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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",
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||||
"\n",
|
||||
"Licensed under the MIT License."
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||||
]
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||||
},
|
||||
{
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"cell_type": "markdown",
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||||
"metadata": {},
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"source": [
|
||||
"# 00. Installation and configuration\n",
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"\n",
|
||||
"## Prerequisites:\n",
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||||
"\n",
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||||
"### 1. Install Azure ML SDK\n",
|
||||
"Follow [SDK installation instructions](https://docs.microsoft.com/azure/machine-learning/service/how-to-configure-environment).\n",
|
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"\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",
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||||
"\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",
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||||
"# if ACI is not registered, run this command.\n",
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||||
"# note you need to be the subscription owner in order to execute this command successfully.\n",
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"(myenv) $ az provider register -n Microsoft.ContainerInstance\n",
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"```\n",
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"\n",
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"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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]
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},
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{
|
||||
"cell_type": "code",
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||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"tags": [
|
||||
"install"
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||||
]
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},
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"outputs": [],
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"source": [
|
||||
"# Check core SDK version number for debugging purposes\n",
|
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"import azureml.core\n",
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"\n",
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"print(\"SDK Version:\", azureml.core.VERSION)"
|
||||
]
|
||||
},
|
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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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"## Initialize an Azure ML Workspace\n",
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"### What is an Azure ML Workspace and why do I need one?\n",
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"\n",
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"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",
|
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"\n",
|
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"### What do I need\n",
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"\n",
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||||
"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",
|
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"You can also easily create a new resource group using azure-cli.\n",
|
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"\n",
|
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"```sh\n",
|
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"(myenv) $ az group create -n my_resource_group -l eastus2\n",
|
||||
"```\n",
|
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"\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": [],
|
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"source": [
|
||||
"import os\n",
|
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"\n",
|
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"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",
|
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"workspace_region = os.environ.get(\"WORKSPACE_REGION\", \"eastus2\")"
|
||||
]
|
||||
},
|
||||
{
|
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"cell_type": "markdown",
|
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"metadata": {},
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"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
|
||||
}
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||||
@@ -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]==1.0.2"]
|
||||
|
||||
# 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]==1.0.6"]
|
||||
|
||||
# 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"
|
||||
106
NBSETUP.md
106
NBSETUP.md
@@ -1,106 +0,0 @@
|
||||
# Setting up environment
|
||||
|
||||
---
|
||||
|
||||
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
|
||||
|
||||
|
||||
|
||||
## **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 and a Jupyter notebook server
|
||||
pip install azureml-sdk[notebooks]
|
||||
|
||||
# install the data prep component
|
||||
pip install azureml-dataprep
|
||||
|
||||
# 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:
|
||||
|
||||
[](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 dataprep components
|
||||
pip install azureml-dataprep
|
||||
|
||||
# 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
|
||||
79
README.md
79
README.md
@@ -1,56 +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
|
||||
|
||||
## Quick installation
|
||||
```sh
|
||||
pip install azureml-sdk
|
||||
```
|
||||
Read more detailed instructions on [how to set up your environment](./NBSETUP.md) using Azure Notebook service, your own Jupyter notebook server, or Docker.
|
||||
1. [](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.
|
||||
|
||||
|
||||
|
||||
## How to navigate and use the example notebooks?
|
||||
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.
|
||||
|
||||
If you want to...
|
||||
## **Use your own notebook server**
|
||||
|
||||
* ...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](./how-to-use-azureml/machine-learning-pipelines/pipeline-mpi-batch-prediction.ipynb).
|
||||
* ...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).
|
||||
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.
|
||||
|
||||
## Tutorials
|
||||
> 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.
|
||||
|
||||
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
|
||||
# Contributing
|
||||
|
||||
The [How to use Azure ML](./how-to-use-azureml) folder contains specific examples demonstrating the features of the Azure Machine Learning SDK
|
||||
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.
|
||||
|
||||
- [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
|
||||
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.
|
||||
|
||||
---
|
||||
## 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).
|
||||
|
||||
---
|
||||
|
||||
## Projects using Azure Machine Learning
|
||||
|
||||
Visit following repos to see projects contributed by Azure ML users:
|
||||
|
||||
- [Fine tune natural language processing 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)
|
||||
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.
|
||||
24
cli/score.py
Normal file
24
cli/score.py
Normal file
@@ -0,0 +1,24 @@
|
||||
import pickle
|
||||
import json
|
||||
import numpy
|
||||
from sklearn.externals import joblib
|
||||
from sklearn.linear_model import Ridge
|
||||
from azureml.core.model import Model
|
||||
|
||||
def init():
|
||||
global model
|
||||
# note here "sklearn_regression_model.pkl" is the name of the model registered under
|
||||
# this is a different behavior than before when the code is run locally, even though the code is the same.
|
||||
model_path = Model.get_model_path('sklearn_regression_model.pkl')
|
||||
# deserialize the model file back into a sklearn model
|
||||
model = joblib.load(model_path)
|
||||
|
||||
# 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)
|
||||
except Exception as e:
|
||||
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))
|
||||
@@ -1,376 +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": [
|
||||
"# 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, inferencing, 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 1.0.18 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 note 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 = \"cpucluster\"\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 cpucluster\")\n",
|
||||
"except ComputeTargetException:\n",
|
||||
" print(\"Creating new cpucluster\")\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 = \"gpucluster\"\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 gpucluster\")\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 in notebook](./how-to-use-azureml/training/train-in-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
|
||||
}
|
||||
@@ -1,409 +0,0 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Copyright (c) Microsoft Corporation. All rights reserved.\n",
|
||||
"\n",
|
||||
"Licensed under the MIT License."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# NVIDIA RAPIDS in Azure Machine Learning"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"The [RAPIDS](https://www.developer.nvidia.com/rapids) suite of software libraries from NVIDIA enables the execution of end-to-end data science and analytics pipelines entirely on GPUs. In many machine learning projects, a significant portion of the model training time is spent in setting up the data; this stage of the process is known as Extraction, Transformation and Loading, or ETL. By using the DataFrame API for ETL\u00c2\u00a0and GPU-capable ML algorithms in RAPIDS, data preparation and training models can be done in GPU-accelerated end-to-end pipelines without incurring serialization costs between the pipeline stages. This notebook demonstrates how to use NVIDIA RAPIDS to prepare data and train model\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.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\u00e2\u20ac\u2122s Single-Family Loan Performance Data](http://www.fanniemae.com/portal/funding-the-market/data/loan-performance-data.html). Refer to the 'Available mortgage datasets' section in [instructions](https://rapidsai.github.io/demos/datasets/mortgage-data) to get sample data.\n",
|
||||
"\n",
|
||||
"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."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"<font color='red'>Important</font>: The following step assumes the data is uploaded to the Workspace's default data store under a folder named 'mortgagedata2000_01'. Note that uploading data to the Workspace's default data store is not necessary and the data can be referenced from any datastore, e.g., from Azure Blob or File service, once it is added as a datastore to the workspace. The path_on_datastore parameter needs to be updated, depending on where the data is available. The directory where the data is available should have the following folder structure, as the process_data.py script expects this directory structure:\n",
|
||||
"* _<data directory>_/acq\n",
|
||||
"* _<data directory>_/perf\n",
|
||||
"* _names.csv_\n",
|
||||
"\n",
|
||||
"The 'acq' and 'perf' refer to directories containing data files. The _<data directory>_ is the path specified in _path_on_datastore_ parameter in the step below."
|
||||
]
|
||||
},
|
||||
{
|
||||
"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='<local directory that has data>', target_path='mortgagedata2000_01', 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='mortgagedata2000_01')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Create AML run configuration to launch a machine learning job"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"AML allows the option of using existing Docker images with prebuilt conda environments. The following step use an existing image from [Docker Hub](https://hub.docker.com/r/rapidsai/rapidsai/)."
|
||||
]
|
||||
},
|
||||
{
|
||||
"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",
|
||||
"# use conda environment named 'rapids' available in the Docker image\n",
|
||||
"# this conda environment does not include azureml-defaults package that is required for using AML functionality like metrics tracking, model management etc.\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",
|
||||
"# if registry is not mentioned the image is pulled from Docker Hub\n",
|
||||
"run_config.environment.docker.base_image = \"rapidsai/rapidsai:cuda9.2_ubuntu16.04_root\"\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):\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",
|
||||
" gpu_count_data_partition_mapping = {1: 2, 2: 4, 3: 5, 4: 7}\n",
|
||||
" part_count = gpu_count_data_partition_mapping[gpu_count]\n",
|
||||
"\n",
|
||||
" end_year = 2000\n",
|
||||
" if gpu_count > 2:\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()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"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",
|
||||
"# train using CPU, use GPU for both ETL and training\n",
|
||||
"run_rapids_experiment(cpu_predictor, num_gpu)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"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",
|
||||
"# train using CPU, use GPU for ETL\n",
|
||||
"run_rapids_experiment(cpu_predictor, num_gpu)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"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
|
||||
}
|
||||
@@ -1,500 +0,0 @@
|
||||
# License Info: https://github.com/rapidsai/notebooks/blob/master/LICENSE
|
||||
import numpy as np
|
||||
import datetime
|
||||
import dask_xgboost as dxgb_gpu
|
||||
import dask
|
||||
import dask_cudf
|
||||
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
|
||||
|
||||
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')
|
||||
|
||||
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]
|
||||
print('IPADDR is {0}'.format(IPADDR))
|
||||
|
||||
cmd = "/rapids/notebooks/utils/dask-setup.sh 0"
|
||||
process = subprocess.Popen(cmd.split(), stdout=subprocess.PIPE)
|
||||
output, error = process.communicate()
|
||||
|
||||
cmd = "/rapids/notebooks/utils/dask-setup.sh rapids " + str(num_gpu) + " 8786 8787 8790 " + str(IPADDR) + " MASTER"
|
||||
process = subprocess.Popen(cmd.split(), stdout=subprocess.PIPE)
|
||||
output, error = process.communicate()
|
||||
|
||||
print(output.decode())
|
||||
|
||||
import dask
|
||||
from dask.delayed import delayed
|
||||
from dask.distributed import Client, wait
|
||||
|
||||
_client = IPADDR + str(":8786")
|
||||
|
||||
client = dask.distributed.Client(_client)
|
||||
|
||||
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(year=2000, quarter=1, perf_file=""):
|
||||
ml_arrays = run_dask_task(delayed(run_gpu_workflow),
|
||||
quarter=quarter,
|
||||
year=year,
|
||||
perf_file=perf_file)
|
||||
return 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(quarter=1, year=2000, perf_file="", **kwargs):
|
||||
names = gpu_load_names()
|
||||
acq_gdf = gpu_load_acquisition_csv(acquisition_path= acq_data_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(**kwargs):
|
||||
""" 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_names_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(index=False)
|
||||
|
||||
|
||||
# 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)
|
||||
|
||||
# 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(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)
|
||||
|
||||
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()
|
||||
labels = None
|
||||
|
||||
print('str(gpu_dfs) is {0}'.format(str(gpu_dfs)))
|
||||
|
||||
wait(gpu_dfs)
|
||||
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')
|
||||
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](./training/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/).
|
||||
@@ -1,264 +0,0 @@
|
||||
# Table of Contents
|
||||
1. [Automated ML Introduction](#introduction)
|
||||
1. [Running samples in Azure Notebooks](#jupyter)
|
||||
1. [Running samples in Azure Databricks](#databricks)
|
||||
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.
|
||||
|
||||
Below are the three execution environments supported by AutoML.
|
||||
|
||||
|
||||
<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.
|
||||
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>
|
||||
## Running samples in 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>
|
||||
## 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 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 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. See the specific sections below for Windows, Mac and Linux. It can take about 10 minutes to execute.
|
||||
## 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 AutoML
|
||||
|
||||
<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 Auto 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 Auto ML for regression
|
||||
- Uses local compute for training
|
||||
|
||||
- [auto-ml-remote-execution.ipynb](remote-execution/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
|
||||
|
||||
- [auto-ml-remote-amlcompute.ipynb](remote-batchai/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 automl settings as kwargs
|
||||
|
||||
- [auto-ml-remote-attach.ipynb](remote-attach/auto-ml-remote-attach.ipynb)
|
||||
- Dataset: Scikit learn's [20newsgroup](http://scikit-learn.org/stable/datasets/twenty_newsgroups.html)
|
||||
- handling text data with preprocess flag
|
||||
- Reading data from a blob store for remote executions
|
||||
- using pandas dataframes for reading data
|
||||
|
||||
- [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 AutoML Runs for a given project
|
||||
- Get details for a AutoML Run. (Automl settings, run widget & all metrics)
|
||||
- Download fitted pipeline for any iteration
|
||||
|
||||
- [auto-ml-remote-execution-with-datastore.ipynb](remote-execution-with-datastore/auto-ml-remote-execution-with-datastore.ipynb)
|
||||
- Dataset: Scikit learn's [20newsgroup](http://scikit-learn.org/stable/datasets/twenty_newsgroups.html)
|
||||
- Download the data and store it in DataStore.
|
||||
|
||||
- [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 Auto 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-dataprep.ipynb](dataprep/auto-ml-dataprep.ipynb)
|
||||
- Using DataPrep for reading data
|
||||
|
||||
- [auto-ml-dataprep-remote-execution.ipynb](dataprep-remote-execution/auto-ml-dataprep-remote-execution.ipynb)
|
||||
- Using DataPrep 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 Auto 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 AutoML 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 AutoML forecasting model on multiple time-series
|
||||
|
||||
<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. 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>`.
|
||||
|
||||
## 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.
|
||||
|
||||
## 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
|
||||
AutoML 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
|
||||
AutoML 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. 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 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.
|
||||
@@ -1,22 +0,0 @@
|
||||
name: azure_automl
|
||||
dependencies:
|
||||
# The python interpreter version.
|
||||
# Currently Azure ML only supports 3.5.2 and later.
|
||||
- python>=3.5.2,<3.6.8
|
||||
- nb_conda
|
||||
- matplotlib==2.1.0
|
||||
- numpy>=1.11.0,<1.15.0
|
||||
- cython
|
||||
- urllib3<1.24
|
||||
- scipy>=1.0.0,<=1.1.0
|
||||
- scikit-learn>=0.18.0,<=0.19.1
|
||||
- pandas>=0.22.0,<0.23.0
|
||||
- tensorflow>=1.12.0
|
||||
- py-xgboost<=0.80
|
||||
|
||||
- pip:
|
||||
# Required packages for AzureML execution, history, and data preparation.
|
||||
- azureml-sdk[automl,explain]
|
||||
- azureml-widgets
|
||||
- pandas_ml
|
||||
|
||||
@@ -1,23 +0,0 @@
|
||||
name: azure_automl
|
||||
dependencies:
|
||||
# The python interpreter version.
|
||||
# Currently Azure ML only supports 3.5.2 and later.
|
||||
- python>=3.5.2,<3.6.8
|
||||
- nb_conda
|
||||
- matplotlib==2.1.0
|
||||
- numpy>=1.15.3
|
||||
- cython
|
||||
- urllib3<1.24
|
||||
- scipy>=1.0.0,<=1.1.0
|
||||
- scikit-learn>=0.18.0,<=0.19.1
|
||||
- pandas>=0.22.0,<0.23.0
|
||||
- tensorflow>=1.12.0
|
||||
- py-xgboost<=0.80
|
||||
|
||||
- pip:
|
||||
# Required packages for AzureML execution, history, and data preparation.
|
||||
- azureml-sdk[automl,explain]
|
||||
- azureml-widgets
|
||||
- pandas_ml
|
||||
|
||||
|
||||
@@ -1,51 +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
|
||||
|
||||
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
|
||||
|
||||
: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
|
||||
|
||||
|
||||
@@ -1,55 +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 &&
|
||||
pip install numpy==1.15.3 &&
|
||||
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
|
||||
|
||||
|
||||
|
||||
@@ -1,503 +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": [
|
||||
"# 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",
|
||||
"# project folder\n",
|
||||
"project_folder = './sample_projects/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['Project Directory'] = project_folder\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, ], [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_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",
|
||||
" n_cross_validations = 2,\n",
|
||||
" verbosity = logging.INFO,\n",
|
||||
" X = X_train, \n",
|
||||
" y = y_train,\n",
|
||||
" path = project_folder)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"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. 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": [
|
||||
"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": [
|
||||
"from azureml.core.conda_dependencies import CondaDependencies\n",
|
||||
"\n",
|
||||
"myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn'], pip_packages=['azureml-sdk[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-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 a Container 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)\n",
|
||||
"\n",
|
||||
"if image.creation_state == 'Failed':\n",
|
||||
" print(\"Image build log at: \" + image.image_build_log_uri)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Deploy the Image as a 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": [
|
||||
"### Delete a 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"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#aci_service.get_logs()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"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
|
||||
}
|
||||
@@ -1,381 +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": [
|
||||
"# 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 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": [
|
||||
"import logging\n",
|
||||
"\n",
|
||||
"from matplotlib import pyplot as plt\n",
|
||||
"import numpy as np\n",
|
||||
"import pandas as pd\n",
|
||||
"from sklearn import datasets\n",
|
||||
"\n",
|
||||
"import azureml.core\n",
|
||||
"from azureml.core.experiment import Experiment\n",
|
||||
"from azureml.core.workspace import Workspace\n",
|
||||
"from azureml.train.automl import AutoMLConfig"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"ws = Workspace.from_config()\n",
|
||||
"\n",
|
||||
"# Choose a name for the experiment and specify the project folder.\n",
|
||||
"experiment_name = 'automl-local-whitelist'\n",
|
||||
"project_folder = './sample_projects/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['Project Directory'] = project_folder\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, ], [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.|\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",
|
||||
" n_cross_validations = 3,\n",
|
||||
" verbosity = logging.INFO,\n",
|
||||
" X = X_train, \n",
|
||||
" y = y_train,\n",
|
||||
" enable_tf=True,\n",
|
||||
" whitelist_models=[\"TensorFlowLinearClassifier\", \"TensorFlowDNN\"],\n",
|
||||
" path = project_folder)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"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
|
||||
}
|
||||
@@ -1,396 +0,0 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Copyright (c) Microsoft Corporation. All rights reserved.\n",
|
||||
"\n",
|
||||
"Licensed under the MIT License."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# 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": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"ws = Workspace.from_config()\n",
|
||||
"\n",
|
||||
"# Choose a name for the experiment and specify the project folder.\n",
|
||||
"experiment_name = 'automl-classification'\n",
|
||||
"project_folder = './sample_projects/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['Project Directory'] = project_folder\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",
|
||||
"|**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, ], [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",
|
||||
" iteration_timeout_minutes = 60,\n",
|
||||
" iterations = 25,\n",
|
||||
" n_cross_validations = 3,\n",
|
||||
" verbosity = logging.INFO,\n",
|
||||
" X = X_train, \n",
|
||||
" y = y_train,\n",
|
||||
" path = project_folder)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"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": {},
|
||||
"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
|
||||
}
|
||||
@@ -1,498 +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": [
|
||||
"# Automated Machine Learning\n",
|
||||
"_**Prepare Data using `azureml.dataprep` for Remote Execution (DSVM)**_\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 the `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 [configuration](../../../configuration.ipynb) before running this notebook.\n",
|
||||
"\n",
|
||||
"In this notebook you will learn how to:\n",
|
||||
"1. Define data loading and preparation steps in a `Dataflow` using `azureml.dataprep`.\n",
|
||||
"2. Pass the `Dataflow` to AutoML for a local run.\n",
|
||||
"3. Pass the `Dataflow` to AutoML for a remote run."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Setup\n",
|
||||
"\n",
|
||||
"Currently, Data Prep only supports __Ubuntu 16__ and __Red Hat Enterprise Linux 7__. We are working on supporting more linux distros."
|
||||
]
|
||||
},
|
||||
{
|
||||
"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",
|
||||
"import time\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.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-remote-dsvm'\n",
|
||||
"# project folder\n",
|
||||
"project_folder = './sample_projects/automl-dataprep-remote-dsvm'\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",
|
||||
"outputDf = pd.DataFrame(data = output, index = [''])\n",
|
||||
"outputDf.T"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Data"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# You can use `auto_read_file` which intelligently figures out delimiters and datatypes of a file.\n",
|
||||
"# The data referenced here was pulled from `sklearn.datasets.load_digits()`.\n",
|
||||
"simple_example_data_root = 'https://dprepdata.blob.core.windows.net/automl-notebook-data/'\n",
|
||||
"X = dprep.auto_read_file(simple_example_data_root + 'X.csv').skip(1) # Remove the header row.\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": [
|
||||
"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 datasets."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"X.skip(1).head(5)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"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\" : False,\n",
|
||||
" \"verbosity\" : logging.INFO,\n",
|
||||
" \"n_cross_validations\": 3\n",
|
||||
"}"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Create or Attach a Remote Linux DSVM"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"dsvm_name = 'mydsvmc'\n",
|
||||
"\n",
|
||||
"try:\n",
|
||||
" while ws.compute_targets[dsvm_name].provisioning_state == 'Creating':\n",
|
||||
" time.sleep(1)\n",
|
||||
" \n",
|
||||
" dsvm_compute = DsvmCompute(ws, dsvm_name)\n",
|
||||
" print('Found existing DVSM.')\n",
|
||||
"except:\n",
|
||||
" print('Creating a 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)\n",
|
||||
" print(\"Waiting one minute for ssh to be accessible\")\n",
|
||||
" time.sleep(90) # Wait for ssh to be accessible"
|
||||
]
|
||||
},
|
||||
{
|
||||
"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",
|
||||
"conda_run_config = RunConfiguration(framework=\"python\")\n",
|
||||
"\n",
|
||||
"conda_run_config.target = dsvm_compute\n",
|
||||
"\n",
|
||||
"cd = CondaDependencies.create(pip_packages=['azureml-sdk[automl]'], conda_packages=['numpy'])\n",
|
||||
"conda_run_config.environment.python.conda_dependencies = cd"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Pass Data with `Dataflow` Objects\n",
|
||||
"\n",
|
||||
"The `Dataflow` objects captured above can also be passed to the `submit` method for a remote run. AutoML will serialize the `Dataflow` object and send it to the 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=conda_run_config,\n",
|
||||
" X = X,\n",
|
||||
" y = y,\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": [
|
||||
"## 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"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from sklearn import datasets\n",
|
||||
"\n",
|
||||
"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",
|
||||
"from matplotlib import pyplot as plt\n",
|
||||
"import numpy as np\n",
|
||||
"\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()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Appendix"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Capture the `Dataflow` Objects for Later Use in AutoML\n",
|
||||
"\n",
|
||||
"`Dataflow` objects are immutable and are composed of a list of data preparation steps. A `Dataflow` object 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.auto_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": [
|
||||
"print(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": {
|
||||
"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
|
||||
}
|
||||
@@ -1,449 +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": [
|
||||
"# Automated Machine Learning\n",
|
||||
"_**Prepare Data using `azureml.dataprep` 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 the `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 [configuration](../../../configuration.ipynb) before running this notebook.\n",
|
||||
"\n",
|
||||
"In this notebook you will learn how to:\n",
|
||||
"1. Define data loading and preparation steps in a `Dataflow` using `azureml.dataprep`.\n",
|
||||
"2. Pass the `Dataflow` to AutoML for a local run.\n",
|
||||
"3. Pass the `Dataflow` to AutoML for a remote run."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Setup\n",
|
||||
"\n",
|
||||
"Currently, Data Prep only supports __Ubuntu 16__ and __Red Hat Enterprise Linux 7__. We are working on supporting more linux distros."
|
||||
]
|
||||
},
|
||||
{
|
||||
"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",
|
||||
"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-local'\n",
|
||||
"# project folder\n",
|
||||
"project_folder = './sample_projects/automl-dataprep-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['Project Directory'] = project_folder\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": [
|
||||
"# You can use `auto_read_file` which intelligently figures out delimiters and datatypes of a file.\n",
|
||||
"# The data referenced here was pulled from `sklearn.datasets.load_digits()`.\n",
|
||||
"simple_example_data_root = 'https://dprepdata.blob.core.windows.net/automl-notebook-data/'\n",
|
||||
"X = dprep.auto_read_file(simple_example_data_root + 'X.csv').skip(1) # Remove the header row.\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 datasets."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"X.skip(1).head(5)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"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\" : False,\n",
|
||||
" \"verbosity\" : logging.INFO,\n",
|
||||
" \"n_cross_validations\": 3\n",
|
||||
"}"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Pass Data with `Dataflow` Objects\n",
|
||||
"\n",
|
||||
"The `Dataflow` objects captured above can be passed to the `submit` method for a 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": "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"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from sklearn import datasets\n",
|
||||
"\n",
|
||||
"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",
|
||||
"from matplotlib import pyplot as plt\n",
|
||||
"import numpy as np\n",
|
||||
"\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()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Appendix"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Capture the `Dataflow` Objects for Later Use in AutoML\n",
|
||||
"\n",
|
||||
"`Dataflow` objects are immutable and are composed of a list of data preparation steps. A `Dataflow` object 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.auto_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": [
|
||||
"print(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": {
|
||||
"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
|
||||
}
|
||||
@@ -1,342 +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": [
|
||||
"# 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",
|
||||
"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"
|
||||
]
|
||||
},
|
||||
{
|
||||
"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
|
||||
}
|
||||
@@ -1,381 +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": [
|
||||
"# Automated Machine Learning\n",
|
||||
"_**Energy Demand Forecasting**_\n",
|
||||
"\n",
|
||||
"## Contents\n",
|
||||
"1. [Introduction](#Introduction)\n",
|
||||
"1. [Setup](#Setup)\n",
|
||||
"1. [Data](#Data)\n",
|
||||
"1. [Train](#Train)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Introduction\n",
|
||||
"In this example, we show how AutoML can be used for energy demand forecasting.\n",
|
||||
"\n",
|
||||
"Make sure you have executed the [configuration](../../../configuration.ipynb) before running this notebook.\n",
|
||||
"\n",
|
||||
"In this notebook you would see\n",
|
||||
"1. Creating an Experiment in an existing Workspace\n",
|
||||
"2. Instantiating AutoMLConfig with new task type \"forecasting\" for timeseries data training, and other timeseries related settings: for this dataset we use the basic one: \"time_column_name\" \n",
|
||||
"3. Training the Model using local compute\n",
|
||||
"4. Exploring the results\n",
|
||||
"5. Testing the fitted model"
|
||||
]
|
||||
},
|
||||
{
|
||||
"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",
|
||||
"# Squash warning messages for cleaner output in the notebook\n",
|
||||
"warnings.showwarning = lambda *args, **kwargs: None\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"from azureml.core.workspace import Workspace\n",
|
||||
"from azureml.core.experiment import Experiment\n",
|
||||
"from azureml.train.automl import AutoMLConfig\n",
|
||||
"from matplotlib import pyplot as plt\n",
|
||||
"from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"As part of the setup you have already created a <b>Workspace</b>. 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": [
|
||||
"ws = Workspace.from_config()\n",
|
||||
"\n",
|
||||
"# choose a name for the run history container in the workspace\n",
|
||||
"experiment_name = 'automl-energydemandforecasting'\n",
|
||||
"# project folder\n",
|
||||
"project_folder = './sample_projects/automl-local-energydemandforecasting'\n",
|
||||
"\n",
|
||||
"experiment = Experiment(ws, experiment_name)\n",
|
||||
"\n",
|
||||
"output = {}\n",
|
||||
"output['SDK version'] = azureml.core.VERSION\n",
|
||||
"output['Subscription ID'] = ws.subscription_id\n",
|
||||
"output['Workspace'] = ws.name\n",
|
||||
"output['Resource Group'] = ws.resource_group\n",
|
||||
"output['Location'] = ws.location\n",
|
||||
"output['Project Directory'] = project_folder\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 energy demanding data from file, and preview data."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"data = pd.read_csv(\"nyc_energy.csv\", parse_dates=['timeStamp'])\n",
|
||||
"data.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Split the data to train and test\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"train = data[data['timeStamp'] < '2017-02-01']\n",
|
||||
"test = data[data['timeStamp'] >= '2017-02-01']\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Prepare the test data, we will feed X_test to the fitted model and get prediction"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"y_test = test.pop('demand').values\n",
|
||||
"X_test = test"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Split the train data to train and valid\n",
|
||||
"\n",
|
||||
"Use one month's data as valid data\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"X_train = train[train['timeStamp'] < '2017-01-01']\n",
|
||||
"X_valid = train[train['timeStamp'] >= '2017-01-01']\n",
|
||||
"y_train = X_train.pop('demand').values\n",
|
||||
"y_valid = X_valid.pop('demand').values\n",
|
||||
"print(X_train.shape)\n",
|
||||
"print(y_train.shape)\n",
|
||||
"print(X_valid.shape)\n",
|
||||
"print(y_valid.shape)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"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",
|
||||
"|**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**|Data used to evaluate a model in a iteration. (sparse) array-like, shape = [n_samples, n_features]|\n",
|
||||
"|**y_valid**|Data used to evaluate a model in a iteration. (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": [
|
||||
"time_column_name = 'timeStamp'\n",
|
||||
"automl_settings = {\n",
|
||||
" \"time_column_name\": time_column_name,\n",
|
||||
"}\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"automl_config = AutoMLConfig(task = 'forecasting',\n",
|
||||
" debug_log = 'automl_nyc_energy_errors.log',\n",
|
||||
" primary_metric='normalized_root_mean_squared_error',\n",
|
||||
" iterations = 10,\n",
|
||||
" iteration_timeout_minutes = 5,\n",
|
||||
" X = X_train,\n",
|
||||
" y = y_train,\n",
|
||||
" X_valid = X_valid,\n",
|
||||
" y_valid = y_valid,\n",
|
||||
" path=project_folder,\n",
|
||||
" verbosity = logging.INFO,\n",
|
||||
" **automl_settings)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"You can call the submit method on the experiment object and pass the run configuration. For Local runs the execution is synchronous. Depending on the data and number of iterations this can run for while.\n",
|
||||
"You will see the currently running iterations printing to the console."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"local_run = experiment.submit(automl_config, show_output=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"local_run"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### 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": [
|
||||
"### Test the Best Fitted Model\n",
|
||||
"\n",
|
||||
"Predict on training and test set, and calculate residual values."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"y_pred = fitted_model.predict(X_test)\n",
|
||||
"y_pred"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Use the Check Data Function to remove the nan values from y_test to avoid error when calculate metrics "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"if len(y_test) != len(y_pred):\n",
|
||||
" raise ValueError(\n",
|
||||
" 'the true values and prediction values do not have equal length.')\n",
|
||||
"elif len(y_test) == 0:\n",
|
||||
" raise ValueError(\n",
|
||||
" 'y_true and y_pred are empty.')\n",
|
||||
"\n",
|
||||
"# if there is any non-numeric element in the y_true or y_pred,\n",
|
||||
"# the ValueError exception will be thrown.\n",
|
||||
"y_test_f = np.array(y_test).astype(float)\n",
|
||||
"y_pred_f = np.array(y_pred).astype(float)\n",
|
||||
"\n",
|
||||
"# remove entries both in y_true and y_pred where at least\n",
|
||||
"# one element in y_true or y_pred is missing\n",
|
||||
"y_test = y_test_f[~(np.isnan(y_test_f) | np.isnan(y_pred_f))]\n",
|
||||
"y_pred = y_pred_f[~(np.isnan(y_test_f) | np.isnan(y_pred_f))]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Calculate metrics for the prediction\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(\"[Test Data] \\nRoot Mean squared error: %.2f\" % np.sqrt(mean_squared_error(y_test, y_pred)))\n",
|
||||
"# Explained variance score: 1 is perfect prediction\n",
|
||||
"print('mean_absolute_error score: %.2f' % mean_absolute_error(y_test, y_pred))\n",
|
||||
"print('R2 score: %.2f' % r2_score(y_test, y_pred))\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\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()"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"authors": [
|
||||
{
|
||||
"name": "xiaga"
|
||||
}
|
||||
],
|
||||
"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
|
||||
}
|
||||
File diff suppressed because it is too large
Load Diff
@@ -1,425 +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": [
|
||||
"# Automated Machine Learning\n",
|
||||
"_**Orange Juice Sales Forecasting**_\n",
|
||||
"\n",
|
||||
"## Contents\n",
|
||||
"1. [Introduction](#Introduction)\n",
|
||||
"1. [Setup](#Setup)\n",
|
||||
"1. [Data](#Data)\n",
|
||||
"1. [Train](#Train)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Introduction\n",
|
||||
"In this example, we use AutoML to find and tune a time-series forecasting model.\n",
|
||||
"\n",
|
||||
"Make sure you have executed the [configuration notebook](../../../configuration.ipynb) before running this notebook.\n",
|
||||
"\n",
|
||||
"In this notebook, you will:\n",
|
||||
"1. Create an Experiment in an existing Workspace\n",
|
||||
"2. Instantiate an AutoMLConfig \n",
|
||||
"3. Find and train a forecasting model using local compute\n",
|
||||
"4. Evaluate the performance of the model\n",
|
||||
"\n",
|
||||
"The examples in the follow code samples use the University of Chicago's Dominick's Finer Foods dataset to forecast orange juice sales. Dominick's was a grocery chain in the Chicago metropolitan area."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Setup"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import azureml.core\n",
|
||||
"import pandas as pd\n",
|
||||
"import numpy as np\n",
|
||||
"import logging\n",
|
||||
"import warnings\n",
|
||||
"# Squash warning messages for cleaner output in the notebook\n",
|
||||
"warnings.showwarning = lambda *args, **kwargs: None\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"from azureml.core.workspace import Workspace\n",
|
||||
"from azureml.core.experiment import Experiment\n",
|
||||
"from azureml.train.automl import AutoMLConfig\n",
|
||||
"from sklearn.metrics import mean_absolute_error, mean_squared_error"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"As part of the setup you have already created a <b>Workspace</b>. To run AutoML, you also need to create an <b>Experiment</b>. An Experiment is a named object in a Workspace which represents a predictive task, the output of which is a trained model and a set of evaluation metrics for the model. "
|
||||
]
|
||||
},
|
||||
{
|
||||
"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-ojsalesforecasting'\n",
|
||||
"# project folder\n",
|
||||
"project_folder = './sample_projects/automl-local-ojsalesforecasting'\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['Run History Name'] = experiment_name\n",
|
||||
"pd.set_option('display.max_colwidth', -1)\n",
|
||||
"outputDf = pd.DataFrame(data = output, index = [''])\n",
|
||||
"outputDf.T"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Data\n",
|
||||
"You are now ready to load the historical orange juice sales data. We will load the CSV file into a plain pandas DataFrame; the time column in the CSV is called _WeekStarting_, so it will be specially parsed into the datetime type."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"time_column_name = 'WeekStarting'\n",
|
||||
"data = pd.read_csv(\"dominicks_OJ.csv\", parse_dates=[time_column_name])\n",
|
||||
"data.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Each row in the DataFrame holds a quantity of weekly sales for an OJ brand at a single store. The data also includes the sales price, a flag indicating if the OJ brand was advertised in the store that week, and some customer demographic information based on the store location. For historical reasons, the data also include the logarithm of the sales quantity. The Dominick's grocery data is commonly used to illustrate econometric modeling techniques where logarithms of quantities are generally preferred. \n",
|
||||
"\n",
|
||||
"The task is now to build a time-series model for the _Quantity_ column. It is important to note that this dataset is comprised of many individual time-series - one for each unique combination of _Store_ and _Brand_. To distinguish the individual time-series, we thus define the **grain** - the columns whose values determine the boundaries between time-series: "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"grain_column_names = ['Store', 'Brand']\n",
|
||||
"nseries = data.groupby(grain_column_names).ngroups\n",
|
||||
"print('Data contains {0} individual time-series.'.format(nseries))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"For demonstration purposes, we extract sales time-series for just a few of the stores:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"use_stores = [2, 5, 8]\n",
|
||||
"data_subset = data[data.Store.isin(use_stores)]\n",
|
||||
"nseries = data_subset.groupby(grain_column_names).ngroups\n",
|
||||
"print('Data subset contains {0} individual time-series.'.format(nseries))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Data Splitting\n",
|
||||
"We now split the data into a training and a testing set for later forecast evaluation. The test set will contain the final 20 weeks of observed sales for each time-series. The splits should be stratified by series, so we use a group-by statement on the grain columns."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"n_test_periods = 20\n",
|
||||
"\n",
|
||||
"def split_last_n_by_grain(df, n):\n",
|
||||
" \"\"\"Group df by grain and split on last n rows for each group.\"\"\"\n",
|
||||
" df_grouped = (df.sort_values(time_column_name) # Sort by ascending time\n",
|
||||
" .groupby(grain_column_names, group_keys=False))\n",
|
||||
" df_head = df_grouped.apply(lambda dfg: dfg.iloc[:-n])\n",
|
||||
" df_tail = df_grouped.apply(lambda dfg: dfg.iloc[-n:])\n",
|
||||
" return df_head, df_tail\n",
|
||||
"\n",
|
||||
"X_train, X_test = split_last_n_by_grain(data_subset, n_test_periods)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Modeling\n",
|
||||
"\n",
|
||||
"For forecasting tasks, AutoML uses pre-processing and estimation steps that are specific to time-series. AutoML will undertake the following pre-processing steps:\n",
|
||||
"* Detect time-series sample frequency (e.g. hourly, daily, weekly) and create new records for absent time points to make the series regular. A regular time series has a well-defined frequency and has a value at every sample point in a contiguous time span \n",
|
||||
"* Impute missing values in the target (via forward-fill) and feature columns (using median column values) \n",
|
||||
"* Create grain-based features to enable fixed effects across different series\n",
|
||||
"* Create time-based features to assist in learning seasonal patterns\n",
|
||||
"* Encode categorical variables to numeric quantities\n",
|
||||
"\n",
|
||||
"AutoML will currently train a single, regression-type model across **all** time-series in a given training set. This allows the model to generalize across related series.\n",
|
||||
"\n",
|
||||
"You are almost ready to start an AutoML training job. First, we need to separate the target column from the rest of the DataFrame: "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"target_column_name = 'Quantity'\n",
|
||||
"y_train = X_train.pop(target_column_name).values"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Train\n",
|
||||
"\n",
|
||||
"The AutoMLConfig object defines the settings and data for an AutoML training job. Here, we set necessary inputs like the task type, the number of AutoML iterations to try, the training data, and cross-validation parameters. \n",
|
||||
"\n",
|
||||
"For forecasting tasks, there are some additional parameters that can be set: the name of the column holding the date/time, the grain column names, and the maximum forecast horizon. A time column is required for forecasting, while the grain is optional. If a grain is not given, AutoML assumes that the whole dataset is a single time-series. We also pass a list of columns to drop prior to modeling. The _logQuantity_ column is completely correlated with the target quantity, so it must be removed to prevent a target leak.\n",
|
||||
"\n",
|
||||
"The forecast horizon is given in units of the time-series frequency; for instance, the OJ series frequency is weekly, so a horizon of 20 means that a trained model will estimate sales up-to 20 weeks beyond the latest date in the training data for each series. In this example, we set the maximum horizon to the number of samples per series in the test set (n_test_periods). Generally, the value of this parameter will be dictated by business needs. For example, a demand planning organizaion that needs to estimate the next month of sales would set the horizon accordingly. \n",
|
||||
"\n",
|
||||
"Finally, a note about the cross-validation (CV) procedure for time-series data. AutoML uses out-of-sample error estimates to select a best pipeline/model, so it is important that the CV fold splitting is done correctly. Time-series can violate the basic statistical assumptions of the canonical K-Fold CV strategy, so AutoML implements a [rolling origin validation](https://robjhyndman.com/hyndsight/tscv/) procedure to create CV folds for time-series data. To use this procedure, you just need to specify the desired number of CV folds in the AutoMLConfig object. It is also possible to bypass CV and use your own validation set by setting the *X_valid* and *y_valid* parameters of AutoMLConfig.\n",
|
||||
"\n",
|
||||
"Here is a summary of AutoMLConfig parameters used for training the OJ model:\n",
|
||||
"\n",
|
||||
"|Property|Description|\n",
|
||||
"|-|-|\n",
|
||||
"|**task**|forecasting|\n",
|
||||
"|**primary_metric**|This is the metric that you want to optimize.<br> Forecasting supports the following primary metrics <br><i>spearman_correlation</i><br><i>normalized_root_mean_squared_error</i><br><i>r2_score</i><br><i>normalized_mean_absolute_error</i>\n",
|
||||
"|**iterations**|Number of iterations. In each iteration, Auto ML trains a specific pipeline on the given data|\n",
|
||||
"|**X**|Training matrix of features as a pandas DataFrame, shape = [n_training_samples, n_features]|\n",
|
||||
"|**y**|Target values as a numpy.ndarray, shape = [n_training_samples, ]|\n",
|
||||
"|**n_cross_validations**|Number of cross-validation folds to use for model/pipeline selection|\n",
|
||||
"|**enable_ensembling**|Allow AutoML to create ensembles of the best performing models\n",
|
||||
"|**debug_log**|Log file path for writing debugging information\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.|\n",
|
||||
"|**time_column_name**|Name of the datetime column in the input data|\n",
|
||||
"|**grain_column_names**|Name(s) of the columns defining individual series in the input data|\n",
|
||||
"|**drop_column_names**|Name(s) of columns to drop prior to modeling|\n",
|
||||
"|**max_horizon**|Maximum desired forecast horizon in units of time-series frequency|"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"time_series_settings = {\n",
|
||||
" 'time_column_name': time_column_name,\n",
|
||||
" 'grain_column_names': grain_column_names,\n",
|
||||
" 'drop_column_names': ['logQuantity'],\n",
|
||||
" 'max_horizon': n_test_periods\n",
|
||||
"}\n",
|
||||
"\n",
|
||||
"automl_config = AutoMLConfig(task='forecasting',\n",
|
||||
" debug_log='automl_oj_sales_errors.log',\n",
|
||||
" primary_metric='normalized_root_mean_squared_error',\n",
|
||||
" iterations=10,\n",
|
||||
" X=X_train,\n",
|
||||
" y=y_train,\n",
|
||||
" n_cross_validations=5,\n",
|
||||
" enable_ensembling=False,\n",
|
||||
" path=project_folder,\n",
|
||||
" verbosity=logging.INFO,\n",
|
||||
" **time_series_settings)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"You can now submit a new training run. For local runs, the execution is synchronous. Depending on the data and number of iterations this operation may take several minutes.\n",
|
||||
"Information from each iteration will be printed to the console."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"local_run = experiment.submit(automl_config, show_output=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"local_run"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Retrieve the Best Model\n",
|
||||
"Each run within an Experiment stores serialized (i.e. pickled) pipelines from the AutoML iterations. We can now retrieve the pipeline with the best performance on the validation dataset:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"best_run, fitted_pipeline = local_run.get_output()\n",
|
||||
"fitted_pipeline.steps"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Make Predictions from the Best Fitted Model\n",
|
||||
"Now that we have retrieved the best pipeline/model, it can be used to make predictions on test data. First, we remove the target values from the test set:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"y_test = X_test.pop(target_column_name).values"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"X_test.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"To produce predictions on the test set, we need to know the feature values at all dates in the test set. This requirement is somewhat reasonable for the OJ sales data since the features mainly consist of price, which is usually set in advance, and customer demographics which are approximately constant for each store over the 20 week forecast horizon in the testing data. \n",
|
||||
"\n",
|
||||
"The target predictions can be retrieved by calling the `predict` method on the best model:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"y_pred = fitted_pipeline.predict(X_test)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Calculate evaluation metrics for the prediction\n",
|
||||
"To evaluate the accuracy of the forecast, we'll compare against the actual sales quantities for some select metrics, included the mean absolute percentage error (MAPE)."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def MAPE(actual, pred):\n",
|
||||
" \"\"\"\n",
|
||||
" Calculate mean absolute percentage error.\n",
|
||||
" Remove NA and values where actual is close to zero\n",
|
||||
" \"\"\"\n",
|
||||
" not_na = ~(np.isnan(actual) | np.isnan(pred))\n",
|
||||
" not_zero = ~np.isclose(actual, 0.0)\n",
|
||||
" actual_safe = actual[not_na & not_zero]\n",
|
||||
" pred_safe = pred[not_na & not_zero]\n",
|
||||
" APE = 100*np.abs((actual_safe - pred_safe)/actual_safe)\n",
|
||||
" return np.mean(APE)\n",
|
||||
"\n",
|
||||
"print(\"[Test Data] \\nRoot Mean squared error: %.2f\" % np.sqrt(mean_squared_error(y_test, y_pred)))\n",
|
||||
"print('mean_absolute_error score: %.2f' % mean_absolute_error(y_test, y_pred))\n",
|
||||
"print('MAPE: %.2f' % MAPE(y_test, y_pred))"
|
||||
]
|
||||
}
|
||||
],
|
||||
"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
|
||||
}
|
||||
File diff suppressed because it is too large
Load Diff
@@ -1,379 +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": [
|
||||
"# Automated Machine Learning\n",
|
||||
"_**Blacklisting Models, Early Termination, and Handling Missing Data**_\n",
|
||||
"\n",
|
||||
"## Contents\n",
|
||||
"1. [Introduction](#Introduction)\n",
|
||||
"1. [Setup](#Setup)\n",
|
||||
"1. [Data](#Data)\n",
|
||||
"1. [Train](#Train)\n",
|
||||
"1. [Results](#Results)\n",
|
||||
"1. [Test](#Test)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Introduction\n",
|
||||
"In this example we use the scikit-learn's [digit dataset](http://scikit-learn.org/stable/datasets/index.html#optical-recognition-of-handwritten-digits-dataset) to showcase how you can use AutoML for handling missing values in data. We also provide a stopping metric indicating a target for the primary metrics so that AutoML can terminate the run without necessarly going through all the iterations. Finally, if you want to avoid a certain pipeline, we allow you to specify a blacklist of algorithms that AutoML will ignore for this run.\n",
|
||||
"\n",
|
||||
"Make sure you have executed the [configuration](../../../configuration.ipynb) before running this notebook.\n",
|
||||
"\n",
|
||||
"In this notebook you will learn how to:\n",
|
||||
"1. Create an `Experiment` in an existing `Workspace`.\n",
|
||||
"2. Configure AutoML using `AutoMLConfig`.\n",
|
||||
"4. Train the model.\n",
|
||||
"5. Explore the results.\n",
|
||||
"6. Test the best fitted model.\n",
|
||||
"\n",
|
||||
"In addition this notebook showcases the following features\n",
|
||||
"- **Blacklisting** certain pipelines\n",
|
||||
"- Specifying **target metrics** to indicate stopping criteria\n",
|
||||
"- Handling **missing data** in the input"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Setup\n",
|
||||
"\n",
|
||||
"As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import logging\n",
|
||||
"\n",
|
||||
"from matplotlib import pyplot as plt\n",
|
||||
"import numpy as np\n",
|
||||
"import pandas as pd\n",
|
||||
"from sklearn import datasets\n",
|
||||
"\n",
|
||||
"import azureml.core\n",
|
||||
"from azureml.core.experiment import Experiment\n",
|
||||
"from azureml.core.workspace import Workspace\n",
|
||||
"from azureml.train.automl import AutoMLConfig"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"ws = Workspace.from_config()\n",
|
||||
"\n",
|
||||
"# Choose a name for the experiment.\n",
|
||||
"experiment_name = 'automl-local-missing-data'\n",
|
||||
"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",
|
||||
"outputDf = pd.DataFrame(data = output, index = [''])\n",
|
||||
"outputDf.T"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Data"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"digits = datasets.load_digits()\n",
|
||||
"X_train = digits.data[10:,:]\n",
|
||||
"y_train = digits.target[10:]\n",
|
||||
"\n",
|
||||
"# Add missing values in 75% of the lines.\n",
|
||||
"missing_rate = 0.75\n",
|
||||
"n_missing_samples = int(np.floor(X_train.shape[0] * missing_rate))\n",
|
||||
"missing_samples = np.hstack((np.zeros(X_train.shape[0] - n_missing_samples, dtype=np.bool), np.ones(n_missing_samples, dtype=np.bool)))\n",
|
||||
"rng = np.random.RandomState(0)\n",
|
||||
"rng.shuffle(missing_samples)\n",
|
||||
"missing_features = rng.randint(0, X_train.shape[1], n_missing_samples)\n",
|
||||
"X_train[np.where(missing_samples)[0], missing_features] = np.nan"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df = pd.DataFrame(data = X_train)\n",
|
||||
"df['Label'] = pd.Series(y_train, index=df.index)\n",
|
||||
"df.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Train\n",
|
||||
"\n",
|
||||
"Instantiate an `AutoMLConfig` object to specify the settings and data used to run the experiment. This includes setting `experiment_exit_score`, which should cause the run to complete before the `iterations` count is reached.\n",
|
||||
"\n",
|
||||
"|Property|Description|\n",
|
||||
"|-|-|\n",
|
||||
"|**task**|classification or regression|\n",
|
||||
"|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics: <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>average_precision_score_weighted</i><br><i>norm_macro_recall</i><br><i>precision_score_weighted</i>|\n",
|
||||
"|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
|
||||
"|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
|
||||
"|**n_cross_validations**|Number of cross validation splits.|\n",
|
||||
"|**preprocess**|Setting this to *True* enables AutoML to perform preprocessing on the input to handle *missing data*, and to perform some common *feature extraction*.|\n",
|
||||
"|**experiment_exit_score**|*double* value indicating the target for *primary_metric*. <br>Once the target is surpassed the run terminates.|\n",
|
||||
"|**blacklist_models**|*List* of *strings* indicating machine learning algorithms for AutoML to avoid in this run.<br><br> Allowed values for **Classification**<br><i>LogisticRegression</i><br><i>SGD</i><br><i>MultinomialNaiveBayes</i><br><i>BernoulliNaiveBayes</i><br><i>SVM</i><br><i>LinearSVM</i><br><i>KNN</i><br><i>DecisionTree</i><br><i>RandomForest</i><br><i>ExtremeRandomTrees</i><br><i>LightGBM</i><br><i>GradientBoosting</i><br><i>TensorFlowDNN</i><br><i>TensorFlowLinearClassifier</i><br><br>Allowed values for **Regression**<br><i>ElasticNet</i><br><i>GradientBoosting</i><br><i>DecisionTree</i><br><i>KNN</i><br><i>LassoLars</i><br><i>SGD</i><br><i>RandomForest</i><br><i>ExtremeRandomTrees</i><br><i>LightGBM</i><br><i>TensorFlowLinearRegressor</i><br><i>TensorFlowDNN</i>|\n",
|
||||
"|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
|
||||
"|**y**|(sparse) array-like, shape = [n_samples, ], [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",
|
||||
" iteration_timeout_minutes = 60,\n",
|
||||
" iterations = 20,\n",
|
||||
" n_cross_validations = 5,\n",
|
||||
" preprocess = True,\n",
|
||||
" experiment_exit_score = 0.9984,\n",
|
||||
" blacklist_models = ['KNN','LinearSVM'],\n",
|
||||
" verbosity = logging.INFO,\n",
|
||||
" X = X_train, \n",
|
||||
" y = y_train,\n",
|
||||
" path = project_folder)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Call the `submit` method on the experiment object and pass the run configuration. Execution of local runs is synchronous. Depending on the data and the number of iterations this can run for a while.\n",
|
||||
"In this example, we specify `show_output = True` to print currently running iterations to the console."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"local_run = experiment.submit(automl_config, show_output = True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"local_run"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Results"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Widget for Monitoring Runs\n",
|
||||
"\n",
|
||||
"The widget will first report a \"loading\" status while running the first iteration. After completing the first iteration, an auto-updating graph and table will be shown. The widget will refresh once per minute, so you should see the graph update as child runs complete.\n",
|
||||
"\n",
|
||||
"**Note:** The widget displays a link at the bottom. Use this link to open a web interface to explore the individual run details."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.widgets import RunDetails\n",
|
||||
"RunDetails(local_run).show() "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"\n",
|
||||
"#### Retrieve All Child Runs\n",
|
||||
"You can also use SDK methods to fetch all the child runs and see individual metrics that we log."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"children = list(local_run.get_children())\n",
|
||||
"metricslist = {}\n",
|
||||
"for run in children:\n",
|
||||
" properties = run.get_properties()\n",
|
||||
" metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}\n",
|
||||
" metricslist[int(properties['iteration'])] = metrics\n",
|
||||
"\n",
|
||||
"rundata = pd.DataFrame(metricslist).sort_index(1)\n",
|
||||
"rundata"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Retrieve the Best Model\n",
|
||||
"\n",
|
||||
"Below we select the best pipeline from our iterations. The `get_output` method returns the best run and the fitted model. The Model includes the pipeline and any pre-processing. Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"best_run, fitted_model = local_run.get_output()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Best Model Based on Any Other Metric\n",
|
||||
"Show the run and the model which has the smallest `accuracy` value:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# lookup_metric = \"accuracy\"\n",
|
||||
"# best_run, fitted_model = local_run.get_output(metric = lookup_metric)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Model from a Specific Iteration\n",
|
||||
"Show the run and the model from the third iteration:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# iteration = 3\n",
|
||||
"# best_run, fitted_model = local_run.get_output(iteration = iteration)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Test"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"digits = datasets.load_digits()\n",
|
||||
"X_test = digits.data[:10, :]\n",
|
||||
"y_test = digits.target[:10]\n",
|
||||
"images = digits.images[:10]\n",
|
||||
"\n",
|
||||
"# Randomly select digits and test.\n",
|
||||
"for index in np.random.choice(len(y_test), 2, replace = False):\n",
|
||||
" print(index)\n",
|
||||
" predicted = fitted_model.predict(X_test[index:index + 1])[0]\n",
|
||||
" label = y_test[index]\n",
|
||||
" title = \"Label value = %d Predicted value = %d \" % (label, predicted)\n",
|
||||
" fig = plt.figure(1, figsize=(3,3))\n",
|
||||
" ax1 = fig.add_axes((0,0,.8,.8))\n",
|
||||
" ax1.set_title(title)\n",
|
||||
" plt.imshow(images[index], cmap = plt.cm.gray_r, interpolation = 'nearest')\n",
|
||||
" plt.show()\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"authors": [
|
||||
{
|
||||
"name": "savitam"
|
||||
}
|
||||
],
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.6"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
@@ -1,348 +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": [
|
||||
"# Automated Machine Learning\n",
|
||||
"_**Explain classification model and visualize the explanation**_\n",
|
||||
"\n",
|
||||
"## Contents\n",
|
||||
"1. [Introduction](#Introduction)\n",
|
||||
"1. [Setup](#Setup)\n",
|
||||
"1. [Data](#Data)\n",
|
||||
"1. [Train](#Train)\n",
|
||||
"1. [Results](#Results)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Introduction\n",
|
||||
"In this example we use the sklearn's [iris dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_iris.html) to showcase how you can use the AutoML Classifier for a simple classification problem.\n",
|
||||
"\n",
|
||||
"Make sure you have executed the [configuration](../../../configuration.ipynb) before running this notebook.\n",
|
||||
"\n",
|
||||
"In this notebook you would see\n",
|
||||
"1. Creating an Experiment in an existing Workspace\n",
|
||||
"2. Instantiating AutoMLConfig\n",
|
||||
"3. Training the Model using local compute and explain the model\n",
|
||||
"4. Visualization model's feature importance in widget\n",
|
||||
"5. Explore best model's explanation"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Setup\n",
|
||||
"\n",
|
||||
"As part of the setup you have already created a <b>Workspace</b>. For AutoML you would need to create an <b>Experiment</b>. An <b>Experiment</b> is a named object in a <b>Workspace</b>, which is used to run experiments."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import logging\n",
|
||||
"\n",
|
||||
"import pandas as pd\n",
|
||||
"import azureml.core\n",
|
||||
"from azureml.core.experiment import Experiment\n",
|
||||
"from azureml.core.workspace import Workspace\n",
|
||||
"from azureml.train.automl import AutoMLConfig"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"ws = Workspace.from_config()\n",
|
||||
"\n",
|
||||
"# choose a name for experiment\n",
|
||||
"experiment_name = 'automl-model-explanation'\n",
|
||||
"# project folder\n",
|
||||
"project_folder = './sample_projects/automl-model-explanation'\n",
|
||||
"\n",
|
||||
"experiment=Experiment(ws, experiment_name)\n",
|
||||
"\n",
|
||||
"output = {}\n",
|
||||
"output['SDK version'] = azureml.core.VERSION\n",
|
||||
"output['Subscription ID'] = ws.subscription_id\n",
|
||||
"output['Workspace Name'] = ws.name\n",
|
||||
"output['Resource Group'] = ws.resource_group\n",
|
||||
"output['Location'] = ws.location\n",
|
||||
"output['Project Directory'] = project_folder\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": [
|
||||
"from sklearn import datasets\n",
|
||||
"\n",
|
||||
"iris = datasets.load_iris()\n",
|
||||
"y = iris.target\n",
|
||||
"X = iris.data\n",
|
||||
"\n",
|
||||
"features = iris.feature_names\n",
|
||||
"\n",
|
||||
"from sklearn.model_selection import train_test_split\n",
|
||||
"X_train, X_test, y_train, y_test = train_test_split(X,\n",
|
||||
" y,\n",
|
||||
" test_size=0.1,\n",
|
||||
" random_state=100,\n",
|
||||
" stratify=y)\n",
|
||||
"\n",
|
||||
"X_train = pd.DataFrame(X_train, columns=features)\n",
|
||||
"X_test = pd.DataFrame(X_test, columns=features)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Train\n",
|
||||
"\n",
|
||||
"Instantiate a AutoMLConfig object. This defines the settings and data used to run the experiment.\n",
|
||||
"\n",
|
||||
"|Property|Description|\n",
|
||||
"|-|-|\n",
|
||||
"|**task**|classification or regression|\n",
|
||||
"|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics: <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>average_precision_score_weighted</i><br><i>norm_macro_recall</i><br><i>precision_score_weighted</i>|\n",
|
||||
"|**max_time_sec**|Time limit in minutes for each iterations|\n",
|
||||
"|**iterations**|Number of iterations. In each iteration Auto ML trains the data with a specific pipeline|\n",
|
||||
"|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
|
||||
"|**y**|(sparse) array-like, shape = [n_samples, ], [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]|\n",
|
||||
"|**y_valid**|(sparse) array-like, shape = [n_samples, ], [n_samples, n_classes]|\n",
|
||||
"|**model_explainability**|Indicate to explain each trained pipeline or not |\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",
|
||||
" iteration_timeout_minutes = 200,\n",
|
||||
" iterations = 10,\n",
|
||||
" verbosity = logging.INFO,\n",
|
||||
" X = X_train, \n",
|
||||
" y = y_train,\n",
|
||||
" X_valid = X_test,\n",
|
||||
" y_valid = y_test,\n",
|
||||
" model_explainability=True,\n",
|
||||
" path=project_folder)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"You can call the submit method on the experiment object and pass the run configuration. For Local runs the execution is synchronous. Depending on the data and number of iterations this can run for while.\n",
|
||||
"You will see the currently running iterations printing to the console."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"local_run = experiment.submit(automl_config, show_output=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"local_run"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Results"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Widget for monitoring runs\n",
|
||||
"\n",
|
||||
"The widget will sit on \"loading\" until the first iteration completed, then you will see an auto-updating graph and table show up. It refreshed once per minute, so you should see the graph update as child runs complete.\n",
|
||||
"\n",
|
||||
"NOTE: The widget displays a link at the bottom. This links to a web-ui to explore the individual run details."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.widgets import RunDetails\n",
|
||||
"RunDetails(local_run).show() "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Retrieve the Best Model\n",
|
||||
"\n",
|
||||
"Below we select the best pipeline from our iterations. The *get_output* method on automl_classifier returns the best run and the fitted model for the last *fit* invocation. There are overloads on *get_output* that allow you to retrieve the best run and fitted model for *any* logged metric or a particular *iteration*."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"best_run, fitted_model = local_run.get_output()\n",
|
||||
"print(best_run)\n",
|
||||
"print(fitted_model)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Best Model 's explanation\n",
|
||||
"\n",
|
||||
"Retrieve the explanation from the best_run. And explanation information includes:\n",
|
||||
"\n",
|
||||
"1.\tshap_values: The explanation information generated by shap lib\n",
|
||||
"2.\texpected_values: The expected value of the model applied to set of X_train data.\n",
|
||||
"3.\toverall_summary: The model level feature importance values sorted in descending order\n",
|
||||
"4.\toverall_imp: The feature names sorted in the same order as in overall_summary\n",
|
||||
"5.\tper_class_summary: The class level feature importance values sorted in descending order. Only available for the classification case\n",
|
||||
"6.\tper_class_imp: The feature names sorted in the same order as in per_class_summary. Only available for the classification case"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.train.automl.automlexplainer import retrieve_model_explanation\n",
|
||||
"\n",
|
||||
"shap_values, expected_values, overall_summary, overall_imp, per_class_summary, per_class_imp = \\\n",
|
||||
" retrieve_model_explanation(best_run)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(overall_summary)\n",
|
||||
"print(overall_imp)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(per_class_summary)\n",
|
||||
"print(per_class_imp)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Beside retrieve the existed model explanation information, explain the model with different train/test data"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.train.automl.automlexplainer import explain_model\n",
|
||||
"\n",
|
||||
"shap_values, expected_values, overall_summary, overall_imp, per_class_summary, per_class_imp = \\\n",
|
||||
" explain_model(fitted_model, X_train, X_test)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(overall_summary)\n",
|
||||
"print(overall_imp)"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"authors": [
|
||||
{
|
||||
"name": "xif"
|
||||
}
|
||||
],
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.6"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
@@ -1,400 +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": [
|
||||
"# Automated Machine Learning\n",
|
||||
"_**Regression 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"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Introduction\n",
|
||||
"In this example we use the scikit-learn's [diabetes dataset](http://scikit-learn.org/stable/datasets/index.html#diabetes-dataset) to showcase how you can use AutoML for a simple regression 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",
|
||||
"\n",
|
||||
"import azureml.core\n",
|
||||
"from azureml.core.experiment import Experiment\n",
|
||||
"from azureml.core.workspace import Workspace\n",
|
||||
"from azureml.train.automl import AutoMLConfig"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"ws = Workspace.from_config()\n",
|
||||
"\n",
|
||||
"# Choose a name for the experiment and specify the project folder.\n",
|
||||
"experiment_name = 'automl-local-regression'\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",
|
||||
"outputDf = pd.DataFrame(data = output, index = [''])\n",
|
||||
"outputDf.T"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Data\n",
|
||||
"This uses scikit-learn's [load_diabetes](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_diabetes.html) method."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Load the diabetes dataset, a well-known built-in small dataset that comes with scikit-learn.\n",
|
||||
"from sklearn.datasets import load_diabetes\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": [
|
||||
"## Train\n",
|
||||
"\n",
|
||||
"Instantiate an `AutoMLConfig` object to specify the settings and data used to run the experiment.\n",
|
||||
"\n",
|
||||
"|Property|Description|\n",
|
||||
"|-|-|\n",
|
||||
"|**task**|classification or regression|\n",
|
||||
"|**primary_metric**|This is the metric that you want to optimize. Regression supports the following primary metrics: <br><i>spearman_correlation</i><br><i>normalized_root_mean_squared_error</i><br><i>r2_score</i><br><i>normalized_mean_absolute_error</i>|\n",
|
||||
"|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
|
||||
"|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
|
||||
"|**n_cross_validations**|Number of cross validation splits.|\n",
|
||||
"|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
|
||||
"|**y**|(sparse) array-like, shape = [n_samples, ], [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",
|
||||
" iteration_timeout_minutes = 10,\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": [
|
||||
"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 `root_mean_squared_error` value (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",
|
||||
"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"
|
||||
]
|
||||
},
|
||||
{
|
||||
"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",
|
||||
"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",
|
||||
"\n",
|
||||
"# Plot a 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",
|
||||
"\n",
|
||||
"# Plot a 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": {
|
||||
"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
|
||||
}
|
||||
@@ -1,555 +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": [
|
||||
"# Automated Machine Learning\n",
|
||||
"_**Remote Execution using 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 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 would see\n",
|
||||
"1. Create an `Experiment` in an existing `Workspace`.\n",
|
||||
"2. Create or Attach existing AmlCompute to a workspace.\n",
|
||||
"3. Configure AutoML using `AutoMLConfig`.\n",
|
||||
"4. Train the model using AmlCompute\n",
|
||||
"5. Explore the results.\n",
|
||||
"6. Test the best fitted model.\n",
|
||||
"\n",
|
||||
"In addition this notebook showcases the following features\n",
|
||||
"- **Parallel** executions for iterations\n",
|
||||
"- **Asynchronous** tracking of progress\n",
|
||||
"- **Cancellation** of individual iterations or the entire run\n",
|
||||
"- Retrieving models for any iteration or logged metric\n",
|
||||
"- Specifying AutoML settings as `**kwargs`"
|
||||
]
|
||||
},
|
||||
{
|
||||
"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",
|
||||
"import os\n",
|
||||
"import csv\n",
|
||||
"\n",
|
||||
"from matplotlib import pyplot as plt\n",
|
||||
"import numpy as np\n",
|
||||
"import pandas as pd\n",
|
||||
"from sklearn import datasets\n",
|
||||
"\n",
|
||||
"import azureml.core\n",
|
||||
"from azureml.core.experiment import Experiment\n",
|
||||
"from azureml.core.workspace import Workspace\n",
|
||||
"from azureml.train.automl import AutoMLConfig"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"ws = Workspace.from_config()\n",
|
||||
"\n",
|
||||
"# Choose a name for the run history container in the workspace.\n",
|
||||
"experiment_name = 'automl-remote-amlcompute'\n",
|
||||
"project_folder = './project'\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",
|
||||
"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](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for your AutoML run. In this tutorial, you create `AmlCompute` as your training compute resource.\n",
|
||||
"\n",
|
||||
"**Creation of AmlCompute takes approximately 5 minutes.** If the AmlCompute with that name is already in your workspace this code will skip the creation process.\n",
|
||||
"\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](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 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",
|
||||
" # 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": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Data\n",
|
||||
"For remote executions, you need to make the data accessible from the remote compute.\n",
|
||||
"This can be done by uploading the data to DataStore.\n",
|
||||
"In this example, we upload scikit-learn's [load_digits](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html) data."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"data_train = datasets.load_digits()\n",
|
||||
"\n",
|
||||
"if not os.path.isdir('data'):\n",
|
||||
" os.mkdir('data')\n",
|
||||
" \n",
|
||||
"if not os.path.exists(project_folder):\n",
|
||||
" os.makedirs(project_folder)\n",
|
||||
" \n",
|
||||
"pd.DataFrame(data_train.data).to_csv(\"data/X_train.tsv\", index=False, header=False, quoting=csv.QUOTE_ALL, sep=\"\\t\")\n",
|
||||
"pd.DataFrame(data_train.target).to_csv(\"data/y_train.tsv\", index=False, header=False, sep=\"\\t\")\n",
|
||||
"\n",
|
||||
"ds = ws.get_default_datastore()\n",
|
||||
"ds.upload(src_dir='./data', target_path='bai_data', overwrite=True, show_progress=True)\n",
|
||||
"\n",
|
||||
"from azureml.core.runconfig import DataReferenceConfiguration\n",
|
||||
"dr = DataReferenceConfiguration(datastore_name=ds.name, \n",
|
||||
" path_on_datastore='bai_data', \n",
|
||||
" path_on_compute='/tmp/azureml_runs',\n",
|
||||
" mode='download', # download files from datastore to compute target\n",
|
||||
" overwrite=False)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core.runconfig import RunConfiguration\n",
|
||||
"from azureml.core.conda_dependencies import CondaDependencies\n",
|
||||
"\n",
|
||||
"# create a new RunConfig object\n",
|
||||
"conda_run_config = RunConfiguration(framework=\"python\")\n",
|
||||
"\n",
|
||||
"# Set compute target to AmlCompute\n",
|
||||
"conda_run_config.target = compute_target\n",
|
||||
"conda_run_config.environment.docker.enabled = True\n",
|
||||
"conda_run_config.environment.docker.base_image = azureml.core.runconfig.DEFAULT_CPU_IMAGE\n",
|
||||
"\n",
|
||||
"# set the data reference of the run coonfiguration\n",
|
||||
"conda_run_config.data_references = {ds.name: dr}\n",
|
||||
"\n",
|
||||
"cd = CondaDependencies.create(pip_packages=['azureml-sdk[automl]'], conda_packages=['numpy'])\n",
|
||||
"conda_run_config.environment.python.conda_dependencies = cd"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%%writefile $project_folder/get_data.py\n",
|
||||
"\n",
|
||||
"import pandas as pd\n",
|
||||
"\n",
|
||||
"def get_data():\n",
|
||||
" X_train = pd.read_csv(\"/tmp/azureml_runs/bai_data/X_train.tsv\", delimiter=\"\\t\", header=None, quotechar='\"')\n",
|
||||
" y_train = pd.read_csv(\"/tmp/azureml_runs/bai_data/y_train.tsv\", delimiter=\"\\t\", header=None, quotechar='\"')\n",
|
||||
"\n",
|
||||
" return { \"X\" : X_train.values, \"y\" : y_train[0].values }\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Train\n",
|
||||
"\n",
|
||||
"You can specify `automl_settings` as `**kwargs` as well. Also note that you can use a `get_data()` function for local excutions too.\n",
|
||||
"\n",
|
||||
"**Note:** When using AmlCompute, you can't pass Numpy arrays directly to the fit method.\n",
|
||||
"\n",
|
||||
"|Property|Description|\n",
|
||||
"|-|-|\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",
|
||||
"|**max_concurrent_iterations**|Maximum 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",
|
||||
" \"iteration_timeout_minutes\": 2,\n",
|
||||
" \"iterations\": 20,\n",
|
||||
" \"n_cross_validations\": 5,\n",
|
||||
" \"primary_metric\": 'AUC_weighted',\n",
|
||||
" \"preprocess\": False,\n",
|
||||
" \"max_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",
|
||||
" run_configuration=conda_run_config,\n",
|
||||
" data_script = project_folder + \"/get_data.py\",\n",
|
||||
" **automl_settings\n",
|
||||
" )\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Call the `submit` method on the experiment object and pass the run configuration. For remote runs the execution is asynchronous, so you will see the iterations get populated as they complete. You can interact with the widgets and models even when the experiment is running to retrieve the best model up to that point. Once you are satisfied with the model, you can cancel a particular iteration or the whole run.\n",
|
||||
"In this example, we specify `show_output = False` to suppress console output while the run is in progress."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"remote_run = experiment.submit(automl_config, show_output = False)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"remote_run"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Results\n",
|
||||
"\n",
|
||||
"#### Loading executed runs\n",
|
||||
"In case you need to load a previously executed run, enable the cell below and replace the `run_id` value."
|
||||
]
|
||||
},
|
||||
{
|
||||
"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 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",
|
||||
"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. Use this link to open a web interface 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.widgets import RunDetails\n",
|
||||
"RunDetails(remote_run).show() "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Wait until 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": [
|
||||
"### Cancelling 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 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 = 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 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 the model from the third 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": [
|
||||
"## 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"
|
||||
]
|
||||
},
|
||||
{
|
||||
"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
|
||||
}
|
||||
@@ -1,515 +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": [
|
||||
"# Automated Machine Learning\n",
|
||||
"_**Remote Execution using attach**_\n",
|
||||
"\n",
|
||||
"## Contents\n",
|
||||
"1. [Introduction](#Introduction)\n",
|
||||
"1. [Setup](#Setup)\n",
|
||||
"1. [Data](#Data)\n",
|
||||
"1. [Train](#Train)\n",
|
||||
"1. [Results](#Results)\n",
|
||||
"1. [Test](#Test)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Introduction\n",
|
||||
"In this example we use the scikit-learn's [20newsgroup](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.fetch_20newsgroups.html) to showcase how you can use AutoML to handle text data with remote attach.\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. Attach an existing DSVM to a workspace.\n",
|
||||
"3. Configure AutoML using `AutoMLConfig`.\n",
|
||||
"4. Train the model using the DSVM.\n",
|
||||
"5. Explore the results.\n",
|
||||
"6. Test the best fitted model.\n",
|
||||
"\n",
|
||||
"In addition this notebook showcases the following features\n",
|
||||
"- **Parallel** executions for iterations\n",
|
||||
"- **Asynchronous** tracking of progress\n",
|
||||
"- **Cancellation** of individual iterations or the entire run\n",
|
||||
"- Retrieving models for any iteration or logged metric\n",
|
||||
"- Specifying AutoML settings as `**kwargs`\n",
|
||||
"- Handling **text** data using the `preprocess` flag"
|
||||
]
|
||||
},
|
||||
{
|
||||
"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 os\n",
|
||||
"\n",
|
||||
"import numpy as np\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.train.automl import AutoMLConfig"
|
||||
]
|
||||
},
|
||||
{
|
||||
"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-attach'\n",
|
||||
"project_folder = './sample_projects/automl-remote-attach'\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",
|
||||
"outputDf = pd.DataFrame(data = output, index = [''])\n",
|
||||
"outputDf.T"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Attach a Remote Linux DSVM\n",
|
||||
"To use a remote Docker compute target:\n",
|
||||
"1. Create a Linux DSVM in Azure, following these [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_run`s. 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, user name and password below.\n",
|
||||
"\n",
|
||||
"**Note:** By default, SSH runs on port 22 and you don't need to change the port number below. If you've configured SSH to use a different port, change `dsvm_ssh_port` accordinglyaddress. [Read more](https://docs.microsoft.com/en-us/azure/virtual-machines/troubleshooting/detailed-troubleshoot-ssh-connection) on changing SSH ports for security reasons."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core.compute import ComputeTarget, RemoteCompute\n",
|
||||
"import time\n",
|
||||
"\n",
|
||||
"# Add your VM information below\n",
|
||||
"# If a compute with the specified compute_name already exists, it will be used and the dsvm_ip_addr, dsvm_ssh_port, \n",
|
||||
"# dsvm_username and dsvm_password will be ignored.\n",
|
||||
"compute_name = 'mydsvmb'\n",
|
||||
"dsvm_ip_addr = '<<ip_addr>>'\n",
|
||||
"dsvm_ssh_port = 22\n",
|
||||
"dsvm_username = '<<username>>'\n",
|
||||
"dsvm_password = '<<password>>'\n",
|
||||
"\n",
|
||||
"if compute_name in ws.compute_targets:\n",
|
||||
" print('Using existing compute.')\n",
|
||||
" dsvm_compute = ws.compute_targets[compute_name]\n",
|
||||
"else:\n",
|
||||
" attach_config = RemoteCompute.attach_configuration(address=dsvm_ip_addr, username=dsvm_username, password=dsvm_password, ssh_port=dsvm_ssh_port)\n",
|
||||
" ComputeTarget.attach(workspace=ws, name=compute_name, attach_configuration=attach_config)\n",
|
||||
"\n",
|
||||
" while ws.compute_targets[compute_name].provisioning_state == 'Creating':\n",
|
||||
" time.sleep(1)\n",
|
||||
"\n",
|
||||
" dsvm_compute = ws.compute_targets[compute_name]\n",
|
||||
" \n",
|
||||
" if dsvm_compute.provisioning_state == 'Failed':\n",
|
||||
" print('Attached failed.')\n",
|
||||
" print(dsvm_compute.provisioning_errors)\n",
|
||||
" dsvm_compute.detach()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core.runconfig import RunConfiguration\n",
|
||||
"from azureml.core.conda_dependencies import CondaDependencies\n",
|
||||
"\n",
|
||||
"# create a new RunConfig object\n",
|
||||
"conda_run_config = RunConfiguration(framework=\"python\")\n",
|
||||
"\n",
|
||||
"# Set compute target to the Linux DSVM\n",
|
||||
"conda_run_config.target = dsvm_compute\n",
|
||||
"\n",
|
||||
"cd = CondaDependencies.create(pip_packages=['azureml-sdk[automl]'], conda_packages=['numpy'])\n",
|
||||
"conda_run_config.environment.python.conda_dependencies = cd"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Data\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",
|
||||
"In this example, 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 numpy as np\n",
|
||||
"from sklearn.datasets import fetch_20newsgroups\n",
|
||||
"\n",
|
||||
"def get_data():\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 = np.array(data_train.data).reshape((len(data_train.data),1))\n",
|
||||
" y_train = np.array(data_train.target)\n",
|
||||
" \n",
|
||||
" return { \"X\" : X_train, \"y\" : y_train }"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Train\n",
|
||||
"\n",
|
||||
"You can specify `automl_settings` as `**kwargs` as well. Also note that you can use a `get_data()` function for local excutions too.\n",
|
||||
"\n",
|
||||
"**Note:** When using Remote DSVM, you can't pass Numpy arrays directly to the fit method.\n",
|
||||
"\n",
|
||||
"|Property|Description|\n",
|
||||
"|-|-|\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",
|
||||
"|**max_concurrent_iterations**|Maximum number of iterations that would be executed in parallel. This should be less than the number of cores on the DSVM.|\n",
|
||||
"|**preprocess**|Setting this to *True* enables AutoML to perform preprocessing on the input to handle *missing data*, and to perform some common *feature extraction*.|\n",
|
||||
"|**enable_cache**|Setting this to *True* enables preprocess done once and reuse the same preprocessed data for all the iterations. Default value is True.\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",
|
||||
" \"iteration_timeout_minutes\": 60,\n",
|
||||
" \"iterations\": 4,\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",
|
||||
" run_configuration=conda_run_config,\n",
|
||||
" data_script = project_folder + \"/get_data.py\",\n",
|
||||
" **automl_settings\n",
|
||||
" )\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Call the `submit` method on the experiment object and pass the run configuration. For remote runs the execution is asynchronous, so you will see the iterations get populated as they complete. You can interact with the widgets and models even when the experiment is running to retrieve 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": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"remote_run"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Results\n",
|
||||
"#### 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",
|
||||
"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. 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": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Wait until the run finishes.\n",
|
||||
"remote_run.wait_for_completion(show_output = True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"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": [
|
||||
"\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": [
|
||||
"### 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": [
|
||||
"### 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 which has the smallest `accuracy` value:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"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": [
|
||||
"## Test"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Load test data.\n",
|
||||
"from pandas_ml import ConfusionMatrix\n",
|
||||
"from sklearn.datasets import fetch_20newsgroups\n",
|
||||
"\n",
|
||||
"remove = ('headers', 'footers', 'quotes')\n",
|
||||
"categories = [\n",
|
||||
" 'alt.atheism',\n",
|
||||
" 'talk.religion.misc',\n",
|
||||
" 'comp.graphics',\n",
|
||||
" 'sci.space',\n",
|
||||
" ]\n",
|
||||
"\n",
|
||||
"data_test = fetch_20newsgroups(subset = 'test', categories = categories,\n",
|
||||
" shuffle = True, random_state = 42,\n",
|
||||
" remove = remove)\n",
|
||||
"\n",
|
||||
"X_test = np.array(data_test.data).reshape((len(data_test.data),1))\n",
|
||||
"y_test = data_test.target\n",
|
||||
"\n",
|
||||
"# Test our best pipeline.\n",
|
||||
"\n",
|
||||
"y_pred = fitted_model.predict(X_test)\n",
|
||||
"y_pred_strings = [data_test.target_names[i] for i in y_pred]\n",
|
||||
"y_test_strings = [data_test.target_names[i] for i in y_test]\n",
|
||||
"\n",
|
||||
"cm = ConfusionMatrix(y_test_strings, y_pred_strings)\n",
|
||||
"print(cm)\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": "3.6.6"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
@@ -1,555 +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": [
|
||||
"# Automated Machine Learning\n",
|
||||
"_**Remote Execution using 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 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 would see\n",
|
||||
"1. Create an `Experiment` in an existing `Workspace`.\n",
|
||||
"2. Create or Attach existing AmlCompute to a workspace.\n",
|
||||
"3. Configure AutoML using `AutoMLConfig`.\n",
|
||||
"4. Train the model using AmlCompute\n",
|
||||
"5. Explore the results.\n",
|
||||
"6. Test the best fitted model.\n",
|
||||
"\n",
|
||||
"In addition this notebook showcases the following features\n",
|
||||
"- **Parallel** executions for iterations\n",
|
||||
"- **Asynchronous** tracking of progress\n",
|
||||
"- **Cancellation** of individual iterations or the entire run\n",
|
||||
"- Retrieving models for any iteration or logged metric\n",
|
||||
"- Specifying AutoML settings as `**kwargs`"
|
||||
]
|
||||
},
|
||||
{
|
||||
"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",
|
||||
"import os\n",
|
||||
"import csv\n",
|
||||
"\n",
|
||||
"from matplotlib import pyplot as plt\n",
|
||||
"import numpy as np\n",
|
||||
"import pandas as pd\n",
|
||||
"from sklearn import datasets\n",
|
||||
"\n",
|
||||
"import azureml.core\n",
|
||||
"from azureml.core.experiment import Experiment\n",
|
||||
"from azureml.core.workspace import Workspace\n",
|
||||
"from azureml.train.automl import AutoMLConfig"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"ws = Workspace.from_config()\n",
|
||||
"\n",
|
||||
"# Choose a name for the run history container in the workspace.\n",
|
||||
"experiment_name = 'automl-remote-amlcompute'\n",
|
||||
"project_folder = './project'\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",
|
||||
"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](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for your AutoML run. In this tutorial, you create `AmlCompute` as your training compute resource.\n",
|
||||
"\n",
|
||||
"**Creation of AmlCompute takes approximately 5 minutes.** If the AmlCompute with that name is already in your workspace this code will skip the creation process.\n",
|
||||
"\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](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 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",
|
||||
" # 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": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Data\n",
|
||||
"For remote executions, you need to make the data accessible from the remote compute.\n",
|
||||
"This can be done by uploading the data to DataStore.\n",
|
||||
"In this example, we upload scikit-learn's [load_digits](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html) data."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"data_train = datasets.load_digits()\n",
|
||||
"\n",
|
||||
"if not os.path.isdir('data'):\n",
|
||||
" os.mkdir('data')\n",
|
||||
" \n",
|
||||
"if not os.path.exists(project_folder):\n",
|
||||
" os.makedirs(project_folder)\n",
|
||||
" \n",
|
||||
"pd.DataFrame(data_train.data).to_csv(\"data/X_train.tsv\", index=False, header=False, quoting=csv.QUOTE_ALL, sep=\"\\t\")\n",
|
||||
"pd.DataFrame(data_train.target).to_csv(\"data/y_train.tsv\", index=False, header=False, sep=\"\\t\")\n",
|
||||
"\n",
|
||||
"ds = ws.get_default_datastore()\n",
|
||||
"ds.upload(src_dir='./data', target_path='bai_data', overwrite=True, show_progress=True)\n",
|
||||
"\n",
|
||||
"from azureml.core.runconfig import DataReferenceConfiguration\n",
|
||||
"dr = DataReferenceConfiguration(datastore_name=ds.name, \n",
|
||||
" path_on_datastore='bai_data', \n",
|
||||
" path_on_compute='/tmp/azureml_runs',\n",
|
||||
" mode='download', # download files from datastore to compute target\n",
|
||||
" overwrite=False)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core.runconfig import RunConfiguration\n",
|
||||
"from azureml.core.conda_dependencies import CondaDependencies\n",
|
||||
"\n",
|
||||
"# create a new RunConfig object\n",
|
||||
"conda_run_config = RunConfiguration(framework=\"python\")\n",
|
||||
"\n",
|
||||
"# Set compute target to AmlCompute\n",
|
||||
"conda_run_config.target = compute_target\n",
|
||||
"conda_run_config.environment.docker.enabled = True\n",
|
||||
"conda_run_config.environment.docker.base_image = azureml.core.runconfig.DEFAULT_CPU_IMAGE\n",
|
||||
"\n",
|
||||
"# set the data reference of the run coonfiguration\n",
|
||||
"conda_run_config.data_references = {ds.name: dr}\n",
|
||||
"\n",
|
||||
"cd = CondaDependencies.create(pip_packages=['azureml-sdk[automl]'], conda_packages=['numpy'])\n",
|
||||
"conda_run_config.environment.python.conda_dependencies = cd"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%%writefile $project_folder/get_data.py\n",
|
||||
"\n",
|
||||
"import pandas as pd\n",
|
||||
"\n",
|
||||
"def get_data():\n",
|
||||
" X_train = pd.read_csv(\"/tmp/azureml_runs/bai_data/X_train.tsv\", delimiter=\"\\t\", header=None, quotechar='\"')\n",
|
||||
" y_train = pd.read_csv(\"/tmp/azureml_runs/bai_data/y_train.tsv\", delimiter=\"\\t\", header=None, quotechar='\"')\n",
|
||||
"\n",
|
||||
" return { \"X\" : X_train.values, \"y\" : y_train[0].values }\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Train\n",
|
||||
"\n",
|
||||
"You can specify `automl_settings` as `**kwargs` as well. Also note that you can use a `get_data()` function for local excutions too.\n",
|
||||
"\n",
|
||||
"**Note:** When using AmlCompute, you can't pass Numpy arrays directly to the fit method.\n",
|
||||
"\n",
|
||||
"|Property|Description|\n",
|
||||
"|-|-|\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",
|
||||
"|**max_concurrent_iterations**|Maximum 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",
|
||||
" \"iteration_timeout_minutes\": 2,\n",
|
||||
" \"iterations\": 20,\n",
|
||||
" \"n_cross_validations\": 5,\n",
|
||||
" \"primary_metric\": 'AUC_weighted',\n",
|
||||
" \"preprocess\": False,\n",
|
||||
" \"max_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",
|
||||
" run_configuration=conda_run_config,\n",
|
||||
" data_script = project_folder + \"/get_data.py\",\n",
|
||||
" **automl_settings\n",
|
||||
" )\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Call the `submit` method on the experiment object and pass the run configuration. For remote runs the execution is asynchronous, so you will see the iterations get populated as they complete. You can interact with the widgets and models even when the experiment is running to retrieve the best model up to that point. Once you are satisfied with the model, you can cancel a particular iteration or the whole run.\n",
|
||||
"In this example, we specify `show_output = False` to suppress console output while the run is in progress."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"remote_run = experiment.submit(automl_config, show_output = False)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"remote_run"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Results\n",
|
||||
"\n",
|
||||
"#### Loading executed runs\n",
|
||||
"In case you need to load a previously executed run, enable the cell below and replace the `run_id` value."
|
||||
]
|
||||
},
|
||||
{
|
||||
"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 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",
|
||||
"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. Use this link to open a web interface 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.widgets import RunDetails\n",
|
||||
"RunDetails(remote_run).show() "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Wait until 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": [
|
||||
"### Cancelling 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 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 = 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 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 the model from the third 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": [
|
||||
"## 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"
|
||||
]
|
||||
},
|
||||
{
|
||||
"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
|
||||
}
|
||||
@@ -1,583 +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": [
|
||||
"# Automated Machine Learning\n",
|
||||
"_**Remote Execution with DataStore**_\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",
|
||||
"This sample accesses a data file on a remote DSVM through DataStore. Advantages of using data store are:\n",
|
||||
"1. DataStore secures the access details.\n",
|
||||
"2. DataStore supports read, write to blob and file store\n",
|
||||
"3. AutoML natively supports copying data from DataStore to DSVM\n",
|
||||
"\n",
|
||||
"Make sure you have executed the [configuration](../../../configuration.ipynb) before running this notebook.\n",
|
||||
"\n",
|
||||
"In this notebook you would see\n",
|
||||
"1. Storing data in DataStore.\n",
|
||||
"2. get_data returning data from DataStore."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Setup\n",
|
||||
"\n",
|
||||
"As part of the setup you have already created a <b>Workspace</b>. For AutoML you would need to create an <b>Experiment</b>. An <b>Experiment</b> is a named object in a <b>Workspace</b>, which is used to run experiments."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import logging\n",
|
||||
"import os\n",
|
||||
"import time\n",
|
||||
"\n",
|
||||
"import numpy as np\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.workspace import Workspace\n",
|
||||
"from azureml.train.automl import AutoMLConfig"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"ws = Workspace.from_config()\n",
|
||||
"\n",
|
||||
"# choose a name for experiment\n",
|
||||
"experiment_name = 'automl-remote-datastore-file'\n",
|
||||
"# project folder\n",
|
||||
"project_folder = './sample_projects/automl-remote-datastore-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",
|
||||
"outputDf = pd.DataFrame(data = output, index = [''])\n",
|
||||
"outputDf.T"
|
||||
]
|
||||
},
|
||||
{
|
||||
"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://docs.microsoft.com/en-us/azure/virtual-machines/troubleshooting/detailed-troubleshoot-ssh-connection) on this."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"compute_target_name = 'mydsvmc'\n",
|
||||
"\n",
|
||||
"try:\n",
|
||||
" while ws.compute_targets[compute_target_name].provisioning_state == 'Creating':\n",
|
||||
" time.sleep(1)\n",
|
||||
" \n",
|
||||
" dsvm_compute = DsvmCompute(workspace=ws, name=compute_target_name)\n",
|
||||
" print('found existing:', dsvm_compute.name)\n",
|
||||
"except:\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)\n",
|
||||
" print(\"Waiting one minute for ssh to be accessible\")\n",
|
||||
" time.sleep(90) # Wait for ssh to be accessible"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Data\n",
|
||||
"\n",
|
||||
"### Copy data file to local\n",
|
||||
"\n",
|
||||
"Download the data file.\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"if not os.path.isdir('data'):\n",
|
||||
" os.mkdir('data') "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from sklearn.datasets import fetch_20newsgroups\n",
|
||||
"import csv\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",
|
||||
"pd.DataFrame(data_train.data).to_csv(\"data/X_train.tsv\", index=False, header=False, quoting=csv.QUOTE_ALL, sep=\"\\t\")\n",
|
||||
"pd.DataFrame(data_train.target).to_csv(\"data/y_train.tsv\", index=False, header=False, sep=\"\\t\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Upload data to the cloud"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Now make the data accessible remotely by uploading that data from your local machine into Azure so it can be accessed for remote training. The datastore is a convenient construct associated with your workspace for you to upload/download data, and interact with it from your remote compute targets. It is backed by Azure blob storage account.\n",
|
||||
"\n",
|
||||
"The data.tsv files are uploaded into a directory named data at the root of the datastore."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#blob_datastore = Datastore(ws, blob_datastore_name)\n",
|
||||
"ds = ws.get_default_datastore()\n",
|
||||
"print(ds.datastore_type, ds.account_name, ds.container_name)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# ds.upload_files(\"data.tsv\")\n",
|
||||
"ds.upload(src_dir='./data', target_path='data', overwrite=True, show_progress=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Configure & Run\n",
|
||||
"\n",
|
||||
"First let's create a DataReferenceConfigruation object to inform the system what data folder to download to the compute target.\n",
|
||||
"The path_on_compute should be an absolute path to ensure that the data files are downloaded only once. The get_data method should use this same path to access the data files."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core.runconfig import DataReferenceConfiguration\n",
|
||||
"dr = DataReferenceConfiguration(datastore_name=ds.name, \n",
|
||||
" path_on_datastore='data', \n",
|
||||
" path_on_compute='/tmp/azureml_runs',\n",
|
||||
" mode='download', # download files from datastore to compute target\n",
|
||||
" overwrite=False)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core.runconfig import RunConfiguration\n",
|
||||
"from azureml.core.conda_dependencies import CondaDependencies\n",
|
||||
"\n",
|
||||
"# create a new RunConfig object\n",
|
||||
"conda_run_config = RunConfiguration(framework=\"python\")\n",
|
||||
"\n",
|
||||
"# Set compute target to the Linux DSVM\n",
|
||||
"conda_run_config.target = dsvm_compute\n",
|
||||
"# set the data reference of the run coonfiguration\n",
|
||||
"conda_run_config.data_references = {ds.name: dr}\n",
|
||||
"\n",
|
||||
"cd = CondaDependencies.create(pip_packages=['azureml-sdk[automl]'], conda_packages=['numpy'])\n",
|
||||
"conda_run_config.environment.python.conda_dependencies = cd"
|
||||
]
|
||||
},
|
||||
{
|
||||
"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).\n",
|
||||
"\n",
|
||||
"The read_csv uses the path_on_compute value specified in the DataReferenceConfiguration call plus the path_on_datastore folder and then the actual file name."
|
||||
]
|
||||
},
|
||||
{
|
||||
"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",
|
||||
"\n",
|
||||
"def get_data():\n",
|
||||
" X_train = pd.read_csv(\"/tmp/azureml_runs/data/X_train.tsv\", delimiter=\"\\t\", header=None, quotechar='\"')\n",
|
||||
" y_train = pd.read_csv(\"/tmp/azureml_runs/data/y_train.tsv\", delimiter=\"\\t\", header=None, quotechar='\"')\n",
|
||||
"\n",
|
||||
" return { \"X\" : X_train.values, \"y\" : y_train[0].values }"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Train\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 AutoMLConfig.</i>\n",
|
||||
"\n",
|
||||
"|Property|Description|\n",
|
||||
"|-|-|\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 Auto ML trains a specific pipeline with the data|\n",
|
||||
"|**n_cross_validations**|Number of cross validation splits|\n",
|
||||
"|**max_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",
|
||||
"|**enable_cache**|Setting this to *True* enables preprocess done once and reuse the same preprocessed data for all the iterations. Default value is True.|\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",
|
||||
" \"iteration_timeout_minutes\": 60,\n",
|
||||
" \"iterations\": 4,\n",
|
||||
" \"n_cross_validations\": 5,\n",
|
||||
" \"primary_metric\": 'AUC_weighted',\n",
|
||||
" \"preprocess\": True,\n",
|
||||
" \"max_cores_per_iteration\": 1,\n",
|
||||
" \"verbosity\": logging.INFO\n",
|
||||
"}\n",
|
||||
"automl_config = AutoMLConfig(task = 'classification',\n",
|
||||
" debug_log = 'automl_errors.log',\n",
|
||||
" path=project_folder,\n",
|
||||
" run_configuration=conda_run_config,\n",
|
||||
" #compute_target = dsvm_compute,\n",
|
||||
" data_script = project_folder + \"/get_data.py\",\n",
|
||||
" **automl_settings\n",
|
||||
" )"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"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": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"remote_run"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Results\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.widgets import RunDetails\n",
|
||||
"RunDetails(remote_run).show() "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Wait until 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",
|
||||
"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": [
|
||||
"### 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": [
|
||||
"### 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. 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": [
|
||||
"## Test\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Load test data.\n",
|
||||
"from pandas_ml import ConfusionMatrix\n",
|
||||
"\n",
|
||||
"data_test = fetch_20newsgroups(subset = 'test', categories = categories,\n",
|
||||
" shuffle = True, random_state = 42,\n",
|
||||
" remove = remove)\n",
|
||||
"\n",
|
||||
"X_test = np.array(data_test.data).reshape((len(data_test.data),1))\n",
|
||||
"y_test = data_test.target\n",
|
||||
"\n",
|
||||
"# Test our best pipeline.\n",
|
||||
"\n",
|
||||
"y_pred = fitted_model.predict(X_test)\n",
|
||||
"y_pred_strings = [data_test.target_names[i] for i in y_pred]\n",
|
||||
"y_test_strings = [data_test.target_names[i] for i in y_test]\n",
|
||||
"\n",
|
||||
"cm = ConfusionMatrix(y_test_strings, y_pred_strings)\n",
|
||||
"print(cm)\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": "3.6.6"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
@@ -1,527 +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": [
|
||||
"# Automated Machine Learning\n",
|
||||
"_**Remote Execution using DSVM (Ubuntu)**_\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 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 wiil learn how to:\n",
|
||||
"1. Create an `Experiment` in an existing `Workspace`.\n",
|
||||
"2. Attach an existing DSVM to a workspace.\n",
|
||||
"3. Configure AutoML using `AutoMLConfig`.\n",
|
||||
"4. Train the model using the DSVM.\n",
|
||||
"5. Explore the results.\n",
|
||||
"6. Test the best fitted model.\n",
|
||||
"\n",
|
||||
"In addition, this notebook showcases the following features:\n",
|
||||
"- **Parallel** executions for iterations\n",
|
||||
"- **Asynchronous** tracking of progress\n",
|
||||
"- **Cancellation** of individual iterations or the entire run\n",
|
||||
"- Retrieving models for any iteration or logged metric\n",
|
||||
"- Specifying AutoML settings as `**kwargs`"
|
||||
]
|
||||
},
|
||||
{
|
||||
"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",
|
||||
"import os\n",
|
||||
"import time\n",
|
||||
"import csv\n",
|
||||
"\n",
|
||||
"from matplotlib import pyplot as plt\n",
|
||||
"import numpy as np\n",
|
||||
"import pandas as pd\n",
|
||||
"from sklearn import datasets\n",
|
||||
"\n",
|
||||
"import azureml.core\n",
|
||||
"from azureml.core.experiment import Experiment\n",
|
||||
"from azureml.core.workspace import Workspace\n",
|
||||
"from azureml.train.automl import AutoMLConfig"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"ws = Workspace.from_config()\n",
|
||||
"\n",
|
||||
"# Choose a name for the run history container in the workspace.\n",
|
||||
"experiment_name = 'automl-remote-dsvm'\n",
|
||||
"project_folder = './project'\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",
|
||||
"outputDf = pd.DataFrame(data = output, index = [''])\n",
|
||||
"outputDf.T"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Create a Remote Linux DSVM\n",
|
||||
"**Note:** If creation fails with a message about Marketplace purchase eligibilty, start creation of a DSVM through the [Azure portal](https://portal.azure.com), and select \"Want to create programmatically\" to enable programmatic creation. Once you've enabled this setting, you can exit the portal without actually creating the DSVM, and creation of the DSVM through the notebook should work.\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core.compute import DsvmCompute\n",
|
||||
"\n",
|
||||
"dsvm_name = 'mydsvma'\n",
|
||||
"try:\n",
|
||||
" dsvm_compute = DsvmCompute(ws, dsvm_name)\n",
|
||||
" print('Found an existing DSVM.')\n",
|
||||
"except:\n",
|
||||
" print('Creating a new DSVM.')\n",
|
||||
" dsvm_config = DsvmCompute.provisioning_configuration(vm_size = \"Standard_D2s_v3\")\n",
|
||||
" dsvm_compute = DsvmCompute.create(ws, name = dsvm_name, provisioning_configuration = dsvm_config)\n",
|
||||
" dsvm_compute.wait_for_completion(show_output = True)\n",
|
||||
" print(\"Waiting one minute for ssh to be accessible\")\n",
|
||||
" time.sleep(90) # Wait for ssh to be accessible"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Data\n",
|
||||
"For remote executions, you need to make the data accessible from the remote compute.\n",
|
||||
"This can be done by uploading the data to DataStore.\n",
|
||||
"In this example, we upload scikit-learn's [load_digits](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html) data."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"data_train = datasets.load_digits()\n",
|
||||
"\n",
|
||||
"if not os.path.isdir('data'):\n",
|
||||
" os.mkdir('data')\n",
|
||||
" \n",
|
||||
"if not os.path.exists(project_folder):\n",
|
||||
" os.makedirs(project_folder)\n",
|
||||
" \n",
|
||||
"pd.DataFrame(data_train.data).to_csv(\"data/X_train.tsv\", index=False, header=False, quoting=csv.QUOTE_ALL, sep=\"\\t\")\n",
|
||||
"pd.DataFrame(data_train.target).to_csv(\"data/y_train.tsv\", index=False, header=False, sep=\"\\t\")\n",
|
||||
"\n",
|
||||
"ds = ws.get_default_datastore()\n",
|
||||
"ds.upload(src_dir='./data', target_path='re_data', overwrite=True, show_progress=True)\n",
|
||||
"\n",
|
||||
"from azureml.core.runconfig import DataReferenceConfiguration\n",
|
||||
"dr = DataReferenceConfiguration(datastore_name=ds.name, \n",
|
||||
" path_on_datastore='re_data', \n",
|
||||
" path_on_compute='/tmp/azureml_runs',\n",
|
||||
" mode='download', # download files from datastore to compute target\n",
|
||||
" overwrite=False)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core.runconfig import RunConfiguration\n",
|
||||
"from azureml.core.conda_dependencies import CondaDependencies\n",
|
||||
"\n",
|
||||
"# create a new RunConfig object\n",
|
||||
"conda_run_config = RunConfiguration(framework=\"python\")\n",
|
||||
"\n",
|
||||
"# Set compute target to the Linux DSVM\n",
|
||||
"conda_run_config.target = dsvm_compute\n",
|
||||
"\n",
|
||||
"# set the data reference of the run coonfiguration\n",
|
||||
"conda_run_config.data_references = {ds.name: dr}\n",
|
||||
"\n",
|
||||
"cd = CondaDependencies.create(pip_packages=['azureml-sdk[automl]'], conda_packages=['numpy'])\n",
|
||||
"conda_run_config.environment.python.conda_dependencies = cd"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%%writefile $project_folder/get_data.py\n",
|
||||
"\n",
|
||||
"import pandas as pd\n",
|
||||
"\n",
|
||||
"def get_data():\n",
|
||||
" X_train = pd.read_csv(\"/tmp/azureml_runs/re_data/X_train.tsv\", delimiter=\"\\t\", header=None, quotechar='\"')\n",
|
||||
" y_train = pd.read_csv(\"/tmp/azureml_runs/re_data/y_train.tsv\", delimiter=\"\\t\", header=None, quotechar='\"')\n",
|
||||
"\n",
|
||||
" return { \"X\" : X_train.values, \"y\" : y_train[0].values }\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Train\n",
|
||||
"\n",
|
||||
"You can specify `automl_settings` as `**kwargs` as well. Also note that you can use a `get_data()` function for local excutions too.\n",
|
||||
"\n",
|
||||
"**Note:** When using Remote DSVM, you can't pass Numpy arrays directly to the fit method.\n",
|
||||
"\n",
|
||||
"|Property|Description|\n",
|
||||
"|-|-|\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",
|
||||
"|**max_concurrent_iterations**|Maximum number of iterations to execute 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",
|
||||
" \"iteration_timeout_minutes\": 10,\n",
|
||||
" \"iterations\": 20,\n",
|
||||
" \"n_cross_validations\": 5,\n",
|
||||
" \"primary_metric\": 'AUC_weighted',\n",
|
||||
" \"preprocess\": False,\n",
|
||||
" \"max_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",
|
||||
" run_configuration=conda_run_config,\n",
|
||||
" data_script = project_folder + \"/get_data.py\",\n",
|
||||
" **automl_settings\n",
|
||||
" )\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"**Note:** The first run on a new DSVM may take several minutes to prepare the environment."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Call the `submit` method on the experiment object and pass the run configuration. For remote runs the execution is asynchronous, so you will see the iterations get populated as they complete. You can interact with the widgets and models even when the experiment is running to retrieve the best model up to that point. Once you are satisfied with the model, you can cancel a particular iteration or the whole run.\n",
|
||||
"\n",
|
||||
"In this example, we specify `show_output = False` to suppress console output while the run is in progress."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"remote_run = experiment.submit(automl_config, show_output = False)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"remote_run"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Results\n",
|
||||
"\n",
|
||||
"#### Loading Executed Runs\n",
|
||||
"In case you need to load a previously executed run, enable the cell below and replace the `run_id` value."
|
||||
]
|
||||
},
|
||||
{
|
||||
"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 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",
|
||||
"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. 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": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Wait until 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": [
|
||||
"### Cancelling 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 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 = 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 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 the model from the third 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": [
|
||||
"## 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": [
|
||||
"#### Test Our Best Fitted Model"
|
||||
]
|
||||
},
|
||||
{
|
||||
"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
|
||||
}
|
||||
@@ -1,240 +0,0 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Copyright (c) Microsoft Corporation. All rights reserved.\n",
|
||||
"\n",
|
||||
"Licensed under the MIT License."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Automated Machine Learning\n",
|
||||
"_**Sample Weight**_\n",
|
||||
"\n",
|
||||
"## Contents\n",
|
||||
"1. [Introduction](#Introduction)\n",
|
||||
"1. [Setup](#Setup)\n",
|
||||
"1. [Train](#Train)\n",
|
||||
"1. [Test](#Test)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Introduction\n",
|
||||
"In this example we use the scikit-learn's [digit dataset](http://scikit-learn.org/stable/datasets/index.html#optical-recognition-of-handwritten-digits-dataset) to showcase how you can use sample weight with AutoML. Sample weight is used where some sample values are more important than others.\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 configure AutoML to use `sample_weight` and you will see the difference sample weight makes to the test results."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Setup\n",
|
||||
"\n",
|
||||
"As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import logging\n",
|
||||
"\n",
|
||||
"from matplotlib import pyplot as plt\n",
|
||||
"import numpy as np\n",
|
||||
"import pandas as pd\n",
|
||||
"from sklearn import datasets\n",
|
||||
"\n",
|
||||
"import azureml.core\n",
|
||||
"from azureml.core.experiment import Experiment\n",
|
||||
"from azureml.core.workspace import Workspace\n",
|
||||
"from azureml.train.automl import AutoMLConfig"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"ws = Workspace.from_config()\n",
|
||||
"\n",
|
||||
"# Choose names for the regular and the sample weight experiments.\n",
|
||||
"experiment_name = 'non_sample_weight_experiment'\n",
|
||||
"sample_weight_experiment_name = 'sample_weight_experiment'\n",
|
||||
"\n",
|
||||
"project_folder = './sample_projects/sample_weight'\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",
|
||||
"outputDf = pd.DataFrame(data = output, index = [''])\n",
|
||||
"outputDf.T"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Train\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_train = digits.data[100:,:]\n",
|
||||
"y_train = 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_train])\n",
|
||||
"\n",
|
||||
"automl_classifier = AutoMLConfig(task = 'classification',\n",
|
||||
" debug_log = 'automl_errors.log',\n",
|
||||
" primary_metric = 'AUC_weighted',\n",
|
||||
" iteration_timeout_minutes = 60,\n",
|
||||
" iterations = 10,\n",
|
||||
" n_cross_validations = 2,\n",
|
||||
" verbosity = logging.INFO,\n",
|
||||
" X = X_train, \n",
|
||||
" y = y_train,\n",
|
||||
" path = project_folder)\n",
|
||||
"\n",
|
||||
"automl_sample_weight = AutoMLConfig(task = 'classification',\n",
|
||||
" debug_log = 'automl_errors.log',\n",
|
||||
" primary_metric = 'AUC_weighted',\n",
|
||||
" iteration_timeout_minutes = 60,\n",
|
||||
" iterations = 10,\n",
|
||||
" n_cross_validations = 2,\n",
|
||||
" verbosity = logging.INFO,\n",
|
||||
" X = X_train, \n",
|
||||
" y = y_train,\n",
|
||||
" sample_weight = sample_weight,\n",
|
||||
" path = project_folder)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Call the `submit` method on the experiment objects 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_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": [
|
||||
"## Test\n",
|
||||
"\n",
|
||||
"#### Load Test Data"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"digits = datasets.load_digits()\n",
|
||||
"X_test = digits.data[:100, :]\n",
|
||||
"y_test = digits.target[:100]\n",
|
||||
"images = digits.images[:100]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Compare the Models\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_test)):\n",
|
||||
" predicted = fitted_model.predict(X_test[index:index + 1])[0]\n",
|
||||
" predicted_sample_weight = fitted_model_sample_weight.predict(X_test[index:index + 1])[0]\n",
|
||||
" label = y_test[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": {
|
||||
"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
|
||||
}
|
||||
@@ -1,380 +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": [
|
||||
"# Automated Machine Learning\n",
|
||||
"_**Train Test Split and Handling Sparse Data**_\n",
|
||||
"\n",
|
||||
"## Contents\n",
|
||||
"1. [Introduction](#Introduction)\n",
|
||||
"1. [Setup](#Setup)\n",
|
||||
"1. [Data](#Data)\n",
|
||||
"1. [Train](#Train)\n",
|
||||
"1. [Results](#Results)\n",
|
||||
"1. [Test](#Test)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Introduction\n",
|
||||
"In this example we use the scikit-learn's [20newsgroup](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.fetch_20newsgroups.html) to showcase how you can use AutoML for handling sparse data and how to specify custom cross validations splits.\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",
|
||||
"4. Train the model.\n",
|
||||
"5. Explore the results.\n",
|
||||
"6. Test the best fitted model.\n",
|
||||
"\n",
|
||||
"In addition this notebook showcases the following features\n",
|
||||
"- Explicit train test splits \n",
|
||||
"- Handling **sparse data** in the input"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Setup\n",
|
||||
"\n",
|
||||
"As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import logging\n",
|
||||
"\n",
|
||||
"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.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 = 'sparse-data-train-test-split'\n",
|
||||
"# project folder\n",
|
||||
"project_folder = './sample_projects/sparse-data-train-test-split'\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",
|
||||
"outputDf = pd.DataFrame(data = output, index = [''])\n",
|
||||
"outputDf.T"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## 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_valid, y_train, y_valid = 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_valid = vectorizer.transform(X_valid)\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_valid.shape[0], X_valid.shape[1]]\n",
|
||||
"summary_df"
|
||||
]
|
||||
},
|
||||
{
|
||||
"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",
|
||||
"|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
|
||||
"|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
|
||||
"|**preprocess**|Setting this to *True* enables AutoML to perform preprocessing on the input to handle *missing data*, and to perform some common *feature extraction*.<br>**Note:** If input data is sparse, you cannot use *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",
|
||||
" iteration_timeout_minutes = 60,\n",
|
||||
" iterations = 5,\n",
|
||||
" preprocess = False,\n",
|
||||
" verbosity = logging.INFO,\n",
|
||||
" X = X_train, \n",
|
||||
" y = y_train,\n",
|
||||
" X_valid = X_valid, \n",
|
||||
" y_valid = y_valid, \n",
|
||||
" path = project_folder)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Call the `submit` method on the experiment object and pass the run configuration. Execution of local runs is synchronous. Depending on the data and the number of iterations this can run for a while.\n",
|
||||
"In this example, we specify `show_output = True` to print currently running iterations to the console."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"local_run = experiment.submit(automl_config, show_output=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"local_run"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Results"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Widget for Monitoring Runs\n",
|
||||
"\n",
|
||||
"The widget will first report a \"loading\" status while running the first iteration. After completing the first iteration, an auto-updating graph and table will be shown. The widget will refresh once per minute, so you should see the graph update as child runs complete.\n",
|
||||
"\n",
|
||||
"**Note:** The widget displays a link at the bottom. Use this link to open a web interface to explore the individual run details."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.widgets import RunDetails\n",
|
||||
"RunDetails(local_run).show() "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"\n",
|
||||
"#### Retrieve All Child Runs\n",
|
||||
"You can also use SDK methods to fetch all the child runs and see individual metrics that we log."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"children = list(local_run.get_children())\n",
|
||||
"metricslist = {}\n",
|
||||
"for run in children:\n",
|
||||
" properties = run.get_properties()\n",
|
||||
" metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}\n",
|
||||
" metricslist[int(properties['iteration'])] = metrics\n",
|
||||
" \n",
|
||||
"rundata = pd.DataFrame(metricslist).sort_index(1)\n",
|
||||
"rundata"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Retrieve the Best Model\n",
|
||||
"\n",
|
||||
"Below we select the best pipeline from our iterations. The `get_output` method returns the best run and the fitted model. The Model includes the pipeline and any pre-processing. Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"best_run, fitted_model = local_run.get_output()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Best Model Based on Any Other Metric\n",
|
||||
"Show the run and the model which has the smallest `accuracy` value:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# lookup_metric = \"accuracy\"\n",
|
||||
"# best_run, fitted_model = local_run.get_output(metric = lookup_metric)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Model from a Specific Iteration\n",
|
||||
"Show the run and the model from the third iteration:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# iteration = 3\n",
|
||||
"# best_run, fitted_model = local_run.get_output(iteration = iteration)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Test"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Load test data.\n",
|
||||
"from pandas_ml import ConfusionMatrix\n",
|
||||
"\n",
|
||||
"data_test = fetch_20newsgroups(subset = 'test', categories = categories,\n",
|
||||
" shuffle = True, random_state = 42,\n",
|
||||
" remove = remove)\n",
|
||||
"\n",
|
||||
"X_test = vectorizer.transform(data_test.data)\n",
|
||||
"y_test = data_test.target\n",
|
||||
"\n",
|
||||
"# Test our best pipeline.\n",
|
||||
"\n",
|
||||
"y_pred = fitted_model.predict(X_test)\n",
|
||||
"y_pred_strings = [data_test.target_names[i] for i in y_pred]\n",
|
||||
"y_test_strings = [data_test.target_names[i] for i in y_test]\n",
|
||||
"\n",
|
||||
"cm = ConfusionMatrix(y_test_strings, y_pred_strings)\n",
|
||||
"print(cm)\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": "3.6.6"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
@@ -1,201 +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": [
|
||||
"# 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",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Introduction\n",
|
||||
"\n",
|
||||
"In this example we will explore AutoML's subsampling feature. This is useful for training on large datasets to speed up the convergence.\n",
|
||||
"\n",
|
||||
"The setup is quiet similar to a normal classification, with the exception of the `enable_subsampling` option. Keep in mind that even with the `enable_subsampling` flag set, subsampling will only be run for large datasets (>= 50k rows) and large (>= 85) or no iteration restrictions.\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Setup\n",
|
||||
"\n",
|
||||
"As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import logging\n",
|
||||
"\n",
|
||||
"import numpy as np\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.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 and specify the project folder.\n",
|
||||
"experiment_name = 'automl-subsampling'\n",
|
||||
"project_folder = './sample_projects/automl-subsampling'\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": [
|
||||
"## Data\n",
|
||||
"\n",
|
||||
"We will create a simple dataset using the numpy sin function just for this example. We need just over 50k rows."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"base = np.arange(60000)\n",
|
||||
"cos = np.cos(base)\n",
|
||||
"y = np.round(np.sin(base)).astype('int')\n",
|
||||
"\n",
|
||||
"# Exclude the first 100 rows from training so that they can be used for test.\n",
|
||||
"X_train = np.hstack((base.reshape(-1, 1), cos.reshape(-1, 1)))\n",
|
||||
"y_train = y"
|
||||
]
|
||||
},
|
||||
{
|
||||
"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",
|
||||
"|**enable_subsampling**|This enables subsampling as an option. However it does not guarantee subsampling will be used. It also depends on how large the dataset is and how many iterations it's expected to run at a minimum.|\n",
|
||||
"|**iterations**|Number of iterations. Subsampling requires a lot of iterations at smaller percent so in order for subsampling to be used we need to set iterations to be a high number.|\n",
|
||||
"|**experiment_timeout_minutes**|The experiment timeout, it's set to 5 right now to shorten the demo but it should probably be higher if we want to finish all the iterations.|\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"automl_config = AutoMLConfig(task = 'classification',\n",
|
||||
" debug_log = 'automl_errors.log',\n",
|
||||
" primary_metric = 'accuracy',\n",
|
||||
" iterations = 85,\n",
|
||||
" experiment_timeout_minutes = 5,\n",
|
||||
" n_cross_validations = 2,\n",
|
||||
" verbosity = logging.INFO,\n",
|
||||
" X = X_train, \n",
|
||||
" y = y_train,\n",
|
||||
" enable_subsampling=True,\n",
|
||||
" path = project_folder)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"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": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"authors": [
|
||||
{
|
||||
"name": "rogehe"
|
||||
}
|
||||
],
|
||||
"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
|
||||
}
|
||||
Binary file not shown.
@@ -1,29 +0,0 @@
|
||||
Azure Databricks is a managed Spark offering on Azure and customers already use it for advanced analytics. It provides a collaborative Notebook based environment with CPU or GPU based compute cluster.
|
||||
|
||||
In this section, you will find sample notebooks on how to use Azure Machine Learning SDK with Azure Databricks. You can train a model using Spark MLlib and then deploy the model to ACI/AKS from within Azure Databricks. You can also use Automated ML capability (**public preview**) of Azure ML SDK with Azure Databricks.
|
||||
|
||||
- Customers who use Azure Databricks for advanced analytics can now use the same cluster to run experiments with or without automated machine learning.
|
||||
- You can keep the data within the same cluster.
|
||||
- You can leverage the local worker nodes with autoscale and auto termination capabilities.
|
||||
- You can use multiple cores of your Azure Databricks cluster to perform simultenous training.
|
||||
- You can further tune the model generated by automated machine learning if you chose to.
|
||||
- Every run (including the best run) is available as a pipeline, which you can tune further if needed.
|
||||
- The model trained using Azure Databricks can be registered in Azure ML SDK workspace and then deployed to Azure managed compute (ACI or AKS) using the Azure Machine learning SDK.
|
||||
|
||||
Please follow our [Azure doc](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-environment#azure-databricks) to install the sdk in your Azure Databricks cluster before trying any of the sample notebooks.
|
||||
|
||||
**Single file** -
|
||||
The following archive contains all the sample notebooks. You can the run notebooks after importing [DBC](Databricks_AMLSDK_1-4_6.dbc) in your Databricks workspace instead of downloading individually.
|
||||
|
||||
Notebooks 1-4 have to be run sequentially & 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.
|
||||
|
||||
Notebook 6 is an Automated ML sample notebook for Classification.
|
||||
|
||||
Learn more about [how to use Azure Databricks as a development environment](https://docs.microsoft.com/azure/machine-learning/service/how-to-configure-environment#azure-databricks) for Azure Machine Learning service.
|
||||
|
||||
**Databricks as a Compute Target from AML Pipelines**
|
||||
You can use Azure Databricks as a compute target from [Azure Machine Learning Pipelines](https://docs.microsoft.com/en-us/azure/machine-learning/service/concept-ml-pipelines). Take a look at this notebook for details: [aml-pipelines-use-databricks-as-compute-target.ipynb](aml-pipelines-use-databricks-as-compute-target.ipynb).
|
||||
|
||||
For more on SDK concepts, please refer to [notebooks](https://github.com/Azure/MachineLearningNotebooks).
|
||||
|
||||
**Please let us know your feedback.**
|
||||
@@ -1,714 +0,0 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Copyright (c) Microsoft Corporation. All rights reserved. \n",
|
||||
"Licensed under the MIT License."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Using Databricks as a Compute Target from Azure Machine Learning Pipeline\n",
|
||||
"To use Databricks as a compute target from [Azure Machine Learning Pipeline](https://docs.microsoft.com/en-us/azure/machine-learning/service/concept-ml-pipelines), a [DatabricksStep](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.databricks_step.databricksstep?view=azure-ml-py) is used. This notebook demonstrates the use of DatabricksStep in Azure Machine Learning Pipeline.\n",
|
||||
"\n",
|
||||
"The notebook will show:\n",
|
||||
"1. Running an arbitrary Databricks notebook that the customer has in Databricks workspace\n",
|
||||
"2. Running an arbitrary Python script that the customer has in DBFS\n",
|
||||
"3. Running an arbitrary Python script that is available on local computer (will upload to DBFS, and then run in Databricks) \n",
|
||||
"4. Running a JAR job that the customer has in DBFS.\n",
|
||||
"\n",
|
||||
"## Before you begin:\n",
|
||||
"\n",
|
||||
"1. **Create an Azure Databricks workspace** in the same subscription where you have your Azure Machine Learning workspace. You will need details of this workspace later on to define DatabricksStep. [Click here](https://ms.portal.azure.com/#blade/HubsExtension/Resources/resourceType/Microsoft.Databricks%2Fworkspaces) for more information.\n",
|
||||
"2. **Create PAT (access token)**: Manually create a Databricks access token at the Azure Databricks portal. See [this](https://docs.databricks.com/api/latest/authentication.html#generate-a-token) for more information.\n",
|
||||
"3. **Add demo notebook to ADB**: This notebook has a sample you can use as is. Launch Azure Databricks attached to your Azure Machine Learning workspace and add a new notebook. \n",
|
||||
"4. **Create/attach a Blob storage** for use from ADB"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Add demo notebook to ADB Workspace\n",
|
||||
"Copy and paste the below code to create a new notebook in your ADB workspace."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"```python\n",
|
||||
"# direct access\n",
|
||||
"dbutils.widgets.get(\"myparam\")\n",
|
||||
"p = getArgument(\"myparam\")\n",
|
||||
"print (\"Param -\\'myparam':\")\n",
|
||||
"print (p)\n",
|
||||
"\n",
|
||||
"dbutils.widgets.get(\"input\")\n",
|
||||
"i = getArgument(\"input\")\n",
|
||||
"print (\"Param -\\'input':\")\n",
|
||||
"print (i)\n",
|
||||
"\n",
|
||||
"dbutils.widgets.get(\"output\")\n",
|
||||
"o = getArgument(\"output\")\n",
|
||||
"print (\"Param -\\'output':\")\n",
|
||||
"print (o)\n",
|
||||
"\n",
|
||||
"n = i + \"/testdata.txt\"\n",
|
||||
"df = spark.read.csv(n)\n",
|
||||
"\n",
|
||||
"display (df)\n",
|
||||
"\n",
|
||||
"data = [('value1', 'value2')]\n",
|
||||
"df2 = spark.createDataFrame(data)\n",
|
||||
"\n",
|
||||
"z = o + \"/output.txt\"\n",
|
||||
"df2.write.csv(z)\n",
|
||||
"```"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Azure Machine Learning and Pipeline SDK-specific imports"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import os\n",
|
||||
"import azureml.core\n",
|
||||
"from azureml.core.runconfig import JarLibrary\n",
|
||||
"from azureml.core.compute import ComputeTarget, DatabricksCompute\n",
|
||||
"from azureml.exceptions import ComputeTargetException\n",
|
||||
"from azureml.core import Workspace, Experiment\n",
|
||||
"from azureml.pipeline.core import Pipeline, PipelineData\n",
|
||||
"from azureml.pipeline.steps import DatabricksStep\n",
|
||||
"from azureml.core.datastore import Datastore\n",
|
||||
"from azureml.data.data_reference import DataReference\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. Make sure the config file is present at .\\config.json"
|
||||
]
|
||||
},
|
||||
{
|
||||
"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": [
|
||||
"## Attach Databricks compute target\n",
|
||||
"Next, you need to add your Databricks workspace to Azure Machine Learning as a compute target and give it a name. You will use this name to refer to your Databricks workspace compute target inside Azure Machine Learning.\n",
|
||||
"\n",
|
||||
"- **Resource Group** - The resource group name of your Azure Machine Learning workspace\n",
|
||||
"- **Databricks Workspace Name** - The workspace name of your Azure Databricks workspace\n",
|
||||
"- **Databricks Access Token** - The access token you created in ADB\n",
|
||||
"\n",
|
||||
"**The Databricks workspace need to be present in the same subscription as your AML workspace**"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Replace with your account info before running.\n",
|
||||
" \n",
|
||||
"db_compute_name=os.getenv(\"DATABRICKS_COMPUTE_NAME\", \"<my-databricks-compute-name>\") # Databricks compute name\n",
|
||||
"db_resource_group=os.getenv(\"DATABRICKS_RESOURCE_GROUP\", \"<my-db-resource-group>\") # Databricks resource group\n",
|
||||
"db_workspace_name=os.getenv(\"DATABRICKS_WORKSPACE_NAME\", \"<my-db-workspace-name>\") # Databricks workspace name\n",
|
||||
"db_access_token=os.getenv(\"DATABRICKS_ACCESS_TOKEN\", \"<my-access-token>\") # Databricks access token\n",
|
||||
" \n",
|
||||
"try:\n",
|
||||
" databricks_compute = DatabricksCompute(workspace=ws, name=db_compute_name)\n",
|
||||
" print('Compute target {} already exists'.format(db_compute_name))\n",
|
||||
"except ComputeTargetException:\n",
|
||||
" print('Compute not found, will use below parameters to attach new one')\n",
|
||||
" print('db_compute_name {}'.format(db_compute_name))\n",
|
||||
" print('db_resource_group {}'.format(db_resource_group))\n",
|
||||
" print('db_workspace_name {}'.format(db_workspace_name))\n",
|
||||
" print('db_access_token {}'.format(db_access_token))\n",
|
||||
" \n",
|
||||
" config = DatabricksCompute.attach_configuration(\n",
|
||||
" resource_group = db_resource_group,\n",
|
||||
" workspace_name = db_workspace_name,\n",
|
||||
" access_token= db_access_token)\n",
|
||||
" databricks_compute=ComputeTarget.attach(ws, db_compute_name, config)\n",
|
||||
" databricks_compute.wait_for_completion(True)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Data Connections with Inputs and Outputs\n",
|
||||
"The DatabricksStep supports Azure Bloband ADLS for inputs and outputs. You also will need to define a [Secrets](https://docs.azuredatabricks.net/user-guide/secrets/index.html) scope to enable authentication to external data sources such as Blob and ADLS from Databricks.\n",
|
||||
"\n",
|
||||
"- Databricks documentation on [Azure Blob](https://docs.azuredatabricks.net/spark/latest/data-sources/azure/azure-storage.html)\n",
|
||||
"- Databricks documentation on [ADLS](https://docs.databricks.com/spark/latest/data-sources/azure/azure-datalake.html)\n",
|
||||
"\n",
|
||||
"### Type of Data Access\n",
|
||||
"Databricks allows to interact with Azure Blob and ADLS in two ways.\n",
|
||||
"- **Direct Access**: Databricks allows you to interact with Azure Blob or ADLS URIs directly. The input or output URIs will be mapped to a Databricks widget param in the Databricks notebook.\n",
|
||||
"- **Mounting**: You will be supplied with additional parameters and secrets that will enable you to mount your ADLS or Azure Blob input or output location in your Databricks notebook."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Direct Access: Python sample code\n",
|
||||
"If you have a data reference named \"input\" it will represent the URI of the input and you can access it directly in the Databricks python notebook like so:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"```python\n",
|
||||
"dbutils.widgets.get(\"input\")\n",
|
||||
"y = getArgument(\"input\")\n",
|
||||
"df = spark.read.csv(y)\n",
|
||||
"```"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Mounting: Python sample code for Azure Blob\n",
|
||||
"Given an Azure Blob data reference named \"input\" the following widget params will be made available in the Databricks notebook:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"```python\n",
|
||||
"# This contains the input URI\n",
|
||||
"dbutils.widgets.get(\"input\")\n",
|
||||
"myinput_uri = getArgument(\"input\")\n",
|
||||
"\n",
|
||||
"# How to get the input datastore name inside ADB notebook\n",
|
||||
"# This contains the name of a Databricks secret (in the predefined \"amlscope\" secret scope) \n",
|
||||
"# that contians an access key or sas for the Azure Blob input (this name is obtained by appending \n",
|
||||
"# the name of the input with \"_blob_secretname\". \n",
|
||||
"dbutils.widgets.get(\"input_blob_secretname\") \n",
|
||||
"myinput_blob_secretname = getArgument(\"input_blob_secretname\")\n",
|
||||
"\n",
|
||||
"# This contains the required configuration for mounting\n",
|
||||
"dbutils.widgets.get(\"input_blob_config\")\n",
|
||||
"myinput_blob_config = getArgument(\"input_blob_config\")\n",
|
||||
"\n",
|
||||
"# Usage\n",
|
||||
"dbutils.fs.mount(\n",
|
||||
" source = myinput_uri,\n",
|
||||
" mount_point = \"/mnt/input\",\n",
|
||||
" extra_configs = {myinput_blob_config:dbutils.secrets.get(scope = \"amlscope\", key = myinput_blob_secretname)})\n",
|
||||
"```"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Mounting: Python sample code for ADLS\n",
|
||||
"Given an ADLS data reference named \"input\" the following widget params will be made available in the Databricks notebook:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"```python\n",
|
||||
"# This contains the input URI\n",
|
||||
"dbutils.widgets.get(\"input\") \n",
|
||||
"myinput_uri = getArgument(\"input\")\n",
|
||||
"\n",
|
||||
"# This contains the client id for the service principal \n",
|
||||
"# that has access to the adls input\n",
|
||||
"dbutils.widgets.get(\"input_adls_clientid\") \n",
|
||||
"myinput_adls_clientid = getArgument(\"input_adls_clientid\")\n",
|
||||
"\n",
|
||||
"# This contains the name of a Databricks secret (in the predefined \"amlscope\" secret scope) \n",
|
||||
"# that contains the secret for the above mentioned service principal\n",
|
||||
"dbutils.widgets.get(\"input_adls_secretname\") \n",
|
||||
"myinput_adls_secretname = getArgument(\"input_adls_secretname\")\n",
|
||||
"\n",
|
||||
"# This contains the refresh url for the mounting configs\n",
|
||||
"dbutils.widgets.get(\"input_adls_refresh_url\") \n",
|
||||
"myinput_adls_refresh_url = getArgument(\"input_adls_refresh_url\")\n",
|
||||
"\n",
|
||||
"# Usage \n",
|
||||
"configs = {\"dfs.adls.oauth2.access.token.provider.type\": \"ClientCredential\",\n",
|
||||
" \"dfs.adls.oauth2.client.id\": myinput_adls_clientid,\n",
|
||||
" \"dfs.adls.oauth2.credential\": dbutils.secrets.get(scope = \"amlscope\", key =myinput_adls_secretname),\n",
|
||||
" \"dfs.adls.oauth2.refresh.url\": myinput_adls_refresh_url}\n",
|
||||
"\n",
|
||||
"dbutils.fs.mount(\n",
|
||||
" source = myinput_uri,\n",
|
||||
" mount_point = \"/mnt/output\",\n",
|
||||
" extra_configs = configs)\n",
|
||||
"```"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Use Databricks from Azure Machine Learning Pipeline\n",
|
||||
"To use Databricks as a compute target from Azure Machine Learning Pipeline, a DatabricksStep is used. Let's define a datasource (via DataReference) and intermediate data (via PipelineData) to be used in DatabricksStep."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Use the default blob storage\n",
|
||||
"def_blob_store = Datastore(ws, \"workspaceblobstore\")\n",
|
||||
"print('Datastore {} will be used'.format(def_blob_store.name))\n",
|
||||
"\n",
|
||||
"# We are uploading a sample file in the local directory to be used as a datasource\n",
|
||||
"def_blob_store.upload_files(files=[\"./testdata.txt\"], target_path=\"dbtest\", overwrite=False)\n",
|
||||
"\n",
|
||||
"step_1_input = DataReference(datastore=def_blob_store, path_on_datastore=\"dbtest\",\n",
|
||||
" data_reference_name=\"input\")\n",
|
||||
"\n",
|
||||
"step_1_output = PipelineData(\"output\", datastore=def_blob_store)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Add a DatabricksStep\n",
|
||||
"Adds a Databricks notebook as a step in a Pipeline.\n",
|
||||
"- ***name:** Name of the Module\n",
|
||||
"- **inputs:** List of input connections for data consumed by this step. Fetch this inside the notebook using dbutils.widgets.get(\"input\")\n",
|
||||
"- **outputs:** List of output port definitions for outputs produced by this step. Fetch this inside the notebook using dbutils.widgets.get(\"output\")\n",
|
||||
"- **existing_cluster_id:** Cluster ID of an existing Interactive cluster on the Databricks workspace. If you are providing this, do not provide any of the parameters below that are used to create a new cluster such as spark_version, node_type, etc.\n",
|
||||
"- **spark_version:** Version of spark for the databricks run cluster. default value: 4.0.x-scala2.11\n",
|
||||
"- **node_type:** Azure vm node types for the databricks run cluster. default value: Standard_D3_v2\n",
|
||||
"- **num_workers:** Specifies a static number of workers for the databricks run cluster\n",
|
||||
"- **min_workers:** Specifies a min number of workers to use for auto-scaling the databricks run cluster\n",
|
||||
"- **max_workers:** Specifies a max number of workers to use for auto-scaling the databricks run cluster\n",
|
||||
"- **spark_env_variables:** Spark environment variables for the databricks run cluster (dictionary of {str:str}). default value: {'PYSPARK_PYTHON': '/databricks/python3/bin/python3'}\n",
|
||||
"- **notebook_path:** Path to the notebook in the databricks instance. If you are providing this, do not provide python script related paramaters or JAR related parameters.\n",
|
||||
"- **notebook_params:** Parameters for the databricks notebook (dictionary of {str:str}). Fetch this inside the notebook using dbutils.widgets.get(\"myparam\")\n",
|
||||
"- **python_script_path:** The path to the python script in the DBFS or S3. If you are providing this, do not provide python_script_name which is used for uploading script from local machine.\n",
|
||||
"- **python_script_params:** Parameters for the python script (list of str)\n",
|
||||
"- **main_class_name:** The name of the entry point in a JAR module. If you are providing this, do not provide any python script or notebook related parameters.\n",
|
||||
"- **jar_params:** Parameters for the JAR module (list of str)\n",
|
||||
"- **python_script_name:** name of a python script on your local machine (relative to source_directory). If you are providing this do not provide python_script_path which is used to execute a remote python script; or any of the JAR or notebook related parameters.\n",
|
||||
"- **source_directory:** folder that contains the script and other files\n",
|
||||
"- **hash_paths:** list of paths to hash to detect a change in source_directory (script file is always hashed)\n",
|
||||
"- **run_name:** Name in databricks for this run\n",
|
||||
"- **timeout_seconds:** Timeout for the databricks run\n",
|
||||
"- **runconfig:** Runconfig to use. Either pass runconfig or each library type as a separate parameter but do not mix the two\n",
|
||||
"- **maven_libraries:** maven libraries for the databricks run\n",
|
||||
"- **pypi_libraries:** pypi libraries for the databricks run\n",
|
||||
"- **egg_libraries:** egg libraries for the databricks run\n",
|
||||
"- **jar_libraries:** jar libraries for the databricks run\n",
|
||||
"- **rcran_libraries:** rcran libraries for the databricks run\n",
|
||||
"- **compute_target:** Azure Databricks compute\n",
|
||||
"- **allow_reuse:** Whether the step should reuse previous results when run with the same settings/inputs\n",
|
||||
"- **version:** Optional version tag to denote a change in functionality for the step\n",
|
||||
"\n",
|
||||
"\\* *denotes required fields* \n",
|
||||
"*You must provide exactly one of num_workers or min_workers and max_workers paramaters* \n",
|
||||
"*You must provide exactly one of databricks_compute or databricks_compute_name parameters*\n",
|
||||
"\n",
|
||||
"## Use runconfig to specify library dependencies\n",
|
||||
"You can use a runconfig to specify the library dependencies for your cluster in Databricks. The runconfig will contain a databricks section as follows:\n",
|
||||
"\n",
|
||||
"```yaml\n",
|
||||
"environment:\n",
|
||||
"# Databricks details\n",
|
||||
" databricks:\n",
|
||||
"# List of maven libraries.\n",
|
||||
" mavenLibraries:\n",
|
||||
" - coordinates: org.jsoup:jsoup:1.7.1\n",
|
||||
" repo: ''\n",
|
||||
" exclusions:\n",
|
||||
" - slf4j:slf4j\n",
|
||||
" - '*:hadoop-client'\n",
|
||||
"# List of PyPi libraries\n",
|
||||
" pypiLibraries:\n",
|
||||
" - package: beautifulsoup4\n",
|
||||
" repo: ''\n",
|
||||
"# List of RCran libraries\n",
|
||||
" rcranLibraries:\n",
|
||||
" -\n",
|
||||
"# Coordinates.\n",
|
||||
" package: ada\n",
|
||||
"# Repo\n",
|
||||
" repo: http://cran.us.r-project.org\n",
|
||||
"# List of JAR libraries\n",
|
||||
" jarLibraries:\n",
|
||||
" -\n",
|
||||
"# Coordinates.\n",
|
||||
" library: dbfs:/mnt/libraries/library.jar\n",
|
||||
"# List of Egg libraries\n",
|
||||
" eggLibraries:\n",
|
||||
" -\n",
|
||||
"# Coordinates.\n",
|
||||
" library: dbfs:/mnt/libraries/library.egg\n",
|
||||
"```\n",
|
||||
"\n",
|
||||
"You can then create a RunConfiguration object using this file and pass it as the runconfig parameter to DatabricksStep.\n",
|
||||
"```python\n",
|
||||
"from azureml.core.runconfig import RunConfiguration\n",
|
||||
"\n",
|
||||
"runconfig = RunConfiguration()\n",
|
||||
"runconfig.load(path='<directory_where_runconfig_is_stored>', name='<runconfig_file_name>')\n",
|
||||
"```"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### 1. Running the demo notebook already added to the Databricks workspace\n",
|
||||
"Create a notebook in the Azure Databricks workspace, and provide the path to that notebook as the value associated with the environment variable \"DATABRICKS_NOTEBOOK_PATH\". This will then set the variable notebook_path when you run the code cell below:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"notebook_path=os.getenv(\"DATABRICKS_NOTEBOOK_PATH\", \"<my-databricks-notebook-path>\") # Databricks notebook path\n",
|
||||
"\n",
|
||||
"dbNbStep = DatabricksStep(\n",
|
||||
" name=\"DBNotebookInWS\",\n",
|
||||
" inputs=[step_1_input],\n",
|
||||
" outputs=[step_1_output],\n",
|
||||
" num_workers=1,\n",
|
||||
" notebook_path=notebook_path,\n",
|
||||
" notebook_params={'myparam': 'testparam'},\n",
|
||||
" run_name='DB_Notebook_demo',\n",
|
||||
" compute_target=databricks_compute,\n",
|
||||
" allow_reuse=True\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Build and submit the Experiment"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#PUBLISHONLY\n",
|
||||
"#steps = [dbNbStep]\n",
|
||||
"#pipeline = Pipeline(workspace=ws, steps=steps)\n",
|
||||
"#pipeline_run = Experiment(ws, 'DB_Notebook_demo').submit(pipeline)\n",
|
||||
"#pipeline_run.wait_for_completion()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### View Run Details"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#PUBLISHONLY\n",
|
||||
"#from azureml.widgets import RunDetails\n",
|
||||
"#RunDetails(pipeline_run).show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### 2. Running a Python script from DBFS\n",
|
||||
"This shows how to run a Python script in DBFS. \n",
|
||||
"\n",
|
||||
"To complete this, you will need to first upload the Python script in your local machine to DBFS using the [CLI](https://docs.azuredatabricks.net/user-guide/dbfs-databricks-file-system.html). The CLI command is given below:\n",
|
||||
"\n",
|
||||
"```\n",
|
||||
"dbfs cp ./train-db-dbfs.py dbfs:/train-db-dbfs.py\n",
|
||||
"```\n",
|
||||
"\n",
|
||||
"The code in the below cell assumes that you have completed the previous step of uploading the script `train-db-dbfs.py` to the root folder in DBFS."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"python_script_path = os.getenv(\"DATABRICKS_PYTHON_SCRIPT_PATH\", \"<my-databricks-python-script-path>\") # Databricks python script path\n",
|
||||
"\n",
|
||||
"dbPythonInDbfsStep = DatabricksStep(\n",
|
||||
" name=\"DBPythonInDBFS\",\n",
|
||||
" inputs=[step_1_input],\n",
|
||||
" num_workers=1,\n",
|
||||
" python_script_path=python_script_path,\n",
|
||||
" python_script_params={'--input_data'},\n",
|
||||
" run_name='DB_Python_demo',\n",
|
||||
" compute_target=databricks_compute,\n",
|
||||
" allow_reuse=True\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Build and submit the Experiment"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#PUBLISHONLY\n",
|
||||
"#steps = [dbPythonInDbfsStep]\n",
|
||||
"#pipeline = Pipeline(workspace=ws, steps=steps)\n",
|
||||
"#pipeline_run = Experiment(ws, 'DB_Python_demo').submit(pipeline)\n",
|
||||
"#pipeline_run.wait_for_completion()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### View Run Details"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#PUBLISHONLY\n",
|
||||
"#from azureml.widgets import RunDetails\n",
|
||||
"#RunDetails(pipeline_run).show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### 3. Running a Python script in Databricks that currenlty is in local computer\n",
|
||||
"To run a Python script that is currently in your local computer, follow the instructions below. \n",
|
||||
"\n",
|
||||
"The commented out code below code assumes that you have `train-db-local.py` in the `scripts` subdirectory under the current working directory.\n",
|
||||
"\n",
|
||||
"In this case, the Python script will be uploaded first to DBFS, and then the script will be run in Databricks."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"python_script_name = \"train-db-local.py\"\n",
|
||||
"source_directory = \".\"\n",
|
||||
"\n",
|
||||
"dbPythonInLocalMachineStep = DatabricksStep(\n",
|
||||
" name=\"DBPythonInLocalMachine\",\n",
|
||||
" inputs=[step_1_input],\n",
|
||||
" num_workers=1,\n",
|
||||
" python_script_name=python_script_name,\n",
|
||||
" source_directory=source_directory,\n",
|
||||
" run_name='DB_Python_Local_demo',\n",
|
||||
" compute_target=databricks_compute,\n",
|
||||
" allow_reuse=True\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Build and submit the Experiment"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"steps = [dbPythonInLocalMachineStep]\n",
|
||||
"pipeline = Pipeline(workspace=ws, steps=steps)\n",
|
||||
"pipeline_run = Experiment(ws, 'DB_Python_Local_demo').submit(pipeline)\n",
|
||||
"pipeline_run.wait_for_completion()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### View Run Details"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.widgets import RunDetails\n",
|
||||
"RunDetails(pipeline_run).show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### 4. Running a JAR job that is alreay added in DBFS\n",
|
||||
"To run a JAR job that is already uploaded to DBFS, follow the instructions below. You will first upload the JAR file to DBFS using the [CLI](https://docs.azuredatabricks.net/user-guide/dbfs-databricks-file-system.html).\n",
|
||||
"\n",
|
||||
"The commented out code in the below cell assumes that you have uploaded `train-db-dbfs.jar` to the root folder in DBFS. You can upload `train-db-dbfs.jar` to the root folder in DBFS using this commandline so you can use `jar_library_dbfs_path = \"dbfs:/train-db-dbfs.jar\"`:\n",
|
||||
"\n",
|
||||
"```\n",
|
||||
"dbfs cp ./train-db-dbfs.jar dbfs:/train-db-dbfs.jar\n",
|
||||
"```"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"main_jar_class_name = \"com.microsoft.aeva.Main\"\n",
|
||||
"jar_library_dbfs_path = os.getenv(\"DATABRICKS_JAR_LIB_PATH\", \"<my-databricks-jar-lib-path>\") # Databricks jar library path\n",
|
||||
"\n",
|
||||
"dbJarInDbfsStep = DatabricksStep(\n",
|
||||
" name=\"DBJarInDBFS\",\n",
|
||||
" inputs=[step_1_input],\n",
|
||||
" num_workers=1,\n",
|
||||
" main_class_name=main_jar_class_name,\n",
|
||||
" jar_params={'arg1', 'arg2'},\n",
|
||||
" run_name='DB_JAR_demo',\n",
|
||||
" jar_libraries=[JarLibrary(jar_library_dbfs_path)],\n",
|
||||
" compute_target=databricks_compute,\n",
|
||||
" allow_reuse=True\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Build and submit the Experiment"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#PUBLISHONLY\n",
|
||||
"#steps = [dbJarInDbfsStep]\n",
|
||||
"#pipeline = Pipeline(workspace=ws, steps=steps)\n",
|
||||
"#pipeline_run = Experiment(ws, 'DB_JAR_demo').submit(pipeline)\n",
|
||||
"#pipeline_run.wait_for_completion()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### View Run Details"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#PUBLISHONLY\n",
|
||||
"#from azureml.widgets import RunDetails\n",
|
||||
"#RunDetails(pipeline_run).show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Next: ADLA as a Compute Target\n",
|
||||
"To use ADLA as a compute target from Azure Machine Learning Pipeline, a AdlaStep is used. This [notebook](./aml-pipelines-use-adla-as-compute-target.ipynb) demonstrates the use of AdlaStep in Azure Machine Learning Pipeline."
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"authors": [
|
||||
{
|
||||
"name": "diray"
|
||||
}
|
||||
],
|
||||
"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": "3.6.2"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
@@ -1,366 +0,0 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Azure ML & Azure Databricks notebooks by Parashar Shah.\n",
|
||||
"\n",
|
||||
"Copyright (c) Microsoft Corporation. All rights reserved.\n",
|
||||
"\n",
|
||||
"Licensed under the MIT License."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#Model Building"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import os\n",
|
||||
"import pprint\n",
|
||||
"import numpy as np\n",
|
||||
"\n",
|
||||
"from pyspark.ml import Pipeline, PipelineModel\n",
|
||||
"from pyspark.ml.feature import OneHotEncoder, StringIndexer, VectorAssembler\n",
|
||||
"from pyspark.ml.classification import LogisticRegression\n",
|
||||
"from pyspark.ml.evaluation import BinaryClassificationEvaluator\n",
|
||||
"from pyspark.ml.tuning import CrossValidator, ParamGridBuilder"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import azureml.core\n",
|
||||
"\n",
|
||||
"# Check core SDK version number\n",
|
||||
"print(\"SDK version:\", azureml.core.VERSION)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Set auth to be used by workspace related APIs.\n",
|
||||
"# For automation or CI/CD ServicePrincipalAuthentication can be used.\n",
|
||||
"# https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.authentication.serviceprincipalauthentication?view=azure-ml-py\n",
|
||||
"auth = None"
|
||||
]
|
||||
},
|
||||
{
|
||||
"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.from_config(auth = auth)\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": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#get the train and test datasets\n",
|
||||
"train_data_path = \"AdultCensusIncomeTrain\"\n",
|
||||
"test_data_path = \"AdultCensusIncomeTest\"\n",
|
||||
"\n",
|
||||
"train = spark.read.parquet(train_data_path)\n",
|
||||
"test = spark.read.parquet(test_data_path)\n",
|
||||
"\n",
|
||||
"print(\"train: ({}, {})\".format(train.count(), len(train.columns)))\n",
|
||||
"print(\"test: ({}, {})\".format(test.count(), len(test.columns)))\n",
|
||||
"\n",
|
||||
"train.printSchema()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#Define Model"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"label = \"income\"\n",
|
||||
"dtypes = dict(train.dtypes)\n",
|
||||
"dtypes.pop(label)\n",
|
||||
"\n",
|
||||
"si_xvars = []\n",
|
||||
"ohe_xvars = []\n",
|
||||
"featureCols = []\n",
|
||||
"for idx,key in enumerate(dtypes):\n",
|
||||
" if dtypes[key] == \"string\":\n",
|
||||
" featureCol = \"-\".join([key, \"encoded\"])\n",
|
||||
" featureCols.append(featureCol)\n",
|
||||
" \n",
|
||||
" tmpCol = \"-\".join([key, \"tmp\"])\n",
|
||||
" # string-index and one-hot encode the string column\n",
|
||||
" #https://spark.apache.org/docs/2.3.0/api/java/org/apache/spark/ml/feature/StringIndexer.html\n",
|
||||
" #handleInvalid: Param for how to handle invalid data (unseen labels or NULL values). \n",
|
||||
" #Options are 'skip' (filter out rows with invalid data), 'error' (throw an error), \n",
|
||||
" #or 'keep' (put invalid data in a special additional bucket, at index numLabels). Default: \"error\"\n",
|
||||
" si_xvars.append(StringIndexer(inputCol=key, outputCol=tmpCol, handleInvalid=\"skip\"))\n",
|
||||
" ohe_xvars.append(OneHotEncoder(inputCol=tmpCol, outputCol=featureCol))\n",
|
||||
" else:\n",
|
||||
" featureCols.append(key)\n",
|
||||
"\n",
|
||||
"# string-index the label column into a column named \"label\"\n",
|
||||
"si_label = StringIndexer(inputCol=label, outputCol='label')\n",
|
||||
"\n",
|
||||
"# assemble the encoded feature columns in to a column named \"features\"\n",
|
||||
"assembler = VectorAssembler(inputCols=featureCols, outputCol=\"features\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core.run import Run\n",
|
||||
"from azureml.core.experiment import Experiment\n",
|
||||
"import numpy as np\n",
|
||||
"import os\n",
|
||||
"import shutil\n",
|
||||
"\n",
|
||||
"model_name = \"AdultCensus_runHistory.mml\"\n",
|
||||
"model_dbfs = os.path.join(\"/dbfs\", model_name)\n",
|
||||
"run_history_name = 'spark-ml-notebook'\n",
|
||||
"\n",
|
||||
"# start a training run by defining an experiment\n",
|
||||
"myexperiment = Experiment(ws, \"Ignite_AI_Talk\")\n",
|
||||
"root_run = myexperiment.start_logging()\n",
|
||||
"\n",
|
||||
"# Regularization Rates - \n",
|
||||
"regs = [0.0001, 0.001, 0.01, 0.1]\n",
|
||||
" \n",
|
||||
"# try a bunch of regularization rate in a Logistic Regression model\n",
|
||||
"for reg in regs:\n",
|
||||
" print(\"Regularization rate: {}\".format(reg))\n",
|
||||
" # create a bunch of child runs\n",
|
||||
" with root_run.child_run(\"reg-\" + str(reg)) as run:\n",
|
||||
" # create a new Logistic Regression model.\n",
|
||||
" lr = LogisticRegression(regParam=reg)\n",
|
||||
" \n",
|
||||
" # put together the pipeline\n",
|
||||
" pipe = Pipeline(stages=[*si_xvars, *ohe_xvars, si_label, assembler, lr])\n",
|
||||
"\n",
|
||||
" # train the model\n",
|
||||
" model_p = pipe.fit(train)\n",
|
||||
" \n",
|
||||
" # make prediction\n",
|
||||
" pred = model_p.transform(test)\n",
|
||||
" \n",
|
||||
" # evaluate. note only 2 metrics are supported out of the box by Spark ML.\n",
|
||||
" bce = BinaryClassificationEvaluator(rawPredictionCol='rawPrediction')\n",
|
||||
" au_roc = bce.setMetricName('areaUnderROC').evaluate(pred)\n",
|
||||
" au_prc = bce.setMetricName('areaUnderPR').evaluate(pred)\n",
|
||||
"\n",
|
||||
" print(\"Area under ROC: {}\".format(au_roc))\n",
|
||||
" print(\"Area Under PR: {}\".format(au_prc))\n",
|
||||
" \n",
|
||||
" # log reg, au_roc, au_prc and feature names in run history\n",
|
||||
" run.log(\"reg\", reg)\n",
|
||||
" run.log(\"au_roc\", au_roc)\n",
|
||||
" run.log(\"au_prc\", au_prc)\n",
|
||||
" run.log_list(\"columns\", train.columns)\n",
|
||||
"\n",
|
||||
" # save model\n",
|
||||
" model_p.write().overwrite().save(model_name)\n",
|
||||
" \n",
|
||||
" # upload the serialized model into run history record\n",
|
||||
" mdl, ext = model_name.split(\".\")\n",
|
||||
" model_zip = mdl + \".zip\"\n",
|
||||
" shutil.make_archive(mdl, 'zip', model_dbfs)\n",
|
||||
" run.upload_file(\"outputs/\" + model_name, model_zip) \n",
|
||||
" #run.upload_file(\"outputs/\" + model_name, path_or_stream = model_dbfs) #cannot deal with folders\n",
|
||||
"\n",
|
||||
" # now delete the serialized model from local folder since it is already uploaded to run history \n",
|
||||
" shutil.rmtree(model_dbfs)\n",
|
||||
" os.remove(model_zip)\n",
|
||||
" \n",
|
||||
"# Declare run completed\n",
|
||||
"root_run.complete()\n",
|
||||
"root_run_id = root_run.id\n",
|
||||
"print (\"run id:\", root_run.id)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"metrics = root_run.get_metrics(recursive=True)\n",
|
||||
"best_run_id = max(metrics, key = lambda k: metrics[k]['au_roc'])\n",
|
||||
"print(best_run_id, metrics[best_run_id]['au_roc'], metrics[best_run_id]['reg'])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#Get the best run\n",
|
||||
"child_runs = {}\n",
|
||||
"\n",
|
||||
"for r in root_run.get_children():\n",
|
||||
" child_runs[r.id] = r\n",
|
||||
" \n",
|
||||
"best_run = child_runs[best_run_id]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#Download the model from the best run to a local folder\n",
|
||||
"best_model_file_name = \"best_model.zip\"\n",
|
||||
"best_run.download_file(name = 'outputs/' + model_name, output_file_path = best_model_file_name)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#Model Evaluation"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"##unzip the model to dbfs (as load() seems to require that) and load it.\n",
|
||||
"if os.path.isfile(model_dbfs) or os.path.isdir(model_dbfs):\n",
|
||||
" shutil.rmtree(model_dbfs)\n",
|
||||
"shutil.unpack_archive(best_model_file_name, model_dbfs)\n",
|
||||
"\n",
|
||||
"model_p_best = PipelineModel.load(model_name)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# make prediction\n",
|
||||
"pred = model_p_best.transform(test)\n",
|
||||
"output = pred[['hours_per_week','age','workclass','marital_status','income','prediction']]\n",
|
||||
"display(output.limit(5))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# evaluate. note only 2 metrics are supported out of the box by Spark ML.\n",
|
||||
"bce = BinaryClassificationEvaluator(rawPredictionCol='rawPrediction')\n",
|
||||
"au_roc = bce.setMetricName('areaUnderROC').evaluate(pred)\n",
|
||||
"au_prc = bce.setMetricName('areaUnderPR').evaluate(pred)\n",
|
||||
"\n",
|
||||
"print(\"Area under ROC: {}\".format(au_roc))\n",
|
||||
"print(\"Area Under PR: {}\".format(au_prc))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#Model Persistence"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"##NOTE: by default the model is saved to and loaded from /dbfs/ instead of cwd!\n",
|
||||
"model_p_best.write().overwrite().save(model_name)\n",
|
||||
"print(\"saved model to {}\".format(model_dbfs))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%sh\n",
|
||||
"\n",
|
||||
"ls -la /dbfs/AdultCensus_runHistory.mml/*"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"dbutils.notebook.exit(\"success\")"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"authors": [
|
||||
{
|
||||
"name": "pasha"
|
||||
}
|
||||
],
|
||||
"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"
|
||||
},
|
||||
"name": "build-model-run-history-03",
|
||||
"notebookId": 3836944406456339
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 1
|
||||
}
|
||||
@@ -1,310 +0,0 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Azure ML & Azure Databricks notebooks by Parashar Shah.\n",
|
||||
"\n",
|
||||
"Copyright (c) Microsoft Corporation. All rights reserved.\n",
|
||||
"\n",
|
||||
"Licensed under the MIT License."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Please ensure you have run all previous notebooks in sequence before running this.\n",
|
||||
"\n",
|
||||
"Please Register Azure Container Instance(ACI) using Azure Portal: https://docs.microsoft.com/en-us/azure/azure-resource-manager/resource-manager-supported-services#portal in your subscription before using the SDK to deploy your ML model to ACI."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import azureml.core\n",
|
||||
"\n",
|
||||
"# Check core SDK version number\n",
|
||||
"print(\"SDK version:\", azureml.core.VERSION)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Set auth to be used by workspace related APIs.\n",
|
||||
"# For automation or CI/CD ServicePrincipalAuthentication can be used.\n",
|
||||
"# https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.authentication.serviceprincipalauthentication?view=azure-ml-py\n",
|
||||
"auth = None"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core import Workspace\n",
|
||||
"\n",
|
||||
"ws = Workspace.from_config(auth = auth)\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": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"##NOTE: service deployment always gets the model from the current working dir.\n",
|
||||
"import os\n",
|
||||
"\n",
|
||||
"model_name = \"AdultCensus_runHistory.mml\" # \n",
|
||||
"model_name_dbfs = os.path.join(\"/dbfs\", model_name)\n",
|
||||
"\n",
|
||||
"print(\"copy model from dbfs to local\")\n",
|
||||
"model_local = \"file:\" + os.getcwd() + \"/\" + model_name\n",
|
||||
"dbutils.fs.cp(model_name, model_local, True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#Register the model\n",
|
||||
"from azureml.core.model import Model\n",
|
||||
"mymodel = Model.register(model_path = model_name, # this points to a local file\n",
|
||||
" model_name = model_name, # this is the name the model is registered as, am using same name for both path and name. \n",
|
||||
" description = \"ADB trained model by Parashar\",\n",
|
||||
" workspace = ws)\n",
|
||||
"\n",
|
||||
"print(mymodel.name, mymodel.description, mymodel.version)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#%%writefile score_sparkml.py\n",
|
||||
"score_sparkml = \"\"\"\n",
|
||||
" \n",
|
||||
"import json\n",
|
||||
" \n",
|
||||
"def init():\n",
|
||||
" # One-time initialization of PySpark and predictive model\n",
|
||||
" import pyspark\n",
|
||||
" from azureml.core.model import Model\n",
|
||||
" from pyspark.ml import PipelineModel\n",
|
||||
" \n",
|
||||
" global trainedModel\n",
|
||||
" global spark\n",
|
||||
" \n",
|
||||
" spark = pyspark.sql.SparkSession.builder.appName(\"ADB and AML notebook by Parashar\").getOrCreate()\n",
|
||||
" model_name = \"{model_name}\" #interpolated\n",
|
||||
" model_path = Model.get_model_path(model_name)\n",
|
||||
" trainedModel = PipelineModel.load(model_path)\n",
|
||||
" \n",
|
||||
"def run(input_json):\n",
|
||||
" if isinstance(trainedModel, Exception):\n",
|
||||
" return json.dumps({{\"trainedModel\":str(trainedModel)}})\n",
|
||||
" \n",
|
||||
" try:\n",
|
||||
" sc = spark.sparkContext\n",
|
||||
" input_list = json.loads(input_json)\n",
|
||||
" input_rdd = sc.parallelize(input_list)\n",
|
||||
" input_df = spark.read.json(input_rdd)\n",
|
||||
" \n",
|
||||
" # Compute prediction\n",
|
||||
" prediction = trainedModel.transform(input_df)\n",
|
||||
" #result = prediction.first().prediction\n",
|
||||
" predictions = prediction.collect()\n",
|
||||
" \n",
|
||||
" #Get each scored result\n",
|
||||
" preds = [str(x['prediction']) for x in predictions]\n",
|
||||
" result = \",\".join(preds)\n",
|
||||
" # you can return any data type as long as it is JSON-serializable\n",
|
||||
" return result.tolist()\n",
|
||||
" except Exception as e:\n",
|
||||
" result = str(e)\n",
|
||||
" return result\n",
|
||||
" \n",
|
||||
"\"\"\".format(model_name=model_name)\n",
|
||||
" \n",
|
||||
"exec(score_sparkml)\n",
|
||||
" \n",
|
||||
"with open(\"score_sparkml.py\", \"w\") as file:\n",
|
||||
" file.write(score_sparkml)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core.conda_dependencies import CondaDependencies \n",
|
||||
"\n",
|
||||
"myacienv = CondaDependencies.create(conda_packages=['scikit-learn','numpy','pandas']) #showing how to add libs as an eg. - not needed for this model.\n",
|
||||
"\n",
|
||||
"with open(\"mydeployenv.yml\",\"w\") as f:\n",
|
||||
" f.write(myacienv.serialize_to_string())"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#deploy to ACI\n",
|
||||
"from azureml.core.webservice import AciWebservice, Webservice\n",
|
||||
"\n",
|
||||
"myaci_config = AciWebservice.deploy_configuration(\n",
|
||||
" cpu_cores = 2, \n",
|
||||
" memory_gb = 2, \n",
|
||||
" tags = {'name':'Databricks Azure ML ACI'}, \n",
|
||||
" description = 'This is for ADB and AML example. Azure Databricks & Azure ML SDK demo with ACI by Parashar.')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# this will take 10-15 minutes to finish\n",
|
||||
"\n",
|
||||
"service_name = \"aciws\"\n",
|
||||
"runtime = \"spark-py\" \n",
|
||||
"driver_file = \"score_sparkml.py\"\n",
|
||||
"my_conda_file = \"mydeployenv.yml\"\n",
|
||||
"\n",
|
||||
"# image creation\n",
|
||||
"from azureml.core.image import ContainerImage\n",
|
||||
"myimage_config = ContainerImage.image_configuration(execution_script = driver_file, \n",
|
||||
" runtime = runtime, \n",
|
||||
" conda_file = my_conda_file)\n",
|
||||
"\n",
|
||||
"# Webservice creation\n",
|
||||
"myservice = Webservice.deploy_from_model(\n",
|
||||
" workspace=ws, \n",
|
||||
" name=service_name,\n",
|
||||
" deployment_config = myaci_config,\n",
|
||||
" models = [mymodel],\n",
|
||||
" image_config = myimage_config\n",
|
||||
" )\n",
|
||||
"\n",
|
||||
"myservice.wait_for_deployment(show_output=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"help(Webservice)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# List images by ws\n",
|
||||
"\n",
|
||||
"for 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))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#for using the Web HTTP API \n",
|
||||
"print(myservice.scoring_uri)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import json\n",
|
||||
"\n",
|
||||
"#get the some sample data\n",
|
||||
"test_data_path = \"AdultCensusIncomeTest\"\n",
|
||||
"test = spark.read.parquet(test_data_path).limit(5)\n",
|
||||
"\n",
|
||||
"test_json = json.dumps(test.toJSON().collect())\n",
|
||||
"\n",
|
||||
"print(test_json)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#using data defined above predict if income is >50K (1) or <=50K (0)\n",
|
||||
"myservice.run(input_data=test_json)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#comment to not delete the web service\n",
|
||||
"myservice.delete()"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"authors": [
|
||||
{
|
||||
"name": "pasha"
|
||||
}
|
||||
],
|
||||
"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"
|
||||
},
|
||||
"name": "deploy-to-aci-04",
|
||||
"notebookId": 3836944406456376
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 1
|
||||
}
|
||||
@@ -1,236 +0,0 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Azure ML & Azure Databricks notebooks by Parashar Shah.\n",
|
||||
"\n",
|
||||
"Copyright (c) Microsoft Corporation. All rights reserved.\n",
|
||||
"\n",
|
||||
"Licensed under the MIT License."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"This notebook uses image from ACI notebook for deploying to AKS."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import azureml.core\n",
|
||||
"\n",
|
||||
"# Check core SDK version number\n",
|
||||
"print(\"SDK version:\", azureml.core.VERSION)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Set auth to be used by workspace related APIs.\n",
|
||||
"# For automation or CI/CD ServicePrincipalAuthentication can be used.\n",
|
||||
"# https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.authentication.serviceprincipalauthentication?view=azure-ml-py\n",
|
||||
"auth = None"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core import Workspace\n",
|
||||
"\n",
|
||||
"ws = Workspace.from_config(auth = auth)\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": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# List images by ws\n",
|
||||
"\n",
|
||||
"from azureml.core.image import ContainerImage\n",
|
||||
"for 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))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core.image import Image\n",
|
||||
"myimage = Image(workspace=ws, name=\"aciws\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#create AKS compute\n",
|
||||
"#it may take 20-25 minutes to create a new cluster\n",
|
||||
"\n",
|
||||
"from azureml.core.compute import AksCompute, ComputeTarget\n",
|
||||
"\n",
|
||||
"# Use the default configuration (can also provide parameters to customize)\n",
|
||||
"prov_config = AksCompute.provisioning_configuration()\n",
|
||||
"\n",
|
||||
"aks_name = 'ps-aks-demo2' \n",
|
||||
"\n",
|
||||
"# Create the cluster\n",
|
||||
"aks_target = ComputeTarget.create(workspace = ws, \n",
|
||||
" name = aks_name, \n",
|
||||
" provisioning_configuration = prov_config)\n",
|
||||
"\n",
|
||||
"aks_target.wait_for_completion(show_output = True)\n",
|
||||
"\n",
|
||||
"print(aks_target.provisioning_state)\n",
|
||||
"print(aks_target.provisioning_errors)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core.webservice import Webservice\n",
|
||||
"help( Webservice.deploy_from_image)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core.webservice import Webservice, AksWebservice\n",
|
||||
"from azureml.core.image import ContainerImage\n",
|
||||
"\n",
|
||||
"#Set the web service configuration (using default here with app insights)\n",
|
||||
"aks_config = AksWebservice.deploy_configuration(enable_app_insights=True)\n",
|
||||
"\n",
|
||||
"#unique service name\n",
|
||||
"service_name ='ps-aks-service'\n",
|
||||
"\n",
|
||||
"# Webservice creation using single command, there is a variant to use image directly as well.\n",
|
||||
"aks_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",
|
||||
"\n",
|
||||
"aks_service.wait_for_deployment(show_output=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"aks_service.deployment_status"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#for using the Web HTTP API \n",
|
||||
"print(aks_service.scoring_uri)\n",
|
||||
"print(aks_service.get_keys())"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import json\n",
|
||||
"\n",
|
||||
"#get the some sample data\n",
|
||||
"test_data_path = \"AdultCensusIncomeTest\"\n",
|
||||
"test = spark.read.parquet(test_data_path).limit(5)\n",
|
||||
"\n",
|
||||
"test_json = json.dumps(test.toJSON().collect())\n",
|
||||
"\n",
|
||||
"print(test_json)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#using data defined above predict if income is >50K (1) or <=50K (0)\n",
|
||||
"aks_service.run(input_data=test_json)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#comment to not delete the web service\n",
|
||||
"aks_service.delete()\n",
|
||||
"#image.delete()\n",
|
||||
"#model.delete()\n",
|
||||
"aks_target.delete() "
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"authors": [
|
||||
{
|
||||
"name": "pasha"
|
||||
}
|
||||
],
|
||||
"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"
|
||||
},
|
||||
"name": "deploy-to-aks-existingimage-05",
|
||||
"notebookId": 1030695628045968
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 1
|
||||
}
|
||||
@@ -1,172 +0,0 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Azure ML & Azure Databricks notebooks by Parashar Shah.\n",
|
||||
"\n",
|
||||
"Copyright (c) Microsoft Corporation. All rights reserved.\n",
|
||||
"\n",
|
||||
"Licensed under the MIT License."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#Data Ingestion"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import os\n",
|
||||
"import urllib"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Download AdultCensusIncome.csv from Azure CDN. This file has 32,561 rows.\n",
|
||||
"dataurl = \"https://amldockerdatasets.azureedge.net/AdultCensusIncome.csv\"\n",
|
||||
"datafile = \"AdultCensusIncome.csv\"\n",
|
||||
"datafile_dbfs = os.path.join(\"/dbfs\", datafile)\n",
|
||||
"\n",
|
||||
"if os.path.isfile(datafile_dbfs):\n",
|
||||
" print(\"found {} at {}\".format(datafile, datafile_dbfs))\n",
|
||||
"else:\n",
|
||||
" print(\"downloading {} to {}\".format(datafile, datafile_dbfs))\n",
|
||||
" urllib.request.urlretrieve(dataurl, datafile_dbfs)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Create a Spark dataframe out of the csv file.\n",
|
||||
"data_all = sqlContext.read.format('csv').options(header='true', inferSchema='true', ignoreLeadingWhiteSpace='true', ignoreTrailingWhiteSpace='true').load(datafile)\n",
|
||||
"print(\"({}, {})\".format(data_all.count(), len(data_all.columns)))\n",
|
||||
"data_all.printSchema()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#renaming columns\n",
|
||||
"columns_new = [col.replace(\"-\", \"_\") for col in data_all.columns]\n",
|
||||
"data_all = data_all.toDF(*columns_new)\n",
|
||||
"data_all.printSchema()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"display(data_all.limit(5))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#Data Preparation"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Choose feature columns and the label column.\n",
|
||||
"label = \"income\"\n",
|
||||
"xvars = set(data_all.columns) - {label}\n",
|
||||
"\n",
|
||||
"print(\"label = {}\".format(label))\n",
|
||||
"print(\"features = {}\".format(xvars))\n",
|
||||
"\n",
|
||||
"data = data_all.select([*xvars, label])\n",
|
||||
"\n",
|
||||
"# Split data into train and test.\n",
|
||||
"train, test = data.randomSplit([0.75, 0.25], seed=123)\n",
|
||||
"\n",
|
||||
"print(\"train ({}, {})\".format(train.count(), len(train.columns)))\n",
|
||||
"print(\"test ({}, {})\".format(test.count(), len(test.columns)))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#Data Persistence"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Write the train and test data sets to intermediate storage\n",
|
||||
"train_data_path = \"AdultCensusIncomeTrain\"\n",
|
||||
"test_data_path = \"AdultCensusIncomeTest\"\n",
|
||||
"\n",
|
||||
"train_data_path_dbfs = os.path.join(\"/dbfs\", \"AdultCensusIncomeTrain\")\n",
|
||||
"test_data_path_dbfs = os.path.join(\"/dbfs\", \"AdultCensusIncomeTest\")\n",
|
||||
"\n",
|
||||
"train.write.mode('overwrite').parquet(train_data_path)\n",
|
||||
"test.write.mode('overwrite').parquet(test_data_path)\n",
|
||||
"print(\"train and test datasets saved to {} and {}\".format(train_data_path_dbfs, test_data_path_dbfs))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"authors": [
|
||||
{
|
||||
"name": "pasha"
|
||||
}
|
||||
],
|
||||
"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"
|
||||
},
|
||||
"name": "ingest-data-02",
|
||||
"notebookId": 3836944406456362
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 1
|
||||
}
|
||||
@@ -1,176 +0,0 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Azure ML & Azure Databricks notebooks by Parashar Shah.\n",
|
||||
"\n",
|
||||
"Copyright (c) Microsoft Corporation. All rights reserved.\n",
|
||||
"\n",
|
||||
"Licensed under the MIT License."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"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. You can select the option to attach the library to all clusters or just one cluster.\n",
|
||||
"\n",
|
||||
"**install azureml-sdk**\n",
|
||||
"* Source: Upload Python Egg or PyPi\n",
|
||||
"* PyPi Name: `azureml-sdk[databricks]`\n",
|
||||
"* Select Install Library"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import azureml.core\n",
|
||||
"\n",
|
||||
"# Check core SDK version number - based on build number of preview/master.\n",
|
||||
"print(\"SDK version:\", azureml.core.VERSION)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Please specify the Azure subscription Id, resource group name, workspace name, and the region in which you want to create the Azure Machine Learning Workspace.\n",
|
||||
"\n",
|
||||
"You can get the value of your Azure subscription ID from the Azure Portal, and then selecting Subscriptions from the menu on the left.\n",
|
||||
"\n",
|
||||
"For the resource_group, use the name of the resource group that contains your Azure Databricks Workspace.\n",
|
||||
"\n",
|
||||
"NOTE: If you provide a resource group name that does not exist, the resource group will be automatically created. This may or may not succeed in your environment, depending on the permissions you have on your Azure Subscription."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# subscription_id = \"<your-subscription-id>\"\n",
|
||||
"# resource_group = \"<your-existing-resource-group>\"\n",
|
||||
"# workspace_name = \"<a-new-or-existing-workspace; it is unrelated to Databricks workspace>\"\n",
|
||||
"# workspace_region = \"<your-resource group-region>\""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Set auth to be used by workspace related APIs.\n",
|
||||
"# For automation or CI/CD ServicePrincipalAuthentication can be used.\n",
|
||||
"# https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.authentication.serviceprincipalauthentication?view=azure-ml-py\n",
|
||||
"auth = None"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# import the Workspace class and check the azureml SDK version\n",
|
||||
"# exist_ok checks if workspace exists or not.\n",
|
||||
"\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",
|
||||
" auth = auth,\n",
|
||||
" exist_ok=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#get workspace details\n",
|
||||
"ws.get_details()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"ws = Workspace(workspace_name = workspace_name,\n",
|
||||
" subscription_id = subscription_id,\n",
|
||||
" resource_group = resource_group,\n",
|
||||
" auth = auth)\n",
|
||||
"\n",
|
||||
"# persist the subscription id, resource group name, and workspace name in aml_config/config.json.\n",
|
||||
"ws.write_config()\n",
|
||||
"#if you need to give a different path/filename please use this\n",
|
||||
"#write_config(path=\"/databricks/driver/aml_config/\",file_name=<alias_conf.cfg>)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"help(Workspace)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"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.from_config(auth = auth)\n",
|
||||
"#ws = Workspace.from_config(<full path>)\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')"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"authors": [
|
||||
{
|
||||
"name": "pasha"
|
||||
}
|
||||
],
|
||||
"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"
|
||||
},
|
||||
"name": "installation-and-configuration-01",
|
||||
"notebookId": 3688394266452835
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 1
|
||||
}
|
||||
@@ -1,633 +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": [
|
||||
"# Automated ML on Azure Databricks\n",
|
||||
"\n",
|
||||
"In this example we use the scikit-learn's <a href=\"http://scikit-learn.org/stable/datasets/index.html#optical-recognition-of-handwritten-digits-dataset\" target=\"_blank\">digit dataset</a> to showcase how you can use AutoML for a simple classification problem.\n",
|
||||
"\n",
|
||||
"In this notebook you will learn how to:\n",
|
||||
"1. Create Azure Machine Learning Workspace object and initialize your notebook directory to easily reload this object from a configuration file.\n",
|
||||
"2. Create an `Experiment` in an existing `Workspace`.\n",
|
||||
"3. Configure Automated ML using `AutoMLConfig`.\n",
|
||||
"4. Train the model using Azure Databricks.\n",
|
||||
"5. Explore the results.\n",
|
||||
"6. Test the best fitted model.\n",
|
||||
"\n",
|
||||
"Before running this notebook, please follow the <a href=\"https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/azure-databricks\" target=\"_blank\">readme for using Automated ML on Azure Databricks</a> for installing necessary libraries to your cluster."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"We support installing AML SDK with Automated ML as library from GUI. When attaching a library follow <a href=\"https://docs.databricks.com/user-guide/libraries.html\" target=\"_blank\">this link</a> and add the below string as your PyPi package. You can select the option to attach the library to all clusters or just one cluster.\n",
|
||||
"\n",
|
||||
"**azureml-sdk with automated ml**\n",
|
||||
"* Source: Upload Python Egg or PyPi\n",
|
||||
"* PyPi Name: `azureml-sdk[automl_databricks]`\n",
|
||||
"* Select Install Library"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Check the Azure ML Core SDK Version to Validate Your Installation"
|
||||
]
|
||||
},
|
||||
{
|
||||
"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 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, 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 Azure ML library and specify following information:\n",
|
||||
"* A name for your workspace. You can choose one.\n",
|
||||
"* Your subscription id. Use the `id` value from the `az account show` command output above.\n",
|
||||
"* The resource group name. The resource group organizes Azure resources and provides a default region for the resources in the group. The resource group will be created if it doesn't exist. Resource groups can be created and viewed in the [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 = \"<Your SubscriptionId>\" #you should be owner or contributor\n",
|
||||
"resource_group = \"<Resource group - new or existing>\" #you should be owner or contributor\n",
|
||||
"workspace_name = \"<workspace to be created>\" #your workspace name\n",
|
||||
"workspace_region = \"<azureregion>\" #your region"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Creating a Workspace\n",
|
||||
"If you already have access to an Azure ML workspace you want to use, you can skip this cell. Otherwise, this cell will create an Azure ML workspace for you in the specified 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 administrator to provide you with the appropriate permissions or to provision the required resources.\n",
|
||||
"\n",
|
||||
"**Note:** Creation of a new workspace can take several minutes."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Import the Workspace class and check the Azure ML 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": {},
|
||||
"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": [
|
||||
"## Create a Folder to Host Sample Projects\n",
|
||||
"Finally, 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": [
|
||||
"## Create an Experiment\n",
|
||||
"\n",
|
||||
"As part of the setup you have already created an Azure ML `Workspace` object. For Automated ML 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",
|
||||
"import os\n",
|
||||
"import random\n",
|
||||
"import time\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",
|
||||
"\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": [
|
||||
"# Choose a name for the experiment and specify the project folder.\n",
|
||||
"experiment_name = 'automl-local-classification'\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": [
|
||||
"## Registering Datastore"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Datastore is the way to save connection information to a storage service (e.g. Azure Blob, Azure Data Lake, Azure SQL) information to your workspace so you can access them without exposing credentials in your code. The first thing you will need to do is register a datastore, you can refer to our [python SDK documentation](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.datastore.datastore?view=azure-ml-py) on how to register datastores. __Note: for best security practices, please do not check in code that contains registering datastores with secrets into your source control__\n",
|
||||
"\n",
|
||||
"The code below registers a datastore pointing to a publicly readable blob container."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core import Datastore\n",
|
||||
"\n",
|
||||
"datastore_name = 'demo_training'\n",
|
||||
"Datastore.register_azure_blob_container(\n",
|
||||
" workspace = ws, \n",
|
||||
" datastore_name = datastore_name, \n",
|
||||
" container_name = 'automl-notebook-data', \n",
|
||||
" account_name = 'dprepdata'\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Below is an example on how to register a private blob container\n",
|
||||
"```python\n",
|
||||
"datastore = Datastore.register_azure_blob_container(\n",
|
||||
" workspace = ws, \n",
|
||||
" datastore_name = 'example_datastore', \n",
|
||||
" container_name = 'example-container', \n",
|
||||
" account_name = 'storageaccount',\n",
|
||||
" account_key = 'accountkey'\n",
|
||||
")\n",
|
||||
"```\n",
|
||||
"The example below shows how to register an Azure Data Lake store. Please make sure you have granted the necessary permissions for the service principal to access the data lake.\n",
|
||||
"```python\n",
|
||||
"datastore = Datastore.register_azure_data_lake(\n",
|
||||
" workspace = ws,\n",
|
||||
" datastore_name = 'example_datastore',\n",
|
||||
" store_name = 'adlsstore',\n",
|
||||
" tenant_id = 'tenant-id-of-service-principal',\n",
|
||||
" client_id = 'client-id-of-service-principal',\n",
|
||||
" client_secret = 'client-secret-of-service-principal'\n",
|
||||
")\n",
|
||||
"```"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Load Training Data Using DataPrep"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Automated ML takes a Dataflow as input.\n",
|
||||
"\n",
|
||||
"If you are familiar with Pandas and have done your data preparation work in Pandas already, you can use the `read_pandas_dataframe` method in dprep to convert the DataFrame to a Dataflow.\n",
|
||||
"```python\n",
|
||||
"df = pd.read_csv(...)\n",
|
||||
"# apply some transforms\n",
|
||||
"dprep.read_pandas_dataframe(df, temp_folder='/path/accessible/by/both/driver/and/worker')\n",
|
||||
"```\n",
|
||||
"\n",
|
||||
"If you just need to ingest data without doing any preparation, you can directly use AzureML Data Prep (Data Prep) to do so. The code below demonstrates this scenario. Data Prep also has data preparation capabilities, we have many [sample notebooks](https://github.com/Microsoft/AMLDataPrepDocs) demonstrating the capabilities.\n",
|
||||
"\n",
|
||||
"You will get the datastore you registered previously and pass it to Data Prep for reading. The data comes from the digits dataset: `sklearn.datasets.load_digits()`. `DataPath` points to a specific location within a datastore. "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import azureml.dataprep as dprep\n",
|
||||
"from azureml.data.datapath import DataPath\n",
|
||||
"\n",
|
||||
"datastore = Datastore.get(workspace = ws, name = datastore_name)\n",
|
||||
"\n",
|
||||
"X_train = dprep.read_csv(DataPath(datastore = datastore, path_on_datastore = 'X.csv')) \n",
|
||||
"y_train = dprep.read_csv(DataPath(datastore = datastore, path_on_datastore = 'y.csv')).to_long(dprep.ColumnSelector(term='.*', use_regex = True))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Review the Data Preparation Result\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 datasets."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"X_train.get_profile()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"y_train.get_profile()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Configure AutoML\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",
|
||||
"|**primary_metric**|This is the metric that you want to optimize. Regression supports the following primary metrics: <br><i>spearman_correlation</i><br><i>normalized_root_mean_squared_error</i><br><i>r2_score</i><br><i>normalized_mean_absolute_error</i>|\n",
|
||||
"|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
|
||||
"|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
|
||||
"|**n_cross_validations**|Number of cross validation splits.|\n",
|
||||
"|**spark_context**|Spark Context object. for Databricks, use spark_context=sc|\n",
|
||||
"|**max_concurrent_iterations**|Maximum number of iterations to execute in parallel. This should be <= number of worker nodes in your Azure Databricks cluster.|\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.|\n",
|
||||
"|**preprocess**|set this to True to enable pre-processing of data eg. string to numeric using one-hot encoding|\n",
|
||||
"|**exit_score**|Target score for experiment. It is associated with the metric. eg. exit_score=0.995 will exit experiment after that|"
|
||||
]
|
||||
},
|
||||
{
|
||||
"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 = 10,\n",
|
||||
" iterations = 5,\n",
|
||||
" preprocess = True,\n",
|
||||
" n_cross_validations = 10,\n",
|
||||
" max_concurrent_iterations = 2, #change it based on number of worker nodes\n",
|
||||
" verbosity = logging.INFO,\n",
|
||||
" spark_context=sc, #databricks/spark related\n",
|
||||
" X = X_train, \n",
|
||||
" y = y_train,\n",
|
||||
" path = project_folder)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Train the Models\n",
|
||||
"\n",
|
||||
"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."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"local_run = experiment.submit(automl_config, show_output = False) # for higher runs please use show_output=False and use the below"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Explore the Results"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Portal URL for Monitoring Runs\n",
|
||||
"\n",
|
||||
"The following will provide a link to the web interface to explore individual run details and status. In the future we might support output displayed in the notebook."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"displayHTML(\"<a href={} target='_blank'>Your experiment in Azure Portal: {}</a>\".format(local_run.get_portal_url(), local_run.id))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"The following will show the child runs and waits for the parent run to complete."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Retrieve All Child Runs after the experiment is completed (in portal)\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 after the above run is complete \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 after the above run is complete based on the child run\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": [
|
||||
"### Test the Best Fitted Model\n",
|
||||
"\n",
|
||||
"#### Load Test Data - you can split the dataset beforehand & pass Train dataset to AutoML and use Test dataset to evaluate the best model."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from sklearn import datasets\n",
|
||||
"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 digits and see how our model works. This is just an example to show you."
|
||||
]
|
||||
},
|
||||
{
|
||||
"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",
|
||||
" display(fig)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"When deploying an automated ML trained model, please specify _pippackages=['azureml-sdk[automl]']_ in your CondaDependencies.\n",
|
||||
"\n",
|
||||
"Please refer to only the **Deploy** section in this notebook - <a href=\"https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/automated-machine-learning/classification-with-deployment\" target=\"_blank\">Deployment of Automated ML trained model</a>"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"authors": [
|
||||
{
|
||||
"name": "savitam"
|
||||
},
|
||||
{
|
||||
"name": "wamartin"
|
||||
}
|
||||
],
|
||||
"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"
|
||||
},
|
||||
"name": "auto-ml-classification-local-adb",
|
||||
"notebookId": 587284549713154
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 1
|
||||
}
|
||||
Some files were not shown because too many files have changed in this diff Show More
Reference in New Issue
Block a user