge(alpha=0.03)\n",
    "reg.fit(data['train']['X'], data['train']['y'])\n",
    "preds = reg.predict(data['test']['X'])\n",
    "run.log('mse', mean_squared_error(data['test']['y'], preds))\n",
    "joblib.dump(value=reg, filename='model.pkl')\n",
    "run.upload_file(name='outputs/model.pkl', path_or_stream='./model.pkl')\n",
    "\n",
    "run.complete()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can browse to the recorded run. Please make sure you use Chrome to navigate the run history page."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "run"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simple parameter sweep\n",
    "Sweep over alpha values of a sklearn ridge model, and capture metrics and trained model in the Azure ML experiment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import os\n",
    "from tqdm import tqdm\n",
    "\n",
    "model_name = \"model.pkl\"\n",
    "\n",
    "# list of numbers from 0 to 1.0 with a 0.05 interval\n",
    "alphas = np.arange(0.0, 1.0, 0.05)\n",
    "\n",
    "# try a bunch of alpha values in a Linear Regression (Ridge) model\n",
    "for alpha in tqdm(alphas):\n",
    "    # create a bunch of runs, each train a model with a different alpha value\n",
    "    with experiment.start_logging() as run:\n",
    "        # Use Ridge algorithm to build a regression model\n",
    "        reg = Ridge(alpha=alpha)\n",
    "        reg.fit(X=data[\"train\"][\"X\"], y=data[\"train\"][\"y\"])\n",
    "        preds = reg.predict(X=data[\"test\"][\"X\"])\n",
    "        mse = mean_squared_error(y_true=data[\"test\"][\"y\"], y_pred=preds)\n",
    "\n",
    "        # log alpha, mean_squared_error and feature names in run history\n",
    "        run.log(name=\"alpha\", value=alpha)\n",
    "        run.log(name=\"mse\", value=mse)\n",
    "        run.log_list(name=\"columns\", value=columns)\n",
    "\n",
    "        with open(model_name, \"wb\") as file:\n",
    "            joblib.dump(value=reg, filename=file)\n",
    "        \n",
    "        # upload the serialized model into run history record\n",
    "        run.upload_file(name=\"outputs/\" + model_name, path_or_stream=model_name)\n",
    "\n",
    "        # now delete the serialized model from local folder since it is already uploaded to run history \n",
    "        os.remove(path=model_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# now let's take a look at the experiment in Azure portal.\n",
    "experiment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Select best model from the experiment\n",
    "Load all experiment run metrics recursively from the experiment into a dictionary object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "runs = {}\n",
    "run_metrics = {}\n",
    "\n",
    "for r in tqdm(experiment.get_runs()):\n",
    "    metrics = r.get_metrics()\n",
    "    if 'mse' in metrics.keys():\n",
    "        runs[r.id] = r\n",
    "        run_metrics[r.id] = metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now find the run with the lowest Mean Squared Error value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_run_id = min(run_metrics, key = lambda k: run_metrics[k]['mse'])\n",
    "best_run = runs[best_run_id]\n",
    "print('Best run is:', best_run_id)\n",
    "print('Metrics:', run_metrics[best_run_id])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can add tags to your runs to make them easier to catalog"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "query history"
    ]
   },
   "outputs": [],
   "source": [
    "best_run.tag(key=\"Description\", value=\"The best one\")\n",
    "best_run.get_tags()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot MSE over alpha\n",
    "\n",
    "Let's observe the best model visually by plotting the MSE values over alpha values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "best_alpha = run_metrics[best_run_id]['alpha']\n",
    "min_mse = run_metrics[best_run_id]['mse']\n",
    "\n",
    "alpha_mse = np.array([(run_metrics[k]['alpha'], run_metrics[k]['mse']) for k in run_metrics.keys()])\n",
    "sorted_alpha_mse = alpha_mse[alpha_mse[:,0].argsort()]\n",
    "\n",
    "plt.plot(sorted_alpha_mse[:,0], sorted_alpha_mse[:,1], 'r--')\n",
    "plt.plot(sorted_alpha_mse[:,0], sorted_alpha_mse[:,1], 'bo')\n",
    "\n",
    "plt.xlabel('alpha', fontsize = 14)\n",
    "plt.ylabel('mean squared error', fontsize = 14)\n",
    "plt.title('MSE over alpha', fontsize = 16)\n",
    "\n",
    "# plot arrow\n",
    "plt.arrow(x = best_alpha, y = min_mse + 39, dx = 0, dy = -26, ls = '-', lw = 0.4,\n",
    "          width = 0, head_width = .03, head_length = 8)\n",
    "\n",
    "# plot \"best run\" text\n",
    "plt.text(x = best_alpha - 0.08, y = min_mse + 50, s = 'Best Run', fontsize = 14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Register the best model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find the model file saved in the run record of best run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "query history"
    ]
   },
   "outputs": [],
   "source": [
    "for f in best_run.get_file_names():\n",
    "    print(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can register this model in the model registry of the workspace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "register model from history"
    ]
   },
   "outputs": [],
   "source": [
    "model = best_run.register_model(model_name='best_model', model_path='outputs/model.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Verify that the model has been registered properly. If you have done this several times you'd see the version number auto-increases each time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "register model from history"
    ]
   },
   "outputs": [],
   "source": [
    "from azureml.core.model import Model\n",
    "models = Model.list(workspace=ws, name='best_model')\n",
    "for m in models:\n",
    "    print(m.name, m.version)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also download the registered model. Afterwards, you should see a `model.pkl` file in the current directory. You can then use it for local testing if you'd like."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "download file"
    ]
   },
   "outputs": [],
   "source": [
    "# remove the model file if it is already on disk\n",
    "if os.path.isfile('model.pkl'): \n",
    "    os.remove('model.pkl')\n",
    "# download the model\n",
    "model.download(target_dir=\"./\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scoring script\n",
    "\n",
    "Now we are ready to build a Docker image and deploy the model in it as a web service. The first step is creating the scoring script. For convenience, we have created the scoring script for you. It is printed below as text, but you can also run `%pfile ./score.py` in a cell to show the file.\n",
    "\n",
    "Tbe scoring script consists of two functions: `init` that is used to load the model to memory when starting the container, and `run` that makes the prediction when web service is called. Please pay special attention to how the model is loaded in the `init()` function. When Docker image is built for this model, the actual model file is downloaded and placed on disk, and `get_model_path` function returns the local path where the model is placed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('./score.py', 'r') as scoring_script:\n",
    "    print(scoring_script.read())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create environment dependency file\n",
    "\n",
    "We need a environment dependency file `myenv.yml` to specify which libraries are needed by the scoring script when building the Docker image for web service deployment. We can manually create this file, or we can use the `CondaDependencies` API to automatically create this file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.core.conda_dependencies import CondaDependencies \n",
    "\n",
    "myenv = CondaDependencies()\n",
    "myenv.add_conda_package(\"scikit-learn\")\n",
    "myenv.add_pip_package(\"pynacl==1.2.1\")\n",
    "print(myenv.serialize_to_string())\n",
    "\n",
    "with open(\"myenv.yml\",\"w\") as f:\n",
    "    f.write(myenv.serialize_to_string())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Deploy web service into an Azure Container Instance\n",
    "The deployment process takes the registered model and your scoring scrip, and builds a Docker image. It then deploys the Docker image into Azure Container Instance as a running container with an HTTP endpoint readying for scoring calls. Read more about [Azure Container Instance](https://azure.microsoft.com/en-us/services/container-instances/).\n",
    "\n",
    "Note ACI is great for quick and cost-effective dev/test deployment scenarios. For production workloads, please use [Azure Kubernentes Service (AKS)](https://azure.microsoft.com/en-us/services/kubernetes-service/) instead. Please follow in struction in [this notebook](11.production-deploy-to-aks.ipynb) to see how that can be done from Azure ML.\n",
    " \n",
    "** Note: ** The web service creation can take 6-7 minutes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "deploy service",
     "aci"
    ]
   },
   "outputs": [],
   "source": [
    "from azureml.core.webservice import AciWebservice, Webservice\n",
    "\n",
    "aciconfig = AciWebservice.deploy_configuration(cpu_cores=1, \n",
    "                                               memory_gb=1, \n",
    "                                               tags={'sample name': 'AML 101'}, \n",
    "                                               description='This is a great example.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note the below `WebService.deploy_from_model()` function takes a model object registered under the workspace. It then bakes the model file in the Docker image so it can be looked-up using the `Model.get_model_path()` function in `score.py`. \n",
    "\n",
    "If you have a local model file instead of a registered model object, you can also use the `WebService.deploy()` function which would register the model and then deploy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "deploy service",
     "aci"
    ]
   },
   "outputs": [],
   "source": [
    "from azureml.core.image import ContainerImage\n",
    "image_config = ContainerImage.image_configuration(execution_script=\"score.py\", \n",
    "                                    runtime=\"python\", \n",
    "                                    conda_file=\"myenv.yml\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "deploy service",
     "aci"
    ]
   },
   "outputs": [],
   "source": [
    "%%time\n",
    "# this will take 5-10 minutes to finish\n",
    "# you can also use \"az container list\" command to find the ACI being deployed\n",
    "service = Webservice.deploy_from_model(name='my-aci-svc',\n",
    "                                       deployment_config=aciconfig,\n",
    "                                       models=[model],\n",
    "                                       image_config=image_config,\n",
    "                                       workspace=ws)\n",
    "\n",
    "service.wait_for_deployment(show_output=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## Test web service"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "deploy service",
     "aci"
    ]
   },
   "outputs": [],
   "source": [
    "print('web service is hosted in ACI:', service.scoring_uri)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the `run` API to call the web service with one row of data to get a prediction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "deploy service",
     "aci"
    ]
   },
   "outputs": [],
   "source": [
    "import json\n",
    "# score the first row from the test set.\n",
    "test_samples = json.dumps({\"data\": X_test[0:1, :].tolist()})\n",
    "service.run(input_data = test_samples)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Feed the entire test set and calculate the errors (residual values)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "deploy service",
     "aci"
    ]
   },
   "outputs": [],
   "source": [
    "# score the entire test set.\n",
    "test_samples = json.dumps({'data': X_test.tolist()})\n",
    "\n",
    "result = json.loads(service.run(input_data = test_samples))['result']\n",
    "residual = result - y_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also send raw HTTP request to test the web service."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "deploy service",
     "aci"
    ]
   },
   "outputs": [],
   "source": [
    "import requests\n",
    "import json\n",
    "\n",
    "# 2 rows of input data, each with 10 made-up numerical features\n",
    "input_data = \"{\\\"data\\\": [[1, 2, 3, 4, 5, 6, 7, 8, 9, 10], [10, 9, 8, 7, 6, 5, 4, 3, 2, 1]]}\"\n",
    "\n",
    "headers = {'Content-Type':'application/json'}\n",
    "\n",
    "# for AKS deployment you'd need to the service key in the header as well\n",
    "# api_key = service.get_key()\n",
    "# headers = {'Content-Type':'application/json',  'Authorization':('Bearer '+ api_key)} \n",
    "\n",
    "resp = requests.post(service.scoring_uri, input_data, headers = headers)\n",
    "print(resp.text)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Residual graph\n",
    "Plot a residual value graph to chart the errors on the entire test set. Observe the nice bell curve."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "f, (a0, a1) = plt.subplots(1, 2, gridspec_kw={'width_ratios':[3, 1], 'wspace':0, 'hspace': 0})\n",
    "f.suptitle('Residual Values', fontsize = 18)\n",
    "\n",
    "f.set_figheight(6)\n",
    "f.set_figwidth(14)\n",
    "\n",
    "a0.plot(residual, 'bo', alpha=0.4);\n",
    "a0.plot([0,90], [0,0], 'r', lw=2)\n",
    "a0.set_ylabel('residue values', fontsize=14)\n",
    "a0.set_xlabel('test data set', fontsize=14)\n",
    "\n",
    "a1.hist(residual, orientation='horizontal', color='blue', bins=10, histtype='step');\n",
    "a1.hist(residual, orientation='horizontal', color='blue', alpha=0.2, bins=10);\n",
    "a1.set_yticklabels([])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Delete ACI to clean up"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Deleting ACI is super fast!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "deploy service",
     "aci"
    ]
   },
   "outputs": [],
   "source": [
    "%%time\n",
    "service.delete()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.6",
   "language": "python",
   "name": "python36"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
 }
--- a/01.getting-started/02.train-on-local/02.train-on-local.ipynb
+++ b/01.getting-started/02.train-on-local/02.train-on-local.ipynb
@@ -1,465 +0,0 @@
 {
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
    "\n",
    "Licensed under the MIT License."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 02. Train locally\n",
    "* Create or load workspace.\n",
    "* Create scripts locally.\n",
    "* Create `train.py` in a folder, along with a `my.lib` file.\n",
    "* Configure & execute a local run in a user-managed Python environment.\n",
    "* Configure & execute a local run in a system-managed Python environment.\n",
    "* Configure & execute a local run in a Docker environment.\n",
    "* Query run metrics to find the best model\n",
    "* Register model for operationalization."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prerequisites\n",
    "Make sure you go through the [00. Installation and Configuration](00.configuration.ipynb) Notebook first if you haven't."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Check core SDK version number\n",
    "import azureml.core\n",
    "\n",
    "print(\"SDK version:\", azureml.core.VERSION)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initialize Workspace\n",
    "\n",
    "Initialize a workspace object from persisted configuration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.core.workspace import Workspace\n",
    "\n",
    "ws = Workspace.from_config()\n",
    "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep='\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create An Experiment\n",
    "**Experiment** is a logical container in an Azure ML Workspace. It hosts run records which can include run metrics and output artifacts from your experiments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.core import Experiment\n",
    "experiment_name = 'train-on-local'\n",
    "exp = Experiment(workspace=ws, name=experiment_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## View `train.py`\n",
    "\n",
    "`train.py` is already created for you."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('./train.py', 'r') as f:\n",
    "    print(f.read())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note `train.py` also references a `mylib.py` file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('./mylib.py', 'r') as f:\n",
    "    print(f.read())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configure & Run\n",
    "### User-managed environment\n",
    "Below, we use a user-managed run, which means you are responsible to ensure all the necessary packages are available in the Python environment you choose to run the script."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.core.runconfig import RunConfiguration\n",
    "\n",
    "# Editing a run configuration property on-fly.\n",
    "run_config_user_managed = RunConfiguration()\n",
    "\n",
    "run_config_user_managed.environment.python.user_managed_dependencies = True\n",
    "\n",
    "# You can choose a specific Python environment by pointing to a Python path \n",
    "#run_config.environment.python.interpreter_path = '/home/johndoe/miniconda3/envs/sdk2/bin/python'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Submit script to run in the user-managed environment\n",
    "Note whole script folder is submitted for execution, including the `mylib.py` file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.core import ScriptRunConfig\n",
    "\n",
    "src = ScriptRunConfig(source_directory='./', script='train.py', run_config=run_config_user_managed)\n",
    "run = exp.submit(src)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Get run history details"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "run"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Block to wait till run finishes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "run.wait_for_completion(show_output=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### System-managed environment\n",
    "You can also ask the system to build a new conda environment and execute your scripts in it. The environment is built once and will be reused in subsequent executions as long as the conda dependencies remain unchanged. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.core.runconfig import RunConfiguration\n",
    "from azureml.core.conda_dependencies import CondaDependencies\n",
    "\n",
    "run_config_system_managed = RunConfiguration()\n",
    "\n",
    "run_config_system_managed.environment.python.user_managed_dependencies = False\n",
    "run_config_system_managed.prepare_environment = True\n",
    "\n",
    "# Specify conda dependencies with scikit-learn\n",
    "cd = CondaDependencies.create(conda_packages=['scikit-learn'])\n",
    "run_config_system_managed.environment.python.conda_dependencies = cd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Submit script to run in the system-managed environment\n",
    "A new conda environment is built based on the conda dependencies object. If you are running this for the first time,  this might take up to 5 mninutes. But this conda environment is reused so long as you don't change the conda dependencies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "src = ScriptRunConfig(source_directory=\"./\", script='train.py', run_config=run_config_system_managed)\n",
    "run = exp.submit(src)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Get run history details"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "run"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Block and wait till run finishes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "run.wait_for_completion(show_output = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Docker-based execution\n",
    "**IMPORTANT**: You must have Docker engine installed locally in order to use this execution mode. If your kernel is already running in a Docker container, such as **Azure Notebooks**, this mode will **NOT** work.\n",
    "\n",
    "You can also ask the system to pull down a Docker image and execute your scripts in it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.core.runconfig import RunConfiguration\n",
    "from azureml.core.conda_dependencies import CondaDependencies\n",
    "\n",
    "run_config_docker = RunConfiguration()\n",
    "\n",
    "run_config_docker.environment.python.user_managed_dependencies = False\n",
    "run_config_docker.prepare_environment = True\n",
    "run_config_docker.environment.docker.enabled = True\n",
    "run_config_docker.environment.docker.base_image = azureml.core.runconfig.DEFAULT_CPU_IMAGE\n",
    "\n",
    "# Specify conda dependencies with scikit-learn\n",
    "cd = CondaDependencies.create(conda_packages=['scikit-learn'])\n",
    "run_config_docker.environment.python.conda_dependencies = cd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Submit script to run in the system-managed environment\n",
    "A new conda environment is built based on the conda dependencies object. If you are running this for the first time, this might take up to 5 mninutes. But this conda environment is reused so long as you don't change the conda dependencies.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "src = ScriptRunConfig(source_directory=\"./\", script='train.py', run_config=run_config_docker)\n",
    "run = exp.submit(src)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Get run history details\n",
    "run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "run.wait_for_completion(show_output=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Query run metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "query history",
     "get metrics"
    ]
   },
   "outputs": [],
   "source": [
    "# get all metris logged in the run\n",
    "run.get_metrics()\n",
    "metrics = run.get_metrics()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's find the model that has the lowest MSE value logged."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "best_alpha = metrics['alpha'][np.argmin(metrics['mse'])]\n",
    "\n",
    "print('When alpha is {1:0.2f}, we have min MSE {0:0.2f}.'.format(\n",
    "    min(metrics['mse']), \n",
    "    best_alpha\n",
    "))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also list all the files that are associated with this run record"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "run.get_file_names()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We know the model `ridge_0.40.pkl` is the best performing model from the eariler queries. So let's register it with the workspace."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# supply a model name, and the full path to the serialized model file.\n",
    "model = run.register_model(model_name='best_ridge_model', model_path='./outputs/ridge_0.40.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(model.name, model.version, model.url)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now you can deploy this model following the example in the 01 notebook."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.6",
   "language": "python",
   "name": "python36"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
 }
--- a/01.getting-started/03.train-on-aci/03.train-on-aci.ipynb
+++ b/01.getting-started/03.train-on-aci/03.train-on-aci.ipynb
@@ -1,284 +0,0 @@
 {
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
    "\n",
    "Licensed under the MIT License."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 03. Train on Azure Container Instance\n",
    "\n",
    "* Create Workspace\n",
    "* Create `train.py` in the project folder.\n",
    "* Configure an ACI (Azure Container Instance) run\n",
    "* Execute in ACI"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prerequisites\n",
    "Make sure you go through the [00. Installation and Configuration](00.configuration.ipynb) Notebook first if you haven't."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Check core SDK version number\n",
    "import azureml.core\n",
    "\n",
    "print(\"SDK version:\", azureml.core.VERSION)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initialize Workspace\n",
    "\n",
    "Initialize a workspace object from persisted configuration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "create workspace"
    ]
   },
   "outputs": [],
   "source": [
    "from azureml.core import Workspace\n",
    "\n",
    "ws = Workspace.from_config()\n",
    "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create An Experiment\n",
    "\n",
    "**Experiment** is a logical container in an Azure ML Workspace. It hosts run records which can include run metrics and output artifacts from your experiments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.core import Experiment\n",
    "experiment_name = 'train-on-aci'\n",
    "experiment = Experiment(workspace = ws, name = experiment_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Remote execution on ACI\n",
    "\n",
    "The training script `train.py` is already created for you. Let's have a look."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('./train.py', 'r') as f:\n",
    "    print(f.read())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configure for using ACI\n",
    "Linux-based ACI is available in `West US`, `East US`, `West Europe`, `North Europe`, `West US 2`, `Southeast Asia`, `Australia East`, `East US 2`, and `Central US` regions. See details [here](https://docs.microsoft.com/en-us/azure/container-instances/container-instances-quotas#region-availability)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "configure run"
    ]
   },
   "outputs": [],
   "source": [
    "from azureml.core.runconfig import RunConfiguration\n",
    "from azureml.core.conda_dependencies import CondaDependencies\n",
    "\n",
    "# create a new runconfig object\n",
    "run_config = RunConfiguration()\n",
    "\n",
    "# signal that you want to use ACI to execute script.\n",
    "run_config.target = \"containerinstance\"\n",
    "\n",
    "# ACI container group is only supported in certain regions, which can be different than the region the Workspace is in.\n",
    "run_config.container_instance.region = 'eastus2'\n",
    "\n",
    "# set the ACI CPU and Memory \n",
    "run_config.container_instance.cpu_cores = 1\n",
    "run_config.container_instance.memory_gb = 2\n",
    "\n",
    "# enable Docker \n",
    "run_config.environment.docker.enabled = True\n",
    "\n",
    "# set Docker base image to the default CPU-based image\n",
    "run_config.environment.docker.base_image = azureml.core.runconfig.DEFAULT_CPU_IMAGE\n",
    "\n",
    "# use conda_dependencies.yml to create a conda environment in the Docker image for execution\n",
    "run_config.environment.python.user_managed_dependencies = False\n",
    "\n",
    "# auto-prepare the Docker image when used for execution (if it is not already prepared)\n",
    "run_config.auto_prepare_environment = True\n",
    "\n",
    "# specify CondaDependencies obj\n",
    "run_config.environment.python.conda_dependencies = CondaDependencies.create(conda_packages=['scikit-learn'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Submit the Experiment\n",
    "Finally, run the training job on the ACI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "remote run",
     "aci"
    ]
   },
   "outputs": [],
   "source": [
    "%%time \n",
    "from azureml.core.script_run_config import ScriptRunConfig\n",
    "\n",
    "script_run_config = ScriptRunConfig(source_directory='./',\n",
    "                                    script='train.py',\n",
    "                                    run_config=run_config)\n",
    "\n",
    "run = experiment.submit(script_run_config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "query history"
    ]
   },
   "outputs": [],
   "source": [
    "# Show run details\n",
    "run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "remote run",
     "aci"
    ]
   },
   "outputs": [],
   "source": [
    "%%time\n",
    "# Shows output of the run on stdout.\n",
    "run.wait_for_completion(show_output=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "get metrics"
    ]
   },
   "outputs": [],
   "source": [
    "# get all metris logged in the run\n",
    "run.get_metrics()\n",
    "metrics = run.get_metrics()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "print('When alpha is {1:0.2f}, we have min MSE {0:0.2f}.'.format(\n",
    "    min(metrics['mse']), \n",
    "    metrics['alpha'][np.argmin(metrics['mse'])]\n",
    "))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# show all the files stored within the run record\n",
    "run.get_file_names()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now you can take a model produced here, register it and then deploy as a web service."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.6",
   "language": "python",
   "name": "python36"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
 }
--- a/01.getting-started/04.train-on-remote-vm/04.train-on-remote-vm.ipynb
+++ b/01.getting-started/04.train-on-remote-vm/04.train-on-remote-vm.ipynb
@@ -1,613 +0,0 @@
 {
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
    "\n",
    "Licensed under the MIT License."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 04. Train in a remote Linux VM\n",
    "* Create Workspace\n",
    "* Create `train.py` file\n",
    "* Create (or attach) DSVM as compute resource.\n",
    "* Upoad data files into default datastore\n",
    "* Configure & execute a run in a few different ways\n",
    "    - Use system-built conda\n",
    "    - Use existing Python environment\n",
    "    - Use Docker \n",
    "* Find the best model in the run"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prerequisites\n",
    "Make sure you go through the [00. Installation and Configuration](00.configuration.ipynb) Notebook first if you haven't."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Check core SDK version number\n",
    "import azureml.core\n",
    "\n",
    "print(\"SDK version:\", azureml.core.VERSION)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initialize Workspace\n",
    "\n",
    "Initialize a workspace object from persisted configuration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.core import Workspace\n",
    "\n",
    "ws = Workspace.from_config()\n",
    "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep='\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Experiment\n",
    "\n",
    "**Experiment** is a logical container in an Azure ML Workspace. It hosts run records which can include run metrics and output artifacts from your experiments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "experiment_name = 'train-on-remote-vm'\n",
    "\n",
    "from azureml.core import Experiment\n",
    "exp = Experiment(workspace=ws, name=experiment_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's also create a local folder to hold the training script."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "script_folder = './vm-run'\n",
    "os.makedirs(script_folder, exist_ok=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Upload data files into datastore\n",
    "Every workspace comes with a default datastore (and you can register more) which is backed by the Azure blob storage account associated with the workspace. We can use it to transfer data from local to the cloud, and access it from the compute target."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get the default datastore\n",
    "ds = ws.get_default_datastore()\n",
    "print(ds.name, ds.datastore_type, ds.account_name, ds.container_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load diabetes data from `scikit-learn` and save it as 2 local files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_diabetes\n",
    "import numpy as np\n",
    "\n",
    "training_data = load_diabetes()\n",
    "np.save(file='./feeatures.npy', arr=training_data['data'])\n",
    "np.save(file='./labels.npy', arr=training_data['target'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's upload the 2 files into the default datastore under a path named `diabetes`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds.upload_files(['./feeatures.npy', './labels.npy'], target_path='diabetes', overwrite=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## View `train.py`\n",
    "\n",
    "For convenience, we created a training script for you. It is printed below as a text, but you can also run `%pfile ./train.py` in a cell to show the file. Please pay special attention on how we are loading the features and labels from files in the `data_folder` path, which is passed in as an argument of the training script (shown later)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# copy train.py into the script folder\n",
    "import shutil\n",
    "shutil.copy('./train.py', os.path.join(script_folder, 'train.py'))\n",
    "\n",
    "with open(os.path.join(script_folder, './train.py'), 'r') as training_script:\n",
    "    print(training_script.read())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Linux DSVM as a compute target\n",
    "\n",
    "**Note**: If creation fails with a message about Marketplace purchase eligibilty, go to portal.azure.com, start creating DSVM there, and select \"Want to create programmatically\" to enable programmatic creation. Once you've enabled it, you can exit without actually creating VM.\n",
    " \n",
    "**Note**: By default SSH runs on port 22 and you don't need to specify it. But if for security reasons you switch to a different port (such as 5022), you can specify the port number in the provisioning configuration object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.core.compute import DsvmCompute\n",
    "from azureml.core.compute_target import ComputeTargetException\n",
    "\n",
    "compute_target_name = 'mydsvm'\n",
    "\n",
    "try:\n",
    "    dsvm_compute = DsvmCompute(workspace=ws, name=compute_target_name)\n",
    "    print('found existing:', dsvm_compute.name)\n",
    "except ComputeTargetException:\n",
    "    print('creating new.')\n",
    "    dsvm_config = DsvmCompute.provisioning_configuration(vm_size=\"Standard_D2_v2\")\n",
    "    dsvm_compute = DsvmCompute.create(ws, name=compute_target_name, provisioning_configuration=dsvm_config)\n",
    "    dsvm_compute.wait_for_completion(show_output=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Attach an existing Linux DSVM\n",
    "You can also attach an existing Linux VM as a compute target. The default port is 22."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.core.compute import RemoteCompute \n",
    "# if you want to connect using SSH key instead of username/password you can provide parameters private_key_file and private_key_passphrase \n",
    "attached_dsvm_compute = RemoteCompute.attach(workspace=ws,\n",
    "                                             name=\"attached_vm\",\n",
    "                                             username='<usename>',\n",
    "                                             address='<ip_adress_or_fqdn>',\n",
    "                                             ssh_port=22,\n",
    "                                             password='<password>')\n",
    "attached_dsvm_compute.wait_for_completion(show_output=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configure & Run\n",
    "First let's create a `DataReferenceConfigruation` object to inform the system what data folder to download to the copmute target."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.core.runconfig import DataReferenceConfiguration\n",
    "dr = DataReferenceConfiguration(datastore_name=ds.name, \n",
    "                   path_on_datastore='diabetes', \n",
    "                   mode='download', # download files from datastore to compute target\n",
    "                   overwrite=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can try a few different ways to run the training script in the VM."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conda run\n",
    "You can ask the system to build a conda environment based on your dependency specification, and submit your script to run there. Once the environment is built, and if you don't change your dependencies, it will be reused in subsequent runs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.core.runconfig import RunConfiguration\n",
    "from azureml.core.conda_dependencies import CondaDependencies\n",
    "\n",
    "# create a new RunConfig object\n",
    "conda_run_config = RunConfiguration(framework=\"python\")\n",
    "\n",
    "# Set compute target to the Linux DSVM\n",
    "conda_run_config.target = dsvm_compute.name\n",
    "\n",
    "# set the data reference of the run coonfiguration\n",
    "conda_run_config.data_references = {ds.name: dr}\n",
    "\n",
    "# specify CondaDependencies obj\n",
    "conda_run_config.environment.python.conda_dependencies = CondaDependencies.create(conda_packages=['scikit-learn'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.core import Run\n",
    "from azureml.core import ScriptRunConfig\n",
    "\n",
    "src = ScriptRunConfig(source_directory=script_folder, \n",
    "                      script='train.py', \n",
    "                      run_config=conda_run_config, \n",
    "                      # pass the datastore reference as a parameter to the training script\n",
    "                      arguments=['--data-folder', str(ds.as_download())] \n",
    "                     ) \n",
    "run = exp.submit(config=src)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "run.wait_for_completion(show_output=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Show the run object. You can navigate to the Azure portal to see detailed information about the run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "run"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Native VM run\n",
    "You can also configure to use an exiting Python environment in the VM to execute the script without asking the system to create a conda environment for you."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a new RunConfig object\n",
    "vm_run_config = RunConfiguration(framework=\"python\")\n",
    "\n",
    "# Set compute target to the Linux DSVM\n",
    "vm_run_config.target = dsvm_compute.name\n",
    "\n",
    "# set the data reference of the run coonfiguration\n",
    "conda_run_config.data_references = {ds.name: dr}\n",
    "\n",
    "# Let system know that you will configure the Python environment yourself.\n",
    "vm_run_config.environment.python.user_managed_dependencies = True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The below run will likely fail because `train.py` needs dependency `azureml`, `scikit-learn` and others, which are not found in that Python environment. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "src = ScriptRunConfig(source_directory=script_folder, \n",
    "                      script='train.py', \n",
    "                      run_config=vm_run_config,\n",
    "                      # pass the datastore reference as a parameter to the training script\n",
    "                      arguments=['--data-folder', str(ds.as_download())])\n",
    "run = exp.submit(config=src)\n",
    "run.wait_for_completion(show_output=True)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "You can choose to SSH into the VM and install Azure ML SDK, and any other missing dependencies, in that Python environment. For demonstration purposes, we simply are going to create another script `train2.py` that doesn't have azureml dependencies, and submit it instead."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%writefile $script_folder/train2.py\n",
    "print('####################################')\n",
    "print('Hello World (without Azure ML SDK)!')\n",
    "print('####################################')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's try again. And this time it should work fine."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "src = ScriptRunConfig(source_directory=script_folder, \n",
    "                      script='train2.py', \n",
    "                      run_config=vm_run_config)\n",
    "run = exp.submit(config=src)\n",
    "run.wait_for_completion(show_output=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note even in this case you get a run record with some basic statistics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "run"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Configure a Docker run with new conda environment on the VM\n",
    "You can execute in a Docker container in the VM. If you choose this option, the system will pull down a base Docker image, build a new conda environment in it if you ask for (you can also skip this if you are using a customer Docker image when a preconfigured Python environment), start a container, and run your script in there. This image is also uploaded into your ACR (Azure Container Registry) assoicated with your workspace, an reused if your dependencies don't change in the subsequent runs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.core.runconfig import RunConfiguration\n",
    "from azureml.core.conda_dependencies import CondaDependencies\n",
    "\n",
    "\n",
    "# Load the \"cpu-dsvm.runconfig\" file (created by the above attach operation) in memory\n",
    "docker_run_config = RunConfiguration(framework=\"python\")\n",
    "\n",
    "# Set compute target to the Linux DSVM\n",
    "docker_run_config.target = dsvm_compute.name\n",
    "\n",
    "# Use Docker in the remote VM\n",
    "docker_run_config.environment.docker.enabled = True\n",
    "\n",
    "# Use CPU base image from DockerHub\n",
    "docker_run_config.environment.docker.base_image = azureml.core.runconfig.DEFAULT_CPU_IMAGE\n",
    "print('Base Docker image is:', docker_run_config.environment.docker.base_image)\n",
    "\n",
    "# set the data reference of the run coonfiguration\n",
    "docker_run_config.data_references = {ds.name: dr}\n",
    "\n",
    "# specify CondaDependencies obj\n",
    "docker_run_config.environment.python.conda_dependencies = CondaDependencies.create(conda_packages=['scikit-learn'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Submit the Experiment\n",
    "Submit script to run in the Docker image in the remote VM. If you run this for the first time, the system will download the base image, layer in packages specified in the `conda_dependencies.yml` file on top of the base image, create a container and then execute the script in the container."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "src = ScriptRunConfig(source_directory=script_folder, \n",
    "                      script='train.py', \n",
    "                      run_config=docker_run_config,\n",
    "                      # pass the datastore reference as a parameter to the training script\n",
    "                      arguments=['--data-folder', str(ds.as_download())])\n",
    "run = exp.submit(config=src)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "run.wait_for_completion(show_output=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### View run history details"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "run"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Find the best model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have tried various execution modes, we can find the best model from the last run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get all metris logged in the run\n",
    "run.get_metrics()\n",
    "metrics = run.get_metrics()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# find the index where MSE is the smallest\n",
    "indices = list(range(0, len(metrics['mse'])))\n",
    "min_mse_index = min(indices, key=lambda x: metrics['mse'][x])\n",
    "\n",
    "print('When alpha is {1:0.2f}, we have min MSE {0:0.2f}.'.format(\n",
    "    metrics['mse'][min_mse_index], \n",
    "    metrics['alpha'][min_mse_index]\n",
    "))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clean up compute resource"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dsvm_compute.delete()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.6",
   "language": "python",
   "name": "python36"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
 }
--- a/01.getting-started/05.train-in-spark/05.train-in-spark.ipynb
+++ b/01.getting-started/05.train-in-spark/05.train-in-spark.ipynb
@@ -1,321 +0,0 @@
 {
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
    "\n",
    "Licensed under the MIT License."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 05. Train in Spark\n",
    "* Create Workspace\n",
    "* Create Experiment\n",
    "* Copy relevant files to the script folder\n",
    "* Configure and Run"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prerequisites\n",
    "Make sure you go through the [00. Installation and Configuration](00.configuration.ipynb) Notebook first if you haven't."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Check core SDK version number\n",
    "import azureml.core\n",
    "\n",
    "print(\"SDK version:\", azureml.core.VERSION)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initialize Workspace\n",
    "\n",
    "Initialize a workspace object from persisted configuration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.core import Workspace\n",
    "\n",
    "ws = Workspace.from_config()\n",
    "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep='\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Experiment\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "experiment_name = 'train-on-spark'\n",
    "\n",
    "from azureml.core import Experiment\n",
    "exp = Experiment(workspace=ws, name=experiment_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## View `train-spark.py`\n",
    "\n",
    "For convenience, we created a training script for you. It is printed below as a text, but you can also run `%pfile ./train-spark.py` in a cell to show the file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('train-spark.py', 'r') as training_script:\n",
    "    print(training_script.read())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configure & Run"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Configure an ACI run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.core.runconfig import RunConfiguration\n",
    "from azureml.core.conda_dependencies import CondaDependencies\n",
    "\n",
    "# use pyspark framework\n",
    "aci_run_config = RunConfiguration(framework=\"pyspark\")\n",
    "\n",
    "# use ACI to run the Spark job\n",
    "aci_run_config.target = 'containerinstance'\n",
    "aci_run_config.container_instance.region = 'eastus2'\n",
    "aci_run_config.container_instance.cpu_cores = 1\n",
    "aci_run_config.container_instance.memory_gb = 2\n",
    "\n",
    "# specify base Docker image to use\n",
    "aci_run_config.environment.docker.enabled = True\n",
    "aci_run_config.environment.docker.base_image = azureml.core.runconfig.DEFAULT_MMLSPARK_CPU_IMAGE\n",
    "\n",
    "# specify CondaDependencies\n",
    "cd = CondaDependencies()\n",
    "cd.add_conda_package('numpy')\n",
    "aci_run_config.environment.python.conda_dependencies = cd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Submit script to ACI to run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.core import ScriptRunConfig\n",
    "\n",
    "script_run_config = ScriptRunConfig(source_directory = '.',\n",
    "                                    script= 'train-spark.py',\n",
    "                                    run_config = aci_run_config)\n",
    "run = exp.submit(script_run_config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "run.wait_for_completion(show_output=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Attach an HDI cluster\n",
    "Now we can use a real Spark cluster, HDInsight for Spark, to run this job. To use HDI commpute target:\n",
    " 1. Create an Spark for HDI cluster in Azure. Here is some [quick instructions](https://docs.microsoft.com/en-us/azure/machine-learning/desktop-workbench/how-to-create-dsvm-hdi). Make sure you use the Ubuntu flavor, NOT CentOS.\n",
    " 2. Enter the IP address, username and password below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.core.compute import HDInsightCompute\n",
    "from azureml.exceptions import ComputeTargetException\n",
    "\n",
    "try:\n",
    "    # if you want to connect using SSH key instead of username/password you can provide parameters private_key_file and private_key_passphrase\n",
    "    hdi_compute = HDInsightCompute.attach(workspace=ws, \n",
    "                                          name=\"myhdi\", \n",
    "                                          address=\"myhdi-ssh.azurehdinsight.net\", \n",
    "                                          ssh_port=22, \n",
    "                                          username='<ssh-username>', \n",
    "                                          password='<ssh-pwd>')\n",
    "\n",
    "except ComputeTargetException as e:\n",
    "    print(\"Caught = {}\".format(e.message))\n",
    "    \n",
    "        \n",
    "hdi_compute.wait_for_completion(show_output=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Configure HDI run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.core.runconfig import RunConfiguration\n",
    "from azureml.core.conda_dependencies import CondaDependencies\n",
    "\n",
    "\n",
    "# Load the \"cpu-dsvm.runconfig\" file (created by the above attach operation) in memory\n",
    "hdi_run_config = RunConfiguration(framework=\"pyspark\")\n",
    "\n",
    "# Set compute target to the Linux DSVM\n",
    "hdi_run_config.target = hdi_compute.name\n",
    "\n",
    "# Ask system to provision a new one based on the conda_dependencies.yml file\n",
    "hdi_run_config.environment.python.user_managed_dependencies = False\n",
    "\n",
    "# specify CondaDependencies obj\n",
    "cd = CondaDependencies()\n",
    "cd.add_conda_package('numpy')\n",
    "hdi_run_config.environment.python.conda_dependencies = cd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Submit the script to HDI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.core import ScriptRunConfig\n",
    "\n",
    "script_run_config = ScriptRunConfig(source_directory = '.',\n",
    "                                    script= 'train-spark.py',\n",
    "                                    run_config = hdi_run_config)\n",
    "run = exp.submit(script_run_config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get the URL of the run history web page\n",
    "run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get all metris logged in the run\n",
    "metrics = run.get_metrics()\n",
    "print(metrics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
 }
--- a/01.getting-started/10.register-model-create-image-deploy-service/10.register-model-create-image-deploy-service.ipynb
+++ b/01.getting-started/10.register-model-create-image-deploy-service/10.register-model-create-image-deploy-service.ipynb
@@ -1,421 +0,0 @@
 {
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
    "\n",
    "Licensed under the MIT License."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 10. Register Model, Create Image and Deploy Service\n",
    "\n",
    "This example shows how to deploy a web service in step-by-step fashion:\n",
    "\n",
    " 1. Register model\n",
    " 2. Query versions of models and select one to deploy\n",
    " 3. Create Docker image\n",
    " 4. Query versions of images\n",
    " 5. Deploy the image as web service\n",
    " \n",
    "**IMPORTANT**:\n",
    " * This notebook requires you to first complete \"01.SDK-101-Train-and-Deploy-to-ACI.ipynb\" Notebook\n",
    " \n",
    "The 101 Notebook taught you how to deploy a web service directly from model in one step. This Notebook shows a more advanced approach that gives you more control over model versions and Docker image versions.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prerequisites\n",
    "Make sure you go through the [00. Installation and Configuration](00.configuration.ipynb) Notebook first if you haven't."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Check core SDK version number\n",
    "import azureml.core\n",
    "\n",
    "print(\"SDK version:\", azureml.core.VERSION)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initialize Workspace\n",
    "\n",
    "Initialize a workspace object from persisted configuration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "create workspace"
    ]
   },
   "outputs": [],
   "source": [
    "from azureml.core import Workspace\n",
    "\n",
    "ws = Workspace.from_config()\n",
    "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Register Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can add tags and descriptions to your models. Note you need to have a `sklearn_linreg_model.pkl` file in the current directory. This file is generated by the 01 notebook. The below call registers that file as a model with the same name `sklearn_linreg_model.pkl` in the workspace.\n",
    "\n",
    "Using tags, you can track useful information such as the name and version of the machine learning library used to train the model. Note that tags must be alphanumeric."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "register model from file"
    ]
   },
   "outputs": [],
   "source": [
    "from azureml.core.model import Model\n",
    "import sklearn\n",
    "\n",
    "library_version = \"sklearn\"+sklearn.__version__.replace(\".\",\"x\")\n",
    "\n",
    "model = Model.register(model_path = \"sklearn_regression_model.pkl\",\n",
    "                       model_name = \"sklearn_regression_model.pkl\",\n",
    "                       tags = {'area': \"diabetes\", 'type': \"regression\", 'version': library_version},\n",
    "                       description = \"Ridge regression model to predict diabetes\",\n",
    "                       workspace = ws)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can explore the registered models within your workspace and query by tag. Models are versioned. If you call the register_model command many times with same model name, you will get multiple versions of the model with increasing version numbers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "register model from file"
    ]
   },
   "outputs": [],
   "source": [
    "regression_models = Model.list(workspace=ws, tags=['area'])\n",
    "for m in regression_models:\n",
    "    print(\"Name:\", m.name,\"\\tVersion:\", m.version, \"\\tDescription:\", m.description, m.tags)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can pick a specific model to deploy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(model.name, model.description, model.version, sep = '\\t')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create Docker Image"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Show `score.py`. Note that the `sklearn_regression_model.pkl` in the `get_model_path` call is referring to a model named `sklearn_linreg_model.pkl` registered under the workspace. It is NOT referenceing the local file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%writefile score.py\n",
    "import pickle\n",
    "import json\n",
    "import numpy\n",
    "from sklearn.externals import joblib\n",
    "from sklearn.linear_model import Ridge\n",
    "from azureml.core.model import Model\n",
    "\n",
    "def init():\n",
    "    global model\n",
    "    # note here \"sklearn_regression_model.pkl\" is the name of the model registered under\n",
    "    # this is a different behavior than before when the code is run locally, even though the code is the same.\n",
    "    model_path = Model.get_model_path('sklearn_regression_model.pkl')\n",
    "    # deserialize the model file back into a sklearn model\n",
    "    model = joblib.load(model_path)\n",
    "\n",
    "# note you can pass in multiple rows for scoring\n",
    "def run(raw_data):\n",
    "    try:\n",
    "        data = json.loads(raw_data)['data']\n",
    "        data = numpy.array(data)\n",
    "        result = model.predict(data)\n",
    "    except Exception as e:\n",
    "        result = str(e)\n",
    "    return json.dumps({\"result\": result.tolist()})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.core.conda_dependencies import CondaDependencies \n",
    "\n",
    "myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn'])\n",
    "myenv.add_pip_package(\"pynacl==1.2.1\")\n",
    "\n",
    "with open(\"myenv.yml\",\"w\") as f:\n",
    "    f.write(myenv.serialize_to_string())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that following command can take few minutes. \n",
    "\n",
    "You can add tags and descriptions to images. Also, an image can contain multiple models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "create image"
    ]
   },
   "outputs": [],
   "source": [
    "from azureml.core.image import Image, ContainerImage\n",
    "\n",
    "image_config = ContainerImage.image_configuration(runtime= \"python\",\n",
    "                                 execution_script=\"score.py\",\n",
    "                                 conda_file=\"myenv.yml\",\n",
    "                                 tags = {'area': \"diabetes\", 'type': \"regression\"},\n",
    "                                 description = \"Image with ridge regression model\")\n",
    "\n",
    "image = Image.create(name = \"myimage1\",\n",
    "                     # this is the model object \n",
    "                     models = [model],\n",
    "                     image_config = image_config, \n",
    "                     workspace = ws)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "create image"
    ]
   },
   "outputs": [],
   "source": [
    "image.wait_for_creation(show_output = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "List images by tag and find out the detailed build log for debugging."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "create image"
    ]
   },
   "outputs": [],
   "source": [
    "for i in Image.list(workspace = ws,tags = [\"area\"]):\n",
    "    print('{}(v.{} [{}]) stored at {} with build log {}'.format(i.name, i.version, i.creation_state, i.image_location, i.image_build_log_uri))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Deploy image as web service on Azure Container Instance\n",
    "\n",
    "Note that the service creation can take few minutes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "deploy service",
     "aci"
    ]
   },
   "outputs": [],
   "source": [
    "from azureml.core.webservice import AciWebservice\n",
    "\n",
    "aciconfig = AciWebservice.deploy_configuration(cpu_cores = 1, \n",
    "                                               memory_gb = 1, \n",
    "                                               tags = {'area': \"diabetes\", 'type': \"regression\"}, \n",
    "                                               description = 'Predict diabetes using regression model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "deploy service",
     "aci"
    ]
   },
   "outputs": [],
   "source": [
    "from azureml.core.webservice import Webservice\n",
    "\n",
    "aci_service_name = 'my-aci-service-2'\n",
    "print(aci_service_name)\n",
    "aci_service = Webservice.deploy_from_image(deployment_config = aciconfig,\n",
    "                                           image = image,\n",
    "                                           name = aci_service_name,\n",
    "                                           workspace = ws)\n",
    "aci_service.wait_for_deployment(True)\n",
    "print(aci_service.state)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test web service"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Call the web service with some dummy input data to get a prediction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "deploy service",
     "aci"
    ]
   },
   "outputs": [],
   "source": [
    "import json\n",
    "\n",
    "test_sample = json.dumps({'data': [\n",
    "    [1,2,3,4,5,6,7,8,9,10], \n",
    "    [10,9,8,7,6,5,4,3,2,1]\n",
    "]})\n",
    "test_sample = bytes(test_sample,encoding = 'utf8')\n",
    "\n",
    "prediction = aci_service.run(input_data = test_sample)\n",
    "print(prediction)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Delete ACI to clean up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "deploy service",
     "aci"
    ]
   },
   "outputs": [],
   "source": [
    "aci_service.delete()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.6",
   "language": "python",
   "name": "python36"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
 }
--- a/01.getting-started/11.production-deploy-to-aks/11.production-deploy-to-aks.ipynb
+++ b/01.getting-started/11.production-deploy-to-aks/11.production-deploy-to-aks.ipynb
@@ -1,335 +0,0 @@
 {
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
    "\n",
    "Licensed under the MIT License."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Deploying a web service to Azure Kubernetes Service (AKS)\n",
    "This notebook shows the steps for deploying a service: registering a model, creating an image, provisioning a cluster (one time action), and deploying a service to it. \n",
    "We then test and delete the service, image and model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.core import Workspace\n",
    "from azureml.core.compute import AksCompute, ComputeTarget\n",
    "from azureml.core.webservice import Webservice, AksWebservice\n",
    "from azureml.core.image import Image\n",
    "from azureml.core.model import Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import azureml.core\n",
    "print(azureml.core.VERSION)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Get workspace\n",
    "Load existing workspace from the config file info."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.core.workspace import Workspace\n",
    "\n",
    "ws = Workspace.from_config()\n",
    "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Register the model\n",
    "Register an existing trained model, add descirption and tags."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Register the model\n",
    "from azureml.core.model import Model\n",
    "model = Model.register(model_path = \"sklearn_regression_model.pkl\", # this points to a local file\n",
    "                       model_name = \"sklearn_regression_model.pkl\", # this is the name the model is registered as\n",
    "                       tags = {'area': \"diabetes\", 'type': \"regression\"},\n",
    "                       description = \"Ridge regression model to predict diabetes\",\n",
    "                       workspace = ws)\n",
    "\n",
    "print(model.name, model.description, model.version)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create an image\n",
    "Create an image using the registered model the script that will load and run the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%writefile score.py\n",
    "import pickle\n",
    "import json\n",
    "import numpy\n",
    "from sklearn.externals import joblib\n",
    "from sklearn.linear_model import Ridge\n",
    "from azureml.core.model import Model\n",
    "\n",
    "def init():\n",
    "    global model\n",
    "    # note here \"sklearn_regression_model.pkl\" is the name of the model registered under\n",
    "    # this is a different behavior than before when the code is run locally, even though the code is the same.\n",
    "    model_path = Model.get_model_path('sklearn_regression_model.pkl')\n",
    "    # deserialize the model file back into a sklearn model\n",
    "    model = joblib.load(model_path)\n",
    "\n",
    "# note you can pass in multiple rows for scoring\n",
    "def run(raw_data):\n",
    "    try:\n",
    "        data = json.loads(raw_data)['data']\n",
    "        data = numpy.array(data)\n",
    "        result = model.predict(data)\n",
    "    except Exception as e:\n",
    "        result = str(e)\n",
    "    return json.dumps({\"result\": result.tolist()})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.core.conda_dependencies import CondaDependencies \n",
    "\n",
    "myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn'])\n",
    "\n",
    "with open(\"myenv.yml\",\"w\") as f:\n",
    "    f.write(myenv.serialize_to_string())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.core.image import ContainerImage\n",
    "\n",
    "image_config = ContainerImage.image_configuration(execution_script = \"score.py\",\n",
    "                                                  runtime = \"python\",\n",
    "                                                  conda_file = \"myenv.yml\",\n",
    "                                                  description = \"Image with ridge regression model\",\n",
    "                                                  tags = {'area': \"diabetes\", 'type': \"regression\"}\n",
    "                                                 )\n",
    "\n",
    "image = ContainerImage.create(name = \"myimage1\",\n",
    "                              # this is the model object\n",
    "                              models = [model],\n",
    "                              image_config = image_config,\n",
    "                              workspace = ws)\n",
    "\n",
    "image.wait_for_creation(show_output = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Provision the AKS Cluster\n",
    "This is a one time setup. You can reuse this cluster for multiple deployments after it has been created. If you delete the cluster or the resource group that contains it, then you would have to recreate it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use the default configuration (can also provide parameters to customize)\n",
    "prov_config = AksCompute.provisioning_configuration()\n",
    "\n",
    "aks_name = 'my-aks-9' \n",
    "# Create the cluster\n",
    "aks_target = ComputeTarget.create(workspace = ws, \n",
    "                                  name = aks_name, \n",
    "                                  provisioning_configuration = prov_config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "aks_target.wait_for_completion(show_output = True)\n",
    "print(aks_target.provisioning_state)\n",
    "print(aks_target.provisioning_errors)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Optional step: Attach existing AKS cluster\n",
    "\n",
    "If you have existing AKS cluster in your Azure subscription, you can attach it to the Workspace."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "# Use the default configuration (can also provide parameters to customize)\n",
    "resource_id = '/subscriptions/92c76a2f-0e1c-4216-b65e-abf7a3f34c1e/resourcegroups/raymondsdk0604/providers/Microsoft.ContainerService/managedClusters/my-aks-0605d37425356b7d01'\n",
    "\n",
    "create_name='my-existing-aks' \n",
    "# Create the cluster\n",
    "aks_target = AksCompute.attach(workspace=ws, name=create_name, resource_id=resource_id)\n",
    "# Wait for the operation to complete\n",
    "aks_target.wait_for_completion(True)\n",
    "'''"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Deploy web service to AKS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Set the web service configuration (using default here)\n",
    "aks_config = AksWebservice.deploy_configuration()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "aks_service_name ='aks-service-1'\n",
    "\n",
    "aks_service = Webservice.deploy_from_image(workspace = ws, \n",
    "                                           name = aks_service_name,\n",
    "                                           image = image,\n",
    "                                           deployment_config = aks_config,\n",
    "                                           deployment_target = aks_target)\n",
    "aks_service.wait_for_deployment(show_output = True)\n",
    "print(aks_service.state)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test the web service\n",
    "We test the web sevice by passing data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "import json\n",
    "\n",
    "test_sample = json.dumps({'data': [\n",
    "    [1,2,3,4,5,6,7,8,9,10], \n",
    "    [10,9,8,7,6,5,4,3,2,1]\n",
    "]})\n",
    "test_sample = bytes(test_sample,encoding = 'utf8')\n",
    "\n",
    "prediction = aks_service.run(input_data = test_sample)\n",
    "print(prediction)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Clean up\n",
    "Delete the service, image and model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "aks_service.delete()\n",
    "image.delete()\n",
    "model.delete()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.6",
   "language": "python",
   "name": "python36"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
 }
--- a/01.getting-started/12.enable-data-collection-for-models-in-aks/12.enable-data-collection-for-models-in-aks.ipynb
+++ b/01.getting-started/12.enable-data-collection-for-models-in-aks/12.enable-data-collection-for-models-in-aks.ipynb
@@ -1,447 +0,0 @@
 {
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Enabling Data Collection for Models in Production\n",
    "With this notebook, you can learn how to collect input model data from your Azure Machine Learning service in an Azure Blob storage. Once enabled, this data collected gives you the opportunity:\n",
    "\n",
    "* Monitor data drifts as production data enters your model\n",
    "* Make better decisions on when to retrain or optimize your model\n",
    "* Retrain your model with the data collected\n",
    "\n",
    "## What data is collected?\n",
    "* Model input data (voice, images, and video are not supported) from services deployed in Azure Kubernetes Cluster (AKS)\n",
    "* Model predictions using production input data.\n",
    "\n",
    "**Note:** pre-aggregation or pre-calculations on this data are done by user and not included in this version of the product.\n",
    "\n",
    "## What is different compared to standard production deployment process?\n",
    "1. Update scoring file.\n",
    "2. Update yml file with new dependency.\n",
    "3. Update aks configuration.\n",
    "4. Build new image and deploy it. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Import your dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.core import Workspace, Run\n",
    "from azureml.core.compute import AksCompute, ComputeTarget\n",
    "from azureml.core.webservice import Webservice, AksWebservice\n",
    "from azureml.core.image import Image\n",
    "from azureml.core.model import Model\n",
    "\n",
    "import azureml.core\n",
    "print(azureml.core.VERSION)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Set up your configuration and create a workspace\n",
    "Follow Notebook 00 instructions to do this.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ws = Workspace.from_config()\n",
    "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Register Model\n",
    "Register an existing trained model, add descirption and tags."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Register the model\n",
    "from azureml.core.model import Model\n",
    "model = Model.register(model_path = \"sklearn_regression_model.pkl\", # this points to a local file\n",
    "                       model_name = \"sklearn_regression_model.pkl\", # this is the name the model is registered as\n",
    "                       tags = {'area': \"diabetes\", 'type': \"regression\"},\n",
    "                       description = \"Ridge regression model to predict diabetes\",\n",
    "                       workspace = ws)\n",
    "\n",
    "print(model.name, model.description, model.version)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. *Update your scoring file with Data Collection*\n",
    "The file below, compared to the file used in notebook 11, has the following changes:\n",
    "### a. Import the module\n",
    "```python \n",
    "from azureml.monitoring import ModelDataCollector```\n",
    "### b. In your init function add:\n",
    "```python \n",
    "global inputs_dc, prediction_d\n",
    "inputs_dc = ModelDataCollector(\"best_model\", identifier=\"inputs\", feature_names=[\"feat1\", \"feat2\", \"feat3\". \"feat4\", \"feat5\", \"Feat6\"])\n",
    "prediction_dc = ModelDataCollector(\"best_model\", identifier=\"predictions\", feature_names=[\"prediction1\", \"prediction2\"])```\n",
    "    \n",
    "* Identifier: Identifier is later used for building the folder structure in your Blob, it can be used to divide \"raw\" data versus \"processed\".\n",
    "* CorrelationId: is an optional parameter, you do not need to set it up if your model doesn't require it. Having a correlationId in place does help you for easier mapping with other data. (Examples include: LoanNumber, CustomerId, etc.)\n",
    "* Feature Names: These need to be set up in the order of your features in order for them to have column names when the .csv is created.\n",
    "\n",
    "### c. In your run function add:\n",
    "```python\n",
    "inputs_dc.collect(data)\n",
    "prediction_dc.collect(result)```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%writefile score.py\n",
    "import pickle\n",
    "import json\n",
    "import numpy \n",
    "from sklearn.externals import joblib\n",
    "from sklearn.linear_model import Ridge\n",
    "from azureml.core.model import Model\n",
    "from azureml.monitoring import ModelDataCollector\n",
    "import time\n",
    "\n",
    "def init():\n",
    "    global model\n",
    "    print (\"model initialized\" + time.strftime(\"%H:%M:%S\"))\n",
    "    # note here \"sklearn_regression_model.pkl\" is the name of the model registered under the workspace\n",
    "    # this call should return the path to the model.pkl file on the local disk.\n",
    "    model_path = Model.get_model_path(model_name = 'sklearn_regression_model.pkl')\n",
    "    # deserialize the model file back into a sklearn model\n",
    "    model = joblib.load(model_path)\n",
    "    global inputs_dc, prediction_dc\n",
    "    # this setup will help us save our inputs under the \"inputs\" path in our Azure Blob\n",
    "    inputs_dc = ModelDataCollector(model_name=\"sklearn_regression_model\", identifier=\"inputs\", feature_names=[\"feat1\", \"feat2\"]) \n",
    "    # this setup will help us save our ipredictions under the \"predictions\" path in our Azure Blob\n",
    "    prediction_dc = ModelDataCollector(\"sklearn_regression_model\", identifier=\"predictions\", feature_names=[\"prediction1\", \"prediction2\"]) \n",
    "  \n",
    "# note you can pass in multiple rows for scoring\n",
    "def run(raw_data):\n",
    "    global inputs_dc, prediction_dc\n",
    "    try:\n",
    "        data = json.loads(raw_data)['data']\n",
    "        data = numpy.array(data)\n",
    "        result = model.predict(data)\n",
    "        print (\"saving input data\" + time.strftime(\"%H:%M:%S\"))\n",
    "        inputs_dc.collect(data) #this call is saving our input data into our blob\n",
    "        prediction_dc.collect(result)#this call is saving our prediction data into our blob\n",
    "        print (\"saving prediction data\" + time.strftime(\"%H:%M:%S\"))\n",
    "        return json.dumps({\"result\": result.tolist()})\n",
    "    except Exception as e:\n",
    "        result = str(e)\n",
    "        print (result + time.strftime(\"%H:%M:%S\"))\n",
    "        return json.dumps({\"error\": result})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. *Update your myenv.yml file with the required module*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.core.conda_dependencies import CondaDependencies \n",
    "\n",
    "myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn'])\n",
    "myenv.add_pip_package(\"azureml-monitoring\")\n",
    "\n",
    "with open(\"myenv.yml\",\"w\") as f:\n",
    "    f.write(myenv.serialize_to_string())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Create your new Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.core.image import ContainerImage\n",
    "\n",
    "image_config = ContainerImage.image_configuration(execution_script = \"score.py\",\n",
    "                                                  runtime = \"python\",\n",
    "                                                  conda_file = \"myenv.yml\",\n",
    "                                                  description = \"Image with ridge regression model\",\n",
    "                                                  tags = {'area': \"diabetes\", 'type': \"regression\"}\n",
    "                                                 )\n",
    "\n",
    "image = ContainerImage.create(name = \"myimage1\",\n",
    "                              # this is the model object\n",
    "                              models = [model],\n",
    "                              image_config = image_config,\n",
    "                              workspace = ws)\n",
    "\n",
    "image.wait_for_creation(show_output = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(model.name, model.description, model.version)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. Deploy to AKS service"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create AKS compute if you haven't done so (Notebook 11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use the default configuration (can also provide parameters to customize)\n",
    "prov_config = AksCompute.provisioning_configuration()\n",
    "\n",
    "aks_name = 'my-aks-test1' \n",
    "# Create the cluster\n",
    "aks_target = ComputeTarget.create(workspace = ws, \n",
    "                                  name = aks_name, \n",
    "                                  provisioning_configuration = prov_config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "aks_target.wait_for_completion(show_output = True)\n",
    "print(aks_target.provisioning_state)\n",
    "print(aks_target.provisioning_errors)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you already have a cluster you can attach the service to it:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "scrolled": true
   },
   "source": [
    "```python \n",
    "    %%time\n",
    "    resource_id = '/subscriptions/<subscriptionid>/resourcegroups/<resourcegroupname>/providers/Microsoft.ContainerService/managedClusters/<aksservername>'\n",
    "    create_name= 'myaks4'\n",
    "    aks_target = AksCompute.attach(workspace = ws, \n",
    "                                      name = create_name, \n",
    "                                      #esource_id=resource_id)\n",
    "    ## Wait for the operation to complete\n",
    "    aks_target.wait_for_provisioning(True)```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### a. *Activate Data Collection and App Insights through updating AKS Webservice configuration*\n",
    "In order to enable Data Collection and App Insights in your service you will need to update your AKS configuration file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Set the web service configuration\n",
    "aks_config = AksWebservice.deploy_configuration(collect_model_data=True, enable_app_insights=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### b. Deploy your service"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "aks_service_name ='aks-w-dc2'\n",
    "\n",
    "aks_service = Webservice.deploy_from_image(workspace = ws, \n",
    "                                           name = aks_service_name,\n",
    "                                           image = image,\n",
    "                                           deployment_config = aks_config,\n",
    "                                           deployment_target = aks_target\n",
    "                                           )\n",
    "aks_service.wait_for_deployment(show_output = True)\n",
    "print(aks_service.state)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8. Test your service and send some data\n",
    "**Note**: It will take around 15 mins for your data to appear in your blob.\n",
    "The data will appear in your Azure Blob following this format:\n",
    "\n",
    "/modeldata/subscriptionid/resourcegroupname/workspacename/webservicename/modelname/modelversion/identifier/year/month/day/data.csv "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "import json\n",
    "\n",
    "test_sample = json.dumps({'data': [\n",
    "    [1,2,3,4,54,6,7,8,88,10], \n",
    "    [10,9,8,37,36,45,4,33,2,1]\n",
    "]})\n",
    "test_sample = bytes(test_sample,encoding = 'utf8')\n",
    "\n",
    "prediction = aks_service.run(input_data = test_sample)\n",
    "print(prediction)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9. Validate you data and analyze it\n",
    "You can look into your data following this path format in your Azure Blob (it takes up to 15 minutes for the data to appear):\n",
    "\n",
    "/modeldata/**subscriptionid>**/**resourcegroupname>**/**workspacename>**/**webservicename>**/**modelname>**/**modelversion>>**/**identifier>**/*year/month/day*/data.csv \n",
    "\n",
    "For doing further analysis you have multiple options:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### a. Create DataBricks cluter and connect it to your blob\n",
    "https://docs.microsoft.com/en-us/azure/azure-databricks/quickstart-create-databricks-workspace-portal or in your databricks workspace you can look for the template \"Azure Blob Storage Import Example Notebook\".\n",
    "\n",
    "\n",
    "Here is an example for setting up the file location to extract the relevant data:\n",
    "\n",
    "<code> file_location = \"wasbs://mycontainer@storageaccountname.blob.core.windows.net/unknown/unknown/unknown-bigdataset-unknown/my_iterate_parking_inputs/2018/&deg;/&deg;/data.csv\" \n",
    "file_type = \"csv\"</code>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### b. Connect Blob to Power Bi (Small Data only)\n",
    "1. Download and Open PowerBi Desktop\n",
    "2. Select “Get Data” and click on “Azure Blob Storage” >> Connect\n",
    "3. Add your storage account and enter your storage key.\n",
    "4. Select the container where your Data Collection is stored and click on Edit. \n",
    "5. In the query editor, click under “Name” column and add your Storage account Model path into the filter. Note: if you want to only look into files from a specific year or month, just expand the filter path. For example, just look into March data: /modeldata/subscriptionid>/resourcegroupname>/workspacename>/webservicename>/modelname>/modelversion>/identifier>/year>/3\n",
    "6. Click on the double arrow aside the “Content” column to combine the files. \n",
    "7. Click OK and the data will preload.\n",
    "8. You can now click Close and Apply and start building your custom reports on your Model Input data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Disable Data Collection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "aks_service.update(collect_model_data=False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.6",
   "language": "python",
   "name": "python36"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
 }
--- a/Dockerfiles/1.0.10/Dockerfile
+++ b/Dockerfiles/1.0.10/Dockerfile
@@ -0,0 +1,29 @@
 FROM continuumio/miniconda:4.5.11
 # install git
 RUN apt-get update && apt-get upgrade -y && apt-get install -y git
 # create a new conda environment named azureml
 RUN conda create -n azureml -y -q Python=3.6
 # install additional packages used by sample notebooks. this is optional
 RUN ["/bin/bash", "-c", "source activate azureml && conda install -y tqdm cython matplotlib scikit-learn"]
 # install azurmel-sdk components
 RUN ["/bin/bash", "-c", "source activate azureml && pip install azureml-sdk[notebooks]==1.0.10"]
 # clone Azure ML GitHub sample notebooks
 RUN cd /home && git clone -b "azureml-sdk-1.0.10" --single-branch https://github.com/Azure/MachineLearningNotebooks.git
 # generate jupyter configuration file
 RUN ["/bin/bash", "-c", "source activate azureml && mkdir ~/.jupyter && cd ~/.jupyter && jupyter notebook --generate-config"]
 # set an emtpy token for Jupyter to remove authentication. 
 # this is NOT recommended for production environment
 RUN echo "c.NotebookApp.token = ''" >> ~/.jupyter/jupyter_notebook_config.py
 # open up port 8887 on the container
 EXPOSE 8887
 # start Jupyter notebook server on port 8887 when the container starts
 CMD /bin/bash -c "cd /home/MachineLearningNotebooks && source activate azureml && jupyter notebook --port 8887 --no-browser --ip 0.0.0.0 --allow-root"
--- a/Dockerfiles/1.0.15/Dockerfile
+++ b/Dockerfiles/1.0.15/Dockerfile
@@ -0,0 +1,29 @@
 FROM continuumio/miniconda:4.5.11
 # install git
 RUN apt-get update && apt-get upgrade -y && apt-get install -y git
 # create a new conda environment named azureml
 RUN conda create -n azureml -y -q Python=3.6
 # install additional packages used by sample notebooks. this is optional
 RUN ["/bin/bash", "-c", "source activate azureml && conda install -y tqdm cython matplotlib scikit-learn"]
 # install azurmel-sdk components
 RUN ["/bin/bash", "-c", "source activate azureml && pip install azureml-sdk[notebooks]==1.0.15"]
 # clone Azure ML GitHub sample notebooks
 RUN cd /home && git clone -b "azureml-sdk-1.0.15" --single-branch https://github.com/Azure/MachineLearningNotebooks.git
 # generate jupyter configuration file
 RUN ["/bin/bash", "-c", "source activate azureml && mkdir ~/.jupyter && cd ~/.jupyter && jupyter notebook --generate-config"]
 # set an emtpy token for Jupyter to remove authentication. 
 # this is NOT recommended for production environment
 RUN echo "c.NotebookApp.token = ''" >> ~/.jupyter/jupyter_notebook_config.py
 # open up port 8887 on the container
 EXPOSE 8887
 # start Jupyter notebook server on port 8887 when the container starts
 CMD /bin/bash -c "cd /home/MachineLearningNotebooks && source activate azureml && jupyter notebook --port 8887 --no-browser --ip 0.0.0.0 --allow-root"
--- a/Dockerfiles/1.0.17/Dockerfile
+++ b/Dockerfiles/1.0.17/Dockerfile
@@ -0,0 +1,29 @@
 FROM continuumio/miniconda:4.5.11
 # install git
 RUN apt-get update && apt-get upgrade -y && apt-get install -y git
 # create a new conda environment named azureml
 RUN conda create -n azureml -y -q Python=3.6
 # install additional packages used by sample notebooks. this is optional
 RUN ["/bin/bash", "-c", "source activate azureml && conda install -y tqdm cython matplotlib scikit-learn"]
 # install azurmel-sdk components
 RUN ["/bin/bash", "-c", "source activate azureml && pip install azureml-sdk[notebooks]==1.0.17"]
 # clone Azure ML GitHub sample notebooks
 RUN cd /home && git clone -b "azureml-sdk-1.0.17" --single-branch https://github.com/Azure/MachineLearningNotebooks.git
 # generate jupyter configuration file
 RUN ["/bin/bash", "-c", "source activate azureml && mkdir ~/.jupyter && cd ~/.jupyter && jupyter notebook --generate-config"]
 # set an emtpy token for Jupyter to remove authentication. 
 # this is NOT recommended for production environment
 RUN echo "c.NotebookApp.token = ''" >> ~/.jupyter/jupyter_notebook_config.py
 # open up port 8887 on the container
 EXPOSE 8887
 # start Jupyter notebook server on port 8887 when the container starts
 CMD /bin/bash -c "cd /home/MachineLearningNotebooks && source activate azureml && jupyter notebook --port 8887 --no-browser --ip 0.0.0.0 --allow-root"
--- a/Dockerfiles/1.0.18/Dockerfile
+++ b/Dockerfiles/1.0.18/Dockerfile
@@ -0,0 +1,29 @@
 FROM continuumio/miniconda:4.5.11
 # install git
 RUN apt-get update && apt-get upgrade -y && apt-get install -y git
 # create a new conda environment named azureml
 RUN conda create -n azureml -y -q Python=3.6
 # install additional packages used by sample notebooks. this is optional
 RUN ["/bin/bash", "-c", "source activate azureml && conda install -y tqdm cython matplotlib scikit-learn"]
 # install azurmel-sdk components
 RUN ["/bin/bash", "-c", "source activate azureml && pip install azureml-sdk[notebooks]==1.0.18"]
 # clone Azure ML GitHub sample notebooks
 RUN cd /home && git clone -b "azureml-sdk-1.0.18" --single-branch https://github.com/Azure/MachineLearningNotebooks.git
 # generate jupyter configuration file
 RUN ["/bin/bash", "-c", "source activate azureml && mkdir ~/.jupyter && cd ~/.jupyter && jupyter notebook --generate-config"]
 # set an emtpy token for Jupyter to remove authentication. 
 # this is NOT recommended for production environment
 RUN echo "c.NotebookApp.token = ''" >> ~/.jupyter/jupyter_notebook_config.py
 # open up port 8887 on the container
 EXPOSE 8887
 # start Jupyter notebook server on port 8887 when the container starts
 CMD /bin/bash -c "cd /home/MachineLearningNotebooks && source activate azureml && jupyter notebook --port 8887 --no-browser --ip 0.0.0.0 --allow-root"
--- a/Dockerfiles/1.0.2/Dockerfile
+++ b/Dockerfiles/1.0.2/Dockerfile
@@ -0,0 +1,29 @@
 FROM continuumio/miniconda:4.5.11
 # install git
 RUN apt-get update && apt-get upgrade -y && apt-get install -y git
 # create a new conda environment named azureml
 RUN conda create -n azureml -y -q Python=3.6
 # install additional packages used by sample notebooks. this is optional
 RUN ["/bin/bash", "-c", "source activate azureml && conda install -y tqdm cython matplotlib scikit-learn"]
 # install azurmel-sdk components
 RUN ["/bin/bash", "-c", "source activate azureml && pip install azureml-sdk[notebooks]==1.0.2"]
 # clone Azure ML GitHub sample notebooks
 RUN cd /home && git clone -b "azureml-sdk-1.0.2" --single-branch https://github.com/Azure/MachineLearningNotebooks.git
 # generate jupyter configuration file
 RUN ["/bin/bash", "-c", "source activate azureml && mkdir ~/.jupyter && cd ~/.jupyter && jupyter notebook --generate-config"]
 # set an emtpy token for Jupyter to remove authentication. 
 # this is NOT recommended for production environment
 RUN echo "c.NotebookApp.token = ''" >> ~/.jupyter/jupyter_notebook_config.py
 # open up port 8887 on the container
 EXPOSE 8887
 # start Jupyter notebook server on port 8887 when the container starts
 CMD /bin/bash -c "cd /home/MachineLearningNotebooks && source activate azureml && jupyter notebook --port 8887 --no-browser --ip 0.0.0.0 --allow-root"
--- a/Dockerfiles/1.0.21/Dockerfile
+++ b/Dockerfiles/1.0.21/Dockerfile
@@ -0,0 +1,29 @@
 FROM continuumio/miniconda:4.5.11
 # install git
 RUN apt-get update && apt-get upgrade -y && apt-get install -y git
 # create a new conda environment named azureml
 RUN conda create -n azureml -y -q Python=3.6
 # install additional packages used by sample notebooks. this is optional
 RUN ["/bin/bash", "-c", "source activate azureml && conda install -y tqdm cython matplotlib scikit-learn"]
 # install azurmel-sdk components
 RUN ["/bin/bash", "-c", "source activate azureml && pip install azureml-sdk[notebooks]==1.0.21"]
 # clone Azure ML GitHub sample notebooks
 RUN cd /home && git clone -b "azureml-sdk-1.0.21" --single-branch https://github.com/Azure/MachineLearningNotebooks.git
 # generate jupyter configuration file
 RUN ["/bin/bash", "-c", "source activate azureml && mkdir ~/.jupyter && cd ~/.jupyter && jupyter notebook --generate-config"]
 # set an emtpy token for Jupyter to remove authentication. 
 # this is NOT recommended for production environment
 RUN echo "c.NotebookApp.token = ''" >> ~/.jupyter/jupyter_notebook_config.py
 # open up port 8887 on the container
 EXPOSE 8887
 # start Jupyter notebook server on port 8887 when the container starts
 CMD /bin/bash -c "cd /home/MachineLearningNotebooks && source activate azureml && jupyter notebook --port 8887 --no-browser --ip 0.0.0.0 --allow-root"
--- a/Dockerfiles/1.0.23/Dockerfile
+++ b/Dockerfiles/1.0.23/Dockerfile
@@ -0,0 +1,29 @@
 FROM continuumio/miniconda:4.5.11
 # install git
 RUN apt-get update && apt-get upgrade -y && apt-get install -y git
 # create a new conda environment named azureml
 RUN conda create -n azureml -y -q Python=3.6
 # install additional packages used by sample notebooks. this is optional
 RUN ["/bin/bash", "-c", "source activate azureml && conda install -y tqdm cython matplotlib scikit-learn"]
 # install azurmel-sdk components
 RUN ["/bin/bash", "-c", "source activate azureml && pip install azureml-sdk[notebooks]==1.0.23"]
 # clone Azure ML GitHub sample notebooks
 RUN cd /home && git clone -b "azureml-sdk-1.0.23" --single-branch https://github.com/Azure/MachineLearningNotebooks.git
 # generate jupyter configuration file
 RUN ["/bin/bash", "-c", "source activate azureml && mkdir ~/.jupyter && cd ~/.jupyter && jupyter notebook --generate-config"]
 # set an emtpy token for Jupyter to remove authentication. 
 # this is NOT recommended for production environment
 RUN echo "c.NotebookApp.token = ''" >> ~/.jupyter/jupyter_notebook_config.py
 # open up port 8887 on the container
 EXPOSE 8887
 # start Jupyter notebook server on port 8887 when the container starts
 CMD /bin/bash -c "cd /home/MachineLearningNotebooks && source activate azureml && jupyter notebook --port 8887 --no-browser --ip 0.0.0.0 --allow-root"
--- a/Dockerfiles/1.0.30/Dockerfile
+++ b/Dockerfiles/1.0.30/Dockerfile
@@ -0,0 +1,29 @@
 FROM continuumio/miniconda:4.5.11
 # install git
 RUN apt-get update && apt-get upgrade -y && apt-get install -y git
 # create a new conda environment named azureml
 RUN conda create -n azureml -y -q Python=3.6
 # install additional packages used by sample notebooks. this is optional
 RUN ["/bin/bash", "-c", "source activate azureml && conda install -y tqdm cython matplotlib scikit-learn"]
 # install azurmel-sdk components
 RUN ["/bin/bash", "-c", "source activate azureml && pip install azureml-sdk[notebooks]==1.0.30"]
 # clone Azure ML GitHub sample notebooks
 RUN cd /home && git clone -b "azureml-sdk-1.0.30" --single-branch https://github.com/Azure/MachineLearningNotebooks.git
 # generate jupyter configuration file
 RUN ["/bin/bash", "-c", "source activate azureml && mkdir ~/.jupyter && cd ~/.jupyter && jupyter notebook --generate-config"]
 # set an emtpy token for Jupyter to remove authentication. 
 # this is NOT recommended for production environment
 RUN echo "c.NotebookApp.token = ''" >> ~/.jupyter/jupyter_notebook_config.py
 # open up port 8887 on the container
 EXPOSE 8887
 # start Jupyter notebook server on port 8887 when the container starts
 CMD /bin/bash -c "cd /home/MachineLearningNotebooks && source activate azureml && jupyter notebook --port 8887 --no-browser --ip 0.0.0.0 --allow-root"
--- a/Dockerfiles/1.0.33/Dockerfile
+++ b/Dockerfiles/1.0.33/Dockerfile
@@ -0,0 +1,29 @@
 FROM continuumio/miniconda:4.5.11
 # install git
 RUN apt-get update && apt-get upgrade -y && apt-get install -y git
 # create a new conda environment named azureml
 RUN conda create -n azureml -y -q Python=3.6
 # install additional packages used by sample notebooks. this is optional
 RUN ["/bin/bash", "-c", "source activate azureml && conda install -y tqdm cython matplotlib scikit-learn"]
 # install azurmel-sdk components
 RUN ["/bin/bash", "-c", "source activate azureml && pip install azureml-sdk[notebooks]==1.0.33"]
 # clone Azure ML GitHub sample notebooks
 RUN cd /home && git clone -b "azureml-sdk-1.0.33" --single-branch https://github.com/Azure/MachineLearningNotebooks.git
 # generate jupyter configuration file
 RUN ["/bin/bash", "-c", "source activate azureml && mkdir ~/.jupyter && cd ~/.jupyter && jupyter notebook --generate-config"]
 # set an emtpy token for Jupyter to remove authentication. 
 # this is NOT recommended for production environment
 RUN echo "c.NotebookApp.token = ''" >> ~/.jupyter/jupyter_notebook_config.py
 # open up port 8887 on the container
 EXPOSE 8887
 # start Jupyter notebook server on port 8887 when the container starts
 CMD /bin/bash -c "cd /home/MachineLearningNotebooks && source activate azureml && jupyter notebook --port 8887 --no-browser --ip 0.0.0.0 --allow-root"
--- a/Dockerfiles/1.0.41/Dockerfile
+++ b/Dockerfiles/1.0.41/Dockerfile
@@ -0,0 +1,29 @@
 FROM continuumio/miniconda:4.5.11
 # install git
 RUN apt-get update && apt-get upgrade -y && apt-get install -y git
 # create a new conda environment named azureml
 RUN conda create -n azureml -y -q Python=3.6
 # install additional packages used by sample notebooks. this is optional
 RUN ["/bin/bash", "-c", "source activate azureml && conda install -y tqdm cython matplotlib scikit-learn"]
 # install azurmel-sdk components
 RUN ["/bin/bash", "-c", "source activate azureml && pip install azureml-sdk[notebooks]==1.0.41"]
 # clone Azure ML GitHub sample notebooks
 RUN cd /home && git clone -b "azureml-sdk-1.0.41" --single-branch https://github.com/Azure/MachineLearningNotebooks.git
 # generate jupyter configuration file
 RUN ["/bin/bash", "-c", "source activate azureml && mkdir ~/.jupyter && cd ~/.jupyter && jupyter notebook --generate-config"]
 # set an emtpy token for Jupyter to remove authentication. 
 # this is NOT recommended for production environment
 RUN echo "c.NotebookApp.token = ''" >> ~/.jupyter/jupyter_notebook_config.py
 # open up port 8887 on the container
 EXPOSE 8887
 # start Jupyter notebook server on port 8887 when the container starts
 CMD /bin/bash -c "cd /home/MachineLearningNotebooks && source activate azureml && jupyter notebook --port 8887 --no-browser --ip 0.0.0.0 --allow-root"
--- a/Dockerfiles/1.0.43/Dockerfile
+++ b/Dockerfiles/1.0.43/Dockerfile
@@ -0,0 +1,29 @@
 FROM continuumio/miniconda:4.5.11
 # install git
 RUN apt-get update && apt-get upgrade -y && apt-get install -y git
 # create a new conda environment named azureml
 RUN conda create -n azureml -y -q Python=3.6
 # install additional packages used by sample notebooks. this is optional
 RUN ["/bin/bash", "-c", "source activate azureml && conda install -y tqdm cython matplotlib scikit-learn"]
 # install azurmel-sdk components
 RUN ["/bin/bash", "-c", "source activate azureml && pip install azureml-sdk[notebooks]==1.0.43"]
 # clone Azure ML GitHub sample notebooks
 RUN cd /home && git clone -b "azureml-sdk-1.0.43" --single-branch https://github.com/Azure/MachineLearningNotebooks.git
 # generate jupyter configuration file
 RUN ["/bin/bash", "-c", "source activate azureml && mkdir ~/.jupyter && cd ~/.jupyter && jupyter notebook --generate-config"]
 # set an emtpy token for Jupyter to remove authentication. 
 # this is NOT recommended for production environment
 RUN echo "c.NotebookApp.token = ''" >> ~/.jupyter/jupyter_notebook_config.py
 # open up port 8887 on the container
 EXPOSE 8887
 # start Jupyter notebook server on port 8887 when the container starts
 CMD /bin/bash -c "cd /home/MachineLearningNotebooks && source activate azureml && jupyter notebook --port 8887 --no-browser --ip 0.0.0.0 --allow-root"
--- a/Dockerfiles/1.0.6/Dockerfile
+++ b/Dockerfiles/1.0.6/Dockerfile
@@ -0,0 +1,29 @@
 FROM continuumio/miniconda:4.5.11
 # install git
 RUN apt-get update && apt-get upgrade -y && apt-get install -y git
 # create a new conda environment named azureml
 RUN conda create -n azureml -y -q Python=3.6
 # install additional packages used by sample notebooks. this is optional
 RUN ["/bin/bash", "-c", "source activate azureml && conda install -y tqdm cython matplotlib scikit-learn"]
 # install azurmel-sdk components
 RUN ["/bin/bash", "-c", "source activate azureml && pip install azureml-sdk[notebooks]==1.0.6"]
 # clone Azure ML GitHub sample notebooks
 RUN cd /home && git clone -b "azureml-sdk-1.0.6" --single-branch https://github.com/Azure/MachineLearningNotebooks.git
 # generate jupyter configuration file
 RUN ["/bin/bash", "-c", "source activate azureml && mkdir ~/.jupyter && cd ~/.jupyter && jupyter notebook --generate-config"]
 # set an emtpy token for Jupyter to remove authentication. 
 # this is NOT recommended for production environment
 RUN echo "c.NotebookApp.token = ''" >> ~/.jupyter/jupyter_notebook_config.py
 # open up port 8887 on the container
 EXPOSE 8887
 # start Jupyter notebook server on port 8887 when the container starts
 CMD /bin/bash -c "cd /home/MachineLearningNotebooks && source activate azureml && jupyter notebook --port 8887 --no-browser --ip 0.0.0.0 --allow-root"
--- a/Dockerfiles/1.0.8/Dockerfile
+++ b/Dockerfiles/1.0.8/Dockerfile
@@ -0,0 +1,29 @@
 FROM continuumio/miniconda:4.5.11
 # install git
 RUN apt-get update && apt-get upgrade -y && apt-get install -y git
 # create a new conda environment named azureml
 RUN conda create -n azureml -y -q Python=3.6
 # install additional packages used by sample notebooks. this is optional
 RUN ["/bin/bash", "-c", "source activate azureml && conda install -y tqdm cython matplotlib scikit-learn"]
 # install azurmel-sdk components
 RUN ["/bin/bash", "-c", "source activate azureml && pip install azureml-sdk[notebooks]==1.0.8"]
 # clone Azure ML GitHub sample notebooks
 RUN cd /home && git clone -b "azureml-sdk-1.0.8" --single-branch https://github.com/Azure/MachineLearningNotebooks.git
 # generate jupyter configuration file
 RUN ["/bin/bash", "-c", "source activate azureml && mkdir ~/.jupyter && cd ~/.jupyter && jupyter notebook --generate-config"]
 # set an emtpy token for Jupyter to remove authentication. 
 # this is NOT recommended for production environment
 RUN echo "c.NotebookApp.token = ''" >> ~/.jupyter/jupyter_notebook_config.py
 # open up port 8887 on the container
 EXPOSE 8887
 # start Jupyter notebook server on port 8887 when the container starts
 CMD /bin/bash -c "cd /home/MachineLearningNotebooks && source activate azureml && jupyter notebook --port 8887 --no-browser --ip 0.0.0.0 --allow-root"
--- a/Licenses/sdk-license/LICENSE
+++ b/Licenses/sdk-license/LICENSE
@@ -0,0 +1,14 @@
 This software is made available to you on the condition that you agree to
 [your agreement][1] governing your use of Azure.
 If you do not have an existing agreement governing your use of Azure, you agree that 
 your agreement governing use of Azure is the [Microsoft Online Subscription Agreement][2]
 (which incorporates the [Online Services Terms][3]).
 By using the software you agree to these terms. This software may collect data
 that is transmitted to Microsoft. Please see the [Microsoft Privacy Statement][4]
 to learn more about how Microsoft processes personal data.
 [1]: https://azure.microsoft.com/en-us/support/legal/
 [2]: https://azure.microsoft.com/en-us/support/legal/subscription-agreement/
 [3]: http://www.microsoftvolumelicensing.com/DocumentSearch.aspx?Mode=3&DocumentTypeId=46
 [4]: http://go.microsoft.com/fwlink/?LinkId=248681 
--- a/Licenses/sdk-preview-license/LICENSE
+++ b/Licenses/sdk-preview-license/LICENSE
--- a/NBSETUP.md
+++ b/NBSETUP.md
@@ -0,0 +1,95 @@
 # Set up your notebook environment for Azure Machine Learning
 To run the notebooks in this repository use one of following options.
 ## **Option 1: Use Azure Notebooks**
 Azure Notebooks is a hosted Jupyter-based notebook service in the Azure cloud. Azure Machine Learning Python SDK is already pre-installed in the Azure Notebooks `Python 3.6` kernel.
 1. [![Azure Notebooks](https://notebooks.azure.com/launch.png)](https://aka.ms/aml-clone-azure-notebooks)
 [Import sample notebooks ](https://aka.ms/aml-clone-azure-notebooks) into Azure Notebooks
 1. Follow the instructions in the [Configuration](configuration.ipynb) notebook to create and connect to a workspace
 1. Open one of the sample notebooks
    **Make sure the Azure Notebook kernel is set to `Python 3.6`** when you open a notebook by choosing Kernel > Change Kernel > Python 3.6 from the menus.
 ## **Option 2: Use your own notebook server**
 ### Quick installation
 We recommend you create a Python virtual environment ([Miniconda](https://conda.io/miniconda.html) preferred but [virtualenv](https://virtualenv.pypa.io/en/latest/) works too) and install the SDK in it.
 ```sh
 # install just the base SDK
 pip install azureml-sdk
 # clone the sample repoistory
 git clone https://github.com/Azure/MachineLearningNotebooks.git
 # below steps are optional
 # install the base SDK, Jupyter notebook server and tensorboard
 pip install azureml-sdk[notebooks,tensorboard]
 # install model explainability component
 pip install azureml-sdk[explain]
 # install automated ml components
 pip install azureml-sdk[automl]
 # install experimental features (not ready for production use)
 pip install azureml-sdk[contrib]
 ```
 Note the _extras_ (the keywords inside the square brackets) can be combined. For example:
 ```sh
 # install base SDK, Jupyter notebook and automated ml components
 pip install azureml-sdk[notebooks,automl]
 ```
 ### Full instructions
 [Install the Azure Machine Learning SDK](https://docs.microsoft.com/en-us/azure/machine-learning/service/quickstart-create-workspace-with-python)
 Please make sure you start with the [Configuration](configuration.ipynb) notebook to create and connect to a workspace.
 ### Video walkthrough:
 [!VIDEO https://youtu.be/VIsXeTuW3FU]
 ## **Option 3: Use Docker**
 You need to have Docker engine installed locally and running. Open a command line window and type the following command. 
 __Note:__ We use version `1.0.10` below as an exmaple, but you can replace that with any available version number you like.
 ```sh
 # clone the sample repoistory
 git clone https://github.com/Azure/MachineLearningNotebooks.git
 # change current directory to the folder 
 # where Dockerfile of the specific SDK version is located.
 cd MachineLearningNotebooks/Dockerfiles/1.0.10
 # build a Docker image with the a name (azuremlsdk for example) 
 # and a version number tag (1.0.10 for example).
 # this can take several minutes depending on your computer speed and network bandwidth.
 docker build . -t azuremlsdk:1.0.10
 # launch the built Docker container which also automatically starts
 # a Jupyter server instance listening on port 8887 of the host machine
 docker run -it -p 8887:8887 azuremlsdk:1.0.10
 ```
 Now you can point your browser to http://localhost:8887. We recommend that you start from the `configuration.ipynb` notebook at the root directory.
 If you need additional Azure ML SDK components, you can either modify the Docker files before you build the Docker images to add additional steps, or install them through command line in the live container after you build the Docker image. For example:
 ```sh
 # install the core SDK and automated ml components
 pip install azureml-sdk[automl]
 # install the core SDK and model explainability component
 pip install azureml-sdk[explain]
 # install the core SDK and experimental components
 pip install azureml-sdk[contrib]
 ```
 Drag and Drop
 The image will be downloaded by Fatkun
--- a/README.md
+++ b/README.md
@@ -1,41 +1,76 @@
-# Sample notebooks for Azure Machine Learning service
+# Azure Machine Learning service example notebooks
-To run the notebooks in this repository use one of these methods:
+This repository contains example notebooks demonstrating the [Azure Machine Learning](https://azure.microsoft.com/en-us/services/machine-learning-service/) Python SDK which allows you to build, train, deploy and manage machine learning solutions using Azure.  The AML SDK allows you the choice of using local or cloud compute resources, while managing and maintaining the complete data science workflow from the cloud.
-## Use Azure Notebooks - Jupyter based notebooks in the Azure cloud
+![Azure ML Workflow](https://raw.githubusercontent.com/MicrosoftDocs/azure-docs/master/articles/machine-learning/service/media/concept-azure-machine-learning-architecture/workflow.png)
 1. [![Azure Notebooks](https://notebooks.azure.com/launch.png)](https://aka.ms/aml-clone-azure-notebooks)
 [Import sample notebooks ](https://aka.ms/aml-clone-azure-notebooks) into Azure Notebooks.
 1. Follow the instructions in the [00.configuration](00.configuration.ipynb) notebook to create and connect to a workspace.
 1. Open one of the sample notebooks.
    **Make sure the Azure Notebook kernel is set to `Python 3.6`** when you open a notebook.  
    ![set kernel to Python 3.6](images/python36.png)
-## **Use your own notebook server**
+## Quick installation
 ```sh
 pip install azureml-sdk
 ```
 Read more detailed instructions on [how to set up your environment](./NBSETUP.md) using Azure Notebook service, your own Jupyter notebook server, or Docker.
-1. Setup a Jupyter Notebook server and [install the Azure Machine Learning SDK](https://aka.ms/aml-how-to-configure-environment).
+## How to navigate and use the example notebooks?
-1. Clone [this repository](https://aka.ms/aml-notebooks).
+If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, you should always run the [Configuration](./configuration.ipynb) notebook first when setting up a notebook library on a new machine or in a new environment. It configures your notebook library to connect to an Azure Machine Learning workspace, and sets up your workspace and compute to be used by many of the other examples. 
 1. You may need to install other packages for specific notebooks
 1. Start your notebook server.
 1. Follow the instructions in the [00.configuration](00.configuration.ipynb) notebook to create and connect to a workspace.
 1. Open one of the sample notebooks.
-> Note: **Looking for automated machine learning samples?**
+If you want to...
 > For your convenience, you can use an installation script instead of the steps below for the automated ML notebooks. Go to the [automl folder README](automl/README.md) and follow the instructions.  The script installs all  packages needed for notebooks in that folder.
-# Contributing
+ * ...try out and explore Azure ML, start with image classification tutorials: [Part 1 (Training)](./tutorials/img-classification-part1-training.ipynb) and [Part 2 (Deployment)](./tutorials/img-classification-part2-deploy.ipynb).
 * ...prepare your data and do automated machine learning, start with regression tutorials: [Part 1 (Data Prep)](./tutorials/regression-part1-data-prep.ipynb) and [Part 2 (Automated ML)](./tutorials/regression-part2-automated-ml.ipynb).
 * ...learn about experimentation and tracking run history, first [train within Notebook](./how-to-use-azureml/training/train-within-notebook/train-within-notebook.ipynb), then try [training on remote VM](./how-to-use-azureml/training/train-on-remote-vm/train-on-remote-vm.ipynb) and [using logging APIs](./how-to-use-azureml/training/logging-api/logging-api.ipynb).
 * ...train deep learning models at scale, first learn about [Machine Learning Compute](./how-to-use-azureml/training/train-on-amlcompute/train-on-amlcompute.ipynb), and then try [distributed hyperparameter tuning](./how-to-use-azureml/training-with-deep-learning/train-hyperparameter-tune-deploy-with-pytorch/train-hyperparameter-tune-deploy-with-pytorch.ipynb) and [distributed training](./how-to-use-azureml/training-with-deep-learning/distributed-pytorch-with-horovod/distributed-pytorch-with-horovod.ipynb).
 * ...deploy models as a realtime scoring service, first learn the basics by [training within Notebook and deploying to Azure Container Instance](./how-to-use-azureml/training/train-within-notebook/train-within-notebook.ipynb), then learn how to [register and manage models, and create Docker images](./how-to-use-azureml/deployment/register-model-create-image-deploy-service/register-model-create-image-deploy-service.ipynb), and [production deploy models on Azure Kubernetes Cluster](./how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks.ipynb).
 * ...deploy models as a batch scoring service, first [train a model within Notebook](./how-to-use-azureml/training/train-within-notebook/train-within-notebook.ipynb), learn how to [register and manage models](./how-to-use-azureml/deployment/register-model-create-image-deploy-service/register-model-create-image-deploy-service.ipynb), then [create Machine Learning Compute for scoring compute](./how-to-use-azureml/training/train-on-amlcompute/train-on-amlcompute.ipynb), and [use Machine Learning Pipelines to deploy your model](https://aka.ms/pl-batch-scoring).
 * ...monitor your deployed models, learn about using [App Insights](./how-to-use-azureml/deployment/enable-app-insights-in-production-service/enable-app-insights-in-production-service.ipynb) and [model data collection](./how-to-use-azureml/deployment/enable-data-collection-for-models-in-aks/enable-data-collection-for-models-in-aks.ipynb).
-This project welcomes contributions and suggestions.  Most contributions require you to agree to a
+## Tutorials
 Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us
 the rights to use your contribution. For details, visit https://cla.microsoft.com.
-When you submit a pull request, a CLA-bot will automatically determine whether you need to provide
+The [Tutorials](./tutorials) folder contains notebooks for the tutorials described in the [Azure Machine Learning documentation](https://aka.ms/aml-docs).
 a CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the instructions
 provided by the bot. You will only need to do this once across all repos using our CLA.
-This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).
+## How to use Azure ML
-For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or
+
-contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments.
+The [How to use Azure ML](./how-to-use-azureml) folder contains specific examples demonstrating the features of the Azure Machine Learning SDK
 - [Training](./how-to-use-azureml/training) - Examples of how to build models using Azure ML's logging and execution capabilities on local and remote compute targets
 - [Training with Deep Learning](./how-to-use-azureml/training-with-deep-learning) - Examples demonstrating how to build deep learning models using estimators and parameter sweeps
 - [Manage Azure ML Service](./how-to-use-azureml/manage-azureml-service) - Examples how to perform tasks, such as authenticate against Azure ML service in different ways.
 - [Automated Machine Learning](./how-to-use-azureml/automated-machine-learning) - Examples using Automated Machine Learning to automatically generate optimal machine learning pipelines and models
 - [Machine Learning Pipelines](./how-to-use-azureml/machine-learning-pipelines) - Examples showing how to create and use reusable pipelines for training and batch scoring
 - [Deployment](./how-to-use-azureml/deployment) - Examples showing how to deploy and manage machine learning models and solutions
 - [Azure Databricks](./how-to-use-azureml/azure-databricks) - Examples showing how to use Azure ML with Azure Databricks
 - [Monitor Models](./how-to-use-azureml/monitor-models) - Examples showing how to enable model monitoring services such as DataDrift
 ---
 ## Documentation
 * Quickstarts, end-to-end tutorials, and how-tos on the [official documentation site for Azure Machine Learning service](https://docs.microsoft.com/en-us/azure/machine-learning/service/).
 * [Python SDK reference](https://docs.microsoft.com/en-us/python/api/overview/azure/ml/intro?view=azure-ml-py)
 * Azure ML Data Prep SDK [overview](https://aka.ms/data-prep-sdk), [Python SDK reference](https://aka.ms/aml-data-prep-apiref), and [tutorials and how-tos](https://aka.ms/aml-data-prep-notebooks).
 ---
 ## Community Repository 
 Visit this [community repository](https://github.com/microsoft/MLOps/tree/master/examples) to find useful end-to-end sample notebooks. Also, please follow these [contribution guidelines](https://github.com/microsoft/MLOps/blob/master/contributing.md) when contributing to this repository.   
 ## Projects using Azure Machine Learning
 Visit following repos to see projects contributed by Azure ML users:
 - [AMLSamples](https://github.com/Azure/AMLSamples) Number of end-to-end examples, including face recognition, predictive maintenance, customer churn and sentiment analysis.
 - [Learn about Natural Language Processing best practices using Azure Machine Learning service](https://github.com/microsoft/nlp)
 - [Pre-Train BERT models using Azure Machine Learning service](https://github.com/Microsoft/AzureML-BERT)
 - [Fashion MNIST with Azure ML SDK](https://github.com/amynic/azureml-sdk-fashion)
 - [UMass Amherst Student Samples](https://github.com/katiehouse3/microsoft-azure-ml-notebooks) - A number of end-to-end machine learning notebooks, including machine translation, image classification, and customer churn, created by students in the 696DS course at UMass Amherst.
 ## Data/Telemetry 
 This repository collects usage data and sends it to Mircosoft to help improve our products and services. Read Microsoft's [privacy statement to learn more](https://privacy.microsoft.com/en-US/privacystatement)
 To opt out of tracking, please go to the raw markdown or .ipynb files and remove the following line of code:
 ```sh
    "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/README.png)"
 ```
 This URL will be slightly different depending on the file. 
 ![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/README.png)
--- a/automl/00.configuration.ipynb
+++ b/automl/00.configuration.ipynb
@@ -1,265 +0,0 @@
 {
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
    "\n",
    "Licensed under the MIT License."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AutoML 00. configuration\n",
    "\n",
    "In this example you will create an Azure Machine Learning Workspace and initialize your notebook directory to easily use this workspace.  Typically you will only need to run this once per notebook directory, and all other notebooks in this directory or any sub-directories will automatically use the settings you indicate here.\n",
    "\n",
    "\n",
    "## Prerequisites:\n",
    "\n",
    "Before running this notebook, run the automl_setup script described in README.md.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Connect to your Azure Subscription\n",
    "\n",
    "In order to use an AML Workspace, first you need access to an Azure Subscription.  You can [create your own](https://azure.microsoft.com/en-us/free/) or get your existing subscription information from the [Azure portal](https://portal.azure.com).\n",
    "\n",
    "First login to azure and follow prompts to authenticate. Then check that your subscription is correct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!az login"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!az account show"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you have multiple subscriptions and need to change the active one, you can use a command\n",
    "```shell\n",
    "az account set -s <subscription-id>\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Register Machine Learning Services Resource Provider\n",
    "\n",
    "This step is required to use the Azure ML services backing the SDK."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# register the new RP\n",
    "!az provider register -n Microsoft.MachineLearningServices\n",
    "\n",
    "# check the registration status\n",
    "!az provider show -n Microsoft.MachineLearningServices"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check core SDK version number for validate your installation and for debugging purposes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import azureml.core\n",
    "\n",
    "print(\"SDK Version:\", azureml.core.VERSION)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initialize an Azure ML Workspace\n",
    "### What is an Azure ML Workspace and why do I need one?\n",
    "\n",
    "An AML Workspace is an Azure resource that organaizes and coordinates the actions of many other Azure resources to assist in executing and sharing machine learning workflows.  In particular, an AML Workspace coordinates storage, databases, and compute resources providing added functionality for machine learning experimentation, operationalization, and the monitoring of operationalized models.\n",
    "\n",
    "\n",
    "### What do I need\n",
    "\n",
    "To create or access an Azure ML Workspace, you will need to import the AML library and specify following information:\n",
    "* A name for your workspace. You can choose one.\n",
    "* Your subscription id. Use *id* value from *az account show* output above. \n",
    "* The resource group name. Resource group organizes Azure resources and provides default region for the resources in the group. You can either specify a new one, in which case it gets created for your Workspace, or use an existing one or create a new one from [Azure portal](https://portal.azure.com)\n",
    "* Supported regions include `eastus2`, `eastus`,`westcentralus`, `southeastasia`, `westeurope`, `australiaeast`, `westus2`, `southcentralus`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "subscription_id = \"<subscription_id>\"\n",
    "resource_group = \"myrg\"\n",
    "workspace_name = \"myws\"\n",
    "workspace_region = \"eastus2\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating a workspace\n",
    "If you already have access to an AML Workspace you want to use, you can skip this cell.  Otherwise, this cell will create an AML workspace for you in a subscription provided you have the correct permissions for the given `subscription_id`.\n",
    "\n",
    "This will fail when:\n",
    "1. The workspace already exists\n",
    "2. You do not have permission to create a workspace in the resource group\n",
    "3. You are not a subscription owner or contributor and no Azure ML workspaces have ever been created in this subscription\n",
    "\n",
    "If workspace creation fails for any reason other than already existing, please work with your IT admin to provide you with the appropriate permissions or to provision the required resources.\n",
    "\n",
    "**Note** The workspace creation can take several minutes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import the Workspace class and check the azureml SDK version\n",
    "from azureml.core import Workspace\n",
    "\n",
    "ws = Workspace.create(name = workspace_name,\n",
    "                      subscription_id = subscription_id,\n",
    "                      resource_group = resource_group, \n",
    "                      location = workspace_region)\n",
    "ws.get_details()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configuring your local environment\n",
    "You can validate that you have access to the specified workspace and write a configuration file to the default configuration location, `./aml_config/config.json`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.core import Workspace\n",
    "\n",
    "ws = Workspace(workspace_name = workspace_name,\n",
    "               subscription_id = subscription_id,\n",
    "               resource_group = resource_group)\n",
    "\n",
    "# persist the subscription id, resource group name, and workspace name in aml_config/config.json.\n",
    "ws.write_config()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can then load the workspace from this config file from any notebook in the current directory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load workspace configuratio from ./aml_config/config.json file.\n",
    "my_workspace = Workspace.from_config()\n",
    "my_workspace.get_details()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create a folder to host all sample projects\n",
    "Lastly, create a folder where all the sample projects will be hosted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "sample_projects_folder = './sample_projects'\n",
    "\n",
    "if not os.path.isdir(sample_projects_folder):\n",
    "    os.mkdir(sample_projects_folder)\n",
    "    \n",
    "print('Sample projects will be created in {}.'.format(sample_projects_folder))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Success!\n",
    "Great, you are ready to move on to the rest of the sample notebooks."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.6",
   "language": "python",
   "name": "python36"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
 }
--- a/automl/01.auto-ml-classification.ipynb
+++ b/automl/01.auto-ml-classification.ipynb
@@ -1,399 +0,0 @@
 {
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
    "\n",
    "Licensed under the MIT License."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AutoML 01: Classification with local compute\n",
    "\n",
    "In this example we use the scikit learn's [digit dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html) to showcase how you can use AutoML for a simple classification problem.\n",
    "\n",
    "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook.\n",
    "\n",
    "In this notebook you would see\n",
    "1. Creating an Experiment in an existing Workspace\n",
    "2. Instantiating AutoMLConfig\n",
    "3. Training the Model using local compute\n",
    "4. Exploring the results\n",
    "5. Testing the fitted model\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Experiment\n",
    "\n",
    "As part of the setup you have already created a <b>Workspace</b>. For AutoML you would need to create an <b>Experiment</b>. An <b>Experiment</b> is a named object in a <b>Workspace</b>, which is used to run experiments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "import os\n",
    "import random\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.pyplot import imshow\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn import datasets\n",
    "\n",
    "import azureml.core\n",
    "from azureml.core.experiment import Experiment\n",
    "from azureml.core.workspace import Workspace\n",
    "from azureml.train.automl import AutoMLConfig\n",
    "from azureml.train.automl.run import AutoMLRun"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ws = Workspace.from_config()\n",
    "\n",
    "# choose a name for experiment\n",
    "experiment_name = 'automl-local-classification'\n",
    "# project folder\n",
    "project_folder = './sample_projects/automl-local-classification'\n",
    "\n",
    "experiment=Experiment(ws, experiment_name)\n",
    "\n",
    "output = {}\n",
    "output['SDK version'] = azureml.core.VERSION\n",
    "output['Subscription ID'] = ws.subscription_id\n",
    "output['Workspace Name'] = ws.name\n",
    "output['Resource Group'] = ws.resource_group\n",
    "output['Location'] = ws.location\n",
    "output['Project Directory'] = project_folder\n",
    "output['Experiment Name'] = experiment.name\n",
    "pd.set_option('display.max_colwidth', -1)\n",
    "pd.DataFrame(data = output, index = ['']).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Diagnostics\n",
    "\n",
    "Opt-in diagnostics for better experience, quality, and security of future releases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.telemetry import set_diagnostics_collection\n",
    "set_diagnostics_collection(send_diagnostics=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Digits Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "\n",
    "digits = datasets.load_digits()\n",
    "\n",
    "# Exclude the first 100 rows from training so that they can be used for test.\n",
    "X_digits = digits.data[100:,:]\n",
    "y_digits = digits.target[100:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Instantiate Auto ML Config\n",
    "\n",
    "Instantiate a AutoMLConfig object. This defines the settings and data used to run the experiment.\n",
    "\n",
    "|Property|Description|\n",
    "|-|-|\n",
    "|**task**|classification or regression|\n",
    "|**primary_metric**|This is the metric that you want to optimize.<br> Classification supports the following primary metrics <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>balanced_accuracy</i><br><i>average_precision_score_weighted</i><br><i>precision_score_weighted</i>|\n",
    "|**max_time_sec**|Time limit in seconds for each iteration|\n",
    "|**iterations**|Number of iterations. In each iteration Auto ML trains a specific pipeline with the data |\n",
    "|**n_cross_validations**|Number of cross validation splits|\n",
    "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
    "|**y**|(sparse) array-like, shape = [n_samples, ], [n_samples, n_classes]<br>Multi-class targets. An indicator matrix turns on multilabel classification.  This should be an array of integers. |\n",
    "|**path**|Relative path to the project folder.  AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder. |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "automl_config = AutoMLConfig(task = 'classification',\n",
    "                             debug_log = 'automl_errors.log',\n",
    "                             primary_metric = 'AUC_weighted',\n",
    "                             max_time_sec = 3600,\n",
    "                             iterations = 50,\n",
    "                             n_cross_validations = 3,\n",
    "                             verbosity = logging.INFO,\n",
    "                             X = X_digits, \n",
    "                             y = y_digits,\n",
    "                             path=project_folder)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training the Model\n",
    "\n",
    "You can call the submit method on the experiment object and pass the run configuration. For Local runs the execution is synchronous. Depending on the data and number of iterations this can run for while.\n",
    "You will see the currently running iterations printing to the console."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "local_run = experiment.submit(automl_config, show_output=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Optionally, you can continue an interrupted local run by calling continue_experiment without the <b>iterations</b> parameter, or run more iterations to a completed run by specifying the <b>iterations</b> parameter:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "local_run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "local_run = local_run.continue_experiment(X = X_digits, \n",
    "                                          y = y_digits, \n",
    "                                          show_output = True,\n",
    "                                          iterations = 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring the results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Widget for monitoring runs\n",
    "\n",
    "The widget will sit on \"loading\" until the first iteration completed, then you will see an auto-updating graph and table show up. It refreshed once per minute, so you should see the graph update as child runs complete.\n",
    "\n",
    "NOTE: The widget displays a link at the bottom. This links to a web-ui to explore the individual run details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.train.widgets import RunDetails\n",
    "RunDetails(local_run).show() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "#### Retrieve All Child Runs\n",
    "You can also use sdk methods to fetch all the child runs and see individual metrics that we log. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "children = list(local_run.get_children())\n",
    "metricslist = {}\n",
    "for run in children:\n",
    "    properties = run.get_properties()\n",
    "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}    \n",
    "    metricslist[int(properties['iteration'])] = metrics\n",
    "\n",
    "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
    "rundata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Retrieve the Best Model\n",
    "\n",
    "Below we select the best pipeline from our iterations. The *get_output* method on automl_classifier returns the best run and the fitted model for the last *fit* invocation. There are overloads on *get_output* that allow you to retrieve the best run and fitted model for *any* logged metric or a particular *iteration*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_run, fitted_model = local_run.get_output()\n",
    "print(best_run)\n",
    "print(fitted_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Best Model based on any other metric\n",
    "Give me the run and the model that has the smallest `log_loss`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lookup_metric = \"log_loss\"\n",
    "best_run, fitted_model = local_run.get_output(metric = lookup_metric)\n",
    "print(best_run)\n",
    "print(fitted_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model from a specific iteration\n",
    "Give me the run and the model from the 3rd iteration:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "iteration = 3\n",
    "third_run, third_model = local_run.get_output(iteration = iteration)\n",
    "print(third_run)\n",
    "print(third_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Testing the Fitted Model \n",
    "\n",
    "#### Load Test Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "digits = datasets.load_digits()\n",
    "X_digits = digits.data[:10, :]\n",
    "y_digits = digits.target[:10]\n",
    "images = digits.images[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Testing our best pipeline\n",
    "We will try to predict 2 digits and see how our model works."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Randomly select digits and test\n",
    "for index in np.random.choice(len(y_digits), 2):\n",
    "    print(index)\n",
    "    predicted = fitted_model.predict(X_digits[index:index + 1])[0]\n",
    "    label = y_digits[index]\n",
    "    title = \"Label value = %d  Predicted value = %d \" % ( label,predicted)\n",
    "    fig = plt.figure(1, figsize=(3,3))\n",
    "    ax1 = fig.add_axes((0,0,.8,.8))\n",
    "    ax1.set_title(title)\n",
    "    plt.imshow(images[index], cmap=plt.cm.gray_r, interpolation='nearest')\n",
    "    plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.6",
   "language": "python",
   "name": "python36"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
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 }
--- a/automl/02.auto-ml-regression.ipynb
+++ b/automl/02.auto-ml-regression.ipynb
@@ -1,409 +0,0 @@
 {
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
    "\n",
    "Licensed under the MIT License."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AutoML 02: Regression with local compute\n",
    "\n",
    "In this example we use the scikit learn's [diabetes dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_diabetes.html) to showcase how you can use AutoML for a simple regression problem.\n",
    "\n",
    "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook.\n",
    "\n",
    "In this notebook you would see\n",
    "1. Creating an Experiment using an existing Workspace\n",
    "2. Instantiating AutoMLConfig\n",
    "3. Training the Model using local compute\n",
    "4. Exploring the results\n",
    "5. Testing the fitted model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Experiment\n",
    "\n",
    "As part of the setup you have already created a <b>Workspace</b>. For AutoML you would need to create an <b>Experiment</b>. An <b>Experiment</b> is a named object in a <b>Workspace</b>, which is used to run experiments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "import os\n",
    "import random\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.pyplot import imshow\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn import datasets\n",
    "\n",
    "import azureml.core\n",
    "from azureml.core.experiment import Experiment\n",
    "from azureml.core.workspace import Workspace\n",
    "from azureml.train.automl import AutoMLConfig\n",
    "from azureml.train.automl.run import AutoMLRun"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ws = Workspace.from_config()\n",
    "\n",
    "# choose a name for the experiment\n",
    "experiment_name = 'automl-local-regression'\n",
    "# project folder\n",
    "project_folder = './sample_projects/automl-local-regression'\n",
    "\n",
    "experiment = Experiment(ws, experiment_name)\n",
    "\n",
    "output = {}\n",
    "output['SDK version'] = azureml.core.VERSION\n",
    "output['Subscription ID'] = ws.subscription_id\n",
    "output['Workspace Name'] = ws.name\n",
    "output['Resource Group'] = ws.resource_group\n",
    "output['Location'] = ws.location\n",
    "output['Project Directory'] = project_folder\n",
    "output['Experiment Name'] = experiment.name\n",
    "pd.set_option('display.max_colwidth', -1)\n",
    "pd.DataFrame(data = output, index = ['']).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Diagnostics\n",
    "\n",
    "Opt-in diagnostics for better experience, quality, and security of future releases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.telemetry import set_diagnostics_collection\n",
    "set_diagnostics_collection(send_diagnostics=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load diabetes dataset, a well-known built-in small dataset that comes with scikit-learn\n",
    "from sklearn.datasets import load_diabetes\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X, y = load_diabetes(return_X_y = True)\n",
    "\n",
    "columns = ['age', 'gender', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Instantiate Auto ML Config\n",
    "\n",
    "Instantiate a AutoMLConfig object. This defines the settings and data used to run the experiment.\n",
    "\n",
    "|Property|Description|\n",
    "|-|-|\n",
    "|**task**|classification or regression|\n",
    "|**primary_metric**|This is the metric that you want to optimize.<br> Regression supports the following primary metrics <br><i>spearman_correlation</i><br><i>normalized_root_mean_squared_error</i><br><i>r2_score</i><br><i>normalized_mean_absolute_error</i><br><i>normalized_root_mean_squared_log_error</i>|\n",
    "|**max_time_sec**|Time limit in seconds for each iteration|\n",
    "|**iterations**|Number of iterations. In each iteration Auto ML trains a specific pipeline with the data|\n",
    "|**n_cross_validations**|Number of cross validation splits|\n",
    "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
    "|**y**|(sparse) array-like, shape = [n_samples, ], [n_samples, n_classes]<br>Multi-class targets. An indicator matrix turns on multilabel classification.  This should be an array of integers. |\n",
    "|**path**|Relative path to the project folder.  AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder.|"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "automl_config = AutoMLConfig(task='regression',\n",
    "                             max_time_sec = 600,\n",
    "                             iterations = 10,\n",
    "                             primary_metric = 'spearman_correlation', \n",
    "                             n_cross_validations = 5,\n",
    "                             debug_log = 'automl.log',\n",
    "                             verbosity = logging.INFO,\n",
    "                             X = X_train, \n",
    "                             y = y_train,\n",
    "                             path=project_folder)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training the Model\n",
    "\n",
    "You can call the submit method on the experiment object and pass the run configuration. For Local runs the execution is synchronous. Depending on the data and number of iterations this can run for while.\n",
    "You will see the currently running iterations printing to the console."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "local_run = experiment.submit(automl_config, show_output=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "local_run"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring the results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Widget for monitoring runs\n",
    "\n",
    "The widget will sit on \"loading\" until the first iteration completed, then you will see an auto-updating graph and table show up. It refreshed once per minute, so you should see the graph update as child runs complete.\n",
    "\n",
    "NOTE: The widget displays a link at the bottom. This links to a web-ui to explore the individual run details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.train.widgets import RunDetails\n",
    "RunDetails(local_run).show() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "#### Retrieve All Child Runs\n",
    "You can also use sdk methods to fetch all the child runs and see individual metrics that we log. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "children = list(local_run.get_children())\n",
    "metricslist = {}\n",
    "for run in children:\n",
    "    properties = run.get_properties()\n",
    "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}    \n",
    "    metricslist[int(properties['iteration'])] = metrics\n",
    "    \n",
    "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
    "rundata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Retrieve the Best Model\n",
    "\n",
    "Below we select the best pipeline from our iterations. The *get_output* method on automl_classifier returns the best run and the fitted model for the last *fit* invocation. There are overloads on *get_output* that allow you to retrieve the best run and fitted model for *any* logged metric or a particular *iteration*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_run, fitted_model = local_run.get_output()\n",
    "print(best_run)\n",
    "print(fitted_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Best Model based on any other metric\n",
    "Show the run and model that has the smallest `root_mean_squared_error` (which turned out to be the same as the one with largest `spearman_correlation` value):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lookup_metric = \"root_mean_squared_error\"\n",
    "best_run, fitted_model = local_run.get_output(metric=lookup_metric)\n",
    "print(best_run)\n",
    "print(fitted_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model from a specific iteration\n",
    "\n",
    "Simply show the run and model from the 3rd iteration:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "iteration = 3\n",
    "third_run, third_model = local_run.get_output(iteration = iteration)\n",
    "print(third_run)\n",
    "print(third_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Testing the Fitted Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Predict on training and test set, and calculate residual values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_train = fitted_model.predict(X_train)\n",
    "y_residual_train = y_train - y_pred_train\n",
    "\n",
    "y_pred_test = fitted_model.predict(X_test)\n",
    "y_residual_test = y_test - y_pred_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn import datasets\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "\n",
    "# set up a multi-plot chart\n",
    "f, (a0, a1) = plt.subplots(1, 2, gridspec_kw = {'width_ratios':[1, 1], 'wspace':0, 'hspace': 0})\n",
    "f.suptitle('Regression Residual Values', fontsize = 18)\n",
    "f.set_figheight(6)\n",
    "f.set_figwidth(16)\n",
    "\n",
    "# plot residual values of training set\n",
    "a0.axis([0, 360, -200, 200])\n",
    "a0.plot(y_residual_train, 'bo', alpha = 0.5)\n",
    "a0.plot([-10,360],[0,0], 'r-', lw = 3)\n",
    "a0.text(16,170,'RMSE = {0:.2f}'.format(np.sqrt(mean_squared_error(y_train, y_pred_train))), fontsize = 12)\n",
    "a0.text(16,140,'R2 score = {0:.2f}'.format(r2_score(y_train, y_pred_train)), fontsize = 12)\n",
    "a0.set_xlabel('Training samples', fontsize = 12)\n",
    "a0.set_ylabel('Residual Values', fontsize = 12)\n",
    "# plot histogram\n",
    "a0.hist(y_residual_train, orientation = 'horizontal', color = 'b', bins = 10, histtype = 'step');\n",
    "a0.hist(y_residual_train, orientation = 'horizontal', color = 'b', alpha = 0.2, bins = 10);\n",
    "\n",
    "# plot residual values of test set\n",
    "a1.axis([0, 90, -200, 200])\n",
    "a1.plot(y_residual_test, 'bo', alpha = 0.5)\n",
    "a1.plot([-10,360],[0,0], 'r-', lw = 3)\n",
    "a1.text(5,170,'RMSE = {0:.2f}'.format(np.sqrt(mean_squared_error(y_test, y_pred_test))), fontsize = 12)\n",
    "a1.text(5,140,'R2 score = {0:.2f}'.format(r2_score(y_test, y_pred_test)), fontsize = 12)\n",
    "a1.set_xlabel('Test samples', fontsize = 12)\n",
    "a1.set_yticklabels([])\n",
    "# plot histogram\n",
    "a1.hist(y_residual_test, orientation = 'horizontal', color = 'b', bins = 10, histtype = 'step');\n",
    "a1.hist(y_residual_test, orientation = 'horizontal', color = 'b', alpha = 0.2, bins = 10);\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
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--- a/automl/03.auto-ml-remote-execution.ipynb
+++ b/automl/03.auto-ml-remote-execution.ipynb
@@ -1,471 +0,0 @@
 {
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
    "\n",
    "Licensed under the MIT License."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AutoML 03:  Remote Execution using DSVM (Ubuntu)\n",
    "\n",
    "In this example we use the scikit learn's [digit dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html) to showcase how you can use AutoML for a simple classification problem.\n",
    "\n",
    "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook.\n",
    "\n",
    "In this notebook you would see\n",
    "1. Creating an Experiment using an existing Workspace\n",
    "2. Attaching an existing DSVM to a workspace\n",
    "3. Instantiating AutoMLConfig \n",
    "4. Training the Model using the DSVM\n",
    "5. Exploring the results\n",
    "6. Testing the fitted model\n",
    "\n",
    "In addition this notebook showcases the following features\n",
    "- **Parallel** Executions for iterations\n",
    "- Asyncronous tracking of progress\n",
    "- **Cancelling** individual iterations or the entire run\n",
    "- Retrieving models for any iteration or logged metric\n",
    "- specify automl settings as **kwargs**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Experiment\n",
    "\n",
    "As part of the setup you have already created a workspace. For AutoML you would need to create a <b>Experiment</b>. An <b>Experiment</b> is a named object in a <b>Workspace</b>, which is used to run experiments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "import os\n",
    "import random\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.pyplot import imshow\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn import datasets\n",
    "\n",
    "import azureml.core\n",
    "from azureml.core.experiment import Experiment\n",
    "from azureml.core.workspace import Workspace\n",
    "from azureml.train.automl import AutoMLConfig\n",
    "from azureml.train.automl.run import AutoMLRun"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ws = Workspace.from_config()\n",
    "\n",
    "# choose a name for the run history container in the workspace\n",
    "experiment_name = 'automl-remote-dsvm4'\n",
    "# project folder\n",
    "project_folder = './sample_projects/automl-remote-dsvm4'\n",
    "\n",
    "experiment=Experiment(ws, experiment_name)\n",
    "\n",
    "output = {}\n",
    "output['SDK version'] = azureml.core.VERSION\n",
    "output['Subscription ID'] = ws.subscription_id\n",
    "output['Workspace Name'] = ws.name\n",
    "output['Resource Group'] = ws.resource_group\n",
    "output['Location'] = ws.location\n",
    "output['Project Directory'] = project_folder\n",
    "output['Experiment Name'] = experiment.name\n",
    "pd.set_option('display.max_colwidth', -1)\n",
    "pd.DataFrame(data = output, index = ['']).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Diagnostics\n",
    "\n",
    "Opt-in diagnostics for better experience, quality, and security of future releases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.telemetry import set_diagnostics_collection\n",
    "set_diagnostics_collection(send_diagnostics=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create a Remote Linux DSVM\n",
    "Note: If creation fails with a message about Marketplace purchase eligibilty, go to portal.azure.com, start creating DSVM there, and select \"Want to create programmatically\" to enable programmatic creation. Once you've enabled it, you can exit without actually creating VM.\n",
    "\n",
    "**Note**: By default SSH runs on port 22 and you don't need to specify it. But if for security reasons you can switch to a different port (such as 5022), you can append the port number to the address. [Read more](https://render.githubusercontent.com/documentation/sdk/ssh-issue.md) on this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.core.compute import DsvmCompute\n",
    "\n",
    "dsvm_name = 'mydsvm'\n",
    "try:\n",
    "    dsvm_compute = DsvmCompute(ws, dsvm_name)\n",
    "    print('found existing dsvm.')\n",
    "except:\n",
    "    print('creating new dsvm.')\n",
    "    dsvm_config = DsvmCompute.provisioning_configuration(vm_size = \"Standard_D2_v2\")\n",
    "    dsvm_compute = DsvmCompute.create(ws, name = dsvm_name, provisioning_configuration = dsvm_config)\n",
    "    dsvm_compute.wait_for_completion(show_output = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Get Data File\n",
    "For remote executions you should author a get_data.py file containing a get_data() function. This file should be in the root directory of the project. You can encapsulate code to read data either from a blob storage or local disk in this file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not os.path.exists(project_folder):\n",
    "    os.makedirs(project_folder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%writefile $project_folder/get_data.py\n",
    "\n",
    "from sklearn import datasets\n",
    "from scipy import sparse\n",
    "import numpy as np\n",
    "\n",
    "def get_data():\n",
    "    \n",
    "    digits = datasets.load_digits()\n",
    "    X_digits = digits.data[100:,:]\n",
    "    y_digits = digits.target[100:]\n",
    "\n",
    "    return { \"X\" : X_digits, \"y\" : y_digits }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Instantiate AutoML <a class=\"anchor\" id=\"Instatiate-AutoML-Remote-DSVM\"></a>\n",
    "\n",
    "You can specify automl_settings as **kwargs** as well. Also note that you can use the get_data() symantic for local excutions too. \n",
    "\n",
    "<i>Note: For Remote DSVM and Batch AI you cannot pass Numpy arrays directly to the fit method.</i>\n",
    "\n",
    "|Property|Description|\n",
    "|-|-|\n",
    "|**primary_metric**|This is the metric that you want to optimize.<br> Classification supports the following primary metrics <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>balanced_accuracy</i><br><i>average_precision_score_weighted</i><br><i>precision_score_weighted</i>|\n",
    "|**max_time_sec**|Time limit in seconds for each iteration|\n",
    "|**iterations**|Number of iterations. In each iteration Auto ML trains a specific pipeline with the data|\n",
    "|**n_cross_validations**|Number of cross validation splits|\n",
    "|**concurrent_iterations**|Max number of iterations that would be executed in parallel.  This should be less than the number of cores on the DSVM."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "automl_settings = {\n",
    "    \"max_time_sec\": 600,\n",
    "    \"iterations\": 20,\n",
    "    \"n_cross_validations\": 5,\n",
    "    \"primary_metric\": 'AUC_weighted',\n",
    "    \"preprocess\": False,\n",
    "    \"concurrent_iterations\": 2,\n",
    "    \"verbosity\": logging.INFO\n",
    "}\n",
    "\n",
    "automl_config = AutoMLConfig(task = 'classification',\n",
    "                             debug_log = 'automl_errors.log',\n",
    "                             path=project_folder, \n",
    "                             compute_target = dsvm_compute,\n",
    "                             data_script = project_folder + \"/get_data.py\",\n",
    "                             **automl_settings\n",
    "                            )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Note</b> that the first run on a new DSVM may take a several minutes to preparing the environment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "remote_run = experiment.submit(automl_config, show_output=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring the Results\n",
    "\n",
    "#### Loading executed runs\n",
    "In case you need to load a previously executed run given a run id please enable the below cell"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "remote_run = AutoMLRun(experiment=experiment, run_id='AutoML_480d3ed6-fc94-44aa-8f4e-0b945db9d3ef')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Widget for monitoring runs\n",
    "\n",
    "The widget will sit on \"loading\" until the first iteration completed, then you will see an auto-updating graph and table show up. It refreshed once per minute, so you should see the graph update as child runs complete.\n",
    "\n",
    "You can click on a pipeline to see run properties and output logs.  Logs are also available on the DSVM under /tmp/azureml_run/{iterationid}/azureml-logs\n",
    "\n",
    "NOTE: The widget displays a link at the bottom. This links to a web-ui to explore the individual run details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.train.widgets import RunDetails\n",
    "RunDetails(remote_run).show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# wait till the run finishes\n",
    "remote_run.wait_for_completion(show_output = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "#### Retrieve All Child Runs\n",
    "You can also use sdk methods to fetch all the child runs and see individual metrics that we log. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "children = list(remote_run.get_children())\n",
    "metricslist = {}\n",
    "for run in children:\n",
    "    properties = run.get_properties()\n",
    "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}    \n",
    "    metricslist[int(properties['iteration'])] = metrics\n",
    "\n",
    "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
    "rundata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Canceling runs\n",
    "\n",
    "You can cancel ongoing remote runs using the *cancel()* and *cancel_iteration()* functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cancel the ongoing experiment and stop scheduling new iterations\n",
    "# remote_run.cancel()\n",
    "\n",
    "# Cancel iteration 1 and move onto iteration 2\n",
    "# remote_run.cancel_iteration(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Retrieve the Best Model\n",
    "\n",
    "Below we select the best pipeline from our iterations. The *get_output* method on automl_classifier returns the best run and the fitted model for the last *fit* invocation. There are overloads on *get_output* that allow you to retrieve the best run and fitted model for *any* logged metric or a particular *iteration*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_run, fitted_model = remote_run.get_output()\n",
    "print(best_run)\n",
    "print(fitted_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Best Model based on any other metric\n",
    "Show the run/model which has the smallest `log_loss` value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lookup_metric = \"log_loss\"\n",
    "best_run, fitted_model = remote_run.get_output(metric = lookup_metric)\n",
    "print(best_run)\n",
    "print(fitted_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model from a specific iteration\n",
    "Show the run and model from the 3rd iteration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "iteration = 3\n",
    "third_run, third_model = remote_run.get_output(iteration=iteration)\n",
    "print(third_run)\n",
    "print(third_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Testing the Fitted Model <a class=\"anchor\" id=\"Testing-the-Fitted-Model-Remote-DSVM\"></a>\n",
    "\n",
    "#### Load Test Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "digits = datasets.load_digits()\n",
    "X_digits = digits.data[:10, :]\n",
    "y_digits = digits.target[:10]\n",
    "images = digits.images[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Testing our best pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Randomly select digits and test\n",
    "for index in np.random.choice(len(y_digits), 2):\n",
    "    print(index)\n",
    "    predicted = fitted_model.predict(X_digits[index:index + 1])[0]\n",
    "    label = y_digits[index]\n",
    "    title = \"Label value = %d  Predicted value = %d \" % ( label,predicted)\n",
    "    fig = plt.figure(1, figsize=(3,3))\n",
    "    ax1 = fig.add_axes((0,0,.8,.8))\n",
    "    ax1.set_title(title)\n",
    "    plt.imshow(images[index], cmap=plt.cm.gray_r, interpolation='nearest')\n",
    "    plt.show()"
   ]
  }
 ],
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   "display_name": "Python 3.6",
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    "name": "ipython",
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--- a/automl/03b.auto-ml-remote-batchai.ipynb
+++ b/automl/03b.auto-ml-remote-batchai.ipynb
@@ -1,522 +0,0 @@
 {
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
    "\n",
    "Licensed under the MIT License."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AutoML 03:  Remote Execution using Batch AI\n",
    "\n",
    "In this example we use the scikit learn's [diabetes dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_diabetes.html) to showcase how you can use AutoML for a simple classification problem.\n",
    "\n",
    "Make sure you have executed the [setup](setup.ipynb) before running this notebook.\n",
    "\n",
    "In this notebook you would see\n",
    "1. Creating an Experiment using an existing Workspace\n",
    "2. Attaching an existing Batch AI compute to a workspace\n",
    "3. Instantiating AutoMLConfig \n",
    "4. Training the Model using the Batch AI\n",
    "5. Exploring the results\n",
    "6. Testing the fitted model\n",
    "\n",
    "In addition this notebook showcases the following features\n",
    "- **Parallel** Executions for iterations\n",
    "- Asyncronous tracking of progress\n",
    "- **Cancelling** individual iterations or the entire run\n",
    "- Retrieving models for any iteration or logged metric\n",
    "- specify automl settings as **kwargs**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Experiment\n",
    "\n",
    "As part of the setup you have already created a workspace. For AutoML you would need to create a <b>Experiment</b>. An <b>Experiment</b> is a named object in a <b>Workspace</b>, which is used to run experiments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "import os\n",
    "import random\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.pyplot import imshow\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn import datasets\n",
    "\n",
    "import azureml.core\n",
    "from azureml.core.experiment import Experiment\n",
    "from azureml.core.workspace import Workspace\n",
    "from azureml.train.automl import AutoMLConfig\n",
    "from azureml.train.automl.run import AutoMLRun"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ws = Workspace.from_config()\n",
    "\n",
    "# choose a name for the run history container in the workspace\n",
    "experiment_name = 'automl-remote-batchai'\n",
    "# project folder\n",
    "project_folder = './sample_projects/automl-remote-batchai'\n",
    "\n",
    "experiment=Experiment(ws, experiment_name)\n",
    "\n",
    "output = {}\n",
    "output['SDK version'] = azureml.core.VERSION\n",
    "output['Subscription ID'] = ws.subscription_id\n",
    "output['Workspace Name'] = ws.name\n",
    "output['Resource Group'] = ws.resource_group\n",
    "output['Location'] = ws.location\n",
    "output['Project Directory'] = project_folder\n",
    "output['Experiment Name'] = experiment.name\n",
    "pd.set_option('display.max_colwidth', -1)\n",
    "pd.DataFrame(data = output, index = ['']).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Diagnostics\n",
    "\n",
    "Opt-in diagnostics for better experience, quality, and security of future releases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.telemetry import set_diagnostics_collection\n",
    "set_diagnostics_collection(send_diagnostics=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Batch AI Cluster\n",
    "The cluster is created as Machine Learning Compute and will appear under your workspace.\n",
    "\n",
    "<b>Note</b>: The cluster creation can take over 10 minutes, please be patient.\n",
    "\n",
    "As with other Azure services, there are limits on certain resources (for eg. BatchAI cluster size) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.core.compute import BatchAiCompute\n",
    "from azureml.core.compute import ComputeTarget\n",
    "\n",
    "# choose a name for your cluster\n",
    "batchai_cluster_name = ws.name + \"cpu\"\n",
    "\n",
    "found = False\n",
    "# see if this compute target already exists in the workspace\n",
    "for ct in ws.compute_targets():\n",
    "    print(ct.name, ct.type)\n",
    "    if (ct.name == batchai_cluster_name and ct.type == 'BatchAI'):\n",
    "        found = True\n",
    "        print('found compute target. just use it.')\n",
    "        compute_target = ct\n",
    "        break\n",
    "        \n",
    "if not found:\n",
    "    print('creating a new compute target...')\n",
    "    provisioning_config = BatchAiCompute.provisioning_configuration(vm_size = \"STANDARD_D2_V2\", # for GPU, use \"STANDARD_NC6\"\n",
    "                                                                #vm_priority = 'lowpriority', # optional\n",
    "                                                                autoscale_enabled = True,\n",
    "                                                                cluster_min_nodes = 1, \n",
    "                                                                cluster_max_nodes = 4)\n",
    "\n",
    "    # create the cluster\n",
    "    compute_target = ComputeTarget.create(ws,batchai_cluster_name, provisioning_config)\n",
    "    \n",
    "    # can poll for a minimum number of nodes and for a specific timeout. \n",
    "    # if no min node count is provided it will use the scale settings for the cluster\n",
    "    compute_target.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)\n",
    "    \n",
    "     # For a more detailed view of current BatchAI cluster status, use the 'status' property    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Get Data File\n",
    "For remote executions you should author a get_data.py file containing a get_data() function. This file should be in the root directory of the project. You can encapsulate code to read data either from a blob storage or local disk in this file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not os.path.exists(project_folder):\n",
    "    os.makedirs(project_folder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%writefile $project_folder/get_data.py\n",
    "\n",
    "from sklearn import datasets\n",
    "from scipy import sparse\n",
    "import numpy as np\n",
    "\n",
    "def get_data():\n",
    "    \n",
    "    digits = datasets.load_digits()\n",
    "    X_digits = digits.data\n",
    "    y_digits = digits.target\n",
    "\n",
    "    return { \"X\" : X_digits, \"y\" : y_digits }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Instantiate AutoML <a class=\"anchor\" id=\"Instatiate-AutoML-Remote-DSVM\"></a>\n",
    "\n",
    "You can specify automl_settings as **kwargs** as well. Also note that you can use the get_data() symantic for local excutions too. \n",
    "\n",
    "<i>Note: For Remote DSVM and Batch AI you cannot pass Numpy arrays directly to the fit method.</i>\n",
    "\n",
    "|Property|Description|\n",
    "|-|-|\n",
    "|**primary_metric**|This is the metric that you want to optimize.<br> Classification supports the following primary metrics <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>balanced_accuracy</i><br><i>average_precision_score_weighted</i><br><i>precision_score_weighted</i>|\n",
    "|**max_time_sec**|Time limit in seconds for each iteration|\n",
    "|**iterations**|Number of iterations. In each iteration Auto ML trains a specific pipeline with the data|\n",
    "|**n_cross_validations**|Number of cross validation splits|\n",
    "|**concurrent_iterations**|Max number of iterations that would be executed in parallel.  This should be less than the number of cores on the DSVM."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "automl_settings = {\n",
    "    \"max_time_sec\": 120,\n",
    "    \"iterations\": 20,\n",
    "    \"n_cross_validations\": 5,\n",
    "    \"primary_metric\": 'AUC_weighted',\n",
    "    \"preprocess\": False,\n",
    "    \"concurrent_iterations\": 5,\n",
    "    \"verbosity\": logging.INFO\n",
    "}\n",
    "\n",
    "automl_config = AutoMLConfig(task = 'classification',\n",
    "                             debug_log = 'automl_errors.log',\n",
    "                             path=project_folder,\n",
    "                             compute_target = compute_target,\n",
    "                             data_script = project_folder + \"/get_data.py\",\n",
    "                             **automl_settings\n",
    "                            )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "remote_run = experiment.submit(automl_config, show_output=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring the Results\n",
    "\n",
    "#### Loading executed runs\n",
    "In case you need to load a previously executed run given a run id please enable the below cell"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "remote_run = AutoMLRun(experiment=experiment, run_id='AutoML_5db13491-c92a-4f1d-b622-8ab8d973a058')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Widget for monitoring runs\n",
    "\n",
    "The widget will sit on \"loading\" until the first iteration completed, then you will see an auto-updating graph and table show up. It refreshed once per minute, so you should see the graph update as child runs complete.\n",
    "\n",
    "You can click on a pipeline to see run properties and output logs.  Logs are also available on the DSVM under /tmp/azureml_run/{iterationid}/azureml-logs\n",
    "\n",
    "NOTE: The widget displays a link at the bottom. This links to a web-ui to explore the individual run details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "remote_run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.train.widgets import RunDetails\n",
    "RunDetails(remote_run).show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# wait till the run finishes\n",
    "remote_run.wait_for_completion(show_output = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "#### Retrieve All Child Runs\n",
    "You can also use sdk methods to fetch all the child runs and see individual metrics that we log. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "children = list(remote_run.get_children())\n",
    "metricslist = {}\n",
    "for run in children:\n",
    "    properties = run.get_properties()\n",
    "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}    \n",
    "    metricslist[int(properties['iteration'])] = metrics\n",
    "\n",
    "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
    "rundata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Canceling runs\n",
    "\n",
    "You can cancel ongoing remote runs using the *cancel()* and *cancel_iteration()* functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cancel the ongoing experiment and stop scheduling new iterations\n",
    "# remote_run.cancel()\n",
    "\n",
    "# Cancel iteration 1 and move onto iteration 2\n",
    "# remote_run.cancel_iteration(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Retrieve the Best Model\n",
    "\n",
    "Below we select the best pipeline from our iterations. The *get_output* method on automl_classifier returns the best run and the fitted model for the last *fit* invocation. There are overloads on *get_output* that allow you to retrieve the best run and fitted model for *any* logged metric or a particular *iteration*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_run, fitted_model = remote_run.get_output()\n",
    "print(best_run)\n",
    "print(fitted_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Best Model based on any other metric\n",
    "Show the run/model which has the smallest `log_loss` value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lookup_metric = \"log_loss\"\n",
    "best_run, fitted_model = remote_run.get_output(metric = lookup_metric)\n",
    "print(best_run)\n",
    "print(fitted_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model from a specific iteration\n",
    "Show the run and model from the 3rd iteration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "iteration = 3\n",
    "third_run, third_model = remote_run.get_output(iteration=iteration)\n",
    "print(third_run)\n",
    "print(third_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Register fitted model for deployment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "description = 'AutoML Model'\n",
    "tags = None\n",
    "remote_run.register_model(description=description, tags=tags)\n",
    "remote_run.model_id # Use this id to deploy the model as a web service in Azure"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Testing the Fitted Model <a class=\"anchor\" id=\"Testing-the-Fitted-Model-Remote-DSVM\"></a>\n",
    "\n",
    "#### Load Test Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "digits = datasets.load_digits()\n",
    "X_digits = digits.data[:10, :]\n",
    "y_digits = digits.target[:10]\n",
    "images = digits.images[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Testing our best pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Randomly select digits and test\n",
    "for index in np.random.choice(len(y_digits), 2):\n",
    "    print(index)\n",
    "    predicted = fitted_model.predict(X_digits[index:index + 1])[0]\n",
    "    label = y_digits[index]\n",
    "    title = \"Label value = %d  Predicted value = %d \" % ( label,predicted)\n",
    "    fig = plt.figure(1, figsize=(3,3))\n",
    "    ax1 = fig.add_axes((0,0,.8,.8))\n",
    "    ax1.set_title(title)\n",
    "    plt.imshow(images[index], cmap=plt.cm.gray_r, interpolation='nearest')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.6",
   "language": "python",
   "name": "python36"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
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 }
--- a/automl/04.auto-ml-remote-execution-text-data-blob-store.ipynb
+++ b/automl/04.auto-ml-remote-execution-text-data-blob-store.ipynb
@@ -1,495 +0,0 @@
 {
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
    "\n",
    "Licensed under the MIT License."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Auto ML : Remote Execution with Text data from Blobstorage\n",
    "\n",
    "In this example we use the [Burning Man 2016 dataset](https://innovate.burningman.org/datasets-page/) to showcase how you can use AutoML to handle text data from a Azure blobstorage.\n",
    "\n",
    "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook.\n",
    "\n",
    "In this notebook you would see\n",
    "1. Creating an Experiment using an existing Workspace\n",
    "2. Attaching an existing DSVM to a workspace\n",
    "3. Instantiating AutoMLConfig \n",
    "4. Training the Model using the DSVM\n",
    "5. Exploring the results\n",
    "6. Testing the fitted model\n",
    "\n",
    "In addition this notebook showcases the following features\n",
    "- **Parallel** Executions for iterations\n",
    "- Asyncronous tracking of progress\n",
    "- **Cancelling** individual iterations or the entire run\n",
    "- Retrieving models for any iteration or logged metric\n",
    "- specify automl settings as **kwargs**\n",
    "- handling **text** data with **preprocess** flag\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Experiment\n",
    "\n",
    "As part of the setup you have already created a <b>Workspace</b>. For AutoML you would need to create an <b>Experiment</b>. An <b>Experiment</b> is a named object in a <b>Workspace</b>, which is used to run experiments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "import os\n",
    "import random\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.pyplot import imshow\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn import datasets\n",
    "\n",
    "import azureml.core\n",
    "from azureml.core.experiment import Experiment\n",
    "from azureml.core.workspace import Workspace\n",
    "from azureml.train.automl import AutoMLConfig\n",
    "from azureml.train.automl.run import AutoMLRun"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ws = Workspace.from_config()\n",
    "\n",
    "# choose a name for the run history container in the workspace\n",
    "experiment_name = 'automl-remote-dsvm-blobstore'\n",
    "# project folder\n",
    "project_folder = './sample_projects/automl-remote-dsvm-blobstore'\n",
    "\n",
    "experiment = Experiment(ws, experiment_name)\n",
    "\n",
    "output = {}\n",
    "output['SDK version'] = azureml.core.VERSION\n",
    "output['Subscription ID'] = ws.subscription_id\n",
    "output['Workspace'] = ws.name\n",
    "output['Resource Group'] = ws.resource_group\n",
    "output['Location'] = ws.location\n",
    "output['Project Directory'] = project_folder\n",
    "output['Experiment Name'] = experiment.name\n",
    "pd.set_option('display.max_colwidth', -1)\n",
    "pd.DataFrame(data=output, index=['']).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Diagnostics\n",
    "\n",
    "Opt-in diagnostics for better experience, quality, and security of future releases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.telemetry import set_diagnostics_collection\n",
    "set_diagnostics_collection(send_diagnostics=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Attach a Remote Linux DSVM\n",
    "To use remote docker commpute target:\n",
    "1. Create a Linux DSVM in Azure. Here is some [quick instructions](https://docs.microsoft.com/en-us/azure/machine-learning/desktop-workbench/how-to-create-dsvm-hdi). Make sure you use the Ubuntu flavor, NOT CentOS.  Make sure that disk space is available under /tmp because AutoML creates files under /tmp/azureml_runs. The DSVM should have more cores than the number of parallel runs that you plan to enable. It should also have at least 4Gb per core.\n",
    "2. Enter the IP address, username and password below\n",
    "\n",
    "**Note**: By default SSH runs on port 22 and you don't need to specify it. But if for security reasons you can switch to a different port (such as 5022), you can append the port number to the address. [Read more](https://render.githubusercontent.com/documentation/sdk/ssh-issue.md) on this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.core.compute import RemoteCompute\n",
    "\n",
    "# Add your VM information below\n",
    "dsvm_name     = 'mydsvm1'\n",
    "dsvm_ip_addr  = '<<ip_addr>>'\n",
    "dsvm_username = '<<username>>'\n",
    "dsvm_password = '<<password>>'\n",
    "\n",
    "dsvm_compute = RemoteCompute.attach(workspace=ws, name=dsvm_name, address=dsvm_ip_addr, username=dsvm_username, password=dsvm_password, ssh_port=22)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Get Data File\n",
    "For remote executions you should author a get_data.py file containing a get_data() function. This file should be in the root directory of the project. You can encapsulate code to read data either from a blob storage or local disk in this file.\n",
    "\n",
    "The *get_data()* function returns a [dictionary](README.md#getdata)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not os.path.exists(project_folder):\n",
    "    os.makedirs(project_folder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%writefile $project_folder/get_data.py\n",
    "\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "def get_data():\n",
    "    # Burning man 2016 data\n",
    "    df = pd.read_csv(\"https://automldemods.blob.core.windows.net/datasets/PlayaEvents2016,_1.6MB,_3.4k-rows.cleaned.2.tsv\",\n",
    "                     delimiter=\"\\t\", quotechar='\"')\n",
    "    # get integer labels\n",
    "    le = LabelEncoder()\n",
    "    le.fit(df[\"Label\"].values)\n",
    "    y = le.transform(df[\"Label\"].values)\n",
    "    df = df.drop([\"Label\"], axis=1)\n",
    "\n",
    "    df_train, _, y_train, _ = train_test_split(df, y, test_size=0.1, random_state=42)\n",
    "\n",
    "    return { \"X\" : df, \"y\" : y }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### View data\n",
    "\n",
    "You can execute the *get_data()* function locally to view the *train* data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%run  $project_folder/get_data.py\n",
    "data_dict = get_data()\n",
    "df = data_dict[\"X\"]\n",
    "y = data_dict[\"y\"]\n",
    "pd.set_option('display.max_colwidth', 15)\n",
    "df['Label'] = pd.Series(y, index=df.index)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Instantiate AutoML <a class=\"anchor\" id=\"Instatiate-AutoML-Remote-DSVM\"></a>\n",
    "\n",
    "You can specify automl_settings as **kwargs** as well. Also note that you can use the get_data() symantic for local excutions too. \n",
    "\n",
    "<i>Note: For Remote DSVM and Batch AI you cannot pass Numpy arrays directly to the fit method.</i>\n",
    "\n",
    "|Property|Description|\n",
    "|-|-|\n",
    "|**primary_metric**|This is the metric that you want to optimize.<br> Classification supports the following primary metrics <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>balanced_accuracy</i><br><i>average_precision_score_weighted</i><br><i>precision_score_weighted</i>|\n",
    "|**max_time_sec**|Time limit in seconds for each iteration|\n",
    "|**iterations**|Number of iterations. In each iteration Auto ML trains a specific pipeline with the data|\n",
    "|**n_cross_validations**|Number of cross validation splits|\n",
    "|**concurrent_iterations**|Max number of iterations that would be executed in parallel.  This should be less than the number of cores on the DSVM\n",
    "|**preprocess**| *True/False* <br>Setting this to *True* enables AutoML to perform preprocessing <br>on the input to handle *missing data*, and perform some common *feature extraction*|\n",
    "|**max_cores_per_iteration**| Indicates how many cores on the compute target would be used to train a single pipeline.<br> Default is *1*, you can set it to *-1* to use all cores|"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "automl_settings = {\n",
    "    \"max_time_sec\": 3600,\n",
    "    \"iterations\": 10,\n",
    "    \"n_cross_validations\": 5,\n",
    "    \"primary_metric\": 'AUC_weighted',\n",
    "    \"preprocess\": True,\n",
    "    \"max_cores_per_iteration\": 2\n",
    "}\n",
    "\n",
    "automl_config = AutoMLConfig(task = 'classification',\n",
    "                             path=project_folder,\n",
    "                             compute_target = dsvm_compute,\n",
    "                             data_script = project_folder + \"/get_data.py\",\n",
    "                             **automl_settings\n",
    "                            )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training the Model <a class=\"anchor\" id=\"Training-the-model-Remote-DSVM\"></a>\n",
    "\n",
    "For remote runs the execution is asynchronous, so you will see the iterations get populated as they complete. You can interact with the widgets/models even when the experiment is running to retreive the best model up to that point. Once you are satisfied with the model you can cancel a particular iteration or the whole run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "remote_run = experiment.submit(automl_config)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring the Results <a class=\"anchor\" id=\"Exploring-the-Results-Remote-DSVM\"></a>\n",
    "#### Widget for monitoring runs\n",
    "\n",
    "The widget will sit on \"loading\" until the first iteration completed, then you will see an auto-updating graph and table show up. It refreshed once per minute, so you should see the graph update as child runs complete.\n",
    "\n",
    "You can click on a pipeline to see run properties and output logs. Logs are also available on the DSVM under /tmp/azureml_run/{iterationid}/azureml-logs\n",
    "\n",
    "NOTE: The widget displays a link at the bottom. This links to a web-ui to explore the individual run details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.train.widgets import RunDetails\n",
    "RunDetails(remote_run).show() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "#### Retrieve All Child Runs\n",
    "You can also use sdk methods to fetch all the child runs and see individual metrics that we log. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "children = list(remote_run.get_children())\n",
    "metricslist = {}\n",
    "for run in children:\n",
    "    properties = run.get_properties()\n",
    "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}    \n",
    "    metricslist[int(properties['iteration'])] = metrics\n",
    "\n",
    "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
    "rundata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Canceling runs\n",
    "You can cancel ongoing remote runs using the *cancel()* and *cancel_iteration()* functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cancel the ongoing experiment and stop scheduling new iterations\n",
    "remote_run.cancel()\n",
    "\n",
    "# Cancel iteration 1 and move onto iteration 2\n",
    "# remote_run.cancel_iteration(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Retrieve the Best Model\n",
    "\n",
    "Below we select the best pipeline from our iterations. The *get_output* method on automl_classifier returns the best run and the fitted model for the last *fit* invocation. There are overloads on *get_output* that allow you to retrieve the best run and fitted model for *any* logged metric or a particular *iteration*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_run, fitted_model = remote_run.get_output()\n",
    "print(best_run)\n",
    "print(fitted_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Best Model based on any other metric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# lookup_metric = \"accuracy\"\n",
    "# best_run, fitted_model = remote_run.get_output(metric=lookup_metric)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model from a specific iteration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "iteration = 0\n",
    "zero_run, zero_model = remote_run.get_output(iteration=iteration)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Register fitted model for deployment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "description = 'AutoML Model'\n",
    "tags = None\n",
    "remote_run.register_model(description=description, tags=tags)\n",
    "remote_run.model_id # Use this id to deploy the model as a web service in Azure"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Testing the Fitted Model <a class=\"anchor\" id=\"Testing-the-Fitted-Model-Remote-DSVM\"></a>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from pandas_ml import ConfusionMatrix\n",
    "\n",
    "df = pd.read_csv(\"https://automldemods.blob.core.windows.net/datasets/PlayaEvents2016,_1.6MB,_3.4k-rows.cleaned.2.tsv\",\n",
    "                     delimiter=\"\\t\", quotechar='\"')\n",
    "\n",
    "# get integer labels\n",
    "le = LabelEncoder()\n",
    "le.fit(df[\"Label\"].values)\n",
    "y = le.transform(df[\"Label\"].values)\n",
    "df = df.drop([\"Label\"], axis=1)\n",
    "\n",
    "_, df_test, _, y_test = train_test_split(df, y, test_size=0.1, random_state=42)\n",
    "\n",
    "\n",
    "ypred = fitted_model.predict(df_test.values)\n",
    "\n",
    "\n",
    "ypred_strings = le.inverse_transform(ypred)\n",
    "ytest_strings = le.inverse_transform(y_test)\n",
    "\n",
    "cm = ConfusionMatrix(ytest_strings, ypred_strings)\n",
    "\n",
    "print(cm)\n",
    "\n",
    "cm.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.6",
   "language": "python",
   "name": "python36"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
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--- a/automl/05.auto-ml-missing-data-Blacklist-Early-Termination.ipynb
+++ b/automl/05.auto-ml-missing-data-Blacklist-Early-Termination.ipynb
@@ -1,396 +0,0 @@
 {
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
    "\n",
    "Licensed under the MIT License."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AutoML 05 : Blacklisting models, Early termination and handling missing data\n",
    "\n",
    "In this example we use the scikit learn's [digit dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html) to showcase how you can use AutoML for handling missing values in data. We also provide a stopping metric indicating a target for the primary metric so that AutoML can terminate the run without necessarly going through all the iterations. Finally, if you want to avoid a certain pipeline, we allow you to specify a black list of algos that AutoML will ignore for this run.\n",
    "\n",
    "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook.\n",
    "\n",
    "In this notebook you would see\n",
    "1. Creating an Experiment using an existing Workspace\n",
    "2. Instantiating AutoMLConfig\n",
    "4. Training the Model\n",
    "5. Exploring the results\n",
    "6. Testing the fitted model\n",
    "\n",
    "In addition this notebook showcases the following features\n",
    "- **Blacklist** certain pipelines\n",
    "- Specify a **target metrics** to indicate stopping criteria\n",
    "- Handling **Missing Data** in the input\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## Create Experiment\n",
    "\n",
    "As part of the setup you have already created a <b>Workspace</b>. For AutoML you would need to create an <b>Experiment</b>. An <b>Experiment</b> is a named object in a <b>Workspace</b>, which is used to run experiments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "import os\n",
    "import random\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.pyplot import imshow\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn import datasets\n",
    "\n",
    "import azureml.core\n",
    "from azureml.core.experiment import Experiment\n",
    "from azureml.core.workspace import Workspace\n",
    "from azureml.train.automl import AutoMLConfig\n",
    "from azureml.train.automl.run import AutoMLRun"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ws = Workspace.from_config()\n",
    "\n",
    "# choose a name for the experiment\n",
    "experiment_name = 'automl-local-missing-data'\n",
    "# project folder\n",
    "project_folder = './sample_projects/automl-local-missing-data'\n",
    "\n",
    "experiment=Experiment(ws, experiment_name)\n",
    "\n",
    "output = {}\n",
    "output['SDK version'] = azureml.core.VERSION\n",
    "output['Subscription ID'] = ws.subscription_id\n",
    "output['Workspace'] = ws.name\n",
    "output['Resource Group'] = ws.resource_group\n",
    "output['Location'] = ws.location\n",
    "output['Project Directory'] = project_folder\n",
    "output['Experiment Name'] = experiment.name\n",
    "pd.set_option('display.max_colwidth', -1)\n",
    "pd.DataFrame(data=output, index=['']).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Diagnostics\n",
    "\n",
    "Opt-in diagnostics for better experience, quality, and security of future releases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.telemetry import set_diagnostics_collection\n",
    "set_diagnostics_collection(send_diagnostics=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating Missing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import sparse\n",
    "\n",
    "digits = datasets.load_digits()\n",
    "X_digits = digits.data[10:,:]\n",
    "y_digits = digits.target[10:]\n",
    "\n",
    "# Add missing values in 75% of the lines\n",
    "missing_rate = 0.75\n",
    "n_missing_samples = int(np.floor(X_digits.shape[0] * missing_rate))\n",
    "missing_samples = np.hstack((np.zeros(X_digits.shape[0] - n_missing_samples, dtype=np.bool), np.ones(n_missing_samples, dtype=np.bool)))\n",
    "rng = np.random.RandomState(0)\n",
    "rng.shuffle(missing_samples)\n",
    "missing_features = rng.randint(0, X_digits.shape[1], n_missing_samples)\n",
    "X_digits[np.where(missing_samples)[0], missing_features] = np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(data=X_digits)\n",
    "df['Label'] = pd.Series(y_digits, index=df.index)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Instantiate Auto ML Config\n",
    "\n",
    "\n",
    "This defines the settings and data used to run the experiment.\n",
    "\n",
    "|Property|Description|\n",
    "|-|-|\n",
    "|**task**|classification or regression|\n",
    "|**primary_metric**|This is the metric that you want to optimize.<br> Classification supports the following primary metrics <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>balanced_accuracy</i><br><i>average_precision_score_weighted</i><br><i>precision_score_weighted</i>|\n",
    "|**max_time_sec**|Time limit in seconds for each iteration|\n",
    "|**iterations**|Number of iterations. In each iteration Auto ML trains the data with a specific pipeline|\n",
    "|**n_cross_validations**|Number of cross validation splits|\n",
    "|**preprocess**| *True/False* <br>Setting this to *True* enables Auto ML to perform preprocessing <br>on the input to handle *missing data*, and perform some common *feature extraction*|\n",
    "|**exit_score**|*double* value indicating the target for *primary_metric*. <br> Once the target is surpassed the run terminates|\n",
    "|**blacklist_algos**|*Array* of *strings* indicating pipelines to ignore for Auto ML.<br><br> Allowed values for **Classification**<br><i>LogisticRegression</i><br><i>SGDClassifierWrapper</i><br><i>NBWrapper</i><br><i>BernoulliNB</i><br><i>SVCWrapper</i><br><i>LinearSVMWrapper</i><br><i>KNeighborsClassifier</i><br><i>DecisionTreeClassifier</i><br><i>RandomForestClassifier</i><br><i>ExtraTreesClassifier</i><br><i>LightGBMClassifier</i><br><br>Allowed values for **Regression**<br><i>ElasticNet<i><br><i>GradientBoostingRegressor<i><br><i>DecisionTreeRegressor<i><br><i>KNeighborsRegressor<i><br><i>LassoLars<i><br><i>SGDRegressor<i><br><i>RandomForestRegressor<i><br><i>ExtraTreesRegressor<i>|\n",
    "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
    "|**y**|(sparse) array-like, shape = [n_samples, ], [n_samples, n_classes]<br>Multi-class targets. An indicator matrix turns on multilabel classification.  This should be an array of integers. |\n",
    "|**path**|Relative path to the project folder.  AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder. |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "automl_config = AutoMLConfig(task = 'classification',\n",
    "                             debug_log = 'automl_errors.log',\n",
    "                             primary_metric = 'AUC_weighted',\n",
    "                             max_time_sec = 3600,\n",
    "                             iterations = 20,\n",
    "                             n_cross_validations = 5,\n",
    "                             preprocess = True,\n",
    "                             exit_score = 0.994,\n",
    "                             blacklist_algos = ['KNeighborsClassifier','LinearSVMWrapper'],\n",
    "                             verbosity = logging.INFO,\n",
    "                             X = X_digits, \n",
    "                             y = y_digits,\n",
    "                             path=project_folder)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training the Model\n",
    "\n",
    "You can call the submit method on the experiment object and pass the run configuration. For Local runs the execution is synchronous. Depending on the data and number of iterations this can run for while.\n",
    "You will see the currently running iterations printing to the console."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "local_run = experiment.submit(automl_config, show_output=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring the results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Widget for monitoring runs\n",
    "\n",
    "The widget will sit on \"loading\" until the first iteration completed, then you will see an auto-updating graph and table show up. It refreshed once per minute, so you should see the graph update as child runs complete.\n",
    "\n",
    "NOTE: The widget will display a link at the bottom. This will not currently work, but will eventually link to a web-ui to explore the individual run details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.train.widgets import RunDetails\n",
    "RunDetails(local_run).show() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "#### Retrieve All Child Runs\n",
    "You can also use sdk methods to fetch all the child runs and see individual metrics that we log. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "children = list(local_run.get_children())\n",
    "metricslist = {}\n",
    "for run in children:\n",
    "    properties = run.get_properties()\n",
    "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}    \n",
    "    metricslist[int(properties['iteration'])] = metrics\n",
    "\n",
    "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
    "rundata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Retrieve the Best Model\n",
    "\n",
    "Below we select the best pipeline from our iterations. Each pipeline is a tuple of three elements. The first element is the score for the pipeline the second element is the string description of the pipeline and the last element are the pipeline objects used for each fold in the cross-validation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_run, fitted_model = local_run.get_output()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Best Model based on any other metric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# lookup_metric = \"accuracy\"\n",
    "# best_run, fitted_model = local_run.get_output(metric=lookup_metric)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model from a specific iteration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# iteration = 3\n",
    "# best_run, fitted_model = local_run.get_output(iteration=iteration)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Register fitted model for deployment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "description = 'AutoML Model'\n",
    "tags = None\n",
    "local_run.register_model(description=description, tags=tags)\n",
    "local_run.model_id # Use this id to deploy the model as a web service in Azure"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Testing the Fitted Model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "digits = datasets.load_digits()\n",
    "X_digits = digits.data[:10, :]\n",
    "y_digits = digits.target[:10]\n",
    "images = digits.images[:10]\n",
    "\n",
    "#Randomly select digits and test\n",
    "for index in np.random.choice(len(y_digits), 2):\n",
    "    print(index)\n",
    "    predicted = fitted_model.predict(X_digits[index:index + 1])[0]\n",
    "    label = y_digits[index]\n",
    "    title = \"Label value = %d  Predicted value = %d \" % ( label,predicted)\n",
    "    fig = plt.figure(1, figsize=(3,3))\n",
    "    ax1 = fig.add_axes((0,0,.8,.8))\n",
    "    ax1.set_title(title)\n",
    "    plt.imshow(images[index], cmap=plt.cm.gray_r, interpolation='nearest')\n",
    "    plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.6",
   "language": "python",
   "name": "python36"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "pygments_lexer": "ipython3",
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--- a/automl/06.auto-ml-sparse-data-custom-cv-split.ipynb
+++ b/automl/06.auto-ml-sparse-data-custom-cv-split.ipynb
@@ -1,418 +0,0 @@
 {
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
    "\n",
    "Licensed under the MIT License."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AutoML 06: Custom CV splits, handling sparse data\n",
    "\n",
    "In this example we use the scikit learn's [20newsgroup](In this example we use the scikit learn's [digit dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html) to showcase how you can use AutoML for handling sparse data and specify custom cross validation splits.\n",
    "\n",
    "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook.\n",
    "\n",
    "In this notebook you would see\n",
    "1. Creating an Experiment using an existing Workspace\n",
    "2. Instantiating AutoMLConfig\n",
    "4. Training the Model\n",
    "5. Exploring the results\n",
    "6. Testing the fitted model\n",
    "\n",
    "In addition this notebook showcases the following features\n",
    "- **Custom CV** splits \n",
    "- Handling **Sparse Data** in the input"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Experiment\n",
    "\n",
    "As part of the setup you have already created a <b>Workspace</b>. For AutoML you would need to create an <b>Experiment</b>. An <b>Experiment</b> is a named object in a <b>Workspace</b>, which is used to run experiments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "import os\n",
    "import random\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.pyplot import imshow\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn import datasets\n",
    "\n",
    "import azureml.core\n",
    "from azureml.core.experiment import Experiment\n",
    "from azureml.core.workspace import Workspace\n",
    "from azureml.train.automl import AutoMLConfig\n",
    "from azureml.train.automl.run import AutoMLRun"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ws = Workspace.from_config()\n",
    "\n",
    "# choose a name for the experiment\n",
    "experiment_name = 'automl-local-missing-data'\n",
    "# project folder\n",
    "project_folder = './sample_projects/automl-local-missing-data'\n",
    "\n",
    "experiment = Experiment(ws, experiment_name)\n",
    "\n",
    "output = {}\n",
    "output['SDK version'] = azureml.core.VERSION\n",
    "output['Subscription ID'] = ws.subscription_id\n",
    "output['Workspace'] = ws.name\n",
    "output['Resource Group'] = ws.resource_group\n",
    "output['Location'] = ws.location\n",
    "output['Project Directory'] = project_folder\n",
    "output['Experiment Name'] = experiment.name\n",
    "pd.set_option('display.max_colwidth', -1)\n",
    "pd.DataFrame(data=output, index=['']).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Diagnostics\n",
    "\n",
    "Opt-in diagnostics for better experience, quality, and security of future releases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.telemetry import set_diagnostics_collection\n",
    "set_diagnostics_collection(send_diagnostics=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating Sparse Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import fetch_20newsgroups\n",
    "from sklearn.feature_extraction.text import HashingVectorizer\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "remove = ('headers', 'footers', 'quotes')\n",
    "categories = [\n",
    "    'alt.atheism',\n",
    "    'talk.religion.misc',\n",
    "    'comp.graphics',\n",
    "    'sci.space',\n",
    "]\n",
    "data_train = fetch_20newsgroups(subset='train', categories=categories,\n",
    "                                shuffle=True, random_state=42,\n",
    "                                remove=remove)\n",
    "\n",
    "X_train, X_validation, y_train, y_validation = train_test_split(data_train.data, data_train.target, test_size=0.33, random_state=42)\n",
    "\n",
    "\n",
    "vectorizer = HashingVectorizer(stop_words='english', alternate_sign=False,\n",
    "                               n_features=2**16)\n",
    "X_train = vectorizer.transform(X_train)\n",
    "X_validation = vectorizer.transform(X_validation)\n",
    "\n",
    "summary_df = pd.DataFrame(index = ['No of Samples', 'No of Features'])\n",
    "summary_df['Train Set'] = [X_train.shape[0], X_train.shape[1]]\n",
    "summary_df['Validation Set'] = [X_validation.shape[0], X_validation.shape[1]]\n",
    "summary_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Instantiate Auto ML Config\n",
    "\n",
    "This defines the settings and data used to run the experiment.\n",
    "\n",
    "|Property|Description|\n",
    "|-|-|\n",
    "|**task**|classification or regression|\n",
    "|**primary_metric**|This is the metric that you want to optimize.<br> Classification supports the following primary metrics <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>balanced_accuracy</i><br><i>average_precision_score_weighted</i><br><i>precision_score_weighted</i>|\n",
    "|**max_time_sec**|Time limit in seconds for each iteration|\n",
    "|**iterations**|Number of iterations. In each iteration Auto ML trains a specific pipeline with the data|\n",
    "|**preprocess**| *True/False* <br>Setting this to *True* enables Auto ML to perform preprocessing <br>on the input to handle *missing data*, and perform some common *feature extraction*<br>*Note: If input data is Sparse you cannot use preprocess=True*|\n",
    "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
    "|**y**|(sparse) array-like, shape = [n_samples, ], [n_samples, n_classes]<br>Multi-class targets. An indicator matrix turns on multilabel classification.  This should be an array of integers. |\n",
    "|**X_valid**|(sparse) array-like, shape = [n_samples, n_features] for the custom Validation set|\n",
    "|**y_valid**|(sparse) array-like, shape = [n_samples, ], [n_samples, n_classes]<br>Multi-class targets. An indicator matrix turns on multilabel classification. for the custom Validation set|\n",
    "|**path**|Relative path to the project folder.  AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder.|"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "automl_config = AutoMLConfig(task = 'classification',\n",
    "                             debug_log='automl_errors.log',\n",
    "                             primary_metric='AUC_weighted',\n",
    "                             max_time_sec=3600,\n",
    "                             iterations=5,\n",
    "                             preprocess=False,\n",
    "                             verbosity=logging.INFO,\n",
    "                             X = X_train, \n",
    "                             y = y_train,\n",
    "                             X_valid = X_validation, \n",
    "                             y_valid = y_validation, \n",
    "                             path=project_folder)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training the Model\n",
    "\n",
    "You can call the submit method on the experiment object and pass the run configuration. For Local runs the execution is synchronous. Depending on the data and number of iterations this can run for while.\n",
    "You will see the currently running iterations printing to the console."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "local_run = experiment.submit(automl_config, show_output=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring the results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Widget for monitoring runs\n",
    "\n",
    "The widget will sit on \"loading\" until the first iteration completed, then you will see an auto-updating graph and table show up. It refreshed once per minute, so you should see the graph update as child runs complete.\n",
    "\n",
    "NOTE: The widget displays a link at the bottom. This links to a web-ui to explore the individual run details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.train.widgets import RunDetails\n",
    "RunDetails(local_run).show() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "#### Retrieve All Child Runs\n",
    "You can also use sdk methods to fetch all the child runs and see individual metrics that we log. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "children = list(local_run.get_children())\n",
    "metricslist = {}\n",
    "for run in children:\n",
    "    properties = run.get_properties()\n",
    "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}    \n",
    "    metricslist[int(properties['iteration'])] = metrics\n",
    "    \n",
    "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
    "rundata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Retrieve the Best Model\n",
    "\n",
    "Below we select the best pipeline from our iterations. The *get_output* method on automl_classifier returns the best run and the fitted model for the last *fit* invocation. There are overloads on *get_output* that allow you to retrieve the best run and fitted model for *any* logged metric or a particular *iteration*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_run, fitted_model = local_run.get_output()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Best Model based on any other metric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# lookup_metric = \"accuracy\"\n",
    "# best_run, fitted_model = local_run.get_output(metric=lookup_metric)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model from a specific iteration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# iteration = 3\n",
    "# best_run, fitted_model = local_run.get_output(iteration=iteration)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Register fitted model for deployment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "description = 'AutoML Model'\n",
    "tags = None\n",
    "local_run.register_model(description=description, tags=tags)\n",
    "local_run.model_id # Use this id to deploy the model as a web service in Azure"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Testing the Fitted Model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "digits = datasets.load_digits()### Testing the Fitted Model\n",
    "\n",
    "#### Load Test Data\n",
    "import sklearn\n",
    "from pandas_ml import ConfusionMatrix\n",
    "\n",
    "remove = ('headers', 'footers', 'quotes')\n",
    "categories = [\n",
    "    'alt.atheism',\n",
    "    'talk.religion.misc',\n",
    "    'comp.graphics',\n",
    "    'sci.space',\n",
    "]\n",
    "\n",
    "\n",
    "data_test = fetch_20newsgroups(subset='test', categories=categories,\n",
    "                                shuffle=True, random_state=42,\n",
    "                                remove=remove)\n",
    "\n",
    "vectorizer = HashingVectorizer(stop_words='english', alternate_sign=False,\n",
    "                               n_features=2**16)\n",
    "\n",
    "X_test = vectorizer.transform(data_test.data)\n",
    "y_test = data_test.target\n",
    "\n",
    "#### Testing our best pipeline\n",
    "\n",
    "ypred = fitted_model.predict(X_test)\n",
    "ypred_strings = [categories[i] for i in ypred]\n",
    "ytest_strings = [categories[i] for i in y_test]\n",
    "\n",
    "cm = ConfusionMatrix(ytest_strings, ypred_strings)\n",
    "print(cm)\n",
    "cm.plot()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.6",
   "language": "python",
   "name": "python36"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
 }
--- a/automl/07.auto-ml-exploring-previous-runs.ipynb
+++ b/automl/07.auto-ml-exploring-previous-runs.ipynb
@@ -1,326 +0,0 @@
 {
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
    "\n",
    "Licensed under the MIT License."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AutoML 07: Exploring previous runs\n",
    "\n",
    "In this example we present some examples on navigating previously executed runs. We also show how you can download a fitted model for any previous run.\n",
    "\n",
    "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook.\n",
    "\n",
    "In this notebook you would see\n",
    "1. List all Experiments for the workspace\n",
    "2. List AutoML runs for an Experiment\n",
    "3. Get details for a AutoML Run. (Automl settings, run widget & all metrics)\n",
    "4. Download fitted pipeline for any iteration\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# List all AutoML Experiments in a Workspace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "import os\n",
    "import random\n",
    "import re\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.pyplot import imshow\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn import datasets\n",
    "\n",
    "import azureml.core\n",
    "from azureml.core.experiment import Experiment\n",
    "from azureml.core.run import Run\n",
    "from azureml.core.workspace import Workspace\n",
    "from azureml.train.automl import AutoMLConfig\n",
    "from azureml.train.automl.run import AutoMLRun"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ws = Workspace.from_config()\n",
    "experiment_list = Experiment.list(workspace=ws)\n",
    "\n",
    "summary_df = pd.DataFrame(index = ['No of Runs'])\n",
    "pattern = re.compile('^AutoML_[^_]*$')\n",
    "for experiment in experiment_list:\n",
    "    all_runs = list(experiment.get_runs())\n",
    "    automl_runs = []\n",
    "    for run in all_runs:\n",
    "        if(pattern.match(run.id)):\n",
    "            automl_runs.append(run)    \n",
    "    summary_df[experiment.name] = [len(automl_runs)]\n",
    "    \n",
    "pd.set_option('display.max_colwidth', -1)\n",
    "summary_df.T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Diagnostics\n",
    "\n",
    "Opt-in diagnostics for better experience, quality, and security of future releases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.telemetry import set_diagnostics_collection\n",
    "set_diagnostics_collection(send_diagnostics=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# List AutoML runs for an Experiment\n",
    "You can set <i>Experiment</i> name with any experiment name from the result of the Experiment.list cell to load the AutoML runs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "experiment_name = 'automl-local-classification' # Replace this with any project name from previous cell\n",
    "\n",
    "proj = ws.experiments()[experiment_name]\n",
    "summary_df = pd.DataFrame(index = ['Type', 'Status', 'Primary Metric', 'Iterations', 'Compute', 'Name'])\n",
    "pattern = re.compile('^AutoML_[^_]*$')\n",
    "all_runs = list(proj.get_runs(properties={'azureml.runsource': 'automl'}))\n",
    "for run in all_runs:\n",
    "    if(pattern.match(run.id)):\n",
    "        properties = run.get_properties()\n",
    "        tags = run.get_tags()\n",
    "        amlsettings = eval(properties['RawAMLSettingsString'])\n",
    "        if 'iterations' in tags:\n",
    "            iterations = tags['iterations']\n",
    "        else:\n",
    "            iterations = properties['num_iterations']\n",
    "        summary_df[run.id] = [amlsettings['task_type'], run.get_details()['status'], properties['primary_metric'], iterations, properties['target'], amlsettings['name']]\n",
    "    \n",
    "from IPython.display import HTML\n",
    "projname_html = HTML(\"<h3>{}</h3>\".format(proj.name))\n",
    "\n",
    "from IPython.display import display\n",
    "display(projname_html)\n",
    "display(summary_df.T)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Get Details for a Auto ML Run\n",
    "\n",
    "Copy the project name and run id from the previous cell output to find more details on a particular run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "run_id = '' # Filling your own run_id\n",
    "\n",
    "from azureml.train.widgets import RunDetails\n",
    "\n",
    "experiment = Experiment(ws, experiment_name)\n",
    "ml_run = AutoMLRun(experiment=experiment, run_id=run_id)\n",
    "\n",
    "summary_df = pd.DataFrame(index = ['Type', 'Status', 'Primary Metric', 'Iterations', 'Compute', 'Name', 'Start Time', 'End Time'])\n",
    "properties = ml_run.get_properties()\n",
    "tags = ml_run.get_tags()\n",
    "status = ml_run.get_details()\n",
    "amlsettings = eval(properties['RawAMLSettingsString'])\n",
    "if 'iterations' in tags:\n",
    "    iterations = tags['iterations']\n",
    "else:\n",
    "    iterations = properties['num_iterations']\n",
    "start_time = None\n",
    "if 'startTimeUtc' in status:\n",
    "    start_time = status['startTimeUtc']\n",
    "end_time = None\n",
    "if 'endTimeUtc' in status:\n",
    "    end_time = status['endTimeUtc']\n",
    "summary_df[ml_run.id] = [amlsettings['task_type'], status['status'], properties['primary_metric'], iterations, properties['target'], amlsettings['name'], start_time, end_time]\n",
    "display(HTML('<h3>Runtime Details</h3>'))\n",
    "display(summary_df)\n",
    "\n",
    "#settings_df = pd.DataFrame(data=amlsettings, index=[''])\n",
    "display(HTML('<h3>AutoML Settings</h3>'))\n",
    "display(amlsettings)\n",
    "\n",
    "display(HTML('<h3>Iterations</h3>'))\n",
    "RunDetails(ml_run).show() \n",
    "\n",
    "children = list(ml_run.get_children())\n",
    "metricslist = {}\n",
    "for run in children:\n",
    "    properties = run.get_properties()\n",
    "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}    \n",
    "    metricslist[int(properties['iteration'])] = metrics\n",
    "\n",
    "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
    "display(HTML('<h3>Metrics</h3>'))\n",
    "display(rundata)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Download fitted models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Download best model for any given metric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "metric = 'AUC_weighted' # Replace with a metric name\n",
    "best_run, fitted_model = ml_run.get_output(metric=metric)\n",
    "fitted_model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Download model for any given iteration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "iteration = 4 # Replace with an interation number\n",
    "best_run, fitted_model = ml_run.get_output(iteration=iteration)\n",
    "fitted_model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Register fitted model for deployment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "description = 'AutoML Model'\n",
    "tags = None\n",
    "ml_run.register_model(description=description, tags=tags)\n",
    "ml_run.model_id # Use this id to deploy the model as a web service in Azure"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Register best model for any given metric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "metric = 'AUC_weighted' # Replace with a metric name\n",
    "description = 'AutoML Model'\n",
    "tags = None\n",
    "ml_run.register_model(description=description, tags=tags, metric=metric)\n",
    "ml_run.model_id # Use this id to deploy the model as a web service in Azure"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Register model for any given iteration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "iteration = 4 # Replace with an interation number\n",
    "description = 'AutoML Model'\n",
    "tags = None\n",
    "ml_run.register_model(description=description, tags=tags, iteration=iteration)\n",
    "ml_run.model_id # Use this id to deploy the model as a web service in Azure"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.6",
   "language": "python",
   "name": "python36"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
 }
--- a/automl/08.auto-ml-remote-execution-with-text-file-on-DSVM.ipynb
+++ b/automl/08.auto-ml-remote-execution-with-text-file-on-DSVM.ipynb
@@ -1,480 +0,0 @@
 {
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
    "\n",
    "Licensed under the MIT License."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AutoML 08: Remote Execution with Text file\n",
    "\n",
    "In this sample accesses a data file on a remote DSVM.  This is more efficient than reading the file from Blob storage in the get_data method.\n",
    "\n",
    "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook.\n",
    "\n",
    "In this notebook you would see\n",
    "1. Configuring the DSVM to allow files to be access directly by the get_data method.\n",
    "2. get_data returning data from a local file.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Experiment\n",
    "\n",
    "As part of the setup you have already created a <b>Workspace</b>. For AutoML you would need to create an <b>Experiment</b>. An <b>Experiment</b> is a named object in a <b>Workspace</b>, which is used to run experiments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "import os\n",
    "import random\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.pyplot import imshow\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn import datasets\n",
    "\n",
    "import azureml.core\n",
    "from azureml.core.experiment import Experiment\n",
    "from azureml.core.workspace import Workspace\n",
    "from azureml.train.automl import AutoMLConfig\n",
    "from azureml.train.automl.run import AutoMLRun"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ws = Workspace.from_config()\n",
    "\n",
    "# choose a name for experiment\n",
    "experiment_name = 'automl-remote-dsvm-file'\n",
    "# project folder\n",
    "project_folder = './sample_projects/automl-remote-dsvm-file'\n",
    "\n",
    "experiment=Experiment(ws, experiment_name)\n",
    "\n",
    "output = {}\n",
    "output['SDK version'] = azureml.core.VERSION\n",
    "output['Subscription ID'] = ws.subscription_id\n",
    "output['Workspace'] = ws.name\n",
    "output['Resource Group'] = ws.resource_group\n",
    "output['Location'] = ws.location\n",
    "output['Project Directory'] = project_folder\n",
    "output['Experiment Name'] = experiment.name\n",
    "pd.set_option('display.max_colwidth', -1)\n",
    "pd.DataFrame(data=output, index=['']).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Diagnostics\n",
    "\n",
    "Opt-in diagnostics for better experience, quality, and security of future releases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.telemetry import set_diagnostics_collection\n",
    "set_diagnostics_collection(send_diagnostics=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create a Remote Linux DSVM\n",
    "Note: If creation fails with a message about Marketplace purchase eligibilty, go to portal.azure.com, start creating DSVM there, and select \"Want to create programmatically\" to enable programmatic creation. Once you've enabled it, you can exit without actually creating VM.\n",
    "\n",
    "**Note**: By default SSH runs on port 22 and you don't need to specify it. But if for security reasons you can switch to a different port (such as 5022), you can append the port number to the address. [Read more](https://render.githubusercontent.com/documentation/sdk/ssh-issue.md) on this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.core.compute import DsvmCompute\n",
    "\n",
    "dsvm_name = 'mydsvm'\n",
    "try:\n",
    "    dsvm_compute = DsvmCompute(ws, dsvm_name)\n",
    "    print('found existing dsvm.')\n",
    "except:\n",
    "    print('creating new dsvm.')\n",
    "    dsvm_config = DsvmCompute.provisioning_configuration(vm_size = \"Standard_D2_v2\")\n",
    "    dsvm_compute = DsvmCompute.create(ws, name = dsvm_name, provisioning_configuration = dsvm_config)\n",
    "    dsvm_compute.wait_for_completion(show_output = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Copy data file to the DSVM\n",
    "Download the data file.\n",
    "Copy the data file to the DSVM under the folder: /tmp/data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"https://automldemods.blob.core.windows.net/datasets/PlayaEvents2016,_1.6MB,_3.4k-rows.cleaned.2.tsv\",\n",
    "                     delimiter=\"\\t\", quotechar='\"')\n",
    "df.to_csv(\"data.tsv\", sep=\"\\t\", quotechar='\"', index=False)\n",
    "\n",
    "# Now copy the file data.tsv to the folder /tmp/data on the DSVM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Get Data File\n",
    "For remote executions you should author a get_data.py file containing a get_data() function. This file should be in the root directory of the project. You can encapsulate code to read data either from a blob storage or local disk in this file.\n",
    "\n",
    "The *get_data()* function returns a [dictionary](README.md#getdata)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not os.path.exists(project_folder):\n",
    "    os.makedirs(project_folder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%writefile $project_folder/get_data.py\n",
    "\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "import os\n",
    "\n",
    "def get_data():\n",
    "    # Burning man 2016 data\n",
    "    df = pd.read_csv('/tmp/data/data.tsv',\n",
    "                     delimiter=\"\\t\", quotechar='\"')\n",
    "    # get integer labels\n",
    "    le = LabelEncoder()\n",
    "    le.fit(df[\"Label\"].values)\n",
    "    y = le.transform(df[\"Label\"].values)\n",
    "    df = df.drop([\"Label\"], axis=1)\n",
    "\n",
    "    df_train, _, y_train, _ = train_test_split(df, y, test_size=0.1, random_state=42)\n",
    "\n",
    "    return { \"X\" : df.values, \"y\" : y }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Instantiate AutoML <a class=\"anchor\" id=\"Instatiate-AutoML-Remote-DSVM\"></a>\n",
    "\n",
    "You can specify automl_settings as **kwargs** as well. Also note that you can use the get_data() symantic for local excutions too. \n",
    "\n",
    "<i>Note: For Remote DSVM and Batch AI you cannot pass Numpy arrays directly to the fit method.</i>\n",
    "\n",
    "|Property|Description|\n",
    "|-|-|\n",
    "|**primary_metric**|This is the metric that you want to optimize.<br> Classification supports the following primary metrics <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>balanced_accuracy</i><br><i>average_precision_score_weighted</i><br><i>precision_score_weighted</i>|\n",
    "|**max_time_sec**|Time limit in seconds for each iteration|\n",
    "|**iterations**|Number of iterations. In each iteration Auto ML trains a specific pipeline with the data|\n",
    "|**n_cross_validations**|Number of cross validation splits|\n",
    "|**concurrent_iterations**|Max number of iterations that would be executed in parallel.  This should be less than the number of cores on the DSVM\n",
    "|**preprocess**| *True/False* <br>Setting this to *True* enables Auto ML to perform preprocessing <br>on the input to handle *missing data*, and perform some common *feature extraction*|\n",
    "|**max_cores_per_iteration**| Indicates how many cores on the compute target would be used to train a single pipeline.<br> Default is *1*, you can set it to *-1* to use all cores|"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "automl_settings = {\n",
    "    \"max_time_sec\": 3600,\n",
    "    \"iterations\": 10,\n",
    "    \"n_cross_validations\": 5,\n",
    "    \"primary_metric\": 'AUC_weighted',\n",
    "    \"preprocess\": True,\n",
    "    \"max_cores_per_iteration\": 2,\n",
    "    \"verbosity\": logging.INFO\n",
    "}\n",
    "automl_config = AutoMLConfig(task = 'classification',\n",
    "                             debug_log = 'automl_errors.log',\n",
    "                             path=project_folder,\n",
    "                             compute_target = dsvm_compute,\n",
    "                             data_script = project_folder + \"/get_data.py\",\n",
    "                             **automl_settings\n",
    "                            )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training the Model <a class=\"anchor\" id=\"Training-the-model-Remote-DSVM\"></a>\n",
    "\n",
    "For remote runs the execution is asynchronous, so you will see the iterations get populated as they complete. You can interact with the widgets/models even when the experiment is running to retreive the best model up to that point. Once you are satisfied with the model you can cancel a particular iteration or the whole run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "remote_run = experiment.submit(automl_config, show_output=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring the Results <a class=\"anchor\" id=\"Exploring-the-Results-Remote-DSVM\"></a>\n",
    "#### Widget for monitoring runs\n",
    "\n",
    "The widget will sit on \"loading\" until the first iteration completed, then you will see an auto-updating graph and table show up. It refreshed once per minute, so you should see the graph update as child runs complete.\n",
    "\n",
    "You can click on a pipeline to see run properties and output logs. Logs are also available on the DSVM under /tmp/azureml_run/{iterationid}/azureml-logs\n",
    "\n",
    "NOTE: The widget displays a link at the bottom. This links to a web-ui to explore the individual run details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.train.widgets import RunDetails\n",
    "RunDetails(remote_run).show() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "#### Retrieve All Child Runs\n",
    "You can also use sdk methods to fetch all the child runs and see individual metrics that we log. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "children = list(remote_run.get_children())\n",
    "metricslist = {}\n",
    "for run in children:\n",
    "    properties = run.get_properties()\n",
    "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}    \n",
    "    metricslist[int(properties['iteration'])] = metrics\n",
    "\n",
    "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
    "rundata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Canceling runs\n",
    "You can cancel ongoing remote runs using the *cancel()* and *cancel_iteration()* functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cancel the ongoing experiment and stop scheduling new iterations\n",
    "# remote_run.cancel()\n",
    "\n",
    "# Cancel iteration 1 and move onto iteration 2\n",
    "# remote_run.cancel_iteration(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Retrieve the Best Model\n",
    "\n",
    "Below we select the best pipeline from our iterations. The *get_output* method on automl_classifier returns the best run and the fitted model for the last *fit* invocation. There are overloads on *get_output* that allow you to retrieve the best run and fitted model for *any* logged metric or a particular *iteration*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_run, fitted_model = remote_run.get_output()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Best Model based on any other metric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# lookup_metric = \"accuracy\"\n",
    "# best_run, fitted_model = remote_run.get_output(metric=lookup_metric)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model from a specific iteration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# iteration = 1\n",
    "# best_run, fitted_model = remote_run.get_output(iteration=iteration)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Register fitted model for deployment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "description = 'AutoML Model'\n",
    "tags = None\n",
    "remote_run.register_model(description=description, tags=tags)\n",
    "remote_run.model_id # Use this id to deploy the model as a web service in Azure"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Testing the Fitted Model <a class=\"anchor\" id=\"Testing-the-Fitted-Model-Remote-DSVM\"></a>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from pandas_ml import ConfusionMatrix\n",
    "\n",
    "df = pd.read_csv(\"https://automldemods.blob.core.windows.net/datasets/PlayaEvents2016,_1.6MB,_3.4k-rows.cleaned.2.tsv\",\n",
    "                     delimiter=\"\\t\", quotechar='\"')\n",
    "\n",
    "# get integer labels\n",
    "le = LabelEncoder()\n",
    "le.fit(df[\"Label\"].values)\n",
    "y = le.transform(df[\"Label\"].values)\n",
    "df = df.drop([\"Label\"], axis=1)\n",
    "\n",
    "_, df_test, _, y_test = train_test_split(df, y, test_size=0.1, random_state=42)\n",
    "\n",
    "ypred = fitted_model.predict(df_test.values)\n",
    "\n",
    "ypred_strings = le.inverse_transform(ypred)\n",
    "ytest_strings = le.inverse_transform(y_test)\n",
    "\n",
    "cm = ConfusionMatrix(ytest_strings, ypred_strings)\n",
    "\n",
    "print(cm)\n",
    "\n",
    "cm.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.6",
   "language": "python",
   "name": "python36"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
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 }
--- a/automl/09.auto-ml-classification-with-deployment.ipynb
+++ b/automl/09.auto-ml-classification-with-deployment.ipynb
@@ -1,500 +0,0 @@
 {
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
    "\n",
    "Licensed under the MIT License."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AutoML 09: Classification with deployment\n",
    "\n",
    "In this example we use the scikit learn's [digit dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html) to showcase how you can use AutoML for a simple classification problem.\n",
    "\n",
    "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook.\n",
    "\n",
    "In this notebook you would see\n",
    "1. Creating an Experiment using an existing Workspace\n",
    "2. Instantiating AutoMLConfig\n",
    "3. Training the Model using local compute\n",
    "4. Exploring the results\n",
    "5. Registering the model\n",
    "6. Creating Image and creating aci service\n",
    "7. Testing the aci service\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Experiment\n",
    "\n",
    "As part of the setup you have already created a <b>Workspace</b>. For AutoML you would need to create an <b>Experiment</b>. An <b>Experiment</b> is a named object in a <b>Workspace</b>, which is used to run experiments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import logging\n",
    "import os\n",
    "import random\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.pyplot import imshow\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn import datasets\n",
    "\n",
    "import azureml.core\n",
    "from azureml.core.experiment import Experiment\n",
    "from azureml.core.workspace import Workspace\n",
    "from azureml.train.automl import AutoMLConfig\n",
    "from azureml.train.automl.run import AutoMLRun"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ws = Workspace.from_config()\n",
    "\n",
    "# choose a name for experiment\n",
    "experiment_name =  'automl-local-classification'\n",
    "# project folder\n",
    "project_folder = './sample_projects/automl-local-classification'\n",
    "\n",
    "experiment=Experiment(ws, experiment_name)\n",
    "\n",
    "output = {}\n",
    "output['SDK version'] = azureml.core.VERSION\n",
    "output['Subscription ID'] = ws.subscription_id\n",
    "output['Workspace'] = ws.name\n",
    "output['Resource Group'] = ws.resource_group\n",
    "output['Location'] = ws.location\n",
    "output['Project Directory'] = project_folder\n",
    "output['Experiment Name'] = experiment.name\n",
    "pd.set_option('display.max_colwidth', -1)\n",
    "pd.DataFrame(data=output, index=['']).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Diagnostics\n",
    "\n",
    "Opt-in diagnostics for better experience, quality, and security of future releases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.telemetry import set_diagnostics_collection\n",
    "set_diagnostics_collection(send_diagnostics=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Instantiate Auto ML Config\n",
    "\n",
    "Instantiate a AutoMLConfig object. This defines the settings and data used to run the experiment.\n",
    "\n",
    "|Property|Description|\n",
    "|-|-|\n",
    "|**task**|classification or regression|\n",
    "|**primary_metric**|This is the metric that you want to optimize.<br> Classification supports the following primary metrics <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>balanced_accuracy</i><br><i>average_precision_score_weighted</i><br><i>precision_score_weighted</i>|\n",
    "|**max_time_sec**|Time limit in seconds for each iteration|\n",
    "|**iterations**|Number of iterations. In each iteration Auto ML trains a specific pipeline with the data|\n",
    "|**n_cross_validations**|Number of cross validation splits|\n",
    "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
    "|**y**|(sparse) array-like, shape = [n_samples, ], [n_samples, n_classes]<br>Multi-class targets. An indicator matrix turns on multilabel classification.  This should be an array of integers. |\n",
    "|**path**|Relative path to the project folder.  AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder. |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "digits = datasets.load_digits()\n",
    "X_digits = digits.data[10:,:]\n",
    "y_digits = digits.target[10:]\n",
    "\n",
    "automl_config = AutoMLConfig(task = 'classification',\n",
    "                             name=experiment_name,\n",
    "                             debug_log='automl_errors.log',\n",
    "                             primary_metric='AUC_weighted',\n",
    "                             max_time_sec=1200,\n",
    "                             iterations=10,\n",
    "                             n_cross_validations=2,\n",
    "                             verbosity=logging.INFO,\n",
    "                             X = X_digits, \n",
    "                             y = y_digits,\n",
    "                             path=project_folder)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training the Model\n",
    "\n",
    "You can call the submit method on the experiment object and pass the run configuration. For Local runs the execution is synchronous. Depending on the data and number of iterations this can run for while.\n",
    "You will see the currently running iterations printing to the console."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "local_run = experiment.submit(automl_config, show_output=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Retrieve the Best Model\n",
    "\n",
    "Below we select the best pipeline from our iterations. The *get_output* method on automl_classifier returns the best run and the fitted model for the last *fit* invocation. There are overloads on *get_output* that allow you to retrieve the best run and fitted model for *any* logged metric or a particular *iteration*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_run, fitted_model = local_run.get_output()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Register fitted model for deployment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "description = 'AutoML Model'\n",
    "tags = None\n",
    "model = local_run.register_model(description=description, tags=tags, iteration=8)\n",
    "local_run.model_id # This will be written to the script file later in the notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create Scoring script ###"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%writefile score.py\n",
    "import pickle\n",
    "import json\n",
    "import numpy\n",
    "from sklearn.externals import joblib\n",
    "from azureml.core.model import Model\n",
    "\n",
    "\n",
    "def init():\n",
    "    global model\n",
    "    model_path = Model.get_model_path(model_name = '<<modelid>>') # this name is model.id of model that we want to deploy\n",
    "    # deserialize the model file back into a sklearn model\n",
    "    model = joblib.load(model_path)\n",
    "\n",
    "def run(rawdata):\n",
    "    try:\n",
    "        data = json.loads(rawdata)['data']\n",
    "        data = numpy.array(data)\n",
    "        result = model.predict(data)\n",
    "    except Exception as e:\n",
    "        result = str(e)\n",
    "        return json.dumps({\"error\": result})\n",
    "    return json.dumps({\"result\":result.tolist()})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create yml file for env"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To ensure the consistence the fit results with the training results, the sdk dependence versions need to be the same as the environment that trains the model. Details about retrieving the versions can be found in notebook 12.auto-ml-retrieve-the-training-sdk-versions.ipynb."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "experiment_name = 'automl-local-classification'\n",
    "\n",
    "experiment = Experiment(ws, experiment_name)\n",
    "ml_run = AutoMLRun(experiment=experiment, run_id=local_run.id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dependencies = ml_run.get_run_sdk_dependencies(iteration=7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for p in ['azureml-train-automl', 'azureml-sdk', 'azureml-core']:\n",
    "    print('{}\\t{}'.format(p, dependencies[p]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%writefile myenv.yml\n",
    "name: myenv\n",
    "channels:\n",
    "  - defaults\n",
    "dependencies:\n",
    "  - pip:\n",
    "    - numpy==1.14.2\n",
    "    - scikit-learn==0.19.2\n",
    "    - azureml-sdk[notebooks,automl]==<<azureml-version>> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Substitute the actual version number in the environment file.\n",
    "\n",
    "conda_env_file_name = 'myenv.yml'\n",
    "\n",
    "with open(conda_env_file_name, 'r') as cefr:\n",
    "    content = cefr.read()\n",
    "\n",
    "with open(conda_env_file_name, 'w') as cefw:\n",
    "    cefw.write(content.replace('<<azureml-version>>', dependencies['azureml-sdk']))\n",
    "\n",
    "# Substitute the actual model id in the script file.\n",
    "\n",
    "script_file_name = 'score.py'\n",
    "\n",
    "with open(script_file_name, 'r') as cefr:\n",
    "    content = cefr.read()\n",
    "\n",
    "with open(script_file_name, 'w') as cefw:\n",
    "    cefw.write(content.replace('<<modelid>>', local_run.model_id))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create Image ###"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.core.image import Image, ContainerImage\n",
    "\n",
    "image_config = ContainerImage.image_configuration(runtime= \"python\",\n",
    "                                 execution_script = script_file_name,\n",
    "                                 conda_file = conda_env_file_name,\n",
    "                                 tags = {'area': \"digits\", 'type': \"automl_classification\"},\n",
    "                                 description = \"Image for automl classification sample\")\n",
    "\n",
    "image = Image.create(name = \"automlsampleimage\",\n",
    "                     # this is the model object \n",
    "                     models = [model],\n",
    "                     image_config = image_config, \n",
    "                     workspace = ws)\n",
    "\n",
    "image.wait_for_creation(show_output = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Deploy Image as web service on Azure Container Instance ###"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.core.webservice import AciWebservice\n",
    "\n",
    "aciconfig = AciWebservice.deploy_configuration(cpu_cores = 1, \n",
    "                                               memory_gb = 1, \n",
    "                                               tags = {'area': \"digits\", 'type': \"automl_classification\"}, \n",
    "                                               description = 'sample service for Automl Classification')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.core.webservice import Webservice\n",
    "\n",
    "aci_service_name = 'automl-sample-01'\n",
    "print(aci_service_name)\n",
    "aci_service = Webservice.deploy_from_image(deployment_config = aciconfig,\n",
    "                                           image = image,\n",
    "                                           name = aci_service_name,\n",
    "                                           workspace = ws)\n",
    "aci_service.wait_for_deployment(True)\n",
    "print(aci_service.state)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### To delete a service ##"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#aci_service.delete()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### To get logs from deployed service ###"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#aci_service.get_logs()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test Web Service ###"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Randomly select digits and test\n",
    "digits = datasets.load_digits()\n",
    "X_digits = digits.data[:10, :]\n",
    "y_digits = digits.target[:10]\n",
    "images = digits.images[:10]\n",
    "\n",
    "for index in np.random.choice(len(y_digits), 3):\n",
    "    print(index)\n",
    "    test_sample = json.dumps({'data':X_digits[index:index + 1].tolist()})\n",
    "    predicted = aci_service.run(input_data = test_sample)\n",
    "    label = y_digits[index]\n",
    "    predictedDict = json.loads(predicted)\n",
    "    title = \"Label value = %d  Predicted value = %s \" % ( label,predictedDict['result'][0])\n",
    "    fig = plt.figure(1, figsize=(3,3))\n",
    "    ax1 = fig.add_axes((0,0,.8,.8))\n",
    "    ax1.set_title(title)\n",
    "    plt.imshow(images[index], cmap=plt.cm.gray_r, interpolation='nearest')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.6",
   "language": "python",
   "name": "python36"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
 }
--- a/automl/10.auto-ml-multi-output-example.ipynb
+++ b/automl/10.auto-ml-multi-output-example.ipynb
@@ -1,292 +0,0 @@
 {
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
    "\n",
    "Licensed under the MIT License."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AutoML 10: Multi output Example for AutoML"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook shows an example to use AutoML to train the multi output problems by leveraging the correlation between the outputs using indicator vectors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "import os\n",
    "import random\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.pyplot import imshow\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn import datasets\n",
    "\n",
    "import azureml.core\n",
    "from azureml.core.experiment import Experiment\n",
    "from azureml.core.workspace import Workspace\n",
    "from azureml.train.automl import AutoMLConfig\n",
    "from azureml.train.automl.run import AutoMLRun"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Diagnostics\n",
    "\n",
    "Opt-in diagnostics for better experience, quality, and security of future releases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.telemetry import set_diagnostics_collection\n",
    "set_diagnostics_collection(send_diagnostics=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transformer functions\n",
    "The transformation of the input are happening for input X and Y as following, e.g. Y = {y_1, y_2}, then X becomes\n",
    "    \n",
    "X 1 0\n",
    "     \n",
    "X 0 1\n",
    "\n",
    "and Y becomes,\n",
    "\n",
    "y_1\n",
    "\n",
    "y_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import sparse\n",
    "from scipy import linalg\n",
    "\n",
    "#Transformer functions\n",
    "def multi_output_transform_x_y(X, Y):\n",
    "    X_new = multi_output_transformer_x(X, Y.shape[1])\n",
    "    y_new = multi_output_transform_y(Y)\n",
    "    return X_new, y_new\n",
    "\n",
    "def multi_output_transformer_x(X, number_of_columns_Y):\n",
    "    indicator_vecs = linalg.block_diag(*([np.ones((X.shape[0], 1))] * number_of_columns_Y))\n",
    "    if sparse.issparse(X):\n",
    "        X_new = sparse.vstack(np.tile(X, number_of_columns_Y))\n",
    "        indicator_vecs = sparse.coo_matrix(indicator_vecs)\n",
    "        X_new = sparse.hstack((X_new, indicator_vecs))\n",
    "    else:\n",
    "        X_new = np.tile(X, (number_of_columns_Y, 1))\n",
    "        X_new = np.hstack((X_new, indicator_vecs))\n",
    "    return X_new\n",
    "\n",
    "def multi_output_transform_y(Y):\n",
    "    return Y.reshape(-1, order=\"F\")\n",
    "    \n",
    "def multi_output_inverse_transform_y(y, number_of_columns_y):\n",
    "    return y.reshape((-1, number_of_columns_y), order=\"F\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## AutoML experiment set up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ws = Workspace.from_config()\n",
    "\n",
    "# choose a name for experiment\n",
    "experiment_name = 'automl-local-multi-output'\n",
    "# project folder\n",
    "project_folder = './sample_projects/automl-local-multi-output'\n",
    "\n",
    "experiment=Experiment(ws, experiment_name)\n",
    "\n",
    "output = {}\n",
    "output['SDK version'] = azureml.core.VERSION\n",
    "output['Subscription ID'] = ws.subscription_id\n",
    "output['Workspace'] = ws.name\n",
    "output['Resource Group'] = ws.resource_group\n",
    "output['Location'] = ws.location\n",
    "output['Project Directory'] = project_folder\n",
    "output['Experiment Name'] = experiment.name\n",
    "pd.set_option('display.max_colwidth', -1)\n",
    "pd.DataFrame(data=output, index=['']).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create a random dataset for the test purpose "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rng = np.random.RandomState(1)\n",
    "X_train = np.sort(200 * rng.rand(600, 1) - 100, axis=0)\n",
    "Y_train = np.array([np.pi * np.sin(X_train).ravel(), np.pi * np.cos(X_train).ravel()]).T\n",
    "Y_train += (0.5 - rng.rand(*Y_train.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perform X and Y transformation using transformer function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_transformed, y_train_transformed = multi_output_transform_x_y(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "automl_config = AutoMLConfig(task = 'regression',\n",
    "                             debug_log='automl_errors_multi.log',\n",
    "                             primary_metric='r2_score',\n",
    "                             iterations=10,\n",
    "                             n_cross_validations=2,\n",
    "                             verbosity=logging.INFO,\n",
    "                             X=X_train_transformed,\n",
    "                             y=y_train_transformed,\n",
    "                             path=project_folder)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fit the transformed data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "local_run = experiment.submit(automl_config, show_output=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the best fit model\n",
    "best_run, fitted_model = local_run.get_output()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate random data set for predicting\n",
    "X_predict = np.sort(200 * rng.rand(200, 1) - 100, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Transform predict data\n",
    "X_predict_transformed = multi_output_transformer_x(X_predict, Y_train.shape[1])\n",
    "# Predict and inverse transform the prediction\n",
    "y_predict = fitted_model.predict(X_predict_transformed)\n",
    "Y_predict = multi_output_inverse_transform_y(y_predict, Y_train.shape[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(Y_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.6",
   "language": "python",
   "name": "python36"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
 }
--- a/automl/11.auto-ml-sample-weight.ipynb
+++ b/automl/11.auto-ml-sample-weight.ipynb
@@ -1,251 +0,0 @@
 {
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
    "\n",
    "Licensed under the MIT License."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AutoML 11: Sample weight\n",
    "\n",
    "In this example we use the scikit learn's [digit dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html) to showcase how you can use sample weight with the AutoML Classifier.\n",
    "Sample weight is used where some sample values are more important than others.\n",
    "\n",
    "Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook.\n",
    "\n",
    "In this notebook you would see\n",
    "1. How to specifying sample_weight\n",
    "2. The difference that it makes to test results\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Experiment\n",
    "\n",
    "As part of the setup you have already created a <b>Workspace</b>. For AutoML you would need to create an <b>Experiment</b>. An <b>Experiment</b> is a named object in a <b>Workspace</b>, which is used to run experiments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "import os\n",
    "import random\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.pyplot import imshow\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn import datasets\n",
    "\n",
    "import azureml.core\n",
    "from azureml.core.experiment import Experiment\n",
    "from azureml.core.workspace import Workspace\n",
    "from azureml.train.automl import AutoMLConfig\n",
    "from azureml.train.automl.run import AutoMLRun"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ws = Workspace.from_config()\n",
    "\n",
    "# choose a name for experiment\n",
    "experiment_name = 'non_sample_weight_experiment'\n",
    "sample_weight_experiment_name = 'sample_weight_experiment'\n",
    "\n",
    "# project folder\n",
    "project_folder = './sample_projects/automl-local-classification'\n",
    "\n",
    "experiment=Experiment(ws, experiment_name)\n",
    "sample_weight_experiment=Experiment(ws, sample_weight_experiment_name)\n",
    "\n",
    "output = {}\n",
    "output['SDK version'] = azureml.core.VERSION\n",
    "output['Subscription ID'] = ws.subscription_id\n",
    "output['Workspace Name'] = ws.name\n",
    "output['Resource Group'] = ws.resource_group\n",
    "output['Location'] = ws.location\n",
    "output['Project Directory'] = project_folder\n",
    "output['Experiment Name'] = experiment.name\n",
    "pd.set_option('display.max_colwidth', -1)\n",
    "pd.DataFrame(data = output, index = ['']).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Diagnostics\n",
    "\n",
    "Opt-in diagnostics for better experience, quality, and security of future releases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.telemetry import set_diagnostics_collection\n",
    "set_diagnostics_collection(send_diagnostics=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Instantiate Auto ML Config\n",
    "\n",
    "Instantiate two AutoMLConfig Objects. One will be used with sample_weight and one without."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "digits = datasets.load_digits()\n",
    "X_digits = digits.data[100:,:]\n",
    "y_digits = digits.target[100:]\n",
    "\n",
    "# The example makes the sample weight 0.9 for the digit 4 and 0.1 for all other digits.\n",
    "# This makes the model more likely to classify as 4 if the image it not clear.\n",
    "sample_weight = np.array([(0.9 if x == 4 else 0.01) for x in y_digits])\n",
    "\n",
    "automl_classifier = AutoMLConfig(task = 'classification',\n",
    "                                 debug_log = 'automl_errors.log',\n",
    "                                 primary_metric = 'AUC_weighted',\n",
    "                                 max_time_sec = 3600,\n",
    "                                 iterations = 10,\n",
    "                                 n_cross_validations = 2,\n",
    "                                 verbosity = logging.INFO,\n",
    "                                 X = X_digits, \n",
    "                                 y = y_digits,\n",
    "                                 path=project_folder)\n",
    "\n",
    "automl_sample_weight = AutoMLConfig(task = 'classification',\n",
    "                                    debug_log = 'automl_errors.log',\n",
    "                                    primary_metric = 'AUC_weighted',\n",
    "                                    max_time_sec = 3600,\n",
    "                                    iterations = 10,\n",
    "                                    n_cross_validations = 2,\n",
    "                                    verbosity = logging.INFO,\n",
    "                                    X = X_digits, \n",
    "                                    y = y_digits,\n",
    "                                    sample_weight = sample_weight,\n",
    "                                    path=project_folder)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training the Models\n",
    "\n",
    "Call the submit method on the experiment and pass the configuration. For Local runs the execution is synchronous. Depending on the data and number of iterations this can run for while.\n",
    "You will see the currently running iterations printing to the console."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "local_run = experiment.submit(automl_classifier, show_output=True)\n",
    "sample_weight_run = sample_weight_experiment.submit(automl_sample_weight, show_output=True)\n",
    "\n",
    "best_run, fitted_model = local_run.get_output()\n",
    "best_run_sample_weight, fitted_model_sample_weight = sample_weight_run.get_output()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Testing the Fitted Models\n",
    "\n",
    "#### Load Test Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "digits = datasets.load_digits()\n",
    "X_digits = digits.data[:100, :]\n",
    "y_digits = digits.target[:100]\n",
    "images = digits.images[:100]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Compare the pipelines\n",
    "The prediction from the sample weight model is more likely to correctly predict 4's.  However, it is also more likely to predict 4 for some images that are not labelled as 4."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Randomly select digits and test\n",
    "for index in range(0,len(y_digits)):\n",
    "    predicted = fitted_model.predict(X_digits[index:index + 1])[0]\n",
    "    predicted_sample_weight = fitted_model_sample_weight.predict(X_digits[index:index + 1])[0]\n",
    "    label = y_digits[index]\n",
    "    if predicted == 4 or predicted_sample_weight == 4 or label == 4:\n",
    "        title = \"Label value = %d  Predicted value = %d Prediced with sample weight = %d\" % ( label,predicted,predicted_sample_weight)\n",
    "        fig = plt.figure(1, figsize=(3,3))\n",
    "        ax1 = fig.add_axes((0,0,.8,.8))\n",
    "        ax1.set_title(title)\n",
    "        plt.imshow(images[index], cmap=plt.cm.gray_r, interpolation='nearest')\n",
    "        plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.6",
   "language": "python",
   "name": "python36"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
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 }
--- a/automl/12.auto-ml-retrieve-the-training-sdk-versions.ipynb
+++ b/automl/12.auto-ml-retrieve-the-training-sdk-versions.ipynb
@@ -1,240 +0,0 @@
 {
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
    "\n",
    "Licensed under the MIT License."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AutoML 12: Retrieving Training SDK Versions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "import os\n",
    "import random\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.pyplot import imshow\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn import datasets\n",
    "\n",
    "import azureml.core\n",
    "from azureml.core.experiment import Experiment\n",
    "from azureml.core.workspace import Workspace\n",
    "from azureml.train.automl import AutoMLConfig\n",
    "from azureml.train.automl.run import AutoMLRun\n",
    "from azureml.train.automl.utilities import get_sdk_dependencies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Diagnostics\n",
    "\n",
    "Opt-in diagnostics for better experience, quality, and security of future releases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.telemetry import set_diagnostics_collection\n",
    "set_diagnostics_collection(send_diagnostics=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Retrieve the SDK versions in the current env"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To retrieve the SDK versions in the current env, simple running get_sdk_dependencies()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "get_sdk_dependencies()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Training Model Using AutoML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ws = Workspace.from_config()\n",
    "\n",
    "# choose a name for experiment\n",
    "experiment_name = 'automl-local-classification'\n",
    "# project folder\n",
    "project_folder = './sample_projects/automl-local-classification'\n",
    "\n",
    "experiment=Experiment(ws, experiment_name)\n",
    "\n",
    "output = {}\n",
    "output['SDK version'] = azureml.core.VERSION\n",
    "output['Subscription ID'] = ws.subscription_id\n",
    "output['Workspace'] = ws.name\n",
    "output['Resource Group'] = ws.resource_group\n",
    "output['Location'] = ws.location\n",
    "output['Project Directory'] = project_folder\n",
    "output['Experiment Name'] = experiment.name\n",
    "pd.set_option('display.max_colwidth', -1)\n",
    "pd.DataFrame(data=output, index=['']).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "digits = datasets.load_digits()\n",
    "X_digits = digits.data[10:,:]\n",
    "y_digits = digits.target[10:]\n",
    "\n",
    "automl_config = AutoMLConfig(task = 'classification',\n",
    "                             debug_log='automl_errors.log',\n",
    "                             primary_metric='AUC_weighted',\n",
    "                             iterations=3,\n",
    "                             n_cross_validations=2,\n",
    "                             verbosity=logging.INFO,\n",
    "                             X = X_digits, \n",
    "                             y = y_digits,\n",
    "                             path=project_folder)\n",
    "\n",
    "local_run = experiment.submit(automl_config, show_output=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Retrieve the SDK versions from RunHistory"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get the SDK versions from RunHistory, first the RunId need to be recorded. This can either be done by copy it from the output message or retieve if after each run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "run_id = local_run.id\n",
    "print(run_id)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Initialize a new AutoMLRunClass."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "experiment_name = 'automl-local-classification'\n",
    "#run_id = 'AutoML_c0585b1f-a0e6-490b-84c7-3a099468b28e'\n",
    "\n",
    "experiment = Experiment(ws, experiment_name)\n",
    "ml_run = AutoMLRun(experiment=experiment, run_id=run_id)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get parent training SDK versions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ml_run.get_run_sdk_dependencies()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get the traning SDK versions of a specific run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ml_run.get_run_sdk_dependencies(iteration=2)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.6",
   "language": "python",
   "name": "python36"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
 }
--- a/automl/13.auto-ml-dataprep.ipynb
+++ b/automl/13.auto-ml-dataprep.ipynb
@@ -1,567 +0,0 @@
 {
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
    "\n",
    "Licensed under the MIT License."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AutoML 13: Prepare Data using `azureml.dataprep`\n",
    "In this example we showcase how you can use `azureml.dataprep` SDK to load and prepare data for AutoML. `azureml.dataprep` can also be used standalone - full documentation can be found [here](https://github.com/Microsoft/PendletonDocs).\n",
    "\n",
    "Make sure you have executed the [setup](00.configuration.ipynb) before running this notebook.\n",
    "\n",
    "In this notebook you would see\n",
    "1. Defining data loading and preparation steps in a `Dataflow` using `azureml.dataprep`\n",
    "2. Passing the `Dataflow` to AutoML for local run\n",
    "3. Passing the `Dataflow` to AutoML for remote run"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Install `azureml.dataprep` SDK"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Please restart your kernel after the below installs.\n",
    "\n",
    "Tornado must be downgraded to a pre-5 version due to a known Tornado x Jupyter event loop bug."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install azureml-dataprep\n",
    "!pip install tornado==4.5.1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Diagnostics\n",
    "\n",
    "Opt-in diagnostics for better experience, quality, and security of future releases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.telemetry import set_diagnostics_collection\n",
    "set_diagnostics_collection(send_diagnostics = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Experiment\n",
    "\n",
    "As part of the setup you have already created a <b>Workspace</b>. For AutoML you would need to create an <b>Experiment</b>. An <b>Experiment</b> is a named object in a <b>Workspace</b>, which is used to run experiments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "import os\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "import azureml.core\n",
    "from azureml.core.compute import DsvmCompute\n",
    "from azureml.core.experiment import Experiment\n",
    "from azureml.core.runconfig import CondaDependencies\n",
    "from azureml.core.runconfig import RunConfiguration\n",
    "from azureml.core.workspace import Workspace\n",
    "import azureml.dataprep as dprep\n",
    "from azureml.train.automl import AutoMLConfig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ws = Workspace.from_config()\n",
    " \n",
    "# choose a name for experiment\n",
    "experiment_name = 'automl-dataprep-classification'\n",
    "# project folder\n",
    "project_folder = './sample_projects/automl-dataprep-classification'\n",
    " \n",
    "experiment = Experiment(ws, experiment_name)\n",
    " \n",
    "output = {}\n",
    "output['SDK version'] = azureml.core.VERSION\n",
    "output['Subscription ID'] = ws.subscription_id\n",
    "output['Workspace Name'] = ws.name\n",
    "output['Resource Group'] = ws.resource_group\n",
    "output['Location'] = ws.location\n",
    "output['Project Directory'] = project_folder\n",
    "output['Experiment Name'] = experiment.name\n",
    "pd.set_option('display.max_colwidth', -1)\n",
    "pd.DataFrame(data = output, index = ['']).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading Data using DataPrep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# You can use `smart_read_file` which intelligently figures out delimiters and datatypes of a file\n",
    "# data pulled from sklearn.datasets.load_digits()\n",
    "simple_example_data_root = 'https://dprepdata.blob.core.windows.net/automl-notebook-data/'\n",
    "X = dprep.smart_read_file(simple_example_data_root + 'X.csv').skip(1)  # remove header\n",
    "\n",
    "# You can also use `read_csv` and `to_*` transformations to read (with overridable delimiter).\n",
    "# and convert column types manually.\n",
    "# Here we read a comma delimited file and convert all columns to integers.\n",
    "y = dprep.read_csv(simple_example_data_root + 'y.csv').to_long(dprep.ColumnSelector(term='.*', use_regex = True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Review the Data Preparation Result\n",
    "\n",
    "You can peek the result of a Dataflow at any range using `skip(i)` and `head(j)`. Doing so evaluates only `j` records for all the steps in the Dataflow, which makes it fast even against large dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X.skip(1).head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Instantiate AutoML Settings\n",
    "\n",
    "This creates a general Auto ML Settings applicable for both Local and Remote runs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "automl_settings = {\n",
    "    \"max_time_sec\": 600,\n",
    "    \"iterations\": 2,\n",
    "    \"primary_metric\": 'AUC_weighted',\n",
    "    \"preprocess\": False,\n",
    "    \"verbosity\": logging.INFO,\n",
    "    \"n_cross_validations\" : 3\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Local Run"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pass data with Dataflows\n",
    "\n",
    "The `Dataflow` objects captured above can be passed to `submit` method for local run. AutoML will retrieve the results from the `Dataflow` for model training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "automl_config = AutoMLConfig(task = 'classification',\n",
    "                             debug_log = 'automl_errors.log',\n",
    "                             X = X,\n",
    "                             y = y,\n",
    "                             **automl_settings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "local_run = experiment.submit(automl_config, show_output=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Remote Run\n",
    "*Note: This feature might not work properly in your workspace region before the October update. You may jump to the \"Exploring the results\" section below to explore other features AutoML and DataPrep has to offer.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create or Attach a Remote Linux DSVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dsvm_name = 'mydsvm'\n",
    "try:\n",
    "    dsvm_compute = DsvmCompute(ws, dsvm_name)\n",
    "    print('found existing dsvm.')\n",
    "except:\n",
    "    print('creating new dsvm.')\n",
    "    dsvm_config = DsvmCompute.provisioning_configuration(vm_size = \"Standard_D2_v2\")\n",
    "    dsvm_compute = DsvmCompute.create(ws, name = dsvm_name, provisioning_configuration = dsvm_config)\n",
    "    dsvm_compute.wait_for_completion(show_output = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Update Conda Dependency file to have AutoML and DataPrep SDK\n",
    "\n",
    "Currently AutoML and DataPrep SDK is not installed with Azure ML SDK by default. Due to this we update the conda dependency file to add such dependencies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cd = CondaDependencies()\n",
    "cd.add_pip_package(pip_package='azureml-dataprep')\n",
    "cd.add_pip_package(pip_package='tornado==4.5.1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a RunConfiguration with DSVM name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "run_config = RunConfiguration(conda_dependencies=cd)\n",
    "run_config.target = dsvm_compute\n",
    "run_config.auto_prepare_environment = True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pass data with Dataflows\n",
    "\n",
    "The `Dataflow` objects captured above can also be passed to `submit` method for remote run. AutoML will serialize the `Dataflow` and send to remote compute target. The `Dataflow` will not be evaluated locally."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "automl_config = AutoMLConfig(task = 'classification',\n",
    "                             debug_log = 'automl_errors.log',\n",
    "                             path = project_folder,\n",
    "                             run_configuration = run_config,\n",
    "                             X = X,\n",
    "                             y = y,\n",
    "                             **automl_settings)\n",
    "# Please uncomment the line below to try out remote run with dataprep. \n",
    "# This feature might not work properly in your workspace region before the October update.\n",
    "# remote_run = experiment.submit(automl_config, show_output = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring the results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Widget for monitoring runs\n",
    "\n",
    "The widget will sit on \"loading\" until the first iteration completed, then you will see an auto-updating graph and table show up. It refreshed once per minute, so you should see the graph update as child runs complete.\n",
    "\n",
    "NOTE: The widget displays a link at the bottom. This links to a web-ui to explore the individual run details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.train.widgets import RunDetails\n",
    "RunDetails(local_run).show() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Retrieve all child runs\n",
    "You can also use SDK methods to fetch all the child runs and see individual metrics that we log."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "children = list(local_run.get_children())\n",
    "metricslist = {}\n",
    "for run in children:\n",
    "    properties = run.get_properties()\n",
    "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}\n",
    "    metricslist[int(properties['iteration'])] = metrics\n",
    "    \n",
    "import pandas as pd\n",
    "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
    "rundata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Retrieve the Best Model\n",
    "\n",
    "Below we select the best pipeline from our iterations. The *get_output* method on automl_classifier returns the best run and the fitted model for the last *fit* invocation. There are overloads on *get_output* that allow you to retrieve the best run and fitted model for *any* logged metric or a particular *iteration*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_run, fitted_model = local_run.get_output()\n",
    "print(best_run)\n",
    "print(fitted_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Best Model based on any other metric\n",
    "Give me the run and the model that has the smallest `log_loss`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lookup_metric = \"log_loss\"\n",
    "best_run, fitted_model = local_run.get_output(metric = lookup_metric)\n",
    "print(best_run)\n",
    "print(fitted_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Best Model based on any iteration\n",
    "Give me the run and the model from the 1st iteration:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "iteration = 0\n",
    "best_run, fitted_model = local_run.get_output(iteration = iteration)\n",
    "print(best_run)\n",
    "print(fitted_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Testing the Fitted Model \n",
    "\n",
    "#### Load Test Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "\n",
    "digits = datasets.load_digits()\n",
    "X_digits = digits.data[:10, :]\n",
    "y_digits = digits.target[:10]\n",
    "images = digits.images[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Testing our best pipeline\n",
    "We will try to predict 2 digits and see how our model works."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Randomly select digits and test\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.pyplot import imshow\n",
    "import random\n",
    "import numpy as np\n",
    "\n",
    "for index in np.random.choice(len(y_digits), 2):\n",
    "    print(index)\n",
    "    predicted = fitted_model.predict(X_digits[index:index + 1])[0]\n",
    "    label = y_digits[index]\n",
    "    title = \"Label value = %d  Predicted value = %d \" % ( label,predicted)\n",
    "    fig = plt.figure(1, figsize=(3,3))\n",
    "    ax1 = fig.add_axes((0,0,.8,.8))\n",
    "    ax1.set_title(title)\n",
    "    plt.imshow(images[index], cmap=plt.cm.gray_r, interpolation='nearest')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Appendix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Capture the Dataflows to use for AutoML later\n",
    "\n",
    "`Dataflow` objects are immutable. Each of them is composed of a list of data preparation steps. A `Dataflow` can be branched at any point for further usage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sklearn.digits.data + target\n",
    "digits_complete = dprep.smart_read_file('https://dprepdata.blob.core.windows.net/automl-notebook-data/digits-complete.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`digits_complete` (sourced from `sklearn.datasets.load_digits()`)is forked into `dflow_X` to capture all the feature columns and `dflow_y` to capture the label column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "digits_complete.to_pandas_dataframe().shape\n",
    "labels_column = 'Column64'\n",
    "dflow_X = digits_complete.drop_columns(columns = [labels_column])\n",
    "dflow_y = digits_complete.keep_columns(columns = [labels_column])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.6",
   "language": "python",
   "name": "python36"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
 }
--- a/automl/README.md
+++ b/automl/README.md
@@ -1,262 +0,0 @@
 # Table of Contents
 1. [Automated ML Introduction](#introduction)
 1. [Running samples in Azure Notebooks](#jupyter)
 1. [Running samples in a Local Conda environment](#localconda)
 1. [Automated ML SDK Sample Notebooks](#samples)
 1. [Documentation](#documentation)
 1. [Running using python command](#pythoncommand)
 1. [Troubleshooting](#troubleshooting)
 <a name="introduction"></a>
 # Automated ML introduction 
 Automated machine learning (automated ML) builds high quality machine learning models for you by automating model and hyperparameter selection. Bring a labelled dataset that you want to build a model for, automated ML will give you a high quality machine learning model that you can use for predictions.
 If you are new to Data Science, AutoML will help you get jumpstarted by simplifying machine learning model building. It abstracts you from needing to perform model selection, hyperparameter selection and in one step creates a high quality trained model for you to use.
 If you are an experienced data scientist, AutoML will help increase your productivity by intelligently performing the model and hyperparameter selection for your training and generates high quality models much quicker than manually specifying several combinations of the parameters and running training jobs. AutoML provides visibility and access to all the training jobs and the performance characteristics of the models to help you further tune the pipeline if you desire.
 <a name="jupyter"></a>
 ## Running samples in Azure Notebooks - Jupyter based notebooks in the Azure cloud 
 1. [![Azure Notebooks](https://notebooks.azure.com/launch.png)](https://aka.ms/aml-clone-azure-notebooks)
 [Import sample notebooks ](https://aka.ms/aml-clone-azure-notebooks) into Azure Notebooks.
 1. Follow the instructions in the [../00.configuration](00.configuration.ipynb) notebook to create and connect to a workspace.
 1. Open one of the sample notebooks.
    **Make sure the Azure Notebook kernel is set to `Python 3.6`** when you open a notebook. 
    ![set kernel to Python 3.6](../images/python36.png)
 <a name="localconda"></a>
 ## Running samples in a Local Conda environment 
 To run these notebook on your own notebook server, use these installation instructions.
 The instructions below will install everything you need and then start a Jupyter notebook.  To start your Jupyter notebook manually, use:
 ```
 conda activate azure_automl
 jupyter notebook
 ```
 or on Mac:
 ```
 source activate azure_automl
 jupyter notebook
 ```
 ### 1. Install mini-conda from [here](https://conda.io/miniconda.html), choose Python 3.7 or higher. 
 - **Note**: if you already have conda installed, you can keep using it but it should be version 4.4.10 or later (as shown by: conda -V).  If you have a previous version installed, you can update it using the command: conda update conda.
 There's no need to install mini-conda specifically.
 ### 2. Downloading the sample notebooks
 - Download the sample notebooks from [GitHub](https://github.com/Azure/MachineLearningNotebooks) as zip and extract the contents to a local directory.  The AutoML sample notebooks are in the "automl" folder.
 ### 3. Setup a new conda environment
 The **automl/automl_setup** script creates a new conda environment, installs the necessary packages, configures the widget and starts a jupyter notebook.
 It takes the conda environment name as an optional parameter.  The default conda environment name is azure_automl.  The exact command depends on the operating system.  It can take about 30 minutes to execute.
 ## Windows
 Start a conda command windows, cd to the **automl** folder where the sample notebooks were extracted and then run: 
 ```
 automl_setup
 ```
 ## Mac
 Install "Command line developer tools" if it is not already installed (you can use the command: `xcode-select --install`).
 Start a Terminal windows, cd to the **automl** folder where the sample notebooks were extracted and then run: 
 ```
 bash automl_setup_mac.sh
 ```
 ## Linux
 cd to the **automl** folder where the sample notebooks were extracted and then run: 
 ```
 bash automl_setup_linux.sh
 ```
 ### 4. Running configuration.ipynb
 - Before running any samples you next need to run the configuration notebook. Click on 00.configuration.ipynb notebook
 - Execute the cells in the notebook to Register Machine Learning Services Resource Provider and create a workspace. (*instructions in notebook*)
 ### 5. Running Samples
 - Please make sure you use the Python [conda env:azure_automl] kernel when trying the sample Notebooks.
 - Follow the instructions in the individual notebooks to explore various features in AutoML
 <a name="samples"></a>
 # Automated ML SDK Sample Notebooks 
 - [00.configuration.ipynb](00.configuration.ipynb)
    - Register Machine Learning Services Resource Provider
    - Create new Azure ML Workspace
    - Save Workspace configuration file
 - [01.auto-ml-classification.ipynb](01.auto-ml-classification.ipynb)
    - Dataset: scikit learn's [digit dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits)
    - Simple example of using Auto ML for classification 
    - Uses local compute for training
 - [02.auto-ml-regression.ipynb](02.auto-ml-regression.ipynb)
    - Dataset: scikit learn's [diabetes dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_diabetes.html)
    - Simple example of using Auto ML for regression 
    - Uses local compute for training
 - [03.auto-ml-remote-execution.ipynb](03.auto-ml-remote-execution.ipynb)
    - Dataset: scikit learn's [digit dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits)
    - Example of using Auto ML for classification using a remote linux DSVM for training
    - Parallel execution of iterations
    - Async tracking of progress
    - Cancelling individual iterations or entire run
    - Retrieving models for any iteration or logged metric
    - Specify automl settings as kwargs
 - [03b.auto-ml-remote-batchai.ipynb](03b.auto-ml-remote-batchai.ipynb)
    - Dataset: scikit learn's [digit dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits)
    - Example of using automated ML for classification using a remote Batch AI compute for training
    - Parallel execution of iterations
    - Async tracking of progress
    - Cancelling individual iterations or entire run
    - Retrieving models for any iteration or logged metric
    - Specify automl settings as kwargs
 - [04.auto-ml-remote-execution-text-data-blob-store.ipynb](04.auto-ml-remote-execution-text-data-blob-store.ipynb)
    - Dataset: [Burning Man 2016 dataset](https://innovate.burningman.org/datasets-page/)
    - handling text data with preprocess flag
    - Reading data from a blob store for remote executions
    - using pandas dataframes for reading data    
 - [05.auto-ml-missing-data-blacklist-early-termination.ipynb](05.auto-ml-missing-data-blacklist-early-termination.ipynb)
    - Dataset: scikit learn's [digit dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits)
    - Blacklist certain pipelines
    - Specify a target metrics to indicate stopping criteria
    - Handling Missing Data in the input
 - [06.auto-ml-sparse-data-custom-cv-split.ipynb](06.auto-ml-sparse-data-custom-cv-split.ipynb)
    - Dataset: Scikit learn's [20newsgroup](http://scikit-learn.org/stable/datasets/twenty_newsgroups.html) 
    - Handle sparse datasets
    - Specify custom train and validation set
 - [07.auto-ml-exploring-previous-runs.ipynb](07.auto-ml-exploring-previous-runs)
    - List all projects for the workspace
    - List all AutoML Runs for a given project
    - Get details for a AutoML Run. (Automl settings, run widget & all metrics)
    - Downlaod fitted pipeline for any iteration
 - [08.auto-ml-remote-execution-with-text-file-on-DSVM](08.auto-ml-remote-execution-with-text-file-on-DSVM.ipynb)
    - Dataset: scikit learn's [digit dataset](https://innovate.burningman.org/datasets-page/)
    - Download the data and store it in the DSVM to improve performance.
 - [09.auto-ml-classification-with-deployment.ipynb](09.auto-ml-classification-with-deployment.ipynb)
    - Dataset: scikit learn's [digit dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits)
    - Simple example of using Auto ML for classification 
    - Registering the model
    - Creating Image and creating aci service
    - Testing the aci service
 - [10.auto-ml-multi-output-example.ipynb](10.auto-ml-multi-output-example.ipynb)
    - Dataset: scikit learn's random example using multi-output pipeline(http://scikit-learn.org/stable/auto_examples/ensemble/plot_random_forest_regression_multioutput.html#sphx-glr-auto-examples-ensemble-plot-random-forest-regression-multioutput-py)
    - Simple example of using Auto ML for multi output regression
    - Handle both the dense and sparse metrix
 - [11.auto-ml-sample-weight.ipynb](11.auto-ml-sample-weight.ipynb)
    - How to specifying sample_weight
    - The difference that it makes to test results
 - [12.auto-ml-retrieve-the-training-sdk-versions.ipynb](12.auto-ml-retrieve-the-training-sdk-versions.ipynb)
    - How to get current and training env SDK versions
 - [13.auto-ml-dataprep.ipynb](13.auto-ml-dataprep.ipynb)
    - Using DataPrep for reading data
 <a name="documentation"></a>
 # Documentation 
 ## Table of Contents
 1. [Automated ML Settings ](#automlsettings)
 1. [Cross validation split options](#cvsplits)
 1. [Get Data Syntax](#getdata)
 1. [Data pre-processing and featurization](#preprocessing)
 <a name="automlsettings"></a>
 ## Automated ML Settings 
 |Property|Description|Default|
 |-|-|-|
 |**primary_metric**|This is the metric that you want to optimize.<br><br> Classification supports the following primary metrics <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>balanced_accuracy</i><br><i>average_precision_score_weighted</i><br><i>precision_score_weighted</i><br><br> Regression supports the following primary metrics <br><i>spearman_correlation</i><br><i>normalized_root_mean_squared_error</i><br><i>r2_score</i><br><i>normalized_mean_absolute_error</i><br><i>normalized_root_mean_squared_log_error</i>| Classification: accuracy <br><br> Regression: spearman_correlation
 |**max_time_sec**|Time limit in seconds for each iteration|None|
 |**iterations**|Number of iterations. In each iteration trains the data with a specific pipeline.  To get the best result, use at least 100. |100|
 |**n_cross_validations**|Number of cross validation splits|None|
 |**validation_size**|Size of validation set as percentage of all training samples|None|
 |**concurrent_iterations**|Max number of iterations that would be executed in parallel|1|
 |**preprocess**|*True/False* <br>Setting this to *True* enables preprocessing <br>on the input to handle missing data, and perform some common feature extraction<br>*Note: If input data is Sparse you cannot use preprocess=True*|False|
 |**max_cores_per_iteration**| Indicates how many cores on the compute target would be used to train a single pipeline.<br> You can set it to *-1* to use all cores|1|
 |**exit_score**|*double* value indicating the target for *primary_metric*. <br> Once the target is surpassed the run terminates|None|
 |**blacklist_algos**|*Array* of *strings* indicating pipelines to ignore for Auto ML.<br><br> Allowed values for **Classification**<br><i>LogisticRegression</i><br><i>SGDClassifierWrapper</i><br><i>NBWrapper</i><br><i>BernoulliNB</i><br><i>SVCWrapper</i><br><i>LinearSVMWrapper</i><br><i>KNeighborsClassifier</i><br><i>DecisionTreeClassifier</i><br><i>RandomForestClassifier</i><br><i>ExtraTreesClassifier</i><br><i>gradient boosting</i><br><i>LightGBMClassifier</i><br><br>Allowed values for **Regression**<br><i>ElasticNet</i><br><i>GradientBoostingRegressor</i><br><i>DecisionTreeRegressor</i><br><i>KNeighborsRegressor</i><br><i>LassoLars</i><br><i>SGDRegressor</i><br><i>RandomForestRegressor</i><br><i>ExtraTreesRegressor</i>|None|
 <a name="cvsplits"></a>
 ## Cross validation split options
 ### K-Folds Cross Validation
 Use *n_cross_validations* setting to specify the number of cross validations. The training data set will be randomly split into *n_cross_validations* folds of equal size. During each cross validation round, one of the folds will be used for validation of the model trained on the remaining folds. This process repeats for *n_cross_validations* rounds until each fold is used once as validation set. Finally, the average scores accross all *n_cross_validations* rounds will be reported, and the corresponding model will be retrained on the whole training data set.
 ### Monte Carlo Cross Validation (a.k.a. Repeated Random Sub-Sampling)
 Use *validation_size* to specify the percentage of the training data set that should be used for validation, and use *n_cross_validations* to specify the number of cross validations. During each cross validation round, a subset of size *validation_size* will be randomly selected for validation of the model trained on the remaining data. Finally, the average scores accross all *n_cross_validations* rounds will be reported, and the corresponding model will be retrained on the whole training data set.
 ### Custom train and validation set
 You can specify seperate train and validation set either through the get_data() or directly to the fit method.
 <a name="getdata"></a>
 ## get_data() syntax 
 The *get_data()* function can be used to return a dictionary with these values:
 |Key|Type|Dependency|Mutually Exclusive with|Description|
 |:-|:-|:-|:-|:-|
 |X|Pandas Dataframe or Numpy Array|y|data_train, label, columns|All features to train with|
 |y|Pandas Dataframe or Numpy Array|X|label|Label data to train with.  For classification, this should be an array of integers. |
 |X_valid|Pandas Dataframe or Numpy Array|X, y, y_valid|data_train, label|*Optional* All features to validate with.  If this is not specified, X is split between train and validate|
 |y_valid|Pandas Dataframe or Numpy Array|X, y, X_valid|data_train, label|*Optional* The label data to validate with.  If this is not specified, y is split between train and validate|
 |sample_weight|Pandas Dataframe or Numpy Array|y|data_train, label, columns|*Optional* A weight value for each label. Higher values indicate that the sample is more important.|
 |sample_weight_valid|Pandas Dataframe or Numpy Array|y_valid|data_train, label, columns|*Optional* A weight value for each validation label. Higher values indicate that the sample is more important.  If this is not specified, sample_weight is split between train and validate|
 |data_train|Pandas Dataframe|label|X, y, X_valid, y_valid|All data (features+label) to train with|
 |label|string|data_train|X, y, X_valid, y_valid|Which column in data_train represents the label|
 |columns|Array of strings|data_train||*Optional* Whitelist of columns to use for features|
 |cv_splits_indices|Array of integers|data_train||*Optional* List of indexes to split the data for cross validation|
 <a name="preprocessing"></a>
 ## Data pre-processing and featurization 
 If you use `preprocess=True`, the following data preprocessing steps are performed automatically for you:
 1. Dropping high cardinality or no variance features
    - Features with no useful information are dropped from training and validation sets. These include features with all values missing, same value across all rows or with extremely high cardinality (e.g., hashes, IDs or GUIDs).
 2. Missing value imputation
    - For numerical features, missing values are imputed with average of values in the column.
    - For categorical features, missing values are imputed with most frequent value.
 3. Generating additional features
    - For DateTime features: Year, Month, Day, Day of week, Day of year, Quarter, Week of the year, Hour, Minute, Second.
    - For Text features: Term frequency based on bi-grams and tri-grams, Count vectorizer.
 4. Transformations and encodings
    - Numeric features with very few unique values are transformed into categorical features.
 <a name="pythoncommand"></a>
 # Running using python command 
 Jupyter notebook provides a File / Download as / Python (.py) option for saving the notebook as a Python file.
 You can then run this file using the python command.
 However, on Windows the file needs to be modified before it can be run.
 The following condition must be added to the main code in the file:
    if __name__ == "__main__":
 The main code of the file must be indented so that it is under this condition.
 <a name="troubleshooting"></a>
 # Troubleshooting 
 ## Iterations fail and the log contains "MemoryError"
 This can be caused by insufficient memory on the DSVM.  AutoML loads all training data into memory.  So, the available memory should be more than the training data size.
 If you are using a remote DSVM, memory is needed for each concurrent iteration.  The concurrent_iterations setting specifies the maximum concurrent iterations.  For example, if the training data size is 8Gb and concurrent_iterations is set to 10, the minimum memory required is at least 80Gb.
 To resolve this issue, allocate a DSVM with more memory or reduce the value specified for concurrent_iterations.
 ## Iterations show as "Not Responding" in the RunDetails widget.
 This can be caused by too many concurrent iterations for a remote DSVM.  Each concurrent iteration usually takes 100% of a core when it is running.  Some iterations can use multiple cores.  So, the concurrent_iterations setting should always be less than the number of cores of the DSVM.
 To resolve this issue, try reducing the value specified for the concurrent_iterations setting.
--- a/automl/automl_env.yml
+++ b/automl/automl_env.yml
@@ -1,20 +0,0 @@
 name: azure_automl
 dependencies:
  # The python interpreter version.
  # Currently Azure ML only supports 3.5.2 and later.
 - python=3.6
 - nb_conda
 - matplotlib
 - numpy>=1.11.0,<1.16.0
 - scipy>=0.19.0,<0.20.0
 - scikit-learn>=0.18.0,<=0.19.1
 - pandas>=0.19.0,<0.23.0
 - pip:
    # Required packages for AzureML execution, history, and data preparation.
  - --extra-index-url https://pypi.python.org/simple
  - azureml-sdk[automl]
  - azureml-train-widgets
  - azure-cli
  - pandas_ml
--- a/automl/automl_setup.cmd
+++ b/automl/automl_setup.cmd
@@ -1,41 +0,0 @@
@echo off
 set conda_env_name=%1
 IF "%conda_env_name%"=="" SET conda_env_name="azure_automl"
 call conda activate %conda_env_name% 2>nul:
 if not errorlevel 1 (
  call conda env update --file automl_env.yml -n %conda_env_name%
  if errorlevel 1 goto ErrorExit
 ) else (
  call conda env create -f automl_env.yml -n %conda_env_name%
 )
 call conda activate %conda_env_name% 2>nul:
 if errorlevel 1 goto ErrorExit
 call pip install psutil
 call jupyter nbextension install --py azureml.train.widgets
 if errorlevel 1 goto ErrorExit
 call jupyter nbextension enable --py azureml.train.widgets
 if errorlevel 1 goto ErrorExit
 echo.
 echo.
 echo ***************************************
 echo * AutoML setup completed successfully *
 echo ***************************************
 echo.
 echo Starting jupyter notebook - please run notebook 00.configuration
 echo.
 jupyter notebook --log-level=50
 goto End
 :ErrorExit
 echo Install failed
 :End
--- a/automl/automl_setup_linux.sh
+++ b/automl/automl_setup_linux.sh
@@ -1,34 +0,0 @@
 #!/bin/bash
 CONDA_ENV_NAME=$1
 if [ "$CONDA_ENV_NAME" == "" ]
 then
  CONDA_ENV_NAME="azure_automl"
 fi
 if source activate $CONDA_ENV_NAME 2> /dev/null
 then
   conda env update -file automl_env.yml -n $CONDA_ENV_NAME
 else
   conda env create -f automl_env.yml -n $CONDA_ENV_NAME &&
   source activate $CONDA_ENV_NAME &&
   jupyter nbextension install --py azureml.train.widgets --user &&
   jupyter nbextension enable --py azureml.train.widgets --user &&
   echo "" &&
   echo "" &&
   echo "***************************************" &&
   echo "* AutoML setup completed successfully *" &&
   echo "***************************************" &&
   echo "" &&
   echo "Starting jupyter notebook - please run notebook 00.configuration" &&
   echo "" &&
   jupyter notebook --log-level=50
 fi
 if [ $? -gt 0 ]
 then
   echo "Installation failed"
 fi
--- a/automl/automl_setup_mac.sh
+++ b/automl/automl_setup_mac.sh
@@ -1,35 +0,0 @@
 #!/bin/bash
 CONDA_ENV_NAME=$1
 if [ "$CONDA_ENV_NAME" == "" ]
 then
  CONDA_ENV_NAME="azure_automl"
 fi
 if source activate $CONDA_ENV_NAME 2> /dev/null
 then
   conda env update -file automl_env.yml -n $CONDA_ENV_NAME
 else
   conda env create -f automl_env.yml -n $CONDA_ENV_NAME &&
   source activate $CONDA_ENV_NAME &&
   conda install lightgbm -c conda-forge -y &&
   jupyter nbextension install --py azureml.train.widgets --user &&
   jupyter nbextension enable --py azureml.train.widgets --user &&
   echo "" &&
   echo "" &&
   echo "***************************************" &&
   echo "* AutoML setup completed successfully *" &&
   echo "***************************************" &&
   echo "" &&
   echo "Starting jupyter notebook - please run notebook 00.configuration" &&
   echo "" &&
   jupyter notebook --log-level=50
 fi
 if [ $? -gt 0 ]
 then
   echo "Installation failed"
 fi
--- a/configuration.ipynb
+++ b/configuration.ipynb
@@ -0,0 +1,383 @@
 {
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
        "\n",
        "Licensed under the MIT License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/configuration.png)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Configuration\n",
        "\n",
        "_**Setting up your Azure Machine Learning services workspace and configuring your notebook library**_\n",
        "\n",
        "---\n",
        "---\n",
        "\n",
        "## Table of Contents\n",
        "\n",
        "1. [Introduction](#Introduction)\n",
        "    1. What is an Azure Machine Learning workspace\n",
        "1. [Setup](#Setup)\n",
        "    1. Azure subscription\n",
        "    1. Azure ML SDK and other library installation\n",
        "    1. Azure Container Instance registration\n",
        "1. [Configure your Azure ML Workspace](#Configure%20your%20Azure%20ML%20workspace)\n",
        "    1. Workspace parameters\n",
        "    1. Access your workspace\n",
        "    1. Create a new workspace\n",
        "    1. Create compute resources\n",
        "1. [Next steps](#Next%20steps)\n",
        "\n",
        "---\n",
        "\n",
        "## Introduction\n",
        "\n",
        "This notebook configures your library of notebooks to connect to an Azure Machine Learning (ML) workspace.  In this case, a library contains all of the notebooks in the current folder and any nested folders.  You can configure this notebook library to use an existing workspace or create a new workspace.\n",
        "\n",
        "Typically you will need to run this notebook only once per notebook library as all other notebooks will use connection information that is written here.  If you want to redirect your notebook library to work with a different workspace, then you should re-run this notebook.\n",
        "\n",
        "In this notebook you will\n",
        "* Learn about getting an Azure subscription\n",
        "* Specify your workspace parameters\n",
        "* Access or create your workspace\n",
        "* Add a default compute cluster for your workspace\n",
        "\n",
        "### What is an Azure Machine Learning workspace\n",
        "\n",
        "An Azure ML Workspace is an Azure resource that organizes and coordinates the actions of many other Azure resources to assist in executing and sharing machine learning workflows.  In particular, an Azure ML Workspace coordinates storage, databases, and compute resources providing added functionality for machine learning experimentation, deployment, inference, and the monitoring of deployed models."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Setup\n",
        "\n",
        "This section describes activities required before you can access any Azure ML services functionality."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### 1. Azure Subscription\n",
        "\n",
        "In order to create an Azure ML Workspace, first you need access to an Azure subscription.  An Azure subscription allows you to manage storage, compute, and other assets in the Azure cloud.  You can [create a new subscription](https://azure.microsoft.com/en-us/free/) or access existing subscription information from the [Azure portal](https://portal.azure.com).  Later in this notebook you will need information such as your subscription ID in order to create and access AML workspaces.\n",
        "\n",
        "### 2. Azure ML SDK and other library installation\n",
        "\n",
        "If you are running in your own environment, follow [SDK installation instructions](https://docs.microsoft.com/azure/machine-learning/service/how-to-configure-environment).  If you are running in Azure Notebooks or another Microsoft managed environment, the SDK is already installed.\n",
        "\n",
        "Also install following libraries to your environment. Many of the example notebooks depend on them\n",
        "\n",
        "```\n",
        "(myenv) $ conda install -y matplotlib tqdm scikit-learn\n",
        "```\n",
        "\n",
        "Once installation is complete, the following cell checks the Azure ML SDK version:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [
          "install"
        ]
      },
      "outputs": [],
      "source": [
        "import azureml.core\n",
        "\n",
        "print(\"This notebook was created using version 1.0.69 of the Azure ML SDK\")\n",
        "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "If you are using an older version of the SDK then this notebook was created using, you should upgrade your SDK.\n",
        "\n",
        "### 3. Azure Container Instance registration\n",
        "Azure Machine Learning uses of [Azure Container Instance (ACI)](https://azure.microsoft.com/services/container-instances) to deploy dev/test web services. An Azure subscription needs to be registered to use ACI.  If you or the subscription owner have not yet registered ACI on your subscription, you will need to use the [Azure CLI](https://docs.microsoft.com/en-us/cli/azure/install-azure-cli?view=azure-cli-latest) and execute the following commands.  Note that if you ran through the AML [quickstart](https://docs.microsoft.com/en-us/azure/machine-learning/service/quickstart-get-started) you have already registered ACI. \n",
        "\n",
        "```shell\n",
        "# check to see if ACI is already registered\n",
        "(myenv) $ az provider show -n Microsoft.ContainerInstance -o table\n",
        "\n",
        "# if ACI is not registered, run this command.\n",
        "# note you need to be the subscription owner in order to execute this command successfully.\n",
        "(myenv) $ az provider register -n Microsoft.ContainerInstance\n",
        "```\n",
        "\n",
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Configure your Azure ML workspace\n",
        "\n",
        "### Workspace parameters\n",
        "\n",
        "To use an AML Workspace, you will need to import the Azure ML SDK and supply the following information:\n",
        "* Your subscription id\n",
        "* A resource group name\n",
        "* (optional) The region that will host your workspace\n",
        "* A name for your workspace\n",
        "\n",
        "You can get your subscription ID from the [Azure portal](https://portal.azure.com).\n",
        "\n",
        "You will also need access to a [_resource group_](https://docs.microsoft.com/en-us/azure/azure-resource-manager/resource-group-overview#resource-groups), which organizes Azure resources and provides a default region for the resources in a group.  You can see what resource groups to which you have access, or create a new one in the [Azure portal](https://portal.azure.com).  If you don't have a resource group, the create workspace command will create one for you using the name you provide.\n",
        "\n",
        "The region to host your workspace will be used if you are creating a new workspace.  You do not need to specify this if you are using an existing workspace. You can find the list of supported regions [here](https://azure.microsoft.com/en-us/global-infrastructure/services/?products=machine-learning-service).  You should pick a region that is close to your location or that contains your data.\n",
        "\n",
        "The name for your workspace is unique within the subscription and should be descriptive enough to discern among other AML Workspaces.  The subscription may be used only by you, or it may be used by your department or your entire enterprise, so choose a name that makes sense for your situation.\n",
        "\n",
        "The following cell allows you to specify your workspace parameters.  This cell uses the python method `os.getenv` to read values from environment variables which is useful for automation.  If no environment variable exists, the parameters will be set to the specified default values.  \n",
        "\n",
        "If you ran the Azure Machine Learning [quickstart](https://docs.microsoft.com/en-us/azure/machine-learning/service/quickstart-get-started) in Azure Notebooks, you already have a configured workspace!  You can go to your Azure Machine Learning Getting Started library, view *config.json* file, and copy-paste the values for subscription ID, resource group and workspace name below.\n",
        "\n",
        "Replace the default values in the cell below with your workspace parameters"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import os\n",
        "\n",
        "subscription_id = os.getenv(\"SUBSCRIPTION_ID\", default=\"<my-subscription-id>\")\n",
        "resource_group = os.getenv(\"RESOURCE_GROUP\", default=\"<my-resource-group>\")\n",
        "workspace_name = os.getenv(\"WORKSPACE_NAME\", default=\"<my-workspace-name>\")\n",
        "workspace_region = os.getenv(\"WORKSPACE_REGION\", default=\"eastus2\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Access your workspace\n",
        "\n",
        "The following cell uses the Azure ML SDK to attempt to load the workspace specified by your parameters.  If this cell succeeds, your notebook library will be configured to access the workspace from all notebooks using the `Workspace.from_config()` method.  The cell can fail if the specified workspace doesn't exist or you don't have permissions to access it. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.core import Workspace\n",
        "\n",
        "try:\n",
        "    ws = Workspace(subscription_id = subscription_id, resource_group = resource_group, workspace_name = workspace_name)\n",
        "    # write the details of the workspace to a configuration file to the notebook library\n",
        "    ws.write_config()\n",
        "    print(\"Workspace configuration succeeded. Skip the workspace creation steps below\")\n",
        "except:\n",
        "    print(\"Workspace not accessible. Change your parameters or create a new workspace below\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Create a new workspace\n",
        "\n",
        "If you don't have an existing workspace and are the owner of the subscription or resource group, you can create a new workspace.  If you don't have a resource group, the create workspace command will create one for you using the name you provide.\n",
        "\n",
        "**Note**: As with other Azure services, there are limits on certain resources (for example AmlCompute quota) associated with the Azure ML service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota.\n",
        "\n",
        "This cell will create an Azure ML workspace for you in a subscription provided you have the correct permissions.\n",
        "\n",
        "This will fail if:\n",
        "* You do not have permission to create a workspace in the resource group\n",
        "* You do not have permission to create a resource group if it's non-existing.\n",
        "* You are not a subscription owner or contributor and no Azure ML workspaces have ever been created in this subscription\n",
        "\n",
        "If workspace creation fails, please work with your IT admin to provide you with the appropriate permissions or to provision the required resources."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [
          "create workspace"
        ]
      },
      "outputs": [],
      "source": [
        "from azureml.core import Workspace\n",
        "\n",
        "# Create the workspace using the specified parameters\n",
        "ws = Workspace.create(name = workspace_name,\n",
        "                      subscription_id = subscription_id,\n",
        "                      resource_group = resource_group, \n",
        "                      location = workspace_region,\n",
        "                      create_resource_group = True,\n",
        "                      exist_ok = True)\n",
        "ws.get_details()\n",
        "\n",
        "# write the details of the workspace to a configuration file to the notebook library\n",
        "ws.write_config()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Create compute resources for your training experiments\n",
        "\n",
        "Many of the sample notebooks use Azure ML managed compute (AmlCompute) to train models using a dynamically scalable pool of compute. In this section you will create default compute clusters for use by the other notebooks and any other operations you choose.\n",
        "\n",
        "To create a cluster, you need to specify a compute configuration that specifies the type of machine to be used and the scalability behaviors.  Then you choose a name for the cluster that is unique within the workspace that can be used to address the cluster later.\n",
        "\n",
        "The cluster parameters are:\n",
        "* vm_size - this describes the virtual machine type and size used in the cluster.  All machines in the cluster are the same type.  You can get the list of vm sizes available in your region by using the CLI command\n",
        "\n",
        "```shell\n",
        "az vm list-skus -o tsv\n",
        "```\n",
        "* min_nodes - this sets the minimum size of the cluster.  If you set the minimum to 0 the cluster will shut down all nodes while not in use.  Setting this number to a value higher than 0 will allow for faster start-up times, but you will also be billed when the cluster is not in use.\n",
        "* max_nodes - this sets the maximum size of the cluster.  Setting this to a larger number allows for more concurrency and a greater distributed processing of scale-out jobs.\n",
        "\n",
        "\n",
        "To create a **CPU** cluster now, run the cell below. The autoscale settings mean that the cluster will scale down to 0 nodes when inactive and up to 4 nodes when busy."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.core.compute import ComputeTarget, AmlCompute\n",
        "from azureml.core.compute_target import ComputeTargetException\n",
        "\n",
        "# Choose a name for your CPU cluster\n",
        "cpu_cluster_name = \"cpu-cluster\"\n",
        "\n",
        "# Verify that cluster does not exist already\n",
        "try:\n",
        "    cpu_cluster = ComputeTarget(workspace=ws, name=cpu_cluster_name)\n",
        "    print(\"Found existing cpu-cluster\")\n",
        "except ComputeTargetException:\n",
        "    print(\"Creating new cpu-cluster\")\n",
        "    \n",
        "    # Specify the configuration for the new cluster\n",
        "    compute_config = AmlCompute.provisioning_configuration(vm_size=\"STANDARD_D2_V2\",\n",
        "                                                           min_nodes=0,\n",
        "                                                           max_nodes=4)\n",
        "\n",
        "    # Create the cluster with the specified name and configuration\n",
        "    cpu_cluster = ComputeTarget.create(ws, cpu_cluster_name, compute_config)\n",
        "    \n",
        "    # Wait for the cluster to complete, show the output log\n",
        "    cpu_cluster.wait_for_completion(show_output=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "To create a **GPU** cluster, run the cell below. Note that your subscription must have sufficient quota for GPU VMs or the command will fail. To increase quota, see [these instructions](https://docs.microsoft.com/en-us/azure/azure-supportability/resource-manager-core-quotas-request). "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.core.compute import ComputeTarget, AmlCompute\n",
        "from azureml.core.compute_target import ComputeTargetException\n",
        "\n",
        "# Choose a name for your GPU cluster\n",
        "gpu_cluster_name = \"gpu-cluster\"\n",
        "\n",
        "# Verify that cluster does not exist already\n",
        "try:\n",
        "    gpu_cluster = ComputeTarget(workspace=ws, name=gpu_cluster_name)\n",
        "    print(\"Found existing gpu cluster\")\n",
        "except ComputeTargetException:\n",
        "    print(\"Creating new gpu-cluster\")\n",
        "    \n",
        "    # Specify the configuration for the new cluster\n",
        "    compute_config = AmlCompute.provisioning_configuration(vm_size=\"STANDARD_NC6\",\n",
        "                                                           min_nodes=0,\n",
        "                                                           max_nodes=4)\n",
        "    # Create the cluster with the specified name and configuration\n",
        "    gpu_cluster = ComputeTarget.create(ws, gpu_cluster_name, compute_config)\n",
        "\n",
        "    # Wait for the cluster to complete, show the output log\n",
        "    gpu_cluster.wait_for_completion(show_output=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "---\n",
        "\n",
        "## Next steps\n",
        "\n",
        "In this notebook you configured this notebook library to connect easily to an Azure ML workspace.  You can copy this notebook to your own libraries to connect them to you workspace, or use it to bootstrap new workspaces completely.\n",
        "\n",
        "If you came here from another notebook, you can return there and complete that exercise, or you can try out the [Tutorials](./tutorials) or jump into \"how-to\" notebooks and start creating and deploying models.  A good place to start is the [train within notebook](./how-to-use-azureml/training/train-within-notebook) example that walks through a simplified but complete end to end machine learning process."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "authors": [
      {
        "name": "roastala"
      }
    ],
    "kernelspec": {
      "display_name": "Python 3.6",
      "language": "python",
      "name": "python36"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.5"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
--- a/configuration.yml
+++ b/configuration.yml
@@ -0,0 +1,4 @@
 name: configuration
 dependencies:
 - pip:
  - azureml-sdk
--- a/contrib/RAPIDS/azure-ml-with-nvidia-rapids.ipynb
+++ b/contrib/RAPIDS/azure-ml-with-nvidia-rapids.ipynb
@@ -0,0 +1,554 @@
 {
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
        "\n",
        "Licensed under the MIT License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/contrib/RAPIDS/azure-ml-with-nvidia-rapids/azure-ml-with-nvidia-rapids.png)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# NVIDIA RAPIDS in Azure Machine Learning"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "The [RAPIDS](https://www.developer.nvidia.com/rapids) suite of software libraries from NVIDIA enables the execution of end-to-end data science and analytics pipelines entirely on GPUs. In many machine learning projects, a significant portion of the model training time is spent in setting up the data; this stage of the process is known as Extraction, Transformation and Loading, or ETL. By using the DataFrame API for ETL\u00c2\u00a0and GPU-capable ML algorithms in RAPIDS, data preparation and training models can be done in GPU-accelerated end-to-end pipelines without incurring serialization costs between the pipeline stages. This notebook demonstrates how to use NVIDIA RAPIDS to prepare data and train model\u00c3\u201a\u00c2\u00a0in Azure.\n",
        " \n",
        "In this notebook, we will do the following:\n",
        " \n",
        "* Create an Azure Machine Learning Workspace\n",
        "* Create an AMLCompute target\n",
        "* Use a script to process our data and train a model\n",
        "* Obtain the data required to run this sample\n",
        "* Create an AML run configuration to launch a machine learning job\n",
        "* Run the script to prepare data for training and train the model\n",
        " \n",
        "Prerequisites:\n",
        "* An Azure subscription to create a Machine Learning Workspace\n",
        "* Familiarity with the Azure ML SDK (refer to [notebook samples](https://github.com/Azure/MachineLearningNotebooks))\n",
        "* A Jupyter notebook environment with Azure Machine Learning SDK installed. Refer to instructions to [setup the environment](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-environment#local)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Verify if Azure ML SDK is installed"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import azureml.core\n",
        "print(\"SDK version:\", azureml.core.VERSION)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import os\n",
        "from azureml.core import Workspace, Experiment\n",
        "from azureml.core.conda_dependencies import CondaDependencies\n",
        "from azureml.core.compute import AmlCompute, ComputeTarget\n",
        "from azureml.data.data_reference import DataReference\n",
        "from azureml.core.runconfig import RunConfiguration\n",
        "from azureml.core import ScriptRunConfig\n",
        "from azureml.widgets import RunDetails"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Create Azure ML Workspace"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "The following step is optional if you already have a workspace. If you want to use an existing workspace, then\n",
        "skip this workspace creation step and move on to the next step to load the workspace.\n",
        " \n",
        "<font color='red'>Important</font>: in the code cell below, be sure to set the correct values for the subscription_id, \n",
        "resource_group, workspace_name, region before executing this code cell."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "subscription_id = os.environ.get(\"SUBSCRIPTION_ID\", \"<subscription_id>\")\n",
        "resource_group = os.environ.get(\"RESOURCE_GROUP\", \"<resource_group>\")\n",
        "workspace_name = os.environ.get(\"WORKSPACE_NAME\", \"<workspace_name>\")\n",
        "workspace_region = os.environ.get(\"WORKSPACE_REGION\", \"<region>\")\n",
        "\n",
        "ws = Workspace.create(workspace_name, subscription_id=subscription_id, resource_group=resource_group, location=workspace_region)\n",
        "\n",
        "# write config to a local directory for future use\n",
        "ws.write_config()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Load existing Workspace"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "ws = Workspace.from_config()\n",
        "\n",
        "# if a locally-saved configuration file for the workspace is not available, use the following to load workspace\n",
        "# ws = Workspace(subscription_id=subscription_id, resource_group=resource_group, workspace_name=workspace_name)\n",
        "\n",
        "print('Workspace name: ' + ws.name, \n",
        "      'Azure region: ' + ws.location, \n",
        "      'Subscription id: ' + ws.subscription_id, \n",
        "      'Resource group: ' + ws.resource_group, sep = '\\n')\n",
        "\n",
        "scripts_folder = \"scripts_folder\"\n",
        "\n",
        "if not os.path.isdir(scripts_folder):\n",
        "    os.mkdir(scripts_folder)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Create AML Compute Target"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Because NVIDIA RAPIDS requires P40 or V100 GPUs, the user needs to specify compute targets from one of [NC_v3](https://docs.microsoft.com/en-us/azure/virtual-machines/windows/sizes-gpu#ncv3-series), [NC_v2](https://docs.microsoft.com/en-us/azure/virtual-machines/windows/sizes-gpu#ncv2-series), [ND](https://docs.microsoft.com/en-us/azure/virtual-machines/windows/sizes-gpu#nd-series) or [ND_v2](https://docs.microsoft.com/en-us/azure/virtual-machines/windows/sizes-gpu#ndv2-series-preview) virtual machine types in Azure; these are the families of virtual machines in Azure that are provisioned with these GPUs.\n",
        " \n",
        "Pick one of the supported VM SKUs based on the number of GPUs you want to use for ETL and training in RAPIDS.\n",
        " \n",
        "The script in this notebook is implemented for single-machine scenarios. An example supporting multiple nodes will be published later."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "gpu_cluster_name = \"gpucluster\"\n",
        "\n",
        "if gpu_cluster_name in ws.compute_targets:\n",
        "    gpu_cluster = ws.compute_targets[gpu_cluster_name]\n",
        "    if gpu_cluster and type(gpu_cluster) is AmlCompute:\n",
        "        print('Found compute target. Will use {0} '.format(gpu_cluster_name))\n",
        "else:\n",
        "    print(\"creating new cluster\")\n",
        "    # vm_size parameter below could be modified to one of the RAPIDS-supported VM types\n",
        "    provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"Standard_NC6s_v2\", min_nodes=1, max_nodes = 1)\n",
        "\n",
        "    # create the cluster\n",
        "    gpu_cluster = ComputeTarget.create(ws, gpu_cluster_name, provisioning_config)\n",
        "    gpu_cluster.wait_for_completion(show_output=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Script to process data and train model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "The _process&#95;data.py_ script used in the step below is a slightly modified implementation of [RAPIDS Mortgage E2E example](https://github.com/rapidsai/notebooks-contrib/blob/master/intermediate_notebooks/E2E/mortgage/mortgage_e2e.ipynb)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# copy process_data.py into the script folder\n",
        "import shutil\n",
        "shutil.copy('./process_data.py', os.path.join(scripts_folder, 'process_data.py'))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Data required to run this sample"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "This sample uses [Fannie Mae's Single-Family Loan Performance Data](http://www.fanniemae.com/portal/funding-the-market/data/loan-performance-data.html). Once you obtain access to the data, you will need to make this data available in an [Azure Machine Learning Datastore](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-access-data), for use in this sample. The following code shows how to do that."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Downloading Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import tarfile\n",
        "import hashlib\n",
        "from urllib.request import urlretrieve\n",
        "\n",
        "def validate_downloaded_data(path):\n",
        "    if(os.path.isdir(path) and os.path.exists(path + '//names.csv')) :\n",
        "        if(os.path.isdir(path + '//acq' ) and len(os.listdir(path + '//acq')) == 8):\n",
        "            if(os.path.isdir(path + '//perf' ) and len(os.listdir(path + '//perf')) == 11):\n",
        "                print(\"Data has been downloaded and decompressed at: {0}\".format(path))\n",
        "                return True\n",
        "    print(\"Data has not been downloaded and decompressed\")\n",
        "    return False\n",
        "\n",
        "def show_progress(count, block_size, total_size):\n",
        "    global pbar\n",
        "    global processed\n",
        "    \n",
        "    if count == 0:\n",
        "        pbar = ProgressBar(maxval=total_size)\n",
        "        processed = 0\n",
        "    \n",
        "    processed += block_size\n",
        "    processed = min(processed,total_size)\n",
        "    pbar.update(processed)\n",
        "\n",
        "        \n",
        "def download_file(fileroot):\n",
        "    filename = fileroot + '.tgz'\n",
        "    if(not os.path.exists(filename) or hashlib.md5(open(filename, 'rb').read()).hexdigest() != '82dd47135053303e9526c2d5c43befd5' ):\n",
        "        url_format = 'http://rapidsai-data.s3-website.us-east-2.amazonaws.com/notebook-mortgage-data/{0}.tgz'\n",
        "        url = url_format.format(fileroot)\n",
        "        print(\"...Downloading file :{0}\".format(filename))\n",
        "        urlretrieve(url, filename)\n",
        "        pbar.finish()\n",
        "        print(\"...File :{0} finished downloading\".format(filename))\n",
        "    else:\n",
        "        print(\"...File :{0} has been downloaded already\".format(filename))\n",
        "    return filename\n",
        "\n",
        "def decompress_file(filename,path):\n",
        "    tar = tarfile.open(filename)\n",
        "    print(\"...Getting information from {0} about files to decompress\".format(filename))\n",
        "    members = tar.getmembers()\n",
        "    numFiles = len(members)\n",
        "    so_far = 0\n",
        "    for member_info in members:\n",
        "        tar.extract(member_info,path=path)\n",
        "        so_far += 1\n",
        "    print(\"...All {0} files have been decompressed\".format(numFiles))\n",
        "    tar.close()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "fileroot = 'mortgage_2000-2001'\n",
        "path = '.\\\\{0}'.format(fileroot)\n",
        "pbar = None\n",
        "processed = 0\n",
        "\n",
        "if(not validate_downloaded_data(path)):\n",
        "    print(\"Downloading and Decompressing Input Data\")\n",
        "    filename = download_file(fileroot)\n",
        "    decompress_file(filename,path)\n",
        "    print(\"Input Data has been Downloaded and Decompressed\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Uploading Data to Workspace"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "ds = ws.get_default_datastore()\n",
        "\n",
        "# download and uncompress data in a local directory before uploading to data store\n",
        "# directory specified in src_dir parameter below should have the acq, perf directories with data and names.csv file\n",
        "\n",
        "# ---->>>> UNCOMMENT THE BELOW LINE TO UPLOAD YOUR DATA IF NOT DONE SO ALREADY <<<<----\n",
        "# ds.upload(src_dir=path, target_path=fileroot, overwrite=True, show_progress=True)\n",
        "\n",
        "# data already uploaded to the datastore\n",
        "data_ref = DataReference(data_reference_name='data', datastore=ds, path_on_datastore=fileroot)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Create AML run configuration to launch a machine learning job"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "RunConfiguration is used to submit jobs to Azure Machine Learning service. When creating RunConfiguration for a job, users can either \n",
        "1. specify a Docker image with prebuilt conda environment and use it without any modifications to run the job, or \n",
        "2. specify a Docker image as the base image and conda or pip packages as dependnecies to let AML build a new Docker image with a conda environment containing specified dependencies to use in the job\n",
        "\n",
        "The second option is the recommended option in AML. \n",
        "The following steps have code for both options. You can pick the one that is more appropriate for your requirements. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Specify prebuilt conda environment"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "The following code shows how to install RAPIDS using conda. The `rapids.yml` file contains the list of packages necessary to run this tutorial. **NOTE:** Initial build of the image might take up to 20 minutes as the service needs to build and cache the new image; once the image is built the subequent runs use the cached image and the overhead is minimal."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "cd = CondaDependencies(conda_dependencies_file_path='rapids.yml')\n",
        "run_config = RunConfiguration(conda_dependencies=cd)\n",
        "run_config.framework = 'python'\n",
        "run_config.target = gpu_cluster_name\n",
        "run_config.environment.docker.enabled = True\n",
        "run_config.environment.docker.gpu_support = True\n",
        "run_config.environment.docker.base_image = \"mcr.microsoft.com/azureml/base-gpu:intelmpi2018.3-cuda10.0-cudnn7-ubuntu16.04\"\n",
        "run_config.environment.spark.precache_packages = False\n",
        "run_config.data_references={'data':data_ref.to_config()}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Using Docker"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Alternatively, you can specify RAPIDS Docker image."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# run_config = RunConfiguration()\n",
        "# run_config.framework = 'python'\n",
        "# run_config.environment.python.user_managed_dependencies = True\n",
        "# run_config.environment.python.interpreter_path = '/conda/envs/rapids/bin/python'\n",
        "# run_config.target = gpu_cluster_name\n",
        "# run_config.environment.docker.enabled = True\n",
        "# run_config.environment.docker.gpu_support = True\n",
        "# run_config.environment.docker.base_image = \"rapidsai/rapidsai:cuda9.2-runtime-ubuntu18.04\"\n",
        "# # run_config.environment.docker.base_image_registry.address = '<registry_url>' # not required if the base_image is in Docker hub\n",
        "# # run_config.environment.docker.base_image_registry.username = '<user_name>' # needed only for private images\n",
        "# # run_config.environment.docker.base_image_registry.password = '<password>' # needed only for private images\n",
        "# run_config.environment.spark.precache_packages = False\n",
        "# run_config.data_references={'data':data_ref.to_config()}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Wrapper function to submit Azure Machine Learning experiment"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# parameter cpu_predictor indicates if training should be done on CPU. If set to true, GPUs are used *only* for ETL and *not* for training\n",
        "# parameter num_gpu indicates number of GPUs to use among the GPUs available in the VM for ETL and if cpu_predictor is false, for training as well \n",
        "def run_rapids_experiment(cpu_training, gpu_count, part_count):\n",
        "    # any value between 1-4 is allowed here depending the type of VMs available in gpu_cluster\n",
        "    if gpu_count not in [1, 2, 3, 4]:\n",
        "        raise Exception('Value specified for the number of GPUs to use {0} is invalid'.format(gpu_count))\n",
        "\n",
        "    # following data partition mapping is empirical (specific to GPUs used and current data partitioning scheme) and may need to be tweaked\n",
        "    max_gpu_count_data_partition_mapping = {1: 3, 2: 4, 3: 6, 4: 8}\n",
        "    \n",
        "    if part_count > max_gpu_count_data_partition_mapping[gpu_count]:\n",
        "        print(\"Too many partitions for the number of GPUs, exceeding memory threshold\")\n",
        "        \n",
        "    if part_count > 11:\n",
        "        print(\"Warning: Maximum number of partitions available is 11\")\n",
        "        part_count = 11\n",
        "        \n",
        "    end_year = 2000\n",
        "    \n",
        "    if part_count > 4:\n",
        "        end_year = 2001 # use more data with more GPUs\n",
        "\n",
        "    src = ScriptRunConfig(source_directory=scripts_folder, \n",
        "                          script='process_data.py', \n",
        "                          arguments = ['--num_gpu', gpu_count, '--data_dir', str(data_ref),\n",
        "                                      '--part_count', part_count, '--end_year', end_year,\n",
        "                                      '--cpu_predictor', cpu_training\n",
        "                                      ],\n",
        "                          run_config=run_config\n",
        "                         )\n",
        "\n",
        "    exp = Experiment(ws, 'rapidstest')\n",
        "    run = exp.submit(config=src)\n",
        "    RunDetails(run).show()\n",
        "    return run"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Submit experiment (ETL & training on GPU)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "cpu_predictor = False\n",
        "# the value for num_gpu should be less than or equal to the number of GPUs available in the VM\n",
        "num_gpu = 1\n",
        "data_part_count = 1\n",
        "# train using CPU, use GPU for both ETL and training\n",
        "run = run_rapids_experiment(cpu_predictor, num_gpu, data_part_count)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Submit experiment (ETL on GPU, training on CPU)\n",
        "\n",
        "To observe performance difference between GPU-accelerated RAPIDS based training with CPU-only training, set 'cpu_predictor' predictor to 'True' and rerun the experiment"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "cpu_predictor = True\n",
        "# the value for num_gpu should be less than or equal to the number of GPUs available in the VM\n",
        "num_gpu = 1\n",
        "data_part_count = 1\n",
        "# train using CPU, use GPU for ETL\n",
        "run = run_rapids_experiment(cpu_predictor, num_gpu, data_part_count)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Delete cluster"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# delete the cluster\n",
        "# gpu_cluster.delete()"
      ]
    }
  ],
  "metadata": {
    "authors": [
      {
        "name": "ksivas"
      }
    ],
    "kernelspec": {
      "display_name": "Python 3.6",
      "language": "python",
      "name": "python36"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.8"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 4
 }
--- a/contrib/RAPIDS/process_data.py
+++ b/contrib/RAPIDS/process_data.py
@@ -0,0 +1,470 @@
 import numpy as np
 import datetime
 import dask_xgboost as dxgb_gpu
 import dask
 import dask_cudf
 from dask_cuda import LocalCUDACluster
 from dask.delayed import delayed
 from dask.distributed import Client, wait
 import xgboost as xgb
 import cudf
 from cudf.dataframe import DataFrame
 from collections import OrderedDict
 import gc
 from glob import glob
 import os
 import argparse
 def run_dask_task(func, **kwargs):
    task = func(**kwargs)
    return task
 def process_quarter_gpu(client, col_names_path, acq_data_path, year=2000, quarter=1, perf_file=""):
    dask_client = client
    ml_arrays = run_dask_task(delayed(run_gpu_workflow),
                                          col_path=col_names_path,
                                          acq_path=acq_data_path,
                                          quarter=quarter,
                                          year=year,
                                          perf_file=perf_file)
    return dask_client.compute(ml_arrays,
                          optimize_graph=False,
                          fifo_timeout="0ms")
 def null_workaround(df, **kwargs):
    for column, data_type in df.dtypes.items():
        if str(data_type) == "category":
            df[column] = df[column].astype('int32').fillna(-1)
        if str(data_type) in ['int8', 'int16', 'int32', 'int64', 'float32', 'float64']:
            df[column] = df[column].fillna(-1)
    return df
 def run_gpu_workflow(col_path, acq_path, quarter=1, year=2000, perf_file="", **kwargs):
    names = gpu_load_names(col_path=col_path)
    acq_gdf = gpu_load_acquisition_csv(acquisition_path= acq_path + "/Acquisition_"
                                      + str(year) + "Q" + str(quarter) + ".txt")
    acq_gdf = acq_gdf.merge(names, how='left', on=['seller_name'])
    acq_gdf.drop_column('seller_name')
    acq_gdf['seller_name'] = acq_gdf['new']
    acq_gdf.drop_column('new')
    perf_df_tmp = gpu_load_performance_csv(perf_file)
    gdf = perf_df_tmp
    everdf = create_ever_features(gdf)
    delinq_merge = create_delinq_features(gdf)
    everdf = join_ever_delinq_features(everdf, delinq_merge)
    del(delinq_merge)
    joined_df = create_joined_df(gdf, everdf)
    testdf = create_12_mon_features(joined_df)
    joined_df = combine_joined_12_mon(joined_df, testdf)
    del(testdf)
    perf_df = final_performance_delinquency(gdf, joined_df)
    del(gdf, joined_df)
    final_gdf = join_perf_acq_gdfs(perf_df, acq_gdf)
    del(perf_df)
    del(acq_gdf)
    final_gdf = last_mile_cleaning(final_gdf)
    return final_gdf
 def gpu_load_performance_csv(performance_path, **kwargs):
    """ Loads performance data
    Returns
    -------
    GPU DataFrame
    """
    cols = [
        "loan_id", "monthly_reporting_period", "servicer", "interest_rate", "current_actual_upb",
        "loan_age", "remaining_months_to_legal_maturity", "adj_remaining_months_to_maturity",
        "maturity_date", "msa", "current_loan_delinquency_status", "mod_flag", "zero_balance_code",
        "zero_balance_effective_date", "last_paid_installment_date", "foreclosed_after",
        "disposition_date", "foreclosure_costs", "prop_preservation_and_repair_costs",
        "asset_recovery_costs", "misc_holding_expenses", "holding_taxes", "net_sale_proceeds",
        "credit_enhancement_proceeds", "repurchase_make_whole_proceeds", "other_foreclosure_proceeds",
        "non_interest_bearing_upb", "principal_forgiveness_upb", "repurchase_make_whole_proceeds_flag",
        "foreclosure_principal_write_off_amount", "servicing_activity_indicator"
    ]
    dtypes = OrderedDict([
        ("loan_id", "int64"),
        ("monthly_reporting_period", "date"),
        ("servicer", "category"),
        ("interest_rate", "float64"),
        ("current_actual_upb", "float64"),
        ("loan_age", "float64"),
        ("remaining_months_to_legal_maturity", "float64"),
        ("adj_remaining_months_to_maturity", "float64"),
        ("maturity_date", "date"),
        ("msa", "float64"),
        ("current_loan_delinquency_status", "int32"),
        ("mod_flag", "category"),
        ("zero_balance_code", "category"),
        ("zero_balance_effective_date", "date"),
        ("last_paid_installment_date", "date"),
        ("foreclosed_after", "date"),
        ("disposition_date", "date"),
        ("foreclosure_costs", "float64"),
        ("prop_preservation_and_repair_costs", "float64"),
        ("asset_recovery_costs", "float64"),
        ("misc_holding_expenses", "float64"),
        ("holding_taxes", "float64"),
        ("net_sale_proceeds", "float64"),
        ("credit_enhancement_proceeds", "float64"),
        ("repurchase_make_whole_proceeds", "float64"),
        ("other_foreclosure_proceeds", "float64"),
        ("non_interest_bearing_upb", "float64"),
        ("principal_forgiveness_upb", "float64"),
        ("repurchase_make_whole_proceeds_flag", "category"),
        ("foreclosure_principal_write_off_amount", "float64"),
        ("servicing_activity_indicator", "category")
    ])
    print(performance_path)
    return cudf.read_csv(performance_path, names=cols, delimiter='|', dtype=list(dtypes.values()), skiprows=1)
 def gpu_load_acquisition_csv(acquisition_path, **kwargs):
    """ Loads acquisition data
    Returns
    -------
    GPU DataFrame
    """
    cols = [
        'loan_id', 'orig_channel', 'seller_name', 'orig_interest_rate', 'orig_upb', 'orig_loan_term', 
        'orig_date', 'first_pay_date', 'orig_ltv', 'orig_cltv', 'num_borrowers', 'dti', 'borrower_credit_score', 
        'first_home_buyer', 'loan_purpose', 'property_type', 'num_units', 'occupancy_status', 'property_state',
        'zip', 'mortgage_insurance_percent', 'product_type', 'coborrow_credit_score', 'mortgage_insurance_type', 
        'relocation_mortgage_indicator'
    ]
    dtypes = OrderedDict([
        ("loan_id", "int64"),
        ("orig_channel", "category"),
        ("seller_name", "category"),
        ("orig_interest_rate", "float64"),
        ("orig_upb", "int64"),
        ("orig_loan_term", "int64"),
        ("orig_date", "date"),
        ("first_pay_date", "date"),
        ("orig_ltv", "float64"),
        ("orig_cltv", "float64"),
        ("num_borrowers", "float64"),
        ("dti", "float64"),
        ("borrower_credit_score", "float64"),
        ("first_home_buyer", "category"),
        ("loan_purpose", "category"),
        ("property_type", "category"),
        ("num_units", "int64"),
        ("occupancy_status", "category"),
        ("property_state", "category"),
        ("zip", "int64"),
        ("mortgage_insurance_percent", "float64"),
        ("product_type", "category"),
        ("coborrow_credit_score", "float64"),
        ("mortgage_insurance_type", "float64"),
        ("relocation_mortgage_indicator", "category")
    ])
    print(acquisition_path)
    return cudf.read_csv(acquisition_path, names=cols, delimiter='|', dtype=list(dtypes.values()), skiprows=1)
 def gpu_load_names(col_path):
    """ Loads names used for renaming the banks
    Returns
    -------
    GPU DataFrame
    """
    cols = [
        'seller_name', 'new'
    ]
    dtypes = OrderedDict([
        ("seller_name", "category"),
        ("new", "category"),
    ])
    return cudf.read_csv(col_path, names=cols, delimiter='|', dtype=list(dtypes.values()), skiprows=1)
 def create_ever_features(gdf, **kwargs):
    everdf = gdf[['loan_id', 'current_loan_delinquency_status']]
    everdf = everdf.groupby('loan_id', method='hash').max().reset_index()
    del(gdf)
    everdf['ever_30'] = (everdf['current_loan_delinquency_status'] >= 1).astype('int8')
    everdf['ever_90'] = (everdf['current_loan_delinquency_status'] >= 3).astype('int8')
    everdf['ever_180'] = (everdf['current_loan_delinquency_status'] >= 6).astype('int8')
    everdf.drop_column('current_loan_delinquency_status')
    return everdf
 def create_delinq_features(gdf, **kwargs):
    delinq_gdf = gdf[['loan_id', 'monthly_reporting_period', 'current_loan_delinquency_status']]
    del(gdf)
    delinq_30 = delinq_gdf.query('current_loan_delinquency_status >= 1')[['loan_id', 'monthly_reporting_period']].groupby('loan_id', method='hash').min().reset_index()
    delinq_30['delinquency_30'] = delinq_30['monthly_reporting_period']
    delinq_30.drop_column('monthly_reporting_period')
    delinq_90 = delinq_gdf.query('current_loan_delinquency_status >= 3')[['loan_id', 'monthly_reporting_period']].groupby('loan_id', method='hash').min().reset_index()
    delinq_90['delinquency_90'] = delinq_90['monthly_reporting_period']
    delinq_90.drop_column('monthly_reporting_period')
    delinq_180 = delinq_gdf.query('current_loan_delinquency_status >= 6')[['loan_id', 'monthly_reporting_period']].groupby('loan_id', method='hash').min().reset_index()
    delinq_180['delinquency_180'] = delinq_180['monthly_reporting_period']
    delinq_180.drop_column('monthly_reporting_period')
    del(delinq_gdf)
    delinq_merge = delinq_30.merge(delinq_90, how='left', on=['loan_id'], type='hash')
    delinq_merge['delinquency_90'] = delinq_merge['delinquency_90'].fillna(np.dtype('datetime64[ms]').type('1970-01-01').astype('datetime64[ms]'))
    delinq_merge = delinq_merge.merge(delinq_180, how='left', on=['loan_id'], type='hash')
    delinq_merge['delinquency_180'] = delinq_merge['delinquency_180'].fillna(np.dtype('datetime64[ms]').type('1970-01-01').astype('datetime64[ms]'))
    del(delinq_30)
    del(delinq_90)
    del(delinq_180)
    return delinq_merge
 def join_ever_delinq_features(everdf_tmp, delinq_merge, **kwargs):
    everdf = everdf_tmp.merge(delinq_merge, on=['loan_id'], how='left', type='hash')
    del(everdf_tmp)
    del(delinq_merge)
    everdf['delinquency_30'] = everdf['delinquency_30'].fillna(np.dtype('datetime64[ms]').type('1970-01-01').astype('datetime64[ms]'))
    everdf['delinquency_90'] = everdf['delinquency_90'].fillna(np.dtype('datetime64[ms]').type('1970-01-01').astype('datetime64[ms]'))
    everdf['delinquency_180'] = everdf['delinquency_180'].fillna(np.dtype('datetime64[ms]').type('1970-01-01').astype('datetime64[ms]'))
    return everdf
 def create_joined_df(gdf, everdf, **kwargs):
    test = gdf[['loan_id', 'monthly_reporting_period', 'current_loan_delinquency_status', 'current_actual_upb']]
    del(gdf)
    test['timestamp'] = test['monthly_reporting_period']
    test.drop_column('monthly_reporting_period')
    test['timestamp_month'] = test['timestamp'].dt.month
    test['timestamp_year'] = test['timestamp'].dt.year
    test['delinquency_12'] = test['current_loan_delinquency_status']
    test.drop_column('current_loan_delinquency_status')
    test['upb_12'] = test['current_actual_upb']
    test.drop_column('current_actual_upb')
    test['upb_12'] = test['upb_12'].fillna(999999999)
    test['delinquency_12'] = test['delinquency_12'].fillna(-1)
    joined_df = test.merge(everdf, how='left', on=['loan_id'], type='hash')
    del(everdf)
    del(test)
    joined_df['ever_30'] = joined_df['ever_30'].fillna(-1)
    joined_df['ever_90'] = joined_df['ever_90'].fillna(-1)
    joined_df['ever_180'] = joined_df['ever_180'].fillna(-1)
    joined_df['delinquency_30'] = joined_df['delinquency_30'].fillna(-1)
    joined_df['delinquency_90'] = joined_df['delinquency_90'].fillna(-1)
    joined_df['delinquency_180'] = joined_df['delinquency_180'].fillna(-1)
    joined_df['timestamp_year'] = joined_df['timestamp_year'].astype('int32')
    joined_df['timestamp_month'] = joined_df['timestamp_month'].astype('int32')
    return joined_df
 def create_12_mon_features(joined_df, **kwargs):
    testdfs = []
    n_months = 12
    for y in range(1, n_months + 1):
        tmpdf = joined_df[['loan_id', 'timestamp_year', 'timestamp_month', 'delinquency_12', 'upb_12']]
        tmpdf['josh_months'] = tmpdf['timestamp_year'] * 12 + tmpdf['timestamp_month']
        tmpdf['josh_mody_n'] = ((tmpdf['josh_months'].astype('float64') - 24000 - y) / 12).floor()
        tmpdf = tmpdf.groupby(['loan_id', 'josh_mody_n'], method='hash').agg({'delinquency_12': 'max','upb_12': 'min'}).reset_index()
        tmpdf['delinquency_12'] = (tmpdf['delinquency_12']>3).astype('int32')
        tmpdf['delinquency_12'] +=(tmpdf['upb_12']==0).astype('int32')
        tmpdf['upb_12'] = tmpdf['upb_12']
        tmpdf['timestamp_year'] = (((tmpdf['josh_mody_n'] * n_months) + 24000 + (y - 1)) / 12).floor().astype('int16')
        tmpdf['timestamp_month'] = np.int8(y)
        tmpdf.drop_column('josh_mody_n')
        testdfs.append(tmpdf)
        del(tmpdf)
    del(joined_df)
    return cudf.concat(testdfs)
 def combine_joined_12_mon(joined_df, testdf, **kwargs):
    joined_df.drop_column('delinquency_12')
    joined_df.drop_column('upb_12')
    joined_df['timestamp_year'] = joined_df['timestamp_year'].astype('int16')
    joined_df['timestamp_month'] = joined_df['timestamp_month'].astype('int8')
    return joined_df.merge(testdf, how='left', on=['loan_id', 'timestamp_year', 'timestamp_month'], type='hash')
 def final_performance_delinquency(gdf, joined_df, **kwargs):
    merged = null_workaround(gdf)
    joined_df = null_workaround(joined_df)
    merged['timestamp_month'] = merged['monthly_reporting_period'].dt.month
    merged['timestamp_month'] = merged['timestamp_month'].astype('int8')
    merged['timestamp_year'] = merged['monthly_reporting_period'].dt.year
    merged['timestamp_year'] = merged['timestamp_year'].astype('int16')
    merged = merged.merge(joined_df, how='left', on=['loan_id', 'timestamp_year', 'timestamp_month'], type='hash')
    merged.drop_column('timestamp_year')
    merged.drop_column('timestamp_month')
    return merged
 def join_perf_acq_gdfs(perf, acq, **kwargs):
    perf = null_workaround(perf)
    acq = null_workaround(acq)
    return perf.merge(acq, how='left', on=['loan_id'], type='hash')
 def last_mile_cleaning(df, **kwargs):
    drop_list = [
        'loan_id', 'orig_date', 'first_pay_date', 'seller_name',
        'monthly_reporting_period', 'last_paid_installment_date', 'maturity_date', 'ever_30', 'ever_90', 'ever_180',
        'delinquency_30', 'delinquency_90', 'delinquency_180', 'upb_12',
        'zero_balance_effective_date','foreclosed_after', 'disposition_date','timestamp'
    ]
    for column in drop_list:
        df.drop_column(column)
    for col, dtype in df.dtypes.iteritems():
        if str(dtype)=='category':
            df[col] = df[col].cat.codes
        df[col] = df[col].astype('float32')
    df['delinquency_12'] = df['delinquency_12'] > 0
    df['delinquency_12'] = df['delinquency_12'].fillna(False).astype('int32')
    for column in df.columns:
        df[column] = df[column].fillna(-1)
    return df.to_arrow(preserve_index=False)
 def main():
    parser = argparse.ArgumentParser("rapidssample")
    parser.add_argument("--data_dir", type=str, help="location of data")
    parser.add_argument("--num_gpu", type=int, help="Number of GPUs to use", default=1)
    parser.add_argument("--part_count", type=int, help="Number of data files to train against", default=2)
    parser.add_argument("--end_year", type=int, help="Year to end the data load", default=2000)
    parser.add_argument("--cpu_predictor", type=str, help="Flag to use CPU for prediction", default='False')
    parser.add_argument('-f', type=str, default='') # added for notebook execution scenarios
    args = parser.parse_args()
    data_dir = args.data_dir
    num_gpu = args.num_gpu
    part_count = args.part_count
    end_year = args.end_year
    cpu_predictor = args.cpu_predictor.lower() in ('yes', 'true', 't', 'y', '1')
    if cpu_predictor:
        print('Training with CPUs require num gpu = 1')
        num_gpu = 1
    print('data_dir = {0}'.format(data_dir))
    print('num_gpu = {0}'.format(num_gpu))
    print('part_count = {0}'.format(part_count))
    print('end_year = {0}'.format(end_year))
    print('cpu_predictor = {0}'.format(cpu_predictor))
    import subprocess
    cmd = "hostname --all-ip-addresses"
    process = subprocess.Popen(cmd.split(), stdout=subprocess.PIPE)
    output, error = process.communicate()
    IPADDR = str(output.decode()).split()[0]
    cluster = LocalCUDACluster(ip=IPADDR,n_workers=num_gpu)
    client = Client(cluster)
    client
    print(client.ncores())
    # to download data for this notebook, visit https://rapidsai.github.io/demos/datasets/mortgage-data and update the following paths accordingly
    acq_data_path = "{0}/acq".format(data_dir) #"/rapids/data/mortgage/acq"
    perf_data_path = "{0}/perf".format(data_dir) #"/rapids/data/mortgage/perf"
    col_names_path = "{0}/names.csv".format(data_dir) # "/rapids/data/mortgage/names.csv"
    start_year = 2000
    client
    print('--->>> Workers used: {0}'.format(client.ncores()))
    # NOTE: The ETL calculates additional features which are then dropped before creating the XGBoost DMatrix.
    # This can be optimized to avoid calculating the dropped features.
    print("Reading ...")
    t1 = datetime.datetime.now()
    gpu_dfs = []
    gpu_time = 0
    quarter = 1
    year = start_year
    count = 0
    while year <= end_year:
        for file in glob(os.path.join(perf_data_path + "/Performance_" + str(year) + "Q" + str(quarter) + "*")):
            if count < part_count:
                gpu_dfs.append(process_quarter_gpu(client, col_names_path, acq_data_path, year=year, quarter=quarter, perf_file=file))
                count += 1
                print('file: {0}'.format(file))
                print('count: {0}'.format(count))
        quarter += 1
        if quarter == 5:
            year += 1
            quarter = 1
    wait(gpu_dfs)
    t2 = datetime.datetime.now()
    print("Reading time: {0}".format(str(t2-t1)))
    print('--->>> Number of data parts: {0}'.format(len(gpu_dfs)))
    dxgb_gpu_params = {
        'nround':            100,
        'max_depth':         8,
        'max_leaves':        2**8,
        'alpha':             0.9,
        'eta':               0.1,
        'gamma':             0.1,
        'learning_rate':     0.1,
        'subsample':         1,
        'reg_lambda':        1,
        'scale_pos_weight':  2,
        'min_child_weight':  30,
        'tree_method':       'gpu_hist',
        'n_gpus':            1, 
        'distributed_dask':  True,
        'loss':              'ls',
        'objective':         'reg:squarederror',
        'max_features':      'auto',
        'criterion':         'friedman_mse',
        'grow_policy':       'lossguide',
        'verbose':           True
    }
    if cpu_predictor:
        print('\n---->>>> Training using CPUs <<<<----\n')
        dxgb_gpu_params['predictor'] = 'cpu_predictor'
        dxgb_gpu_params['tree_method'] = 'hist'
        dxgb_gpu_params['objective'] = 'reg:linear'
    else:
        print('\n---->>>> Training using GPUs <<<<----\n')
    print('Training parameters are {0}'.format(dxgb_gpu_params))
    gpu_dfs = [delayed(DataFrame.from_arrow)(gpu_df) for gpu_df in gpu_dfs[:part_count]]
    gpu_dfs = [gpu_df for gpu_df in gpu_dfs]
    wait(gpu_dfs)
    tmp_map = [(gpu_df, list(client.who_has(gpu_df).values())[0]) for gpu_df in gpu_dfs]
    new_map = {}
    for key, value in tmp_map:
        if value not in new_map:
            new_map[value] = [key]
        else:
            new_map[value].append(key)
    del(tmp_map)
    gpu_dfs = []
    for list_delayed in new_map.values():
        gpu_dfs.append(delayed(cudf.concat)(list_delayed))
    del(new_map)
    gpu_dfs = [(gpu_df[['delinquency_12']], gpu_df[delayed(list)(gpu_df.columns.difference(['delinquency_12']))]) for gpu_df in gpu_dfs]
    gpu_dfs = [(gpu_df[0].persist(), gpu_df[1].persist()) for gpu_df in gpu_dfs]
    gpu_dfs = [dask.delayed(xgb.DMatrix)(gpu_df[1], gpu_df[0]) for gpu_df in gpu_dfs]
    gpu_dfs = [gpu_df.persist() for gpu_df in gpu_dfs]
    gc.collect()
    wait(gpu_dfs)
    # TRAIN THE MODEL
    labels = None
    t1 = datetime.datetime.now()
    bst = dxgb_gpu.train(client, dxgb_gpu_params, gpu_dfs, labels, num_boost_round=dxgb_gpu_params['nround'])
    t2 = datetime.datetime.now()
    print('\n---->>>> Training time: {0} <<<<----\n'.format(str(t2-t1)))
    print('Exiting script')
 if __name__ == '__main__':
    main()
--- a/databricks/01.Installation_and_Configuration.ipynb
+++ b/databricks/01.Installation_and_Configuration.ipynb
@@ -1 +0,0 @@
 {"cells":[{"cell_type":"markdown","source":["Azure ML & Azure Databricks notebooks by Parashar Shah.\n\nCopyright (c) Microsoft Corporation. All rights reserved.\n\nLicensed under the MIT License."],"metadata":{}},{"cell_type":"markdown","source":["Please ensure you have run this notebook before proceeding."],"metadata":{}},{"cell_type":"markdown","source":["Now we support installing AML SDK as library from GUI. When attaching a library follow this https://docs.databricks.com/user-guide/libraries.html and add the below string as your PyPi package (during private preview). You can select the option to attach the library to all clusters or just one cluster.\n\nProvide this full string to install the SDK:\n\nazureml-sdk[databricks]"],"metadata":{}},{"cell_type":"code","source":["import azureml.core\n\n# Check core SDK version number - based on build number of preview/master.\nprint(\"SDK version:\", azureml.core.VERSION)"],"metadata":{},"outputs":[],"execution_count":4},{"cell_type":"code","source":["subscription_id = \"<your-subscription-id>\"\nresource_group = \"<your-existing-resource-group>\"\nworkspace_name = \"<a-new-or-existing-workspace; it is unrelated to Databricks workspace>\"\nworkspace_region = \"<your-resource group-region>\""],"metadata":{},"outputs":[],"execution_count":5},{"cell_type":"code","source":["# import the Workspace class and check the azureml SDK version\n# exist_ok checks if workspace exists or not.\n\nfrom azureml.core import Workspace\n\nws = Workspace.create(name = workspace_name,\n                      subscription_id = subscription_id,\n                      resource_group = resource_group, \n                      location = workspace_region,\n                      exist_ok=True)\n\nws.get_details()"],"metadata":{},"outputs":[],"execution_count":6},{"cell_type":"code","source":["ws = Workspace(workspace_name = workspace_name,\n               subscription_id = subscription_id,\n               resource_group = resource_group)\n\n# persist the subscription id, resource group name, and workspace name in aml_config/config.json.\nws.write_config()"],"metadata":{},"outputs":[],"execution_count":7},{"cell_type":"code","source":["%sh\ncat /databricks/driver/aml_config/config.json"],"metadata":{},"outputs":[],"execution_count":8},{"cell_type":"code","source":["# import the Workspace class and check the azureml SDK version\nfrom azureml.core import Workspace\n\nws = Workspace.from_config()\nprint('Workspace name: ' + ws.name, \n      'Azure region: ' + ws.location, \n      'Subscription id: ' + ws.subscription_id, \n      'Resource group: ' + ws.resource_group, sep = '\\n')"],"metadata":{},"outputs":[],"execution_count":9},{"cell_type":"code","source":["dbutils.notebook.exit(\"success\")"],"metadata":{},"outputs":[],"execution_count":10},{"cell_type":"code","source":[""],"metadata":{},"outputs":[],"execution_count":11}],"metadata":{"name":"01.Installation_and_Configuration","notebookId":3874566296719377},"nbformat":4,"nbformat_minor":0}
--- a/databricks/02.Ingest_data.ipynb
+++ b/databricks/02.Ingest_data.ipynb
@@ -1 +0,0 @@
 {"cells":[{"cell_type":"markdown","source":["Azure ML & Azure Databricks notebooks by Parashar Shah.\n\nCopyright (c) Microsoft Corporation. All rights reserved.\n\nLicensed under the MIT License."],"metadata":{}},{"cell_type":"markdown","source":["Please ensure you have run all previous notebooks in sequence before running this."],"metadata":{}},{"cell_type":"markdown","source":["#Data Ingestion"],"metadata":{}},{"cell_type":"code","source":["import os\nimport urllib"],"metadata":{},"outputs":[],"execution_count":4},{"cell_type":"code","source":["# Download AdultCensusIncome.csv from Azure CDN. This file has 32,561 rows.\nbasedataurl = \"https://amldockerdatasets.azureedge.net\"\ndatafile = \"AdultCensusIncome.csv\"\ndatafile_dbfs = os.path.join(\"/dbfs\", datafile)\n\nif os.path.isfile(datafile_dbfs):\n    print(\"found {} at {}\".format(datafile, datafile_dbfs))\nelse:\n    print(\"downloading {} to {}\".format(datafile, datafile_dbfs))\n    urllib.request.urlretrieve(os.path.join(basedataurl, datafile), datafile_dbfs)"],"metadata":{},"outputs":[],"execution_count":5},{"cell_type":"code","source":["# Create a Spark dataframe out of the csv file.\ndata_all = sqlContext.read.format('csv').options(header='true', inferSchema='true', ignoreLeadingWhiteSpace='true', ignoreTrailingWhiteSpace='true').load(datafile)\nprint(\"({}, {})\".format(data_all.count(), len(data_all.columns)))\ndata_all.printSchema()"],"metadata":{},"outputs":[],"execution_count":6},{"cell_type":"code","source":["#renaming columns\ncolumns_new = [col.replace(\"-\", \"_\") for col in data_all.columns]\ndata_all = data_all.toDF(*columns_new)\ndata_all.printSchema()"],"metadata":{},"outputs":[],"execution_count":7},{"cell_type":"code","source":["display(data_all.limit(5))"],"metadata":{},"outputs":[],"execution_count":8},{"cell_type":"markdown","source":["#Data Preparation"],"metadata":{}},{"cell_type":"code","source":["# Choose feature columns and the label column.\nlabel = \"income\"\nxvals_all = set(data_all.columns) - {label}\n\n#dbutils.widgets.remove(\"xvars_multiselect\")\ndbutils.widgets.removeAll()\n\ndbutils.widgets.multiselect('xvars_multiselect', 'hours_per_week', xvals_all)\nxvars_multiselect = dbutils.widgets.get(\"xvars_multiselect\")\nxvars = xvars_multiselect.split(\",\")\n\nprint(\"label = {}\".format(label))\nprint(\"features = {}\".format(xvars))\n\ndata = data_all.select([*xvars, label])\n\n# Split data into train and test.\ntrain, test = data.randomSplit([0.75, 0.25], seed=123)\n\nprint(\"train ({}, {})\".format(train.count(), len(train.columns)))\nprint(\"test ({}, {})\".format(test.count(), len(test.columns)))"],"metadata":{},"outputs":[],"execution_count":10},{"cell_type":"markdown","source":["#Data Persistence"],"metadata":{}},{"cell_type":"code","source":["# Write the train and test data sets to intermediate storage\ntrain_data_path = \"AdultCensusIncomeTrain\"\ntest_data_path = \"AdultCensusIncomeTest\"\n\ntrain_data_path_dbfs = os.path.join(\"/dbfs\", \"AdultCensusIncomeTrain\")\ntest_data_path_dbfs = os.path.join(\"/dbfs\", \"AdultCensusIncomeTest\")\n\ntrain.write.mode('overwrite').parquet(train_data_path)\ntest.write.mode('overwrite').parquet(test_data_path)\nprint(\"train and test datasets saved to {} and {}\".format(train_data_path_dbfs, test_data_path_dbfs))"],"metadata":{},"outputs":[],"execution_count":12},{"cell_type":"code","source":["dbutils.notebook.exit(\"success\")"],"metadata":{},"outputs":[],"execution_count":13},{"cell_type":"code","source":[""],"metadata":{},"outputs":[],"execution_count":14}],"metadata":{"name":"02.Ingest_data","notebookId":3874566296719393},"nbformat":4,"nbformat_minor":0}
--- a/databricks/03a.Build_model.ipynb
+++ b/databricks/03a.Build_model.ipynb
--- a/databricks/03b.Build_model_runHistory.ipynb
+++ b/databricks/03b.Build_model_runHistory.ipynb
--- a/databricks/04.Deploy_to_ACI.ipynb
+++ b/databricks/04.Deploy_to_ACI.ipynb
--- a/databricks/05.Deploy_to_AKS_existingImage.ipynb
+++ b/databricks/05.Deploy_to_AKS_existingImage.ipynb
@@ -1 +0,0 @@
 {"cells":[{"cell_type":"markdown","source":["Azure ML & Azure Databricks notebooks by Parashar Shah.\n\nCopyright (c) Microsoft Corporation. All rights reserved.\n\nLicensed under the MIT License."],"metadata":{}},{"cell_type":"markdown","source":["Please ensure you have run all previous notebooks in sequence before running this. This notebook uses image from ACI notebook for deploying to AKS."],"metadata":{}},{"cell_type":"code","source":["from azureml.core import Workspace\nimport azureml.core\n\n# Check core SDK version number\nprint(\"SDK version:\", azureml.core.VERSION)\n\n#'''\nws = Workspace.from_config()\nprint('Workspace name: ' + ws.name, \n      'Azure region: ' + ws.location, \n      'Subscription id: ' + ws.subscription_id, \n      'Resource group: ' + ws.resource_group, sep = '\\n')\n#'''"],"metadata":{},"outputs":[],"execution_count":3},{"cell_type":"code","source":["# List images by ws\n\nfrom azureml.core.image import ContainerImage\nfor i in ContainerImage.list(workspace = ws):\n    print('{}(v.{} [{}]) stored at {} with build log {}'.format(i.name, i.version, i.creation_state, i.image_location, i.image_build_log_uri))"],"metadata":{},"outputs":[],"execution_count":4},{"cell_type":"code","source":["from azureml.core.image import Image\nmyimage = Image(workspace=ws, id=\"aciws:25\")"],"metadata":{},"outputs":[],"execution_count":5},{"cell_type":"code","source":["#create AKS compute\n#it may take 20-25 minutes to create a new cluster\n\nfrom azureml.core.compute import AksCompute, ComputeTarget\n\n# Use the default configuration (can also provide parameters to customize)\nprov_config = AksCompute.provisioning_configuration()\n\naks_name = 'ps-aks-clus2' \n\n# Create the cluster\naks_target = ComputeTarget.create(workspace = ws, \n                                  name = aks_name, \n                                  provisioning_configuration = prov_config)\n\naks_target.wait_for_completion(show_output = True)\n\nprint(aks_target.provisioning_state)\nprint(aks_target.provisioning_errors)"],"metadata":{},"outputs":[],"execution_count":6},{"cell_type":"code","source":["from azureml.core.webservice import Webservice\nhelp( Webservice.deploy_from_image)"],"metadata":{},"outputs":[],"execution_count":7},{"cell_type":"code","source":["from azureml.core.webservice import Webservice, AksWebservice\nfrom azureml.core.image import ContainerImage\n\n#Set the web service configuration (using default here)\naks_config = AksWebservice.deploy_configuration()\n\n#unique service name\nservice_name ='ps-aks-service'\n\n# Webservice creation using single command, there is a variant to use image directly as well.\naks_service = Webservice.deploy_from_image(\n  workspace=ws, \n  name=service_name,\n  deployment_config = aks_config,\n  image = myimage,\n  deployment_target = aks_target\n    )\n\naks_service.wait_for_deployment(show_output=True)"],"metadata":{},"outputs":[],"execution_count":8},{"cell_type":"code","source":["#for using the Web HTTP API \nprint(aks_service.scoring_uri)\nprint(aks_service.get_keys())"],"metadata":{},"outputs":[],"execution_count":9},{"cell_type":"code","source":["import json\n\n#get the some sample data\ntest_data_path = \"AdultCensusIncomeTest\"\ntest = spark.read.parquet(test_data_path).limit(5)\n\ntest_json = json.dumps(test.toJSON().collect())\n\nprint(test_json)"],"metadata":{},"outputs":[],"execution_count":10},{"cell_type":"code","source":["#using data defined above predict if income is >50K (1) or <=50K (0)\naks_service.run(input_data=test_json)"],"metadata":{},"outputs":[],"execution_count":11},{"cell_type":"code","source":["#comment to not delete the web service\naks_service.delete()\n#image.delete()\n#model.delete()\n#aks_target.delete()"],"metadata":{},"outputs":[],"execution_count":12},{"cell_type":"code","source":[""],"metadata":{},"outputs":[],"execution_count":13}],"metadata":{"name":"04.DeploytoACI","notebookId":3874566296719318},"nbformat":4,"nbformat_minor":0}
--- a/databricks/Databricks_AMLSDK_github.dbc
+++ b/databricks/Databricks_AMLSDK_github.dbc
--- a/databricks/readme.md
+++ b/databricks/readme.md
@@ -1,29 +0,0 @@
 # Azure Databricks - Azure Machine Learning SDK Sample Notebooks
 **NOTE**: With the latest version of Azure Machine Learning SDK, there are some API changes due to which previous version of notebooks will not work.
 Please remove the previous SDK version and install the latest SDK by installing **azureml-sdk[databricks]** as a PyPi library in Azure Databricks workspace. 
 **NOTE**: Please create your Azure Databricks cluster as v4.x (high concurrency preferred) with **Python 3** (dropdown).
 **NOTE**: Some packages like psutil upgrade libs that can cause a conflict, please install such packages by freezing lib version. Eg. "pstuil **cryptography==1.5 pyopenssl==16.0.0 ipython=2.2.0**" to avoid install error. This issue is related to Databricks and not related to AML SDK.
 **NOTE**: You should at least have contributor access to your Azure subcription to run some of the notebooks.
 The iPython Notebooks have to be run sequentially after making changes based on your subscription. The corresponding DBC archive contains all the notebooks and can be imported into your Databricks workspace. You can the run notebooks after importing .dbc instead of downloading individually.  
 This set of notebooks are related to Income prediction experiment based on this [dataset](https://archive.ics.uci.edu/ml/datasets/adult) and demonstrate how to data prep, train and operationalize a Spark ML model with Azure ML Python SDK from within Azure Databricks. For details on SDK concepts, please refer to [notebooks](https://github.com/Azure/MachineLearningNotebooks)
 (Recommended) [Azure Databricks AML SDK notebooks](Databricks_AMLSDK_github.dbc) A single DBC package to import all notebooks in your Azure Databricks workspace.
 01. [Installation and Configuration](01.Installation_and_Configuration.ipynb): Install the Azure ML Python SDK and Initialize an Azure ML Workspace and save the Workspace configuration file.
 02. [Ingest data](02.Ingest_data.ipynb): Download the Adult Census Income dataset and split it into train and test sets.
 03. [Build model](03a.Build_model.ipynb): Train a binary classification model in Azure Databricks with a Spark ML Pipeline.
 04. [Build model with Run History](03b.Build_model_runHistory.ipynb): Train model and also capture run history (tracking) with Azure ML Python SDK.
 05. [Deploy to ACI](04.Deploy_to_ACI.ipynb): Deploy model to Azure Container Instance (ACI) with Azure ML Python SDK.
 06. [Deploy to AKS](04.Deploy_to_AKS_existingImage.ipynb): Deploy model to Azure Kubernetis Service (AKS) with Azure ML Python SDK from an existing Image with model, conda and score file.
 Copyright (c) Microsoft Corporation. All rights reserved.
 All notebooks in this folder are licensed under the MIT License. 
 Apache®, Apache Spark, and Spark® are either registered trademarks or trademarks of the Apache Software Foundation in the United States and/or other countries.
--- a/how-to-use-azureml/README.md
+++ b/how-to-use-azureml/README.md
@@ -0,0 +1,17 @@
 ## Examples to get started with Azure Machine Learning service
 Learn how to use Azure Machine Learning services for experimentation and model management.
 As a pre-requisite, run the [configuration Notebook](../configuration.ipynb) notebook first to set up your Azure ML Workspace. Then, run the notebooks in following recommended order.
 * [train-within-notebook](./training/train-within-notebook): Train a model hile tracking run history, and learn how to deploy the model as web service to Azure Container Instance.
 * [train-on-local](./training/train-on-local): Learn how to submit a run to local computer and use Azure ML managed run configuration.
 * [train-on-amlcompute](./training/train-on-amlcompute): Use a 1-n node Azure ML managed compute cluster for remote runs on Azure CPU or GPU infrastructure.
 * [train-on-remote-vm](./training/train-on-remote-vm): Use Data Science Virtual Machine as a target for remote runs.
 * [logging-api](./track-and-monitor-experiments/logging-api): Learn about the details of logging metrics to run history.
 * [register-model-create-image-deploy-service](./deployment/register-model-create-image-deploy-service): Learn about the details of model management.
 * [production-deploy-to-aks](./deployment/production-deploy-to-aks) Deploy a model to production at scale on Azure Kubernetes Service.
 * [enable-data-collection-for-models-in-aks](./deployment/enable-data-collection-for-models-in-aks) Learn about data collection APIs for deployed model.
 * [enable-app-insights-in-production-service](./deployment/enable-app-insights-in-production-service) Learn how to use App Insights with production web service.
 Find quickstarts, end-to-end tutorials, and how-tos on the [official documentation site for Azure Machine Learning service](https://docs.microsoft.com/en-us/azure/machine-learning/service/).
--- a/how-to-use-azureml/automated-machine-learning/README.md
+++ b/how-to-use-azureml/automated-machine-learning/README.md
@@ -0,0 +1,299 @@
 # Table of Contents
 1. [Automated ML Introduction](#introduction)
 1. [Setup using Azure Notebooks](#jupyter)
 1. [Setup using Azure Databricks](#databricks)
 1. [Setup using a Local Conda environment](#localconda)
 1. [Automated ML SDK Sample Notebooks](#samples)
 1. [Documentation](#documentation)
 1. [Running using python command](#pythoncommand)
 1. [Troubleshooting](#troubleshooting)
 <a name="introduction"></a>
 # Automated ML introduction
 Automated machine learning (automated ML) builds high quality machine learning models for you by automating model and hyperparameter selection. Bring a labelled dataset that you want to build a model for, automated ML will give you a high quality machine learning model that you can use for predictions.
 If you are new to Data Science, automated ML will help you get jumpstarted by simplifying machine learning model building. It abstracts you from needing to perform model selection, hyperparameter selection and in one step creates a high quality trained model for you to use.
 If you are an experienced data scientist, automated ML will help increase your productivity by intelligently performing the model and hyperparameter selection for your training and generates high quality models much quicker than manually specifying several combinations of the parameters and running training jobs. Automated ML provides visibility and access to all the training jobs and the performance characteristics of the models to help you further tune the pipeline if you desire.
 Below are the three execution environments supported by automated ML.
 <a name="jupyter"></a>
 ## Setup using Azure Notebooks - Jupyter based notebooks in the Azure cloud
 1. [![Azure Notebooks](https://notebooks.azure.com/launch.png)](https://aka.ms/aml-clone-azure-notebooks)
 [Import sample notebooks ](https://aka.ms/aml-clone-azure-notebooks) into Azure Notebooks.
 1. Follow the instructions in the [configuration](../../configuration.ipynb) notebook to create and connect to a workspace.
 1. Open one of the sample notebooks.
 <a name="databricks"></a>
 ## Setup using Azure Databricks
 **NOTE**: Please create your Azure Databricks cluster as v4.x (high concurrency preferred) with **Python 3** (dropdown).
 **NOTE**: You should at least have contributor access to your Azure subcription to run the notebook.
 - Please remove the previous SDK version if there is any and install the latest SDK by installing **azureml-sdk[automl_databricks]** as a PyPi library in Azure Databricks workspace.
 - You can find the detail Readme instructions at [GitHub](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/azure-databricks).
 - Download the sample notebook automl-databricks-local-01.ipynb from [GitHub](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/azure-databricks) and import into the Azure databricks workspace.
 - Attach the notebook to the cluster.
 <a name="localconda"></a>
 ## Setup using a Local Conda environment
 To run these notebook on your own notebook server, use these installation instructions.
 The instructions below will install everything you need and then start a Jupyter notebook.
 ### 1. Install mini-conda from [here](https://conda.io/miniconda.html), choose 64-bit Python 3.7 or higher.
 - **Note**: if you already have conda installed, you can keep using it but it should be version 4.4.10 or later (as shown by: conda -V).  If you have a previous version installed, you can update it using the command: conda update conda.
 There's no need to install mini-conda specifically.
 ### 2. Downloading the sample notebooks
 - Download the sample notebooks from [GitHub](https://github.com/Azure/MachineLearningNotebooks) as zip and extract the contents to a local directory.  The automated ML sample notebooks are in the "automated-machine-learning" folder.
 ### 3. Setup a new conda environment
 The **automl_setup** script creates a new conda environment, installs the necessary packages, configures the widget and starts a jupyter notebook. It takes the conda environment name as an optional parameter.  The default conda environment name is azure_automl.  The exact command depends on the operating system.  See the specific sections below for Windows, Mac and Linux.  It can take about 10 minutes to execute.
 Packages installed by the **automl_setup** script:
    <ul><li>python</li><li>nb_conda</li><li>matplotlib</li><li>numpy</li><li>cython</li><li>urllib3</li><li>scipy</li><li>scikit-learn</li><li>pandas</li><li>tensorflow</li><li>py-xgboost</li><li>azureml-sdk</li><li>azureml-widgets</li><li>pandas-ml</li></ul>
 For more details refer to the [automl_env.yml](./automl_env.yml)
 ## Windows
 Start an **Anaconda Prompt** window, cd to the **how-to-use-azureml/automated-machine-learning** folder where the sample notebooks were extracted and then run:
 ```
 automl_setup
 ```
 ## Mac
 Install "Command line developer tools" if it is not already installed (you can use the command: `xcode-select --install`).
 Start a Terminal windows, cd to the **how-to-use-azureml/automated-machine-learning** folder where the sample notebooks were extracted and then run:
 ```
 bash automl_setup_mac.sh
 ```
 ## Linux
 cd to the **how-to-use-azureml/automated-machine-learning** folder where the sample notebooks were extracted and then run:
 ```
 bash automl_setup_linux.sh
 ```
 ### 4. Running configuration.ipynb
 - Before running any samples you next need to run the configuration notebook. Click on [configuration](../../configuration.ipynb) notebook
 - Execute the cells in the notebook to Register Machine Learning Services Resource Provider and create a workspace. (*instructions in notebook*)
 ### 5. Running Samples
 - Please make sure you use the Python [conda env:azure_automl] kernel when trying the sample Notebooks.
 - Follow the instructions in the individual notebooks to explore various features in automated ML.
 ### 6. Starting jupyter notebook manually
 To start your Jupyter notebook manually, use:
 ```
 conda activate azure_automl
 jupyter notebook
 ```
 or on Mac or Linux:
 ```
 source activate azure_automl
 jupyter notebook
 ```
 <a name="samples"></a>
 # Automated ML SDK Sample Notebooks
 - [auto-ml-classification.ipynb](classification/auto-ml-classification.ipynb)
    - Dataset: scikit learn's [digit dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits)
    - Simple example of using automated ML for classification
    - Uses local compute for training
 - [auto-ml-regression.ipynb](regression/auto-ml-regression.ipynb)
    - Dataset: scikit learn's [diabetes dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_diabetes.html)
    - Simple example of using automated ML for regression
    - Uses local compute for training
 - [auto-ml-remote-amlcompute.ipynb](remote-amlcompute/auto-ml-remote-amlcompute.ipynb)
    - Dataset: scikit learn's [digit dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits)
    - Example of using automated ML for classification using remote AmlCompute for training
    - Parallel execution of iterations
    - Async tracking of progress
    - Cancelling individual iterations or entire run
    - Retrieving models for any iteration or logged metric
    - Specify automated ML settings as kwargs
 - [auto-ml-missing-data-blacklist-early-termination.ipynb](missing-data-blacklist-early-termination/auto-ml-missing-data-blacklist-early-termination.ipynb)
    - Dataset: scikit learn's [digit dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits)
    - Blacklist certain pipelines
    - Specify a target metrics to indicate stopping criteria
    - Handling Missing Data in the input
 - [auto-ml-sparse-data-train-test-split.ipynb](sparse-data-train-test-split/auto-ml-sparse-data-train-test-split.ipynb)
    - Dataset: Scikit learn's [20newsgroup](http://scikit-learn.org/stable/datasets/twenty_newsgroups.html)
    - Handle sparse datasets
    - Specify custom train and validation set
 - [auto-ml-exploring-previous-runs.ipynb](exploring-previous-runs/auto-ml-exploring-previous-runs.ipynb)
    - List all projects for the workspace
    - List all automated ML Runs for a given project
    - Get details for a automated ML Run. (automated ML settings, run widget & all metrics)
    - Download fitted pipeline for any iteration
 - [auto-ml-classification-with-deployment.ipynb](classification-with-deployment/auto-ml-classification-with-deployment.ipynb)
    - Dataset: scikit learn's [digit dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits)
    - Simple example of using automated ML for classification
    - Registering the model
    - Creating Image and creating aci service
    - Testing the aci service
 - [auto-ml-sample-weight.ipynb](sample-weight/auto-ml-sample-weight.ipynb)
    - How to specifying sample_weight
    - The difference that it makes to test results
 - [auto-ml-subsampling-local.ipynb](subsampling/auto-ml-subsampling-local.ipynb)
    - How to enable subsampling
 - [auto-ml-dataset.ipynb](dataprep/auto-ml-dataset.ipynb)
    - Using Dataset for reading data
 - [auto-ml-dataset-remote-execution.ipynb](dataprep-remote-execution/auto-ml-dataset-remote-execution.ipynb)
    - Using Dataset for reading data with remote execution
 - [auto-ml-classification-with-whitelisting.ipynb](classification-with-whitelisting/auto-ml-classification-with-whitelisting.ipynb)
    - Dataset: scikit learn's [digit dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits)
    - Simple example of using automated ML for classification with whitelisting tensorflow models.
    - Uses local compute for training
 - [auto-ml-forecasting-energy-demand.ipynb](forecasting-energy-demand/auto-ml-forecasting-energy-demand.ipynb)
    - Dataset: [NYC energy demand data](forecasting-a/nyc_energy.csv)
    - Example of using automated ML for training a forecasting model
 - [auto-ml-forecasting-orange-juice-sales.ipynb](forecasting-orange-juice-sales/auto-ml-forecasting-orange-juice-sales.ipynb)
    - Dataset: [Dominick's grocery sales of orange juice](forecasting-b/dominicks_OJ.csv)
    - Example of training an automated ML forecasting model on multiple time-series
 - [auto-ml-classification-with-onnx.ipynb](classification-with-onnx/auto-ml-classification-with-onnx.ipynb)
    - Dataset: scikit learn's [iris dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_iris.html)
    - Simple example of using automated ML for classification with ONNX models
    - Uses local compute for training
 - [auto-ml-remote-amlcompute-with-onnx.ipynb](remote-amlcompute-with-onnx/auto-ml-remote-amlcompute-with-onnx.ipynb)
    - Dataset: scikit learn's [iris dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_iris.html)
    - Example of using automated ML for classification using remote AmlCompute for training
    - Train the models with ONNX compatible config on
    - Parallel execution of iterations
    - Async tracking of progress
    - Cancelling individual iterations or entire run
    - Retrieving the ONNX models and do the inference with them
 - [auto-ml-bank-marketing-subscribers-with-deployment.ipynb](bank-marketing-subscribers-with-deployment/auto-ml-bank-marketing-with-deployment.ipynb)
    - Dataset: UCI's [bank marketing dataset](https://www.kaggle.com/janiobachmann/bank-marketing-dataset)
    - Simple example of using automated ML for classification to predict term deposit subscriptions for a bank
    - Uses azure compute for training
 - [auto-ml-creditcard-with-deployment.ipynb](credit-card-fraud-detection-with-deployment/auto-ml-creditcard-with-deployment.ipynb)
    - Dataset: Kaggle's [credit card fraud detection dataset](https://www.kaggle.com/mlg-ulb/creditcardfraud)
    - Simple example of using automated ML for classification to fraudulent credit card transactions
    - Uses azure compute for training
 - [auto-ml-hardware-performance-with-deployment.ipynb](hardware-performance-prediction-with-deployment/auto-ml-hardware-performance-with-deployment.ipynb)
    - Dataset: UCI's [computer hardware dataset](https://archive.ics.uci.edu/ml/datasets/Computer+Hardware)
    - Simple example of using automated ML for regression to predict the performance of certain combinations of hardware components
    - Uses azure compute for training
 - [auto-ml-concrete-strength-with-deployment.ipynb](predicting-concrete-strength-with-deployment/auto-ml-concrete-strength-with-deployment.ipynb)
    - Dataset: UCI's [concrete compressive strength dataset](https://www.kaggle.com/pavanraj159/concrete-compressive-strength-data-set)
    - Simple example of using automated ML for regression to predict the strength  predict the compressive strength of concrete based off of different ingredient combinations and quantities of those ingredients
    - Uses azure compute for training
 <a name="documentation"></a>
 See [Configure automated machine learning experiments](https://docs.microsoft.com/azure/machine-learning/service/how-to-configure-auto-train) to learn how more about the the settings and features available for automated machine learning experiments.
 <a name="pythoncommand"></a>
 # Running using python command
 Jupyter notebook provides a File / Download as / Python (.py) option for saving the notebook as a Python file.
 You can then run this file using the python command.
 However, on Windows the file needs to be modified before it can be run.
 The following condition must be added to the main code in the file:
    if __name__ == "__main__":
 The main code of the file must be indented so that it is under this condition.
 <a name="troubleshooting"></a>
 # Troubleshooting
 ## automl_setup fails
 1. On Windows, make sure that you are running automl_setup from an Anconda Prompt window rather than a regular cmd window.  You can launch the "Anaconda Prompt" window by hitting the Start button and typing "Anaconda Prompt".  If you don't see the application "Anaconda Prompt", you might not have conda or mini conda installed.  In that case, you can install it [here](https://conda.io/miniconda.html)
 2. Check that you have conda 64-bit installed rather than 32-bit.  You can check this with the command `conda info`.  The `platform` should be `win-64` for Windows or `osx-64` for Mac.
 3. Check that you have conda 4.4.10 or later.  You can check the version with the command `conda -V`.  If you have a previous version installed, you can update it using the command: `conda update conda`.
 4. On Linux, if the error is `gcc: error trying to exec 'cc1plus': execvp: No such file or directory`, install build essentials using the command `sudo apt-get install build-essential`.
 5. Pass a new name as the first parameter to automl_setup so that it creates a new conda environment. You can view existing conda environments using `conda env list` and remove them with `conda env remove -n <environmentname>`.
 ## automl_setup_linux.sh fails
 If automl_setup_linux.sh fails on Ubuntu Linux with the error: `unable to execute 'gcc': No such file or directory`
 1. Make sure that outbound ports 53 and 80 are enabled.  On an Azure VM, you can do this from the Azure Portal by selecting the VM and clicking on Networking.
 2. Run the command: `sudo apt-get update`
 3. Run the command: `sudo apt-get install build-essential --fix-missing`
 4. Run `automl_setup_linux.sh` again.
 ## configuration.ipynb fails
 1) For local conda, make sure that you have susccessfully run automl_setup first.
 2) Check that the subscription_id is correct.  You can find the subscription_id in the Azure Portal by selecting All Service and then Subscriptions. The characters "<" and ">" should not be included in the subscription_id value.  For example, `subscription_id = "12345678-90ab-1234-5678-1234567890abcd"` has the valid format.
 3) Check that you have Contributor or Owner access to the Subscription.
 4) Check that the region is one of the supported regions: `eastus2`, `eastus`, `westcentralus`, `southeastasia`, `westeurope`, `australiaeast`, `westus2`, `southcentralus`
 5) Check that you have access to the region using the Azure Portal.
 ## workspace.from_config fails
 If the call `ws = Workspace.from_config()` fails:
 1) Make sure that you have run the `configuration.ipynb` notebook successfully.
 2) If you are running a notebook from a folder that is not under the folder where you ran `configuration.ipynb`, copy the folder aml_config and the file config.json that it contains to the new folder.  Workspace.from_config reads the config.json for the notebook folder or it parent folder.
 3) If you are switching to a new subscription, resource group, workspace or region, make sure that you run the `configuration.ipynb` notebook again.  Changing config.json directly will only work if the workspace already exists in the specified resource group under the specified subscription.
 4) If you want to change the region, please change the workspace, resource group or subscription.  `Workspace.create` will not create or update a workspace if it already exists, even if the region specified is different.
 ## Sample notebook fails
 If a sample notebook fails with an error that property, method or library does not exist:
 1) Check that you have selected correct kernel in jupyter notebook.  The kernel is displayed in the top right of the notebook page.  It can be changed using the `Kernel | Change Kernel` menu option.  For Azure Notebooks, it should be `Python 3.6`.  For local conda environments, it should be the conda envioronment name that you specified in automl_setup.  The default is azure_automl.  Note that the kernel is saved as part of the notebook.  So, if you switch to a new conda environment, you will have to select the new kernel in the notebook.
 2) Check that the notebook is for the SDK version that you are using.  You can check the SDK version by executing `azureml.core.VERSION` in a jupyter notebook cell.  You can download previous version of the sample notebooks from GitHub by clicking the `Branch` button, selecting the `Tags` tab and then selecting the version.
 ## Numpy import fails on Windows
 Some Windows environments see an error loading numpy with the latest Python version 3.6.8.  If you see this issue, try with Python version 3.6.7.
 ## Numpy import fails
 Check the tensorflow version in the automated ml conda environment. Supported versions are < 1.13. Uninstall tensorflow from the environment if version is >= 1.13
 You may check the version of tensorflow and uninstall as follows
 1) start a command shell, activate conda environment where automated ml packages are installed
 2) enter `pip freeze` and look for `tensorflow` , if found, the version listed should be < 1.13
 3) If the listed version is a not a supported version,  `pip uninstall tensorflow` in the command shell and enter y for confirmation.
 ## Remote run: DsvmCompute.create fails
 There are several reasons why the DsvmCompute.create can fail.  The reason is usually in the error message but you have to look at the end of the error message for the detailed reason.  Some common reasons are:
 1) `Compute name is invalid, it should start with a letter, be between 2 and 16 character, and only include letters (a-zA-Z), numbers (0-9) and \'-\'.`  Note that underscore is not allowed in the name.
 2) `The requested VM size xxxxx is not available in the current region.`  You can select a different region or vm_size.
 ## Remote run: Unable to establish SSH connection
 Automated ML uses the SSH protocol to communicate with remote DSVMs.  This defaults to port 22.  Possible causes for this error are:
 1) The DSVM is not ready for SSH connections.  When DSVM creation completes, the DSVM might still not be ready to acceept SSH connections.  The sample notebooks have a one minute delay to allow for this.
 2) Your Azure Subscription may restrict the IP address ranges that can access the DSVM on port 22.  You can check this in the Azure Portal by selecting the Virtual Machine and then clicking Networking.  The Virtual Machine name is the name that you provided in the notebook plus 10 alpha numeric characters to make the name unique.  The Inbound Port Rules define what can access the VM on specific ports.  Note that there is a priority priority order.  So, a Deny entry with a low priority number will override a Allow entry with a higher priority number.
 ## Remote run: setup iteration fails
 This is often an issue with the `get_data` method.
 1) Check that the `get_data` method is valid by running it locally.
 2) Make sure that `get_data` isn't referring to any local files.  `get_data` is executed on the remote DSVM.  So, it doesn't have direct access to local data files.  Instead you can store the data files with DataStore.  See [auto-ml-remote-execution-with-datastore.ipynb](remote-execution-with-datastore/auto-ml-remote-execution-with-datastore.ipynb)
 3) You can get to the error log for the setup iteration by clicking the `Click here to see the run in Azure portal` link, click `Back to Experiment`, click on the highest run number and then click on Logs.
 ## Remote run: disk full
 Automated ML creates files under /tmp/azureml_runs for each iteration that it runs.  It creates a folder with the iteration id.  For example: AutoML_9a038a18-77cc-48f1-80fb-65abdbc33abe_93.  Under this, there is a azureml-logs folder, which contains logs.  If you run too many iterations on the same DSVM, these files can fill the disk.
 You can delete the files under /tmp/azureml_runs or just delete the VM and create a new one.
 If your get_data downloads files, make sure the delete them or they can use disk space as well.
 When using DataStore, it is good to specify an absolute path for the files so that they are downloaded just once.  If you specify a relative path, it will download a file for each iteration.
 ## Remote run: Iterations fail and the log contains "MemoryError"
 This can be caused by insufficient memory on the DSVM.  Automated ML loads all training data into memory.  So, the available memory should be more than the training data size.
 If you are using a remote DSVM, memory is needed for each concurrent iteration.  The max_concurrent_iterations setting specifies the maximum concurrent iterations.  For example, if the training data size is 8Gb and max_concurrent_iterations is set to 10, the minimum memory required is at least 80Gb.
 To resolve this issue, allocate a DSVM with more memory or reduce the value specified for max_concurrent_iterations.
 ## Remote run: Iterations show as "Not Responding" in the RunDetails widget.
 This can be caused by too many concurrent iterations for a remote DSVM.  Each concurrent iteration usually takes 100% of a core when it is running.  Some iterations can use multiple cores.  So, the max_concurrent_iterations setting should always be less than the number of cores of the DSVM.
 To resolve this issue, try reducing the value specified for the max_concurrent_iterations setting.
--- a/how-to-use-azureml/automated-machine-learning/automl_env.yml
+++ b/how-to-use-azureml/automated-machine-learning/automl_env.yml
@@ -0,0 +1,27 @@
 name: azure_automl
 dependencies:
  # The python interpreter version.
  # Currently Azure ML only supports 3.5.2 and later.
 - pip
 - python>=3.5.2,<3.6.8
 - nb_conda
 - matplotlib==2.1.0
 - numpy>=1.16.0,<=1.16.2
 - cython
 - urllib3<1.24
 - scipy>=1.0.0,<=1.1.0
 - scikit-learn>=0.19.0,<=0.20.3
 - pandas>=0.22.0,<=0.23.4
 - py-xgboost<=0.80
 - pyarrow>=0.11.0
 - conda-forge::fbprophet==0.5
 - pip:
  # Required packages for AzureML execution, history, and data preparation.
  - azureml-defaults
  - azureml-train-automl
  - azureml-widgets
  - azureml-explain-model
  - azureml-contrib-interpret
  - pandas_ml
--- a/how-to-use-azureml/automated-machine-learning/automl_env_mac.yml
+++ b/how-to-use-azureml/automated-machine-learning/automl_env_mac.yml
@@ -0,0 +1,28 @@
 name: azure_automl
 dependencies:
  # The python interpreter version.
  # Currently Azure ML only supports 3.5.2 and later.
 - pip
 - nomkl
 - python>=3.5.2,<3.6.8
 - nb_conda
 - matplotlib==2.1.0
 - numpy>=1.16.0,<=1.16.2
 - cython
 - urllib3<1.24
 - scipy>=1.0.0,<=1.1.0
 - scikit-learn>=0.19.0,<=0.20.3
 - pandas>=0.22.0,<0.23.0
 - py-xgboost<=0.80
 - pyarrow>=0.11.0
 - conda-forge::fbprophet==0.5
 - pip:
  # Required packages for AzureML execution, history, and data preparation.
  - azureml-defaults
  - azureml-train-automl
  - azureml-widgets
  - azureml-explain-model
  - azureml-contrib-interpret
  - pandas_ml
--- a/how-to-use-azureml/automated-machine-learning/automl_setup.cmd
+++ b/how-to-use-azureml/automated-machine-learning/automl_setup.cmd
@@ -0,0 +1,62 @@
@echo off
 set conda_env_name=%1
 set automl_env_file=%2
 set options=%3
 set PIP_NO_WARN_SCRIPT_LOCATION=0
 IF "%conda_env_name%"=="" SET conda_env_name="azure_automl"
 IF "%automl_env_file%"=="" SET automl_env_file="automl_env.yml"
 IF NOT EXIST %automl_env_file% GOTO YmlMissing
 IF "%CONDA_EXE%"=="" GOTO CondaMissing
 call conda activate %conda_env_name% 2>nul:
 if not errorlevel 1 (
  echo Upgrading azureml-sdk[automl,notebooks,explain] in existing conda environment %conda_env_name%
  call pip install --upgrade azureml-sdk[automl,notebooks,explain]
  if errorlevel 1 goto ErrorExit
 ) else (
  call conda env create -f %automl_env_file% -n %conda_env_name%
 )
 call conda activate %conda_env_name% 2>nul:
 if errorlevel 1 goto ErrorExit
 call python -m ipykernel install --user --name %conda_env_name% --display-name "Python (%conda_env_name%)"
 REM azureml.widgets is now installed as part of the pip install under the conda env.
 REM Removing the old user install so that the notebooks will use the latest widget.
 call jupyter nbextension uninstall --user --py azureml.widgets
 echo.
 echo.
 echo ***************************************
 echo * AutoML setup completed successfully *
 echo ***************************************
 IF NOT "%options%"=="nolaunch" (
  echo.
  echo Starting jupyter notebook - please run the configuration notebook 
  echo.
  jupyter notebook --log-level=50 --notebook-dir='..\..'
 )
 goto End
 :CondaMissing
 echo Please run this script from an Anaconda Prompt window.
 echo You can start an Anaconda Prompt window by
 echo typing Anaconda Prompt on the Start menu.
 echo If you don't see the Anaconda Prompt app, install Miniconda.
 echo If you are running an older version of Miniconda or Anaconda,
 echo you can upgrade using the command: conda update conda
 goto End
 :YmlMissing
 echo File %automl_env_file% not found.
 :ErrorExit
 echo Install failed
 :End
--- a/how-to-use-azureml/automated-machine-learning/automl_setup_linux.sh
+++ b/how-to-use-azureml/automated-machine-learning/automl_setup_linux.sh
@@ -0,0 +1,52 @@
 #!/bin/bash
 CONDA_ENV_NAME=$1
 AUTOML_ENV_FILE=$2
 OPTIONS=$3
 PIP_NO_WARN_SCRIPT_LOCATION=0
 if [ "$CONDA_ENV_NAME" == "" ]
 then
  CONDA_ENV_NAME="azure_automl"
 fi
 if [ "$AUTOML_ENV_FILE" == "" ]
 then
  AUTOML_ENV_FILE="automl_env.yml"
 fi
 if [ ! -f $AUTOML_ENV_FILE ]; then
    echo "File $AUTOML_ENV_FILE not found"
    exit 1
 fi
 if source activate $CONDA_ENV_NAME 2> /dev/null
 then
   echo "Upgrading azureml-sdk[automl,notebooks,explain] in existing conda environment" $CONDA_ENV_NAME
   pip install --upgrade azureml-sdk[automl,notebooks,explain] &&
   jupyter nbextension uninstall --user --py azureml.widgets
 else
   conda env create -f $AUTOML_ENV_FILE -n $CONDA_ENV_NAME &&
   source activate $CONDA_ENV_NAME &&
   python -m ipykernel install --user --name $CONDA_ENV_NAME --display-name "Python ($CONDA_ENV_NAME)" &&
   jupyter nbextension uninstall --user --py azureml.widgets &&
   echo "" &&
   echo "" &&
   echo "***************************************" &&
   echo "* AutoML setup completed successfully *" &&
   echo "***************************************" &&
   if [ "$OPTIONS" != "nolaunch" ]
   then
      echo "" &&
      echo "Starting jupyter notebook - please run the configuration notebook" &&
      echo "" &&
      jupyter notebook --log-level=50 --notebook-dir '../..'
   fi
 fi
 if [ $? -gt 0 ]
 then
   echo "Installation failed"
 fi
--- a/how-to-use-azureml/automated-machine-learning/automl_setup_mac.sh
+++ b/how-to-use-azureml/automated-machine-learning/automl_setup_mac.sh
@@ -0,0 +1,54 @@
 #!/bin/bash
 CONDA_ENV_NAME=$1
 AUTOML_ENV_FILE=$2
 OPTIONS=$3
 PIP_NO_WARN_SCRIPT_LOCATION=0
 if [ "$CONDA_ENV_NAME" == "" ]
 then
  CONDA_ENV_NAME="azure_automl"
 fi
 if [ "$AUTOML_ENV_FILE" == "" ]
 then
  AUTOML_ENV_FILE="automl_env_mac.yml"
 fi
 if [ ! -f $AUTOML_ENV_FILE ]; then
    echo "File $AUTOML_ENV_FILE not found"
    exit 1
 fi
 if source activate $CONDA_ENV_NAME 2> /dev/null
 then
   echo "Upgrading azureml-sdk[automl,notebooks,explain] in existing conda environment" $CONDA_ENV_NAME
   pip install --upgrade azureml-sdk[automl,notebooks,explain] &&
   jupyter nbextension uninstall --user --py azureml.widgets
 else
   conda env create -f $AUTOML_ENV_FILE -n $CONDA_ENV_NAME &&
   source activate $CONDA_ENV_NAME &&
   conda install lightgbm -c conda-forge -y &&
   python -m ipykernel install --user --name $CONDA_ENV_NAME --display-name "Python ($CONDA_ENV_NAME)" &&
   jupyter nbextension uninstall --user --py azureml.widgets &&
   echo "" &&
   echo "" &&
   echo "***************************************" &&
   echo "* AutoML setup completed successfully *" &&
   echo "***************************************" &&
   if [ "$OPTIONS" != "nolaunch" ]
   then
      echo "" &&
      echo "Starting jupyter notebook - please run the configuration notebook" &&
      echo "" &&
      jupyter notebook --log-level=50 --notebook-dir '../..'
   fi
 fi
 if [ $? -gt 0 ]
 then
   echo "Installation failed"
 fi
--- a/how-to-use-azureml/automated-machine-learning/classification-bank-marketing/auto-ml-classification-bank-marketing.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/classification-bank-marketing/auto-ml-classification-bank-marketing.ipynb
@@ -0,0 +1,655 @@
 {
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
        "\n",
        "Licensed under the MIT License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/classification-bank-marketing/auto-ml-classification-bank-marketing.png)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Automated Machine Learning\n",
        "_**Classification with Deployment using a Bank Marketing Dataset**_\n",
        "\n",
        "## Contents\n",
        "1. [Introduction](#Introduction)\n",
        "1. [Setup](#Setup)\n",
        "1. [Train](#Train)\n",
        "1. [Results](#Results)\n",
        "1. [Deploy](#Deploy)\n",
        "1. [Test](#Test)\n",
        "1. [Acknowledgements](#Acknowledgements)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Introduction\n",
        "\n",
        "In this example we use the UCI Bank Marketing dataset to showcase how you can use AutoML for a  classification problem and deploy it to an Azure Container Instance (ACI). The classification goal is to predict if the client will subscribe to a term deposit with the bank.\n",
        "\n",
        "If you are using an Azure Machine Learning Notebook VM, you are all set.  Otherwise, go through the [configuration](../../../configuration.ipynb)  notebook first if you haven't already to establish your connection to the AzureML Workspace. \n",
        "\n",
        "In this notebook you will learn how to:\n",
        "1. Create an experiment using an existing workspace.\n",
        "2. Configure AutoML using `AutoMLConfig`.\n",
        "3. Train the model using local compute.\n",
        "4. Explore the results.\n",
        "5. Register the model.\n",
        "6. Create a container image.\n",
        "7. Create an Azure Container Instance (ACI) service.\n",
        "8. Test the ACI service."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Setup\n",
        "\n",
        "As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import logging\n",
        "\n",
        "from matplotlib import pyplot as plt\n",
        "import pandas as pd\n",
        "import os\n",
        "\n",
        "import azureml.core\n",
        "from azureml.core.experiment import Experiment\n",
        "from azureml.core.workspace import Workspace\n",
        "from azureml.core.dataset import Dataset\n",
        "from azureml.train.automl import AutoMLConfig"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "ws = Workspace.from_config()\n",
        "\n",
        "# choose a name for experiment\n",
        "experiment_name = 'automl-classification-bmarketing'\n",
        "\n",
        "experiment=Experiment(ws, experiment_name)\n",
        "\n",
        "output = {}\n",
        "output['SDK version'] = azureml.core.VERSION\n",
        "output['Subscription ID'] = ws.subscription_id\n",
        "output['Workspace'] = ws.name\n",
        "output['Resource Group'] = ws.resource_group\n",
        "output['Location'] = ws.location\n",
        "output['Experiment Name'] = experiment.name\n",
        "pd.set_option('display.max_colwidth', -1)\n",
        "outputDf = pd.DataFrame(data = output, index = [''])\n",
        "outputDf.T"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Create or Attach existing AmlCompute\n",
        "You will need to create a compute target for your AutoML run. In this tutorial, you create AmlCompute as your training compute resource.\n",
        "#### Creation of AmlCompute takes approximately 5 minutes. \n",
        "If the AmlCompute with that name is already in your workspace this code will skip the creation process.\n",
        "As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read this article on the default limits and how to request more quota."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.core.compute import AmlCompute\n",
        "from azureml.core.compute import ComputeTarget\n",
        "\n",
        "# Choose a name for your cluster.\n",
        "amlcompute_cluster_name = \"automlcl\"\n",
        "\n",
        "found = False\n",
        "# Check if this compute target already exists in the workspace.\n",
        "cts = ws.compute_targets\n",
        "if amlcompute_cluster_name in cts and cts[amlcompute_cluster_name].type == 'AmlCompute':\n",
        "    found = True\n",
        "    print('Found existing compute target.')\n",
        "    compute_target = cts[amlcompute_cluster_name]\n",
        "    \n",
        "if not found:\n",
        "    print('Creating a new compute target...')\n",
        "    provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_D2_V2\", # for GPU, use \"STANDARD_NC6\"\n",
        "                                                                #vm_priority = 'lowpriority', # optional\n",
        "                                                                max_nodes = 6)\n",
        "\n",
        "    # Create the cluster.\n",
        "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, provisioning_config)\n",
        "    \n",
        "print('Checking cluster status...')\n",
        "# Can poll for a minimum number of nodes and for a specific timeout.\n",
        "# If no min_node_count is provided, it will use the scale settings for the cluster.\n",
        "compute_target.wait_for_completion(show_output = True, min_node_count = None, timeout_in_minutes = 20)\n",
        "    \n",
        "# For a more detailed view of current AmlCompute status, use get_status()."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Data\n",
        "\n",
        "Create a run configuration for the remote run."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.core.runconfig import RunConfiguration\n",
        "from azureml.core.conda_dependencies import CondaDependencies\n",
        "import pkg_resources\n",
        "\n",
        "# create a new RunConfig object\n",
        "conda_run_config = RunConfiguration(framework=\"python\")\n",
        "\n",
        "# Set compute target to AmlCompute\n",
        "conda_run_config.target = compute_target\n",
        "conda_run_config.environment.docker.enabled = True\n",
        "\n",
        "cd = CondaDependencies.create(conda_packages=['numpy','py-xgboost<=0.80'])\n",
        "conda_run_config.environment.python.conda_dependencies = cd"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Load Data\n",
        "\n",
        "Load the bank marketing dataset from a csv file containing both training features and labels. The features are inputs to the model, while the training labels represent the expected output of the model."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "data = \"https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/bankmarketing_train.csv\"\n",
        "dataset = Dataset.Tabular.from_delimited_files(data)\n",
        "dataset.take(5).to_pandas_dataframe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Train\n",
        "\n",
        "Instantiate a AutoMLConfig object. This defines the settings and data used to run the experiment.\n",
        "\n",
        "|Property|Description|\n",
        "|-|-|\n",
        "|**task**|classification or regression|\n",
        "|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics: <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>average_precision_score_weighted</i><br><i>norm_macro_recall</i><br><i>precision_score_weighted</i>|\n",
        "|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
        "|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
        "|**n_cross_validations**|Number of cross validation splits.|\n",
        "|**training_data**|Input dataset, containing both features and label column.|\n",
        "|**label_column_name**|The name of the label column.|\n",
        "\n",
        "**_You can find more information about primary metrics_** [here](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-auto-train#primary-metric)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "automl_settings = {\n",
        "    \"iteration_timeout_minutes\": 5,\n",
        "    \"iterations\": 10,\n",
        "    \"n_cross_validations\": 2,\n",
        "    \"primary_metric\": 'AUC_weighted',\n",
        "    \"preprocess\": True,\n",
        "    \"max_concurrent_iterations\": 5,\n",
        "    \"verbosity\": logging.INFO,\n",
        "}\n",
        "\n",
        "automl_config = AutoMLConfig(task = 'classification',\n",
        "                             debug_log = 'automl_errors.log',\n",
        "                             run_configuration=conda_run_config,\n",
        "                             training_data = dataset,\n",
        "                             label_column_name = 'y',\n",
        "                             **automl_settings\n",
        "                            )"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Call the `submit` method on the experiment object and pass the run configuration. Execution of local runs is synchronous. Depending on the data and the number of iterations this can run for a while.\n",
        "In this example, we specify `show_output = True` to print currently running iterations to the console."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "remote_run = experiment.submit(automl_config, show_output = True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "remote_run"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Results"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Widget for Monitoring Runs\n",
        "\n",
        "The widget will first report a \"loading\" status while running the first iteration. After completing the first iteration, an auto-updating graph and table will be shown. The widget will refresh once per minute, so you should see the graph update as child runs complete.\n",
        "\n",
        "**Note:** The widget displays a link at the bottom. Use this link to open a web interface to explore the individual run details"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.widgets import RunDetails\n",
        "RunDetails(remote_run).show() "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Deploy\n",
        "\n",
        "### Retrieve the Best Model\n",
        "\n",
        "Below we select the best pipeline from our iterations. The `get_output` method on `automl_classifier` returns the best run and the fitted model for the last invocation. Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "best_run, fitted_model = remote_run.get_output()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Register the Fitted Model for Deployment\n",
        "If neither `metric` nor `iteration` are specified in the `register_model` call, the iteration with the best primary metric is registered."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "description = 'AutoML Model trained on bank marketing data to predict if a client will subscribe to a term deposit'\n",
        "tags = None\n",
        "model = remote_run.register_model(description = description, tags = tags)\n",
        "\n",
        "print(remote_run.model_id) # This will be written to the script file later in the notebook."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Create Scoring Script\n",
        "The scoring script is required to generate the image for deployment. It contains the code to do the predictions on input data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "%%writefile score.py\n",
        "import pickle\n",
        "import json\n",
        "import numpy\n",
        "import azureml.train.automl\n",
        "from sklearn.externals import joblib\n",
        "from azureml.core.model import Model\n",
        "\n",
        "\n",
        "def init():\n",
        "    global model\n",
        "    model_path = Model.get_model_path(model_name = '<<modelid>>') # this name is model.id of model that we want to deploy\n",
        "    # deserialize the model file back into a sklearn model\n",
        "    model = joblib.load(model_path)\n",
        "\n",
        "def run(rawdata):\n",
        "    try:\n",
        "        data = json.loads(rawdata)['data']\n",
        "        data = np.array(data)\n",
        "        result = model.predict(data)\n",
        "    except Exception as e:\n",
        "        result = str(e)\n",
        "        return json.dumps({\"error\": result})\n",
        "    return json.dumps({\"result\":result.tolist()})"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Create a YAML File for the Environment"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "To ensure the fit results are consistent with the training results, the SDK dependency versions need to be the same as the environment that trains the model. Details about retrieving the versions can be found in notebook [12.auto-ml-retrieve-the-training-sdk-versions](12.auto-ml-retrieve-the-training-sdk-versions.ipynb)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "dependencies = remote_run.get_run_sdk_dependencies(iteration = 1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "for p in ['azureml-train-automl', 'azureml-core']:\n",
        "    print('{}\\t{}'.format(p, dependencies[p]))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn','py-xgboost<=0.80'],\n",
        "                                 pip_packages=['azureml-defaults','azureml-train-automl'])\n",
        "\n",
        "conda_env_file_name = 'myenv.yml'\n",
        "myenv.save_to_file('.', conda_env_file_name)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Substitute the actual version number in the environment file.\n",
        "# This is not strictly needed in this notebook because the model should have been generated using the current SDK version.\n",
        "# However, we include this in case this code is used on an experiment from a previous SDK version.\n",
        "\n",
        "with open(conda_env_file_name, 'r') as cefr:\n",
        "    content = cefr.read()\n",
        "\n",
        "with open(conda_env_file_name, 'w') as cefw:\n",
        "    cefw.write(content.replace(azureml.core.VERSION, dependencies['azureml-train-automl']))\n",
        "\n",
        "# Substitute the actual model id in the script file.\n",
        "\n",
        "script_file_name = 'score.py'\n",
        "\n",
        "with open(script_file_name, 'r') as cefr:\n",
        "    content = cefr.read()\n",
        "\n",
        "with open(script_file_name, 'w') as cefw:\n",
        "    cefw.write(content.replace('<<modelid>>', remote_run.model_id))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Deploy the model as a Web Service on Azure Container Instance"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.core.model import InferenceConfig\n",
        "from azureml.core.webservice import AciWebservice\n",
        "from azureml.core.webservice import Webservice\n",
        "from azureml.core.model import Model\n",
        "\n",
        "inference_config = InferenceConfig(runtime = \"python\", \n",
        "                                   entry_script = script_file_name,\n",
        "                                   conda_file = conda_env_file_name)\n",
        "\n",
        "aciconfig = AciWebservice.deploy_configuration(cpu_cores = 1, \n",
        "                                               memory_gb = 1, \n",
        "                                               tags = {'area': \"bmData\", 'type': \"automl_classification\"}, \n",
        "                                               description = 'sample service for Automl Classification')\n",
        "\n",
        "aci_service_name = 'automl-sample-bankmarketing'\n",
        "print(aci_service_name)\n",
        "aci_service = Model.deploy(ws, aci_service_name, [model], inference_config, aciconfig)\n",
        "aci_service.wait_for_deployment(True)\n",
        "print(aci_service.state)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Delete a Web Service\n",
        "\n",
        "Deletes the specified web service."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "#aci_service.delete()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Get Logs from a Deployed Web Service\n",
        "\n",
        "Gets logs from a deployed web service."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "#aci_service.get_logs()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Test\n",
        "\n",
        "Now that the model is trained split our data in the same way the data was split for training (The difference here is the data is being split locally) and then run the test data through the trained model to get the predicted values."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Load the bank marketing datasets.\n",
        "from numpy import array"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "data = \"https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/bankmarketing_validate.csv\"\n",
        "dataset = Dataset.Tabular.from_delimited_files(data)\n",
        "X_test = dataset.drop_columns(columns=['y'])\n",
        "y_test = dataset.keep_columns(columns=['y'], validate=True)\n",
        "dataset.take(5).to_pandas_dataframe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "X_test = X_test.to_pandas_dataframe()\n",
        "y_test = y_test.to_pandas_dataframe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "y_pred  = fitted_model.predict(X_test)\n",
        "actual = array(y_test)\n",
        "actual = actual[:,0]\n",
        "print(y_pred.shape, \" \", actual.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Calculate metrics for the prediction\n",
        "\n",
        "Now visualize the data on a scatter plot to show what our truth (actual) values are compared to the predicted values \n",
        "from the trained model that was returned."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "%matplotlib notebook\n",
        "test_pred = plt.scatter(actual, y_pred, color='b')\n",
        "test_test = plt.scatter(actual, actual, color='g')\n",
        "plt.legend((test_pred, test_test), ('prediction', 'truth'), loc='upper left', fontsize=8)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Acknowledgements"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "This Bank Marketing dataset is made available under the Creative Commons (CCO: Public Domain) License: https://creativecommons.org/publicdomain/zero/1.0/. Any rights in individual contents of the database are licensed under the Database Contents License: https://creativecommons.org/publicdomain/zero/1.0/ and is available at: https://www.kaggle.com/janiobachmann/bank-marketing-dataset .\n",
        "\n",
        "_**Acknowledgements**_\n",
        "This data set is originally available within the UCI Machine Learning Database: https://archive.ics.uci.edu/ml/datasets/bank+marketing\n",
        "\n",
        "[Moro et al., 2014] S. Moro, P. Cortez and P. Rita. A Data-Driven Approach to Predict the Success of Bank Telemarketing. Decision Support Systems, Elsevier, 62:22-31, June 2014"
      ]
    }
  ],
  "metadata": {
    "authors": [
      {
        "name": "v-rasav"
      }
    ],
    "kernelspec": {
      "display_name": "Python 3.6",
      "language": "python",
      "name": "python36"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.7"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
--- a/how-to-use-azureml/automated-machine-learning/classification-bank-marketing/auto-ml-classification-bank-marketing.yml
+++ b/how-to-use-azureml/automated-machine-learning/classification-bank-marketing/auto-ml-classification-bank-marketing.yml
@@ -0,0 +1,11 @@
 name: auto-ml-classification-bank-marketing
 dependencies:
 - pip:
  - azureml-sdk
  - interpret
  - azureml-defaults
  - azureml-explain-model
  - azureml-train-automl
  - azureml-widgets
  - matplotlib
  - pandas_ml
--- a/how-to-use-azureml/automated-machine-learning/classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.ipynb
@@ -0,0 +1,648 @@
 {
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
        "\n",
        "Licensed under the MIT License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.png)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Automated Machine Learning\n",
        "_**Classification with Deployment using Credit Card Dataset**_\n",
        "\n",
        "## Contents\n",
        "1. [Introduction](#Introduction)\n",
        "1. [Setup](#Setup)\n",
        "1. [Train](#Train)\n",
        "1. [Results](#Results)\n",
        "1. [Deploy](#Deploy)\n",
        "1. [Test](#Test)\n",
        "1. [Acknowledgements](#Acknowledgements)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Introduction\n",
        "\n",
        "In this example we use the associated credit card dataset to showcase how you can use AutoML for a simple classification problem and deploy it to an Azure Container Instance (ACI). The classification goal is to predict if a creditcard transaction  is or is not considered a fraudulent charge.\n",
        "\n",
        "If you are using an Azure Machine Learning Notebook VM, you are all set.  Otherwise, go through the [configuration](../../../configuration.ipynb)  notebook first if you haven't already to establish your connection to the AzureML Workspace. \n",
        "\n",
        "In this notebook you will learn how to:\n",
        "1. Create an experiment using an existing workspace.\n",
        "2. Configure AutoML using `AutoMLConfig`.\n",
        "3. Train the model using local compute.\n",
        "4. Explore the results.\n",
        "5. Register the model.\n",
        "6. Create a container image.\n",
        "7. Create an Azure Container Instance (ACI) service.\n",
        "8. Test the ACI service."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Setup\n",
        "\n",
        "As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import logging\n",
        "\n",
        "from matplotlib import pyplot as plt\n",
        "import pandas as pd\n",
        "import os\n",
        "\n",
        "import azureml.core\n",
        "from azureml.core.experiment import Experiment\n",
        "from azureml.core.workspace import Workspace\n",
        "from azureml.core.dataset import Dataset\n",
        "from azureml.train.automl import AutoMLConfig"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "ws = Workspace.from_config()\n",
        "\n",
        "# choose a name for experiment\n",
        "experiment_name = 'automl-classification-ccard'\n",
        "\n",
        "experiment=Experiment(ws, experiment_name)\n",
        "\n",
        "output = {}\n",
        "output['SDK version'] = azureml.core.VERSION\n",
        "output['Subscription ID'] = ws.subscription_id\n",
        "output['Workspace'] = ws.name\n",
        "output['Resource Group'] = ws.resource_group\n",
        "output['Location'] = ws.location\n",
        "output['Experiment Name'] = experiment.name\n",
        "pd.set_option('display.max_colwidth', -1)\n",
        "outputDf = pd.DataFrame(data = output, index = [''])\n",
        "outputDf.T"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Create or Attach existing AmlCompute\n",
        "You will need to create a compute target for your AutoML run. In this tutorial, you create AmlCompute as your training compute resource.\n",
        "#### Creation of AmlCompute takes approximately 5 minutes. \n",
        "If the AmlCompute with that name is already in your workspace this code will skip the creation process.\n",
        "As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read this article on the default limits and how to request more quota."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.core.compute import AmlCompute\n",
        "from azureml.core.compute import ComputeTarget\n",
        "\n",
        "# Choose a name for your cluster.\n",
        "amlcompute_cluster_name = \"automlcl\"\n",
        "\n",
        "found = False\n",
        "# Check if this compute target already exists in the workspace.\n",
        "cts = ws.compute_targets\n",
        "if amlcompute_cluster_name in cts and cts[amlcompute_cluster_name].type == 'AmlCompute':\n",
        "    found = True\n",
        "    print('Found existing compute target.')\n",
        "    compute_target = cts[amlcompute_cluster_name]\n",
        "    \n",
        "if not found:\n",
        "    print('Creating a new compute target...')\n",
        "    provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_D2_V2\", # for GPU, use \"STANDARD_NC6\"\n",
        "                                                                #vm_priority = 'lowpriority', # optional\n",
        "                                                                max_nodes = 6)\n",
        "\n",
        "    # Create the cluster.\n",
        "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, provisioning_config)\n",
        "    \n",
        "print('Checking cluster status...')\n",
        "# Can poll for a minimum number of nodes and for a specific timeout.\n",
        "# If no min_node_count is provided, it will use the scale settings for the cluster.\n",
        "compute_target.wait_for_completion(show_output = True, min_node_count = None, timeout_in_minutes = 20)\n",
        "\n",
        "# For a more detailed view of current AmlCompute status, use get_status()."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Data\n",
        "\n",
        "Create a run configuration for the remote run."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.core.runconfig import RunConfiguration\n",
        "from azureml.core.conda_dependencies import CondaDependencies\n",
        "import pkg_resources\n",
        "\n",
        "# create a new RunConfig object\n",
        "conda_run_config = RunConfiguration(framework=\"python\")\n",
        "\n",
        "# Set compute target to AmlCompute\n",
        "conda_run_config.target = compute_target\n",
        "conda_run_config.environment.docker.enabled = True\n",
        "\n",
        "cd = CondaDependencies.create(conda_packages=['numpy','py-xgboost<=0.80'])\n",
        "conda_run_config.environment.python.conda_dependencies = cd"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Load Data\n",
        "\n",
        "Load the credit card dataset from a csv file containing both training features and labels. The features are inputs to the model, while the training labels represent the expected output of the model. Next, we'll split the data using random_split and extract the training data for the model."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "data = \"https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/creditcard.csv\"\n",
        "dataset = Dataset.Tabular.from_delimited_files(data)\n",
        "training_data, validation_data = dataset.random_split(percentage=0.8, seed=223)\n",
        "label_column_name = 'Class'\n",
        "X_test = validation_data.drop_columns(columns=[label_column_name])\n",
        "y_test = validation_data.keep_columns(columns=[label_column_name], validate=True)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Train\n",
        "\n",
        "Instantiate a AutoMLConfig object. This defines the settings and data used to run the experiment.\n",
        "\n",
        "|Property|Description|\n",
        "|-|-|\n",
        "|**task**|classification or regression|\n",
        "|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics: <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>average_precision_score_weighted</i><br><i>norm_macro_recall</i><br><i>precision_score_weighted</i>|\n",
        "|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
        "|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
        "|**n_cross_validations**|Number of cross validation splits.|\n",
        "|**training_data**|Input dataset, containing both features and label column.|\n",
        "|**label_column_name**|The name of the label column.|\n",
        "\n",
        "**_You can find more information about primary metrics_** [here](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-auto-train#primary-metric)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "##### If you would like to see even better results increase \"iteration_time_out minutes\" to 10+ mins and increase \"iterations\" to a minimum of 30"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "automl_settings = {\n",
        "    \"iteration_timeout_minutes\": 5,\n",
        "    \"iterations\": 10,\n",
        "    \"n_cross_validations\": 2,\n",
        "    \"primary_metric\": 'average_precision_score_weighted',\n",
        "    \"preprocess\": True,\n",
        "    \"max_concurrent_iterations\": 5,\n",
        "    \"verbosity\": logging.INFO,\n",
        "}\n",
        "\n",
        "automl_config = AutoMLConfig(task = 'classification',\n",
        "                             debug_log = 'automl_errors.log',\n",
        "                             run_configuration=conda_run_config,\n",
        "                             training_data = training_data,\n",
        "                             label_column_name = label_column_name,\n",
        "                             **automl_settings\n",
        "                            )"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Call the `submit` method on the experiment object and pass the run configuration. Execution of local runs is synchronous. Depending on the data and the number of iterations this can run for a while.\n",
        "In this example, we specify `show_output = True` to print currently running iterations to the console."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "remote_run = experiment.submit(automl_config, show_output = True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "remote_run"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Results"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Widget for Monitoring Runs\n",
        "\n",
        "The widget will first report a \"loading\" status while running the first iteration. After completing the first iteration, an auto-updating graph and table will be shown. The widget will refresh once per minute, so you should see the graph update as child runs complete.\n",
        "\n",
        "**Note:** The widget displays a link at the bottom. Use this link to open a web interface to explore the individual run details"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.widgets import RunDetails\n",
        "RunDetails(remote_run).show() "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Deploy\n",
        "\n",
        "### Retrieve the Best Model\n",
        "\n",
        "Below we select the best pipeline from our iterations. The `get_output` method on `automl_classifier` returns the best run and the fitted model for the last invocation. Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "best_run, fitted_model = remote_run.get_output()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Register the Fitted Model for Deployment\n",
        "If neither `metric` nor `iteration` are specified in the `register_model` call, the iteration with the best primary metric is registered."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "description = 'AutoML Model'\n",
        "tags = None\n",
        "model = remote_run.register_model(description = description, tags = tags)\n",
        "\n",
        "print(remote_run.model_id) # This will be written to the script file later in the notebook."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Create Scoring Script\n",
        "The scoring script is required to generate the image for deployment. It contains the code to do the predictions on input data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "%%writefile score.py\n",
        "import pickle\n",
        "import json\n",
        "import numpy\n",
        "import azureml.train.automl\n",
        "from sklearn.externals import joblib\n",
        "from azureml.core.model import Model\n",
        "\n",
        "def init():\n",
        "    global model\n",
        "    model_path = Model.get_model_path(model_name = '<<modelid>>') # this name is model.id of model that we want to deploy\n",
        "    # deserialize the model file back into a sklearn model\n",
        "    model = joblib.load(model_path)\n",
        "\n",
        "def run(rawdata):\n",
        "    try:\n",
        "        data = json.loads(rawdata)['data']\n",
        "        data = numpy.array(data)\n",
        "        result = model.predict(data)\n",
        "    except Exception as e:\n",
        "        result = str(e)\n",
        "        return json.dumps({\"error\": result})\n",
        "    return json.dumps({\"result\":result.tolist()})"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Create a YAML File for the Environment"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "To ensure the fit results are consistent with the training results, the SDK dependency versions need to be the same as the environment that trains the model. Details about retrieving the versions can be found in notebook [12.auto-ml-retrieve-the-training-sdk-versions](12.auto-ml-retrieve-the-training-sdk-versions.ipynb)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "dependencies = remote_run.get_run_sdk_dependencies(iteration = 1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "for p in ['azureml-train-automl', 'azureml-core']:\n",
        "    print('{}\\t{}'.format(p, dependencies[p]))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn','py-xgboost<=0.80'],\n",
        "                                 pip_packages=['azureml-defaults','azureml-train-automl'])\n",
        "\n",
        "conda_env_file_name = 'myenv.yml'\n",
        "myenv.save_to_file('.', conda_env_file_name)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Substitute the actual version number in the environment file.\n",
        "# This is not strictly needed in this notebook because the model should have been generated using the current SDK version.\n",
        "# However, we include this in case this code is used on an experiment from a previous SDK version.\n",
        "\n",
        "with open(conda_env_file_name, 'r') as cefr:\n",
        "    content = cefr.read()\n",
        "\n",
        "with open(conda_env_file_name, 'w') as cefw:\n",
        "    cefw.write(content.replace(azureml.core.VERSION, dependencies['azureml-train-automl']))\n",
        "\n",
        "# Substitute the actual model id in the script file.\n",
        "\n",
        "script_file_name = 'score.py'\n",
        "\n",
        "with open(script_file_name, 'r') as cefr:\n",
        "    content = cefr.read()\n",
        "\n",
        "with open(script_file_name, 'w') as cefw:\n",
        "    cefw.write(content.replace('<<modelid>>', remote_run.model_id))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Deploy the model as a Web Service on Azure Container Instance"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.core.model import InferenceConfig\n",
        "from azureml.core.webservice import AciWebservice\n",
        "from azureml.core.webservice import Webservice\n",
        "from azureml.core.model import Model\n",
        "\n",
        "inference_config = InferenceConfig(runtime = \"python\", \n",
        "                                   entry_script = script_file_name,\n",
        "                                   conda_file = conda_env_file_name)\n",
        "\n",
        "aciconfig = AciWebservice.deploy_configuration(cpu_cores = 1, \n",
        "                                               memory_gb = 1, \n",
        "                                               tags = {'area': \"cards\", 'type': \"automl_classification\"}, \n",
        "                                               description = 'sample service for Automl Classification')\n",
        "\n",
        "aci_service_name = 'automl-sample-creditcard'\n",
        "print(aci_service_name)\n",
        "aci_service = Model.deploy(ws, aci_service_name, [model], inference_config, aciconfig)\n",
        "aci_service.wait_for_deployment(True)\n",
        "print(aci_service.state)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Delete a Web Service\n",
        "\n",
        "Deletes the specified web service."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "#aci_service.delete()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Get Logs from a Deployed Web Service\n",
        "\n",
        "Gets logs from a deployed web service."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "#aci_service.get_logs()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Test\n",
        "\n",
        "Now that the model is trained, split the data in the same way the data was split for training (The difference here is the data is being split locally) and then run the test data through the trained model to get the predicted values."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "#Randomly select and test\n",
        "X_test = X_test.to_pandas_dataframe()\n",
        "y_test = y_test.to_pandas_dataframe()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "y_pred = fitted_model.predict(X_test)\n",
        "y_pred"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Calculate metrics for the prediction\n",
        "\n",
        "Now visualize the data on a scatter plot to show what our truth (actual) values are compared to the predicted values \n",
        "from the trained model that was returned."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "#Randomly select and test\n",
        "# Plot outputs\n",
        "%matplotlib notebook\n",
        "test_pred = plt.scatter(y_test, y_pred, color='b')\n",
        "test_test = plt.scatter(y_test, y_test, color='g')\n",
        "plt.legend((test_pred, test_test), ('prediction', 'truth'), loc='upper left', fontsize=8)\n",
        "plt.show()\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Acknowledgements"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "This Credit Card fraud Detection dataset is made available under the Open Database License: http://opendatacommons.org/licenses/odbl/1.0/. Any rights in individual contents of the database are licensed under the Database Contents License: http://opendatacommons.org/licenses/dbcl/1.0/ and is available at: https://www.kaggle.com/mlg-ulb/creditcardfraud\n",
        "\n",
        "\n",
        "The dataset has been collected and analysed during a research collaboration of Worldline and the Machine Learning Group (http://mlg.ulb.ac.be) of ULB (Universit\u00c3\u00a9 Libre de Bruxelles) on big data mining and fraud detection. More details on current and past projects on related topics are available on https://www.researchgate.net/project/Fraud-detection-5 and the page of the DefeatFraud project\n",
        "Please cite the following works: \n",
        "\u00e2\u20ac\u00a2\tAndrea Dal Pozzolo, Olivier Caelen, Reid A. Johnson and Gianluca Bontempi. Calibrating Probability with Undersampling for Unbalanced Classification. In Symposium on Computational Intelligence and Data Mining (CIDM), IEEE, 2015\n",
        "\u00e2\u20ac\u00a2\tDal Pozzolo, Andrea; Caelen, Olivier; Le Borgne, Yann-Ael; Waterschoot, Serge; Bontempi, Gianluca. Learned lessons in credit card fraud detection from a practitioner perspective, Expert systems with applications,41,10,4915-4928,2014, Pergamon\n",
        "\u00e2\u20ac\u00a2\tDal Pozzolo, Andrea; Boracchi, Giacomo; Caelen, Olivier; Alippi, Cesare; Bontempi, Gianluca. Credit card fraud detection: a realistic modeling and a novel learning strategy, IEEE transactions on neural networks and learning systems,29,8,3784-3797,2018,IEEE\n",
        "o\tDal Pozzolo, Andrea Adaptive Machine learning for credit card fraud detection ULB MLG PhD thesis (supervised by G. Bontempi)\n",
        "\u00e2\u20ac\u00a2\tCarcillo, Fabrizio; Dal Pozzolo, Andrea; Le Borgne, Yann-A\u00c3\u00abl; Caelen, Olivier; Mazzer, Yannis; Bontempi, Gianluca. Scarff: a scalable framework for streaming credit card fraud detection with Spark, Information fusion,41, 182-194,2018,Elsevier\n",
        "\u00e2\u20ac\u00a2\tCarcillo, Fabrizio; Le Borgne, Yann-A\u00c3\u00abl; Caelen, Olivier; Bontempi, Gianluca. Streaming active learning strategies for real-life credit card fraud detection: assessment and visualization, International Journal of Data Science and Analytics, 5,4,285-300,2018,Springer International Publishing"
      ]
    }
  ],
  "metadata": {
    "authors": [
      {
        "name": "v-rasav"
      }
    ],
    "kernelspec": {
      "display_name": "Python 3.6",
      "language": "python",
      "name": "python36"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.7"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
--- a/how-to-use-azureml/automated-machine-learning/classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.yml
+++ b/how-to-use-azureml/automated-machine-learning/classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.yml
@@ -0,0 +1,11 @@
 name: auto-ml-classification-credit-card-fraud
 dependencies:
 - pip:
  - azureml-sdk
  - interpret
  - azureml-defaults
  - azureml-explain-model
  - azureml-train-automl
  - azureml-widgets
  - matplotlib
  - pandas_ml
--- a/how-to-use-azureml/automated-machine-learning/classification-with-deployment/auto-ml-classification-with-deployment.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/classification-with-deployment/auto-ml-classification-with-deployment.ipynb
@@ -0,0 +1,479 @@
 {
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
        "\n",
        "Licensed under the MIT License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/classification-with-deployment/auto-ml-classification-with-deployment.png)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Automated Machine Learning\n",
        "_**Classification with Deployment**_\n",
        "\n",
        "## Contents\n",
        "1. [Introduction](#Introduction)\n",
        "1. [Setup](#Setup)\n",
        "1. [Train](#Train)\n",
        "1. [Deploy](#Deploy)\n",
        "1. [Test](#Test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Introduction\n",
        "\n",
        "In this example we use the scikit learn's [digit dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html) to showcase how you can use AutoML for a simple classification problem and deploy it to an Azure Container Instance (ACI).\n",
        "\n",
        "Make sure you have executed the [configuration](../../../configuration.ipynb) before running this notebook.\n",
        "\n",
        "In this notebook you will learn how to:\n",
        "1. Create an experiment using an existing workspace.\n",
        "2. Configure AutoML using `AutoMLConfig`.\n",
        "3. Train the model using local compute.\n",
        "4. Explore the results.\n",
        "5. Register the model.\n",
        "6. Create a container image.\n",
        "7. Create an Azure Container Instance (ACI) service.\n",
        "8. Test the ACI service."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Setup\n",
        "\n",
        "As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import json\n",
        "import logging\n",
        "\n",
        "from matplotlib import pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn import datasets\n",
        "\n",
        "import azureml.core\n",
        "from azureml.core.experiment import Experiment\n",
        "from azureml.core.workspace import Workspace\n",
        "from azureml.train.automl import AutoMLConfig\n",
        "from azureml.train.automl.run import AutoMLRun"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "ws = Workspace.from_config()\n",
        "\n",
        "# choose a name for experiment\n",
        "experiment_name = 'automl-classification-deployment'\n",
        "\n",
        "experiment=Experiment(ws, experiment_name)\n",
        "\n",
        "output = {}\n",
        "output['SDK version'] = azureml.core.VERSION\n",
        "output['Subscription ID'] = ws.subscription_id\n",
        "output['Workspace'] = ws.name\n",
        "output['Resource Group'] = ws.resource_group\n",
        "output['Location'] = ws.location\n",
        "output['Experiment Name'] = experiment.name\n",
        "pd.set_option('display.max_colwidth', -1)\n",
        "outputDf = pd.DataFrame(data = output, index = [''])\n",
        "outputDf.T"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Train\n",
        "\n",
        "Instantiate a AutoMLConfig object. This defines the settings and data used to run the experiment.\n",
        "\n",
        "|Property|Description|\n",
        "|-|-|\n",
        "|**task**|classification or regression|\n",
        "|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics: <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>average_precision_score_weighted</i><br><i>norm_macro_recall</i><br><i>precision_score_weighted</i>|\n",
        "|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
        "|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
        "|**n_cross_validations**|Number of cross validation splits.|\n",
        "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
        "|**y**|(sparse) array-like, shape = [n_samples, ], Multi-class targets.|"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "digits = datasets.load_digits()\n",
        "X_train = digits.data[10:,:]\n",
        "y_train = digits.target[10:]\n",
        "\n",
        "automl_config = AutoMLConfig(task = 'classification',\n",
        "                             name = experiment_name,\n",
        "                             debug_log = 'automl_errors.log',\n",
        "                             primary_metric = 'AUC_weighted',\n",
        "                             iteration_timeout_minutes = 20,\n",
        "                             iterations = 10,\n",
        "                             verbosity = logging.INFO,\n",
        "                             X = X_train, \n",
        "                             y = y_train)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Call the `submit` method on the experiment object and pass the run configuration. Execution of local runs is synchronous. Depending on the data and the number of iterations this can run for a while.\n",
        "In this example, we specify `show_output = True` to print currently running iterations to the console."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "local_run = experiment.submit(automl_config, show_output = True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "local_run"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Deploy\n",
        "\n",
        "### Retrieve the Best Model\n",
        "\n",
        "Below we select the best pipeline from our iterations. The `get_output` method on `automl_classifier` returns the best run and the fitted model for the last invocation. Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "best_run, fitted_model = local_run.get_output()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Register the Fitted Model for Deployment\n",
        "If neither `metric` nor `iteration` are specified in the `register_model` call, the iteration with the best primary metric is registered."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "description = 'AutoML Model'\n",
        "tags = None\n",
        "model = local_run.register_model(description = description, tags = tags)\n",
        "\n",
        "print(local_run.model_id) # This will be written to the script file later in the notebook."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Create Scoring Script"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "%%writefile score.py\n",
        "import pickle\n",
        "import json\n",
        "import numpy\n",
        "import azureml.train.automl\n",
        "from sklearn.externals import joblib\n",
        "from azureml.core.model import Model\n",
        "\n",
        "\n",
        "def init():\n",
        "    global model\n",
        "    model_path = Model.get_model_path(model_name = '<<modelid>>') # this name is model.id of model that we want to deploy\n",
        "    # deserialize the model file back into a sklearn model\n",
        "    model = joblib.load(model_path)\n",
        "\n",
        "def run(rawdata):\n",
        "    try:\n",
        "        data = json.loads(rawdata)['data']\n",
        "        data = numpy.array(data)\n",
        "        result = model.predict(data)\n",
        "    except Exception as e:\n",
        "        result = str(e)\n",
        "        return json.dumps({\"error\": result})\n",
        "    return json.dumps({\"result\":result.tolist()})"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Create a YAML File for the Environment"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "To ensure the fit results are consistent with the training results, the SDK dependency versions need to be the same as the environment that trains the model. The following cells create a file, myenv.yml, which specifies the dependencies from the run."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "experiment = Experiment(ws, experiment_name)\n",
        "ml_run = AutoMLRun(experiment = experiment, run_id = local_run.id)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "dependencies = ml_run.get_run_sdk_dependencies(iteration = 7)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "for p in ['azureml-train-automl', 'azureml-core']:\n",
        "    print('{}\\t{}'.format(p, dependencies[p]))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.core.conda_dependencies import CondaDependencies\n",
        "\n",
        "myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn','py-xgboost<=0.80'],\n",
        "                                 pip_packages=['azureml-defaults','azureml-train-automl'])\n",
        "\n",
        "conda_env_file_name = 'myenv.yml'\n",
        "myenv.save_to_file('.', conda_env_file_name)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Substitute the actual version number in the environment file.\n",
        "# This is not strictly needed in this notebook because the model should have been generated using the current SDK version.\n",
        "# However, we include this in case this code is used on an experiment from a previous SDK version.\n",
        "\n",
        "with open(conda_env_file_name, 'r') as cefr:\n",
        "    content = cefr.read()\n",
        "\n",
        "with open(conda_env_file_name, 'w') as cefw:\n",
        "    cefw.write(content.replace(azureml.core.VERSION, dependencies['azureml-train-automl']))\n",
        "\n",
        "# Substitute the actual model id in the script file.\n",
        "\n",
        "script_file_name = 'score.py'\n",
        "\n",
        "with open(script_file_name, 'r') as cefr:\n",
        "    content = cefr.read()\n",
        "\n",
        "with open(script_file_name, 'w') as cefw:\n",
        "    cefw.write(content.replace('<<modelid>>', local_run.model_id))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Deploy the model as a Web Service on Azure Container Instance\n",
        "\n",
        "Create the configuration needed for deploying the model as a web service service."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.core.model import InferenceConfig\n",
        "from azureml.core.webservice import AciWebservice\n",
        "\n",
        "inference_config = InferenceConfig(runtime = \"python\", \n",
        "                                   entry_script = script_file_name,\n",
        "                                   conda_file = conda_env_file_name)\n",
        "\n",
        "aciconfig = AciWebservice.deploy_configuration(cpu_cores = 1, \n",
        "                                               memory_gb = 1, \n",
        "                                               tags = {'area': \"digits\", 'type': \"automl_classification\"}, \n",
        "                                               description = 'sample service for Automl Classification')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.core.webservice import Webservice\n",
        "from azureml.core.model import Model\n",
        "\n",
        "aci_service_name = 'automl-sample-01'\n",
        "print(aci_service_name)\n",
        "aci_service = Model.deploy(ws, aci_service_name, [model], inference_config, aciconfig)\n",
        "aci_service.wait_for_deployment(True)\n",
        "print(aci_service.state)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Get the logs from service deployment"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "if aci_service.state != 'Healthy':\n",
        "    # run this command for debugging.\n",
        "    print(aci_service.get_logs())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Delete a Web Service"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "#aci_service.delete()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "#Randomly select digits and test\n",
        "digits = datasets.load_digits()\n",
        "X_test = digits.data[:10, :]\n",
        "y_test = digits.target[:10]\n",
        "images = digits.images[:10]\n",
        "\n",
        "for index in np.random.choice(len(y_test), 3, replace = False):\n",
        "    print(index)\n",
        "    test_sample = json.dumps({'data':X_test[index:index + 1].tolist()})\n",
        "    predicted = aci_service.run(input_data = test_sample)\n",
        "    label = y_test[index]\n",
        "    predictedDict = json.loads(predicted)\n",
        "    title = \"Label value = %d  Predicted value = %s \" % ( label,predictedDict['result'][0])\n",
        "    fig = plt.figure(1, figsize = (3,3))\n",
        "    ax1 = fig.add_axes((0,0,.8,.8))\n",
        "    ax1.set_title(title)\n",
        "    plt.imshow(images[index], cmap = plt.cm.gray_r, interpolation = 'nearest')\n",
        "    plt.show()"
      ]
    }
  ],
  "metadata": {
    "authors": [
      {
        "name": "savitam"
      }
    ],
    "kernelspec": {
      "display_name": "Python 3.6",
      "language": "python",
      "name": "python36"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
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      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
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    }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
--- a/how-to-use-azureml/automated-machine-learning/classification-with-deployment/auto-ml-classification-with-deployment.yml
+++ b/how-to-use-azureml/automated-machine-learning/classification-with-deployment/auto-ml-classification-with-deployment.yml
@@ -0,0 +1,8 @@
 name: auto-ml-classification-with-deployment
 dependencies:
 - pip:
  - azureml-sdk
  - azureml-train-automl
  - azureml-widgets
  - matplotlib
  - pandas_ml
--- a/how-to-use-azureml/automated-machine-learning/classification-with-onnx/auto-ml-classification-with-onnx.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/classification-with-onnx/auto-ml-classification-with-onnx.ipynb
@@ -0,0 +1,375 @@
 {
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
        "\n",
        "Licensed under the MIT License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/classification-with-onnx/auto-ml-classification-with-onnx.png)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Automated Machine Learning\n",
        "_**Classification with Local Compute**_\n",
        "\n",
        "## Contents\n",
        "1. [Introduction](#Introduction)\n",
        "1. [Setup](#Setup)\n",
        "1. [Data](#Data)\n",
        "1. [Train](#Train)\n",
        "1. [Results](#Results)\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Introduction\n",
        "\n",
        "In this example we use the scikit-learn's [iris dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_iris.html) to showcase how you can use AutoML for a simple classification problem.\n",
        "\n",
        "Make sure you have executed the [configuration](../../../configuration.ipynb) before running this notebook.\n",
        "\n",
        "Please find the ONNX related documentations [here](https://github.com/onnx/onnx).\n",
        "\n",
        "In this notebook you will learn how to:\n",
        "1. Create an `Experiment` in an existing `Workspace`.\n",
        "2. Configure AutoML using `AutoMLConfig`.\n",
        "3. Train the model using local compute with ONNX compatible config on.\n",
        "4. Explore the results and save the ONNX model.\n",
        "5. Inference with the ONNX model."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Setup\n",
        "\n",
        "As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import logging\n",
        "\n",
        "from matplotlib import pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn import datasets\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "import azureml.core\n",
        "from azureml.core.experiment import Experiment\n",
        "from azureml.core.workspace import Workspace\n",
        "from azureml.train.automl import AutoMLConfig, constants"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "ws = Workspace.from_config()\n",
        "\n",
        "# Choose a name for the experiment.\n",
        "experiment_name = 'automl-classification-onnx'\n",
        "\n",
        "experiment = Experiment(ws, experiment_name)\n",
        "\n",
        "output = {}\n",
        "output['SDK version'] = azureml.core.VERSION\n",
        "output['Subscription ID'] = ws.subscription_id\n",
        "output['Workspace Name'] = ws.name\n",
        "output['Resource Group'] = ws.resource_group\n",
        "output['Location'] = ws.location\n",
        "output['Experiment Name'] = experiment.name\n",
        "pd.set_option('display.max_colwidth', -1)\n",
        "outputDf = pd.DataFrame(data = output, index = [''])\n",
        "outputDf.T"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Data\n",
        "\n",
        "This uses scikit-learn's [load_iris](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_iris.html) method."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "iris = datasets.load_iris()\n",
        "X_train, X_test, y_train, y_test = train_test_split(iris.data, \n",
        "                                                    iris.target, \n",
        "                                                    test_size=0.2, \n",
        "                                                    random_state=0)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Ensure the x_train and x_test are pandas DataFrame."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Convert the X_train and X_test to pandas DataFrame and set column names,\n",
        "# This is needed for initializing the input variable names of ONNX model, \n",
        "# and the prediction with the ONNX model using the inference helper.\n",
        "X_train = pd.DataFrame(X_train, columns=['c1', 'c2', 'c3', 'c4'])\n",
        "X_test = pd.DataFrame(X_test, columns=['c1', 'c2', 'c3', 'c4'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Train\n",
        "\n",
        "Instantiate an `AutoMLConfig` object to specify the settings and data used to run the experiment.\n",
        "\n",
        "**Note:** Set the parameter enable_onnx_compatible_models=True, if you also want to generate the ONNX compatible models. Please note, the forecasting task and TensorFlow models are not ONNX compatible yet.\n",
        "\n",
        "|Property|Description|\n",
        "|-|-|\n",
        "|**task**|classification or regression|\n",
        "|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics: <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>average_precision_score_weighted</i><br><i>norm_macro_recall</i><br><i>precision_score_weighted</i>|\n",
        "|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
        "|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
        "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
        "|**y**|(sparse) array-like, shape = [n_samples, ], Multi-class targets.|\n",
        "|**enable_onnx_compatible_models**|Enable the ONNX compatible models in the experiment.|"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Set the preprocess=True,  currently the InferenceHelper only supports this mode."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "automl_config = AutoMLConfig(task = 'classification',\n",
        "                             debug_log = 'automl_errors.log',\n",
        "                             primary_metric = 'AUC_weighted',\n",
        "                             iteration_timeout_minutes = 60,\n",
        "                             iterations = 10,\n",
        "                             verbosity = logging.INFO,                             \n",
        "                             X = X_train, \n",
        "                             y = y_train,\n",
        "                             preprocess=True,\n",
        "                             enable_onnx_compatible_models=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Call the `submit` method on the experiment object and pass the run configuration. Execution of local runs is synchronous. Depending on the data and the number of iterations this can run for a while.\n",
        "In this example, we specify `show_output = True` to print currently running iterations to the console."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "local_run = experiment.submit(automl_config, show_output = True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "local_run"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Results"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Widget for Monitoring Runs\n",
        "\n",
        "The widget will first report a \"loading\" status while running the first iteration. After completing the first iteration, an auto-updating graph and table will be shown. The widget will refresh once per minute, so you should see the graph update as child runs complete.\n",
        "\n",
        "**Note:** The widget displays a link at the bottom. Use this link to open a web interface to explore the individual run details."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.widgets import RunDetails\n",
        "RunDetails(local_run).show() "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Retrieve the Best ONNX Model\n",
        "\n",
        "Below we select the best pipeline from our iterations. The `get_output` method returns the best run and the fitted model. The Model includes the pipeline and any pre-processing.  Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*.\n",
        "\n",
        "Set the parameter return_onnx_model=True to retrieve the best ONNX model, instead of the Python model."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "best_run, onnx_mdl = local_run.get_output(return_onnx_model=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Save the best ONNX model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.automl.core.onnx_convert import OnnxConverter\n",
        "onnx_fl_path = \"./best_model.onnx\"\n",
        "OnnxConverter.save_onnx_model(onnx_mdl, onnx_fl_path)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Predict with the ONNX model, using onnxruntime package"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import sys\n",
        "import json\n",
        "from azureml.automl.core.onnx_convert import OnnxConvertConstants\n",
        "\n",
        "if sys.version_info < OnnxConvertConstants.OnnxIncompatiblePythonVersion:\n",
        "    python_version_compatible = True\n",
        "else:\n",
        "    python_version_compatible = False\n",
        "\n",
        "try:\n",
        "    import onnxruntime\n",
        "    from azureml.automl.core.onnx_convert import OnnxInferenceHelper    \n",
        "    onnxrt_present = True\n",
        "except ImportError:\n",
        "    onnxrt_present = False\n",
        "\n",
        "def get_onnx_res(run):\n",
        "    res_path = 'onnx_resource.json'\n",
        "    run.download_file(name=constants.MODEL_RESOURCE_PATH_ONNX, output_file_path=res_path)\n",
        "    with open(res_path) as f:\n",
        "        onnx_res = json.load(f)\n",
        "    return onnx_res\n",
        "\n",
        "if onnxrt_present and python_version_compatible:    \n",
        "    mdl_bytes = onnx_mdl.SerializeToString()\n",
        "    onnx_res = get_onnx_res(best_run)\n",
        "\n",
        "    onnxrt_helper = OnnxInferenceHelper(mdl_bytes, onnx_res)\n",
        "    pred_onnx, pred_prob_onnx = onnxrt_helper.predict(X_test)\n",
        "\n",
        "    print(pred_onnx)\n",
        "    print(pred_prob_onnx)\n",
        "else:\n",
        "    if not python_version_compatible:\n",
        "        print('Please use Python version 3.6 or 3.7 to run the inference helper.')    \n",
        "    if not onnxrt_present:\n",
        "        print('Please install the onnxruntime package to do the prediction with ONNX model.')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "authors": [
      {
        "name": "savitam"
      }
    ],
    "kernelspec": {
      "display_name": "Python 3.6",
      "language": "python",
      "name": "python36"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.6"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
--- a/how-to-use-azureml/automated-machine-learning/classification-with-onnx/auto-ml-classification-with-onnx.yml
+++ b/how-to-use-azureml/automated-machine-learning/classification-with-onnx/auto-ml-classification-with-onnx.yml
@@ -0,0 +1,9 @@
 name: auto-ml-classification-with-onnx
 dependencies:
 - pip:
  - azureml-sdk
  - azureml-train-automl
  - azureml-widgets
  - matplotlib
  - pandas_ml
  - onnxruntime
--- a/how-to-use-azureml/automated-machine-learning/classification-with-whitelisting/auto-ml-classification-with-whitelisting.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/classification-with-whitelisting/auto-ml-classification-with-whitelisting.ipynb
@@ -0,0 +1,395 @@
 {
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
        "\n",
        "Licensed under the MIT License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/classification-with-whitelisting/auto-ml-classification-with-whitelisting.png)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Automated Machine Learning\n",
        "_**Classification using whitelist models**_\n",
        "\n",
        "## Contents\n",
        "1. [Introduction](#Introduction)\n",
        "1. [Setup](#Setup)\n",
        "1. [Data](#Data)\n",
        "1. [Train](#Train)\n",
        "1. [Results](#Results)\n",
        "1. [Test](#Test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Introduction\n",
        "\n",
        "In this example we use the scikit-learn's [digit dataset](http://scikit-learn.org/stable/datasets/index.html#optical-recognition-of-handwritten-digits-dataset) to showcase how you can use AutoML for a simple classification problem.\n",
        "\n",
        "Make sure you have executed the [configuration](../../../configuration.ipynb) before running this notebook.\n",
        "This notebooks shows how can automl can be trained on a selected list of models, see the readme.md for the models.\n",
        "This trains the model exclusively on tensorflow based models.\n",
        "\n",
        "In this notebook you will learn how to:\n",
        "1. Create an `Experiment` in an existing `Workspace`.\n",
        "2. Configure AutoML using `AutoMLConfig`.\n",
        "3. Train the model on a whilelisted models using local compute. \n",
        "4. Explore the results.\n",
        "5. Test the best fitted model."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Setup\n",
        "\n",
        "As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "#Note: This notebook will install tensorflow if not already installed in the enviornment..\n",
        "import logging\n",
        "\n",
        "from matplotlib import pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn import datasets\n",
        "\n",
        "import azureml.core\n",
        "from azureml.core.experiment import Experiment\n",
        "from azureml.core.workspace import Workspace\n",
        "import sys\n",
        "whitelist_models=[\"LightGBM\"]\n",
        "if \"3.7\" != sys.version[0:3]:\n",
        "    try:\n",
        "        import tensorflow as tf1\n",
        "    except ImportError:\n",
        "        from pip._internal import main\n",
        "        main(['install', 'tensorflow>=1.10.0,<=1.12.0'])\n",
        "        logging.getLogger().setLevel(logging.ERROR)\n",
        "    whitelist_models=[\"TensorFlowLinearClassifier\", \"TensorFlowDNN\"]\n",
        "\n",
        "from azureml.train.automl import AutoMLConfig"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "ws = Workspace.from_config()\n",
        "\n",
        "# Choose a name for the experiment.\n",
        "experiment_name = 'automl-local-whitelist'\n",
        "\n",
        "experiment = Experiment(ws, experiment_name)\n",
        "\n",
        "output = {}\n",
        "output['SDK version'] = azureml.core.VERSION\n",
        "output['Subscription ID'] = ws.subscription_id\n",
        "output['Workspace Name'] = ws.name\n",
        "output['Resource Group'] = ws.resource_group\n",
        "output['Location'] = ws.location\n",
        "output['Experiment Name'] = experiment.name\n",
        "pd.set_option('display.max_colwidth', -1)\n",
        "outputDf = pd.DataFrame(data = output, index = [''])\n",
        "outputDf.T"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Data\n",
        "\n",
        "This uses scikit-learn's [load_digits](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html) method."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "digits = datasets.load_digits()\n",
        "\n",
        "# Exclude the first 100 rows from training so that they can be used for test.\n",
        "X_train = digits.data[100:,:]\n",
        "y_train = digits.target[100:]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Train\n",
        "\n",
        "Instantiate an `AutoMLConfig` object to specify the settings and data used to run the experiment.\n",
        "\n",
        "|Property|Description|\n",
        "|-|-|\n",
        "|**task**|classification or regression|\n",
        "|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics: <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>balanced_accuracy</i><br><i>average_precision_score_weighted</i><br><i>precision_score_weighted</i>|\n",
        "|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
        "|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
        "|**n_cross_validations**|Number of cross validation splits.|\n",
        "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
        "|**y**|(sparse) array-like, shape = [n_samples, ], Multi-class targets.|\n",
        "|**whitelist_models**|List of models that AutoML should use.  The possible values are listed [here](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-auto-train#configure-your-experiment-settings).|"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "automl_config = AutoMLConfig(task = 'classification',\n",
        "                             debug_log = 'automl_errors.log',\n",
        "                             primary_metric = 'AUC_weighted',\n",
        "                             iteration_timeout_minutes = 60,\n",
        "                             iterations = 10,\n",
        "                             verbosity = logging.INFO,\n",
        "                             X = X_train, \n",
        "                             y = y_train,\n",
        "                             enable_tf=True,\n",
        "                             whitelist_models=whitelist_models)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Call the `submit` method on the experiment object and pass the run configuration. Execution of local runs is synchronous. Depending on the data and the number of iterations this can run for a while.\n",
        "In this example, we specify `show_output = True` to print currently running iterations to the console."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "local_run = experiment.submit(automl_config, show_output = True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "local_run"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Results"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Widget for Monitoring Runs\n",
        "\n",
        "The widget will first report a \"loading\" status while running the first iteration. After completing the first iteration, an auto-updating graph and table will be shown. The widget will refresh once per minute, so you should see the graph update as child runs complete.\n",
        "\n",
        "**Note:** The widget displays a link at the bottom. Use this link to open a web interface to explore the individual run details."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.widgets import RunDetails\n",
        "RunDetails(local_run).show() "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "\n",
        "#### Retrieve All Child Runs\n",
        "You can also use SDK methods to fetch all the child runs and see individual metrics that we log."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "children = list(local_run.get_children())\n",
        "metricslist = {}\n",
        "for run in children:\n",
        "    properties = run.get_properties()\n",
        "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}\n",
        "    metricslist[int(properties['iteration'])] = metrics\n",
        "\n",
        "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
        "rundata"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Retrieve the Best Model\n",
        "\n",
        "Below we select the best pipeline from our iterations. The `get_output` method returns the best run and the fitted model. The Model includes the pipeline and any pre-processing.  Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "best_run, fitted_model = local_run.get_output()\n",
        "print(best_run)\n",
        "print(fitted_model)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Best Model Based on Any Other Metric\n",
        "Show the run and the model that has the smallest `log_loss` value:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "lookup_metric = \"log_loss\"\n",
        "best_run, fitted_model = local_run.get_output(metric = lookup_metric)\n",
        "print(best_run)\n",
        "print(fitted_model)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Model from a Specific Iteration\n",
        "Show the run and the model from the third iteration:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "iteration = 3\n",
        "third_run, third_model = local_run.get_output(iteration = iteration)\n",
        "print(third_run)\n",
        "print(third_model)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Test\n",
        "\n",
        "#### Load Test Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "digits = datasets.load_digits()\n",
        "X_test = digits.data[:10, :]\n",
        "y_test = digits.target[:10]\n",
        "images = digits.images[:10]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Testing Our Best Fitted Model\n",
        "We will try to predict 2 digits and see how our model works."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Randomly select digits and test.\n",
        "for index in np.random.choice(len(y_test), 2, replace = False):\n",
        "    print(index)\n",
        "    predicted = fitted_model.predict(X_test[index:index + 1])[0]\n",
        "    label = y_test[index]\n",
        "    title = \"Label value = %d  Predicted value = %d \" % (label, predicted)\n",
        "    fig = plt.figure(1, figsize = (3,3))\n",
        "    ax1 = fig.add_axes((0,0,.8,.8))\n",
        "    ax1.set_title(title)\n",
        "    plt.imshow(images[index], cmap = plt.cm.gray_r, interpolation = 'nearest')\n",
        "    plt.show()"
      ]
    }
  ],
  "metadata": {
    "authors": [
      {
        "name": "savitam"
      }
    ],
    "kernelspec": {
      "display_name": "Python 3.6",
      "language": "python",
      "name": "python36"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.6"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
--- a/how-to-use-azureml/automated-machine-learning/classification-with-whitelisting/auto-ml-classification-with-whitelisting.yml
+++ b/how-to-use-azureml/automated-machine-learning/classification-with-whitelisting/auto-ml-classification-with-whitelisting.yml
@@ -0,0 +1,8 @@
 name: auto-ml-classification-with-whitelisting
 dependencies:
 - pip:
  - azureml-sdk
  - azureml-train-automl
  - azureml-widgets
  - matplotlib
  - pandas_ml
--- a/how-to-use-azureml/automated-machine-learning/classification/auto-ml-classification.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/classification/auto-ml-classification.ipynb
@@ -0,0 +1,484 @@
 {
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
        "\n",
        "Licensed under the MIT License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/classification/auto-ml-classification.png)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Automated Machine Learning\n",
        "_**Classification with Local Compute**_\n",
        "\n",
        "## Contents\n",
        "1. [Introduction](#Introduction)\n",
        "1. [Setup](#Setup)\n",
        "1. [Data](#Data)\n",
        "1. [Train](#Train)\n",
        "1. [Results](#Results)\n",
        "1. [Test](#Test)\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Introduction\n",
        "\n",
        "In this example we use the scikit-learn's [digit dataset](http://scikit-learn.org/stable/datasets/index.html#optical-recognition-of-handwritten-digits-dataset) to showcase how you can use AutoML for a simple classification problem.\n",
        "\n",
        "Make sure you have executed the [configuration](../../../configuration.ipynb) before running this notebook.\n",
        "\n",
        "In this notebook you will learn how to:\n",
        "1. Create an `Experiment` in an existing `Workspace`.\n",
        "2. Configure AutoML using `AutoMLConfig`.\n",
        "3. Train the model using local compute.\n",
        "4. Explore the results.\n",
        "5. Test the best fitted model."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Setup\n",
        "\n",
        "As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import logging\n",
        "\n",
        "from matplotlib import pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn import datasets\n",
        "\n",
        "import azureml.core\n",
        "from azureml.core.experiment import Experiment\n",
        "from azureml.core.workspace import Workspace\n",
        "from azureml.train.automl import AutoMLConfig"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Accessing the Azure ML workspace requires authentication with Azure.\n",
        "\n",
        "The default authentication is interactive authentication using the default tenant.  Executing the `ws = Workspace.from_config()` line in the cell below will prompt for authentication the first time that it is run.\n",
        "\n",
        "If you have multiple Azure tenants, you can specify the tenant by replacing the `ws = Workspace.from_config()` line in the cell below with the following:\n",
        "\n",
        "```\n",
        "from azureml.core.authentication import InteractiveLoginAuthentication\n",
        "auth = InteractiveLoginAuthentication(tenant_id = 'mytenantid')\n",
        "ws = Workspace.from_config(auth = auth)\n",
        "```\n",
        "\n",
        "If you need to run in an environment where interactive login is not possible, you can use Service Principal authentication by replacing the `ws = Workspace.from_config()` line in the cell below with the following:\n",
        "\n",
        "```\n",
        "from azureml.core.authentication import ServicePrincipalAuthentication\n",
        "auth = auth = ServicePrincipalAuthentication('mytenantid', 'myappid', 'mypassword')\n",
        "ws = Workspace.from_config(auth = auth)\n",
        "```\n",
        "For more details, see [aka.ms/aml-notebook-auth](http://aka.ms/aml-notebook-auth)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "ws = Workspace.from_config()\n",
        "\n",
        "# Choose a name for the experiment.\n",
        "experiment_name = 'automl-classification'\n",
        "\n",
        "experiment = Experiment(ws, experiment_name)\n",
        "\n",
        "output = {}\n",
        "output['SDK version'] = azureml.core.VERSION\n",
        "output['Subscription ID'] = ws.subscription_id\n",
        "output['Workspace Name'] = ws.name\n",
        "output['Resource Group'] = ws.resource_group\n",
        "output['Location'] = ws.location\n",
        "output['Experiment Name'] = experiment.name\n",
        "pd.set_option('display.max_colwidth', -1)\n",
        "outputDf = pd.DataFrame(data = output, index = [''])\n",
        "outputDf.T"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Data\n",
        "\n",
        "This uses scikit-learn's [load_digits](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html) method."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "digits = datasets.load_digits()\n",
        "\n",
        "# Exclude the first 100 rows from training so that they can be used for test.\n",
        "X_train = digits.data[100:,:]\n",
        "y_train = digits.target[100:]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Train\n",
        "\n",
        "Instantiate an `AutoMLConfig` object to specify the settings and data used to run the experiment.\n",
        "\n",
        "|Property|Description|\n",
        "|-|-|\n",
        "|**task**|classification or regression|\n",
        "|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics: <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>average_precision_score_weighted</i><br><i>norm_macro_recall</i><br><i>precision_score_weighted</i>|\n",
        "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
        "|**y**|(sparse) array-like, shape = [n_samples, ], Multi-class targets.|\n",
        "|**n_cross_validations**|Number of cross validation splits.|\n",
        "|\n",
        "\n",
        "Automated machine learning trains multiple machine learning pipelines.  Each pipelines training is known as an iteration.\n",
        "* You can specify a maximum number of iterations using the `iterations` parameter.\n",
        "* You can specify a maximum time for the run using the `experiment_timeout_minutes` parameter.\n",
        "* If you specify neither the `iterations` nor the `experiment_timeout_minutes`, automated ML keeps running iterations while it continues to see improvements in the scores.\n",
        "\n",
        "The following example doesn't specify `iterations` or `experiment_timeout_minutes` and so runs until the scores stop improving.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "automl_config = AutoMLConfig(task = 'classification',\n",
        "                             primary_metric = 'AUC_weighted',\n",
        "                             X = X_train, \n",
        "                             y = y_train,\n",
        "                             n_cross_validations = 3)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Call the `submit` method on the experiment object and pass the run configuration. Execution of local runs is synchronous. Depending on the data and the number of iterations this can run for a while.\n",
        "In this example, we specify `show_output = True` to print currently running iterations to the console."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "local_run = experiment.submit(automl_config, show_output = True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "local_run"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Optionally, you can continue an interrupted local run by calling `continue_experiment` without the `iterations` parameter, or run more iterations for a completed run by specifying the `iterations` parameter:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "local_run = local_run.continue_experiment(X = X_train, \n",
        "                                          y = y_train, \n",
        "                                          show_output = True,\n",
        "                                          iterations = 5)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Results"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Widget for Monitoring Runs\n",
        "\n",
        "The widget will first report a \"loading\" status while running the first iteration. After completing the first iteration, an auto-updating graph and table will be shown. The widget will refresh once per minute, so you should see the graph update as child runs complete.\n",
        "\n",
        "**Note:** The widget displays a link at the bottom. Use this link to open a web interface to explore the individual run details."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "tags": [
          "widget-rundetails-sample"
        ]
      },
      "outputs": [],
      "source": [
        "from azureml.widgets import RunDetails\n",
        "RunDetails(local_run).show() "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "\n",
        "#### Retrieve All Child Runs\n",
        "You can also use SDK methods to fetch all the child runs and see individual metrics that we log."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "children = list(local_run.get_children())\n",
        "metricslist = {}\n",
        "for run in children:\n",
        "    properties = run.get_properties()\n",
        "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}\n",
        "    metricslist[int(properties['iteration'])] = metrics\n",
        "\n",
        "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
        "rundata"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Retrieve the Best Model\n",
        "\n",
        "Below we select the best pipeline from our iterations. The `get_output` method returns the best run and the fitted model. The Model includes the pipeline and any pre-processing.  Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "best_run, fitted_model = local_run.get_output()\n",
        "print(best_run)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Print the properties of the model\n",
        "The fitted_model is a python object and you can read the different properties of the object.\n",
        "The following shows printing hyperparameters for each step in the pipeline."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from pprint import pprint\n",
        "\n",
        "def print_model(model, prefix=\"\"):\n",
        "    for step in model.steps:\n",
        "        print(prefix + step[0])\n",
        "        if hasattr(step[1], 'estimators') and hasattr(step[1], 'weights'):\n",
        "            pprint({'estimators': list(e[0] for e in step[1].estimators), 'weights': step[1].weights})\n",
        "            print()\n",
        "            for estimator in step[1].estimators:\n",
        "                print_model(estimator[1], estimator[0]+ ' - ')\n",
        "        elif hasattr(step[1], '_base_learners') and hasattr(step[1], '_meta_learner'):\n",
        "            print(\"\\nMeta Learner\")\n",
        "            pprint(step[1]._meta_learner)\n",
        "            print()\n",
        "            for estimator in step[1]._base_learners:\n",
        "                print_model(estimator[1], estimator[0]+ ' - ')\n",
        "        else:\n",
        "            pprint(step[1].get_params())\n",
        "            print()\n",
        "            \n",
        "print_model(fitted_model)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Best Model Based on Any Other Metric\n",
        "Show the run and the model that has the smallest `log_loss` value:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "lookup_metric = \"log_loss\"\n",
        "best_run, fitted_model = local_run.get_output(metric = lookup_metric)\n",
        "print(best_run)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "print_model(fitted_model)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Model from a Specific Iteration\n",
        "Show the run and the model from the third iteration:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "iteration = 3\n",
        "third_run, third_model = local_run.get_output(iteration = iteration)\n",
        "print(third_run)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "print_model(third_model)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Test \n",
        "\n",
        "#### Load Test Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "digits = datasets.load_digits()\n",
        "X_test = digits.data[:10, :]\n",
        "y_test = digits.target[:10]\n",
        "images = digits.images[:10]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Testing Our Best Fitted Model\n",
        "We will try to predict 2 digits and see how our model works."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Randomly select digits and test.\n",
        "for index in np.random.choice(len(y_test), 2, replace = False):\n",
        "    print(index)\n",
        "    predicted = fitted_model.predict(X_test[index:index + 1])[0]\n",
        "    label = y_test[index]\n",
        "    title = \"Label value = %d  Predicted value = %d \" % (label, predicted)\n",
        "    fig = plt.figure(1, figsize = (3,3))\n",
        "    ax1 = fig.add_axes((0,0,.8,.8))\n",
        "    ax1.set_title(title)\n",
        "    plt.imshow(images[index], cmap = plt.cm.gray_r, interpolation = 'nearest')\n",
        "    plt.show()"
      ]
    }
  ],
  "metadata": {
    "authors": [
      {
        "name": "savitam"
      }
    ],
    "kernelspec": {
      "display_name": "Python 3.6",
      "language": "python",
      "name": "python36"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.6"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
--- a/how-to-use-azureml/automated-machine-learning/classification/auto-ml-classification.yml
+++ b/how-to-use-azureml/automated-machine-learning/classification/auto-ml-classification.yml
@@ -0,0 +1,8 @@
 name: auto-ml-classification
 dependencies:
 - pip:
  - azureml-sdk
  - azureml-train-automl
  - azureml-widgets
  - matplotlib
  - pandas_ml
--- a/how-to-use-azureml/automated-machine-learning/dataset-remote-execution/auto-ml-dataset-remote-execution.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/dataset-remote-execution/auto-ml-dataset-remote-execution.ipynb
@@ -0,0 +1,505 @@
 {
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
        "\n",
        "Licensed under the MIT License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/dataprep-remote-execution/auto-ml-dataprep-remote-execution.png)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Automated Machine Learning\n",
        "_**Load Data using `TabularDataset` for Remote Execution (AmlCompute)**_\n",
        "\n",
        "## Contents\n",
        "1. [Introduction](#Introduction)\n",
        "1. [Setup](#Setup)\n",
        "1. [Data](#Data)\n",
        "1. [Train](#Train)\n",
        "1. [Results](#Results)\n",
        "1. [Test](#Test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Introduction\n",
        "In this example we showcase how you can use AzureML Dataset to load data for AutoML.\n",
        "\n",
        "Make sure you have executed the [configuration](../../../configuration.ipynb) before running this notebook.\n",
        "\n",
        "In this notebook you will learn how to:\n",
        "1. Create a `TabularDataset` pointing to the training data.\n",
        "2. Pass the `TabularDataset` to AutoML for a remote run."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Setup"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import logging\n",
        "\n",
        "import pandas as pd\n",
        "\n",
        "import azureml.core\n",
        "from azureml.core.experiment import Experiment\n",
        "from azureml.core.workspace import Workspace\n",
        "from azureml.core.dataset import Dataset\n",
        "from azureml.train.automl import AutoMLConfig"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "ws = Workspace.from_config()\n",
        "\n",
        "# choose a name for experiment\n",
        "experiment_name = 'automl-dataset-remote-bai'\n",
        " \n",
        "experiment = Experiment(ws, experiment_name)\n",
        " \n",
        "output = {}\n",
        "output['SDK version'] = azureml.core.VERSION\n",
        "output['Subscription ID'] = ws.subscription_id\n",
        "output['Workspace Name'] = ws.name\n",
        "output['Resource Group'] = ws.resource_group\n",
        "output['Location'] = ws.location\n",
        "output['Experiment Name'] = experiment.name\n",
        "pd.set_option('display.max_colwidth', -1)\n",
        "outputDf = pd.DataFrame(data = output, index = [''])\n",
        "outputDf.T"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# The data referenced here was a 1MB simple random sample of the Chicago Crime data into a local temporary directory.\n",
        "example_data = 'https://dprepdata.blob.core.windows.net/demo/crime0-random.csv'\n",
        "dataset = Dataset.Tabular.from_delimited_files(example_data)\n",
        "dataset.take(5).to_pandas_dataframe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Review the data\n",
        "\n",
        "You can peek the result of a `TabularDataset` at any range using `skip(i)` and `take(j).to_pandas_dataframe()`. Doing so evaluates only `j` records, which makes it fast even against large datasets.\n",
        "\n",
        "`TabularDataset` objects are immutable and are composed of a list of subsetting transformations (optional)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "training_data = dataset.drop_columns(columns=['FBI Code'])\n",
        "label_column_name = 'Primary Type'"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Train\n",
        "\n",
        "This creates a general AutoML settings object applicable for both local and remote runs."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "automl_settings = {\n",
        "    \"iteration_timeout_minutes\" : 10,\n",
        "    \"iterations\" : 2,\n",
        "    \"primary_metric\" : 'AUC_weighted',\n",
        "    \"preprocess\" : True,\n",
        "    \"verbosity\" : logging.INFO\n",
        "}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Create or Attach an AmlCompute cluster"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.core.compute import AmlCompute\n",
        "from azureml.core.compute import ComputeTarget\n",
        "\n",
        "# Choose a name for your cluster.\n",
        "amlcompute_cluster_name = \"automlc2\"\n",
        "\n",
        "found = False\n",
        "\n",
        "# Check if this compute target already exists in the workspace.\n",
        "\n",
        "cts = ws.compute_targets\n",
        "if amlcompute_cluster_name in cts and cts[amlcompute_cluster_name].type == 'AmlCompute':\n",
        "    found = True\n",
        "    print('Found existing compute target.')\n",
        "    compute_target = cts[amlcompute_cluster_name]\n",
        "\n",
        "if not found:\n",
        "    print('Creating a new compute target...')\n",
        "    provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_D2_V2\", # for GPU, use \"STANDARD_NC6\"\n",
        "                                                                #vm_priority = 'lowpriority', # optional\n",
        "                                                                max_nodes = 6)\n",
        "\n",
        "    # Create the cluster.\\n\",\n",
        "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, provisioning_config)\n",
        "\n",
        "print('Checking cluster status...')\n",
        "# Can poll for a minimum number of nodes and for a specific timeout.\n",
        "# If no min_node_count is provided, it will use the scale settings for the cluster.\n",
        "compute_target.wait_for_completion(show_output = True, min_node_count = None, timeout_in_minutes = 20)\n",
        "\n",
        "# For a more detailed view of current AmlCompute status, use get_status()."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.core.runconfig import RunConfiguration\n",
        "from azureml.core.conda_dependencies import CondaDependencies\n",
        "import pkg_resources\n",
        "\n",
        "# create a new RunConfig object\n",
        "conda_run_config = RunConfiguration(framework=\"python\")\n",
        "\n",
        "# Set compute target to AmlCompute\n",
        "conda_run_config.target = compute_target\n",
        "conda_run_config.environment.docker.enabled = True\n",
        "\n",
        "cd = CondaDependencies.create(conda_packages=['numpy','py-xgboost<=0.80'])\n",
        "conda_run_config.environment.python.conda_dependencies = cd"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Pass Data with `TabularDataset` Objects\n",
        "\n",
        "The `TabularDataset` objects captured above can also be passed to the `submit` method for a remote run. AutoML will serialize the `TabularDataset` object and send it to the remote compute target. The `TabularDataset` will not be evaluated locally."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "automl_config = AutoMLConfig(task = 'classification',\n",
        "                             debug_log = 'automl_errors.log',\n",
        "                             run_configuration=conda_run_config,\n",
        "                             training_data = training_data,\n",
        "                             label_column_name = label_column_name,\n",
        "                             **automl_settings)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "remote_run = experiment.submit(automl_config, show_output = True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "remote_run"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Pre-process cache cleanup\n",
        "The preprocess data gets cache at user default file store. When the run is completed the cache can be cleaned by running below cell"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "remote_run.clean_preprocessor_cache()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Cancelling Runs\n",
        "You can cancel ongoing remote runs using the `cancel` and `cancel_iteration` functions."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Cancel the ongoing experiment and stop scheduling new iterations.\n",
        "# remote_run.cancel()\n",
        "\n",
        "# Cancel iteration 1 and move onto iteration 2.\n",
        "# remote_run.cancel_iteration(1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Results"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Widget for Monitoring Runs\n",
        "\n",
        "The widget will first report a \"loading\" status while running the first iteration. After completing the first iteration, an auto-updating graph and table will be shown. The widget will refresh once per minute, so you should see the graph update as child runs complete.\n",
        "\n",
        "**Note:** The widget displays a link at the bottom. Use this link to open a web interface to explore the individual run details."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.widgets import RunDetails\n",
        "RunDetails(remote_run).show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Retrieve All Child Runs\n",
        "You can also use SDK methods to fetch all the child runs and see individual metrics that we log."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "children = list(remote_run.get_children())\n",
        "metricslist = {}\n",
        "for run in children:\n",
        "    properties = run.get_properties()\n",
        "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}\n",
        "    metricslist[int(properties['iteration'])] = metrics\n",
        "    \n",
        "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
        "rundata"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Retrieve the Best Model\n",
        "\n",
        "Below we select the best pipeline from our iterations. The `get_output` method returns the best run and the fitted model. Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "best_run, fitted_model = remote_run.get_output()\n",
        "print(best_run)\n",
        "print(fitted_model)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Best Model Based on Any Other Metric\n",
        "Show the run and the model that has the smallest `log_loss` value:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "lookup_metric = \"log_loss\"\n",
        "best_run, fitted_model = remote_run.get_output(metric = lookup_metric)\n",
        "print(best_run)\n",
        "print(fitted_model)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Model from a Specific Iteration\n",
        "Show the run and the model from the first iteration:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "iteration = 0\n",
        "best_run, fitted_model = remote_run.get_output(iteration = iteration)\n",
        "print(best_run)\n",
        "print(fitted_model)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Test\n",
        "\n",
        "#### Load Test Data\n",
        "For the test data, it should have the same preparation step as the train data. Otherwise it might get failed at the preprocessing step."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "dataset_test = Dataset.Tabular.from_delimited_files(path='https://dprepdata.blob.core.windows.net/demo/crime0-test.csv')\n",
        "\n",
        "df_test = dataset_test.to_pandas_dataframe()\n",
        "df_test = df_test[pd.notnull(df_test['Primary Type'])]\n",
        "\n",
        "y_test = df_test[['Primary Type']]\n",
        "X_test = df_test.drop(['Primary Type', 'FBI Code'], axis=1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Testing Our Best Fitted Model\n",
        "We will use confusion matrix to see how our model works."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from pandas_ml import ConfusionMatrix\n",
        "\n",
        "ypred = fitted_model.predict(X_test)\n",
        "\n",
        "cm = ConfusionMatrix(y_test['Primary Type'], ypred)\n",
        "\n",
        "print(cm)\n",
        "\n",
        "cm.plot()"
      ]
    }
  ],
  "metadata": {
    "authors": [
      {
        "name": "savitam"
      }
    ],
    "kernelspec": {
      "display_name": "Python 3.6",
      "language": "python",
      "name": "python36"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.5"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
--- a/how-to-use-azureml/automated-machine-learning/dataset-remote-execution/auto-ml-dataset-remote-execution.yml
+++ b/how-to-use-azureml/automated-machine-learning/dataset-remote-execution/auto-ml-dataset-remote-execution.yml
@@ -0,0 +1,11 @@
 name: auto-ml-dataset-remote-execution
 dependencies:
 - pip:
  - azureml-sdk
  - interpret
  - azureml-defaults
  - azureml-explain-model
  - azureml-train-automl
  - azureml-widgets
  - matplotlib
  - pandas_ml
--- a/how-to-use-azureml/automated-machine-learning/dataset/auto-ml-dataset.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/dataset/auto-ml-dataset.ipynb
@@ -0,0 +1,399 @@
 {
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/dataprep/auto-ml-dataprep.png)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
        "\n",
        "Licensed under the MIT License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Automated Machine Learning\n",
        "_**Load Data using `TabularDataset` for Local Execution**_\n",
        "\n",
        "## Contents\n",
        "1. [Introduction](#Introduction)\n",
        "1. [Setup](#Setup)\n",
        "1. [Data](#Data)\n",
        "1. [Train](#Train)\n",
        "1. [Results](#Results)\n",
        "1. [Test](#Test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Introduction\n",
        "In this example we showcase how you can use AzureML Dataset to load data for AutoML.\n",
        "\n",
        "Make sure you have executed the [configuration](../../../configuration.ipynb) before running this notebook.\n",
        "\n",
        "In this notebook you will learn how to:\n",
        "1. Create a `TabularDataset` pointing to the training data.\n",
        "2. Pass the `TabularDataset` to AutoML for a local run."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Setup"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import logging\n",
        "\n",
        "import pandas as pd\n",
        "\n",
        "import azureml.core\n",
        "from azureml.core.experiment import Experiment\n",
        "from azureml.core.workspace import Workspace\n",
        "from azureml.core.dataset import Dataset\n",
        "from azureml.train.automl import AutoMLConfig"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "ws = Workspace.from_config()\n",
        " \n",
        "# choose a name for experiment\n",
        "experiment_name = 'automl-dataset-local'\n",
        " \n",
        "experiment = Experiment(ws, experiment_name)\n",
        " \n",
        "output = {}\n",
        "output['SDK version'] = azureml.core.VERSION\n",
        "output['Subscription ID'] = ws.subscription_id\n",
        "output['Workspace Name'] = ws.name\n",
        "output['Resource Group'] = ws.resource_group\n",
        "output['Location'] = ws.location\n",
        "output['Experiment Name'] = experiment.name\n",
        "pd.set_option('display.max_colwidth', -1)\n",
        "outputDf = pd.DataFrame(data = output, index = [''])\n",
        "outputDf.T"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# The data referenced here was a 1MB simple random sample of the Chicago Crime data into a local temporary directory.\n",
        "example_data = 'https://dprepdata.blob.core.windows.net/demo/crime0-random.csv'\n",
        "dataset = Dataset.Tabular.from_delimited_files(example_data)\n",
        "dataset.take(5).to_pandas_dataframe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Review the data\n",
        "\n",
        "You can peek the result of a `TabularDataset` at any range using `skip(i)` and `take(j).to_pandas_dataframe()`. Doing so evaluates only `j` records, which makes it fast even against large datasets.\n",
        "\n",
        "`TabularDataset` objects are immutable and are composed of a list of subsetting transformations (optional)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "training_data = dataset.drop_columns(columns=['FBI Code'])\n",
        "label_column_name = 'Primary Type'"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Train\n",
        "\n",
        "This creates a general AutoML settings object applicable for both local and remote runs."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "automl_settings = {\n",
        "    \"iteration_timeout_minutes\" : 10,\n",
        "    \"iterations\" : 2,\n",
        "    \"primary_metric\" : 'AUC_weighted',\n",
        "    \"preprocess\" : True,\n",
        "    \"verbosity\" : logging.INFO\n",
        "}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Pass Data with `TabularDataset` Objects\n",
        "\n",
        "The `TabularDataset` objects captured above can be passed to the `submit` method for a local run. AutoML will retrieve the results from the `TabularDataset` for model training."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "automl_config = AutoMLConfig(task = 'classification',\n",
        "                             debug_log = 'automl_errors.log',\n",
        "                             training_data = training_data,\n",
        "                             label_column_name = label_column_name,\n",
        "                             **automl_settings)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "local_run = experiment.submit(automl_config, show_output = True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "local_run"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Results"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Widget for Monitoring Runs\n",
        "\n",
        "The widget will first report a \"loading\" status while running the first iteration. After completing the first iteration, an auto-updating graph and table will be shown. The widget will refresh once per minute, so you should see the graph update as child runs complete.\n",
        "\n",
        "**Note:** The widget displays a link at the bottom. Use this link to open a web interface to explore the individual run details."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.widgets import RunDetails\n",
        "RunDetails(local_run).show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Retrieve All Child Runs\n",
        "You can also use SDK methods to fetch all the child runs and see individual metrics that we log."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "children = list(local_run.get_children())\n",
        "metricslist = {}\n",
        "for run in children:\n",
        "    properties = run.get_properties()\n",
        "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}\n",
        "    metricslist[int(properties['iteration'])] = metrics\n",
        "    \n",
        "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
        "rundata"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Retrieve the Best Model\n",
        "\n",
        "Below we select the best pipeline from our iterations. The `get_output` method returns the best run and the fitted model. Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "best_run, fitted_model = local_run.get_output()\n",
        "print(best_run)\n",
        "print(fitted_model)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Best Model Based on Any Other Metric\n",
        "Show the run and the model that has the smallest `log_loss` value:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "lookup_metric = \"log_loss\"\n",
        "best_run, fitted_model = local_run.get_output(metric = lookup_metric)\n",
        "print(best_run)\n",
        "print(fitted_model)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Model from a Specific Iteration\n",
        "Show the run and the model from the first iteration:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "iteration = 0\n",
        "best_run, fitted_model = local_run.get_output(iteration = iteration)\n",
        "print(best_run)\n",
        "print(fitted_model)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Test\n",
        "\n",
        "#### Load Test Data\n",
        "For the test data, it should have the same preparation step as the train data. Otherwise it might get failed at the preprocessing step."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "dataset_test = Dataset.Tabular.from_delimited_files(path='https://dprepdata.blob.core.windows.net/demo/crime0-test.csv')\n",
        "\n",
        "df_test = dataset_test.to_pandas_dataframe()\n",
        "df_test = df_test[pd.notnull(df_test['Primary Type'])]\n",
        "\n",
        "y_test = df_test[['Primary Type']]\n",
        "X_test = df_test.drop(['Primary Type', 'FBI Code'], axis=1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Testing Our Best Fitted Model\n",
        "We will use confusion matrix to see how our model works."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from pandas_ml import ConfusionMatrix\n",
        "\n",
        "ypred = fitted_model.predict(X_test)\n",
        "\n",
        "cm = ConfusionMatrix(y_test['Primary Type'], ypred)\n",
        "\n",
        "print(cm)\n",
        "\n",
        "cm.plot()"
      ]
    }
  ],
  "metadata": {
    "authors": [
      {
        "name": "savitam"
      }
    ],
    "kernelspec": {
      "display_name": "Python 3.6",
      "language": "python",
      "name": "python36"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.5"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
--- a/how-to-use-azureml/automated-machine-learning/dataset/auto-ml-dataset.yml
+++ b/how-to-use-azureml/automated-machine-learning/dataset/auto-ml-dataset.yml
@@ -0,0 +1,9 @@
 name: auto-ml-dataset
 dependencies:
 - pip:
  - azureml-sdk
  - azureml-train-automl
  - azureml-widgets
  - matplotlib
  - pandas_ml
  - azureml-dataprep[pandas]
--- a/how-to-use-azureml/automated-machine-learning/exploring-previous-runs/auto-ml-exploring-previous-runs.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/exploring-previous-runs/auto-ml-exploring-previous-runs.ipynb
@@ -0,0 +1,349 @@
 {
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
        "\n",
        "Licensed under the MIT License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/exploring-previous-runs/auto-ml-exploring-previous-runs.png)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Automated Machine Learning\n",
        "_**Exploring Previous Runs**_\n",
        "\n",
        "## Contents\n",
        "1. [Introduction](#Introduction)\n",
        "1. [Setup](#Setup)\n",
        "1. [Explore](#Explore)\n",
        "1. [Download](#Download)\n",
        "1. [Register](#Register)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Introduction\n",
        "In this example we present some examples on navigating previously executed runs. We also show how you can download a fitted model for any previous run.\n",
        "\n",
        "Make sure you have executed the [configuration](../../../configuration.ipynb) before running this notebook.\n",
        "\n",
        "In this notebook you will learn how to:\n",
        "1. List all experiments in a workspace.\n",
        "2. List all AutoML runs in an experiment.\n",
        "3. Get details for an AutoML run, including settings, run widget, and all metrics.\n",
        "4. Download a fitted pipeline for any iteration."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Setup"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import json\n",
        "\n",
        "from azureml.core.experiment import Experiment\n",
        "from azureml.core.workspace import Workspace\n",
        "from azureml.train.automl.run import AutoMLRun"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "ws = Workspace.from_config()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Explore"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### List Experiments"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "experiment_list = Experiment.list(workspace=ws)\n",
        "\n",
        "summary_df = pd.DataFrame(index = ['No of Runs'])\n",
        "for experiment in experiment_list:\n",
        "    automl_runs = list(experiment.get_runs(type='automl'))\n",
        "    summary_df[experiment.name] = [len(automl_runs)]\n",
        "    \n",
        "pd.set_option('display.max_colwidth', -1)\n",
        "summary_df.T"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### List runs for an experiment\n",
        "Set `experiment_name` to any experiment name from the result of the Experiment.list cell to load the AutoML runs."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "experiment_name = 'automl-local-classification' # Replace this with any project name from previous cell.\n",
        "\n",
        "proj = ws.experiments[experiment_name]\n",
        "summary_df = pd.DataFrame(index = ['Type', 'Status', 'Primary Metric', 'Iterations', 'Compute', 'Name'])\n",
        "automl_runs = list(proj.get_runs(type='automl'))\n",
        "automl_runs_project = []\n",
        "for run in automl_runs:\n",
        "    properties = run.get_properties()\n",
        "    tags = run.get_tags()\n",
        "    amlsettings = json.loads(properties['AMLSettingsJsonString'])\n",
        "    if 'iterations' in tags:\n",
        "        iterations = tags['iterations']\n",
        "    else:\n",
        "        iterations = properties['num_iterations']\n",
        "    summary_df[run.id] = [amlsettings['task_type'], run.get_details()['status'], properties['primary_metric'], iterations, properties['target'], amlsettings['name']]\n",
        "    if run.get_details()['status'] == 'Completed':\n",
        "        automl_runs_project.append(run.id)\n",
        "    \n",
        "from IPython.display import HTML\n",
        "projname_html = HTML(\"<h3>{}</h3>\".format(proj.name))\n",
        "\n",
        "from IPython.display import display\n",
        "display(projname_html)\n",
        "display(summary_df.T)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Get details for a run\n",
        "\n",
        "Copy the project name and run id from the previous cell output to find more details on a particular run."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "run_id = automl_runs_project[0]  # Replace with your own run_id from above run ids\n",
        "assert (run_id in summary_df.keys()), \"Run id not found! Please set run id to a value from above run ids\"\n",
        "\n",
        "from azureml.widgets import RunDetails\n",
        "\n",
        "experiment = Experiment(ws, experiment_name)\n",
        "ml_run = AutoMLRun(experiment = experiment, run_id = run_id)\n",
        "\n",
        "summary_df = pd.DataFrame(index = ['Type', 'Status', 'Primary Metric', 'Iterations', 'Compute', 'Name', 'Start Time', 'End Time'])\n",
        "properties = ml_run.get_properties()\n",
        "tags = ml_run.get_tags()\n",
        "status = ml_run.get_details()\n",
        "amlsettings = json.loads(properties['AMLSettingsJsonString'])\n",
        "if 'iterations' in tags:\n",
        "    iterations = tags['iterations']\n",
        "else:\n",
        "    iterations = properties['num_iterations']\n",
        "start_time = None\n",
        "if 'startTimeUtc' in status:\n",
        "    start_time = status['startTimeUtc']\n",
        "end_time = None\n",
        "if 'endTimeUtc' in status:\n",
        "    end_time = status['endTimeUtc']\n",
        "summary_df[ml_run.id] = [amlsettings['task_type'], status['status'], properties['primary_metric'], iterations, properties['target'], amlsettings['name'], start_time, end_time]\n",
        "display(HTML('<h3>Runtime Details</h3>'))\n",
        "display(summary_df)\n",
        "\n",
        "#settings_df = pd.DataFrame(data = amlsettings, index = [''])\n",
        "display(HTML('<h3>AutoML Settings</h3>'))\n",
        "display(amlsettings)\n",
        "\n",
        "display(HTML('<h3>Iterations</h3>'))\n",
        "RunDetails(ml_run).show() \n",
        "\n",
        "all_metrics = ml_run.get_metrics(recursive=True)\n",
        "metricslist = {}\n",
        "for run_id, metrics in all_metrics.items():\n",
        "    iteration = int(run_id.split('_')[-1])\n",
        "    float_metrics = {k: v for k, v in metrics.items() if isinstance(v, float)}\n",
        "    metricslist[iteration] = float_metrics\n",
        "\n",
        "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
        "display(HTML('<h3>Metrics</h3>'))\n",
        "display(rundata)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Download"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Download the Best Model for Any Given Metric"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "metric = 'AUC_weighted' # Replace with a metric name.\n",
        "best_run, fitted_model = ml_run.get_output(metric = metric)\n",
        "fitted_model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Download the Model for Any Given Iteration"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "iteration = 1 # Replace with an iteration number.\n",
        "best_run, fitted_model = ml_run.get_output(iteration = iteration)\n",
        "fitted_model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Register"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Register fitted model for deployment\n",
        "If neither `metric` nor `iteration` are specified in the `register_model` call, the iteration with the best primary metric is registered."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "description = 'AutoML Model'\n",
        "tags = None\n",
        "ml_run.register_model(description = description, tags = tags)\n",
        "print(ml_run.model_id) # Use this id to deploy the model as a web service in Azure."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Register the Best Model for Any Given Metric"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "metric = 'AUC_weighted' # Replace with a metric name.\n",
        "description = 'AutoML Model'\n",
        "tags = None\n",
        "ml_run.register_model(description = description, tags = tags, metric = metric)\n",
        "print(ml_run.model_id) # Use this id to deploy the model as a web service in Azure."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Register the Model for Any Given Iteration"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "iteration = 1 # Replace with an iteration number.\n",
        "description = 'AutoML Model'\n",
        "tags = None\n",
        "ml_run.register_model(description = description, tags = tags, iteration = iteration)\n",
        "print(ml_run.model_id) # Use this id to deploy the model as a web service in Azure."
      ]
    }
  ],
  "metadata": {
    "authors": [
      {
        "name": "savitam"
      }
    ],
    "kernelspec": {
      "display_name": "Python 3.6",
      "language": "python",
      "name": "python36"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.6"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
--- a/how-to-use-azureml/automated-machine-learning/exploring-previous-runs/auto-ml-exploring-previous-runs.yml
+++ b/how-to-use-azureml/automated-machine-learning/exploring-previous-runs/auto-ml-exploring-previous-runs.yml
@@ -0,0 +1,8 @@
 name: auto-ml-exploring-previous-runs
 dependencies:
 - pip:
  - azureml-sdk
  - azureml-train-automl
  - azureml-widgets
  - matplotlib
  - pandas_ml
--- a/how-to-use-azureml/automated-machine-learning/forecasting-bike-share/auto-ml-forecasting-bike-share.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/forecasting-bike-share/auto-ml-forecasting-bike-share.ipynb
@@ -0,0 +1,605 @@
 {
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
        "\n",
        "Licensed under the MIT License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/forecasting-bike-share/auto-ml-forecasting-bike-share.png)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Automated Machine Learning\n",
        "**BikeShare Demand Forecasting**\n",
        "\n",
        "## Contents\n",
        "1. [Introduction](#Introduction)\n",
        "1. [Setup](#Setup)\n",
        "1. [Data](#Data)\n",
        "1. [Train](#Train)\n",
        "1. [Evaluate](#Evaluate)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Introduction\n",
        "This notebook demonstrates demand forecasting for a bike-sharing service using AutoML.\n",
        "\n",
        "AutoML highlights here include built-in holiday featurization, accessing engineered feature names, and working with the `forecast` function. Please also look at the additional forecasting notebooks, which document lagging, rolling windows, forecast quantiles, other ways to use the forecast function, and forecaster deployment.\n",
        "\n",
        "Make sure you have executed the [configuration](../../../configuration.ipynb) before running this notebook.\n",
        "\n",
        "Notebook synopsis:\n",
        "1. Creating an Experiment in an existing Workspace\n",
        "2. Configuration and local run of AutoML for a time-series model with lag and holiday features \n",
        "3. Viewing the engineered names for featurized data and featurization summary for all raw features\n",
        "4. Evaluating the fitted model using a rolling test "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Setup\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import azureml.core\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import logging\n",
        "import warnings\n",
        "\n",
        "from pandas.tseries.frequencies import to_offset\n",
        "\n",
        "# Squash warning messages for cleaner output in the notebook\n",
        "warnings.showwarning = lambda *args, **kwargs: None\n",
        "\n",
        "from azureml.core.workspace import Workspace\n",
        "from azureml.core.experiment import Experiment\n",
        "from azureml.train.automl import AutoMLConfig\n",
        "from matplotlib import pyplot as plt\n",
        "from sklearn.metrics import mean_absolute_error, mean_squared_error"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "As part of the setup you have already created a <b>Workspace</b>. To run AutoML, you also need to create an <b>Experiment</b>. An Experiment corresponds to a prediction problem you are trying to solve, while a Run corresponds to a specific approach to the problem."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "ws = Workspace.from_config()\n",
        "\n",
        "# choose a name for the run history container in the workspace\n",
        "experiment_name = 'automl-bikeshareforecasting'\n",
        "\n",
        "experiment = Experiment(ws, experiment_name)\n",
        "\n",
        "output = {}\n",
        "output['SDK version'] = azureml.core.VERSION\n",
        "output['Subscription ID'] = ws.subscription_id\n",
        "output['Workspace'] = ws.name\n",
        "output['Resource Group'] = ws.resource_group\n",
        "output['Location'] = ws.location\n",
        "output['Run History Name'] = experiment_name\n",
        "pd.set_option('display.max_colwidth', -1)\n",
        "outputDf = pd.DataFrame(data = output, index = [''])\n",
        "outputDf.T"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Data\n",
        "Read bike share demand data from file, and preview data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "data = pd.read_csv('bike-no.csv', parse_dates=['date'])\n",
        "data.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Let's set up what we know about the dataset. \n",
        "\n",
        "**Target column** is what we want to forecast.\n",
        "\n",
        "**Time column** is the time axis along which to predict.\n",
        "\n",
        "**Grain** is another word for an individual time series in your dataset. Grains are identified by values of the columns listed `grain_column_names`, for example \"store\" and \"item\" if your data has multiple time series of sales, one series for each combination of store and item sold.\n",
        "\n",
        "This dataset has only one time series. Please see the [orange juice notebook](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales) for an example of a multi-time series dataset."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "target_column_name = 'cnt'\n",
        "time_column_name = 'date'\n",
        "grain_column_names = []"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Split the data\n",
        "\n",
        "The first split we make is into train and test sets. Note we are splitting on time."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "train = data[data[time_column_name] < '2012-09-01']\n",
        "test = data[data[time_column_name] >= '2012-09-01']\n",
        "\n",
        "X_train = train.copy()\n",
        "y_train = X_train.pop(target_column_name).values\n",
        "\n",
        "X_test = test.copy()\n",
        "y_test = X_test.pop(target_column_name).values\n",
        "\n",
        "print(X_train.shape)\n",
        "print(y_train.shape)\n",
        "print(X_test.shape)\n",
        "print(y_test.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Setting forecaster maximum horizon \n",
        "\n",
        "The forecast horizon is the number of periods into the future that the model should predict. Here, we set the horizon to 14 periods (i.e. 14 days). Notice that this is much shorter than the number of days in the test set; we will need to use a rolling test to evaluate the performance on the whole test set. For more discussion of forecast horizons and guiding principles for setting them, please see the [energy demand notebook](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/automated-machine-learning/forecasting-energy-demand).  "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "max_horizon = 14"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Train\n",
        "\n",
        "Instantiate a AutoMLConfig object. This defines the settings and data used to run the experiment.\n",
        "\n",
        "|Property|Description|\n",
        "|-|-|\n",
        "|**task**|forecasting|\n",
        "|**primary_metric**|This is the metric that you want to optimize.<br> Forecasting supports the following primary metrics <br><i>spearman_correlation</i><br><i>normalized_root_mean_squared_error</i><br><i>r2_score</i><br><i>normalized_mean_absolute_error</i>\n",
        "|**iterations**|Number of iterations. In each iteration, Auto ML trains a specific pipeline on the given data|\n",
        "|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
        "|**training_data**|Input dataset, containing both features and label column.|\n",
        "|**label_column_name**|The name of the label column.|\n",
        "|**n_cross_validations**|Number of cross validation splits.|\n",
        "|**country_or_region**|The country/region used to generate holiday features. These should be ISO 3166 two-letter country/region codes (i.e. 'US', 'GB').|\n",
        "\n",
        "This notebook uses the blacklist_models parameter to exclude some models that take a longer time to train on this dataset. You can choose to remove models from the blacklist_models list but you may need to increase the iteration_timeout_minutes parameter value to get results."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "automl_settings = {\n",
        "    'time_column_name': time_column_name,\n",
        "    'max_horizon': max_horizon,\n",
        "    # knowing the country/region allows Automated ML to bring in holidays\n",
        "    'country_or_region': 'US',\n",
        "    'target_lags': 1,\n",
        "    # these columns are a breakdown of the total and therefore a leak\n",
        "    'drop_column_names': ['casual', 'registered']\n",
        "}\n",
        "\n",
        "automl_config = AutoMLConfig(task='forecasting',                             \n",
        "                             primary_metric='normalized_root_mean_squared_error',\n",
        "                             blacklist_models = ['ExtremeRandomTrees'],\n",
        "                             iterations=10,\n",
        "                             iteration_timeout_minutes=5,\n",
        "                             training_data=train,\n",
        "                             label_column_name=target_column_name,\n",
        "                             n_cross_validations=3,                             \n",
        "                             verbosity=logging.INFO,\n",
        "                            **automl_settings)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "We will now run the experiment, starting with 10 iterations of model search. The experiment can be continued for more iterations if more accurate results are required. You will see the currently running iterations printing to the console."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "local_run = experiment.submit(automl_config, show_output=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Displaying the run objects gives you links to the visual tools in the Azure Portal. Go try them!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "local_run"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Retrieve the Best Model\n",
        "Below we select the best pipeline from our iterations. The get_output method on automl_classifier returns the best run and the fitted model for the last fit invocation. There are overloads on get_output that allow you to retrieve the best run and fitted model for any logged metric or a particular iteration."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "best_run, fitted_model = local_run.get_output()\n",
        "fitted_model.steps"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### View the engineered names for featurized data\n",
        "\n",
        "You can accees the engineered feature names generated in time-series featurization. Note that a number of named holiday periods are represented. We recommend that you have at least one year of data when using this feature to ensure that all yearly holidays are captured in the training featurization."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "fitted_model.named_steps['timeseriestransformer'].get_engineered_feature_names()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### View the featurization summary\n",
        "\n",
        "You can also see what featurization steps were performed on different raw features in the user data. For each raw feature in the user data, the following information is displayed:\n",
        "\n",
        "- Raw feature name\n",
        "- Number of engineered features formed out of this raw feature\n",
        "- Type detected\n",
        "- If feature was dropped\n",
        "- List of feature transformations for the raw feature"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Get the featurization summary as a list of JSON\n",
        "featurization_summary = fitted_model.named_steps['timeseriestransformer'].get_featurization_summary()\n",
        "# View the featurization summary as a pandas dataframe\n",
        "pd.DataFrame.from_records(featurization_summary)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Evaluate"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "We now use the best fitted model from the AutoML Run to make forecasts for the test set.  \n",
        "\n",
        "We always score on the original dataset whose schema matches the training set schema."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "X_test.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "We now define some functions for aligning output to input and for producing rolling forecasts over the full test set. As previously stated, the forecast horizon of 14 days is shorter than the length of the test set - which is about 120 days. To get predictions over the full test set, we iterate over the test set, making forecasts 14 days at a time and combining the results. We also make sure that each 14-day forecast uses up-to-date actuals - the current context - to construct lag features. \n",
        "\n",
        "It is a good practice to always align the output explicitly to the input, as the count and order of the rows may have changed during transformations that span multiple rows."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "def align_outputs(y_predicted, X_trans, X_test, y_test, predicted_column_name='predicted',\n",
        "                  horizon_colname='horizon_origin'):\n",
        "    \"\"\"\n",
        "    Demonstrates how to get the output aligned to the inputs\n",
        "    using pandas indexes. Helps understand what happened if\n",
        "    the output's shape differs from the input shape, or if\n",
        "    the data got re-sorted by time and grain during forecasting.\n",
        "    \n",
        "    Typical causes of misalignment are:\n",
        "    * we predicted some periods that were missing in actuals -> drop from eval\n",
        "    * model was asked to predict past max_horizon -> increase max horizon\n",
        "    * data at start of X_test was needed for lags -> provide previous periods\n",
        "    \"\"\"\n",
        "    df_fcst = pd.DataFrame({predicted_column_name : y_predicted,\n",
        "                            horizon_colname: X_trans[horizon_colname]})\n",
        "    # y and X outputs are aligned by forecast() function contract\n",
        "    df_fcst.index = X_trans.index\n",
        "    \n",
        "    # align original X_test to y_test    \n",
        "    X_test_full = X_test.copy()\n",
        "    X_test_full[target_column_name] = y_test\n",
        "\n",
        "    # X_test_full's index does not include origin, so reset for merge\n",
        "    df_fcst.reset_index(inplace=True)\n",
        "    X_test_full = X_test_full.reset_index().drop(columns='index')\n",
        "    together = df_fcst.merge(X_test_full, how='right')\n",
        "    \n",
        "    # drop rows where prediction or actuals are nan \n",
        "    # happens because of missing actuals \n",
        "    # or at edges of time due to lags/rolling windows\n",
        "    clean = together[together[[target_column_name, predicted_column_name]].notnull().all(axis=1)]\n",
        "    return(clean)\n",
        "\n",
        "def do_rolling_forecast(fitted_model, X_test, y_test, max_horizon, freq='D'):\n",
        "    \"\"\"\n",
        "    Produce forecasts on a rolling origin over the given test set.\n",
        "    \n",
        "    Each iteration makes a forecast for the next 'max_horizon' periods \n",
        "    with respect to the current origin, then advances the origin by the horizon time duration. \n",
        "    The prediction context for each forecast is set so that the forecaster uses \n",
        "    the actual target values prior to the current origin time for constructing lag features.\n",
        "    \n",
        "    This function returns a concatenated DataFrame of rolling forecasts.\n",
        "     \"\"\"\n",
        "    df_list = []\n",
        "    origin_time = X_test[time_column_name].min()\n",
        "    while origin_time <= X_test[time_column_name].max():\n",
        "        # Set the horizon time - end date of the forecast\n",
        "        horizon_time = origin_time + max_horizon * to_offset(freq)\n",
        "        \n",
        "        # Extract test data from an expanding window up-to the horizon \n",
        "        expand_wind = (X_test[time_column_name] < horizon_time)\n",
        "        X_test_expand = X_test[expand_wind]\n",
        "        y_query_expand = np.zeros(len(X_test_expand)).astype(np.float)\n",
        "        y_query_expand.fill(np.NaN)\n",
        "        \n",
        "        if origin_time != X_test[time_column_name].min():\n",
        "            # Set the context by including actuals up-to the origin time\n",
        "            test_context_expand_wind = (X_test[time_column_name] < origin_time)\n",
        "            context_expand_wind = (X_test_expand[time_column_name] < origin_time)\n",
        "            y_query_expand[context_expand_wind] = y_test[test_context_expand_wind]\n",
        "        \n",
        "        # Make a forecast out to the maximum horizon\n",
        "        y_fcst, X_trans = fitted_model.forecast(X_test_expand, y_query_expand)\n",
        "        \n",
        "        # Align forecast with test set for dates within the current rolling window \n",
        "        trans_tindex = X_trans.index.get_level_values(time_column_name)\n",
        "        trans_roll_wind = (trans_tindex >= origin_time) & (trans_tindex < horizon_time)\n",
        "        test_roll_wind = expand_wind & (X_test[time_column_name] >= origin_time)\n",
        "        df_list.append(align_outputs(y_fcst[trans_roll_wind], X_trans[trans_roll_wind],\n",
        "                                     X_test[test_roll_wind], y_test[test_roll_wind]))\n",
        "        \n",
        "        # Advance the origin time\n",
        "        origin_time = horizon_time\n",
        "    \n",
        "    return pd.concat(df_list, ignore_index=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "df_all = do_rolling_forecast(fitted_model, X_test, y_test, max_horizon)\n",
        "df_all"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "We now calculate some error metrics for the forecasts and vizualize the predictions vs. the actuals."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "def APE(actual, pred):\n",
        "    \"\"\"\n",
        "    Calculate absolute percentage error.\n",
        "    Returns a vector of APE values with same length as actual/pred.\n",
        "    \"\"\"\n",
        "    return 100*np.abs((actual - pred)/actual)\n",
        "\n",
        "def MAPE(actual, pred):\n",
        "    \"\"\"\n",
        "    Calculate mean absolute percentage error.\n",
        "    Remove NA and values where actual is close to zero\n",
        "    \"\"\"\n",
        "    not_na = ~(np.isnan(actual) | np.isnan(pred))\n",
        "    not_zero = ~np.isclose(actual, 0.0)\n",
        "    actual_safe = actual[not_na & not_zero]\n",
        "    pred_safe = pred[not_na & not_zero]\n",
        "    return np.mean(APE(actual_safe, pred_safe))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "print(\"Simple forecasting model\")\n",
        "rmse = np.sqrt(mean_squared_error(df_all[target_column_name], df_all['predicted']))\n",
        "print(\"[Test Data] \\nRoot Mean squared error: %.2f\" % rmse)\n",
        "mae = mean_absolute_error(df_all[target_column_name], df_all['predicted'])\n",
        "print('mean_absolute_error score: %.2f' % mae)\n",
        "print('MAPE: %.2f' % MAPE(df_all[target_column_name], df_all['predicted']))\n",
        "\n",
        "# Plot outputs\n",
        "%matplotlib inline\n",
        "test_pred = plt.scatter(df_all[target_column_name], df_all['predicted'], color='b')\n",
        "test_test = plt.scatter(y_test, y_test, color='g')\n",
        "plt.legend((test_pred, test_test), ('prediction', 'truth'), loc='upper left', fontsize=8)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "The MAPE seems high; it is being skewed by an actual with a small absolute value. For a more informative evaluation, we can calculate the metrics by forecast horizon:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "df_all.groupby('horizon_origin').apply(\n",
        "    lambda df: pd.Series({'MAPE': MAPE(df[target_column_name], df['predicted']),\n",
        "                          'RMSE': np.sqrt(mean_squared_error(df[target_column_name], df['predicted'])),\n",
        "                          'MAE': mean_absolute_error(df[target_column_name], df['predicted'])}))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "It's also interesting to see the distributions of APE (absolute percentage error) by horizon. On a log scale, the outlying APE in the horizon-3 group is clear."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "df_all_APE = df_all.assign(APE=APE(df_all[target_column_name], df_all['predicted']))\n",
        "APEs = [df_all_APE[df_all['horizon_origin'] == h].APE.values for h in range(1, max_horizon + 1)]\n",
        "\n",
        "%matplotlib inline\n",
        "plt.boxplot(APEs)\n",
        "plt.yscale('log')\n",
        "plt.xlabel('horizon')\n",
        "plt.ylabel('APE (%)')\n",
        "plt.title('Absolute Percentage Errors by Forecast Horizon')\n",
        "\n",
        "plt.show()"
      ]
    }
  ],
  "metadata": {
    "authors": [
      {
        "name": "erwright"
      }
    ],
    "kernelspec": {
      "display_name": "Python 3.6",
      "language": "python",
      "name": "python36"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.8"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
--- a/how-to-use-azureml/automated-machine-learning/forecasting-bike-share/auto-ml-forecasting-bike-share.yml
+++ b/how-to-use-azureml/automated-machine-learning/forecasting-bike-share/auto-ml-forecasting-bike-share.yml
@@ -0,0 +1,9 @@
 name: auto-ml-forecasting-bike-share
 dependencies:
 - pip:
  - azureml-sdk
  - azureml-train-automl
  - azureml-widgets
  - matplotlib
  - pandas_ml
  - statsmodels
--- a/how-to-use-azureml/automated-machine-learning/forecasting-bike-share/bike-no.csv
+++ b/how-to-use-azureml/automated-machine-learning/forecasting-bike-share/bike-no.csv
@@ -0,0 +1,732 @@
 instant,date,season,yr,mnth,weekday,weathersit,temp,atemp,hum,windspeed,casual,registered,cnt
 1,1/1/2011,1,0,1,6,2,0.344167,0.363625,0.805833,0.160446,331,654,985
 2,1/2/2011,1,0,1,0,2,0.363478,0.353739,0.696087,0.248539,131,670,801
 3,1/3/2011,1,0,1,1,1,0.196364,0.189405,0.437273,0.248309,120,1229,1349
 4,1/4/2011,1,0,1,2,1,0.2,0.212122,0.590435,0.160296,108,1454,1562
 5,1/5/2011,1,0,1,3,1,0.226957,0.22927,0.436957,0.1869,82,1518,1600
 6,1/6/2011,1,0,1,4,1,0.204348,0.233209,0.518261,0.0895652,88,1518,1606
 7,1/7/2011,1,0,1,5,2,0.196522,0.208839,0.498696,0.168726,148,1362,1510
 8,1/8/2011,1,0,1,6,2,0.165,0.162254,0.535833,0.266804,68,891,959
 9,1/9/2011,1,0,1,0,1,0.138333,0.116175,0.434167,0.36195,54,768,822
 10,1/10/2011,1,0,1,1,1,0.150833,0.150888,0.482917,0.223267,41,1280,1321
 11,1/11/2011,1,0,1,2,2,0.169091,0.191464,0.686364,0.122132,43,1220,1263
 12,1/12/2011,1,0,1,3,1,0.172727,0.160473,0.599545,0.304627,25,1137,1162
 13,1/13/2011,1,0,1,4,1,0.165,0.150883,0.470417,0.301,38,1368,1406
 14,1/14/2011,1,0,1,5,1,0.16087,0.188413,0.537826,0.126548,54,1367,1421
 15,1/15/2011,1,0,1,6,2,0.233333,0.248112,0.49875,0.157963,222,1026,1248
 16,1/16/2011,1,0,1,0,1,0.231667,0.234217,0.48375,0.188433,251,953,1204
 17,1/17/2011,1,0,1,1,2,0.175833,0.176771,0.5375,0.194017,117,883,1000
 18,1/18/2011,1,0,1,2,2,0.216667,0.232333,0.861667,0.146775,9,674,683
 19,1/19/2011,1,0,1,3,2,0.292174,0.298422,0.741739,0.208317,78,1572,1650
 20,1/20/2011,1,0,1,4,2,0.261667,0.25505,0.538333,0.195904,83,1844,1927
 21,1/21/2011,1,0,1,5,1,0.1775,0.157833,0.457083,0.353242,75,1468,1543
 22,1/22/2011,1,0,1,6,1,0.0591304,0.0790696,0.4,0.17197,93,888,981
 23,1/23/2011,1,0,1,0,1,0.0965217,0.0988391,0.436522,0.2466,150,836,986
 24,1/24/2011,1,0,1,1,1,0.0973913,0.11793,0.491739,0.15833,86,1330,1416
 25,1/25/2011,1,0,1,2,2,0.223478,0.234526,0.616957,0.129796,186,1799,1985
 26,1/26/2011,1,0,1,3,3,0.2175,0.2036,0.8625,0.29385,34,472,506
 27,1/27/2011,1,0,1,4,1,0.195,0.2197,0.6875,0.113837,15,416,431
 28,1/28/2011,1,0,1,5,2,0.203478,0.223317,0.793043,0.1233,38,1129,1167
 29,1/29/2011,1,0,1,6,1,0.196522,0.212126,0.651739,0.145365,123,975,1098
 30,1/30/2011,1,0,1,0,1,0.216522,0.250322,0.722174,0.0739826,140,956,1096
 31,1/31/2011,1,0,1,1,2,0.180833,0.18625,0.60375,0.187192,42,1459,1501
 32,2/1/2011,1,0,2,2,2,0.192174,0.23453,0.829565,0.053213,47,1313,1360
 33,2/2/2011,1,0,2,3,2,0.26,0.254417,0.775417,0.264308,72,1454,1526
 34,2/3/2011,1,0,2,4,1,0.186957,0.177878,0.437826,0.277752,61,1489,1550
 35,2/4/2011,1,0,2,5,2,0.211304,0.228587,0.585217,0.127839,88,1620,1708
 36,2/5/2011,1,0,2,6,2,0.233333,0.243058,0.929167,0.161079,100,905,1005
 37,2/6/2011,1,0,2,0,1,0.285833,0.291671,0.568333,0.1418,354,1269,1623
 38,2/7/2011,1,0,2,1,1,0.271667,0.303658,0.738333,0.0454083,120,1592,1712
 39,2/8/2011,1,0,2,2,1,0.220833,0.198246,0.537917,0.36195,64,1466,1530
 40,2/9/2011,1,0,2,3,2,0.134783,0.144283,0.494783,0.188839,53,1552,1605
 41,2/10/2011,1,0,2,4,1,0.144348,0.149548,0.437391,0.221935,47,1491,1538
 42,2/11/2011,1,0,2,5,1,0.189091,0.213509,0.506364,0.10855,149,1597,1746
 43,2/12/2011,1,0,2,6,1,0.2225,0.232954,0.544167,0.203367,288,1184,1472
 44,2/13/2011,1,0,2,0,1,0.316522,0.324113,0.457391,0.260883,397,1192,1589
 45,2/14/2011,1,0,2,1,1,0.415,0.39835,0.375833,0.417908,208,1705,1913
 46,2/15/2011,1,0,2,2,1,0.266087,0.254274,0.314348,0.291374,140,1675,1815
 47,2/16/2011,1,0,2,3,1,0.318261,0.3162,0.423478,0.251791,218,1897,2115
 48,2/17/2011,1,0,2,4,1,0.435833,0.428658,0.505,0.230104,259,2216,2475
 49,2/18/2011,1,0,2,5,1,0.521667,0.511983,0.516667,0.264925,579,2348,2927
 50,2/19/2011,1,0,2,6,1,0.399167,0.391404,0.187917,0.507463,532,1103,1635
 51,2/20/2011,1,0,2,0,1,0.285217,0.27733,0.407826,0.223235,639,1173,1812
 52,2/21/2011,1,0,2,1,2,0.303333,0.284075,0.605,0.307846,195,912,1107
 53,2/22/2011,1,0,2,2,1,0.182222,0.186033,0.577778,0.195683,74,1376,1450
 54,2/23/2011,1,0,2,3,1,0.221739,0.245717,0.423043,0.094113,139,1778,1917
 55,2/24/2011,1,0,2,4,2,0.295652,0.289191,0.697391,0.250496,100,1707,1807
 56,2/25/2011,1,0,2,5,2,0.364348,0.350461,0.712174,0.346539,120,1341,1461
 57,2/26/2011,1,0,2,6,1,0.2825,0.282192,0.537917,0.186571,424,1545,1969
 58,2/27/2011,1,0,2,0,1,0.343478,0.351109,0.68,0.125248,694,1708,2402
 59,2/28/2011,1,0,2,1,2,0.407273,0.400118,0.876364,0.289686,81,1365,1446
 60,3/1/2011,1,0,3,2,1,0.266667,0.263879,0.535,0.216425,137,1714,1851
 61,3/2/2011,1,0,3,3,1,0.335,0.320071,0.449583,0.307833,231,1903,2134
 62,3/3/2011,1,0,3,4,1,0.198333,0.200133,0.318333,0.225754,123,1562,1685
 63,3/4/2011,1,0,3,5,2,0.261667,0.255679,0.610417,0.203346,214,1730,1944
 64,3/5/2011,1,0,3,6,2,0.384167,0.378779,0.789167,0.251871,640,1437,2077
 65,3/6/2011,1,0,3,0,2,0.376522,0.366252,0.948261,0.343287,114,491,605
 66,3/7/2011,1,0,3,1,1,0.261739,0.238461,0.551304,0.341352,244,1628,1872
 67,3/8/2011,1,0,3,2,1,0.2925,0.3024,0.420833,0.12065,316,1817,2133
 68,3/9/2011,1,0,3,3,2,0.295833,0.286608,0.775417,0.22015,191,1700,1891
 69,3/10/2011,1,0,3,4,3,0.389091,0.385668,0,0.261877,46,577,623
 70,3/11/2011,1,0,3,5,2,0.316522,0.305,0.649565,0.23297,247,1730,1977
 71,3/12/2011,1,0,3,6,1,0.329167,0.32575,0.594583,0.220775,724,1408,2132
 72,3/13/2011,1,0,3,0,1,0.384348,0.380091,0.527391,0.270604,982,1435,2417
 73,3/14/2011,1,0,3,1,1,0.325217,0.332,0.496957,0.136926,359,1687,2046
 74,3/15/2011,1,0,3,2,2,0.317391,0.318178,0.655652,0.184309,289,1767,2056
 75,3/16/2011,1,0,3,3,2,0.365217,0.36693,0.776522,0.203117,321,1871,2192
 76,3/17/2011,1,0,3,4,1,0.415,0.410333,0.602917,0.209579,424,2320,2744
 77,3/18/2011,1,0,3,5,1,0.54,0.527009,0.525217,0.231017,884,2355,3239
 78,3/19/2011,1,0,3,6,1,0.4725,0.466525,0.379167,0.368167,1424,1693,3117
 79,3/20/2011,1,0,3,0,1,0.3325,0.32575,0.47375,0.207721,1047,1424,2471
 80,3/21/2011,2,0,3,1,2,0.430435,0.409735,0.737391,0.288783,401,1676,2077
 81,3/22/2011,2,0,3,2,1,0.441667,0.440642,0.624583,0.22575,460,2243,2703
 82,3/23/2011,2,0,3,3,2,0.346957,0.337939,0.839565,0.234261,203,1918,2121
 83,3/24/2011,2,0,3,4,2,0.285,0.270833,0.805833,0.243787,166,1699,1865
 84,3/25/2011,2,0,3,5,1,0.264167,0.256312,0.495,0.230725,300,1910,2210
 85,3/26/2011,2,0,3,6,1,0.265833,0.257571,0.394167,0.209571,981,1515,2496
 86,3/27/2011,2,0,3,0,2,0.253043,0.250339,0.493913,0.1843,472,1221,1693
 87,3/28/2011,2,0,3,1,1,0.264348,0.257574,0.302174,0.212204,222,1806,2028
 88,3/29/2011,2,0,3,2,1,0.3025,0.292908,0.314167,0.226996,317,2108,2425
 89,3/30/2011,2,0,3,3,2,0.3,0.29735,0.646667,0.172888,168,1368,1536
 90,3/31/2011,2,0,3,4,3,0.268333,0.257575,0.918333,0.217646,179,1506,1685
 91,4/1/2011,2,0,4,5,2,0.3,0.283454,0.68625,0.258708,307,1920,2227
 92,4/2/2011,2,0,4,6,2,0.315,0.315637,0.65375,0.197146,898,1354,2252
 93,4/3/2011,2,0,4,0,1,0.378333,0.378767,0.48,0.182213,1651,1598,3249
 94,4/4/2011,2,0,4,1,1,0.573333,0.542929,0.42625,0.385571,734,2381,3115
 95,4/5/2011,2,0,4,2,2,0.414167,0.39835,0.642083,0.388067,167,1628,1795
 96,4/6/2011,2,0,4,3,1,0.390833,0.387608,0.470833,0.263063,413,2395,2808
 97,4/7/2011,2,0,4,4,1,0.4375,0.433696,0.602917,0.162312,571,2570,3141
 98,4/8/2011,2,0,4,5,2,0.335833,0.324479,0.83625,0.226992,172,1299,1471
 99,4/9/2011,2,0,4,6,2,0.3425,0.341529,0.8775,0.133083,879,1576,2455
 100,4/10/2011,2,0,4,0,2,0.426667,0.426737,0.8575,0.146767,1188,1707,2895
 101,4/11/2011,2,0,4,1,2,0.595652,0.565217,0.716956,0.324474,855,2493,3348
 102,4/12/2011,2,0,4,2,2,0.5025,0.493054,0.739167,0.274879,257,1777,2034
 103,4/13/2011,2,0,4,3,2,0.4125,0.417283,0.819167,0.250617,209,1953,2162
 104,4/14/2011,2,0,4,4,1,0.4675,0.462742,0.540417,0.1107,529,2738,3267
 105,4/15/2011,2,0,4,5,1,0.446667,0.441913,0.67125,0.226375,642,2484,3126
 106,4/16/2011,2,0,4,6,3,0.430833,0.425492,0.888333,0.340808,121,674,795
 107,4/17/2011,2,0,4,0,1,0.456667,0.445696,0.479583,0.303496,1558,2186,3744
 108,4/18/2011,2,0,4,1,1,0.5125,0.503146,0.5425,0.163567,669,2760,3429
 109,4/19/2011,2,0,4,2,2,0.505833,0.489258,0.665833,0.157971,409,2795,3204
 110,4/20/2011,2,0,4,3,1,0.595,0.564392,0.614167,0.241925,613,3331,3944
 111,4/21/2011,2,0,4,4,1,0.459167,0.453892,0.407083,0.325258,745,3444,4189
 112,4/22/2011,2,0,4,5,2,0.336667,0.321954,0.729583,0.219521,177,1506,1683
 113,4/23/2011,2,0,4,6,2,0.46,0.450121,0.887917,0.230725,1462,2574,4036
 114,4/24/2011,2,0,4,0,2,0.581667,0.551763,0.810833,0.192175,1710,2481,4191
 115,4/25/2011,2,0,4,1,1,0.606667,0.5745,0.776667,0.185333,773,3300,4073
 116,4/26/2011,2,0,4,2,1,0.631667,0.594083,0.729167,0.3265,678,3722,4400
 117,4/27/2011,2,0,4,3,2,0.62,0.575142,0.835417,0.3122,547,3325,3872
 118,4/28/2011,2,0,4,4,2,0.6175,0.578929,0.700833,0.320908,569,3489,4058
 119,4/29/2011,2,0,4,5,1,0.51,0.497463,0.457083,0.240063,878,3717,4595
 120,4/30/2011,2,0,4,6,1,0.4725,0.464021,0.503333,0.235075,1965,3347,5312
 121,5/1/2011,2,0,5,0,2,0.451667,0.448204,0.762083,0.106354,1138,2213,3351
 122,5/2/2011,2,0,5,1,2,0.549167,0.532833,0.73,0.183454,847,3554,4401
 123,5/3/2011,2,0,5,2,2,0.616667,0.582079,0.697083,0.342667,603,3848,4451
 124,5/4/2011,2,0,5,3,2,0.414167,0.40465,0.737083,0.328996,255,2378,2633
 125,5/5/2011,2,0,5,4,1,0.459167,0.441917,0.444167,0.295392,614,3819,4433
 126,5/6/2011,2,0,5,5,1,0.479167,0.474117,0.59,0.228246,894,3714,4608
 127,5/7/2011,2,0,5,6,1,0.52,0.512621,0.54125,0.16045,1612,3102,4714
 128,5/8/2011,2,0,5,0,1,0.528333,0.518933,0.631667,0.0746375,1401,2932,4333
 129,5/9/2011,2,0,5,1,1,0.5325,0.525246,0.58875,0.176,664,3698,4362
 130,5/10/2011,2,0,5,2,1,0.5325,0.522721,0.489167,0.115671,694,4109,4803
 131,5/11/2011,2,0,5,3,1,0.5425,0.5284,0.632917,0.120642,550,3632,4182
 132,5/12/2011,2,0,5,4,1,0.535,0.523363,0.7475,0.189667,695,4169,4864
 133,5/13/2011,2,0,5,5,2,0.5125,0.4943,0.863333,0.179725,692,3413,4105
 134,5/14/2011,2,0,5,6,2,0.520833,0.500629,0.9225,0.13495,902,2507,3409
 135,5/15/2011,2,0,5,0,2,0.5625,0.536,0.867083,0.152979,1582,2971,4553
 136,5/16/2011,2,0,5,1,1,0.5775,0.550512,0.787917,0.126871,773,3185,3958
 137,5/17/2011,2,0,5,2,2,0.561667,0.538529,0.837917,0.277354,678,3445,4123
 138,5/18/2011,2,0,5,3,2,0.55,0.527158,0.87,0.201492,536,3319,3855
 139,5/19/2011,2,0,5,4,2,0.530833,0.510742,0.829583,0.108213,735,3840,4575
 140,5/20/2011,2,0,5,5,1,0.536667,0.529042,0.719583,0.125013,909,4008,4917
 141,5/21/2011,2,0,5,6,1,0.6025,0.571975,0.626667,0.12065,2258,3547,5805
 142,5/22/2011,2,0,5,0,1,0.604167,0.5745,0.749583,0.148008,1576,3084,4660
 143,5/23/2011,2,0,5,1,2,0.631667,0.590296,0.81,0.233842,836,3438,4274
 144,5/24/2011,2,0,5,2,2,0.66,0.604813,0.740833,0.207092,659,3833,4492
 145,5/25/2011,2,0,5,3,1,0.660833,0.615542,0.69625,0.154233,740,4238,4978
 146,5/26/2011,2,0,5,4,1,0.708333,0.654688,0.6775,0.199642,758,3919,4677
 147,5/27/2011,2,0,5,5,1,0.681667,0.637008,0.65375,0.240679,871,3808,4679
 148,5/28/2011,2,0,5,6,1,0.655833,0.612379,0.729583,0.230092,2001,2757,4758
 149,5/29/2011,2,0,5,0,1,0.6675,0.61555,0.81875,0.213938,2355,2433,4788
 150,5/30/2011,2,0,5,1,1,0.733333,0.671092,0.685,0.131225,1549,2549,4098
 151,5/31/2011,2,0,5,2,1,0.775,0.725383,0.636667,0.111329,673,3309,3982
 152,6/1/2011,2,0,6,3,2,0.764167,0.720967,0.677083,0.207092,513,3461,3974
 153,6/2/2011,2,0,6,4,1,0.715,0.643942,0.305,0.292287,736,4232,4968
 154,6/3/2011,2,0,6,5,1,0.62,0.587133,0.354167,0.253121,898,4414,5312
 155,6/4/2011,2,0,6,6,1,0.635,0.594696,0.45625,0.123142,1869,3473,5342
 156,6/5/2011,2,0,6,0,2,0.648333,0.616804,0.6525,0.138692,1685,3221,4906
 157,6/6/2011,2,0,6,1,1,0.678333,0.621858,0.6,0.121896,673,3875,4548
 158,6/7/2011,2,0,6,2,1,0.7075,0.65595,0.597917,0.187808,763,4070,4833
 159,6/8/2011,2,0,6,3,1,0.775833,0.727279,0.622083,0.136817,676,3725,4401
 160,6/9/2011,2,0,6,4,2,0.808333,0.757579,0.568333,0.149883,563,3352,3915
 161,6/10/2011,2,0,6,5,1,0.755,0.703292,0.605,0.140554,815,3771,4586
 162,6/11/2011,2,0,6,6,1,0.725,0.678038,0.654583,0.15485,1729,3237,4966
 163,6/12/2011,2,0,6,0,1,0.6925,0.643325,0.747917,0.163567,1467,2993,4460
 164,6/13/2011,2,0,6,1,1,0.635,0.601654,0.494583,0.30535,863,4157,5020
 165,6/14/2011,2,0,6,2,1,0.604167,0.591546,0.507083,0.269283,727,4164,4891
 166,6/15/2011,2,0,6,3,1,0.626667,0.587754,0.471667,0.167912,769,4411,5180
 167,6/16/2011,2,0,6,4,2,0.628333,0.595346,0.688333,0.206471,545,3222,3767
 168,6/17/2011,2,0,6,5,1,0.649167,0.600383,0.735833,0.143029,863,3981,4844
 169,6/18/2011,2,0,6,6,1,0.696667,0.643954,0.670417,0.119408,1807,3312,5119
 170,6/19/2011,2,0,6,0,2,0.699167,0.645846,0.666667,0.102,1639,3105,4744
 171,6/20/2011,2,0,6,1,2,0.635,0.595346,0.74625,0.155475,699,3311,4010
 172,6/21/2011,3,0,6,2,2,0.680833,0.637646,0.770417,0.171025,774,4061,4835
 173,6/22/2011,3,0,6,3,1,0.733333,0.693829,0.7075,0.172262,661,3846,4507
 174,6/23/2011,3,0,6,4,2,0.728333,0.693833,0.703333,0.238804,746,4044,4790
 175,6/24/2011,3,0,6,5,1,0.724167,0.656583,0.573333,0.222025,969,4022,4991
 176,6/25/2011,3,0,6,6,1,0.695,0.643313,0.483333,0.209571,1782,3420,5202
 177,6/26/2011,3,0,6,0,1,0.68,0.637629,0.513333,0.0945333,1920,3385,5305
 178,6/27/2011,3,0,6,1,2,0.6825,0.637004,0.658333,0.107588,854,3854,4708
 179,6/28/2011,3,0,6,2,1,0.744167,0.692558,0.634167,0.144283,732,3916,4648
 180,6/29/2011,3,0,6,3,1,0.728333,0.654688,0.497917,0.261821,848,4377,5225
 181,6/30/2011,3,0,6,4,1,0.696667,0.637008,0.434167,0.185312,1027,4488,5515
 182,7/1/2011,3,0,7,5,1,0.7225,0.652162,0.39625,0.102608,1246,4116,5362
 183,7/2/2011,3,0,7,6,1,0.738333,0.667308,0.444583,0.115062,2204,2915,5119
 184,7/3/2011,3,0,7,0,2,0.716667,0.668575,0.6825,0.228858,2282,2367,4649
 185,7/4/2011,3,0,7,1,2,0.726667,0.665417,0.637917,0.0814792,3065,2978,6043
 186,7/5/2011,3,0,7,2,1,0.746667,0.696338,0.590417,0.126258,1031,3634,4665
 187,7/6/2011,3,0,7,3,1,0.72,0.685633,0.743333,0.149883,784,3845,4629
 188,7/7/2011,3,0,7,4,1,0.75,0.686871,0.65125,0.1592,754,3838,4592
 189,7/8/2011,3,0,7,5,2,0.709167,0.670483,0.757917,0.225129,692,3348,4040
 190,7/9/2011,3,0,7,6,1,0.733333,0.664158,0.609167,0.167912,1988,3348,5336
 191,7/10/2011,3,0,7,0,1,0.7475,0.690025,0.578333,0.183471,1743,3138,4881
 192,7/11/2011,3,0,7,1,1,0.7625,0.729804,0.635833,0.282337,723,3363,4086
 193,7/12/2011,3,0,7,2,1,0.794167,0.739275,0.559167,0.200254,662,3596,4258
 194,7/13/2011,3,0,7,3,1,0.746667,0.689404,0.631667,0.146133,748,3594,4342
 195,7/14/2011,3,0,7,4,1,0.680833,0.635104,0.47625,0.240667,888,4196,5084
 196,7/15/2011,3,0,7,5,1,0.663333,0.624371,0.59125,0.182833,1318,4220,5538
 197,7/16/2011,3,0,7,6,1,0.686667,0.638263,0.585,0.208342,2418,3505,5923
 198,7/17/2011,3,0,7,0,1,0.719167,0.669833,0.604167,0.245033,2006,3296,5302
 199,7/18/2011,3,0,7,1,1,0.746667,0.703925,0.65125,0.215804,841,3617,4458
 200,7/19/2011,3,0,7,2,1,0.776667,0.747479,0.650417,0.1306,752,3789,4541
 201,7/20/2011,3,0,7,3,1,0.768333,0.74685,0.707083,0.113817,644,3688,4332
 202,7/21/2011,3,0,7,4,2,0.815,0.826371,0.69125,0.222021,632,3152,3784
 203,7/22/2011,3,0,7,5,1,0.848333,0.840896,0.580417,0.1331,562,2825,3387
 204,7/23/2011,3,0,7,6,1,0.849167,0.804287,0.5,0.131221,987,2298,3285
 205,7/24/2011,3,0,7,0,1,0.83,0.794829,0.550833,0.169171,1050,2556,3606
 206,7/25/2011,3,0,7,1,1,0.743333,0.720958,0.757083,0.0908083,568,3272,3840
 207,7/26/2011,3,0,7,2,1,0.771667,0.696979,0.540833,0.200258,750,3840,4590
 208,7/27/2011,3,0,7,3,1,0.775,0.690667,0.402917,0.183463,755,3901,4656
 209,7/28/2011,3,0,7,4,1,0.779167,0.7399,0.583333,0.178479,606,3784,4390
 210,7/29/2011,3,0,7,5,1,0.838333,0.785967,0.5425,0.174138,670,3176,3846
 211,7/30/2011,3,0,7,6,1,0.804167,0.728537,0.465833,0.168537,1559,2916,4475
 212,7/31/2011,3,0,7,0,1,0.805833,0.729796,0.480833,0.164813,1524,2778,4302
 213,8/1/2011,3,0,8,1,1,0.771667,0.703292,0.550833,0.156717,729,3537,4266
 214,8/2/2011,3,0,8,2,1,0.783333,0.707071,0.49125,0.20585,801,4044,4845
 215,8/3/2011,3,0,8,3,2,0.731667,0.679937,0.6575,0.135583,467,3107,3574
 216,8/4/2011,3,0,8,4,2,0.71,0.664788,0.7575,0.19715,799,3777,4576
 217,8/5/2011,3,0,8,5,1,0.710833,0.656567,0.630833,0.184696,1023,3843,4866
 218,8/6/2011,3,0,8,6,2,0.716667,0.676154,0.755,0.22825,1521,2773,4294
 219,8/7/2011,3,0,8,0,1,0.7425,0.715292,0.752917,0.201487,1298,2487,3785
 220,8/8/2011,3,0,8,1,1,0.765,0.703283,0.592083,0.192175,846,3480,4326
 221,8/9/2011,3,0,8,2,1,0.775,0.724121,0.570417,0.151121,907,3695,4602
 222,8/10/2011,3,0,8,3,1,0.766667,0.684983,0.424167,0.200258,884,3896,4780
 223,8/11/2011,3,0,8,4,1,0.7175,0.651521,0.42375,0.164796,812,3980,4792
 224,8/12/2011,3,0,8,5,1,0.708333,0.654042,0.415,0.125621,1051,3854,4905
 225,8/13/2011,3,0,8,6,2,0.685833,0.645858,0.729583,0.211454,1504,2646,4150
 226,8/14/2011,3,0,8,0,2,0.676667,0.624388,0.8175,0.222633,1338,2482,3820
 227,8/15/2011,3,0,8,1,1,0.665833,0.616167,0.712083,0.208954,775,3563,4338
 228,8/16/2011,3,0,8,2,1,0.700833,0.645837,0.578333,0.236329,721,4004,4725
 229,8/17/2011,3,0,8,3,1,0.723333,0.666671,0.575417,0.143667,668,4026,4694
 230,8/18/2011,3,0,8,4,1,0.711667,0.662258,0.654583,0.233208,639,3166,3805
 231,8/19/2011,3,0,8,5,2,0.685,0.633221,0.722917,0.139308,797,3356,4153
 232,8/20/2011,3,0,8,6,1,0.6975,0.648996,0.674167,0.104467,1914,3277,5191
 233,8/21/2011,3,0,8,0,1,0.710833,0.675525,0.77,0.248754,1249,2624,3873
 234,8/22/2011,3,0,8,1,1,0.691667,0.638254,0.47,0.27675,833,3925,4758
 235,8/23/2011,3,0,8,2,1,0.640833,0.606067,0.455417,0.146763,1281,4614,5895
 236,8/24/2011,3,0,8,3,1,0.673333,0.630692,0.605,0.253108,949,4181,5130
 237,8/25/2011,3,0,8,4,2,0.684167,0.645854,0.771667,0.210833,435,3107,3542
 238,8/26/2011,3,0,8,5,1,0.7,0.659733,0.76125,0.0839625,768,3893,4661
 239,8/27/2011,3,0,8,6,2,0.68,0.635556,0.85,0.375617,226,889,1115
 240,8/28/2011,3,0,8,0,1,0.707059,0.647959,0.561765,0.304659,1415,2919,4334
 241,8/29/2011,3,0,8,1,1,0.636667,0.607958,0.554583,0.159825,729,3905,4634
 242,8/30/2011,3,0,8,2,1,0.639167,0.594704,0.548333,0.125008,775,4429,5204
 243,8/31/2011,3,0,8,3,1,0.656667,0.611121,0.597917,0.0833333,688,4370,5058
 244,9/1/2011,3,0,9,4,1,0.655,0.614921,0.639167,0.141796,783,4332,5115
 245,9/2/2011,3,0,9,5,2,0.643333,0.604808,0.727083,0.139929,875,3852,4727
 246,9/3/2011,3,0,9,6,1,0.669167,0.633213,0.716667,0.185325,1935,2549,4484
 247,9/4/2011,3,0,9,0,1,0.709167,0.665429,0.742083,0.206467,2521,2419,4940
 248,9/5/2011,3,0,9,1,2,0.673333,0.625646,0.790417,0.212696,1236,2115,3351
 249,9/6/2011,3,0,9,2,3,0.54,0.5152,0.886957,0.343943,204,2506,2710
 250,9/7/2011,3,0,9,3,3,0.599167,0.544229,0.917083,0.0970208,118,1878,1996
 251,9/8/2011,3,0,9,4,3,0.633913,0.555361,0.939565,0.192748,153,1689,1842
 252,9/9/2011,3,0,9,5,2,0.65,0.578946,0.897917,0.124379,417,3127,3544
 253,9/10/2011,3,0,9,6,1,0.66,0.607962,0.75375,0.153608,1750,3595,5345
 254,9/11/2011,3,0,9,0,1,0.653333,0.609229,0.71375,0.115054,1633,3413,5046
 255,9/12/2011,3,0,9,1,1,0.644348,0.60213,0.692174,0.088913,690,4023,4713
 256,9/13/2011,3,0,9,2,1,0.650833,0.603554,0.7125,0.141804,701,4062,4763
 257,9/14/2011,3,0,9,3,1,0.673333,0.6269,0.697083,0.1673,647,4138,4785
 258,9/15/2011,3,0,9,4,2,0.5775,0.553671,0.709167,0.271146,428,3231,3659
 259,9/16/2011,3,0,9,5,2,0.469167,0.461475,0.590417,0.164183,742,4018,4760
 260,9/17/2011,3,0,9,6,2,0.491667,0.478512,0.718333,0.189675,1434,3077,4511
 261,9/18/2011,3,0,9,0,1,0.5075,0.490537,0.695,0.178483,1353,2921,4274
 262,9/19/2011,3,0,9,1,2,0.549167,0.529675,0.69,0.151742,691,3848,4539
 263,9/20/2011,3,0,9,2,2,0.561667,0.532217,0.88125,0.134954,438,3203,3641
 264,9/21/2011,3,0,9,3,2,0.595,0.550533,0.9,0.0964042,539,3813,4352
 265,9/22/2011,3,0,9,4,2,0.628333,0.554963,0.902083,0.128125,555,4240,4795
 266,9/23/2011,4,0,9,5,2,0.609167,0.522125,0.9725,0.0783667,258,2137,2395
 267,9/24/2011,4,0,9,6,2,0.606667,0.564412,0.8625,0.0783833,1776,3647,5423
 268,9/25/2011,4,0,9,0,2,0.634167,0.572637,0.845,0.0503792,1544,3466,5010
 269,9/26/2011,4,0,9,1,2,0.649167,0.589042,0.848333,0.1107,684,3946,4630
 270,9/27/2011,4,0,9,2,2,0.636667,0.574525,0.885417,0.118171,477,3643,4120
 271,9/28/2011,4,0,9,3,2,0.635,0.575158,0.84875,0.148629,480,3427,3907
 272,9/29/2011,4,0,9,4,1,0.616667,0.574512,0.699167,0.172883,653,4186,4839
 273,9/30/2011,4,0,9,5,1,0.564167,0.544829,0.6475,0.206475,830,4372,5202
 274,10/1/2011,4,0,10,6,2,0.41,0.412863,0.75375,0.292296,480,1949,2429
 275,10/2/2011,4,0,10,0,2,0.356667,0.345317,0.791667,0.222013,616,2302,2918
 276,10/3/2011,4,0,10,1,2,0.384167,0.392046,0.760833,0.0833458,330,3240,3570
 277,10/4/2011,4,0,10,2,1,0.484167,0.472858,0.71,0.205854,486,3970,4456
 278,10/5/2011,4,0,10,3,1,0.538333,0.527138,0.647917,0.17725,559,4267,4826
 279,10/6/2011,4,0,10,4,1,0.494167,0.480425,0.620833,0.134954,639,4126,4765
 280,10/7/2011,4,0,10,5,1,0.510833,0.504404,0.684167,0.0223917,949,4036,4985
 281,10/8/2011,4,0,10,6,1,0.521667,0.513242,0.70125,0.0454042,2235,3174,5409
 282,10/9/2011,4,0,10,0,1,0.540833,0.523983,0.7275,0.06345,2397,3114,5511
 283,10/10/2011,4,0,10,1,1,0.570833,0.542925,0.73375,0.0423042,1514,3603,5117
 284,10/11/2011,4,0,10,2,2,0.566667,0.546096,0.80875,0.143042,667,3896,4563
 285,10/12/2011,4,0,10,3,3,0.543333,0.517717,0.90625,0.24815,217,2199,2416
 286,10/13/2011,4,0,10,4,2,0.589167,0.551804,0.896667,0.141787,290,2623,2913
 287,10/14/2011,4,0,10,5,2,0.550833,0.529675,0.71625,0.223883,529,3115,3644
 288,10/15/2011,4,0,10,6,1,0.506667,0.498725,0.483333,0.258083,1899,3318,5217
 289,10/16/2011,4,0,10,0,1,0.511667,0.503154,0.486667,0.281717,1748,3293,5041
 290,10/17/2011,4,0,10,1,1,0.534167,0.510725,0.579583,0.175379,713,3857,4570
 291,10/18/2011,4,0,10,2,2,0.5325,0.522721,0.701667,0.110087,637,4111,4748
 292,10/19/2011,4,0,10,3,3,0.541739,0.513848,0.895217,0.243339,254,2170,2424
 293,10/20/2011,4,0,10,4,1,0.475833,0.466525,0.63625,0.422275,471,3724,4195
 294,10/21/2011,4,0,10,5,1,0.4275,0.423596,0.574167,0.221396,676,3628,4304
 295,10/22/2011,4,0,10,6,1,0.4225,0.425492,0.629167,0.0926667,1499,2809,4308
 296,10/23/2011,4,0,10,0,1,0.421667,0.422333,0.74125,0.0995125,1619,2762,4381
 297,10/24/2011,4,0,10,1,1,0.463333,0.457067,0.772083,0.118792,699,3488,4187
 298,10/25/2011,4,0,10,2,1,0.471667,0.463375,0.622917,0.166658,695,3992,4687
 299,10/26/2011,4,0,10,3,2,0.484167,0.472846,0.720417,0.148642,404,3490,3894
 300,10/27/2011,4,0,10,4,2,0.47,0.457046,0.812917,0.197763,240,2419,2659
 301,10/28/2011,4,0,10,5,2,0.330833,0.318812,0.585833,0.229479,456,3291,3747
 302,10/29/2011,4,0,10,6,3,0.254167,0.227913,0.8825,0.351371,57,570,627
 303,10/30/2011,4,0,10,0,1,0.319167,0.321329,0.62375,0.176617,885,2446,3331
 304,10/31/2011,4,0,10,1,1,0.34,0.356063,0.703333,0.10635,362,3307,3669
 305,11/1/2011,4,0,11,2,1,0.400833,0.397088,0.68375,0.135571,410,3658,4068
 306,11/2/2011,4,0,11,3,1,0.3775,0.390133,0.71875,0.0820917,370,3816,4186
 307,11/3/2011,4,0,11,4,1,0.408333,0.405921,0.702083,0.136817,318,3656,3974
 308,11/4/2011,4,0,11,5,2,0.403333,0.403392,0.6225,0.271779,470,3576,4046
 309,11/5/2011,4,0,11,6,1,0.326667,0.323854,0.519167,0.189062,1156,2770,3926
 310,11/6/2011,4,0,11,0,1,0.348333,0.362358,0.734583,0.0920542,952,2697,3649
 311,11/7/2011,4,0,11,1,1,0.395,0.400871,0.75875,0.057225,373,3662,4035
 312,11/8/2011,4,0,11,2,1,0.408333,0.412246,0.721667,0.0690375,376,3829,4205
 313,11/9/2011,4,0,11,3,1,0.4,0.409079,0.758333,0.0621958,305,3804,4109
 314,11/10/2011,4,0,11,4,2,0.38,0.373721,0.813333,0.189067,190,2743,2933
 315,11/11/2011,4,0,11,5,1,0.324167,0.306817,0.44625,0.314675,440,2928,3368
 316,11/12/2011,4,0,11,6,1,0.356667,0.357942,0.552917,0.212062,1275,2792,4067
 317,11/13/2011,4,0,11,0,1,0.440833,0.43055,0.458333,0.281721,1004,2713,3717
 318,11/14/2011,4,0,11,1,1,0.53,0.524612,0.587083,0.306596,595,3891,4486
 319,11/15/2011,4,0,11,2,2,0.53,0.507579,0.68875,0.199633,449,3746,4195
 320,11/16/2011,4,0,11,3,3,0.456667,0.451988,0.93,0.136829,145,1672,1817
 321,11/17/2011,4,0,11,4,2,0.341667,0.323221,0.575833,0.305362,139,2914,3053
 322,11/18/2011,4,0,11,5,1,0.274167,0.272721,0.41,0.168533,245,3147,3392
 323,11/19/2011,4,0,11,6,1,0.329167,0.324483,0.502083,0.224496,943,2720,3663
 324,11/20/2011,4,0,11,0,2,0.463333,0.457058,0.684583,0.18595,787,2733,3520
 325,11/21/2011,4,0,11,1,3,0.4475,0.445062,0.91,0.138054,220,2545,2765
 326,11/22/2011,4,0,11,2,3,0.416667,0.421696,0.9625,0.118792,69,1538,1607
 327,11/23/2011,4,0,11,3,2,0.440833,0.430537,0.757917,0.335825,112,2454,2566
 328,11/24/2011,4,0,11,4,1,0.373333,0.372471,0.549167,0.167304,560,935,1495
 329,11/25/2011,4,0,11,5,1,0.375,0.380671,0.64375,0.0988958,1095,1697,2792
 330,11/26/2011,4,0,11,6,1,0.375833,0.385087,0.681667,0.0684208,1249,1819,3068
 331,11/27/2011,4,0,11,0,1,0.459167,0.4558,0.698333,0.208954,810,2261,3071
 332,11/28/2011,4,0,11,1,1,0.503478,0.490122,0.743043,0.142122,253,3614,3867
 333,11/29/2011,4,0,11,2,2,0.458333,0.451375,0.830833,0.258092,96,2818,2914
 334,11/30/2011,4,0,11,3,1,0.325,0.311221,0.613333,0.271158,188,3425,3613
 335,12/1/2011,4,0,12,4,1,0.3125,0.305554,0.524583,0.220158,182,3545,3727
 336,12/2/2011,4,0,12,5,1,0.314167,0.331433,0.625833,0.100754,268,3672,3940
 337,12/3/2011,4,0,12,6,1,0.299167,0.310604,0.612917,0.0957833,706,2908,3614
 338,12/4/2011,4,0,12,0,1,0.330833,0.3491,0.775833,0.0839583,634,2851,3485
 339,12/5/2011,4,0,12,1,2,0.385833,0.393925,0.827083,0.0622083,233,3578,3811
 340,12/6/2011,4,0,12,2,3,0.4625,0.4564,0.949583,0.232583,126,2468,2594
 341,12/7/2011,4,0,12,3,3,0.41,0.400246,0.970417,0.266175,50,655,705
 342,12/8/2011,4,0,12,4,1,0.265833,0.256938,0.58,0.240058,150,3172,3322
 343,12/9/2011,4,0,12,5,1,0.290833,0.317542,0.695833,0.0827167,261,3359,3620
 344,12/10/2011,4,0,12,6,1,0.275,0.266412,0.5075,0.233221,502,2688,3190
 345,12/11/2011,4,0,12,0,1,0.220833,0.253154,0.49,0.0665417,377,2366,2743
 346,12/12/2011,4,0,12,1,1,0.238333,0.270196,0.670833,0.06345,143,3167,3310
 347,12/13/2011,4,0,12,2,1,0.2825,0.301138,0.59,0.14055,155,3368,3523
 348,12/14/2011,4,0,12,3,2,0.3175,0.338362,0.66375,0.0609583,178,3562,3740
 349,12/15/2011,4,0,12,4,2,0.4225,0.412237,0.634167,0.268042,181,3528,3709
 350,12/16/2011,4,0,12,5,2,0.375,0.359825,0.500417,0.260575,178,3399,3577
 351,12/17/2011,4,0,12,6,2,0.258333,0.249371,0.560833,0.243167,275,2464,2739
 352,12/18/2011,4,0,12,0,1,0.238333,0.245579,0.58625,0.169779,220,2211,2431
 353,12/19/2011,4,0,12,1,1,0.276667,0.280933,0.6375,0.172896,260,3143,3403
 354,12/20/2011,4,0,12,2,2,0.385833,0.396454,0.595417,0.0615708,216,3534,3750
 355,12/21/2011,1,0,12,3,2,0.428333,0.428017,0.858333,0.2214,107,2553,2660
 356,12/22/2011,1,0,12,4,2,0.423333,0.426121,0.7575,0.047275,227,2841,3068
 357,12/23/2011,1,0,12,5,1,0.373333,0.377513,0.68625,0.274246,163,2046,2209
 358,12/24/2011,1,0,12,6,1,0.3025,0.299242,0.5425,0.190304,155,856,1011
 359,12/25/2011,1,0,12,0,1,0.274783,0.279961,0.681304,0.155091,303,451,754
 360,12/26/2011,1,0,12,1,1,0.321739,0.315535,0.506957,0.239465,430,887,1317
 361,12/27/2011,1,0,12,2,2,0.325,0.327633,0.7625,0.18845,103,1059,1162
 362,12/28/2011,1,0,12,3,1,0.29913,0.279974,0.503913,0.293961,255,2047,2302
 363,12/29/2011,1,0,12,4,1,0.248333,0.263892,0.574167,0.119412,254,2169,2423
 364,12/30/2011,1,0,12,5,1,0.311667,0.318812,0.636667,0.134337,491,2508,2999
 365,12/31/2011,1,0,12,6,1,0.41,0.414121,0.615833,0.220154,665,1820,2485
 366,1/1/2012,1,1,1,0,1,0.37,0.375621,0.6925,0.192167,686,1608,2294
 367,1/2/2012,1,1,1,1,1,0.273043,0.252304,0.381304,0.329665,244,1707,1951
 368,1/3/2012,1,1,1,2,1,0.15,0.126275,0.44125,0.365671,89,2147,2236
 369,1/4/2012,1,1,1,3,2,0.1075,0.119337,0.414583,0.1847,95,2273,2368
 370,1/5/2012,1,1,1,4,1,0.265833,0.278412,0.524167,0.129987,140,3132,3272
 371,1/6/2012,1,1,1,5,1,0.334167,0.340267,0.542083,0.167908,307,3791,4098
 372,1/7/2012,1,1,1,6,1,0.393333,0.390779,0.531667,0.174758,1070,3451,4521
 373,1/8/2012,1,1,1,0,1,0.3375,0.340258,0.465,0.191542,599,2826,3425
 374,1/9/2012,1,1,1,1,2,0.224167,0.247479,0.701667,0.0989,106,2270,2376
 375,1/10/2012,1,1,1,2,1,0.308696,0.318826,0.646522,0.187552,173,3425,3598
 376,1/11/2012,1,1,1,3,2,0.274167,0.282821,0.8475,0.131221,92,2085,2177
 377,1/12/2012,1,1,1,4,2,0.3825,0.381938,0.802917,0.180967,269,3828,4097
 378,1/13/2012,1,1,1,5,1,0.274167,0.249362,0.5075,0.378108,174,3040,3214
 379,1/14/2012,1,1,1,6,1,0.18,0.183087,0.4575,0.187183,333,2160,2493
 380,1/15/2012,1,1,1,0,1,0.166667,0.161625,0.419167,0.251258,284,2027,2311
 381,1/16/2012,1,1,1,1,1,0.19,0.190663,0.5225,0.231358,217,2081,2298
 382,1/17/2012,1,1,1,2,2,0.373043,0.364278,0.716087,0.34913,127,2808,2935
 383,1/18/2012,1,1,1,3,1,0.303333,0.275254,0.443333,0.415429,109,3267,3376
 384,1/19/2012,1,1,1,4,1,0.19,0.190038,0.4975,0.220158,130,3162,3292
 385,1/20/2012,1,1,1,5,2,0.2175,0.220958,0.45,0.20275,115,3048,3163
 386,1/21/2012,1,1,1,6,2,0.173333,0.174875,0.83125,0.222642,67,1234,1301
 387,1/22/2012,1,1,1,0,2,0.1625,0.16225,0.79625,0.199638,196,1781,1977
 388,1/23/2012,1,1,1,1,2,0.218333,0.243058,0.91125,0.110708,145,2287,2432
 389,1/24/2012,1,1,1,2,1,0.3425,0.349108,0.835833,0.123767,439,3900,4339
 390,1/25/2012,1,1,1,3,1,0.294167,0.294821,0.64375,0.161071,467,3803,4270
 391,1/26/2012,1,1,1,4,2,0.341667,0.35605,0.769583,0.0733958,244,3831,4075
 392,1/27/2012,1,1,1,5,2,0.425,0.415383,0.74125,0.342667,269,3187,3456
 393,1/28/2012,1,1,1,6,1,0.315833,0.326379,0.543333,0.210829,775,3248,4023
 394,1/29/2012,1,1,1,0,1,0.2825,0.272721,0.31125,0.24005,558,2685,3243
 395,1/30/2012,1,1,1,1,1,0.269167,0.262625,0.400833,0.215792,126,3498,3624
 396,1/31/2012,1,1,1,2,1,0.39,0.381317,0.416667,0.261817,324,4185,4509
 397,2/1/2012,1,1,2,3,1,0.469167,0.466538,0.507917,0.189067,304,4275,4579
 398,2/2/2012,1,1,2,4,2,0.399167,0.398971,0.672917,0.187187,190,3571,3761
 399,2/3/2012,1,1,2,5,1,0.313333,0.309346,0.526667,0.178496,310,3841,4151
 400,2/4/2012,1,1,2,6,2,0.264167,0.272725,0.779583,0.121896,384,2448,2832
 401,2/5/2012,1,1,2,0,2,0.265833,0.264521,0.687917,0.175996,318,2629,2947
 402,2/6/2012,1,1,2,1,1,0.282609,0.296426,0.622174,0.1538,206,3578,3784
 403,2/7/2012,1,1,2,2,1,0.354167,0.361104,0.49625,0.147379,199,4176,4375
 404,2/8/2012,1,1,2,3,2,0.256667,0.266421,0.722917,0.133721,109,2693,2802
 405,2/9/2012,1,1,2,4,1,0.265,0.261988,0.562083,0.194037,163,3667,3830
 406,2/10/2012,1,1,2,5,2,0.280833,0.293558,0.54,0.116929,227,3604,3831
 407,2/11/2012,1,1,2,6,3,0.224167,0.210867,0.73125,0.289796,192,1977,2169
 408,2/12/2012,1,1,2,0,1,0.1275,0.101658,0.464583,0.409212,73,1456,1529
 409,2/13/2012,1,1,2,1,1,0.2225,0.227913,0.41125,0.167283,94,3328,3422
 410,2/14/2012,1,1,2,2,2,0.319167,0.333946,0.50875,0.141179,135,3787,3922
 411,2/15/2012,1,1,2,3,1,0.348333,0.351629,0.53125,0.1816,141,4028,4169
 412,2/16/2012,1,1,2,4,2,0.316667,0.330162,0.752917,0.091425,74,2931,3005
 413,2/17/2012,1,1,2,5,1,0.343333,0.351629,0.634583,0.205846,349,3805,4154
 414,2/18/2012,1,1,2,6,1,0.346667,0.355425,0.534583,0.190929,1435,2883,4318
 415,2/19/2012,1,1,2,0,2,0.28,0.265788,0.515833,0.253112,618,2071,2689
 416,2/20/2012,1,1,2,1,1,0.28,0.273391,0.507826,0.229083,502,2627,3129
 417,2/21/2012,1,1,2,2,1,0.287826,0.295113,0.594348,0.205717,163,3614,3777
 418,2/22/2012,1,1,2,3,1,0.395833,0.392667,0.567917,0.234471,394,4379,4773
 419,2/23/2012,1,1,2,4,1,0.454167,0.444446,0.554583,0.190913,516,4546,5062
 420,2/24/2012,1,1,2,5,2,0.4075,0.410971,0.7375,0.237567,246,3241,3487
 421,2/25/2012,1,1,2,6,1,0.290833,0.255675,0.395833,0.421642,317,2415,2732
 422,2/26/2012,1,1,2,0,1,0.279167,0.268308,0.41,0.205229,515,2874,3389
 423,2/27/2012,1,1,2,1,1,0.366667,0.357954,0.490833,0.268033,253,4069,4322
 424,2/28/2012,1,1,2,2,1,0.359167,0.353525,0.395833,0.193417,229,4134,4363
 425,2/29/2012,1,1,2,3,2,0.344348,0.34847,0.804783,0.179117,65,1769,1834
 426,3/1/2012,1,1,3,4,1,0.485833,0.475371,0.615417,0.226987,325,4665,4990
 427,3/2/2012,1,1,3,5,2,0.353333,0.359842,0.657083,0.144904,246,2948,3194
 428,3/3/2012,1,1,3,6,2,0.414167,0.413492,0.62125,0.161079,956,3110,4066
 429,3/4/2012,1,1,3,0,1,0.325833,0.303021,0.403333,0.334571,710,2713,3423
 430,3/5/2012,1,1,3,1,1,0.243333,0.241171,0.50625,0.228858,203,3130,3333
 431,3/6/2012,1,1,3,2,1,0.258333,0.255042,0.456667,0.200875,221,3735,3956
 432,3/7/2012,1,1,3,3,1,0.404167,0.3851,0.513333,0.345779,432,4484,4916
 433,3/8/2012,1,1,3,4,1,0.5275,0.524604,0.5675,0.441563,486,4896,5382
 434,3/9/2012,1,1,3,5,2,0.410833,0.397083,0.407083,0.4148,447,4122,4569
 435,3/10/2012,1,1,3,6,1,0.2875,0.277767,0.350417,0.22575,968,3150,4118
 436,3/11/2012,1,1,3,0,1,0.361739,0.35967,0.476957,0.222587,1658,3253,4911
 437,3/12/2012,1,1,3,1,1,0.466667,0.459592,0.489167,0.207713,838,4460,5298
 438,3/13/2012,1,1,3,2,1,0.565,0.542929,0.6175,0.23695,762,5085,5847
 439,3/14/2012,1,1,3,3,1,0.5725,0.548617,0.507083,0.115062,997,5315,6312
 440,3/15/2012,1,1,3,4,1,0.5575,0.532825,0.579583,0.149883,1005,5187,6192
 441,3/16/2012,1,1,3,5,2,0.435833,0.436229,0.842083,0.113192,548,3830,4378
 442,3/17/2012,1,1,3,6,2,0.514167,0.505046,0.755833,0.110704,3155,4681,7836
 443,3/18/2012,1,1,3,0,2,0.4725,0.464,0.81,0.126883,2207,3685,5892
 444,3/19/2012,1,1,3,1,1,0.545,0.532821,0.72875,0.162317,982,5171,6153
 445,3/20/2012,1,1,3,2,1,0.560833,0.538533,0.807917,0.121271,1051,5042,6093
 446,3/21/2012,2,1,3,3,2,0.531667,0.513258,0.82125,0.0895583,1122,5108,6230
 447,3/22/2012,2,1,3,4,1,0.554167,0.531567,0.83125,0.117562,1334,5537,6871
 448,3/23/2012,2,1,3,5,2,0.601667,0.570067,0.694167,0.1163,2469,5893,8362
 449,3/24/2012,2,1,3,6,2,0.5025,0.486733,0.885417,0.192783,1033,2339,3372
 450,3/25/2012,2,1,3,0,2,0.4375,0.437488,0.880833,0.220775,1532,3464,4996
 451,3/26/2012,2,1,3,1,1,0.445833,0.43875,0.477917,0.386821,795,4763,5558
 452,3/27/2012,2,1,3,2,1,0.323333,0.315654,0.29,0.187192,531,4571,5102
 453,3/28/2012,2,1,3,3,1,0.484167,0.47095,0.48125,0.291671,674,5024,5698
 454,3/29/2012,2,1,3,4,1,0.494167,0.482304,0.439167,0.31965,834,5299,6133
 455,3/30/2012,2,1,3,5,2,0.37,0.375621,0.580833,0.138067,796,4663,5459
 456,3/31/2012,2,1,3,6,2,0.424167,0.421708,0.738333,0.250617,2301,3934,6235
 457,4/1/2012,2,1,4,0,2,0.425833,0.417287,0.67625,0.172267,2347,3694,6041
 458,4/2/2012,2,1,4,1,1,0.433913,0.427513,0.504348,0.312139,1208,4728,5936
 459,4/3/2012,2,1,4,2,1,0.466667,0.461483,0.396667,0.100133,1348,5424,6772
 460,4/4/2012,2,1,4,3,1,0.541667,0.53345,0.469583,0.180975,1058,5378,6436
 461,4/5/2012,2,1,4,4,1,0.435,0.431163,0.374167,0.219529,1192,5265,6457
 462,4/6/2012,2,1,4,5,1,0.403333,0.390767,0.377083,0.300388,1807,4653,6460
 463,4/7/2012,2,1,4,6,1,0.4375,0.426129,0.254167,0.274871,3252,3605,6857
 464,4/8/2012,2,1,4,0,1,0.5,0.492425,0.275833,0.232596,2230,2939,5169
 465,4/9/2012,2,1,4,1,1,0.489167,0.476638,0.3175,0.358196,905,4680,5585
 466,4/10/2012,2,1,4,2,1,0.446667,0.436233,0.435,0.249375,819,5099,5918
 467,4/11/2012,2,1,4,3,1,0.348696,0.337274,0.469565,0.295274,482,4380,4862
 468,4/12/2012,2,1,4,4,1,0.3975,0.387604,0.46625,0.290429,663,4746,5409
 469,4/13/2012,2,1,4,5,1,0.4425,0.431808,0.408333,0.155471,1252,5146,6398
 470,4/14/2012,2,1,4,6,1,0.495,0.487996,0.502917,0.190917,2795,4665,7460
 471,4/15/2012,2,1,4,0,1,0.606667,0.573875,0.507917,0.225129,2846,4286,7132
 472,4/16/2012,2,1,4,1,1,0.664167,0.614925,0.561667,0.284829,1198,5172,6370
 473,4/17/2012,2,1,4,2,1,0.608333,0.598487,0.390417,0.273629,989,5702,6691
 474,4/18/2012,2,1,4,3,2,0.463333,0.457038,0.569167,0.167912,347,4020,4367
 475,4/19/2012,2,1,4,4,1,0.498333,0.493046,0.6125,0.0659292,846,5719,6565
 476,4/20/2012,2,1,4,5,1,0.526667,0.515775,0.694583,0.149871,1340,5950,7290
 477,4/21/2012,2,1,4,6,1,0.57,0.542921,0.682917,0.283587,2541,4083,6624
 478,4/22/2012,2,1,4,0,3,0.396667,0.389504,0.835417,0.344546,120,907,1027
 479,4/23/2012,2,1,4,1,2,0.321667,0.301125,0.766667,0.303496,195,3019,3214
 480,4/24/2012,2,1,4,2,1,0.413333,0.405283,0.454167,0.249383,518,5115,5633
 481,4/25/2012,2,1,4,3,1,0.476667,0.470317,0.427917,0.118792,655,5541,6196
 482,4/26/2012,2,1,4,4,2,0.498333,0.483583,0.756667,0.176625,475,4551,5026
 483,4/27/2012,2,1,4,5,1,0.4575,0.452637,0.400833,0.347633,1014,5219,6233
 484,4/28/2012,2,1,4,6,2,0.376667,0.377504,0.489583,0.129975,1120,3100,4220
 485,4/29/2012,2,1,4,0,1,0.458333,0.450121,0.587083,0.116908,2229,4075,6304
 486,4/30/2012,2,1,4,1,2,0.464167,0.457696,0.57,0.171638,665,4907,5572
 487,5/1/2012,2,1,5,2,2,0.613333,0.577021,0.659583,0.156096,653,5087,5740
 488,5/2/2012,2,1,5,3,1,0.564167,0.537896,0.797083,0.138058,667,5502,6169
 489,5/3/2012,2,1,5,4,2,0.56,0.537242,0.768333,0.133696,764,5657,6421
 490,5/4/2012,2,1,5,5,1,0.6275,0.590917,0.735417,0.162938,1069,5227,6296
 491,5/5/2012,2,1,5,6,2,0.621667,0.584608,0.756667,0.152992,2496,4387,6883
 492,5/6/2012,2,1,5,0,2,0.5625,0.546737,0.74,0.149879,2135,4224,6359
 493,5/7/2012,2,1,5,1,2,0.5375,0.527142,0.664167,0.230721,1008,5265,6273
 494,5/8/2012,2,1,5,2,2,0.581667,0.557471,0.685833,0.296029,738,4990,5728
 495,5/9/2012,2,1,5,3,2,0.575,0.553025,0.744167,0.216412,620,4097,4717
 496,5/10/2012,2,1,5,4,1,0.505833,0.491783,0.552083,0.314063,1026,5546,6572
 497,5/11/2012,2,1,5,5,1,0.533333,0.520833,0.360417,0.236937,1319,5711,7030
 498,5/12/2012,2,1,5,6,1,0.564167,0.544817,0.480417,0.123133,2622,4807,7429
 499,5/13/2012,2,1,5,0,1,0.6125,0.585238,0.57625,0.225117,2172,3946,6118
 500,5/14/2012,2,1,5,1,2,0.573333,0.5499,0.789583,0.212692,342,2501,2843
 501,5/15/2012,2,1,5,2,2,0.611667,0.576404,0.794583,0.147392,625,4490,5115
 502,5/16/2012,2,1,5,3,1,0.636667,0.595975,0.697917,0.122512,991,6433,7424
 503,5/17/2012,2,1,5,4,1,0.593333,0.572613,0.52,0.229475,1242,6142,7384
 504,5/18/2012,2,1,5,5,1,0.564167,0.551121,0.523333,0.136817,1521,6118,7639
 505,5/19/2012,2,1,5,6,1,0.6,0.566908,0.45625,0.083975,3410,4884,8294
 506,5/20/2012,2,1,5,0,1,0.620833,0.583967,0.530417,0.254367,2704,4425,7129
 507,5/21/2012,2,1,5,1,2,0.598333,0.565667,0.81125,0.233204,630,3729,4359
 508,5/22/2012,2,1,5,2,2,0.615,0.580825,0.765833,0.118167,819,5254,6073
 509,5/23/2012,2,1,5,3,2,0.621667,0.584612,0.774583,0.102,766,4494,5260
 510,5/24/2012,2,1,5,4,1,0.655,0.6067,0.716667,0.172896,1059,5711,6770
 511,5/25/2012,2,1,5,5,1,0.68,0.627529,0.747083,0.14055,1417,5317,6734
 512,5/26/2012,2,1,5,6,1,0.6925,0.642696,0.7325,0.198992,2855,3681,6536
 513,5/27/2012,2,1,5,0,1,0.69,0.641425,0.697083,0.215171,3283,3308,6591
 514,5/28/2012,2,1,5,1,1,0.7125,0.6793,0.67625,0.196521,2557,3486,6043
 515,5/29/2012,2,1,5,2,1,0.7225,0.672992,0.684583,0.2954,880,4863,5743
 516,5/30/2012,2,1,5,3,2,0.656667,0.611129,0.67,0.134329,745,6110,6855
 517,5/31/2012,2,1,5,4,1,0.68,0.631329,0.492917,0.195279,1100,6238,7338
 518,6/1/2012,2,1,6,5,2,0.654167,0.607962,0.755417,0.237563,533,3594,4127
 519,6/2/2012,2,1,6,6,1,0.583333,0.566288,0.549167,0.186562,2795,5325,8120
 520,6/3/2012,2,1,6,0,1,0.6025,0.575133,0.493333,0.184087,2494,5147,7641
 521,6/4/2012,2,1,6,1,1,0.5975,0.578283,0.487083,0.284833,1071,5927,6998
 522,6/5/2012,2,1,6,2,2,0.540833,0.525892,0.613333,0.209575,968,6033,7001
 523,6/6/2012,2,1,6,3,1,0.554167,0.542292,0.61125,0.077125,1027,6028,7055
 524,6/7/2012,2,1,6,4,1,0.6025,0.569442,0.567083,0.15735,1038,6456,7494
 525,6/8/2012,2,1,6,5,1,0.649167,0.597862,0.467917,0.175383,1488,6248,7736
 526,6/9/2012,2,1,6,6,1,0.710833,0.648367,0.437083,0.144287,2708,4790,7498
 527,6/10/2012,2,1,6,0,1,0.726667,0.663517,0.538333,0.133721,2224,4374,6598
 528,6/11/2012,2,1,6,1,2,0.720833,0.659721,0.587917,0.207713,1017,5647,6664
 529,6/12/2012,2,1,6,2,2,0.653333,0.597875,0.833333,0.214546,477,4495,4972
 530,6/13/2012,2,1,6,3,1,0.655833,0.611117,0.582083,0.343279,1173,6248,7421
 531,6/14/2012,2,1,6,4,1,0.648333,0.624383,0.569583,0.253733,1180,6183,7363
 532,6/15/2012,2,1,6,5,1,0.639167,0.599754,0.589583,0.176617,1563,6102,7665
 533,6/16/2012,2,1,6,6,1,0.631667,0.594708,0.504167,0.166667,2963,4739,7702
 534,6/17/2012,2,1,6,0,1,0.5925,0.571975,0.59875,0.144904,2634,4344,6978
 535,6/18/2012,2,1,6,1,2,0.568333,0.544842,0.777917,0.174746,653,4446,5099
 536,6/19/2012,2,1,6,2,1,0.688333,0.654692,0.69,0.148017,968,5857,6825
 537,6/20/2012,2,1,6,3,1,0.7825,0.720975,0.592083,0.113812,872,5339,6211
 538,6/21/2012,3,1,6,4,1,0.805833,0.752542,0.567917,0.118787,778,5127,5905
 539,6/22/2012,3,1,6,5,1,0.7775,0.724121,0.57375,0.182842,964,4859,5823
 540,6/23/2012,3,1,6,6,1,0.731667,0.652792,0.534583,0.179721,2657,4801,7458
 541,6/24/2012,3,1,6,0,1,0.743333,0.674254,0.479167,0.145525,2551,4340,6891
 542,6/25/2012,3,1,6,1,1,0.715833,0.654042,0.504167,0.300383,1139,5640,6779
 543,6/26/2012,3,1,6,2,1,0.630833,0.594704,0.373333,0.347642,1077,6365,7442
 544,6/27/2012,3,1,6,3,1,0.6975,0.640792,0.36,0.271775,1077,6258,7335
 545,6/28/2012,3,1,6,4,1,0.749167,0.675512,0.4225,0.17165,921,5958,6879
 546,6/29/2012,3,1,6,5,1,0.834167,0.786613,0.48875,0.165417,829,4634,5463
 547,6/30/2012,3,1,6,6,1,0.765,0.687508,0.60125,0.161071,1455,4232,5687
 548,7/1/2012,3,1,7,0,1,0.815833,0.750629,0.51875,0.168529,1421,4110,5531
 549,7/2/2012,3,1,7,1,1,0.781667,0.702038,0.447083,0.195267,904,5323,6227
 550,7/3/2012,3,1,7,2,1,0.780833,0.70265,0.492083,0.126237,1052,5608,6660
 551,7/4/2012,3,1,7,3,1,0.789167,0.732337,0.53875,0.13495,2562,4841,7403
 552,7/5/2012,3,1,7,4,1,0.8275,0.761367,0.457917,0.194029,1405,4836,6241
 553,7/6/2012,3,1,7,5,1,0.828333,0.752533,0.450833,0.146142,1366,4841,6207
 554,7/7/2012,3,1,7,6,1,0.861667,0.804913,0.492083,0.163554,1448,3392,4840
 555,7/8/2012,3,1,7,0,1,0.8225,0.790396,0.57375,0.125629,1203,3469,4672
 556,7/9/2012,3,1,7,1,2,0.710833,0.654054,0.683333,0.180975,998,5571,6569
 557,7/10/2012,3,1,7,2,2,0.720833,0.664796,0.6675,0.151737,954,5336,6290
 558,7/11/2012,3,1,7,3,1,0.716667,0.650271,0.633333,0.151733,975,6289,7264
 559,7/12/2012,3,1,7,4,1,0.715833,0.654683,0.529583,0.146775,1032,6414,7446
 560,7/13/2012,3,1,7,5,2,0.731667,0.667933,0.485833,0.08085,1511,5988,7499
 561,7/14/2012,3,1,7,6,2,0.703333,0.666042,0.699167,0.143679,2355,4614,6969
 562,7/15/2012,3,1,7,0,1,0.745833,0.705196,0.717917,0.166667,1920,4111,6031
 563,7/16/2012,3,1,7,1,1,0.763333,0.724125,0.645,0.164187,1088,5742,6830
 564,7/17/2012,3,1,7,2,1,0.818333,0.755683,0.505833,0.114429,921,5865,6786
 565,7/18/2012,3,1,7,3,1,0.793333,0.745583,0.577083,0.137442,799,4914,5713
 566,7/19/2012,3,1,7,4,1,0.77,0.714642,0.600417,0.165429,888,5703,6591
 567,7/20/2012,3,1,7,5,2,0.665833,0.613025,0.844167,0.208967,747,5123,5870
 568,7/21/2012,3,1,7,6,3,0.595833,0.549912,0.865417,0.2133,1264,3195,4459
 569,7/22/2012,3,1,7,0,2,0.6675,0.623125,0.7625,0.0939208,2544,4866,7410
 570,7/23/2012,3,1,7,1,1,0.741667,0.690017,0.694167,0.138683,1135,5831,6966
 571,7/24/2012,3,1,7,2,1,0.750833,0.70645,0.655,0.211454,1140,6452,7592
 572,7/25/2012,3,1,7,3,1,0.724167,0.654054,0.45,0.1648,1383,6790,8173
 573,7/26/2012,3,1,7,4,1,0.776667,0.739263,0.596667,0.284813,1036,5825,6861
 574,7/27/2012,3,1,7,5,1,0.781667,0.734217,0.594583,0.152992,1259,5645,6904
 575,7/28/2012,3,1,7,6,1,0.755833,0.697604,0.613333,0.15735,2234,4451,6685
 576,7/29/2012,3,1,7,0,1,0.721667,0.667933,0.62375,0.170396,2153,4444,6597
 577,7/30/2012,3,1,7,1,1,0.730833,0.684987,0.66875,0.153617,1040,6065,7105
 578,7/31/2012,3,1,7,2,1,0.713333,0.662896,0.704167,0.165425,968,6248,7216
 579,8/1/2012,3,1,8,3,1,0.7175,0.667308,0.6775,0.141179,1074,6506,7580
 580,8/2/2012,3,1,8,4,1,0.7525,0.707088,0.659583,0.129354,983,6278,7261
 581,8/3/2012,3,1,8,5,2,0.765833,0.722867,0.6425,0.215792,1328,5847,7175
 582,8/4/2012,3,1,8,6,1,0.793333,0.751267,0.613333,0.257458,2345,4479,6824
 583,8/5/2012,3,1,8,0,1,0.769167,0.731079,0.6525,0.290421,1707,3757,5464
 584,8/6/2012,3,1,8,1,2,0.7525,0.710246,0.654167,0.129354,1233,5780,7013
 585,8/7/2012,3,1,8,2,2,0.735833,0.697621,0.70375,0.116908,1278,5995,7273
 586,8/8/2012,3,1,8,3,2,0.75,0.707717,0.672917,0.1107,1263,6271,7534
 587,8/9/2012,3,1,8,4,1,0.755833,0.699508,0.620417,0.1561,1196,6090,7286
 588,8/10/2012,3,1,8,5,2,0.715833,0.667942,0.715833,0.238813,1065,4721,5786
 589,8/11/2012,3,1,8,6,2,0.6925,0.638267,0.732917,0.206479,2247,4052,6299
 590,8/12/2012,3,1,8,0,1,0.700833,0.644579,0.530417,0.122512,2182,4362,6544
 591,8/13/2012,3,1,8,1,1,0.720833,0.662254,0.545417,0.136212,1207,5676,6883
 592,8/14/2012,3,1,8,2,1,0.726667,0.676779,0.686667,0.169158,1128,5656,6784
 593,8/15/2012,3,1,8,3,1,0.706667,0.654037,0.619583,0.169771,1198,6149,7347
 594,8/16/2012,3,1,8,4,1,0.719167,0.654688,0.519167,0.141796,1338,6267,7605
 595,8/17/2012,3,1,8,5,1,0.723333,0.2424,0.570833,0.231354,1483,5665,7148
 596,8/18/2012,3,1,8,6,1,0.678333,0.618071,0.603333,0.177867,2827,5038,7865
 597,8/19/2012,3,1,8,0,2,0.635833,0.603554,0.711667,0.08645,1208,3341,4549
 598,8/20/2012,3,1,8,1,2,0.635833,0.595967,0.734167,0.129979,1026,5504,6530
 599,8/21/2012,3,1,8,2,1,0.649167,0.601025,0.67375,0.0727708,1081,5925,7006
 600,8/22/2012,3,1,8,3,1,0.6675,0.621854,0.677083,0.0702833,1094,6281,7375
 601,8/23/2012,3,1,8,4,1,0.695833,0.637008,0.635833,0.0845958,1363,6402,7765
 602,8/24/2012,3,1,8,5,2,0.7025,0.6471,0.615,0.0721458,1325,6257,7582
 603,8/25/2012,3,1,8,6,2,0.661667,0.618696,0.712917,0.244408,1829,4224,6053
 604,8/26/2012,3,1,8,0,2,0.653333,0.595996,0.845833,0.228858,1483,3772,5255
 605,8/27/2012,3,1,8,1,1,0.703333,0.654688,0.730417,0.128733,989,5928,6917
 606,8/28/2012,3,1,8,2,1,0.728333,0.66605,0.62,0.190925,935,6105,7040
 607,8/29/2012,3,1,8,3,1,0.685,0.635733,0.552083,0.112562,1177,6520,7697
 608,8/30/2012,3,1,8,4,1,0.706667,0.652779,0.590417,0.0771167,1172,6541,7713
 609,8/31/2012,3,1,8,5,1,0.764167,0.6894,0.5875,0.168533,1433,5917,7350
 610,9/1/2012,3,1,9,6,2,0.753333,0.702654,0.638333,0.113187,2352,3788,6140
 611,9/2/2012,3,1,9,0,2,0.696667,0.649,0.815,0.0640708,2613,3197,5810
 612,9/3/2012,3,1,9,1,1,0.7075,0.661629,0.790833,0.151121,1965,4069,6034
 613,9/4/2012,3,1,9,2,1,0.725833,0.686888,0.755,0.236321,867,5997,6864
 614,9/5/2012,3,1,9,3,1,0.736667,0.708983,0.74125,0.187808,832,6280,7112
 615,9/6/2012,3,1,9,4,2,0.696667,0.655329,0.810417,0.142421,611,5592,6203
 616,9/7/2012,3,1,9,5,1,0.703333,0.657204,0.73625,0.171646,1045,6459,7504
 617,9/8/2012,3,1,9,6,2,0.659167,0.611121,0.799167,0.281104,1557,4419,5976
 618,9/9/2012,3,1,9,0,1,0.61,0.578925,0.5475,0.224496,2570,5657,8227
 619,9/10/2012,3,1,9,1,1,0.583333,0.565654,0.50375,0.258713,1118,6407,7525
 620,9/11/2012,3,1,9,2,1,0.5775,0.554292,0.52,0.0920542,1070,6697,7767
 621,9/12/2012,3,1,9,3,1,0.599167,0.570075,0.577083,0.131846,1050,6820,7870
 622,9/13/2012,3,1,9,4,1,0.6125,0.579558,0.637083,0.0827208,1054,6750,7804
 623,9/14/2012,3,1,9,5,1,0.633333,0.594083,0.6725,0.103863,1379,6630,8009
 624,9/15/2012,3,1,9,6,1,0.608333,0.585867,0.501667,0.247521,3160,5554,8714
 625,9/16/2012,3,1,9,0,1,0.58,0.563125,0.57,0.0901833,2166,5167,7333
 626,9/17/2012,3,1,9,1,2,0.580833,0.55305,0.734583,0.151742,1022,5847,6869
 627,9/18/2012,3,1,9,2,2,0.623333,0.565067,0.8725,0.357587,371,3702,4073
 628,9/19/2012,3,1,9,3,1,0.5525,0.540404,0.536667,0.215175,788,6803,7591
 629,9/20/2012,3,1,9,4,1,0.546667,0.532192,0.618333,0.118167,939,6781,7720
 630,9/21/2012,3,1,9,5,1,0.599167,0.571971,0.66875,0.154229,1250,6917,8167
 631,9/22/2012,3,1,9,6,1,0.65,0.610488,0.646667,0.283583,2512,5883,8395
 632,9/23/2012,4,1,9,0,1,0.529167,0.518933,0.467083,0.223258,2454,5453,7907
 633,9/24/2012,4,1,9,1,1,0.514167,0.502513,0.492917,0.142404,1001,6435,7436
 634,9/25/2012,4,1,9,2,1,0.55,0.544179,0.57,0.236321,845,6693,7538
 635,9/26/2012,4,1,9,3,1,0.635,0.596613,0.630833,0.2444,787,6946,7733
 636,9/27/2012,4,1,9,4,2,0.65,0.607975,0.690833,0.134342,751,6642,7393
 637,9/28/2012,4,1,9,5,2,0.619167,0.585863,0.69,0.164179,1045,6370,7415
 638,9/29/2012,4,1,9,6,1,0.5425,0.530296,0.542917,0.227604,2589,5966,8555
 639,9/30/2012,4,1,9,0,1,0.526667,0.517663,0.583333,0.134958,2015,4874,6889
 640,10/1/2012,4,1,10,1,2,0.520833,0.512,0.649167,0.0908042,763,6015,6778
 641,10/2/2012,4,1,10,2,3,0.590833,0.542333,0.871667,0.104475,315,4324,4639
 642,10/3/2012,4,1,10,3,2,0.6575,0.599133,0.79375,0.0665458,728,6844,7572
 643,10/4/2012,4,1,10,4,2,0.6575,0.607975,0.722917,0.117546,891,6437,7328
 644,10/5/2012,4,1,10,5,1,0.615,0.580187,0.6275,0.10635,1516,6640,8156
 645,10/6/2012,4,1,10,6,1,0.554167,0.538521,0.664167,0.268025,3031,4934,7965
 646,10/7/2012,4,1,10,0,2,0.415833,0.419813,0.708333,0.141162,781,2729,3510
 647,10/8/2012,4,1,10,1,2,0.383333,0.387608,0.709583,0.189679,874,4604,5478
 648,10/9/2012,4,1,10,2,2,0.446667,0.438112,0.761667,0.1903,601,5791,6392
 649,10/10/2012,4,1,10,3,1,0.514167,0.503142,0.630833,0.187821,780,6911,7691
 650,10/11/2012,4,1,10,4,1,0.435,0.431167,0.463333,0.181596,834,6736,7570
 651,10/12/2012,4,1,10,5,1,0.4375,0.433071,0.539167,0.235092,1060,6222,7282
 652,10/13/2012,4,1,10,6,1,0.393333,0.391396,0.494583,0.146142,2252,4857,7109
 653,10/14/2012,4,1,10,0,1,0.521667,0.508204,0.640417,0.278612,2080,4559,6639
 654,10/15/2012,4,1,10,1,2,0.561667,0.53915,0.7075,0.296037,760,5115,5875
 655,10/16/2012,4,1,10,2,1,0.468333,0.460846,0.558333,0.182221,922,6612,7534
 656,10/17/2012,4,1,10,3,1,0.455833,0.450108,0.692917,0.101371,979,6482,7461
 657,10/18/2012,4,1,10,4,2,0.5225,0.512625,0.728333,0.236937,1008,6501,7509
 658,10/19/2012,4,1,10,5,2,0.563333,0.537896,0.815,0.134954,753,4671,5424
 659,10/20/2012,4,1,10,6,1,0.484167,0.472842,0.572917,0.117537,2806,5284,8090
 660,10/21/2012,4,1,10,0,1,0.464167,0.456429,0.51,0.166054,2132,4692,6824
 661,10/22/2012,4,1,10,1,1,0.4875,0.482942,0.568333,0.0814833,830,6228,7058
 662,10/23/2012,4,1,10,2,1,0.544167,0.530304,0.641667,0.0945458,841,6625,7466
 663,10/24/2012,4,1,10,3,1,0.5875,0.558721,0.63625,0.0727792,795,6898,7693
 664,10/25/2012,4,1,10,4,2,0.55,0.529688,0.800417,0.124375,875,6484,7359
 665,10/26/2012,4,1,10,5,2,0.545833,0.52275,0.807083,0.132467,1182,6262,7444
 666,10/27/2012,4,1,10,6,2,0.53,0.515133,0.72,0.235692,2643,5209,7852
 667,10/28/2012,4,1,10,0,2,0.4775,0.467771,0.694583,0.398008,998,3461,4459
 668,10/29/2012,4,1,10,1,3,0.44,0.4394,0.88,0.3582,2,20,22
 669,10/30/2012,4,1,10,2,2,0.318182,0.309909,0.825455,0.213009,87,1009,1096
 670,10/31/2012,4,1,10,3,2,0.3575,0.3611,0.666667,0.166667,419,5147,5566
 671,11/1/2012,4,1,11,4,2,0.365833,0.369942,0.581667,0.157346,466,5520,5986
 672,11/2/2012,4,1,11,5,1,0.355,0.356042,0.522083,0.266175,618,5229,5847
 673,11/3/2012,4,1,11,6,2,0.343333,0.323846,0.49125,0.270529,1029,4109,5138
 674,11/4/2012,4,1,11,0,1,0.325833,0.329538,0.532917,0.179108,1201,3906,5107
 675,11/5/2012,4,1,11,1,1,0.319167,0.308075,0.494167,0.236325,378,4881,5259
 676,11/6/2012,4,1,11,2,1,0.280833,0.281567,0.567083,0.173513,466,5220,5686
 677,11/7/2012,4,1,11,3,2,0.295833,0.274621,0.5475,0.304108,326,4709,5035
 678,11/8/2012,4,1,11,4,1,0.352174,0.341891,0.333478,0.347835,340,4975,5315
 679,11/9/2012,4,1,11,5,1,0.361667,0.355413,0.540833,0.214558,709,5283,5992
 680,11/10/2012,4,1,11,6,1,0.389167,0.393937,0.645417,0.0578458,2090,4446,6536
 681,11/11/2012,4,1,11,0,1,0.420833,0.421713,0.659167,0.1275,2290,4562,6852
 682,11/12/2012,4,1,11,1,1,0.485,0.475383,0.741667,0.173517,1097,5172,6269
 683,11/13/2012,4,1,11,2,2,0.343333,0.323225,0.662917,0.342046,327,3767,4094
 684,11/14/2012,4,1,11,3,1,0.289167,0.281563,0.552083,0.199625,373,5122,5495
 685,11/15/2012,4,1,11,4,2,0.321667,0.324492,0.620417,0.152987,320,5125,5445
 686,11/16/2012,4,1,11,5,1,0.345,0.347204,0.524583,0.171025,484,5214,5698
 687,11/17/2012,4,1,11,6,1,0.325,0.326383,0.545417,0.179729,1313,4316,5629
 688,11/18/2012,4,1,11,0,1,0.3425,0.337746,0.692917,0.227612,922,3747,4669
 689,11/19/2012,4,1,11,1,2,0.380833,0.375621,0.623333,0.235067,449,5050,5499
 690,11/20/2012,4,1,11,2,2,0.374167,0.380667,0.685,0.082725,534,5100,5634
 691,11/21/2012,4,1,11,3,1,0.353333,0.364892,0.61375,0.103246,615,4531,5146
 692,11/22/2012,4,1,11,4,1,0.34,0.350371,0.580417,0.0528708,955,1470,2425
 693,11/23/2012,4,1,11,5,1,0.368333,0.378779,0.56875,0.148021,1603,2307,3910
 694,11/24/2012,4,1,11,6,1,0.278333,0.248742,0.404583,0.376871,532,1745,2277
 695,11/25/2012,4,1,11,0,1,0.245833,0.257583,0.468333,0.1505,309,2115,2424
 696,11/26/2012,4,1,11,1,1,0.313333,0.339004,0.535417,0.04665,337,4750,5087
 697,11/27/2012,4,1,11,2,2,0.291667,0.281558,0.786667,0.237562,123,3836,3959
 698,11/28/2012,4,1,11,3,1,0.296667,0.289762,0.50625,0.210821,198,5062,5260
 699,11/29/2012,4,1,11,4,1,0.28087,0.298422,0.555652,0.115522,243,5080,5323
 700,11/30/2012,4,1,11,5,1,0.298333,0.323867,0.649583,0.0584708,362,5306,5668
 701,12/1/2012,4,1,12,6,2,0.298333,0.316904,0.806667,0.0597042,951,4240,5191
 702,12/2/2012,4,1,12,0,2,0.3475,0.359208,0.823333,0.124379,892,3757,4649
 703,12/3/2012,4,1,12,1,1,0.4525,0.455796,0.7675,0.0827208,555,5679,6234
 704,12/4/2012,4,1,12,2,1,0.475833,0.469054,0.73375,0.174129,551,6055,6606
 705,12/5/2012,4,1,12,3,1,0.438333,0.428012,0.485,0.324021,331,5398,5729
 706,12/6/2012,4,1,12,4,1,0.255833,0.258204,0.50875,0.174754,340,5035,5375
 707,12/7/2012,4,1,12,5,2,0.320833,0.321958,0.764167,0.1306,349,4659,5008
 708,12/8/2012,4,1,12,6,2,0.381667,0.389508,0.91125,0.101379,1153,4429,5582
 709,12/9/2012,4,1,12,0,2,0.384167,0.390146,0.905417,0.157975,441,2787,3228
 710,12/10/2012,4,1,12,1,2,0.435833,0.435575,0.925,0.190308,329,4841,5170
 711,12/11/2012,4,1,12,2,2,0.353333,0.338363,0.596667,0.296037,282,5219,5501
 712,12/12/2012,4,1,12,3,2,0.2975,0.297338,0.538333,0.162937,310,5009,5319
 713,12/13/2012,4,1,12,4,1,0.295833,0.294188,0.485833,0.174129,425,5107,5532
 714,12/14/2012,4,1,12,5,1,0.281667,0.294192,0.642917,0.131229,429,5182,5611
 715,12/15/2012,4,1,12,6,1,0.324167,0.338383,0.650417,0.10635,767,4280,5047
 716,12/16/2012,4,1,12,0,2,0.3625,0.369938,0.83875,0.100742,538,3248,3786
 717,12/17/2012,4,1,12,1,2,0.393333,0.4015,0.907083,0.0982583,212,4373,4585
 718,12/18/2012,4,1,12,2,1,0.410833,0.409708,0.66625,0.221404,433,5124,5557
 719,12/19/2012,4,1,12,3,1,0.3325,0.342162,0.625417,0.184092,333,4934,5267
 720,12/20/2012,4,1,12,4,2,0.33,0.335217,0.667917,0.132463,314,3814,4128
 721,12/21/2012,1,1,12,5,2,0.326667,0.301767,0.556667,0.374383,221,3402,3623
 722,12/22/2012,1,1,12,6,1,0.265833,0.236113,0.44125,0.407346,205,1544,1749
 723,12/23/2012,1,1,12,0,1,0.245833,0.259471,0.515417,0.133083,408,1379,1787
 724,12/24/2012,1,1,12,1,2,0.231304,0.2589,0.791304,0.0772304,174,746,920
 725,12/25/2012,1,1,12,2,2,0.291304,0.294465,0.734783,0.168726,440,573,1013
 726,12/26/2012,1,1,12,3,3,0.243333,0.220333,0.823333,0.316546,9,432,441
 727,12/27/2012,1,1,12,4,2,0.254167,0.226642,0.652917,0.350133,247,1867,2114
 728,12/28/2012,1,1,12,5,2,0.253333,0.255046,0.59,0.155471,644,2451,3095
 729,12/29/2012,1,1,12,6,2,0.253333,0.2424,0.752917,0.124383,159,1182,1341
 730,12/30/2012,1,1,12,0,1,0.255833,0.2317,0.483333,0.350754,364,1432,1796
 731,12/31/2012,1,1,12,1,2,0.215833,0.223487,0.5775,0.154846,439,2290,2729
--- a/how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/auto-ml-forecasting-energy-demand.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/auto-ml-forecasting-energy-demand.ipynb
@@ -0,0 +1,684 @@
 {
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
        "\n",
        "Licensed under the MIT License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/auto-ml-forecasting-energy-demand.png)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Automated Machine Learning\n",
        "_**Energy Demand Forecasting**_\n",
        "\n",
        "## Contents\n",
        "1. [Introduction](#Introduction)\n",
        "1. [Setup](#Setup)\n",
        "1. [Data](#Data)\n",
        "1. [Train](#Train)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Introduction\n",
        "In this example, we show how AutoML can be used to forecast a single time-series in the energy demand application area. \n",
        "\n",
        "Make sure you have executed the [configuration](../../../configuration.ipynb) before running this notebook.\n",
        "\n",
        "Notebook synopsis:\n",
        "1. Creating an Experiment in an existing Workspace\n",
        "2. Configuration and local run of AutoML for a simple time-series model\n",
        "3. View engineered features and prediction results\n",
        "4. Configuration and local run of AutoML for a time-series model with lag and rolling window features\n",
        "5. Estimate feature importance"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Setup\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import azureml.core\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import logging\n",
        "import warnings\n",
        "\n",
        "# Squash warning messages for cleaner output in the notebook\n",
        "warnings.showwarning = lambda *args, **kwargs: None\n",
        "\n",
        "from azureml.core.workspace import Workspace\n",
        "from azureml.core.experiment import Experiment\n",
        "from azureml.train.automl import AutoMLConfig\n",
        "from matplotlib import pyplot as plt\n",
        "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "As part of the setup you have already created a <b>Workspace</b>. To run AutoML, you also need to create an <b>Experiment</b>. An Experiment corresponds to a prediction problem you are trying to solve, while a Run corresponds to a specific approach to the problem."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "ws = Workspace.from_config()\n",
        "\n",
        "# choose a name for the run history container in the workspace\n",
        "experiment_name = 'automl-energydemandforecasting'\n",
        "\n",
        "experiment = Experiment(ws, experiment_name)\n",
        "\n",
        "output = {}\n",
        "output['SDK version'] = azureml.core.VERSION\n",
        "output['Subscription ID'] = ws.subscription_id\n",
        "output['Workspace'] = ws.name\n",
        "output['Resource Group'] = ws.resource_group\n",
        "output['Location'] = ws.location\n",
        "output['Run History Name'] = experiment_name\n",
        "pd.set_option('display.max_colwidth', -1)\n",
        "outputDf = pd.DataFrame(data = output, index = [''])\n",
        "outputDf.T"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Data\n",
        "We will use energy consumption data from New York City for model training. The data is stored in a tabular format and includes energy demand and basic weather data at an hourly frequency. Pandas CSV reader is used to read the file into memory. Special attention is given to the \"timeStamp\" column in the data since it contains text which should be parsed as datetime-type objects. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "data = pd.read_csv(\"nyc_energy.csv\", parse_dates=['timeStamp'])\n",
        "data.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "We must now define the schema of this dataset. Every time-series must have a time column and a target. The target quantity is what will be eventually forecasted by a trained model. In this case, the target is the \"demand\" column. The other columns, \"temp\" and \"precip,\" are implicitly designated as features."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Dataset schema\n",
        "time_column_name = 'timeStamp'\n",
        "target_column_name = 'demand'"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Forecast Horizon\n",
        "\n",
        "In addition to the data schema, we must also specify the forecast horizon. A forecast horizon is a time span into the future (or just beyond the latest date in the training data) where forecasts of the target quantity are needed. Choosing a forecast horizon is application specific, but a rule-of-thumb is that **the horizon should be the time-frame where you need actionable decisions based on the forecast.** The horizon usually has a strong relationship with the frequency of the time-series data, that is, the sampling interval of the target quantity and the features. For instance, the NYC energy demand data has an hourly frequency. A decision that requires a demand forecast to the hour is unlikely to be made weeks or months in advance, particularly if we expect weather to be a strong determinant of demand. We may have fairly accurate meteorological forecasts of the hourly temperature and precipitation on a the time-scale of a day or two, however.\n",
        "\n",
        "Given the above discussion, we generally recommend that users set forecast horizons to less than 100 time periods (i.e. less than 100 hours in the NYC energy example). Furthermore, **AutoML's memory use and computation time increase in proportion to the length of the horizon**, so the user should consider carefully how they set this value. If a long horizon forecast really is necessary, it may be good practice to aggregate the series to a coarser time scale.  \n",
        "\n",
        "\n",
        "Forecast horizons in AutoML are given as integer multiples of the time-series frequency. In this example, we set the horizon to 48 hours."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "max_horizon = 48"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Split the data into train and test sets\n",
        "We now split the data into a train and a test set so that we may evaluate model performance. We note that the tail of the dataset contains a large number of NA values in the target column, so we designate the test set as the 48 hour window ending on the latest date of known energy demand. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Find time point to split on\n",
        "latest_known_time = data[~pd.isnull(data[target_column_name])][time_column_name].max()\n",
        "split_time = latest_known_time - pd.Timedelta(hours=max_horizon)\n",
        "\n",
        "# Split into train/test sets\n",
        "X_train = data[data[time_column_name] <= split_time]\n",
        "X_test = data[(data[time_column_name] > split_time) & (data[time_column_name] <= latest_known_time)]\n",
        "\n",
        "# Move the target values into their own arrays \n",
        "y_train = X_train.pop(target_column_name).values\n",
        "y_test = X_test.pop(target_column_name).values"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Train\n",
        "\n",
        "We now instantiate an AutoMLConfig object. This config defines the settings and data used to run the experiment. For forecasting tasks, we must provide extra configuration related to the time-series data schema and forecasting context. Here, only the name of the time column and the maximum forecast horizon are needed. Other settings are described below:\n",
        "\n",
        "|Property|Description|\n",
        "|-|-|\n",
        "|**task**|forecasting|\n",
        "|**primary_metric**|This is the metric that you want to optimize.<br> Forecasting supports the following primary metrics <br><i>spearman_correlation</i><br><i>normalized_root_mean_squared_error</i><br><i>r2_score</i><br><i>normalized_mean_absolute_error</i>\n",
        "|**iterations**|Number of iterations. In each iteration, Auto ML trains a specific pipeline on the given data|\n",
        "|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
        "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
        "|**y**|(sparse) array-like, shape = [n_samples, ], targets values.|\n",
        "|**n_cross_validations**|Number of cross validation splits. Rolling Origin Validation is used to split time-series in a temporally consistent way.|"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "time_series_settings = {\n",
        "    'time_column_name': time_column_name,\n",
        "    'max_horizon': max_horizon\n",
        "}\n",
        "\n",
        "automl_config = AutoMLConfig(task='forecasting',\n",
        "                             debug_log='automl_nyc_energy_errors.log',\n",
        "                             primary_metric='normalized_root_mean_squared_error',\n",
        "                             blacklist_models = ['ExtremeRandomTrees', 'AutoArima'],\n",
        "                             iterations=10,\n",
        "                             iteration_timeout_minutes=5,\n",
        "                             X=X_train,\n",
        "                             y=y_train,\n",
        "                             n_cross_validations=3,\n",
        "                             verbosity = logging.INFO,\n",
        "                             **time_series_settings)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Submitting the configuration will start a new run in this experiment. For local runs, the execution is synchronous. Depending on the data and number of iterations, this can run for a while. Parameters controlling concurrency may speed up the process, depending on your hardware.\n",
        "\n",
        "You will see the currently running iterations printing to the console."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "local_run = experiment.submit(automl_config, show_output=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "local_run"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Retrieve the Best Model\n",
        "Below we select the best pipeline from our iterations. The get_output method on automl_classifier returns the best run and the fitted model for the last fit invocation. There are overloads on get_output that allow you to retrieve the best run and fitted model for any logged metric or a particular iteration."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "best_run, fitted_model = local_run.get_output()\n",
        "fitted_model.steps"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### View the engineered names for featurized data\n",
        "Below we display the engineered feature names generated for the featurized data using the time-series featurization."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "fitted_model.named_steps['timeseriestransformer'].get_engineered_feature_names()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Test the Best Fitted Model\n",
        "\n",
        "For forecasting, we will use the `forecast` function instead of the `predict` function. There are two reasons for this.\n",
        "\n",
        "We need to pass the recent values of the target variable `y`, whereas the scikit-compatible `predict` function only takes the non-target variables `X`. In our case, the test data immediately follows the training data, and we fill the `y` variable with `NaN`. The `NaN` serves as a question mark for the forecaster to fill with the actuals. Using the forecast function will produce forecasts using the shortest possible forecast horizon. The last time at which a definite (non-NaN) value is seen is the _forecast origin_ - the last time when the value of the target is known. \n",
        "\n",
        "Using the `predict` method would result in getting predictions for EVERY horizon the forecaster can predict at. This is useful when training and evaluating the performance of the forecaster at various horizons, but the level of detail is excessive for normal use."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Replace ALL values in y_pred by NaN. \n",
        "# The forecast origin will be at the beginning of the first forecast period\n",
        "# (which is the same time as the end of the last training period).\n",
        "y_query = y_test.copy().astype(np.float)\n",
        "y_query.fill(np.nan)\n",
        "# The featurized data, aligned to y, will also be returned.\n",
        "# This contains the assumptions that were made in the forecast\n",
        "# and helps align the forecast to the original data\n",
        "y_fcst, X_trans = fitted_model.forecast(X_test, y_query)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# limit the evaluation to data where y_test has actuals\n",
        "def align_outputs(y_predicted, X_trans, X_test, y_test, predicted_column_name = 'predicted'):\n",
        "    \"\"\"\n",
        "    Demonstrates how to get the output aligned to the inputs\n",
        "    using pandas indexes. Helps understand what happened if\n",
        "    the output's shape differs from the input shape, or if\n",
        "    the data got re-sorted by time and grain during forecasting.\n",
        "    \n",
        "    Typical causes of misalignment are:\n",
        "    * we predicted some periods that were missing in actuals -> drop from eval\n",
        "    * model was asked to predict past max_horizon -> increase max horizon\n",
        "    * data at start of X_test was needed for lags -> provide previous periods\n",
        "    \"\"\"\n",
        "    df_fcst = pd.DataFrame({predicted_column_name : y_predicted})\n",
        "    # y and X outputs are aligned by forecast() function contract\n",
        "    df_fcst.index = X_trans.index\n",
        "    \n",
        "    # align original X_test to y_test    \n",
        "    X_test_full = X_test.copy()\n",
        "    X_test_full[target_column_name] = y_test\n",
        "\n",
        "    # X_test_full's does not include origin, so reset for merge\n",
        "    df_fcst.reset_index(inplace=True)\n",
        "    X_test_full = X_test_full.reset_index().drop(columns='index')\n",
        "    together = df_fcst.merge(X_test_full, how='right')\n",
        "    \n",
        "    # drop rows where prediction or actuals are nan \n",
        "    # happens because of missing actuals \n",
        "    # or at edges of time due to lags/rolling windows\n",
        "    clean = together[together[[target_column_name, predicted_column_name]].notnull().all(axis=1)]\n",
        "    return(clean)\n",
        "\n",
        "df_all = align_outputs(y_fcst, X_trans, X_test, y_test)\n",
        "df_all.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Looking at `X_trans` is also useful to see what featurization happened to the data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "X_trans"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Calculate accuracy metrics\n",
        "Finally, we calculate some accuracy metrics for the forecast and plot the predictions vs. the actuals over the time range in the test set."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "def MAPE(actual, pred):\n",
        "    \"\"\"\n",
        "    Calculate mean absolute percentage error.\n",
        "    Remove NA and values where actual is close to zero\n",
        "    \"\"\"\n",
        "    not_na = ~(np.isnan(actual) | np.isnan(pred))\n",
        "    not_zero = ~np.isclose(actual, 0.0)\n",
        "    actual_safe = actual[not_na & not_zero]\n",
        "    pred_safe = pred[not_na & not_zero]\n",
        "    APE = 100*np.abs((actual_safe - pred_safe)/actual_safe)\n",
        "    return np.mean(APE)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "print(\"Simple forecasting model\")\n",
        "rmse = np.sqrt(mean_squared_error(df_all[target_column_name], df_all['predicted']))\n",
        "print(\"[Test Data] \\nRoot Mean squared error: %.2f\" % rmse)\n",
        "mae = mean_absolute_error(df_all[target_column_name], df_all['predicted'])\n",
        "print('mean_absolute_error score: %.2f' % mae)\n",
        "print('MAPE: %.2f' % MAPE(df_all[target_column_name], df_all['predicted']))\n",
        "\n",
        "# Plot outputs\n",
        "%matplotlib inline\n",
        "pred, = plt.plot(df_all[time_column_name], df_all['predicted'], color='b')\n",
        "actual, = plt.plot(df_all[time_column_name], df_all[target_column_name], color='g')\n",
        "plt.xticks(fontsize=8)\n",
        "plt.legend((pred, actual), ('prediction', 'truth'), loc='upper left', fontsize=8)\n",
        "plt.title('Prediction vs. Actual Time-Series')\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "The distribution looks a little heavy tailed: we underestimate the excursions of the extremes. A normal-quantile transform of the target might help, but let's first try using some past data with the lags and rolling window transforms.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Using lags and rolling window features"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "We did not use lags in the previous model specification. In effect, the prediction was the result of a simple regression on date, grain and any additional features. This is often a very good prediction as common time series patterns like seasonality and trends can be captured in this manner. Such simple regression is horizon-less: it doesn't matter how far into the future we are predicting, because we are not using past data. In the previous example, the horizon was only used to split the data for cross-validation.\n",
        "\n",
        "Now that we configured target lags, that is the previous values of the target variables, and the prediction is no longer horizon-less. We therefore must still specify the `max_horizon` that the model will learn to forecast. The `target_lags` keyword specifies how far back we will construct the lags of the target variable, and the `target_rolling_window_size` specifies the size of the rolling window over which we will generate the `max`, `min` and `sum` features.\n",
        "\n",
        "This notebook uses the blacklist_models parameter to exclude some models that take a longer time to train on this dataset.  You can choose to remove models from the blacklist_models list but you may need to increase the iteration_timeout_minutes parameter value to get results."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "time_series_settings_with_lags = {\n",
        "    'time_column_name': time_column_name,\n",
        "    'max_horizon': max_horizon,\n",
        "    'target_lags': 12,\n",
        "    'target_rolling_window_size': 4\n",
        "}\n",
        "\n",
        "automl_config_lags = AutoMLConfig(task='forecasting',\n",
        "                                  debug_log='automl_nyc_energy_errors.log',\n",
        "                                  primary_metric='normalized_root_mean_squared_error',\n",
        "                                  blacklist_models=['ElasticNet','ExtremeRandomTrees','GradientBoosting','XGBoostRegressor'],\n",
        "                                  iterations=10,\n",
        "                                  iteration_timeout_minutes=10,\n",
        "                                  X=X_train,\n",
        "                                  y=y_train,\n",
        "                                  n_cross_validations=3,\n",
        "                                  verbosity=logging.INFO,\n",
        "                                  **time_series_settings_with_lags)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "We now start a new local run, this time with lag and rolling window featurization. AutoML applies featurizations in the setup stage, prior to iterating over ML models. The full training set is featurized first, followed by featurization of each of the CV splits. Lag and rolling window features introduce additional complexity, so the run will take longer than in the previous example that lacked these featurizations."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "local_run_lags = experiment.submit(automl_config_lags, show_output=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "best_run_lags, fitted_model_lags = local_run_lags.get_output()\n",
        "y_fcst_lags, X_trans_lags = fitted_model_lags.forecast(X_test, y_query)\n",
        "df_lags = align_outputs(y_fcst_lags, X_trans_lags, X_test, y_test)\n",
        "df_lags.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "X_trans_lags"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "print(\"Forecasting model with lags\")\n",
        "rmse = np.sqrt(mean_squared_error(df_lags[target_column_name], df_lags['predicted']))\n",
        "print(\"[Test Data] \\nRoot Mean squared error: %.2f\" % rmse)\n",
        "mae = mean_absolute_error(df_lags[target_column_name], df_lags['predicted'])\n",
        "print('mean_absolute_error score: %.2f' % mae)\n",
        "print('MAPE: %.2f' % MAPE(df_lags[target_column_name], df_lags['predicted']))\n",
        "\n",
        "# Plot outputs\n",
        "%matplotlib inline\n",
        "pred, = plt.plot(df_lags[time_column_name], df_lags['predicted'], color='b')\n",
        "actual, = plt.plot(df_lags[time_column_name], df_lags[target_column_name], color='g')\n",
        "plt.xticks(fontsize=8)\n",
        "plt.legend((pred, actual), ('prediction', 'truth'), loc='upper left', fontsize=8)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### What features matter for the forecast?\n",
        "The following steps will allow you to compute and visualize engineered feature importance based on your test data for forecasting. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Setup the model explanations for AutoML models\n",
        "The *fitted_model* can generate the following which will be used for getting the engineered and raw feature explanations using *automl_setup_model_explanations*:-\n",
        "1. Featurized data from train samples/test samples \n",
        "2. Gather engineered and raw feature name lists\n",
        "3. Find the classes in your labeled column in classification scenarios\n",
        "\n",
        "The *automl_explainer_setup_obj* contains all the structures from above list. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.train.automl.automl_explain_utilities import AutoMLExplainerSetupClass, automl_setup_model_explanations\n",
        "automl_explainer_setup_obj = automl_setup_model_explanations(fitted_model, X=X_train.copy(), \n",
        "                                                             X_test=X_test.copy(), y=y_train, \n",
        "                                                             task='forecasting')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Initialize the Mimic Explainer for feature importance\n",
        "For explaining the AutoML models, use the *MimicWrapper* from *azureml.explain.model* package. The *MimicWrapper* can be initialized with fields in *automl_explainer_setup_obj*, your workspace and a LightGBM model which acts as a surrogate model to explain the AutoML model (*fitted_model* here). The *MimicWrapper* also takes the *best_run* object where the raw and engineered explanations will be uploaded."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.explain.model.mimic.models.lightgbm_model import LGBMExplainableModel\n",
        "from azureml.explain.model.mimic_wrapper import MimicWrapper\n",
        "explainer = MimicWrapper(ws, automl_explainer_setup_obj.automl_estimator, LGBMExplainableModel, \n",
        "                         init_dataset=automl_explainer_setup_obj.X_transform, run=best_run,\n",
        "                         features=automl_explainer_setup_obj.engineered_feature_names, \n",
        "                         feature_maps=[automl_explainer_setup_obj.feature_map])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Use Mimic Explainer for computing and visualizing engineered feature importance\n",
        "The *explain()* method in *MimicWrapper* can be called with the transformed test samples to get the feature importance for the generated engineered features. You can also use *ExplanationDashboard* to view the dash board visualization of the feature importance values of the generated engineered features by AutoML featurizers."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "engineered_explanations = explainer.explain(['local', 'global'], eval_dataset=automl_explainer_setup_obj.X_test_transform)\n",
        "print(engineered_explanations.get_feature_importance_dict())\n",
        "from azureml.contrib.interpret.visualize import ExplanationDashboard\n",
        "ExplanationDashboard(engineered_explanations, automl_explainer_setup_obj.automl_estimator, automl_explainer_setup_obj.X_test_transform)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Use Mimic Explainer for computing and visualizing raw feature importance\n",
        "The *explain()* method in *MimicWrapper* can be again called with the transformed test samples and setting *get_raw* to *True* to get the feature importance for the raw features. You can also use *ExplanationDashboard* to view the dash board visualization of the feature importance values of the raw features."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "raw_explanations = explainer.explain(['local', 'global'], get_raw=True, \n",
        "                                     raw_feature_names=automl_explainer_setup_obj.raw_feature_names,\n",
        "                                     eval_dataset=automl_explainer_setup_obj.X_test_transform)\n",
        "print(raw_explanations.get_feature_importance_dict())\n",
        "from azureml.contrib.interpret.visualize import ExplanationDashboard\n",
        "ExplanationDashboard(raw_explanations, automl_explainer_setup_obj.automl_pipeline, automl_explainer_setup_obj.X_test_raw)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Please go to the Azure Portal's best run to see the top features chart.\n",
        "\n",
        "The informative features make all sorts of intuitive sense. Temperature is a strong driver of heating and cooling demand in NYC. Apart from that, the daily life cycle, expressed by `hour`, and the weekly cycle, expressed by `wday` drives people's energy use habits."
      ]
    }
  ],
  "metadata": {
    "authors": [
      {
        "name": "erwright"
      }
    ],
    "kernelspec": {
      "display_name": "Python 3.6",
      "language": "python",
      "name": "python36"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.8"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
--- a/how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/auto-ml-forecasting-energy-demand.yml
+++ b/how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/auto-ml-forecasting-energy-demand.yml
@@ -0,0 +1,12 @@
 name: auto-ml-forecasting-energy-demand
 dependencies:
 - pip:
  - azureml-sdk
  - interpret
  - azureml-train-automl
  - azureml-widgets
  - matplotlib
  - pandas_ml
  - statsmodels
  - azureml-explain-model
  - azureml-contrib-interpret
--- a/how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/nyc_energy.csv
+++ b/how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/nyc_energy.csv
--- a/how-to-use-azureml/automated-machine-learning/forecasting-high-frequency/automl-forecasting-function.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/forecasting-high-frequency/automl-forecasting-function.ipynb
@@ -0,0 +1,615 @@
 {
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Automated Machine Learning\n",
        "\n",
        "## Forecasting away from training data\n",
        "\n",
        "This notebook demonstrates the full interface to the `forecast()` function. \n",
        "\n",
        "The best known and most frequent usage of `forecast` enables forecasting on test sets that immediately follows training data. \n",
        "\n",
        "However, in many use cases it is necessary to continue using the model for some time before retraining it. This happens especially in **high frequency forecasting** when forecasts need to be made more frequently than the model can be retrained. Examples are in Internet of Things and predictive cloud resource scaling.\n",
        "\n",
        "Here we show how to use the `forecast()` function when a time gap exists between training data and prediction period.\n",
        "\n",
        "Terminology:\n",
        "* forecast origin: the last period when the target value is known\n",
        "* forecast periods(s): the period(s) for which the value of the target is desired.\n",
        "* forecast horizon: the number of forecast periods\n",
        "* lookback: how many past periods (before forecast origin) the model function depends on. The larger of number of lags and length of rolling window.\n",
        "* prediction context: `lookback` periods immediately preceding the forecast origin\n",
        "\n",
        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/automl-forecasting-function.png)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Setup"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Please make sure you have followed the `configuration.ipynb` notebook so that your ML workspace information is saved in the config file."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import logging\n",
        "import warnings\n",
        "\n",
        "from pandas.tseries.frequencies import to_offset\n",
        "\n",
        "# Squash warning messages for cleaner output in the notebook\n",
        "warnings.showwarning = lambda *args, **kwargs: None\n",
        "\n",
        "np.set_printoptions(precision=4, suppress=True, linewidth=120)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import azureml.core\n",
        "from azureml.core.workspace import Workspace\n",
        "from azureml.core.experiment import Experiment\n",
        "from azureml.train.automl import AutoMLConfig\n",
        "\n",
        "ws = Workspace.from_config()\n",
        "\n",
        "# choose a name for the run history container in the workspace\n",
        "experiment_name = 'automl-forecast-function-demo'\n",
        "\n",
        "experiment = Experiment(ws, experiment_name)\n",
        "\n",
        "output = {}\n",
        "output['SDK version'] = azureml.core.VERSION\n",
        "output['Subscription ID'] = ws.subscription_id\n",
        "output['Workspace'] = ws.name\n",
        "output['Resource Group'] = ws.resource_group\n",
        "output['Location'] = ws.location\n",
        "output['Run History Name'] = experiment_name\n",
        "pd.set_option('display.max_colwidth', -1)\n",
        "outputDf = pd.DataFrame(data = output, index = [''])\n",
        "outputDf.T"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Data\n",
        "For the demonstration purposes we will generate the data artificially and use them for the forecasting."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "TIME_COLUMN_NAME = 'date'\n",
        "GRAIN_COLUMN_NAME = 'grain'\n",
        "TARGET_COLUMN_NAME = 'y'\n",
        "\n",
        "def get_timeseries(train_len: int,\n",
        "                   test_len: int,\n",
        "                   time_column_name: str,\n",
        "                   target_column_name: str,\n",
        "                   grain_column_name: str,\n",
        "                   grains: int = 1,\n",
        "                   freq: str = 'H'):\n",
        "    \"\"\"\n",
        "    Return the time series of designed length.\n",
        "\n",
        "    :param train_len: The length of training data (one series).\n",
        "    :type train_len: int\n",
        "    :param test_len: The length of testing data (one series).\n",
        "    :type test_len: int\n",
        "    :param time_column_name: The desired name of a time column.\n",
        "    :type time_column_name: str\n",
        "    :param\n",
        "    :param grains: The number of grains.\n",
        "    :type grains: int\n",
        "    :param freq: The frequency string representing pandas offset.\n",
        "                 see https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html\n",
        "    :type freq: str\n",
        "    :returns: the tuple of train and test data sets.\n",
        "    :rtype: tuple\n",
        "\n",
        "    \"\"\"\n",
        "    data_train = []  # type: List[pd.DataFrame]\n",
        "    data_test = []  # type: List[pd.DataFrame]\n",
        "    data_length = train_len + test_len\n",
        "    for i in range(grains):\n",
        "        X = pd.DataFrame({\n",
        "            time_column_name: pd.date_range(start='2000-01-01',\n",
        "                                            periods=data_length,\n",
        "                                            freq=freq),\n",
        "            target_column_name: np.arange(data_length).astype(float) + np.random.rand(data_length) + i*5,\n",
        "            'ext_predictor': np.asarray(range(42, 42 + data_length)),\n",
        "            grain_column_name: np.repeat('g{}'.format(i), data_length)\n",
        "        })\n",
        "        data_train.append(X[:train_len])\n",
        "        data_test.append(X[train_len:])\n",
        "    X_train = pd.concat(data_train)\n",
        "    y_train = X_train.pop(target_column_name).values\n",
        "    X_test = pd.concat(data_test)\n",
        "    y_test = X_test.pop(target_column_name).values\n",
        "    return X_train, y_train, X_test, y_test\n",
        "\n",
        "n_test_periods = 6\n",
        "n_train_periods = 30\n",
        "X_train, y_train, X_test, y_test = get_timeseries(train_len=n_train_periods,\n",
        "                                                  test_len=n_test_periods,\n",
        "                                                  time_column_name=TIME_COLUMN_NAME,\n",
        "                                                  target_column_name=TARGET_COLUMN_NAME,\n",
        "                                                  grain_column_name=GRAIN_COLUMN_NAME,\n",
        "                                                  grains=2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Let's see what the training data looks like."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "X_train.tail()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# plot the example time series\n",
        "import matplotlib.pyplot as plt\n",
        "whole_data = X_train.copy()\n",
        "whole_data['y'] = y_train\n",
        "for g in whole_data.groupby('grain'):    \n",
        "    plt.plot(g[1]['date'].values, g[1]['y'].values, label=g[0])\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Create the configuration and train a forecaster\n",
        "First generate the configuration, in which we:\n",
        "* Set metadata columns: target, time column and grain column names.\n",
        "* Ask for 10 iterations through models, last of which will represent the Ensemble of previous ones.\n",
        "* Validate our data using cross validation with rolling window method.\n",
        "* Set normalized root mean squared error as a metric to select the best model.\n",
        "\n",
        "* Finally, we set the task to be forecasting.\n",
        "* By default, we apply the lag lead operator and rolling window to the target value i.e. we use the previous values as a predictor for the future ones."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "lags = [1,2,3]\n",
        "rolling_window_length = 0 # don't do rolling windows\n",
        "max_horizon = n_test_periods\n",
        "time_series_settings = {    \n",
        "    'time_column_name': TIME_COLUMN_NAME,\n",
        "    'grain_column_names': [ GRAIN_COLUMN_NAME ],\n",
        "    'max_horizon': max_horizon,\n",
        "    'target_lags': lags\n",
        "}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Run the model selection and training process."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.core.workspace import Workspace\n",
        "from azureml.core.experiment import Experiment\n",
        "from azureml.train.automl import AutoMLConfig\n",
        "\n",
        "\n",
        "automl_config = AutoMLConfig(task='forecasting',\n",
        "                             debug_log='automl_forecasting_function.log',\n",
        "                             primary_metric='normalized_root_mean_squared_error',                             \n",
        "                             iterations=10,                             \n",
        "                             X=X_train,\n",
        "                             y=y_train,\n",
        "                             n_cross_validations=3,\n",
        "                             verbosity = logging.INFO,\n",
        "                             **time_series_settings)\n",
        "\n",
        "local_run = experiment.submit(automl_config, show_output=True)\n",
        "\n",
        "# Retrieve the best model to use it further.\n",
        "_, fitted_model = local_run.get_output()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Forecasting from the trained model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "In this section we will review the `forecast` interface for two main scenarios: forecasting right after the training data, and the more complex interface for forecasting when there is a gap (in the time sense) between training and testing data."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### X_train is directly followed by the X_test\n",
        "\n",
        "Let's first consider the case when the prediction period immediately follows the training data. This is typical in scenarios where we have the time to retrain the model every time we wish to forecast. Forecasts that are made on daily and slower cadence typically fall into this category. Retraining the model every time benefits the accuracy because the most recent data is often the most informative.\n",
        "\n",
        "![Forecasting after training](forecast_function_at_train.png)\n",
        "\n",
        "The `X_test` and `y_query` below, taken together, form the **forecast request**. The two are interpreted as aligned - `y_query` could actally be a column in `X_test`. `NaN`s in `y_query` are the question marks. These will be filled with the forecasts.\n",
        "\n",
        "When the forecast period immediately follows the training period, the models retain the last few points of data. You can simply fill `y_query` filled with question marks - the model has the data for the lookback already.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Typical path: X_test is known, forecast all upcoming periods"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# The data set contains hourly data, the training set ends at 01/02/2000 at 05:00\n",
        "\n",
        "# These are predictions we are asking the model to make (does not contain thet target column y),\n",
        "# for 6 periods beginning with 2000-01-02 06:00, which immediately follows the training data\n",
        "X_test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "y_query = np.repeat(np.NaN, X_test.shape[0])\n",
        "y_pred_no_gap, xy_nogap =  fitted_model.forecast(X_test, y_query)\n",
        "\n",
        "# xy_nogap contains the predictions in the _automl_target_col column.\n",
        "# Those same numbers are output in y_pred_no_gap\n",
        "xy_nogap"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Distribution forecasts\n",
        "\n",
        "Often the figure of interest is not just the point prediction, but the prediction at some quantile of the distribution. \n",
        "This arises when the forecast is used to control some kind of inventory, for example of grocery items of virtual machines for a cloud service. In such case, the control point is usually something like \"we want the item to be in stock and not run out 99% of the time\". This is called a \"service level\". Here is how you get quantile forecasts."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# specify which quantiles you would like \n",
        "fitted_model.quantiles = [0.01, 0.5, 0.95]\n",
        "# use forecast_quantiles function, not the forecast() one\n",
        "y_pred_quantiles =  fitted_model.forecast_quantiles(X_test, y_query)\n",
        "\n",
        "# it all nicely aligns column-wise\n",
        "pd.concat([X_test.reset_index(), pd.DataFrame({'query' : y_query}), y_pred_quantiles], axis=1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Destination-date forecast: \"just do something\"\n",
        "\n",
        "In some scenarios, the X_test is not known. The forecast is likely to be weak, becaus eit is missing contemporaneous predictors, which we will need to impute. If you still wish to predict forward under the assumption that the last known values will be carried forward, you can forecast out to \"destination date\". The destination date still needs to fit within the maximum horizon from training."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# We will take the destination date as a last date in the test set.\n",
        "dest = max(X_test[TIME_COLUMN_NAME])\n",
        "y_pred_dest, xy_dest = fitted_model.forecast(forecast_destination=dest)\n",
        "\n",
        "# This form also shows how we imputed the predictors which were not given. (Not so well! Use with caution!)\n",
        "xy_dest"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Forecasting away from training data\n",
        "\n",
        "Suppose we trained a model, some time passed, and now we want to apply the model without re-training. If the model \"looks back\" -- uses previous values of the target -- then we somehow need to provide those values to the model.\n",
        "\n",
        "![Forecasting after training](forecast_function_away_from_train.png)\n",
        "\n",
        "The notion of forecast origin comes into play: the forecast origin is **the last period for which we have seen the target value**. This applies per grain, so each grain can have a different forecast origin. \n",
        "\n",
        "The part of data before the forecast origin is the **prediction context**. To provide the context values the model needs when it looks back, we pass definite values in `y_test` (aligned with corresponding times in `X_test`)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# generate the same kind of test data we trained on, \n",
        "# but now make the train set much longer, so that the test set will be in the future\n",
        "X_context, y_context, X_away, y_away = get_timeseries(train_len=42, # train data was 30 steps long\n",
        "                                      test_len=4,\n",
        "                                      time_column_name=TIME_COLUMN_NAME,\n",
        "                                      target_column_name=TARGET_COLUMN_NAME,\n",
        "                                      grain_column_name=GRAIN_COLUMN_NAME,\n",
        "                                      grains=2)\n",
        "\n",
        "# end of the data we trained on\n",
        "print(X_train.groupby(GRAIN_COLUMN_NAME)[TIME_COLUMN_NAME].max())\n",
        "# start of the data we want to predict on\n",
        "print(X_away.groupby(GRAIN_COLUMN_NAME)[TIME_COLUMN_NAME].min())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "There is a gap of 12 hours between end of training and beginning of `X_away`. (It looks like 13 because all timestamps point to the start of the one hour periods.) Using only `X_away` will fail without adding context data for the model to consume."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "try: \n",
        "    y_query = y_away.copy()\n",
        "    y_query.fill(np.NaN)\n",
        "    y_pred_away, xy_away = fitted_model.forecast(X_away, y_query)\n",
        "    xy_away\n",
        "except Exception as e:\n",
        "    print(e)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "How should we read that eror message? The forecast origin is at the last time themodel saw an actual values of `y` (the target). That was at the end of the training data! Because the model received all `NaN` (and not an actual target value), it is attempting to forecast from the end of training data. But the requested forecast periods are past the maximum horizon. We need to provide a define `y` value to establish the forecast origin.\n",
        "\n",
        "We will use this helper function to take the required amount of context from the data preceding the testing data. It's definition is intentionally simplified to keep the idea in the clear."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "def make_forecasting_query(fulldata, time_column_name, target_column_name, forecast_origin, horizon, lookback):\n",
        "\n",
        "    \"\"\"\n",
        "    This function will take the full dataset, and create the query\n",
        "    to predict all values of the grain from the `forecast_origin`\n",
        "    forward for the next `horizon` horizons. Context from previous\n",
        "    `lookback` periods will be included.\n",
        "\n",
        "    \n",
        "\n",
        "    fulldata: pandas.DataFrame           a time series dataset. Needs to contain X and y.\n",
        "    time_column_name: string             which column (must be in fulldata) is the time axis\n",
        "    target_column_name: string           which column (must be in fulldata) is to be forecast\n",
        "    forecast_origin: datetime type       the last time we (pretend to) have target values \n",
        "    horizon: timedelta                   how far forward, in time units (not periods)\n",
        "    lookback: timedelta                  how far back does the model look?\n",
        "\n",
        "    Example:\n",
        "\n",
        "\n",
        "    ```\n",
        "\n",
        "    forecast_origin = pd.to_datetime('2012-09-01') + pd.DateOffset(days=5) # forecast 5 days after end of training\n",
        "    print(forecast_origin)\n",
        "\n",
        "    X_query, y_query = make_forecasting_query(data, \n",
        "                       forecast_origin = forecast_origin,\n",
        "                       horizon = pd.DateOffset(days=7), # 7 days into the future\n",
        "                       lookback = pd.DateOffset(days=1), # model has lag 1 period (day)\n",
        "                      )\n",
        "\n",
        "    ```\n",
        "    \"\"\"\n",
        "\n",
        "    X_past = fulldata[ (fulldata[ time_column_name ] > forecast_origin - lookback) &\n",
        "                       (fulldata[ time_column_name ] <= forecast_origin)\n",
        "                     ]\n",
        "\n",
        "    X_future = fulldata[ (fulldata[ time_column_name ] > forecast_origin) &\n",
        "                         (fulldata[ time_column_name ] <= forecast_origin + horizon)\n",
        "                       ]\n",
        "\n",
        "    y_past = X_past.pop(target_column_name).values.astype(np.float)\n",
        "    y_future = X_future.pop(target_column_name).values.astype(np.float)\n",
        "\n",
        "    # Now take y_future and turn it into question marks\n",
        "    y_query = y_future.copy().astype(np.float)  # because sometimes life hands you an int\n",
        "    y_query.fill(np.NaN)\n",
        "\n",
        "\n",
        "    print(\"X_past is \" + str(X_past.shape) + \" - shaped\")\n",
        "    print(\"X_future is \" + str(X_future.shape) + \" - shaped\")\n",
        "    print(\"y_past is \" + str(y_past.shape) + \" - shaped\")\n",
        "    print(\"y_query is \" + str(y_query.shape) + \" - shaped\")\n",
        "\n",
        "\n",
        "    X_pred = pd.concat([X_past, X_future])\n",
        "    y_pred = np.concatenate([y_past, y_query])\n",
        "    return X_pred, y_pred"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Let's see where the context data ends - it ends, by construction, just before the testing data starts."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "print(X_context.groupby(GRAIN_COLUMN_NAME)[TIME_COLUMN_NAME].agg(['min','max','count']))\n",
        "print(   X_away.groupby(GRAIN_COLUMN_NAME)[TIME_COLUMN_NAME].agg(['min','max','count']))\n",
        "X_context.tail(5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Since the length of the lookback is 3, \n",
        "# we need to add 3 periods from the context to the request\n",
        "# so that the model has the data it needs\n",
        "\n",
        "# Put the X and y back together for a while. \n",
        "# They like each other and it makes them happy.\n",
        "X_context[TARGET_COLUMN_NAME] = y_context\n",
        "X_away[TARGET_COLUMN_NAME] = y_away\n",
        "fulldata = pd.concat([X_context, X_away])\n",
        "\n",
        "# forecast origin is the last point of data, which is one 1-hr period before test\n",
        "forecast_origin = X_away[TIME_COLUMN_NAME].min() - pd.DateOffset(hours=1)\n",
        "# it is indeed the last point of the context\n",
        "assert forecast_origin == X_context[TIME_COLUMN_NAME].max()\n",
        "print(\"Forecast origin: \" + str(forecast_origin))\n",
        "      \n",
        "# the model uses lags and rolling windows to look back in time\n",
        "n_lookback_periods = max(max(lags), rolling_window_length)\n",
        "lookback = pd.DateOffset(hours=n_lookback_periods)\n",
        "\n",
        "horizon = pd.DateOffset(hours=max_horizon)\n",
        "\n",
        "# now make the forecast query from context (refer to figure)\n",
        "X_pred, y_pred = make_forecasting_query(fulldata, TIME_COLUMN_NAME, TARGET_COLUMN_NAME,\n",
        "                                        forecast_origin, horizon, lookback)\n",
        "\n",
        "# show the forecast request aligned\n",
        "X_show = X_pred.copy()\n",
        "X_show[TARGET_COLUMN_NAME] = y_pred\n",
        "X_show"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Note that the forecast origin is at 17:00 for both grains, and periods from 18:00 are to be forecast."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Now everything works\n",
        "y_pred_away, xy_away = fitted_model.forecast(X_pred, y_pred)\n",
        "\n",
        "# show the forecast aligned\n",
        "X_show = xy_away.reset_index()\n",
        "# without the generated features\n",
        "X_show[['date', 'grain', 'ext_predictor', '_automl_target_col']]\n",
        "# prediction is in _automl_target_col"
      ]
    }
  ],
  "metadata": {
    "authors": [
      {
        "name": "erwright, nirovins"
      }
    ],
    "kernelspec": {
      "display_name": "Python 3.6",
      "language": "python",
      "name": "python36"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.7"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
--- a/how-to-use-azureml/automated-machine-learning/forecasting-high-frequency/automl-forecasting-function.yml
+++ b/how-to-use-azureml/automated-machine-learning/forecasting-high-frequency/automl-forecasting-function.yml
@@ -0,0 +1,9 @@
 name: automl-forecasting-function
 dependencies:
 - pip:
  - azureml-sdk
  - azureml-train-automl
  - azureml-widgets
  - pandas_ml
  - statsmodels
  - matplotlib
--- a/how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/auto-ml-forecasting-orange-juice-sales.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/auto-ml-forecasting-orange-juice-sales.ipynb
@@ -0,0 +1,813 @@
 {
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
        "\n",
        "Licensed under the MIT License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/auto-ml-forecasting-orange-juice-sales.png)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Automated Machine Learning\n",
        "_**Orange Juice Sales Forecasting**_\n",
        "\n",
        "## Contents\n",
        "1. [Introduction](#Introduction)\n",
        "1. [Setup](#Setup)\n",
        "1. [Data](#Data)\n",
        "1. [Train](#Train)\n",
        "1. [Predict](#Predict)\n",
        "1. [Operationalize](#Operationalize)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Introduction\n",
        "In this example, we use AutoML to train, select, and operationalize a time-series forecasting model for multiple time-series.\n",
        "\n",
        "Make sure you have executed the [configuration notebook](../../../configuration.ipynb) before running this notebook.\n",
        "\n",
        "The examples in the follow code samples use the University of Chicago's Dominick's Finer Foods dataset to forecast orange juice sales. Dominick's was a grocery chain in the Chicago metropolitan area."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Setup"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import azureml.core\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import logging\n",
        "import warnings\n",
        "\n",
        "# Squash warning messages for cleaner output in the notebook\n",
        "warnings.showwarning = lambda *args, **kwargs: None\n",
        "\n",
        "from azureml.core.workspace import Workspace\n",
        "from azureml.core.experiment import Experiment\n",
        "from azureml.train.automl import AutoMLConfig\n",
        "from sklearn.metrics import mean_absolute_error, mean_squared_error"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "As part of the setup you have already created a <b>Workspace</b>. To run AutoML, you also need to create an <b>Experiment</b>. An Experiment corresponds to a prediction problem you are trying to solve, while a Run corresponds to a specific approach to the problem. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "ws = Workspace.from_config()\n",
        "\n",
        "# choose a name for the run history container in the workspace\n",
        "experiment_name = 'automl-ojforecasting'\n",
        "\n",
        "experiment = Experiment(ws, experiment_name)\n",
        "\n",
        "output = {}\n",
        "output['SDK version'] = azureml.core.VERSION\n",
        "output['Subscription ID'] = ws.subscription_id\n",
        "output['Workspace'] = ws.name\n",
        "output['Resource Group'] = ws.resource_group\n",
        "output['Location'] = ws.location\n",
        "output['Run History Name'] = experiment_name\n",
        "pd.set_option('display.max_colwidth', -1)\n",
        "outputDf = pd.DataFrame(data = output, index = [''])\n",
        "outputDf.T"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Data\n",
        "You are now ready to load the historical orange juice sales data. We will load the CSV file into a plain pandas DataFrame; the time column in the CSV is called _WeekStarting_, so it will be specially parsed into the datetime type."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "time_column_name = 'WeekStarting'\n",
        "data = pd.read_csv(\"dominicks_OJ.csv\", parse_dates=[time_column_name])\n",
        "data.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Each row in the DataFrame holds a quantity of weekly sales for an OJ brand at a single store. The data also includes the sales price, a flag indicating if the OJ brand was advertised in the store that week, and some customer demographic information based on the store location. For historical reasons, the data also include the logarithm of the sales quantity. The Dominick's grocery data is commonly used to illustrate econometric modeling techniques where logarithms of quantities are generally preferred.    \n",
        "\n",
        "The task is now to build a time-series model for the _Quantity_ column. It is important to note that this dataset is comprised of many individual time-series - one for each unique combination of _Store_ and _Brand_. To distinguish the individual time-series, we thus define the **grain** - the columns whose values determine the boundaries between time-series: "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "grain_column_names = ['Store', 'Brand']\n",
        "nseries = data.groupby(grain_column_names).ngroups\n",
        "print('Data contains {0} individual time-series.'.format(nseries))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "For demonstration purposes, we extract sales time-series for just a few of the stores:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "use_stores = [2, 5, 8]\n",
        "data_subset = data[data.Store.isin(use_stores)]\n",
        "nseries = data_subset.groupby(grain_column_names).ngroups\n",
        "print('Data subset contains {0} individual time-series.'.format(nseries))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Data Splitting\n",
        "We now split the data into a training and a testing set for later forecast evaluation. The test set will contain the final 20 weeks of observed sales for each time-series. The splits should be stratified by series, so we use a group-by statement on the grain columns."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "n_test_periods = 20\n",
        "\n",
        "def split_last_n_by_grain(df, n):\n",
        "    \"\"\"Group df by grain and split on last n rows for each group.\"\"\"\n",
        "    df_grouped = (df.sort_values(time_column_name) # Sort by ascending time\n",
        "                  .groupby(grain_column_names, group_keys=False))\n",
        "    df_head = df_grouped.apply(lambda dfg: dfg.iloc[:-n])\n",
        "    df_tail = df_grouped.apply(lambda dfg: dfg.iloc[-n:])\n",
        "    return df_head, df_tail\n",
        "\n",
        "X_train, X_test = split_last_n_by_grain(data_subset, n_test_periods)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Modeling\n",
        "\n",
        "For forecasting tasks, AutoML uses pre-processing and estimation steps that are specific to time-series. AutoML will undertake the following pre-processing steps:\n",
        "* Detect time-series sample frequency (e.g. hourly, daily, weekly) and create new records for absent time points to make the series regular. A regular time series has a well-defined frequency and has a value at every sample point in a contiguous time span \n",
        "* Impute missing values in the target (via forward-fill) and feature columns (using median column values) \n",
        "* Create grain-based features to enable fixed effects across different series\n",
        "* Create time-based features to assist in learning seasonal patterns\n",
        "* Encode categorical variables to numeric quantities\n",
        "\n",
        "AutoML will currently train a single, regression-type model across **all** time-series in a given training set. This allows the model to generalize across related series.\n",
        "\n",
        "You are almost ready to start an AutoML training job. First, we need to separate the target column from the rest of the DataFrame: "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "target_column_name = 'Quantity'\n",
        "y_train = X_train.pop(target_column_name).values"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Train\n",
        "\n",
        "The AutoMLConfig object defines the settings and data for an AutoML training job. Here, we set necessary inputs like the task type, the number of AutoML iterations to try, the training data, and cross-validation parameters. \n",
        "\n",
        "For forecasting tasks, there are some additional parameters that can be set: the name of the column holding the date/time, the grain column names, and the maximum forecast horizon. A time column is required for forecasting, while the grain is optional. If a grain is not given, AutoML assumes that the whole dataset is a single time-series. We also pass a list of columns to drop prior to modeling. The _logQuantity_ column is completely correlated with the target quantity, so it must be removed to prevent a target leak.\n",
        "\n",
        "The forecast horizon is given in units of the time-series frequency; for instance, the OJ series frequency is weekly, so a horizon of 20 means that a trained model will estimate sales up-to 20 weeks beyond the latest date in the training data for each series. In this example, we set the maximum horizon to the number of samples per series in the test set (n_test_periods). Generally, the value of this parameter will be dictated by business needs. For example, a demand planning organizaion that needs to estimate the next month of sales would set the horizon accordingly. Please see the [energy_demand notebook](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/automated-machine-learning/forecasting-energy-demand) for more discussion of forecast horizon.\n",
        "\n",
        "Finally, a note about the cross-validation (CV) procedure for time-series data. AutoML uses out-of-sample error estimates to select a best pipeline/model, so it is important that the CV fold splitting is done correctly. Time-series can violate the basic statistical assumptions of the canonical K-Fold CV strategy, so AutoML implements a [rolling origin validation](https://robjhyndman.com/hyndsight/tscv/) procedure to create CV folds for time-series data. To use this procedure, you just need to specify the desired number of CV folds in the AutoMLConfig object. It is also possible to bypass CV and use your own validation set by setting the *X_valid* and *y_valid* parameters of AutoMLConfig.\n",
        "\n",
        "Here is a summary of AutoMLConfig parameters used for training the OJ model:\n",
        "\n",
        "|Property|Description|\n",
        "|-|-|\n",
        "|**task**|forecasting|\n",
        "|**primary_metric**|This is the metric that you want to optimize.<br> Forecasting supports the following primary metrics <br><i>spearman_correlation</i><br><i>normalized_root_mean_squared_error</i><br><i>r2_score</i><br><i>normalized_mean_absolute_error</i>\n",
        "|**iterations**|Number of iterations. In each iteration, Auto ML trains a specific pipeline on the given data|\n",
        "|**X**|Training matrix of features as a pandas DataFrame, shape = [n_training_samples, n_features]|\n",
        "|**y**|Target values as a numpy.ndarray, shape = [n_training_samples, ]|\n",
        "|**n_cross_validations**|Number of cross-validation folds to use for model/pipeline selection|\n",
        "|**enable_voting_ensemble**|Allow AutoML to create a Voting ensemble of the best performing models\n",
        "|**enable_stack_ensemble**|Allow AutoML to create a Stack ensemble of the best performing models\n",
        "|**debug_log**|Log file path for writing debugging information\n",
        "|**time_column_name**|Name of the datetime column in the input data|\n",
        "|**grain_column_names**|Name(s) of the columns defining individual series in the input data|\n",
        "|**drop_column_names**|Name(s) of columns to drop prior to modeling|\n",
        "|**max_horizon**|Maximum desired forecast horizon in units of time-series frequency|"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "time_series_settings = {\n",
        "    'time_column_name': time_column_name,\n",
        "    'grain_column_names': grain_column_names,\n",
        "    'drop_column_names': ['logQuantity'],\n",
        "    'max_horizon': n_test_periods\n",
        "}\n",
        "\n",
        "automl_config = AutoMLConfig(task='forecasting',\n",
        "                             debug_log='automl_oj_sales_errors.log',\n",
        "                             primary_metric='normalized_mean_absolute_error',\n",
        "                             iterations=10,\n",
        "                             X=X_train,\n",
        "                             y=y_train,\n",
        "                             n_cross_validations=3,\n",
        "                             enable_voting_ensemble=False,\n",
        "                             enable_stack_ensemble=False,\n",
        "                             verbosity=logging.INFO,\n",
        "                             **time_series_settings)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "You can now submit a new training run. For local runs, the execution is synchronous. Depending on the data and number of iterations this operation may take several minutes.\n",
        "Information from each iteration will be printed to the console."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "local_run = experiment.submit(automl_config, show_output=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Retrieve the Best Model\n",
        "Each run within an Experiment stores serialized (i.e. pickled) pipelines from the AutoML iterations. We can now retrieve the pipeline with the best performance on the validation dataset:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "best_run, fitted_pipeline = local_run.get_output()\n",
        "fitted_pipeline.steps"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Forecasting\n",
        "\n",
        "Now that we have retrieved the best pipeline/model, it can be used to make predictions on test data. First, we remove the target values from the test set:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "y_test = X_test.pop(target_column_name).values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "X_test.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "To produce predictions on the test set, we need to know the feature values at all dates in the test set. This requirement is somewhat reasonable for the OJ sales data since the features mainly consist of price, which is usually set in advance, and customer demographics which are approximately constant for each store over the 20 week forecast horizon in the testing data. \n",
        "\n",
        "We will first create a query `y_query`, which is aligned index-for-index to `X_test`. This is a vector of target values where each `NaN` serves the function of the question mark to be replaced by forecast. Passing definite values in the `y` argument allows the `forecast` function to make predictions on data that does not immediately follow the train data which contains `y`. In each grain, the last time point where the model sees a definite value of `y` is that grain's _forecast origin_."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Replace ALL values in y_pred by NaN.\n",
        "# The forecast origin will be at the beginning of the first forecast period.\n",
        "# (Which is the same time as the end of the last training period.)\n",
        "y_query = y_test.copy().astype(np.float)\n",
        "y_query.fill(np.nan)\n",
        "# The featurized data, aligned to y, will also be returned.\n",
        "# This contains the assumptions that were made in the forecast\n",
        "# and helps align the forecast to the original data\n",
        "y_pred, X_trans = fitted_pipeline.forecast(X_test, y_query)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "If you are used to scikit pipelines, perhaps you expected `predict(X_test)`. However, forecasting requires a more general interface that also supplies the past target `y` values. Please use `forecast(X,y)` as `predict(X)` is reserved for internal purposes on forecasting models.\n",
        "\n",
        "The [energy demand forecasting notebook](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/automated-machine-learning/forecasting-energy-demand) demonstrates the use of the forecast function in more detail in the context of using lags and rolling window features. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Evaluate\n",
        "\n",
        "To evaluate the accuracy of the forecast, we'll compare against the actual sales quantities for some select metrics, included the mean absolute percentage error (MAPE). \n",
        "\n",
        "It is a good practice to always align the output explicitly to the input, as the count and order of the rows may have changed during transformations that span multiple rows."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "def align_outputs(y_predicted, X_trans, X_test, y_test, predicted_column_name = 'predicted'):\n",
        "    \"\"\"\n",
        "    Demonstrates how to get the output aligned to the inputs\n",
        "    using pandas indexes. Helps understand what happened if\n",
        "    the output's shape differs from the input shape, or if\n",
        "    the data got re-sorted by time and grain during forecasting.\n",
        "    \n",
        "    Typical causes of misalignment are:\n",
        "    * we predicted some periods that were missing in actuals -> drop from eval\n",
        "    * model was asked to predict past max_horizon -> increase max horizon\n",
        "    * data at start of X_test was needed for lags -> provide previous periods in y\n",
        "    \"\"\"\n",
        "    \n",
        "    df_fcst = pd.DataFrame({predicted_column_name : y_predicted})\n",
        "    # y and X outputs are aligned by forecast() function contract\n",
        "    df_fcst.index = X_trans.index\n",
        "    \n",
        "    # align original X_test to y_test    \n",
        "    X_test_full = X_test.copy()\n",
        "    X_test_full[target_column_name] = y_test\n",
        "\n",
        "    # X_test_full's index does not include origin, so reset for merge\n",
        "    df_fcst.reset_index(inplace=True)\n",
        "    X_test_full = X_test_full.reset_index().drop(columns='index')\n",
        "    together = df_fcst.merge(X_test_full, how='right')\n",
        "    \n",
        "    # drop rows where prediction or actuals are nan \n",
        "    # happens because of missing actuals \n",
        "    # or at edges of time due to lags/rolling windows\n",
        "    clean = together[together[[target_column_name, predicted_column_name]].notnull().all(axis=1)]\n",
        "    return(clean)\n",
        "\n",
        "df_all = align_outputs(y_pred, X_trans, X_test, y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "def MAPE(actual, pred):\n",
        "    \"\"\"\n",
        "    Calculate mean absolute percentage error.\n",
        "    Remove NA and values where actual is close to zero\n",
        "    \"\"\"\n",
        "    not_na = ~(np.isnan(actual) | np.isnan(pred))\n",
        "    not_zero = ~np.isclose(actual, 0.0)\n",
        "    actual_safe = actual[not_na & not_zero]\n",
        "    pred_safe = pred[not_na & not_zero]\n",
        "    APE = 100*np.abs((actual_safe - pred_safe)/actual_safe)\n",
        "    return np.mean(APE)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "print(\"Simple forecasting model\")\n",
        "rmse = np.sqrt(mean_squared_error(df_all[target_column_name], df_all['predicted']))\n",
        "print(\"[Test Data] \\nRoot Mean squared error: %.2f\" % rmse)\n",
        "mae = mean_absolute_error(df_all[target_column_name], df_all['predicted'])\n",
        "print('mean_absolute_error score: %.2f' % mae)\n",
        "print('MAPE: %.2f' % MAPE(df_all[target_column_name], df_all['predicted']))\n",
        "\n",
        "# Plot outputs\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "%matplotlib inline\n",
        "test_pred = plt.scatter(df_all[target_column_name], df_all['predicted'], color='b')\n",
        "test_test = plt.scatter(y_test, y_test, color='g')\n",
        "plt.legend((test_pred, test_test), ('prediction', 'truth'), loc='upper left', fontsize=8)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Operationalize"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "_Operationalization_ means getting the model into the cloud so that other can run it after you close the notebook. We will create a docker running on Azure Container Instances with the model."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "description = 'AutoML OJ forecaster'\n",
        "tags = None\n",
        "model = local_run.register_model(description = description, tags = tags)\n",
        "\n",
        "print(local_run.model_id)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Develop the scoring script\n",
        "\n",
        "Serializing and deserializing complex data frames may be tricky. We first develop the `run()` function of the scoring script locally, then write it into a scoring script. It is much easier to debug any quirks of the scoring function without crossing two compute environments. For this exercise, we handle a common quirk of how pandas dataframes serialize time stamp values."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# this is where we test the run function of the scoring script interactively\n",
        "# before putting it in the scoring script\n",
        "\n",
        "timestamp_columns = ['WeekStarting']\n",
        "\n",
        "def run(rawdata, test_model = None):\n",
        "    \"\"\"\n",
        "    Intended to process 'rawdata' string produced by\n",
        "    \n",
        "    {'X': X_test.to_json(), y' : y_test.to_json()}\n",
        "    \n",
        "    Don't convert the X payload to numpy.array, use it as pandas.DataFrame\n",
        "    \"\"\"\n",
        "    try:\n",
        "        # unpack the data frame with timestamp        \n",
        "        rawobj = json.loads(rawdata)                    # rawobj is now a dict of strings        \n",
        "        X_pred = pd.read_json(rawobj['X'], convert_dates=False)   # load the pandas DF from a json string\n",
        "        for col in timestamp_columns:                             # fix timestamps\n",
        "            X_pred[col] = pd.to_datetime(X_pred[col], unit='ms') \n",
        "        \n",
        "        y_pred = np.array(rawobj['y'])                    # reconstitute numpy array from serialized list\n",
        "        \n",
        "        if test_model is None:\n",
        "            result = model.forecast(X_pred, y_pred)       # use the global model from init function\n",
        "        else:\n",
        "            result = test_model.forecast(X_pred, y_pred)  # use the model on which we are testing\n",
        "        \n",
        "    except Exception as e:\n",
        "        result = str(e)\n",
        "        return json.dumps({\"error\": result})\n",
        "    \n",
        "    forecast_as_list = result[0].tolist()\n",
        "    index_as_df = result[1].index.to_frame().reset_index(drop=True)\n",
        "    \n",
        "    return json.dumps({\"forecast\": forecast_as_list,   # return the minimum over the wire: \n",
        "                       \"index\": index_as_df.to_json()  # no forecast and its featurized values\n",
        "                      })"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# test the run function here before putting in the scoring script\n",
        "import json\n",
        "\n",
        "test_sample = json.dumps({'X': X_test.to_json(), 'y' : y_query.tolist()})\n",
        "response = run(test_sample, fitted_pipeline)\n",
        "\n",
        "# unpack the response, dealing with the timestamp serialization again\n",
        "res_dict = json.loads(response)\n",
        "y_fcst_all = pd.read_json(res_dict['index'])\n",
        "y_fcst_all[time_column_name] = pd.to_datetime(y_fcst_all[time_column_name], unit = 'ms')\n",
        "y_fcst_all['forecast'] = res_dict['forecast']\n",
        "y_fcst_all.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Now that the function works locally in the notebook, let's write it down into the scoring script. The scoring script is authored by the data scientist. Adjust it to taste, adding inputs, outputs and processing as needed."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "%%writefile score_fcast.py\n",
        "import pickle\n",
        "import json\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import azureml.train.automl\n",
        "from sklearn.externals import joblib\n",
        "from azureml.core.model import Model\n",
        "\n",
        "\n",
        "def init():\n",
        "    global model\n",
        "    model_path = Model.get_model_path(model_name = '<<modelid>>') # this name is model.id of model that we want to deploy\n",
        "    # deserialize the model file back into a sklearn model\n",
        "    model = joblib.load(model_path)\n",
        "\n",
        "timestamp_columns = ['WeekStarting']\n",
        "\n",
        "def run(rawdata, test_model = None):\n",
        "    \"\"\"\n",
        "    Intended to process 'rawdata' string produced by\n",
        "    \n",
        "    {'X': X_test.to_json(), y' : y_test.to_json()}\n",
        "    \n",
        "    Don't convert the X payload to numpy.array, use it as pandas.DataFrame\n",
        "    \"\"\"\n",
        "    try:\n",
        "        # unpack the data frame with timestamp        \n",
        "        rawobj = json.loads(rawdata)                    # rawobj is now a dict of strings        \n",
        "        X_pred = pd.read_json(rawobj['X'], convert_dates=False)   # load the pandas DF from a json string\n",
        "        for col in timestamp_columns:                             # fix timestamps\n",
        "            X_pred[col] = pd.to_datetime(X_pred[col], unit='ms') \n",
        "        \n",
        "        y_pred = np.array(rawobj['y'])                    # reconstitute numpy array from serialized list\n",
        "        \n",
        "        if test_model is None:\n",
        "            result = model.forecast(X_pred, y_pred)       # use the global model from init function\n",
        "        else:\n",
        "            result = test_model.forecast(X_pred, y_pred)  # use the model on which we are testing\n",
        "        \n",
        "    except Exception as e:\n",
        "        result = str(e)\n",
        "        return json.dumps({\"error\": result})\n",
        "    \n",
        "    # prepare to send over wire as json\n",
        "    forecast_as_list = result[0].tolist()\n",
        "    index_as_df = result[1].index.to_frame().reset_index(drop=True)\n",
        "    \n",
        "    return json.dumps({\"forecast\": forecast_as_list,   # return the minimum over the wire: \n",
        "                       \"index\": index_as_df.to_json()  # no forecast and its featurized values\n",
        "                      })"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# get the model\n",
        "from azureml.train.automl.run import AutoMLRun\n",
        "\n",
        "experiment = Experiment(ws, experiment_name)\n",
        "ml_run = AutoMLRun(experiment = experiment, run_id = local_run.id)\n",
        "best_iteration = int(str.split(best_run.id,'_')[-1])      # the iteration number is a postfix of the run ID."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# get the best model's dependencies and write them into this file\n",
        "from azureml.core.conda_dependencies import CondaDependencies\n",
        "\n",
        "conda_env_file_name = 'fcast_env.yml'\n",
        "\n",
        "dependencies = ml_run.get_run_sdk_dependencies(iteration = best_iteration)\n",
        "for p in ['azureml-train-automl', 'azureml-core']:\n",
        "    print('{}\\t{}'.format(p, dependencies[p]))\n",
        "\n",
        "myenv = CondaDependencies.create(conda_packages=['numpy>=1.16.0,<=1.16.2','scikit-learn','fbprophet==0.5'], pip_packages=['azureml-defaults','azureml-train-automl'])\n",
        "\n",
        "myenv.save_to_file('.', conda_env_file_name)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# this is the script file name we wrote a few cells above\n",
        "script_file_name = 'score_fcast.py'\n",
        "\n",
        "# Substitute the actual version number in the environment file.\n",
        "# This is not strictly needed in this notebook because the model should have been generated using the current SDK version.\n",
        "# However, we include this in case this code is used on an experiment from a previous SDK version.\n",
        "\n",
        "with open(conda_env_file_name, 'r') as cefr:\n",
        "    content = cefr.read()\n",
        "\n",
        "with open(conda_env_file_name, 'w') as cefw:\n",
        "    cefw.write(content.replace(azureml.core.VERSION, dependencies['azureml-train-automl']))\n",
        "\n",
        "# Substitute the actual model id in the script file.\n",
        "\n",
        "with open(script_file_name, 'r') as cefr:\n",
        "    content = cefr.read()\n",
        "\n",
        "with open(script_file_name, 'w') as cefw:\n",
        "    cefw.write(content.replace('<<modelid>>', local_run.model_id))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Deploy the model as a Web Service on Azure Container Instance"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.core.model import InferenceConfig\n",
        "from azureml.core.webservice import AciWebservice\n",
        "from azureml.core.webservice import Webservice\n",
        "from azureml.core.model import Model\n",
        "\n",
        "inference_config = InferenceConfig(runtime = \"python\", \n",
        "                                   entry_script = script_file_name,\n",
        "                                   conda_file = conda_env_file_name)\n",
        "\n",
        "aciconfig = AciWebservice.deploy_configuration(cpu_cores = 1, \n",
        "                                               memory_gb = 2, \n",
        "                                               tags = {'type': \"automl-forecasting\"},\n",
        "                                               description = \"Automl forecasting sample service\")\n",
        "\n",
        "aci_service_name = 'automl-forecast-01'\n",
        "print(aci_service_name)\n",
        "aci_service = Model.deploy(ws, aci_service_name, [model], inference_config, aciconfig)\n",
        "aci_service.wait_for_deployment(True)\n",
        "print(aci_service.state)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Call the service"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# we send the data to the service serialized into a json string\n",
        "test_sample = json.dumps({'X':X_test.to_json(), 'y' : y_query.tolist()})\n",
        "response = aci_service.run(input_data = test_sample)\n",
        "\n",
        "# translate from networkese to datascientese\n",
        "try: \n",
        "    res_dict = json.loads(response)\n",
        "    y_fcst_all = pd.read_json(res_dict['index'])\n",
        "    y_fcst_all[time_column_name] = pd.to_datetime(y_fcst_all[time_column_name], unit = 'ms')\n",
        "    y_fcst_all['forecast'] = res_dict['forecast']    \n",
        "except:\n",
        "    print(res_dict)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "y_fcst_all.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Delete the web service if desired"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "serv = Webservice(ws, 'automl-forecast-01')\n",
        "# serv.delete()     # don't do it accidentally"
      ]
    }
  ],
  "metadata": {
    "authors": [
      {
        "name": "erwright"
      }
    ],
    "kernelspec": {
      "display_name": "Python 3.6",
      "language": "python",
      "name": "python36"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.7"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
--- a/how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/auto-ml-forecasting-orange-juice-sales.yml
+++ b/how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/auto-ml-forecasting-orange-juice-sales.yml
@@ -0,0 +1,9 @@
 name: auto-ml-forecasting-orange-juice-sales
 dependencies:
 - pip:
  - azureml-sdk
  - azureml-train-automl
  - azureml-widgets
  - matplotlib
  - pandas_ml
  - statsmodels
--- a/how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/dominicks_OJ.csv
+++ b/how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/dominicks_OJ.csv
--- a/how-to-use-azureml/automated-machine-learning/missing-data-blacklist-early-termination/auto-ml-missing-data-blacklist-early-termination.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/missing-data-blacklist-early-termination/auto-ml-missing-data-blacklist-early-termination.ipynb
@@ -0,0 +1,423 @@
 {
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
        "\n",
        "Licensed under the MIT License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/missing-data-blacklist-early-termination/auto-ml-missing-data-blacklist-early-termination.png)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Automated Machine Learning\n",
        "_**Blacklisting Models, Early Termination, and Handling Missing Data**_\n",
        "\n",
        "## Contents\n",
        "1. [Introduction](#Introduction)\n",
        "1. [Setup](#Setup)\n",
        "1. [Data](#Data)\n",
        "1. [Train](#Train)\n",
        "1. [Results](#Results)\n",
        "1. [Test](#Test)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Introduction\n",
        "In this example we use the scikit-learn's [digit dataset](http://scikit-learn.org/stable/datasets/index.html#optical-recognition-of-handwritten-digits-dataset) to showcase how you can use AutoML for handling missing values in data. We also provide a stopping metric indicating a target for the primary metrics so that AutoML can terminate the run without necessarly going through all the iterations. Finally, if you want to avoid a certain pipeline, we allow you to specify a blacklist of algorithms that AutoML will ignore for this run.\n",
        "\n",
        "Make sure you have executed the [configuration](../../../configuration.ipynb) before running this notebook.\n",
        "\n",
        "In this notebook you will learn how to:\n",
        "1. Create an `Experiment` in an existing `Workspace`.\n",
        "2. Configure AutoML using `AutoMLConfig`.\n",
        "3. Train the model.\n",
        "4. Explore the results.\n",
        "5. Viewing the engineered names for featurized data and featurization summary for all raw features.\n",
        "6. Test the best fitted model.\n",
        "\n",
        "In addition this notebook showcases the following features\n",
        "- **Blacklisting** certain pipelines\n",
        "- Specifying **target metrics** to indicate stopping criteria\n",
        "- Handling **missing data** in the input"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Setup\n",
        "\n",
        "As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import logging\n",
        "\n",
        "from matplotlib import pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn import datasets\n",
        "\n",
        "import azureml.core\n",
        "from azureml.core.experiment import Experiment\n",
        "from azureml.core.workspace import Workspace\n",
        "from azureml.train.automl import AutoMLConfig"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "ws = Workspace.from_config()\n",
        "\n",
        "# Choose a name for the experiment.\n",
        "experiment_name = 'automl-local-missing-data'\n",
        "\n",
        "experiment = Experiment(ws, experiment_name)\n",
        "\n",
        "output = {}\n",
        "output['SDK version'] = azureml.core.VERSION\n",
        "output['Subscription ID'] = ws.subscription_id\n",
        "output['Workspace'] = ws.name\n",
        "output['Resource Group'] = ws.resource_group\n",
        "output['Location'] = ws.location\n",
        "output['Experiment Name'] = experiment.name\n",
        "pd.set_option('display.max_colwidth', -1)\n",
        "outputDf = pd.DataFrame(data = output, index = [''])\n",
        "outputDf.T"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "digits = datasets.load_digits()\n",
        "X_train = digits.data[10:,:]\n",
        "y_train = digits.target[10:]\n",
        "\n",
        "# Add missing values in 75% of the lines.\n",
        "missing_rate = 0.75\n",
        "n_missing_samples = int(np.floor(X_train.shape[0] * missing_rate))\n",
        "missing_samples = np.hstack((np.zeros(X_train.shape[0] - n_missing_samples, dtype=np.bool), np.ones(n_missing_samples, dtype=np.bool)))\n",
        "rng = np.random.RandomState(0)\n",
        "rng.shuffle(missing_samples)\n",
        "missing_features = rng.randint(0, X_train.shape[1], n_missing_samples)\n",
        "X_train[np.where(missing_samples)[0], missing_features] = np.nan"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "df = pd.DataFrame(data = X_train)\n",
        "df['Label'] = pd.Series(y_train, index=df.index)\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Train\n",
        "\n",
        "Instantiate an `AutoMLConfig` object to specify the settings and data used to run the experiment.  This includes setting `experiment_exit_score`, which should cause the run to complete before the `iterations` count is reached.\n",
        "\n",
        "|Property|Description|\n",
        "|-|-|\n",
        "|**task**|classification or regression|\n",
        "|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics: <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>average_precision_score_weighted</i><br><i>norm_macro_recall</i><br><i>precision_score_weighted</i>|\n",
        "|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
        "|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
        "|**preprocess**|Setting this to *True* enables AutoML to perform preprocessing on the input to handle *missing data*, and to perform some common *feature extraction*.|\n",
        "|**experiment_exit_score**|*double* value indicating the target for *primary_metric*. <br>Once the target is surpassed the run terminates.|\n",
        "|**blacklist_models**|*List* of *strings* indicating machine learning algorithms for AutoML to avoid in this run.<br><br> Allowed values for **Classification**<br><i>LogisticRegression</i><br><i>SGD</i><br><i>MultinomialNaiveBayes</i><br><i>BernoulliNaiveBayes</i><br><i>SVM</i><br><i>LinearSVM</i><br><i>KNN</i><br><i>DecisionTree</i><br><i>RandomForest</i><br><i>ExtremeRandomTrees</i><br><i>LightGBM</i><br><i>GradientBoosting</i><br><i>TensorFlowDNN</i><br><i>TensorFlowLinearClassifier</i><br><br>Allowed values for **Regression**<br><i>ElasticNet</i><br><i>GradientBoosting</i><br><i>DecisionTree</i><br><i>KNN</i><br><i>LassoLars</i><br><i>SGD</i><br><i>RandomForest</i><br><i>ExtremeRandomTrees</i><br><i>LightGBM</i><br><i>TensorFlowLinearRegressor</i><br><i>TensorFlowDNN</i>|\n",
        "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
        "|**y**|(sparse) array-like, shape = [n_samples, ], Multi-class targets.|"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "automl_config = AutoMLConfig(task = 'classification',\n",
        "                             debug_log = 'automl_errors.log',\n",
        "                             primary_metric = 'AUC_weighted',\n",
        "                             iteration_timeout_minutes = 60,\n",
        "                             iterations = 20,\n",
        "                             preprocess = True,\n",
        "                             experiment_exit_score = 0.9984,\n",
        "                             blacklist_models = ['KNN','LinearSVM'],\n",
        "                             verbosity = logging.INFO,\n",
        "                             X = X_train, \n",
        "                             y = y_train)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Call the `submit` method on the experiment object and pass the run configuration. Execution of local runs is synchronous. Depending on the data and the number of iterations this can run for a while.\n",
        "In this example, we specify `show_output = True` to print currently running iterations to the console."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "local_run = experiment.submit(automl_config, show_output = True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "local_run"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Results"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Widget for Monitoring Runs\n",
        "\n",
        "The widget will first report a \"loading\" status while running the first iteration. After completing the first iteration, an auto-updating graph and table will be shown. The widget will refresh once per minute, so you should see the graph update as child runs complete.\n",
        "\n",
        "**Note:** The widget displays a link at the bottom. Use this link to open a web interface to explore the individual run details."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.widgets import RunDetails\n",
        "RunDetails(local_run).show() "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "\n",
        "#### Retrieve All Child Runs\n",
        "You can also use SDK methods to fetch all the child runs and see individual metrics that we log."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "children = list(local_run.get_children())\n",
        "metricslist = {}\n",
        "for run in children:\n",
        "    properties = run.get_properties()\n",
        "    metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}\n",
        "    metricslist[int(properties['iteration'])] = metrics\n",
        "\n",
        "rundata = pd.DataFrame(metricslist).sort_index(1)\n",
        "rundata"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Retrieve the Best Model\n",
        "\n",
        "Below we select the best pipeline from our iterations. The `get_output` method returns the best run and the fitted model. The Model includes the pipeline and any pre-processing.  Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "best_run, fitted_model = local_run.get_output()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Best Model Based on Any Other Metric\n",
        "Show the run and the model which has the smallest `accuracy` value:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# lookup_metric = \"accuracy\"\n",
        "# best_run, fitted_model = local_run.get_output(metric = lookup_metric)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Model from a Specific Iteration\n",
        "Show the run and the model from the third iteration:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# iteration = 3\n",
        "# best_run, fitted_model = local_run.get_output(iteration = iteration)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### View the engineered names for featurized data\n",
        "Below we display the engineered feature names generated for the featurized data using the preprocessing featurization."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "fitted_model.named_steps['datatransformer'].get_engineered_feature_names()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### View the featurization summary\n",
        "Below we display the featurization that was performed on different raw features in the user data. For each raw feature in the user data, the following information is displayed:-\n",
        "- Raw feature name\n",
        "- Number of engineered features formed out of this raw feature\n",
        "- Type detected\n",
        "- If feature was dropped\n",
        "- List of feature transformations for the raw feature"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Get the featurization summary as a list of JSON\n",
        "featurization_summary = fitted_model.named_steps['datatransformer'].get_featurization_summary()\n",
        "# View the featurization summary as a pandas dataframe\n",
        "pd.DataFrame.from_records(featurization_summary)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "digits = datasets.load_digits()\n",
        "X_test = digits.data[:10, :]\n",
        "y_test = digits.target[:10]\n",
        "images = digits.images[:10]\n",
        "\n",
        "# Randomly select digits and test.\n",
        "for index in np.random.choice(len(y_test), 2, replace = False):\n",
        "    print(index)\n",
        "    predicted = fitted_model.predict(X_test[index:index + 1])[0]\n",
        "    label = y_test[index]\n",
        "    title = \"Label value = %d  Predicted value = %d \" % (label, predicted)\n",
        "    fig = plt.figure(1, figsize=(3,3))\n",
        "    ax1 = fig.add_axes((0,0,.8,.8))\n",
        "    ax1.set_title(title)\n",
        "    plt.imshow(images[index], cmap = plt.cm.gray_r, interpolation = 'nearest')\n",
        "    plt.show()\n"
      ]
    }
  ],
  "metadata": {
    "authors": [
      {
        "name": "savitam"
      }
    ],
    "kernelspec": {
      "display_name": "Python 3.6",
      "language": "python",
      "name": "python36"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.6"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
--- a/how-to-use-azureml/automated-machine-learning/missing-data-blacklist-early-termination/auto-ml-missing-data-blacklist-early-termination.yml
+++ b/how-to-use-azureml/automated-machine-learning/missing-data-blacklist-early-termination/auto-ml-missing-data-blacklist-early-termination.yml
@@ -0,0 +1,8 @@
 name: auto-ml-missing-data-blacklist-early-termination
 dependencies:
 - pip:
  - azureml-sdk
  - azureml-train-automl
  - azureml-widgets
  - matplotlib
  - pandas_ml
--- a/how-to-use-azureml/automated-machine-learning/model-explanation-remote-amlcompute/auto-ml-model-explanations-remote-compute.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/model-explanation-remote-amlcompute/auto-ml-model-explanations-remote-compute.ipynb
@@ -0,0 +1,593 @@
 {
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
        "\n",
        "Licensed under the MIT License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/classification-bank-marketing/auto-ml-classification-bank-marketing.png)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Automated Machine Learning\n",
        "_**Regression on remote compute using Computer Hardware dataset with model explanations**_\n",
        "\n",
        "## Contents\n",
        "1. [Introduction](#Introduction)\n",
        "1. [Setup](#Setup)\n",
        "1. [Train](#Train)\n",
        "1. [Results](#Results)\n",
        "1. [Explanations](#Explanations)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Introduction\n",
        "\n",
        "In this example we use the Hardware Performance Dataset to showcase how you can use AutoML for a simple regression problem. After training AutoML models for this regression data set, we show how you can compute model explanations on your remote compute using a sample explainer script.\n",
        "\n",
        "If you are using an Azure Machine Learning Notebook VM, you are all set.  Otherwise, go through the [configuration](../../../configuration.ipynb)  notebook first if you haven't already to establish your connection to the AzureML Workspace. \n",
        "\n",
        "In this notebook you will learn how to:\n",
        "1. Create an `Experiment` in an existing `Workspace`.\n",
        "2. Configure AutoML using `AutoMLConfig`.\n",
        "3. Train the model using remote compute.\n",
        "4. Explore the results.\n",
        "5. Setup remote compute for computing the model explanations for a given AutoML model.\n",
        "6. Start an AzureML experiment on your remote compute to compute explanations for an AutoML model.\n",
        "7. Download the feature importance for engineered features and visualize the explanations for engineered features. \n",
        "8. Download the feature importance for raw features and visualize the explanations for raw features. \n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Setup\n",
        "\n",
        "As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import logging\n",
        "\n",
        "from matplotlib import pyplot as plt\n",
        "import pandas as pd\n",
        "import os\n",
        "\n",
        "import azureml.core\n",
        "from azureml.core.experiment import Experiment\n",
        "from azureml.core.workspace import Workspace\n",
        "from azureml.core.dataset import Dataset\n",
        "from azureml.train.automl import AutoMLConfig"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "ws = Workspace.from_config()\n",
        "\n",
        "# choose a name for experiment\n",
        "experiment_name = 'automl-regression-computer-hardware'\n",
        "\n",
        "experiment=Experiment(ws, experiment_name)\n",
        "\n",
        "output = {}\n",
        "output['SDK version'] = azureml.core.VERSION\n",
        "output['Subscription ID'] = ws.subscription_id\n",
        "output['Workspace'] = ws.name\n",
        "output['Resource Group'] = ws.resource_group\n",
        "output['Location'] = ws.location\n",
        "output['Experiment Name'] = experiment.name\n",
        "pd.set_option('display.max_colwidth', -1)\n",
        "outputDf = pd.DataFrame(data = output, index = [''])\n",
        "outputDf.T"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Create or Attach existing AmlCompute\n",
        "You will need to create a compute target for your AutoML run. In this tutorial, you create AmlCompute as your training compute resource.\n",
        "#### Creation of AmlCompute takes approximately 5 minutes. \n",
        "If the AmlCompute with that name is already in your workspace this code will skip the creation process.\n",
        "As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read this article on the default limits and how to request more quota."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.core.compute import AmlCompute\n",
        "from azureml.core.compute import ComputeTarget\n",
        "\n",
        "# Choose a name for your cluster.\n",
        "amlcompute_cluster_name = \"automlcl\"\n",
        "\n",
        "found = False\n",
        "# Check if this compute target already exists in the workspace.\n",
        "cts = ws.compute_targets\n",
        "if amlcompute_cluster_name in cts and cts[amlcompute_cluster_name].type == 'AmlCompute':\n",
        "    found = True\n",
        "    print('Found existing compute target.')\n",
        "    compute_target = cts[amlcompute_cluster_name]\n",
        "    \n",
        "if not found:\n",
        "    print('Creating a new compute target...')\n",
        "    provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_D2_V2\", # for GPU, use \"STANDARD_NC6\"\n",
        "                                                                #vm_priority = 'lowpriority', # optional\n",
        "                                                                max_nodes = 6)\n",
        "\n",
        "    # Create the cluster.\n",
        "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, provisioning_config)\n",
        "    \n",
        "print('Checking cluster status...')\n",
        "# Can poll for a minimum number of nodes and for a specific timeout.\n",
        "# If no min_node_count is provided, it will use the scale settings for the cluster.\n",
        "compute_target.wait_for_completion(show_output = True, min_node_count = None, timeout_in_minutes = 20)\n",
        "    \n",
        "# For a more detailed view of current AmlCompute status, use get_status()."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Conda Dependecies for AutoML training experiment\n",
        "\n",
        "Create the conda dependencies for running AutoML experiment on remote compute."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.core.runconfig import RunConfiguration\n",
        "from azureml.core.conda_dependencies import CondaDependencies\n",
        "import pkg_resources\n",
        "\n",
        "# create a new RunConfig object\n",
        "conda_run_config = RunConfiguration(framework=\"python\")\n",
        "\n",
        "# Set compute target to AmlCompute\n",
        "conda_run_config.target = compute_target\n",
        "conda_run_config.environment.docker.enabled = True\n",
        "\n",
        "cd = CondaDependencies.create(conda_packages=['numpy','py-xgboost<=0.80'])\n",
        "conda_run_config.environment.python.conda_dependencies = cd"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Setup Training and Test Data for AutoML experiment\n",
        "\n",
        "Here we create the train and test datasets for hardware performance dataset. We also register the datasets in your workspace using a name so that these datasets may be accessed from the remote compute."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Data source\n",
        "data = \"https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/machineData.csv\"\n",
        "\n",
        "# Create dataset from the url\n",
        "dataset = Dataset.Tabular.from_delimited_files(data)\n",
        "\n",
        "# Split the dataset into train and test datasets\n",
        "train_dataset, test_dataset = dataset.random_split(percentage=0.8, seed=223)\n",
        "\n",
        "# Register the train dataset with your workspace\n",
        "train_dataset.register(workspace = ws, name = 'hardware_performance_train_dataset',\n",
        "                       description = 'hardware performance training data',\n",
        "                       create_new_version=True)\n",
        "\n",
        "# Register the test dataset with your workspace\n",
        "test_dataset.register(workspace = ws, name = 'hardware_performance_test_dataset',\n",
        "                      description = 'hardware performance test data',\n",
        "                      create_new_version=True)\n",
        "\n",
        "# Drop the labeled column from the train dataset\n",
        "X_train = train_dataset.drop_columns(columns=['ERP'])\n",
        "y_train = train_dataset.keep_columns(columns=['ERP'], validate=True)\n",
        "\n",
        "# Drop the labeled column from the test dataset\n",
        "X_test = test_dataset.drop_columns(columns=['ERP'])  \n",
        "\n",
        "# Display the top rows in the train dataset\n",
        "X_train.take(5).to_pandas_dataframe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Train\n",
        "\n",
        "Instantiate an `AutoMLConfig` object to specify the settings and data used to run the experiment.\n",
        "\n",
        "|Property|Description|\n",
        "|-|-|\n",
        "|**task**|classification or regression|\n",
        "|**primary_metric**|This is the metric that you want to optimize. Regression supports the following primary metrics: <br><i>spearman_correlation</i><br><i>normalized_root_mean_squared_error</i><br><i>r2_score</i><br><i>normalized_mean_absolute_error</i>|\n",
        "|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
        "|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
        "|**n_cross_validations**|Number of cross validation splits.|\n",
        "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
        "|**y**|(sparse) array-like, shape = [n_samples, ], targets values.|\n",
        "\n",
        "**_You can find more information about primary metrics_** [here](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-auto-train#primary-metric)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "automl_settings = {\n",
        "    \"iteration_timeout_minutes\": 5,\n",
        "    \"iterations\": 10,\n",
        "    \"n_cross_validations\": 2,\n",
        "    \"primary_metric\": 'spearman_correlation',\n",
        "    \"preprocess\": True,\n",
        "    \"max_concurrent_iterations\": 1,\n",
        "    \"verbosity\": logging.INFO,\n",
        "}\n",
        "\n",
        "automl_config = AutoMLConfig(task = 'regression',\n",
        "                             debug_log = 'automl_errors_model_exp.log',\n",
        "                             run_configuration=conda_run_config,\n",
        "                             X = X_train,\n",
        "                             y = y_train,\n",
        "                             **automl_settings\n",
        "                            )"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Call the `submit` method on the experiment object and pass the run configuration. Execution of local runs is synchronous. Depending on the data and the number of iterations this can run for a while.\n",
        "In this example, we specify `show_output = True` to print currently running iterations to the console."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "remote_run = experiment.submit(automl_config, show_output = True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "remote_run"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Results"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Widget for Monitoring Runs\n",
        "\n",
        "The widget will first report a \"loading\" status while running the first iteration. After completing the first iteration, an auto-updating graph and table will be shown. The widget will refresh once per minute, so you should see the graph update as child runs complete.\n",
        "\n",
        "**Note:** The widget displays a link at the bottom. Use this link to open a web interface to explore the individual run details"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.widgets import RunDetails\n",
        "RunDetails(remote_run).show() "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Explanations\n",
        "This section will walk you through the workflow to compute model explanations for an AutoML model on your remote compute.\n",
        "\n",
        "### Retrieve any AutoML Model for explanations\n",
        "\n",
        "Below we select the some AutoML pipeline from our iterations. The `get_output` method returns the a AutoML run and the fitted model for the last invocation. Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "automl_run, fitted_model = remote_run.get_output(iteration=5)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Setup model explanation run on the remote compute\n",
        "The following section provides details on how to setup an AzureML experiment to run model explanations for an AutoML model on your remote compute."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Sample script used for computing explanations\n",
        "View the sample script for computing the model explanations for your AutoML model on remote compute."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "with open('train_explainer.py', 'r') as cefr:\n",
        "    print(cefr.read())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Substitute values in your sample script\n",
        "The following cell shows how you change the values in the sample script so that you can change the sample script according to your experiment and dataset."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import shutil\n",
        "\n",
        "# create script folder\n",
        "script_folder = './sample_projects/automl-regression-computer-hardware'\n",
        "if not os.path.exists(script_folder):\n",
        "    os.makedirs(script_folder)\n",
        "\n",
        "# Copy the sample script to script folder.\n",
        "shutil.copy('train_explainer.py', script_folder)\n",
        "\n",
        "# Create the explainer script that will run on the remote compute.\n",
        "script_file_name = script_folder + '/train_explainer.py'\n",
        "\n",
        "# Open the sample script for modification\n",
        "with open(script_file_name, 'r') as cefr:\n",
        "    content = cefr.read()\n",
        "\n",
        "# Replace the values in train_explainer.py file with the appropriate values\n",
        "content = content.replace('<<experimnet_name>>', automl_run.experiment.name) # your experiment name.\n",
        "content = content.replace('<<run_id>>', automl_run.id) # Run-id of the AutoML run for which you want to explain the model.\n",
        "content = content.replace('<<target_column_name>>', 'ERP') # Your target column name\n",
        "content = content.replace('<<task>>', 'regression') # Training task type\n",
        "# Name of your training dataset register with your workspace\n",
        "content = content.replace('<<train_dataset_name>>', 'hardware_performance_train_dataset') \n",
        "# Name of your test dataset register with your workspace\n",
        "content = content.replace('<<test_dataset_name>>', 'hardware_performance_test_dataset')\n",
        "\n",
        "# Write sample file into your script folder.\n",
        "with open(script_file_name, 'w') as cefw:\n",
        "    cefw.write(content)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Create conda configuration for model explanations experiment\n",
        "We need `azureml-explain-model`, `azureml-train-automl` and `azureml-core` packages for computing model explanations for your AutoML model on remote compute."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.core.runconfig import RunConfiguration\n",
        "from azureml.core.conda_dependencies import CondaDependencies\n",
        "import pkg_resources\n",
        "\n",
        "# create a new RunConfig object\n",
        "conda_run_config = RunConfiguration(framework=\"python\")\n",
        "\n",
        "# Set compute target to AmlCompute\n",
        "conda_run_config.target = compute_target\n",
        "conda_run_config.environment.docker.enabled = True\n",
        "azureml_pip_packages = [\n",
        "    'azureml-train-automl', 'azureml-core', 'azureml-explain-model'\n",
        "]\n",
        "\n",
        "# specify CondaDependencies obj\n",
        "conda_run_config.environment.python.conda_dependencies = CondaDependencies.create(\n",
        "    conda_packages=['scikit-learn', 'numpy','py-xgboost<=0.80'],\n",
        "    pip_packages=azureml_pip_packages)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Submit the experiment for model explanations\n",
        "Submit the experiment with the above `run_config` and the sample script for computing explanations."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Now submit a run on AmlCompute for model explanations\n",
        "from azureml.core.script_run_config import ScriptRunConfig\n",
        "\n",
        "script_run_config = ScriptRunConfig(source_directory=script_folder,\n",
        "                                    script='train_explainer.py',\n",
        "                                    run_config=conda_run_config)\n",
        "\n",
        "run = experiment.submit(script_run_config)\n",
        "\n",
        "# Show run details\n",
        "run"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "%%time\n",
        "# Shows output of the run on stdout.\n",
        "run.wait_for_completion(show_output=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Feature importance  and  explanation dashboard\n",
        "In this section we describe how you can download the explanation results from the explanations experiment and visualize the feature importance for your AutoML model. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Setup for visualizing the model explanation results\n",
        "For visualizing the explanation results for the *fitted_model* we need to perform the following steps:-\n",
        "1. Featurize test data samples.\n",
        "\n",
        "The *automl_explainer_setup_obj* contains all the structures from above list. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.train.automl.automl_explain_utilities import AutoMLExplainerSetupClass, automl_setup_model_explanations\n",
        "explainer_setup_class = automl_setup_model_explanations(fitted_model, 'regression', X_test=X_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Download engineered feature importance from artifact store\n",
        "You can use *ExplanationClient* to download the engineered feature explanations from the artifact store of the *automl_run*. You can also use ExplanationDashboard to view the dash board visualization of the feature importance values of the engineered features."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.explain.model._internal.explanation_client import ExplanationClient\n",
        "from azureml.contrib.interpret.visualize import ExplanationDashboard\n",
        "client = ExplanationClient.from_run(automl_run)\n",
        "engineered_explanations = client.download_model_explanation(raw=False)\n",
        "print(engineered_explanations.get_feature_importance_dict())\n",
        "ExplanationDashboard(engineered_explanations, explainer_setup_class.automl_estimator, explainer_setup_class.X_test_transform)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Download raw feature importance from artifact store\n",
        "You can use *ExplanationClient* to download the raw feature explanations from the artifact store of the *automl_run*. You can also use ExplanationDashboard to view the dash board visualization of the feature importance values of the raw features."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "raw_explanations = client.download_model_explanation(raw=True)\n",
        "print(raw_explanations.get_feature_importance_dict())\n",
        "ExplanationDashboard(raw_explanations, explainer_setup_class.automl_pipeline, explainer_setup_class.X_test_raw)"
      ]
    }
  ],
  "metadata": {
    "authors": [
      {
        "name": "v-rasav"
      }
    ],
    "kernelspec": {
      "display_name": "Python 3.6",
      "language": "python",
      "name": "python36"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.7"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
--- a/how-to-use-azureml/automated-machine-learning/model-explanation-remote-amlcompute/auto-ml-model-explanations-remote-compute.yml
+++ b/how-to-use-azureml/automated-machine-learning/model-explanation-remote-amlcompute/auto-ml-model-explanations-remote-compute.yml
@@ -0,0 +1,11 @@
 name: auto-ml-model-explanations-remote-compute
 dependencies:
 - pip:
  - azureml-sdk
  - interpret
  - azureml-train-automl
  - azureml-widgets
  - matplotlib
  - pandas_ml
  - azureml-explain-model
  - azureml-contrib-interpret
--- a/how-to-use-azureml/automated-machine-learning/model-explanation-remote-amlcompute/train_explainer.py
+++ b/how-to-use-azureml/automated-machine-learning/model-explanation-remote-amlcompute/train_explainer.py
@@ -0,0 +1,64 @@
 # Copyright (c) Microsoft. All rights reserved.
 # Licensed under the MIT license.
 import os
 from azureml.core.run import Run
 from azureml.core.experiment import Experiment
 from sklearn.externals import joblib
 from azureml.core.dataset import Dataset
 from azureml.train.automl.automl_explain_utilities import AutoMLExplainerSetupClass, automl_setup_model_explanations
 from azureml.explain.model.mimic.models.lightgbm_model import LGBMExplainableModel
 from azureml.explain.model.mimic_wrapper import MimicWrapper
 from automl.client.core.common.constants import MODEL_PATH
 OUTPUT_DIR = './outputs/'
 os.makedirs(OUTPUT_DIR, exist_ok=True)
 # Get workspace from the run context
 run = Run.get_context()
 ws = run.experiment.workspace
 # Get the AutoML run object from the experiment name and the workspace
 experiment = Experiment(ws, '<<experimnet_name>>')
 automl_run = Run(experiment=experiment, run_id='<<run_id>>')
 # Download the best model from the artifact store
 automl_run.download_file(name=MODEL_PATH, output_file_path='model.pkl')
 # Load the AutoML model into memory
 fitted_model = joblib.load('model.pkl')
 # Get the train dataset from the workspace
 train_dataset = Dataset.get_by_name(workspace=ws, name='<<train_dataset_name>>')
 # Drop the lablled column to get the training set.
 X_train = train_dataset.drop_columns(columns=['<<target_column_name>>'])
 y_train = train_dataset.keep_columns(columns=['<<target_column_name>>'], validate=True)
 # Get the train dataset from the workspace
 test_dataset = Dataset.get_by_name(workspace=ws, name='<<test_dataset_name>>')
 # Drop the lablled column to get the testing set.
 X_test = test_dataset.drop_columns(columns=['<<target_column_name>>'])
 # Setup the class for explaining the AtuoML models
 automl_explainer_setup_obj = automl_setup_model_explanations(fitted_model, '<<task>>',
                                                             X=X_train, X_test=X_test,
                                                             y=y_train)
 # Initialize the Mimic Explainer
 explainer = MimicWrapper(ws, automl_explainer_setup_obj.automl_estimator, LGBMExplainableModel,
                         init_dataset=automl_explainer_setup_obj.X_transform, run=automl_run,
                         features=automl_explainer_setup_obj.engineered_feature_names,
                         feature_maps=[automl_explainer_setup_obj.feature_map],
                         classes=automl_explainer_setup_obj.classes)
 # Compute the engineered explanations
 engineered_explanations = explainer.explain(['local', 'global'],
                                            eval_dataset=automl_explainer_setup_obj.X_test_transform)
 # Compute the raw explanations
 raw_explanations = explainer.explain(['local', 'global'], get_raw=True,
                                     raw_feature_names=automl_explainer_setup_obj.raw_feature_names,
                                     eval_dataset=automl_explainer_setup_obj.X_test_transform)
 print("Engineered and raw explanations computed successfully")
--- a/how-to-use-azureml/automated-machine-learning/model-explanation/auto-ml-model-explanation.ipynb
+++ b/how-to-use-azureml/automated-machine-learning/model-explanation/auto-ml-model-explanation.ipynb
@@ -0,0 +1,632 @@
 {
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
        "\n",
        "Licensed under the MIT License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/model-explanation/auto-ml-model-explanation.png)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Automated Machine Learning\n",
        "_**Explain classification model, visualize the explanation and operationalize the explainer along with AutoML model**_\n",
        "\n",
        "## Contents\n",
        "1. [Introduction](#Introduction)\n",
        "1. [Setup](#Setup)\n",
        "1. [Data](#Data)\n",
        "1. [Train](#Train)\n",
        "1. [Results](#Results)\n",
        "1. [Explanations](#Explanations)\n",
        "1. [Operationailze](#Operationailze)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Introduction\n",
        "In this example we use the sklearn's [iris dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_iris.html) to showcase how you can use the AutoML Classifier for a simple classification problem.\n",
        "\n",
        "Make sure you have executed the [configuration](../../../configuration.ipynb) before running this notebook.\n",
        "\n",
        "In this notebook you would see\n",
        "1. Creating an Experiment in an existing Workspace\n",
        "2. Instantiating AutoMLConfig\n",
        "3. Training the Model using local compute and explain the model\n",
        "4. Visualization model's feature importance in widget\n",
        "5. Explore any model's explanation\n",
        "6. Operationalize the AutoML model and the explaination model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Setup\n",
        "\n",
        "As part of the setup you have already created a <b>Workspace</b>. For AutoML you would need to create an <b>Experiment</b>. An <b>Experiment</b> is a named object in a <b>Workspace</b>, which is used to run experiments."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import logging\n",
        "\n",
        "import pandas as pd\n",
        "import azureml.core\n",
        "from azureml.core.experiment import Experiment\n",
        "from azureml.core.workspace import Workspace\n",
        "from azureml.train.automl import AutoMLConfig\n",
        "from azureml.core.dataset import Dataset\n",
        "from azureml.explain.model._internal.explanation_client import ExplanationClient"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "ws = Workspace.from_config()\n",
        "\n",
        "# choose a name for experiment\n",
        "experiment_name = 'automl-model-explanation'\n",
        "\n",
        "experiment=Experiment(ws, experiment_name)\n",
        "\n",
        "output = {}\n",
        "output['SDK version'] = azureml.core.VERSION\n",
        "output['Subscription ID'] = ws.subscription_id\n",
        "output['Workspace Name'] = ws.name\n",
        "output['Resource Group'] = ws.resource_group\n",
        "output['Location'] = ws.location\n",
        "output['Experiment Name'] = experiment.name\n",
        "pd.set_option('display.max_colwidth', -1)\n",
        "outputDf = pd.DataFrame(data = output, index = [''])\n",
        "outputDf.T"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Training Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "train_data = \"https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/bankmarketing_train.csv\"\n",
        "train_dataset = Dataset.Tabular.from_delimited_files(train_data)\n",
        "X_train = train_dataset.drop_columns(columns=['y']).to_pandas_dataframe()\n",
        "y_train = train_dataset.keep_columns(columns=['y'], validate=True).to_pandas_dataframe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Test Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "test_data = \"https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/bankmarketing_test.csv\"\n",
        "test_dataset = Dataset.Tabular.from_delimited_files(test_data)\n",
        "X_test = test_dataset.drop_columns(columns=['y']).to_pandas_dataframe()\n",
        "y_test = test_dataset.keep_columns(columns=['y'], validate=True).to_pandas_dataframe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Train\n",
        "\n",
        "Instantiate a AutoMLConfig object. This defines the settings and data used to run the experiment.\n",
        "\n",
        "|Property|Description|\n",
        "|-|-|\n",
        "|**task**|classification or regression|\n",
        "|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics: <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>average_precision_score_weighted</i><br><i>norm_macro_recall</i><br><i>precision_score_weighted</i>|\n",
        "|**max_time_sec**|Time limit in minutes for each iterations|\n",
        "|**iterations**|Number of iterations. In each iteration Auto ML trains the data with a specific pipeline|\n",
        "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
        "|**y**|(sparse) array-like, shape = [n_samples, ], Multi-class targets.|\n",
        "|**model_explainability**|Indicate to explain each trained pipeline or not |"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "automl_config = AutoMLConfig(task = 'classification',\n",
        "                             debug_log = 'automl_errors.log',\n",
        "                             primary_metric = 'AUC_weighted',\n",
        "                             iteration_timeout_minutes = 200,\n",
        "                             iterations = 10,\n",
        "                             verbosity = logging.INFO,\n",
        "                             preprocess = True,\n",
        "                             X = X_train, \n",
        "                             y = y_train,\n",
        "                             n_cross_validations = 5,\n",
        "                             model_explainability=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "You can call the submit method on the experiment object and pass the run configuration. For Local runs the execution is synchronous. Depending on the data and number of iterations this can run for while.\n",
        "You will see the currently running iterations printing to the console."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "local_run = experiment.submit(automl_config, show_output=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "local_run"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Results"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Widget for monitoring runs\n",
        "\n",
        "The widget will sit on \"loading\" until the first iteration completed, then you will see an auto-updating graph and table show up. It refreshed once per minute, so you should see the graph update as child runs complete.\n",
        "\n",
        "NOTE: The widget displays a link at the bottom. This links to a web-ui to explore the individual run details."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.widgets import RunDetails\n",
        "RunDetails(local_run).show() "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Retrieve the Best Model\n",
        "\n",
        "Below we select the best pipeline from our iterations. The *get_output* method on automl_classifier returns the best run and the fitted model for the last *fit* invocation. There are overloads on *get_output* that allow you to retrieve the best run and fitted model for *any* logged metric or a particular *iteration*."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "best_run, fitted_model = local_run.get_output()\n",
        "print(best_run)\n",
        "print(fitted_model)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Best Model 's explanation\n",
        "\n",
        "Retrieve the explanation from the *best_run* which includes explanations for engineered features and raw features."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Download engineered feature importance from artifact store\n",
        "You can use *ExplanationClient* to download the engineered feature explanations from the artifact store of the *best_run*."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "client = ExplanationClient.from_run(best_run)\n",
        "engineered_explanations = client.download_model_explanation(raw=False)\n",
        "print(engineered_explanations.get_feature_importance_dict())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Download raw feature importance from artifact store\n",
        "You can use *ExplanationClient* to download the raw feature explanations from the artifact store of the *best_run*."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "client = ExplanationClient.from_run(best_run)\n",
        "raw_explanations = client.download_model_explanation(raw=True)\n",
        "print(raw_explanations.get_feature_importance_dict())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Explanations\n",
        "In this section, we will show how to compute model explanations and visualize the explanations using azureml-explain-model package. Besides retrieving an existing model explanation for an AutoML model, you can also explain your AutoML model with different test data. The following steps will allow you to compute and visualize engineered feature importance and raw feature importance based on your test data. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Retrieve any other AutoML model from training"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "automl_run, fitted_model = local_run.get_output(iteration=0)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Setup the model explanations for AutoML models\n",
        "The *fitted_model* can generate the following which will be used for getting the engineered and raw feature explanations using *automl_setup_model_explanations*:-\n",
        "1. Featurized data from train samples/test samples \n",
        "2. Gather engineered and raw feature name lists\n",
        "3. Find the classes in your labeled column in classification scenarios\n",
        "\n",
        "The *automl_explainer_setup_obj* contains all the structures from above list. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.train.automl.automl_explain_utilities import AutoMLExplainerSetupClass, automl_setup_model_explanations\n",
        "\n",
        "automl_explainer_setup_obj = automl_setup_model_explanations(fitted_model, X=X_train, \n",
        "                                                             X_test=X_test, y=y_train, \n",
        "                                                             task='classification')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Initialize the Mimic Explainer for feature importance\n",
        "For explaining the AutoML models, use the *MimicWrapper* from *azureml.explain.model* package. The *MimicWrapper* can be initialized with fields in *automl_explainer_setup_obj*, your workspace and a LightGBM model which acts as a surrogate model to explain the AutoML model (*fitted_model* here). The *MimicWrapper* also takes the *automl_run* object where the raw and engineered explanations will be uploaded."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.explain.model.mimic.models.lightgbm_model import LGBMExplainableModel\n",
        "from azureml.explain.model.mimic_wrapper import MimicWrapper\n",
        "explainer = MimicWrapper(ws, automl_explainer_setup_obj.automl_estimator, LGBMExplainableModel, \n",
        "                         init_dataset=automl_explainer_setup_obj.X_transform, run=automl_run,\n",
        "                         features=automl_explainer_setup_obj.engineered_feature_names, \n",
        "                         feature_maps=[automl_explainer_setup_obj.feature_map],\n",
        "                         classes=automl_explainer_setup_obj.classes)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Use Mimic Explainer for computing and visualizing engineered feature importance\n",
        "The *explain()* method in *MimicWrapper* can be called with the transformed test samples to get the feature importance for the generated engineered features. You can also use *ExplanationDashboard* to view the dash board visualization of the feature importance values of the generated engineered features by AutoML featurizers."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "engineered_explanations = explainer.explain(['local', 'global'], eval_dataset=automl_explainer_setup_obj.X_test_transform)\n",
        "print(engineered_explanations.get_feature_importance_dict())\n",
        "from azureml.contrib.interpret.visualize import ExplanationDashboard\n",
        "ExplanationDashboard(engineered_explanations, automl_explainer_setup_obj.automl_estimator, automl_explainer_setup_obj.X_test_transform)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Use Mimic Explainer for computing and visualizing raw feature importance\n",
        "The *explain()* method in *MimicWrapper* can be again called with the transformed test samples and setting *get_raw* to *True* to get the feature importance for the raw features. You can also use *ExplanationDashboard* to view the dash board visualization of the feature importance values of the raw features."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "raw_explanations = explainer.explain(['local', 'global'], get_raw=True, \n",
        "                                     raw_feature_names=automl_explainer_setup_obj.raw_feature_names,\n",
        "                                     eval_dataset=automl_explainer_setup_obj.X_test_transform)\n",
        "print(raw_explanations.get_feature_importance_dict())\n",
        "from azureml.contrib.interpret.visualize import ExplanationDashboard\n",
        "ExplanationDashboard(raw_explanations, automl_explainer_setup_obj.automl_pipeline, automl_explainer_setup_obj.X_test_raw)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Operationailze\n",
        "In this section we will show how you can operationalize an AutoML model and the explainer which was used to compute the explanations in the previous section.\n",
        "\n",
        "#### Register the AutoML model and the scoring explainer\n",
        "We use the *TreeScoringExplainer* from *azureml.explain.model* package to create the scoring explainer which will be used to compute the raw and engineered feature importances at the inference time. Note that, we initialize the scoring explainer with the *feature_map* that was computed previously. The *feature_map* will be used by the scoring explainer to return the raw feature importance.\n",
        "\n",
        "In the cell below, we pickle the scoring explainer and register the AutoML model and the scoring explainer with the Model Management Service."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.explain.model.scoring.scoring_explainer import TreeScoringExplainer, save\n",
        "\n",
        "# Initialize the ScoringExplainer\n",
        "scoring_explainer = TreeScoringExplainer(explainer.explainer, feature_maps=[automl_explainer_setup_obj.feature_map])\n",
        "\n",
        "# Pickle scoring explainer locally\n",
        "save(scoring_explainer, exist_ok=True)\n",
        "\n",
        "# Register trained automl model present in the 'outputs' folder in the artifacts\n",
        "original_model = automl_run.register_model(model_name='automl_model', \n",
        "                                           model_path='outputs/model.pkl')\n",
        "\n",
        "# Register scoring explainer\n",
        "automl_run.upload_file('scoring_explainer.pkl', 'scoring_explainer.pkl')\n",
        "scoring_explainer_model = automl_run.register_model(model_name='scoring_explainer', model_path='scoring_explainer.pkl')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Create the conda dependencies for setting up the service\n",
        "We need to create the conda dependencies comprising of the *azureml-explain-model*, *azureml-train-automl* and *azureml-defaults* packages. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.core.conda_dependencies import CondaDependencies \n",
        "\n",
        "azureml_pip_packages = [\n",
        "    'azureml-explain-model', 'azureml-train-automl', 'azureml-defaults'\n",
        "]\n",
        " \n",
        "\n",
        "# specify CondaDependencies obj\n",
        "myenv = CondaDependencies.create(conda_packages=['scikit-learn', 'pandas', 'numpy', 'py-xgboost<=0.80'],\n",
        "                                 pip_packages=azureml_pip_packages,\n",
        "                                 pin_sdk_version=True)\n",
        "\n",
        "with open(\"myenv.yml\",\"w\") as f:\n",
        "    f.write(myenv.serialize_to_string())\n",
        "\n",
        "with open(\"myenv.yml\",\"r\") as f:\n",
        "    print(f.read())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### View your scoring file"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "with open(\"score_local_explain.py\",\"r\") as f:\n",
        "    print(f.read())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Deploy the service\n",
        "In the cell below, we deploy the service using the conda file and the scoring file from the previous steps. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.core.webservice import Webservice\n",
        "from azureml.core.model import InferenceConfig\n",
        "from azureml.core.webservice import AciWebservice\n",
        "from azureml.core.model import Model\n",
        "\n",
        "aciconfig = AciWebservice.deploy_configuration(cpu_cores=1, \n",
        "                                               memory_gb=1, \n",
        "                                               tags={\"data\": \"Bank Marketing\",  \n",
        "                                                     \"method\" : \"local_explanation\"}, \n",
        "                                               description='Get local explanations for Bank marketing test data')\n",
        "\n",
        "inference_config = InferenceConfig(runtime= \"python\", \n",
        "                                   entry_script=\"score_local_explain.py\",\n",
        "                                   conda_file=\"myenv.yml\")\n",
        "\n",
        "# Use configs and models generated above\n",
        "service = Model.deploy(ws, 'model-scoring', [scoring_explainer_model, original_model], inference_config, aciconfig)\n",
        "service.wait_for_deployment(show_output=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### View the service logs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "service.get_logs()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Inference using some test data\n",
        "Inference using some test data to see the predicted value from autml model, view the engineered feature importance for the predicted value and raw feature importance for the predicted value."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "if service.state == 'Healthy':\n",
        "    # Serialize the first row of the test data into json\n",
        "    X_test_json = X_test[:1].to_json(orient='records')\n",
        "    print(X_test_json)\n",
        "    # Call the service to get the predictions and the engineered and raw explanations\n",
        "    output = service.run(X_test_json)\n",
        "    # Print the predicted value\n",
        "    print(output['predictions'])\n",
        "    # Print the engineered feature importances for the predicted value\n",
        "    print(output['engineered_local_importance_values'])\n",
        "    # Print the raw feature importances for the predicted value\n",
        "    print(output['raw_local_importance_values'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Delete the service\n",
        "Delete the service once you have finished inferencing."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "service.delete()"
      ]
    }
  ],
  "metadata": {
    "authors": [
      {
        "name": "xif"
      }
    ],
    "kernelspec": {
      "display_name": "Python 3.6",
      "language": "python",
      "name": "python36"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.7"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
--- a/Show More
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		`@@ -1 +0,0 @@`
			{"cells":[{"cell_type":"markdown","source":["Azure ML & Azure Databricks notebooks by Parashar Shah.\n\nCopyright (c) Microsoft Corporation. All rights reserved.\n\nLicensed under the MIT License."],"metadata":{}},{"cell_type":"markdown","source":["Please ensure you have run this notebook before proceeding."],"metadata":{}},{"cell_type":"markdown","source":["Now we support installing AML SDK as library from GUI. When attaching a library follow this https://docs.databricks.com/user-guide/libraries.html and add the below string as your PyPi package (during private preview). You can select the option to attach the library to all clusters or just one cluster.\n\nProvide this full string to install the SDK:\n\nazureml-sdk[databricks]"],"metadata":{}},{"cell_type":"code","source":["import azureml.core\n\n# Check core SDK version number - based on build number of preview/master.\nprint(\"SDK version:\", azureml.core.VERSION)"],"metadata":{},"outputs":[],"execution_count":4},{"cell_type":"code","source":["subscription_id = \"<your-subscription-id>\"\nresource_group = \"<your-existing-resource-group>\"\nworkspace_name = \"<a-new-or-existing-workspace; it is unrelated to Databricks workspace>\"\nworkspace_region = \"<your-resource group-region>\""],"metadata":{},"outputs":[],"execution_count":5},{"cell_type":"code","source":["# import the Workspace class and check the azureml SDK version\n# exist_ok checks if workspace exists or not.\n\nfrom azureml.core import Workspace\n\nws = Workspace.create(name = workspace_name,\n subscription_id = subscription_id,\n resource_group = resource_group, \n location = workspace_region,\n exist_ok=True)\n\nws.get_details()"],"metadata":{},"outputs":[],"execution_count":6},{"cell_type":"code","source":["ws = Workspace(workspace_name = workspace_name,\n subscription_id = subscription_id,\n resource_group = resource_group)\n\n# persist the subscription id, resource group name, and workspace name in aml_config/config.json.\nws.write_config()"],"metadata":{},"outputs":[],"execution_count":7},{"cell_type":"code","source":["%sh\ncat /databricks/driver/aml_config/config.json"],"metadata":{},"outputs":[],"execution_count":8},{"cell_type":"code","source":["# import the Workspace class and check the azureml SDK version\nfrom azureml.core import Workspace\n\nws = Workspace.from_config()\nprint('Workspace name: ' + ws.name, \n 'Azure region: ' + ws.location, \n 'Subscription id: ' + ws.subscription_id, \n 'Resource group: ' + ws.resource_group, sep = '\\n')"],"metadata":{},"outputs":[],"execution_count":9},{"cell_type":"code","source":["dbutils.notebook.exit(\"success\")"],"metadata":{},"outputs":[],"execution_count":10},{"cell_type":"code","source":[""],"metadata":{},"outputs":[],"execution_count":11}],"metadata":{"name":"01.Installation_and_Configuration","notebookId":3874566296719377},"nbformat":4,"nbformat_minor":0}