Explainer_dashboard.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Explainer-dashboard.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyNCAQc73M0IgrMDBoblpV2n",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/raqueeb/data_analyst/blob/main/Explainer_dashboard.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2WkYh5MXYUO8",
        "outputId": "ecb3f608-6543-4a73-b66b-2a14acfbba82"
      },
      "source": [
        "pip install explainerdashboard"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iEMR9lI_YEWs"
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "from explainerdashboard import ClassifierExplainer, ExplainerDashboard\n",
        "from explainerdashboard.datasets import titanic_survive\n",
        "\n",
        "X_train, y_train, X_test, y_test = titanic_survive()"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408
        },
        "id": "qgEB-r80t3EV",
        "outputId": "059c5c39-8c51-4d97-bf7e-60579d92fc57"
      },
      "source": [
        "X_train.head()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Fare</th>\n",
              "      <th>Age</th>\n",
              "      <th>PassengerClass</th>\n",
              "      <th>No_of_siblings_plus_spouses_on_board</th>\n",
              "      <th>No_of_parents_plus_children_on_board</th>\n",
              "      <th>Sex_female</th>\n",
              "      <th>Sex_male</th>\n",
              "      <th>Sex_nan</th>\n",
              "      <th>Deck_A</th>\n",
              "      <th>Deck_B</th>\n",
              "      <th>Deck_C</th>\n",
              "      <th>Deck_D</th>\n",
              "      <th>Deck_E</th>\n",
              "      <th>Deck_F</th>\n",
              "      <th>Deck_G</th>\n",
              "      <th>Deck_T</th>\n",
              "      <th>Deck_Unkown</th>\n",
              "      <th>Embarked_Cherbourg</th>\n",
              "      <th>Embarked_Queenstown</th>\n",
              "      <th>Embarked_Southampton</th>\n",
              "      <th>Embarked_Unknown</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Passenger</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Braund, Mr. Owen Harris</th>\n",
              "      <td>7.2500</td>\n",
              "      <td>22.0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Heikkinen, Miss. Laina</th>\n",
              "      <td>7.9250</td>\n",
              "      <td>26.0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Allen, Mr. William Henry</th>\n",
              "      <td>8.0500</td>\n",
              "      <td>35.0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Moran, Mr. James</th>\n",
              "      <td>8.4583</td>\n",
              "      <td>-999.0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>McCarthy, Mr. Timothy J</th>\n",
              "      <td>51.8625</td>\n",
              "      <td>54.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                             Fare  ...  Embarked_Unknown\n",
              "Passenger                          ...                  \n",
              "Braund, Mr. Owen Harris    7.2500  ...                 0\n",
              "Heikkinen, Miss. Laina     7.9250  ...                 0\n",
              "Allen, Mr. William Henry   8.0500  ...                 0\n",
              "Moran, Mr. James           8.4583  ...                 0\n",
              "McCarthy, Mr. Timothy J   51.8625  ...                 0\n",
              "\n",
              "[5 rows x 21 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vErASwgdqZHv",
        "outputId": "1b8bf7c3-bbb4-4b24-e4c5-bf9293d60a27"
      },
      "source": [
        "model = RandomForestClassifier(n_estimators=50, max_depth=5)\n",
        "model.fit(X_train, y_train)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
              "                       criterion='gini', max_depth=5, max_features='auto',\n",
              "                       max_leaf_nodes=None, max_samples=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=1, min_samples_split=2,\n",
              "                       min_weight_fraction_leaf=0.0, n_estimators=50,\n",
              "                       n_jobs=None, oob_score=False, random_state=None,\n",
              "                       verbose=0, warm_start=False)"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m6Ox_YIxpCRH",
        "outputId": "c1bf9b4a-9acf-4ae6-f0b3-03cdff1548ca"
      },
      "source": [
        "explainer = ClassifierExplainer(\n",
        "                model, X_test, y_test,\n",
        "                # optional:\n",
        "                cats=['Sex', 'Deck', 'Embarked'],\n",
        "                labels=['Not survived', 'Survived'])"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Detected RandomForestClassifier model: Changing class type to RandomForestClassifierExplainer...\n",
            "Note: model_output=='probability', so assuming that raw shap output of RandomForestClassifier is in probability space...\n",
            "Generating self.shap_explainer = shap.TreeExplainer(model)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        },
        "id": "J0HJ3mlqpHVD",
        "outputId": "b7f370d7-f4af-4b47-c9e8-5162fc9fbfec"
      },
      "source": [
        "ExplainerDashboard(explainer, title=\"টাইটানিক অ্যানালাইটিক্স\").run()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Building ExplainerDashboard..\n",
            "Detected google colab environment, setting mode='external'\n",
            "Warning: calculating shap interaction values can be slow! Pass shap_interaction=False to remove interactions tab.\n",
            "Generating layout...\n",
            "Calculating shap values...\n",
            "Calculating prediction probabilities...\n",
            "Calculating metrics...\n",
            "Calculating confusion matrices...\n",
            "Calculating classification_dfs...\n",
            "Calculating roc auc curves...\n",
            "Calculating pr auc curves...\n",
            "Calculating liftcurve_dfs...\n",
            "Calculating shap interaction values... (this may take a while)\n",
            "Reminder: TreeShap computational complexity is O(TLD^2), where T is the number of trees, L is the maximum number of leaves in any tree and D the maximal depth of any tree. So reducing these will speed up the calculation.\n",
            "Calculating dependencies...\n",
            "Calculating permutation importances (if slow, try setting n_jobs parameter)...\n",
            "Calculating pred_percentiles...\n",
            "Calculating predictions...\n",
            "Calculating ShadowDecTree for each individual decision tree...\n",
            "Reminder: you can store the explainer (including calculated dependencies) with explainer.dump('explainer.joblib') and reload with e.g. ClassifierExplainer.from_file('explainer.joblib')\n",
            "Registering callbacks...\n",
            "Starting ExplainerDashboard on http://172.28.0.2:8050\n",
            "You can terminate the dashboard with ExplainerDashboard.terminate(8050)\n",
            "Dash app running on:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "(async (port, path, text, element) => {\n",
              "    if (!google.colab.kernel.accessAllowed) {\n",
              "      return;\n",
              "    }\n",
              "    element.appendChild(document.createTextNode(''));\n",
              "    const url = await google.colab.kernel.proxyPort(port);\n",
              "    const anchor = document.createElement('a');\n",
              "    anchor.href = new URL(path, url).toString();\n",
              "    anchor.target = '_blank';\n",
              "    anchor.setAttribute('data-href', url + path);\n",
              "    anchor.textContent = text;\n",
              "    element.appendChild(anchor);\n",
              "  })(8050, \"/\", \"http://127.0.0.1:8050/\", window.element)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7W4SUb-frQhL"
      },
      "source": [
        ""
      ],
      "execution_count": 6,
      "outputs": []
    }
  ]
}