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00.configuration.ipynb
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00.configuration.ipynb
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@@ -0,0 +1,223 @@
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{
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||||
"cells": [
|
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{
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||||
"cell_type": "markdown",
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"metadata": {},
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||||
"source": [
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||||
"Copyright (c) Microsoft Corporation. All rights reserved.\n",
|
||||
"\n",
|
||||
"Licensed under the MIT License."
|
||||
]
|
||||
},
|
||||
{
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||||
"cell_type": "markdown",
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||||
"metadata": {},
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||||
"source": [
|
||||
"# 00. Installation and configuration\n",
|
||||
"\n",
|
||||
"## Prerequisites:\n",
|
||||
"\n",
|
||||
"### 1. Install Azure ML SDK\n",
|
||||
"Follow [SDK installation instructions](https://docs.microsoft.com/azure/machine-learning/service/how-to-configure-environment).\n",
|
||||
"\n",
|
||||
"### 2. Install some additional packages\n",
|
||||
"This Notebook requires some additional libraries. In the conda environment, run below commands: \n",
|
||||
"```shell\n",
|
||||
"(myenv) $ conda install -y matplotlib tqdm scikit-learn\n",
|
||||
"```\n",
|
||||
"\n",
|
||||
"### 3. Make sure your subscription is registered to use ACI.\n",
|
||||
"This Notebook 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.\n",
|
||||
"```shell\n",
|
||||
"# check to see if ACI is already registered\n",
|
||||
"(myenv) $ az provider show -n Microsoft.ContainerInstance -o table\n",
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||||
"\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",
|
||||
"In this example you will optionally 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",
|
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"\n",
|
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"This notebook also contains optional cells to install and update the require Azure Machine Learning libraries."
|
||||
]
|
||||
},
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||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"tags": [
|
||||
"install"
|
||||
]
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||||
},
|
||||
"outputs": [],
|
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"source": [
|
||||
"# Check core SDK version number for debugging purposes\n",
|
||||
"import azureml.core\n",
|
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"\n",
|
||||
"print(\"SDK Version:\", azureml.core.VERSION)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
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"source": [
|
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"## Initialize an Azure ML Workspace\n",
|
||||
"### What is an Azure ML Workspace and why do I need one?\n",
|
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"\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",
|
||||
"### What do I need\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). Inside your 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)\n",
|
||||
"\n",
|
||||
"You can also easily create a new resource group using azure-cli.\n",
|
||||
"\n",
|
||||
"```sh\n",
|
||||
"(myenv) $ az group create -n my_resource_group -l eastus2\n",
|
||||
"```\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",
|
||||
"Please specify the Azure subscription Id, resource group name, workspace name, and the region in which you want to create the workspace, for example \"eastus2\". "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import os\n",
|
||||
"\n",
|
||||
"subscription_id = os.environ.get(\"SUBSCRIPTION_ID\", \"<my-subscription-id>\")\n",
|
||||
"resource_group = os.environ.get(\"RESOURCE_GROUP\", \"<my-rg>\")\n",
|
||||
"workspace_name = os.environ.get(\"WORKSPACE_NAME\", \"<my-workspace>\")\n",
|
||||
"workspace_region = os.environ.get(\"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.\n",
|
||||
"\n",
|
||||
"This will fail when:\n",
|
||||
"1. You do not have permission to create a workspace in the resource group\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",
|
||||
" exist_ok = True)\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": {
|
||||
"tags": [
|
||||
"create workspace"
|
||||
]
|
||||
},
|
||||
"outputs": [],
|
||||
"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.\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": {
|
||||
"tags": [
|
||||
"create workspace"
|
||||
]
|
||||
},
|
||||
"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": [
|
||||
"## 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": "3.6.4"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
@@ -0,0 +1,810 @@
|
||||
{
|
||||
"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": [
|
||||
"models = ws.models(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",
|
||||
"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
|
||||
}
|
||||
@@ -21,9 +21,7 @@ def run(raw_data):
|
||||
data = json.loads(raw_data)['data']
|
||||
data = np.array(data)
|
||||
result = model.predict(data)
|
||||
|
||||
# you can return any data type as long as it is JSON-serializable
|
||||
return result.tolist()
|
||||
return json.dumps({"result": result.tolist()})
|
||||
except Exception as e:
|
||||
result = str(e)
|
||||
return result
|
||||
return json.dumps({"error": result})
|
||||
465
01.getting-started/02.train-on-local/02.train-on-local.ipynb
Normal file
465
01.getting-started/02.train-on-local/02.train-on-local.ipynb
Normal file
@@ -0,0 +1,465 @@
|
||||
{
|
||||
"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
|
||||
}
|
||||
@@ -15,7 +15,7 @@ os.makedirs('./outputs', exist_ok=True)
|
||||
|
||||
X, y = load_diabetes(return_X_y=True)
|
||||
|
||||
run = Run.get_context()
|
||||
run = Run.get_submitted_run()
|
||||
|
||||
X_train, X_test, y_train, y_test = train_test_split(X, y,
|
||||
test_size=0.2,
|
||||
284
01.getting-started/03.train-on-aci/03.train-on-aci.ipynb
Normal file
284
01.getting-started/03.train-on-aci/03.train-on-aci.ipynb
Normal file
@@ -0,0 +1,284 @@
|
||||
{
|
||||
"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
|
||||
}
|
||||
@@ -14,7 +14,7 @@ os.makedirs('./outputs', exist_ok=True)
|
||||
|
||||
X, y = load_diabetes(return_X_y=True)
|
||||
|
||||
run = Run.get_context()
|
||||
run = Run.get_submitted_run()
|
||||
|
||||
X_train, X_test, y_train, y_test = train_test_split(X, y,
|
||||
test_size=0.2,
|
||||
@@ -0,0 +1,321 @@
|
||||
{
|
||||
"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 VM (MLC managed DSVM)\n",
|
||||
"* Create Workspace\n",
|
||||
"* Create Project\n",
|
||||
"* Create `train.py` file\n",
|
||||
"* Create DSVM as Machine Learning Compute (MLC) resource\n",
|
||||
"* Configure & execute a run in a conda environment in the default miniconda Docker container on DSVM"
|
||||
]
|
||||
},
|
||||
{
|
||||
"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",
|
||||
"\n",
|
||||
"exp = Experiment(workspace = ws, name = experiment_name)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"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."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"with open('./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 append the port number to the address like the example below."
|
||||
]
|
||||
},
|
||||
{
|
||||
"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 as a compute target\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"'''\n",
|
||||
" 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",
|
||||
" dsvm_compute = RemoteCompute.attach(ws,name=\"attach-from-sdk6\",username=<username>,address=<ipaddress>,ssh_port=22,password=<password>)\n",
|
||||
"'''"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Configure & 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 route, you don't need to install anything on the VM yourself. Azure ML execution service will take care of it for you."
|
||||
]
|
||||
},
|
||||
{
|
||||
"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",
|
||||
"run_config = RunConfiguration(framework = \"python\")\n",
|
||||
"\n",
|
||||
"# Set compute target to the Linux DSVM\n",
|
||||
"run_config.target = compute_target_name\n",
|
||||
"\n",
|
||||
"# Use Docker in the remote VM\n",
|
||||
"run_config.environment.docker.enabled = True\n",
|
||||
"\n",
|
||||
"# Use CPU base image from DockerHub\n",
|
||||
"run_config.environment.docker.base_image = azureml.core.runconfig.DEFAULT_CPU_IMAGE\n",
|
||||
"print('Base Docker image is:', run_config.environment.docker.base_image)\n",
|
||||
"\n",
|
||||
"# Ask system to provision a new one based on the conda_dependencies.yml file\n",
|
||||
"run_config.environment.python.user_managed_dependencies = False\n",
|
||||
"\n",
|
||||
"# Prepare the Docker and conda environment automatically when executingfor the first time.\n",
|
||||
"run_config.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",
|
||||
"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": [
|
||||
"from azureml.core import Run\n",
|
||||
"from azureml.core import ScriptRunConfig\n",
|
||||
"\n",
|
||||
"src = ScriptRunConfig(source_directory = '.', script = 'train.py', run_config = run_config)\n",
|
||||
"run = exp.submit(src)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### View run history details"
|
||||
]
|
||||
},
|
||||
{
|
||||
"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": [
|
||||
"### Find the best 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": [
|
||||
"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": "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": "3.6.5"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
@@ -2,8 +2,7 @@
|
||||
# Licensed under the MIT license.
|
||||
|
||||
import os
|
||||
import argparse
|
||||
|
||||
from sklearn.datasets import load_diabetes
|
||||
from sklearn.linear_model import Ridge
|
||||
from sklearn.metrics import mean_squared_error
|
||||
from sklearn.model_selection import train_test_split
|
||||
@@ -13,18 +12,10 @@ from sklearn.externals import joblib
|
||||
import numpy as np
|
||||
|
||||
os.makedirs('./outputs', exist_ok=True)
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('--data-folder', type=str,
|
||||
dest='data_folder', help='data folder')
|
||||
args = parser.parse_args()
|
||||
|
||||
print('Data folder is at:', args.data_folder)
|
||||
print('List all files: ', os.listdir(args.data_folder))
|
||||
X, y = load_diabetes(return_X_y=True)
|
||||
|
||||
X = np.load(os.path.join(args.data_folder, 'features.npy'))
|
||||
y = np.load(os.path.join(args.data_folder, 'labels.npy'))
|
||||
|
||||
run = Run.get_context()
|
||||
run = Run.get_submitted_run()
|
||||
|
||||
X_train, X_test, y_train, y_test = train_test_split(
|
||||
X, y, test_size=0.2, random_state=0)
|
||||
257
01.getting-started/05.train-in-spark/05.train-in-spark.ipynb
Normal file
257
01.getting-started/05.train-in-spark/05.train-in-spark.ipynb
Normal file
@@ -0,0 +1,257 @@
|
||||
{
|
||||
"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",
|
||||
"\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": [
|
||||
"### Attach an HDI cluster\n",
|
||||
"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",
|
||||
"\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_new = HDInsightCompute.attach(ws, \n",
|
||||
" name=\"hdi-attach\", \n",
|
||||
" address=\"hdi-ignite-demo-ssh.azurehdinsight.net\", \n",
|
||||
" ssh_port=22, \n",
|
||||
" username='<username>', \n",
|
||||
" password='<password>')\n",
|
||||
"\n",
|
||||
"except UserErrorException as e:\n",
|
||||
" print(\"Caught = {}\".format(e.message))\n",
|
||||
" print(\"Compute config already attached.\")\n",
|
||||
" \n",
|
||||
" \n",
|
||||
"hdi_compute_new.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",
|
||||
"run_config = RunConfiguration(framework = \"python\")\n",
|
||||
"\n",
|
||||
"# Set compute target to the Linux DSVM\n",
|
||||
"run_config.target = hdi_compute.name\n",
|
||||
"\n",
|
||||
"# Use Docker in the remote VM\n",
|
||||
"# run_config.environment.docker.enabled = True\n",
|
||||
"\n",
|
||||
"# Use CPU base image from DockerHub\n",
|
||||
"# run_config.environment.docker.base_image = azureml.core.runconfig.DEFAULT_CPU_IMAGE\n",
|
||||
"# print('Base Docker image is:', run_config.environment.docker.base_image)\n",
|
||||
"\n",
|
||||
"# Ask system to provision a new one based on the conda_dependencies.yml file\n",
|
||||
"run_config.environment.python.user_managed_dependencies = False\n",
|
||||
"\n",
|
||||
"# Prepare the Docker and conda environment automatically when executingfor the first time.\n",
|
||||
"# run_config.prepare_environment = True\n",
|
||||
"\n",
|
||||
"# specify CondaDependencies obj\n",
|
||||
"# run_config.environment.python.conda_dependencies = CondaDependencies.create(conda_packages=['scikit-learn'])\n",
|
||||
"# load the runconfig object from the \"myhdi.runconfig\" file generated by the attach operaton above."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Submit the script to HDI"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"script_run_config = ScriptRunConfig(source_directory = '.',\n",
|
||||
" script= 'train-spark.py',\n",
|
||||
" run_config = run_config)\n",
|
||||
"run = experiment.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": [
|
||||
"run.wait_for_completion(show_output = True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# get all metris logged in the run\n",
|
||||
"metrics = run.get_metrics()\n",
|
||||
"print(metrics)"
|
||||
]
|
||||
}
|
||||
],
|
||||
"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
|
||||
}
|
||||
@@ -14,10 +14,11 @@ from pyspark.ml.feature import *
|
||||
from pyspark.sql.types import StructType, StructField
|
||||
from pyspark.sql.types import DoubleType, IntegerType, StringType
|
||||
|
||||
|
||||
from azureml.core.run import Run
|
||||
|
||||
# initialize logger
|
||||
run = Run.get_context()
|
||||
run = Run.get_submitted_run()
|
||||
|
||||
# start Spark session
|
||||
spark = pyspark.sql.SparkSession.builder.appName('Iris').getOrCreate()
|
||||
@@ -37,11 +38,7 @@ schema = StructType([
|
||||
StructField("class", StringType())
|
||||
])
|
||||
|
||||
data = spark.read.format("com.databricks.spark.csv") \
|
||||
.option("header", "true") \
|
||||
.schema(schema) \
|
||||
.load("iris.csv")
|
||||
|
||||
data = spark.read.csv('iris.csv', header=False, schema=schema)
|
||||
print("First 10 rows of Iris dataset:")
|
||||
data.show(10)
|
||||
|
||||
@@ -0,0 +1,420 @@
|
||||
{
|
||||
"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 = ws.models(tags=['area'])\n",
|
||||
"for name, m in regression_models.items():\n",
|
||||
" print(\"Name:\", 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",
|
||||
"\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
|
||||
}
|
||||
@@ -0,0 +1,335 @@
|
||||
{
|
||||
"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
|
||||
}
|
||||
@@ -0,0 +1,447 @@
|
||||
{
|
||||
"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/°/°/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
|
||||
}
|
||||
@@ -1,29 +0,0 @@
|
||||
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"
|
||||
@@ -1,29 +0,0 @@
|
||||
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"
|
||||
@@ -1,29 +0,0 @@
|
||||
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"
|
||||
@@ -1,29 +0,0 @@
|
||||
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"
|
||||
@@ -1,29 +0,0 @@
|
||||
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"
|
||||
@@ -1,29 +0,0 @@
|
||||
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"
|
||||
@@ -1,29 +0,0 @@
|
||||
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"
|
||||
@@ -1,29 +0,0 @@
|
||||
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"
|
||||
@@ -1,29 +0,0 @@
|
||||
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"
|
||||
@@ -1,29 +0,0 @@
|
||||
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"
|
||||
@@ -1,29 +0,0 @@
|
||||
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"
|
||||
@@ -1,29 +0,0 @@
|
||||
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"
|
||||
@@ -1,29 +0,0 @@
|
||||
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"
|
||||
@@ -1,14 +0,0 @@
|
||||
|
||||
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
|
||||
95
NBSETUP.md
95
NBSETUP.md
@@ -1,95 +0,0 @@
|
||||
# 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. [](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
|
||||
101
README.md
101
README.md
@@ -1,76 +1,45 @@
|
||||
# Azure Machine Learning service example notebooks
|
||||
# Sample notebooks for Azure Machine Learning service
|
||||
|
||||
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.
|
||||
To run the notebooks in this repository use one of these methods:
|
||||
|
||||
|
||||
## Use Azure Notebooks - Jupyter based notebooks in the Azure cloud
|
||||
|
||||
1. [](https://aka.ms/aml-clone-azure-notebooks)
|
||||
[Import sample notebooks ](https://aka.ms/aml-clone-azure-notebooks) into Azure Notebooks if they are not already there.
|
||||
1. Create a workspace and its configuration file (**config.json**) using [these instructions](https://aka.ms/aml-how-to-configure-environment).
|
||||
1. Select `+New` in the Azure Notebook toolbar to add your **config.json** file to the imported folder.
|
||||
|
||||
1. Open the notebook.
|
||||
|
||||
**Make sure the Azure Notebook kernal is set to `Python 3.6`** when you open a notebook.
|
||||
|
||||
|
||||
|
||||
|
||||
## 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.
|
||||
## **Use your own notebook server**
|
||||
|
||||
## How to navigate and use the example 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. Use [these instructions](https://aka.ms/aml-how-to-configure-environment) to:
|
||||
* Create a workspace and its configuration file (**config.json**).
|
||||
* Configure your notebook server.
|
||||
1. Clone [this repository](https://aka.ms/aml-notebooks).
|
||||
1. Add your **config.json** file to the cloned folder
|
||||
1. You may need to install other packages for specific notebooks
|
||||
1. Start your notebook server.
|
||||
1. Open the notebook you want to run.
|
||||
|
||||
If you want to...
|
||||
> Note: **Looking for automated machine learning samples?**
|
||||
> 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.
|
||||
|
||||
* ...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).
|
||||
# Contributing
|
||||
|
||||
## Tutorials
|
||||
This project welcomes contributions and suggestions. Most contributions require you to agree to a
|
||||
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.
|
||||
|
||||
The [Tutorials](./tutorials) folder contains notebooks for the tutorials described in the [Azure Machine Learning documentation](https://aka.ms/aml-docs).
|
||||
|
||||
## How to use Azure ML
|
||||
When you submit a pull request, a CLA-bot will automatically determine whether you need to provide
|
||||
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.
|
||||
|
||||
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
|
||||
""
|
||||
```
|
||||
This URL will be slightly different depending on the file.
|
||||
|
||||
|
||||
This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).
|
||||
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.
|
||||
|
||||
265
automl/00.configuration.ipynb
Normal file
265
automl/00.configuration.ipynb
Normal file
@@ -0,0 +1,265 @@
|
||||
{
|
||||
"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
|
||||
}
|
||||
399
automl/01.auto-ml-classification.ipynb
Normal file
399
automl/01.auto-ml-classification.ipynb
Normal file
@@ -0,0 +1,399 @@
|
||||
{
|
||||
"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,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
409
automl/02.auto-ml-regression.ipynb
Normal file
409
automl/02.auto-ml-regression.ipynb
Normal file
@@ -0,0 +1,409 @@
|
||||
{
|
||||
"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()"
|
||||
]
|
||||
}
|
||||
],
|
||||
"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
|
||||
}
|
||||
471
automl/03.auto-ml-remote-execution.ipynb
Normal file
471
automl/03.auto-ml-remote-execution.ipynb
Normal file
@@ -0,0 +1,471 @@
|
||||
{
|
||||
"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()"
|
||||
]
|
||||
}
|
||||
],
|
||||
"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
|
||||
}
|
||||
522
automl/03b.auto-ml-remote-batchai.ipynb
Normal file
522
automl/03b.auto-ml-remote-batchai.ipynb
Normal file
@@ -0,0 +1,522 @@
|
||||
{
|
||||
"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,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
495
automl/04.auto-ml-remote-execution-text-data-blob-store.ipynb
Normal file
495
automl/04.auto-ml-remote-execution-text-data-blob-store.ipynb
Normal file
@@ -0,0 +1,495 @@
|
||||
{
|
||||
"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,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
396
automl/05.auto-ml-missing-data-Blacklist-Early-Termination.ipynb
Normal file
396
automl/05.auto-ml-missing-data-Blacklist-Early-Termination.ipynb
Normal file
@@ -0,0 +1,396 @@
|
||||
{
|
||||
"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
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.6"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
418
automl/06.auto-ml-sparse-data-custom-cv-split.ipynb
Normal file
418
automl/06.auto-ml-sparse-data-custom-cv-split.ipynb
Normal file
@@ -0,0 +1,418 @@
|
||||
{
|
||||
"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
|
||||
}
|
||||
326
automl/07.auto-ml-exploring-previous-runs.ipynb
Normal file
326
automl/07.auto-ml-exploring-previous-runs.ipynb
Normal file
@@ -0,0 +1,326 @@
|
||||
{
|
||||
"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
|
||||
}
|
||||
480
automl/08.auto-ml-remote-execution-with-text-file-on-DSVM.ipynb
Normal file
480
automl/08.auto-ml-remote-execution-with-text-file-on-DSVM.ipynb
Normal file
@@ -0,0 +1,480 @@
|
||||
{
|
||||
"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,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
500
automl/09.auto-ml-classification-with-deployment.ipynb
Normal file
500
automl/09.auto-ml-classification-with-deployment.ipynb
Normal file
@@ -0,0 +1,500 @@
|
||||
{
|
||||
"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
|
||||
}
|
||||
292
automl/10.auto-ml-multi-output-example.ipynb
Normal file
292
automl/10.auto-ml-multi-output-example.ipynb
Normal file
@@ -0,0 +1,292 @@
|
||||
{
|
||||
"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
|
||||
}
|
||||
251
automl/11.auto-ml-sample-weight.ipynb
Normal file
251
automl/11.auto-ml-sample-weight.ipynb
Normal file
@@ -0,0 +1,251 @@
|
||||
{
|
||||
"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,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
240
automl/12.auto-ml-retrieve-the-training-sdk-versions.ipynb
Normal file
240
automl/12.auto-ml-retrieve-the-training-sdk-versions.ipynb
Normal file
@@ -0,0 +1,240 @@
|
||||
{
|
||||
"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
|
||||
}
|
||||
567
automl/13.auto-ml-dataprep.ipynb
Normal file
567
automl/13.auto-ml-dataprep.ipynb
Normal file
@@ -0,0 +1,567 @@
|
||||
{
|
||||
"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
|
||||
}
|
||||
264
automl/README.md
Normal file
264
automl/README.md
Normal file
@@ -0,0 +1,264 @@
|
||||
# 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. [](https://aka.ms/aml-clone-azure-notebooks)
|
||||
[Import sample notebooks ](https://aka.ms/aml-clone-azure-notebooks) into Azure Notebooks if they are not already there.
|
||||
1. Create a workspace and its configuration file (**config.json**) using [these instructions](https://aka.ms/aml-how-to-configure-environment).
|
||||
1. Select `+New` in the Azure Notebook toolbar to add your **config.json** file to the imported folder.
|
||||
|
||||
1. Open the notebook.
|
||||
|
||||
**Make sure the Azure Notebook kernal is set to `Python 3.6`** when you open a notebook.
|
||||
|
||||
|
||||
|
||||
<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.
|
||||
|
||||
20
automl/automl_env.yml
Normal file
20
automl/automl_env.yml
Normal file
@@ -0,0 +1,20 @@
|
||||
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
|
||||
|
||||
41
automl/automl_setup.cmd
Normal file
41
automl/automl_setup.cmd
Normal file
@@ -0,0 +1,41 @@
|
||||
@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
|
||||
34
automl/automl_setup_linux.sh
Normal file
34
automl/automl_setup_linux.sh
Normal file
@@ -0,0 +1,34 @@
|
||||
#!/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
|
||||
|
||||
|
||||
35
automl/automl_setup_mac.sh
Normal file
35
automl/automl_setup_mac.sh
Normal file
@@ -0,0 +1,35 @@
|
||||
#!/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
|
||||
|
||||
|
||||
15
cli/myenv.yml
Normal file
15
cli/myenv.yml
Normal file
@@ -0,0 +1,15 @@
|
||||
name: project_environment
|
||||
dependencies:
|
||||
- python=3.6.2
|
||||
- scikit-learn
|
||||
- numpy
|
||||
|
||||
- pip:
|
||||
- numpy==1.14.2
|
||||
- pandas
|
||||
- scipy==1.0.0
|
||||
- scikit-learn==0.19.1
|
||||
# Required packages for AzureML execution, history, and data preparation.
|
||||
- --index-url https://azuremlsdktestpypi.azureedge.net/sdk-release/Preview/E7501C02541B433786111FE8E140CAA1
|
||||
- --extra-index-url https://pypi.python.org/simple
|
||||
- azureml-defaults
|
||||
4
cli/readme.md
Normal file
4
cli/readme.md
Normal file
@@ -0,0 +1,4 @@
|
||||
# CLI Example Content
|
||||
This content can be used in conjunction with our [CLI reference guide.](https://docs.microsoft.com/en-us/azure/machine-learning/service/reference-azure-machine-learning-cli)
|
||||
|
||||
Example content includes training scripts, conda environment files and scoring files.
|
||||
@@ -1,14 +1,10 @@
|
||||
import pickle
|
||||
import json
|
||||
import numpy as np
|
||||
import numpy
|
||||
from sklearn.externals import joblib
|
||||
from sklearn.linear_model import Ridge
|
||||
from azureml.core.model import Model
|
||||
|
||||
from inference_schema.schema_decorators import input_schema, output_schema
|
||||
from inference_schema.parameter_types.numpy_parameter_type import NumpyParameterType
|
||||
|
||||
|
||||
def init():
|
||||
global model
|
||||
# note here "sklearn_regression_model.pkl" is the name of the model registered under
|
||||
@@ -17,18 +13,12 @@ def init():
|
||||
# deserialize the model file back into a sklearn model
|
||||
model = joblib.load(model_path)
|
||||
|
||||
|
||||
input_sample = np.array([[10, 9, 8, 7, 6, 5, 4, 3, 2, 1]])
|
||||
output_sample = np.array([3726.995])
|
||||
|
||||
|
||||
@input_schema('data', NumpyParameterType(input_sample))
|
||||
@output_schema(NumpyParameterType(output_sample))
|
||||
def run(data):
|
||||
# note you can pass in multiple rows for scoring
|
||||
def run(raw_data):
|
||||
try:
|
||||
data = json.loads(raw_data)['data']
|
||||
data = numpy.array(data)
|
||||
result = model.predict(data)
|
||||
# you can return any datatype as long as it is JSON-serializable
|
||||
return result.tolist()
|
||||
except Exception as e:
|
||||
error = str(e)
|
||||
return error
|
||||
result = str(e)
|
||||
return json.dumps({"result": result.tolist()})
|
||||
44
cli/train.py
Normal file
44
cli/train.py
Normal file
@@ -0,0 +1,44 @@
|
||||
# Copyright (c) Microsoft. All rights reserved.
|
||||
# Licensed under the MIT license.
|
||||
|
||||
from sklearn.datasets import load_diabetes
|
||||
from sklearn.linear_model import Ridge
|
||||
from sklearn.metrics import mean_squared_error
|
||||
from sklearn.model_selection import train_test_split
|
||||
from azureml.core.run import Run
|
||||
from sklearn.externals import joblib
|
||||
import os
|
||||
import numpy as np
|
||||
|
||||
os.makedirs('./outputs', exist_ok=True)
|
||||
|
||||
X, y = load_diabetes(return_X_y=True)
|
||||
|
||||
run = Run.get_submitted_run()
|
||||
|
||||
X_train, X_test, y_train, y_test = train_test_split(X, y,
|
||||
test_size=0.2,
|
||||
random_state=0)
|
||||
data = {"train": {"X": X_train, "y": y_train},
|
||||
"test": {"X": X_test, "y": y_test}}
|
||||
|
||||
# list of numbers from 0.0 to 1.0 with a 0.05 interval
|
||||
alphas = np.arange(0.0, 1.0, 0.05)
|
||||
|
||||
for alpha in alphas:
|
||||
# Use Ridge algorithm to create a regression model
|
||||
reg = Ridge(alpha=alpha)
|
||||
reg.fit(data["train"]["X"], data["train"]["y"])
|
||||
|
||||
preds = reg.predict(data["test"]["X"])
|
||||
mse = mean_squared_error(preds, data["test"]["y"])
|
||||
run.log('alpha', alpha)
|
||||
run.log('mse', mse)
|
||||
|
||||
model_file_name = 'ridge_{0:.2f}.pkl'.format(alpha)
|
||||
# save model in the outputs folder so it automatically get uploaded
|
||||
with open(model_file_name, "wb") as file:
|
||||
joblib.dump(value=reg, filename=os.path.join('./outputs/',
|
||||
model_file_name))
|
||||
|
||||
print('alpha is {0:.2f}, and mse is {1:0.2f}'.format(alpha, mse))
|
||||
@@ -1,383 +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": [
|
||||
""
|
||||
]
|
||||
},
|
||||
{
|
||||
"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 1.0.65 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
|
||||
}
|
||||
@@ -1,4 +0,0 @@
|
||||
name: configuration
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
@@ -1,559 +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": [
|
||||
"# 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\u00c3\u201a\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\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",
|
||||
"# 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",
|
||||
"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. just use it. ' + 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_data.py_ script used in the step below is a slightly modified implementation of [RAPIDS E2E example](https://github.com/rapidsai/notebooks/blob/master/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'))\n",
|
||||
"\n",
|
||||
"with open(os.path.join(scripts_folder, './process_data.py'), 'r') as process_data_script:\n",
|
||||
" print(process_data_script.read())"
|
||||
]
|
||||
},
|
||||
{
|
||||
"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": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"<font color='red'>Important</font>: Python package progressbar2 is necessary to run the following cell. If it is not available in your environment where this notebook is running, please install it."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import tarfile\n",
|
||||
"import hashlib\n",
|
||||
"from urllib.request import urlretrieve\n",
|
||||
"from progressbar import ProgressBar\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,show_progress)\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",
|
||||
" show_progress(so_far, 1, numFiles)\n",
|
||||
" so_far += 1\n",
|
||||
" pbar.finish()\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",
|
||||
"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 use an existing image from [Docker Hub](https://hub.docker.com/r/rapidsai/rapidsai/) that has a prebuilt conda environment named 'rapids' when creating a RunConfiguration. Note that this conda environment does not include azureml-defaults package that is required for using AML functionality like metrics tracking, model management etc. This package is automatically installed when you use 'Specify package dependencies' option and that is why it is the recommended option to create RunConfiguraiton in AML."
|
||||
]
|
||||
},
|
||||
{
|
||||
"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": [
|
||||
"#### Specify package dependencies"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"The following code shows how to list package dependencies in a conda environment definition file (rapids.yml) when creating a RunConfiguration"
|
||||
]
|
||||
},
|
||||
{
|
||||
"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 = \"<image>\"\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": "3.6.6"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
@@ -1,495 +0,0 @@
|
||||
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 initialize_rmm_pool():
|
||||
from librmm_cffi import librmm_config as rmm_cfg
|
||||
|
||||
rmm_cfg.use_pool_allocator = True
|
||||
#rmm_cfg.initial_pool_size = 2<<30 # set to 2GiB. Default is 1/2 total GPU memory
|
||||
import cudf
|
||||
return cudf._gdf.rmm_initialize()
|
||||
|
||||
def initialize_rmm_no_pool():
|
||||
from librmm_cffi import librmm_config as rmm_cfg
|
||||
|
||||
rmm_cfg.use_pool_allocator = False
|
||||
import cudf
|
||||
return cudf._gdf.rmm_initialize()
|
||||
|
||||
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()
|
||||
del(gdf)
|
||||
everdf['ever_30'] = (everdf['max_current_loan_delinquency_status'] >= 1).astype('int8')
|
||||
everdf['ever_90'] = (everdf['max_current_loan_delinquency_status'] >= 3).astype('int8')
|
||||
everdf['ever_180'] = (everdf['max_current_loan_delinquency_status'] >= 6).astype('int8')
|
||||
everdf.drop_column('max_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()
|
||||
delinq_30['delinquency_30'] = delinq_30['min_monthly_reporting_period']
|
||||
delinq_30.drop_column('min_monthly_reporting_period')
|
||||
delinq_90 = delinq_gdf.query('current_loan_delinquency_status >= 3')[['loan_id', 'monthly_reporting_period']].groupby('loan_id', method='hash').min()
|
||||
delinq_90['delinquency_90'] = delinq_90['min_monthly_reporting_period']
|
||||
delinq_90.drop_column('min_monthly_reporting_period')
|
||||
delinq_180 = delinq_gdf.query('current_loan_delinquency_status >= 6')[['loan_id', 'monthly_reporting_period']].groupby('loan_id', method='hash').min()
|
||||
delinq_180['delinquency_180'] = delinq_180['min_monthly_reporting_period']
|
||||
delinq_180.drop_column('min_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'})
|
||||
tmpdf['delinquency_12'] = (tmpdf['max_delinquency_12']>3).astype('int32')
|
||||
tmpdf['delinquency_12'] +=(tmpdf['min_upb_12']==0).astype('int32')
|
||||
tmpdf.drop_column('max_delinquency_12')
|
||||
tmpdf['upb_12'] = tmpdf['min_upb_12']
|
||||
tmpdf.drop_column('min_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():
|
||||
#print('XGBOOST_BUILD_DOC is ' + os.environ['XGBOOST_BUILD_DOC'])
|
||||
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))
|
||||
#part_count = part_count + 1 # adding one because the usage below is not inclusive
|
||||
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
|
||||
#end_year = 2000 # end_year is inclusive -- converted to parameter
|
||||
#part_count = 2 # the number of data files to train against -- converted to parameter
|
||||
|
||||
client.run(initialize_rmm_pool)
|
||||
client
|
||||
print(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 ...")
|
||||
print(t2-t1)
|
||||
print('len(gpu_dfs) is {0}'.format(len(gpu_dfs)))
|
||||
|
||||
client.run(cudf._gdf.rmm_finalize)
|
||||
client.run(initialize_rmm_no_pool)
|
||||
client
|
||||
print(client.ncores())
|
||||
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': 'gpu:reg:linear',
|
||||
'max_features': 'auto',
|
||||
'criterion': 'friedman_mse',
|
||||
'grow_policy': 'lossguide',
|
||||
'verbose': True
|
||||
}
|
||||
|
||||
if cpu_predictor:
|
||||
print('Training using CPUs')
|
||||
dxgb_gpu_params['predictor'] = 'cpu_predictor'
|
||||
dxgb_gpu_params['tree_method'] = 'hist'
|
||||
dxgb_gpu_params['objective'] = 'reg:linear'
|
||||
|
||||
else:
|
||||
print('Training using GPUs')
|
||||
|
||||
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)
|
||||
|
||||
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("Training time ...")
|
||||
print(t2-t1)
|
||||
print('str(bst) is {0}'.format(str(bst)))
|
||||
print('Exiting script')
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
1
databricks/01.Installation_and_Configuration.ipynb
Normal file
1
databricks/01.Installation_and_Configuration.ipynb
Normal file
@@ -0,0 +1 @@
|
||||
{"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}
|
||||
1
databricks/02.Ingest_data.ipynb
Normal file
1
databricks/02.Ingest_data.ipynb
Normal file
@@ -0,0 +1 @@
|
||||
{"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}
|
||||
1
databricks/03a.Build_model.ipynb
Normal file
1
databricks/03a.Build_model.ipynb
Normal file
File diff suppressed because one or more lines are too long
1
databricks/03b.Build_model_runHistory.ipynb
Normal file
1
databricks/03b.Build_model_runHistory.ipynb
Normal file
File diff suppressed because one or more lines are too long
1
databricks/04.Deploy_to_ACI.ipynb
Normal file
1
databricks/04.Deploy_to_ACI.ipynb
Normal file
File diff suppressed because one or more lines are too long
1
databricks/05.Deploy_to_AKS_existingImage.ipynb
Normal file
1
databricks/05.Deploy_to_AKS_existingImage.ipynb
Normal file
@@ -0,0 +1 @@
|
||||
{"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}
|
||||
BIN
databricks/Databricks_AMLSDK_github.dbc
Normal file
BIN
databricks/Databricks_AMLSDK_github.dbc
Normal file
Binary file not shown.
26
databricks/readme.md
Normal file
26
databricks/readme.md
Normal file
@@ -0,0 +1,26 @@
|
||||
# Azure Databricks - Azure ML SDK Sample Notebooks
|
||||
|
||||
**NOTE**: With the latest version of our AML SDK, there are some API changes due to which previous version of notebooks will not work.
|
||||
Kindly use this v4 notebooks (updated Sep 18)– if you had installed the AML SDK in your Databricks cluster please update to latest SDK version by installing azureml-sdk[databricks] as a library from GUI.
|
||||
|
||||
**NOTE**: Please create your Azure Databricks cluster as v4.x (high concurrency preferred) with **Python 3** (dropdown). We are extending it to more runtimes asap.
|
||||
|
||||
**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 [Private preview notebooks](https://github.com/Azure/ViennaDocs/tree/master/PrivatePreview/notebooks)
|
||||
|
||||
(Recommended) [Azure Databricks AML SDK notebooks](Databricks_AMLSDK_github.dbc) A single DBC package to import all notebooks in your 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.
|
||||
@@ -1,17 +0,0 @@
|
||||
## 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/).
|
||||
@@ -1,299 +0,0 @@
|
||||
# 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. [](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.
|
||||
@@ -1,27 +0,0 @@
|
||||
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-explain-model
|
||||
- pandas_ml
|
||||
|
||||
@@ -1,28 +0,0 @@
|
||||
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-explain-model
|
||||
- pandas_ml
|
||||
|
||||
@@ -1,62 +0,0 @@
|
||||
@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
|
||||
@@ -1,52 +0,0 @@
|
||||
#!/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
|
||||
|
||||
|
||||
@@ -1,54 +0,0 @@
|
||||
#!/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
|
||||
|
||||
|
||||
|
||||
@@ -1,655 +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": [
|
||||
""
|
||||
]
|
||||
},
|
||||
{
|
||||
"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
|
||||
}
|
||||
@@ -1,10 +0,0 @@
|
||||
name: auto-ml-classification-bank-marketing
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- azureml-defaults
|
||||
- azureml-explain-model
|
||||
- azureml-train-automl
|
||||
- azureml-widgets
|
||||
- matplotlib
|
||||
- pandas_ml
|
||||
@@ -1,648 +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": [
|
||||
""
|
||||
]
|
||||
},
|
||||
{
|
||||
"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
|
||||
}
|
||||
@@ -1,10 +0,0 @@
|
||||
name: auto-ml-classification-credit-card-fraud
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- azureml-defaults
|
||||
- azureml-explain-model
|
||||
- azureml-train-automl
|
||||
- azureml-widgets
|
||||
- matplotlib
|
||||
- pandas_ml
|
||||
@@ -1,479 +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": [
|
||||
""
|
||||
]
|
||||
},
|
||||
{
|
||||
"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",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.6"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
@@ -1,8 +0,0 @@
|
||||
name: auto-ml-classification-with-deployment
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- azureml-train-automl
|
||||
- azureml-widgets
|
||||
- matplotlib
|
||||
- pandas_ml
|
||||
@@ -1,375 +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": [
|
||||
""
|
||||
]
|
||||
},
|
||||
{
|
||||
"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
|
||||
}
|
||||
@@ -1,9 +0,0 @@
|
||||
name: auto-ml-classification-with-onnx
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- azureml-train-automl
|
||||
- azureml-widgets
|
||||
- matplotlib
|
||||
- pandas_ml
|
||||
- onnxruntime
|
||||
@@ -1,395 +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": [
|
||||
""
|
||||
]
|
||||
},
|
||||
{
|
||||
"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
|
||||
}
|
||||
@@ -1,8 +0,0 @@
|
||||
name: auto-ml-classification-with-whitelisting
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- azureml-train-automl
|
||||
- azureml-widgets
|
||||
- matplotlib
|
||||
- pandas_ml
|
||||
@@ -1,484 +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": [
|
||||
""
|
||||
]
|
||||
},
|
||||
{
|
||||
"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
|
||||
}
|
||||
@@ -1,8 +0,0 @@
|
||||
name: auto-ml-classification
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- azureml-train-automl
|
||||
- azureml-widgets
|
||||
- matplotlib
|
||||
- pandas_ml
|
||||
@@ -1,505 +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": [
|
||||
""
|
||||
]
|
||||
},
|
||||
{
|
||||
"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
|
||||
}
|
||||
@@ -1,10 +0,0 @@
|
||||
name: auto-ml-dataset-remote-execution
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- azureml-defaults
|
||||
- azureml-explain-model
|
||||
- azureml-train-automl
|
||||
- azureml-widgets
|
||||
- matplotlib
|
||||
- pandas_ml
|
||||
@@ -1,399 +0,0 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
""
|
||||
]
|
||||
},
|
||||
{
|
||||
"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
|
||||
}
|
||||
@@ -1,9 +0,0 @@
|
||||
name: auto-ml-dataset
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- azureml-train-automl
|
||||
- azureml-widgets
|
||||
- matplotlib
|
||||
- pandas_ml
|
||||
- azureml-dataprep[pandas]
|
||||
@@ -1,349 +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": [
|
||||
""
|
||||
]
|
||||
},
|
||||
{
|
||||
"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
|
||||
}
|
||||
@@ -1,8 +0,0 @@
|
||||
name: auto-ml-exploring-previous-runs
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- azureml-train-automl
|
||||
- azureml-widgets
|
||||
- matplotlib
|
||||
- pandas_ml
|
||||
@@ -1,605 +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": [
|
||||
""
|
||||
]
|
||||
},
|
||||
{
|
||||
"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
|
||||
}
|
||||
@@ -1,9 +0,0 @@
|
||||
name: auto-ml-forecasting-bike-share
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- azureml-train-automl
|
||||
- azureml-widgets
|
||||
- matplotlib
|
||||
- pandas_ml
|
||||
- statsmodels
|
||||
Some files were not shown because too many files have changed in this diff Show More
Reference in New Issue
Block a user