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

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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
        "\n",
        "Licensed under the MIT License."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Uncomment these if explanation packages are not already installed in your environment\n",
        "#!pip install --upgrade azureml-sdk[explain]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Explain a model with the AML explain-model package\n",
        "\n",
        "1. Train a SVM model using Scikit-learn\n",
        "2. Run 'explain_model' in local mode, which doesn't contact any Azure services\n",
        "3. Run 'explain_model' with AML Run History, which leverages Run History Service to store and manage the explanation data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Disclaimer: this notebook is a preview of model explainability, and the APIs shown below are subject to breaking changes"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Train a SVM model, which we will try to explain"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Import Iris dataset\n",
        "from sklearn import datasets\n",
        "iris = datasets.load_iris()"
      ]
    },
    {
      "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(iris.data, iris.target, test_size=0.2, random_state=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Import scikit learn, fit a SVM model\n",
        "def create_scikit_learn_model(X, y):\n",
        "    from sklearn import svm\n",
        "    clf = svm.SVC(gamma=0.001, C=100., probability=True)\n",
        "    model = clf.fit(X, y)\n",
        "    return model\n",
        "model = create_scikit_learn_model(x_train, y_train)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Run model explainer locally"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from azureml.explain.model.tabular_explainer import TabularExplainer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import time\n",
        "start = time.time()\n",
        "\n",
        "explainer = TabularExplainer(model, x_train, features=iris.feature_names)\n",
        "global_explanation = explainer.explain_global(x_test)\n",
        "\n",
        "# importance values for each class, test example, and feature (local importance)\n",
        "local_imp_values = global_explanation.local_importance_values\n",
        "# base prediction with feature importances ignored\n",
        "expected_values = global_explanation.expected_values\n",
        "# global feature importance information\n",
        "global_imp_values = global_explanation.global_importance_values\n",
        "ranked_global_imp_names = global_explanation.get_ranked_global_names()\n",
        "# global per-class feature importance information\n",
        "per_class_imp_values = global_explanation.per_class_values\n",
        "ranked_per_class_imp_names = global_explanation.get_ranked_per_class_names()\n",
        "\n",
        "end = time.time()\n",
        "print(end - start)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Run model explainer 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": [
        "import time\n",
        "start = time.time()\n",
        "explainer = TabularExplainer(model, x_train, features=iris.feature_names, classes=iris.target_names)\n",
        "explanation = explainer.explain_global(x_test)\n",
        "client.upload_model_explanation(explanation)\n",
        "end = time.time()\n",
        "print(end - start)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "explanation_from_run = client.download_model_explanation()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# global feature importance information\n",
        "global_imp_values = explanation_from_run.global_importance_values\n",
        "global_imp_names = explanation_from_run.get_ranked_global_names()\n",
        "# global per-class feature importance information\n",
        "per_class_imp_values = explanation_from_run.per_class_values\n",
        "per_class_imp_names = explanation_from_run.get_ranked_per_class_names()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## This visualization is unsupported, and is not guaranteed to work in the future"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Get the shap values and explore locally\n",
        "import shap\n",
        "import numpy as np\n",
        "shap.initjs()\n",
        "display(shap.force_plot(explanation_from_run.expected_values[1], np.asarray(explanation_from_run.local_importance_values[1]), x_test))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "run.complete()"
      ]
    }
  ],
  "metadata": {
    "authors": [
      {
        "name": "wamartin"
      }
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