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

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Boston Housing Price Prediction with scikit-learn (run model explainer locally)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/explain-model/explain-tabular-data-local/explain-local-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 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"
      }
    ],
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