removing old directories
This commit is contained in:
Ilya Matiach
@@ -1,606 +0,0 @@
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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",
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"Licensed under the MIT License."
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]
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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": [
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""
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]
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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": [
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"# Train using Azure Machine Learning Compute\n",
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"\n",
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"* Initialize a Workspace\n",
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"* Create an Experiment\n",
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"* Introduction to AmlCompute\n",
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"* Submit an AmlCompute run in a few different ways\n",
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" - Provision as a run based compute target \n",
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" - Provision as a persistent compute target (Basic)\n",
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" - Provision as a persistent compute target (Advanced)\n",
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"* Additional operations to perform on AmlCompute\n",
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"* Download model explanation data from the Run History Portal\n",
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"* Print the explanation data"
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]
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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": [
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"## Prerequisites\n",
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"If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the [configuration notebook](../../../configuration.ipynb) first if you haven't."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Check core SDK version number\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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},
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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 a Workspace\n",
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"\n",
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"Initialize a workspace object from persisted configuration"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"tags": [
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"create workspace"
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]
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},
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"outputs": [],
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"source": [
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"from azureml.core import Workspace\n",
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"\n",
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"ws = Workspace.from_config()\n",
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"print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep='\\n')"
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]
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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": [
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"## Create An Experiment\n",
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"\n",
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"**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."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from azureml.core import Experiment\n",
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"experiment_name = 'explainer-remote-run-on-amlcompute'\n",
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"experiment = Experiment(workspace=ws, name=experiment_name)"
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]
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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": [
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"## Introduction to AmlCompute\n",
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"\n",
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"Azure Machine Learning Compute is managed compute infrastructure that allows the user to easily create single to multi-node compute of the appropriate VM Family. It is created **within your workspace region** and is a resource that can be used by other users in your workspace. It autoscales by default to the max_nodes, when a job is submitted, and executes in a containerized environment packaging the dependencies as specified by the user. \n",
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"\n",
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"Since it is managed compute, job scheduling and cluster management are handled internally by Azure Machine Learning service. \n",
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"\n",
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"For more information on Azure Machine Learning Compute, please read [this article](https://docs.microsoft.com/azure/machine-learning/service/how-to-set-up-training-targets#amlcompute)\n",
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"\n",
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"If you are an existing BatchAI customer who is migrating to Azure Machine Learning, please read [this article](https://aka.ms/batchai-retirement)\n",
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"\n",
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"**Note**: As with other Azure services, there are limits on certain resources (for eg. AmlCompute quota) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota.\n",
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"\n",
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"\n",
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"The training script `run_explainer.py` is already created for you. Let's have a look."
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]
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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": [
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"## Submit an AmlCompute run in a few different ways\n",
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"\n",
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"First lets check which VM families are available in your region. Azure is a regional service and some specialized SKUs (especially GPUs) are only available in certain regions. Since AmlCompute is created in the region of your workspace, we will use the supported_vms () function to see if the VM family we want to use ('STANDARD_D2_V2') is supported.\n",
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"\n",
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"You can also pass a different region to check availability and then re-create your workspace in that region through the [configuration notebook](../../../configuration.ipynb)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from azureml.core.compute import ComputeTarget, AmlCompute\n",
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"\n",
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"AmlCompute.supported_vmsizes(workspace=ws)\n",
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"# AmlCompute.supported_vmsizes(workspace=ws, location='southcentralus')"
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]
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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": [
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"### Create project directory\n",
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"\n",
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"Create a directory that will contain all the necessary code from your local machine that you will need access to on the remote resource. This includes the training script, and any additional files your training script depends on"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"import shutil\n",
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"\n",
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"project_folder = './explainer-remote-run-on-amlcompute'\n",
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"os.makedirs(project_folder, exist_ok=True)\n",
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"shutil.copy('run_explainer.py', project_folder)"
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]
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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": [
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"### Provision as a run based compute target\n",
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"\n",
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"You can provision AmlCompute as a compute target at run-time. In this case, the compute is auto-created for your run, scales up to max_nodes that you specify, and then **deleted automatically** after the run completes."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from azureml.core.runconfig import RunConfiguration\n",
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"from azureml.core.conda_dependencies import CondaDependencies\n",
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"from azureml.core.runconfig import DEFAULT_CPU_IMAGE\n",
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"\n",
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"# create a new runconfig object\n",
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"run_config = RunConfiguration()\n",
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"\n",
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"# signal that you want to use AmlCompute to execute script.\n",
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"run_config.target = \"amlcompute\"\n",
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"\n",
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"# AmlCompute will be created in the same region as workspace\n",
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"# Set vm size for AmlCompute\n",
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"run_config.amlcompute.vm_size = 'STANDARD_D2_V2'\n",
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"\n",
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"# enable Docker \n",
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"run_config.environment.docker.enabled = True\n",
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"\n",
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"# set Docker base image to the default CPU-based image\n",
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"run_config.environment.docker.base_image = DEFAULT_CPU_IMAGE\n",
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"\n",
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"# use conda_dependencies.yml to create a conda environment in the Docker image for execution\n",
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"run_config.environment.python.user_managed_dependencies = False\n",
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"\n",
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"azureml_pip_packages = [\n",
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" 'azureml-defaults', 'azureml-contrib-explain-model', 'azureml-core', 'azureml-telemetry',\n",
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" 'azureml-explain-model'\n",
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"]\n",
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"\n",
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"# specify CondaDependencies obj\n",
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"run_config.environment.python.conda_dependencies = CondaDependencies.create(conda_packages=['scikit-learn'],\n",
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" pip_packages=azureml_pip_packages)\n",
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"\n",
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"# Now submit a run on AmlCompute\n",
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"from azureml.core.script_run_config import ScriptRunConfig\n",
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"\n",
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"script_run_config = ScriptRunConfig(source_directory=project_folder,\n",
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" script='run_explainer.py',\n",
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" run_config=run_config)\n",
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"\n",
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"run = experiment.submit(script_run_config)\n",
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"\n",
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"# Show run details\n",
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"run"
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]
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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": [
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"Note: if you need to cancel a run, you can follow [these instructions](https://aka.ms/aml-docs-cancel-run)."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"%%time\n",
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"# Shows output of the run on stdout.\n",
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"run.wait_for_completion(show_output=True)"
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]
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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": [
|
||||
"### Provision as a persistent compute target (Basic)\n",
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"\n",
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||||
"You can provision a persistent AmlCompute resource by simply defining two parameters thanks to smart defaults. By default it autoscales from 0 nodes and provisions dedicated VMs to run your job in a container. This is useful when you want to continously re-use the same target, debug it between jobs or simply share the resource with other users of your workspace.\n",
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"\n",
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"* `vm_size`: VM family of the nodes provisioned by AmlCompute. Simply choose from the supported_vmsizes() above\n",
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"* `max_nodes`: Maximum nodes to autoscale to while running a job on AmlCompute"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
|
||||
"from azureml.core.compute import ComputeTarget, AmlCompute\n",
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"from azureml.core.compute_target import ComputeTargetException\n",
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"\n",
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"# Choose a name for your CPU cluster\n",
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"cpu_cluster_name = \"cpu-cluster\"\n",
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"\n",
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"# Verify that cluster does not exist already\n",
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"try:\n",
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" cpu_cluster = ComputeTarget(workspace=ws, name=cpu_cluster_name)\n",
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" print('Found existing cluster, use it.')\n",
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"except ComputeTargetException:\n",
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" compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D2_V2',\n",
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" max_nodes=4)\n",
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" cpu_cluster = ComputeTarget.create(ws, cpu_cluster_name, compute_config)\n",
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"\n",
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"cpu_cluster.wait_for_completion(show_output=True)"
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]
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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": [
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"### Configure & Run"
|
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]
|
||||
},
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||||
{
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||||
"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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||||
"source": [
|
||||
"from azureml.core.runconfig import RunConfiguration\n",
|
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"from azureml.core.conda_dependencies import CondaDependencies\n",
|
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"\n",
|
||||
"# create a new RunConfig object\n",
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"run_config = RunConfiguration(framework=\"python\")\n",
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"\n",
|
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"# Set compute target to AmlCompute target created in previous step\n",
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"run_config.target = cpu_cluster.name\n",
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"\n",
|
||||
"# enable Docker \n",
|
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"run_config.environment.docker.enabled = True\n",
|
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"\n",
|
||||
"azureml_pip_packages = [\n",
|
||||
" 'azureml-defaults', 'azureml-contrib-explain-model', 'azureml-core', 'azureml-telemetry',\n",
|
||||
" 'azureml-explain-model'\n",
|
||||
"]\n",
|
||||
"\n",
|
||||
"# specify CondaDependencies obj\n",
|
||||
"run_config.environment.python.conda_dependencies = CondaDependencies.create(conda_packages=['scikit-learn'],\n",
|
||||
" pip_packages=azureml_pip_packages)\n",
|
||||
"\n",
|
||||
"from azureml.core import Run\n",
|
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"from azureml.core import ScriptRunConfig\n",
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"\n",
|
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"src = ScriptRunConfig(source_directory=project_folder, \n",
|
||||
" script='run_explainer.py', \n",
|
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" run_config=run_config) \n",
|
||||
"run = experiment.submit(config=src)\n",
|
||||
"run"
|
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]
|
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},
|
||||
{
|
||||
"cell_type": "code",
|
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"execution_count": null,
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"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%%time\n",
|
||||
"# Shows output of the run on stdout.\n",
|
||||
"run.wait_for_completion(show_output=True)"
|
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]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
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"execution_count": null,
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"metadata": {},
|
||||
"outputs": [],
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||||
"source": [
|
||||
"run.get_metrics()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Provision as a persistent compute target (Advanced)\n",
|
||||
"\n",
|
||||
"You can also specify additional properties or change defaults while provisioning AmlCompute using a more advanced configuration. This is useful when you want a dedicated cluster of 4 nodes (for example you can set the min_nodes and max_nodes to 4), or want the compute to be within an existing VNet in your subscription.\n",
|
||||
"\n",
|
||||
"In addition to `vm_size` and `max_nodes`, you can specify:\n",
|
||||
"* `min_nodes`: Minimum nodes (default 0 nodes) to downscale to while running a job on AmlCompute\n",
|
||||
"* `vm_priority`: Choose between 'dedicated' (default) and 'lowpriority' VMs when provisioning AmlCompute. Low Priority VMs use Azure's excess capacity and are thus cheaper but risk your run being pre-empted\n",
|
||||
"* `idle_seconds_before_scaledown`: Idle time (default 120 seconds) to wait after run completion before auto-scaling to min_nodes\n",
|
||||
"* `vnet_resourcegroup_name`: Resource group of the **existing** VNet within which AmlCompute should be provisioned\n",
|
||||
"* `vnet_name`: Name of VNet\n",
|
||||
"* `subnet_name`: Name of SubNet within the VNet"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core.compute import ComputeTarget, AmlCompute\n",
|
||||
"from azureml.core.compute_target import ComputeTargetException\n",
|
||||
"\n",
|
||||
"# Choose a name for your CPU cluster\n",
|
||||
"cpu_cluster_name = \"cpu-cluster\"\n",
|
||||
"\n",
|
||||
"# Verify that cluster does not exist already\n",
|
||||
"try:\n",
|
||||
" cpu_cluster = ComputeTarget(workspace=ws, name=cpu_cluster_name)\n",
|
||||
" print('Found existing cluster, use it.')\n",
|
||||
"except ComputeTargetException:\n",
|
||||
" compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D2_V2',\n",
|
||||
" vm_priority='lowpriority',\n",
|
||||
" min_nodes=2,\n",
|
||||
" max_nodes=4,\n",
|
||||
" idle_seconds_before_scaledown='300',\n",
|
||||
" vnet_resourcegroup_name='<my-resource-group>',\n",
|
||||
" vnet_name='<my-vnet-name>',\n",
|
||||
" subnet_name='<my-subnet-name>')\n",
|
||||
" cpu_cluster = ComputeTarget.create(ws, cpu_cluster_name, compute_config)\n",
|
||||
"\n",
|
||||
"cpu_cluster.wait_for_completion(show_output=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Configure & 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",
|
||||
"# create a new RunConfig object\n",
|
||||
"run_config = RunConfiguration(framework=\"python\")\n",
|
||||
"\n",
|
||||
"# Set compute target to AmlCompute target created in previous step\n",
|
||||
"run_config.target = cpu_cluster.name\n",
|
||||
"\n",
|
||||
"# enable Docker \n",
|
||||
"run_config.environment.docker.enabled = True\n",
|
||||
"\n",
|
||||
"azureml_pip_packages = [\n",
|
||||
" 'azureml-defaults', 'azureml-contrib-explain-model', 'azureml-core', 'azureml-telemetry',\n",
|
||||
" 'azureml-explain-model'\n",
|
||||
"]\n",
|
||||
"\n",
|
||||
"# specify CondaDependencies obj\n",
|
||||
"run_config.environment.python.conda_dependencies = CondaDependencies.create(conda_packages=['scikit-learn'],\n",
|
||||
" pip_packages=azureml_pip_packages)\n",
|
||||
"\n",
|
||||
"from azureml.core import Run\n",
|
||||
"from azureml.core import ScriptRunConfig\n",
|
||||
"\n",
|
||||
"src = ScriptRunConfig(source_directory=project_folder, \n",
|
||||
" script='run_explainer.py', \n",
|
||||
" run_config=run_config) \n",
|
||||
"run = experiment.submit(config=src)\n",
|
||||
"run"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%%time\n",
|
||||
"# Shows output of the run on stdout.\n",
|
||||
"run.wait_for_completion(show_output=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"run.get_metrics()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.contrib.explain.model.explanation.explanation_client import ExplanationClient\n",
|
||||
"\n",
|
||||
"client = ExplanationClient.from_run(run)\n",
|
||||
"# Get the top k (e.g., 4) most important features with their importance values\n",
|
||||
"explanation = client.download_model_explanation(top_k=4)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Additional operations to perform on AmlCompute\n",
|
||||
"\n",
|
||||
"You can perform more operations on AmlCompute such as updating the node counts or deleting the compute. "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Get_status () gets the latest status of the AmlCompute target\n",
|
||||
"cpu_cluster.get_status().serialize()\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Update () takes in the min_nodes, max_nodes and idle_seconds_before_scaledown and updates the AmlCompute target\n",
|
||||
"# cpu_cluster.update(min_nodes=1)\n",
|
||||
"# cpu_cluster.update(max_nodes=10)\n",
|
||||
"cpu_cluster.update(idle_seconds_before_scaledown=300)\n",
|
||||
"# cpu_cluster.update(min_nodes=2, max_nodes=4, idle_seconds_before_scaledown=600)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Delete () is used to deprovision and delete the AmlCompute target. Useful if you want to re-use the compute name \n",
|
||||
"# 'cpu-cluster' in this case but use a different VM family for instance.\n",
|
||||
"\n",
|
||||
"# cpu_cluster.delete()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Download Model Explanation Data"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.contrib.explain.model.explanation.explanation_client import ExplanationClient\n",
|
||||
"\n",
|
||||
"# Get model explanation data\n",
|
||||
"client = ExplanationClient.from_run(run)\n",
|
||||
"explanation = client.download_model_explanation()\n",
|
||||
"local_importance_values = explanation.local_importance_values\n",
|
||||
"expected_values = explanation.expected_values\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Or you can use the saved run.id to retrive the feature importance values\n",
|
||||
"client = ExplanationClient.from_run_id(ws, experiment_name, run.id)\n",
|
||||
"explanation = client.download_model_explanation()\n",
|
||||
"local_importance_values = explanation.local_importance_values\n",
|
||||
"expected_values = explanation.expected_values"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Get the top k (e.g., 4) most important features with their importance values\n",
|
||||
"explanation = client.download_model_explanation(top_k=4)\n",
|
||||
"global_importance_values = explanation.get_ranked_global_values()\n",
|
||||
"global_importance_names = explanation.get_ranked_global_names()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print('global importance values: {}'.format(global_importance_values))\n",
|
||||
"print('global importance names: {}'.format(global_importance_names))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Success!\n",
|
||||
"Great, you are ready to move on to the remaining notebooks."
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"authors": [
|
||||
{
|
||||
"name": "mesameki"
|
||||
}
|
||||
],
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.8"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
@@ -1,6 +0,0 @@
|
||||
name: regression-sklearn-on-amlcompute
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- azureml-explain-model
|
||||
- azureml-contrib-explain-model
|
||||
@@ -1,52 +0,0 @@
|
||||
# Copyright (c) Microsoft. All rights reserved.
|
||||
# Licensed under the MIT license.
|
||||
|
||||
from sklearn import datasets
|
||||
from sklearn.linear_model import Ridge
|
||||
from azureml.explain.model.tabular_explainer import TabularExplainer
|
||||
from azureml.contrib.explain.model.explanation.explanation_client import ExplanationClient
|
||||
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)
|
||||
|
||||
boston_data = datasets.load_boston()
|
||||
|
||||
run = Run.get_context()
|
||||
client = ExplanationClient.from_run(run)
|
||||
|
||||
X_train, X_test, y_train, y_test = train_test_split(boston_data.data,
|
||||
boston_data.target,
|
||||
test_size=0.2,
|
||||
random_state=0)
|
||||
|
||||
alpha = 0.5
|
||||
# Use Ridge algorithm to create a regression model
|
||||
reg = Ridge(alpha)
|
||||
model = reg.fit(X_train, y_train)
|
||||
|
||||
preds = reg.predict(X_test)
|
||||
run.log('alpha', alpha)
|
||||
|
||||
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))
|
||||
|
||||
# Explain predictions on your local machine
|
||||
tabular_explainer = TabularExplainer(model, X_train, features=boston_data.feature_names)
|
||||
|
||||
# Explain overall model predictions (global explanation)
|
||||
# Passing in test dataset for evaluation examples - note it must be a representative sample of the original data
|
||||
# x_train can be passed as well, but with more examples explanations it will
|
||||
# take longer although they may be more accurate
|
||||
global_explanation = tabular_explainer.explain_global(X_test)
|
||||
|
||||
# Uploading model explanation data for storage or visualization in webUX
|
||||
# The explanation can then be downloaded on any compute
|
||||
comment = 'Global explanation on regression model trained on boston dataset'
|
||||
client.upload_model_explanation(global_explanation, comment=comment)
|
||||
@@ -1,279 +0,0 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Breast cancer diagnosis classification with scikit-learn (run model explainer locally)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Copyright (c) Microsoft Corporation. All rights reserved.\n",
|
||||
"\n",
|
||||
"Licensed under the MIT License."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Explain a model with the AML explain-model package\n",
|
||||
"\n",
|
||||
"1. Train a SVM classification model using Scikit-learn\n",
|
||||
"2. Run 'explain_model' with full data in local mode, which doesn't contact any Azure services\n",
|
||||
"3. Run 'explain_model' with summarized data in local mode, which doesn't contact any Azure services\n",
|
||||
"4. Visualize the global and local explanations with the visualization dashboard."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from sklearn.datasets import load_breast_cancer\n",
|
||||
"from sklearn import svm\n",
|
||||
"from azureml.explain.model.tabular_explainer import TabularExplainer"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# 1. Run model explainer locally with full data"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Load the breast cancer diagnosis data"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"breast_cancer_data = load_breast_cancer()\n",
|
||||
"classes = breast_cancer_data.target_names.tolist()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Split data into train and test\n",
|
||||
"from sklearn.model_selection import train_test_split\n",
|
||||
"x_train, x_test, y_train, y_test = train_test_split(breast_cancer_data.data, breast_cancer_data.target, test_size=0.2, random_state=0)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Train a SVM classification model, which you want to explain"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"clf = svm.SVC(gamma=0.001, C=100., probability=True)\n",
|
||||
"model = clf.fit(x_train, y_train)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Explain predictions on your local machine"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"tabular_explainer = TabularExplainer(model, x_train, features=breast_cancer_data.feature_names, classes=classes)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Explain overall model predictions (global explanation)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Passing in test dataset for evaluation examples - note it must be a representative sample of the original data\n",
|
||||
"# x_train can be passed as well, but with more examples explanations will take longer although they may be more accurate\n",
|
||||
"global_explanation = tabular_explainer.explain_global(x_test)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Sorted SHAP values\n",
|
||||
"print('ranked global importance values: {}'.format(global_explanation.get_ranked_global_values()))\n",
|
||||
"# Corresponding feature names\n",
|
||||
"print('ranked global importance names: {}'.format(global_explanation.get_ranked_global_names()))\n",
|
||||
"# feature ranks (based on original order of features)\n",
|
||||
"print('global importance rank: {}'.format(global_explanation.global_importance_rank))\n",
|
||||
"# per class feature names\n",
|
||||
"print('ranked per class feature names: {}'.format(global_explanation.get_ranked_per_class_names()))\n",
|
||||
"# per class feature importance values\n",
|
||||
"print('ranked per class feature values: {}'.format(global_explanation.get_ranked_per_class_values()))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"dict(zip(global_explanation.get_ranked_global_names(), global_explanation.get_ranked_global_values()))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Explain overall model predictions as a collection of local (instance-level) explanations"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# feature shap values for all features and all data points in the training data\n",
|
||||
"print('local importance values: {}'.format(global_explanation.local_importance_values))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Explain local data points (individual instances)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# explain the first member of the test set\n",
|
||||
"instance_num = 0\n",
|
||||
"local_explanation = tabular_explainer.explain_local(x_test[instance_num,:])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# get the prediction for the first member of the test set and explain why model made that prediction\n",
|
||||
"prediction_value = clf.predict(x_test)[instance_num]\n",
|
||||
"\n",
|
||||
"sorted_local_importance_values = local_explanation.get_ranked_local_values()[prediction_value]\n",
|
||||
"sorted_local_importance_names = local_explanation.get_ranked_local_names()[prediction_value]\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"dict(zip(sorted_local_importance_names, sorted_local_importance_values))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# 2. Load visualization dashboard"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Note you will need to have extensions enabled prior to jupyter kernel starting\n",
|
||||
"!jupyter nbextension install --py --sys-prefix azureml.contrib.explain.model.visualize\n",
|
||||
"!jupyter nbextension enable --py --sys-prefix azureml.contrib.explain.model.visualize\n",
|
||||
"# Or, in Jupyter Labs, uncomment below\n",
|
||||
"# jupyter labextension install @jupyter-widgets/jupyterlab-manager\n",
|
||||
"# jupyter labextension install microsoft-mli-widget"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.contrib.explain.model.visualize import ExplanationDashboard"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"ExplanationDashboard(global_explanation, model, x_test)"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"authors": [
|
||||
{
|
||||
"name": "mesameki"
|
||||
}
|
||||
],
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.8"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
@@ -1,6 +0,0 @@
|
||||
name: explain-local-sklearn-binary-classification
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- azureml-explain-model
|
||||
- azureml-contrib-explain-model
|
||||
@@ -1,280 +0,0 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Iris flower classification with scikit-learn (run model explainer locally)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Copyright (c) Microsoft Corporation. All rights reserved.\n",
|
||||
"\n",
|
||||
"Licensed under the MIT License."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Explain a model with the AML explain-model package\n",
|
||||
"\n",
|
||||
"1. Train a SVM classification model using Scikit-learn\n",
|
||||
"2. Run 'explain_model' with full data in local mode, which doesn't contact any Azure services\n",
|
||||
"3. Run 'explain_model' with summarized data in local mode, which doesn't contact any Azure services\n",
|
||||
"4. Visualize the global and local explanations with the visualization dashboard."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from sklearn.datasets import load_iris\n",
|
||||
"from sklearn import svm\n",
|
||||
"from azureml.explain.model.tabular_explainer import TabularExplainer"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# 1. Run model explainer locally with full data"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Load the breast cancer diagnosis data"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"iris = load_iris()\n",
|
||||
"X = iris['data']\n",
|
||||
"y = iris['target']\n",
|
||||
"classes = iris['target_names']\n",
|
||||
"feature_names = iris['feature_names']"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Split data into train and test\n",
|
||||
"from sklearn.model_selection import train_test_split\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 a SVM classification model, which you want to explain"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"clf = svm.SVC(gamma=0.001, C=100., probability=True)\n",
|
||||
"model = clf.fit(x_train, y_train)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Explain predictions on your local machine"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"tabular_explainer = TabularExplainer(model, x_train, features = feature_names, classes=classes)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Explain overall model predictions (global explanation)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"global_explanation = tabular_explainer.explain_global(x_test)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Sorted SHAP values\n",
|
||||
"print('ranked global importance values: {}'.format(global_explanation.get_ranked_global_values()))\n",
|
||||
"# Corresponding feature names\n",
|
||||
"print('ranked global importance names: {}'.format(global_explanation.get_ranked_global_names()))\n",
|
||||
"# feature ranks (based on original order of features)\n",
|
||||
"print('global importance rank: {}'.format(global_explanation.global_importance_rank))\n",
|
||||
"# per class feature names\n",
|
||||
"print('ranked per class feature names: {}'.format(global_explanation.get_ranked_per_class_names()))\n",
|
||||
"# per class feature importance values\n",
|
||||
"print('ranked per class feature values: {}'.format(global_explanation.get_ranked_per_class_values()))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"dict(zip(global_explanation.get_ranked_global_names(), global_explanation.get_ranked_global_values()))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Explain overall model predictions as a collection of local (instance-level) explanations"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# feature shap values for all features and all data points in the training data\n",
|
||||
"print('local importance values: {}'.format(global_explanation.local_importance_values))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Explain local data points (individual instances)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# explain the first member of the test set\n",
|
||||
"instance_num = 0\n",
|
||||
"local_explanation = tabular_explainer.explain_local(x_test[instance_num,:])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# get the prediction for the first member of the test set and explain why model made that prediction\n",
|
||||
"prediction_value = clf.predict(x_test)[instance_num]\n",
|
||||
"\n",
|
||||
"sorted_local_importance_values = local_explanation.get_ranked_local_values()[prediction_value]\n",
|
||||
"sorted_local_importance_names = local_explanation.get_ranked_local_names()[prediction_value]\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"dict(zip(sorted_local_importance_names, sorted_local_importance_values))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Load visualization dashboard"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Note you will need to have extensions enabled prior to jupyter kernel starting\n",
|
||||
"!jupyter nbextension install --py --sys-prefix azureml.contrib.explain.model.visualize\n",
|
||||
"!jupyter nbextension enable --py --sys-prefix azureml.contrib.explain.model.visualize\n",
|
||||
"# Or, in Jupyter Labs, uncomment below\n",
|
||||
"# jupyter labextension install @jupyter-widgets/jupyterlab-manager\n",
|
||||
"# jupyter labextension install microsoft-mli-widget"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.contrib.explain.model.visualize import ExplanationDashboard"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"ExplanationDashboard(global_explanation, model, x_test)"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"authors": [
|
||||
{
|
||||
"name": "mesameki"
|
||||
}
|
||||
],
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.8"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
@@ -1,6 +0,0 @@
|
||||
name: explain-local-sklearn-multiclass-classification
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- azureml-explain-model
|
||||
- azureml-contrib-explain-model
|
||||
@@ -1,272 +0,0 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Boston Housing Price Prediction with scikit-learn (run model explainer locally)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Copyright (c) Microsoft Corporation. All rights reserved.\n",
|
||||
"\n",
|
||||
"Licensed under the MIT License."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Explain a model with the AML explain-model package\n",
|
||||
"\n",
|
||||
"1. Train a GradientBoosting regression model using Scikit-learn\n",
|
||||
"2. Run 'explain_model' with full dataset in local mode, which doesn't contact any Azure services.\n",
|
||||
"3. Run 'explain_model' with summarized dataset in local mode, which doesn't contact any Azure services.\n",
|
||||
"4. Visualize the global and local explanations with the visualization dashboard."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from sklearn import datasets\n",
|
||||
"from sklearn.ensemble import GradientBoostingRegressor\n",
|
||||
"from azureml.explain.model.tabular_explainer import TabularExplainer"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# 1. Run model explainer locally with full data"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Load the Boston house price data"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"boston_data = datasets.load_boston()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Split data into train and test\n",
|
||||
"from sklearn.model_selection import train_test_split\n",
|
||||
"x_train, x_test, y_train, y_test = train_test_split(boston_data.data, boston_data.target, test_size=0.2, random_state=0)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Train a GradientBoosting Regression model, which you want to explain"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"reg = GradientBoostingRegressor(n_estimators=100, max_depth=4,\n",
|
||||
" learning_rate=0.1, loss='huber',\n",
|
||||
" random_state=1)\n",
|
||||
"model = reg.fit(x_train, y_train)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Explain predictions on your local machine"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"tabular_explainer = TabularExplainer(model, x_train, features = boston_data.feature_names)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Explain overall model predictions (global explanation)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Passing in test dataset for evaluation examples - note it must be a representative sample of the original data\n",
|
||||
"# x_train can be passed as well, but with more examples explanations will take longer although they may be more accurate\n",
|
||||
"global_explanation = tabular_explainer.explain_global(x_test)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Sorted SHAP values \n",
|
||||
"print('ranked global importance values: {}'.format(global_explanation.get_ranked_global_values()))\n",
|
||||
"# Corresponding feature names\n",
|
||||
"print('ranked global importance names: {}'.format(global_explanation.get_ranked_global_names()))\n",
|
||||
"# feature ranks (based on original order of features)\n",
|
||||
"print('global importance rank: {}'.format(global_explanation.global_importance_rank))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"dict(zip(global_explanation.get_ranked_global_names(), global_explanation.get_ranked_global_values()))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Explain overall model predictions as a collection of local (instance-level) explanations"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# feature shap values for all features and all data points in the training data\n",
|
||||
"print('local importance values: {}'.format(global_explanation.local_importance_values))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Explain local data points (individual instances)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"local_explanation = tabular_explainer.explain_local(x_test[0,:])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# sorted local feature importance information; reflects the original feature order\n",
|
||||
"sorted_local_importance_names = local_explanation.get_ranked_local_names()\n",
|
||||
"sorted_local_importance_values = local_explanation.get_ranked_local_values()\n",
|
||||
"\n",
|
||||
"print('sorted local importance names: {}'.format(sorted_local_importance_names))\n",
|
||||
"print('sorted local importance values: {}'.format(sorted_local_importance_values))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Load visualization dashboard"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Note you will need to have extensions enabled prior to jupyter kernel starting\n",
|
||||
"!jupyter nbextension install --py --sys-prefix azureml.contrib.explain.model.visualize\n",
|
||||
"!jupyter nbextension enable --py --sys-prefix azureml.contrib.explain.model.visualize\n",
|
||||
"# Or, in Jupyter Labs, uncomment below\n",
|
||||
"# jupyter labextension install @jupyter-widgets/jupyterlab-manager\n",
|
||||
"# jupyter labextension install microsoft-mli-widget"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.contrib.explain.model.visualize import ExplanationDashboard"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"ExplanationDashboard(global_explanation, model, x_test)"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"authors": [
|
||||
{
|
||||
"name": "mesameki"
|
||||
}
|
||||
],
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.8"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
@@ -1,6 +0,0 @@
|
||||
name: explain-local-sklearn-regression
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- azureml-explain-model
|
||||
- azureml-contrib-explain-model
|
||||
@@ -1,337 +0,0 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Summary\n",
|
||||
"From raw data that is a mixture of categoricals and numeric, featurize the categoricals using one hot encoding. Use tabular explainer to get explain object and then get raw feature importances"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Copyright (c) Microsoft Corporation. All rights reserved.\n",
|
||||
"\n",
|
||||
"Licensed under the MIT License."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Explain a model with the AML explain-model package on raw features\n",
|
||||
"\n",
|
||||
"1. Train a Logistic Regression model using Scikit-learn\n",
|
||||
"2. Run 'explain_model' with full dataset in local mode, which doesn't contact any Azure services.\n",
|
||||
"3. Run 'explain_model' with summarized dataset in local mode, which doesn't contact any Azure services.\n",
|
||||
"4. Visualize the global and local explanations with the visualization dashboard."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from sklearn.pipeline import Pipeline\n",
|
||||
"from sklearn.impute import SimpleImputer\n",
|
||||
"from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
|
||||
"from sklearn.linear_model import LogisticRegression\n",
|
||||
"from azureml.explain.model.tabular_explainer import TabularExplainer\n",
|
||||
"import pandas as pd\n",
|
||||
"import numpy as np"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"titanic_url = ('https://raw.githubusercontent.com/amueller/'\n",
|
||||
" 'scipy-2017-sklearn/091d371/notebooks/datasets/titanic3.csv')\n",
|
||||
"data = pd.read_csv(titanic_url)\n",
|
||||
"# fill missing values\n",
|
||||
"data = data.fillna(method=\"ffill\")\n",
|
||||
"data = data.fillna(method=\"bfill\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# 1. Run model explainer locally with full data"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Similar to example [here](https://scikit-learn.org/stable/auto_examples/compose/plot_column_transformer_mixed_types.html#sphx-glr-auto-examples-compose-plot-column-transformer-mixed-types-py), use a subset of columns"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from sklearn.model_selection import train_test_split\n",
|
||||
"\n",
|
||||
"numeric_features = ['age', 'fare']\n",
|
||||
"categorical_features = ['embarked', 'sex', 'pclass']\n",
|
||||
"\n",
|
||||
"y = data['survived'].values\n",
|
||||
"X = data[categorical_features + numeric_features]\n",
|
||||
"\n",
|
||||
"x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"sklearn imports"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from sklearn.pipeline import Pipeline\n",
|
||||
"from sklearn.impute import SimpleImputer\n",
|
||||
"from sklearn.preprocessing import StandardScaler, OneHotEncoder"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"We can explain raw features by either using a `sklearn.compose.ColumnTransformer` or a list of fitted transformer tuples. The cell below uses `sklearn.compose.ColumnTransformer`. In case you want to run the example with the list of fitted transformer tuples, comment the cell below and uncomment the cell that follows after. "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from sklearn.compose import ColumnTransformer\n",
|
||||
"\n",
|
||||
"transformations = ColumnTransformer([\n",
|
||||
" (\"age_fare\", Pipeline(steps=[\n",
|
||||
" ('imputer', SimpleImputer(strategy='median')),\n",
|
||||
" ('scaler', StandardScaler())\n",
|
||||
" ]), [\"age\", \"fare\"]),\n",
|
||||
" (\"embarked\", Pipeline(steps=[\n",
|
||||
" (\"imputer\", SimpleImputer(strategy='constant', fill_value='missing')), \n",
|
||||
" (\"encoder\", OneHotEncoder(sparse=False))]), [\"embarked\"]),\n",
|
||||
" (\"sex_pclass\", OneHotEncoder(sparse=False), [\"sex\", \"pclass\"]) \n",
|
||||
"])\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# Append classifier to preprocessing pipeline.\n",
|
||||
"# Now we have a full prediction pipeline.\n",
|
||||
"clf = Pipeline(steps=[('preprocessor', transformations),\n",
|
||||
" ('classifier', LogisticRegression(solver='lbfgs'))])\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"'''\n",
|
||||
"# Uncomment below if sklearn-pandas is not installed\n",
|
||||
"#!pip install sklearn-pandas\n",
|
||||
"from sklearn_pandas import DataFrameMapper\n",
|
||||
"\n",
|
||||
"# Impute, standardize the numeric features and one-hot encode the categorical features. \n",
|
||||
"\n",
|
||||
"transformations = [\n",
|
||||
" ([\"age\", \"fare\"], Pipeline(steps=[\n",
|
||||
" ('imputer', SimpleImputer(strategy='median')),\n",
|
||||
" ('scaler', StandardScaler())\n",
|
||||
" ])),\n",
|
||||
" ([\"embarked\"], Pipeline(steps=[\n",
|
||||
" (\"imputer\", SimpleImputer(strategy='constant', fill_value='missing')), \n",
|
||||
" (\"encoder\", OneHotEncoder(sparse=False))])),\n",
|
||||
" ([\"sex\", \"pclass\"], OneHotEncoder(sparse=False)) \n",
|
||||
"]\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# Append classifier to preprocessing pipeline.\n",
|
||||
"# Now we have a full prediction pipeline.\n",
|
||||
"clf = Pipeline(steps=[('preprocessor', DataFrameMapper(transformations)),\n",
|
||||
" ('classifier', LogisticRegression(solver='lbfgs'))])\n",
|
||||
"'''"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Train a Logistic Regression model, which you want to explain"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"model = clf.fit(x_train, y_train)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Explain predictions on your local machine"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"tabular_explainer = TabularExplainer(clf.steps[-1][1], initialization_examples=x_train, features=x_train.columns, transformations=transformations)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Passing in test dataset for evaluation examples - note it must be a representative sample of the original data\n",
|
||||
"# x_train can be passed as well, but with more examples explanations will take longer although they may be more accurate\n",
|
||||
"global_explanation = tabular_explainer.explain_global(x_test)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"sorted_global_importance_values = global_explanation.get_ranked_global_values()\n",
|
||||
"sorted_global_importance_names = global_explanation.get_ranked_global_names()\n",
|
||||
"dict(zip(sorted_global_importance_names, sorted_global_importance_values))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Explain overall model predictions as a collection of local (instance-level) explanations"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# explain the first member of the test set\n",
|
||||
"local_explanation = tabular_explainer.explain_local(x_test[:1])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# get the prediction for the first member of the test set and explain why model made that prediction\n",
|
||||
"prediction_value = clf.predict(x_test)[0]\n",
|
||||
"\n",
|
||||
"sorted_local_importance_values = local_explanation.get_ranked_local_values()[prediction_value]\n",
|
||||
"sorted_local_importance_names = local_explanation.get_ranked_local_names()[prediction_value]\n",
|
||||
"\n",
|
||||
"# Sorted local SHAP values\n",
|
||||
"print('ranked local importance values: {}'.format(sorted_local_importance_values))\n",
|
||||
"# Corresponding feature names\n",
|
||||
"print('ranked local importance names: {}'.format(sorted_local_importance_names))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# 2. Load visualization dashboard"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Note you will need to have extensions enabled prior to jupyter kernel starting\n",
|
||||
"!jupyter nbextension install --py --sys-prefix azureml.contrib.explain.model.visualize\n",
|
||||
"!jupyter nbextension enable --py --sys-prefix azureml.contrib.explain.model.visualize\n",
|
||||
"# Or, in Jupyter Labs, uncomment below\n",
|
||||
"# jupyter labextension install @jupyter-widgets/jupyterlab-manager\n",
|
||||
"# jupyter labextension install microsoft-mli-widget"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.contrib.explain.model.visualize import ExplanationDashboard"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"ExplanationDashboard(global_explanation, model, x_test)"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"authors": [
|
||||
{
|
||||
"name": "mesameki"
|
||||
}
|
||||
],
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.8"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
@@ -1,7 +0,0 @@
|
||||
name: explain-sklearn-raw-features
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- azureml-explain-model
|
||||
- azureml-contrib-explain-model
|
||||
- sklearn-pandas
|
||||
@@ -1,262 +0,0 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Breast cancer diagnosis classification with scikit-learn (save model explanations via AML Run History)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Copyright (c) Microsoft Corporation. All rights reserved.\n",
|
||||
"\n",
|
||||
"Licensed under the MIT License."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Explain a model with the AML explain-model package\n",
|
||||
"\n",
|
||||
"1. Train a SVM classification model using Scikit-learn\n",
|
||||
"2. Run 'explain_model' with AML Run History, which leverages run history service to store and manage the explanation data"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from sklearn.datasets import load_breast_cancer\n",
|
||||
"from sklearn import svm\n",
|
||||
"from azureml.explain.model.tabular_explainer import TabularExplainer"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# 1. Run model explainer locally with full data"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Load the breast cancer diagnosis data"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"breast_cancer_data = load_breast_cancer()\n",
|
||||
"classes = breast_cancer_data.target_names.tolist()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Split data into train and test\n",
|
||||
"from sklearn.model_selection import train_test_split\n",
|
||||
"x_train, x_test, y_train, y_test = train_test_split(breast_cancer_data.data, breast_cancer_data.target, test_size=0.2, random_state=0)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Train a SVM classification model, which you want to explain"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"clf = svm.SVC(gamma=0.001, C=100., probability=True)\n",
|
||||
"model = clf.fit(x_train, y_train)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Explain predictions on your local machine"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"tabular_explainer = TabularExplainer(model, x_train, features=breast_cancer_data.feature_names, classes=classes)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Explain overall model predictions (global explanation)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Passing in test dataset for evaluation examples - note it must be a representative sample of the original data\n",
|
||||
"# x_train can be passed as well, but with more examples explanations will take longer although they may be more accurate\n",
|
||||
"global_explanation = tabular_explainer.explain_global(x_test)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# 2. Save Model Explanation With AML Run History"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import azureml.core\n",
|
||||
"from azureml.core import Workspace, Experiment, Run\n",
|
||||
"from azureml.explain.model.tabular_explainer import TabularExplainer\n",
|
||||
"from azureml.contrib.explain.model.explanation.explanation_client import ExplanationClient\n",
|
||||
"# Check core SDK version number\n",
|
||||
"print(\"SDK version:\", azureml.core.VERSION)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"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": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"experiment_name = 'explain_model'\n",
|
||||
"experiment = Experiment(ws, experiment_name)\n",
|
||||
"run = experiment.start_logging()\n",
|
||||
"client = ExplanationClient.from_run(run)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Uploading model explanation data for storage or visualization in webUX\n",
|
||||
"# The explanation can then be downloaded on any compute\n",
|
||||
"client.upload_model_explanation(global_explanation)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Get model explanation data\n",
|
||||
"explanation = client.download_model_explanation()\n",
|
||||
"local_importance_values = explanation.local_importance_values\n",
|
||||
"expected_values = explanation.expected_values"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Get the top k (e.g., 4) most important features with their importance values\n",
|
||||
"explanation = client.download_model_explanation(top_k=4)\n",
|
||||
"global_importance_values = explanation.get_ranked_global_values()\n",
|
||||
"global_importance_names = explanation.get_ranked_global_names()\n",
|
||||
"per_class_names = explanation.get_ranked_per_class_names()[0]\n",
|
||||
"per_class_values = explanation.get_ranked_per_class_values()[0]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print('per class feature importance values: {}'.format(per_class_values))\n",
|
||||
"print('per class feature importance names: {}'.format(per_class_names))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"dict(zip(per_class_names, per_class_values))"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"authors": [
|
||||
{
|
||||
"name": "mesameki"
|
||||
}
|
||||
],
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.8"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
@@ -1,6 +0,0 @@
|
||||
name: explain-run-history-sklearn-classification
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- azureml-explain-model
|
||||
- azureml-contrib-explain-model
|
||||
@@ -1,276 +0,0 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Boston Housing Price Prediction with scikit-learn (save model explanations via AML Run History)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Copyright (c) Microsoft Corporation. All rights reserved.\n",
|
||||
"\n",
|
||||
"Licensed under the MIT License."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Explain a model with the AML explain-model package\n",
|
||||
"\n",
|
||||
"1. Train a GradientBoosting regression model using Scikit-learn\n",
|
||||
"2. Run 'explain_model' with AML Run History, which leverages run history service to store and manage the explanation data"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Save Model Explanation With AML Run History"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#Import Iris dataset\n",
|
||||
"from sklearn import datasets\n",
|
||||
"from sklearn.ensemble import GradientBoostingRegressor\n",
|
||||
"\n",
|
||||
"import azureml.core\n",
|
||||
"from azureml.core import Workspace, Experiment, Run\n",
|
||||
"from azureml.explain.model.tabular_explainer import TabularExplainer\n",
|
||||
"from azureml.contrib.explain.model.explanation.explanation_client import ExplanationClient\n",
|
||||
"# Check core SDK version number\n",
|
||||
"print(\"SDK version:\", azureml.core.VERSION)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"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": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"experiment_name = 'explain_model'\n",
|
||||
"experiment = Experiment(ws, experiment_name)\n",
|
||||
"run = experiment.start_logging()\n",
|
||||
"client = ExplanationClient.from_run(run)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Load the Boston house price data"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"boston_data = datasets.load_boston()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Split data into train and test\n",
|
||||
"from sklearn.model_selection import train_test_split\n",
|
||||
"x_train, x_test, y_train, y_test = train_test_split(boston_data.data, boston_data.target, test_size=0.2, random_state=0)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Train a GradientBoosting Regression model, which you want to explain"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"clf = GradientBoostingRegressor(n_estimators=100, max_depth=4,\n",
|
||||
" learning_rate=0.1, loss='huber',\n",
|
||||
" random_state=1)\n",
|
||||
"model = clf.fit(x_train, y_train)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Explain predictions on your local machine"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"tabular_explainer = TabularExplainer(model, x_train, features=boston_data.feature_names)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Explain overall model predictions (global explanation)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Passing in test dataset for evaluation examples - note it must be a representative sample of the original data\n",
|
||||
"# x_train can be passed as well, but with more examples explanations will take longer although they may be more accurate\n",
|
||||
"global_explanation = tabular_explainer.explain_global(x_test)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Uploading model explanation data for storage or visualization in webUX\n",
|
||||
"# The explanation can then be downloaded on any compute\n",
|
||||
"client.upload_model_explanation(global_explanation)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Get model explanation data\n",
|
||||
"explanation = client.download_model_explanation()\n",
|
||||
"local_importance_values = explanation.local_importance_values\n",
|
||||
"expected_values = explanation.expected_values"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Print the values\n",
|
||||
"print('expected values: {}'.format(expected_values))\n",
|
||||
"print('local importance values: {}'.format(local_importance_values))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Get the top k (e.g., 4) most important features with their importance values\n",
|
||||
"explanation = client.download_model_explanation(top_k=4)\n",
|
||||
"global_importance_values = explanation.get_ranked_global_values()\n",
|
||||
"global_importance_names = explanation.get_ranked_global_names()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print('global importance values: {}'.format(global_importance_values))\n",
|
||||
"print('global importance names: {}'.format(global_importance_names))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Explain individual instance predictions (local explanation) ##### needs to get updated with the new build"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"local_explanation = tabular_explainer.explain_local(x_test[0,:])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# local feature importance information\n",
|
||||
"local_importance_values = local_explanation.local_importance_values\n",
|
||||
"print('local importance values: {}'.format(local_importance_values))"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"authors": [
|
||||
{
|
||||
"name": "mesameki"
|
||||
}
|
||||
],
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.8"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
@@ -1,6 +0,0 @@
|
||||
name: explain-run-history-sklearn-regression
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- azureml-explain-model
|
||||
- azureml-contrib-explain-model
|
||||
Reference in New Issue
Block a user