MachineLearningNotebooks/how-to-use-azureml/explain-model/explain-tabular-data-run-history/explain-run-history-sklearn-regression.ipynb

{
  "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": [
        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/explain-model/explain-tabular-data-run-history/explain-run-history-sklearn-regression.png)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
        "\n",
        "Licensed under the MIT License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "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"
      }
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