IBM_360.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "IBM 360.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "LmuHVMzbXlvJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145
        },
        "outputId": "d81326fa-faf5-4d41-b09f-3fe0a75bb2ad"
      },
      "source": [
        " !git clone https://github.com/Trusted-AI/AIX360"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'AIX360'...\n",
            "remote: Enumerating objects: 70, done.\u001b[K\n",
            "remote: Counting objects: 100% (70/70), done.\u001b[K\n",
            "remote: Compressing objects: 100% (64/64), done.\u001b[K\n",
            "remote: Total 1374 (delta 29), reused 18 (delta 4), pack-reused 1304\u001b[K\n",
            "Receiving objects: 100% (1374/1374), 357.25 MiB | 15.29 MiB/s, done.\n",
            "Resolving deltas: 100% (761/761), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CddBFVNOh63e",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "09ee5e58-5d84-4a95-c51d-bebc05b1ce6d"
      },
      "source": [
        "cd /content/AIX360"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/AIX360\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_13LTUn6h8t6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "f5bed966-1b42-41b7-ea02-5276ae6b2bbb"
      },
      "source": [
        "!pip install -e ."
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Obtaining file:///content/AIX360\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.6/dist-packages (from aix360==0.2.0) (0.16.0)\n",
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            "Requirement already satisfied: Image in /usr/local/lib/python3.6/dist-packages (from aix360==0.2.0) (1.5.32)\n",
            "Collecting tensorflow==1.14\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/de/f0/96fb2e0412ae9692dbf400e5b04432885f677ad6241c088ccc5fe7724d69/tensorflow-1.14.0-cp36-cp36m-manylinux1_x86_64.whl (109.2MB)\n",
            "\u001b[K     |████████████████████████████████| 109.2MB 86kB/s \n",
            "\u001b[?25hRequirement already satisfied: keras==2.3.1 in /usr/local/lib/python3.6/dist-packages (from aix360==0.2.0) (2.3.1)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.6/dist-packages (from aix360==0.2.0) (3.2.2)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from aix360==0.2.0) (1.18.5)\n",
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            "Requirement already satisfied: scipy>=0.17 in /usr/local/lib/python3.6/dist-packages (from aix360==0.2.0) (1.4.1)\n",
            "Collecting xport\n",
            "  Downloading https://files.pythonhosted.org/packages/6a/a0/ade37253fe2c7a457a9a8703e93e4b1517dd53315e3941416ee4f7463f08/xport-2.0.2-py2.py3-none-any.whl\n",
            "Requirement already satisfied: scikit-image in /usr/local/lib/python3.6/dist-packages (from aix360==0.2.0) (0.16.2)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from aix360==0.2.0) (2.23.0)\n",
            "Collecting xgboost==1.0.2\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/70/91/551d37ba472bcbd70a25e667acc65a18a9d053657b13afcf0f87aa24d7bb/xgboost-1.0.2-py3-none-manylinux1_x86_64.whl (109.7MB)\n",
            "\u001b[K     |████████████████████████████████| 109.8MB 53kB/s \n",
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            "Collecting qpsolvers\n",
            "  Downloading https://files.pythonhosted.org/packages/08/bd/5ea67fe9071013b635ca15afbc1860573fa963328e07336315add4ecbd48/qpsolvers-1.4.tar.gz\n",
            "Collecting lime==0.1.1.37\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/b5/e0/60070b461a589b2fee0dbc45df9987f150fca83667c2f8a064cef7dbac6b/lime-0.1.1.37.tar.gz (275kB)\n",
            "\u001b[K     |████████████████████████████████| 276kB 44.0MB/s \n",
            "\u001b[?25hCollecting shap==0.34.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/b8/db/58740847c0df6b346a999e3c375936436f4155354f6b644aa6e203bb40f3/shap-0.34.0.tar.gz (264kB)\n",
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            "Requirement already satisfied: wrapt>=1.11.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.14->aix360==0.2.0) (1.12.1)\n",
            "Collecting tensorflow-estimator<1.15.0rc0,>=1.14.0rc0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/3c/d5/21860a5b11caf0678fbc8319341b0ae21a07156911132e0e71bffed0510d/tensorflow_estimator-1.14.0-py2.py3-none-any.whl (488kB)\n",
            "\u001b[K     |████████████████████████████████| 491kB 41.4MB/s \n",
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            "Collecting tensorboard<1.15.0,>=1.14.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/91/2d/2ed263449a078cd9c8a9ba50ebd50123adf1f8cfbea1492f9084169b89d9/tensorboard-1.14.0-py3-none-any.whl (3.1MB)\n",
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            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests->aix360==0.2.0) (1.24.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->aix360==0.2.0) (2.10)\n",
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            "Requirement already satisfied: packaging in /usr/local/lib/python3.6/dist-packages (from bleach>=2.1.0->aix360==0.2.0) (20.4)\n",
            "Collecting quadprog\n",
            "  Downloading https://files.pythonhosted.org/packages/5a/f0/d4c8866f5d14dfa1a441439f5ce0d2680844651772129c431e78caadfe10/quadprog-0.1.7.tar.gz\n",
            "Collecting progressbar\n",
            "  Downloading https://files.pythonhosted.org/packages/a3/a6/b8e451f6cff1c99b4747a2f7235aa904d2d49e8e1464e0b798272aa84358/progressbar-2.5.tar.gz\n",
            "Requirement already satisfied: tqdm>4.25.0 in /usr/local/lib/python3.6/dist-packages (from shap==0.34.0->aix360==0.2.0) (4.41.1)\n",
            "Requirement already satisfied: dill>=0.3.2 in /usr/local/lib/python3.6/dist-packages (from multiprocess->cvxpy->aix360==0.2.0) (0.3.2)\n",
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            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.15.0,>=1.14.0->tensorflow==1.14->aix360==0.2.0) (3.2.2)\n",
            "Requirement already satisfied: setuptools>=41.0.0 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.15.0,>=1.14.0->tensorflow==1.14->aix360==0.2.0) (49.1.0)\n",
            "Requirement already satisfied: decorator>=4.3.0 in /usr/local/lib/python3.6/dist-packages (from networkx>=2.0->scikit-image->aix360==0.2.0) (4.4.2)\n",
            "Requirement already satisfied: Cython in /usr/local/lib/python3.6/dist-packages (from quadprog->qpsolvers->aix360==0.2.0) (0.29.21)\n",
            "Requirement already satisfied: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.6/dist-packages (from markdown>=2.6.8->tensorboard<1.15.0,>=1.14.0->tensorflow==1.14->aix360==0.2.0) (1.7.0)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.6/dist-packages (from importlib-metadata; python_version < \"3.8\"->markdown>=2.6.8->tensorboard<1.15.0,>=1.14.0->tensorflow==1.14->aix360==0.2.0) (3.1.0)\n",
            "Building wheels for collected packages: qpsolvers, lime, shap, quadprog, progressbar\n",
            "  Building wheel for qpsolvers (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for qpsolvers: filename=qpsolvers-1.4-cp36-none-any.whl size=18454 sha256=be15f85d8673763a99f6023ebe753bee6f246cb1a0edbd7e538d1d59e2e9f64b\n",
            "  Stored in directory: /root/.cache/pip/wheels/ce/a7/5f/2af411a5eea3909096a303f84b7d6ff84e440081e8baccb4f2\n",
            "  Building wheel for lime (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for lime: filename=lime-0.1.1.37-cp36-none-any.whl size=284277 sha256=d269bd588f2ce8f9af0baa1bce530b4b7aae5e8b1d98b9ee7563331b57213b0e\n",
            "  Stored in directory: /root/.cache/pip/wheels/c1/38/e7/50d75d4fb75afa604570dc42f20c5c5f5ab26d3fbe8d6ef27b\n",
            "  Building wheel for shap (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for shap: filename=shap-0.34.0-cp36-cp36m-linux_x86_64.whl size=383179 sha256=ca8ca39685c6988b53d33aafa517e7247d5f4d6b81f7a114f1b5dd566cbf6639\n",
            "  Stored in directory: /root/.cache/pip/wheels/28/7e/28/57bc9fcb77579b0df6561298648249370648c0b91dfe42c8ec\n",
            "  Building wheel for quadprog (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for quadprog: filename=quadprog-0.1.7-cp36-cp36m-linux_x86_64.whl size=299400 sha256=49bac480803d4d939bc1912fb21b79a84816ada26155681605f940c1c139928e\n",
            "  Stored in directory: /root/.cache/pip/wheels/36/dd/b1/849989444c0a5930927b260663019b7da6cff864fc224c2747\n",
            "  Building wheel for progressbar (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for progressbar: filename=progressbar-2.5-cp36-none-any.whl size=12074 sha256=3f1599cfa5fd9c86d5d6c95e4afc36a049eef00d54c195e90a88cd6079dde282\n",
            "  Stored in directory: /root/.cache/pip/wheels/c0/e9/6b/ea01090205e285175842339aa3b491adeb4015206cda272ff0\n",
            "Successfully built qpsolvers lime shap quadprog progressbar\n",
            "Installing collected packages: tensorflow-estimator, tensorboard, tensorflow, xport, xgboost, quadprog, qpsolvers, progressbar, lime, shap, aix360\n",
            "  Found existing installation: tensorflow-estimator 2.2.0\n",
            "    Uninstalling tensorflow-estimator-2.2.0:\n",
            "      Successfully uninstalled tensorflow-estimator-2.2.0\n",
            "  Found existing installation: tensorboard 2.2.2\n",
            "    Uninstalling tensorboard-2.2.2:\n",
            "      Successfully uninstalled tensorboard-2.2.2\n",
            "  Found existing installation: tensorflow 2.2.0\n",
            "    Uninstalling tensorflow-2.2.0:\n",
            "      Successfully uninstalled tensorflow-2.2.0\n",
            "  Found existing installation: xgboost 0.90\n",
            "    Uninstalling xgboost-0.90:\n",
            "      Successfully uninstalled xgboost-0.90\n",
            "  Running setup.py develop for aix360\n",
            "Successfully installed aix360 lime-0.1.1.37 progressbar-2.5 qpsolvers-1.4 quadprog-0.1.7 shap-0.34.0 tensorboard-1.14.0 tensorflow-1.14.0 tensorflow-estimator-1.14.0 xgboost-1.0.2 xport-2.0.2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sTsH67T4oMGw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 253
        },
        "outputId": "9f810be0-dee2-43df-a82b-44a9cbfe774c"
      },
      "source": [
        "from sklearn.datasets import load_breast_cancer\n",
        "bc = load_breast_cancer()\n",
        "import pandas as pd\n",
        "bc_df = pd.DataFrame(bc.data, columns=bc.feature_names)\n",
        "bc_df.head()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mean radius</th>\n",
              "      <th>mean texture</th>\n",
              "      <th>mean perimeter</th>\n",
              "      <th>mean area</th>\n",
              "      <th>mean smoothness</th>\n",
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              "      <th>mean concavity</th>\n",
              "      <th>mean concave points</th>\n",
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              "      <th>mean fractal dimension</th>\n",
              "      <th>radius error</th>\n",
              "      <th>texture error</th>\n",
              "      <th>perimeter error</th>\n",
              "      <th>area error</th>\n",
              "      <th>smoothness error</th>\n",
              "      <th>compactness error</th>\n",
              "      <th>concavity error</th>\n",
              "      <th>concave points error</th>\n",
              "      <th>symmetry error</th>\n",
              "      <th>fractal dimension error</th>\n",
              "      <th>worst radius</th>\n",
              "      <th>worst texture</th>\n",
              "      <th>worst perimeter</th>\n",
              "      <th>worst area</th>\n",
              "      <th>worst smoothness</th>\n",
              "      <th>worst compactness</th>\n",
              "      <th>worst concavity</th>\n",
              "      <th>worst concave points</th>\n",
              "      <th>worst symmetry</th>\n",
              "      <th>worst fractal dimension</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>17.99</td>\n",
              "      <td>10.38</td>\n",
              "      <td>122.80</td>\n",
              "      <td>1001.0</td>\n",
              "      <td>0.11840</td>\n",
              "      <td>0.27760</td>\n",
              "      <td>0.3001</td>\n",
              "      <td>0.14710</td>\n",
              "      <td>0.2419</td>\n",
              "      <td>0.07871</td>\n",
              "      <td>1.0950</td>\n",
              "      <td>0.9053</td>\n",
              "      <td>8.589</td>\n",
              "      <td>153.40</td>\n",
              "      <td>0.006399</td>\n",
              "      <td>0.04904</td>\n",
              "      <td>0.05373</td>\n",
              "      <td>0.01587</td>\n",
              "      <td>0.03003</td>\n",
              "      <td>0.006193</td>\n",
              "      <td>25.38</td>\n",
              "      <td>17.33</td>\n",
              "      <td>184.60</td>\n",
              "      <td>2019.0</td>\n",
              "      <td>0.1622</td>\n",
              "      <td>0.6656</td>\n",
              "      <td>0.7119</td>\n",
              "      <td>0.2654</td>\n",
              "      <td>0.4601</td>\n",
              "      <td>0.11890</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>20.57</td>\n",
              "      <td>17.77</td>\n",
              "      <td>132.90</td>\n",
              "      <td>1326.0</td>\n",
              "      <td>0.08474</td>\n",
              "      <td>0.07864</td>\n",
              "      <td>0.0869</td>\n",
              "      <td>0.07017</td>\n",
              "      <td>0.1812</td>\n",
              "      <td>0.05667</td>\n",
              "      <td>0.5435</td>\n",
              "      <td>0.7339</td>\n",
              "      <td>3.398</td>\n",
              "      <td>74.08</td>\n",
              "      <td>0.005225</td>\n",
              "      <td>0.01308</td>\n",
              "      <td>0.01860</td>\n",
              "      <td>0.01340</td>\n",
              "      <td>0.01389</td>\n",
              "      <td>0.003532</td>\n",
              "      <td>24.99</td>\n",
              "      <td>23.41</td>\n",
              "      <td>158.80</td>\n",
              "      <td>1956.0</td>\n",
              "      <td>0.1238</td>\n",
              "      <td>0.1866</td>\n",
              "      <td>0.2416</td>\n",
              "      <td>0.1860</td>\n",
              "      <td>0.2750</td>\n",
              "      <td>0.08902</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>19.69</td>\n",
              "      <td>21.25</td>\n",
              "      <td>130.00</td>\n",
              "      <td>1203.0</td>\n",
              "      <td>0.10960</td>\n",
              "      <td>0.15990</td>\n",
              "      <td>0.1974</td>\n",
              "      <td>0.12790</td>\n",
              "      <td>0.2069</td>\n",
              "      <td>0.05999</td>\n",
              "      <td>0.7456</td>\n",
              "      <td>0.7869</td>\n",
              "      <td>4.585</td>\n",
              "      <td>94.03</td>\n",
              "      <td>0.006150</td>\n",
              "      <td>0.04006</td>\n",
              "      <td>0.03832</td>\n",
              "      <td>0.02058</td>\n",
              "      <td>0.02250</td>\n",
              "      <td>0.004571</td>\n",
              "      <td>23.57</td>\n",
              "      <td>25.53</td>\n",
              "      <td>152.50</td>\n",
              "      <td>1709.0</td>\n",
              "      <td>0.1444</td>\n",
              "      <td>0.4245</td>\n",
              "      <td>0.4504</td>\n",
              "      <td>0.2430</td>\n",
              "      <td>0.3613</td>\n",
              "      <td>0.08758</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>11.42</td>\n",
              "      <td>20.38</td>\n",
              "      <td>77.58</td>\n",
              "      <td>386.1</td>\n",
              "      <td>0.14250</td>\n",
              "      <td>0.28390</td>\n",
              "      <td>0.2414</td>\n",
              "      <td>0.10520</td>\n",
              "      <td>0.2597</td>\n",
              "      <td>0.09744</td>\n",
              "      <td>0.4956</td>\n",
              "      <td>1.1560</td>\n",
              "      <td>3.445</td>\n",
              "      <td>27.23</td>\n",
              "      <td>0.009110</td>\n",
              "      <td>0.07458</td>\n",
              "      <td>0.05661</td>\n",
              "      <td>0.01867</td>\n",
              "      <td>0.05963</td>\n",
              "      <td>0.009208</td>\n",
              "      <td>14.91</td>\n",
              "      <td>26.50</td>\n",
              "      <td>98.87</td>\n",
              "      <td>567.7</td>\n",
              "      <td>0.2098</td>\n",
              "      <td>0.8663</td>\n",
              "      <td>0.6869</td>\n",
              "      <td>0.2575</td>\n",
              "      <td>0.6638</td>\n",
              "      <td>0.17300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>20.29</td>\n",
              "      <td>14.34</td>\n",
              "      <td>135.10</td>\n",
              "      <td>1297.0</td>\n",
              "      <td>0.10030</td>\n",
              "      <td>0.13280</td>\n",
              "      <td>0.1980</td>\n",
              "      <td>0.10430</td>\n",
              "      <td>0.1809</td>\n",
              "      <td>0.05883</td>\n",
              "      <td>0.7572</td>\n",
              "      <td>0.7813</td>\n",
              "      <td>5.438</td>\n",
              "      <td>94.44</td>\n",
              "      <td>0.011490</td>\n",
              "      <td>0.02461</td>\n",
              "      <td>0.05688</td>\n",
              "      <td>0.01885</td>\n",
              "      <td>0.01756</td>\n",
              "      <td>0.005115</td>\n",
              "      <td>22.54</td>\n",
              "      <td>16.67</td>\n",
              "      <td>152.20</td>\n",
              "      <td>1575.0</td>\n",
              "      <td>0.1374</td>\n",
              "      <td>0.2050</td>\n",
              "      <td>0.4000</td>\n",
              "      <td>0.1625</td>\n",
              "      <td>0.2364</td>\n",
              "      <td>0.07678</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   mean radius  mean texture  ...  worst symmetry  worst fractal dimension\n",
              "0        17.99         10.38  ...          0.4601                  0.11890\n",
              "1        20.57         17.77  ...          0.2750                  0.08902\n",
              "2        19.69         21.25  ...          0.3613                  0.08758\n",
              "3        11.42         20.38  ...          0.6638                  0.17300\n",
              "4        20.29         14.34  ...          0.2364                  0.07678\n",
              "\n",
              "[5 rows x 30 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SJ0hDpdpXm1L",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 199
        },
        "outputId": "abfd6086-bf83-405d-e46e-0a254d2373c5"
      },
      "source": [
        "bc_df.columns"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['mean radius', 'mean texture', 'mean perimeter', 'mean area',\n",
              "       'mean smoothness', 'mean compactness', 'mean concavity',\n",
              "       'mean concave points', 'mean symmetry', 'mean fractal dimension',\n",
              "       'radius error', 'texture error', 'perimeter error', 'area error',\n",
              "       'smoothness error', 'compactness error', 'concavity error',\n",
              "       'concave points error', 'symmetry error', 'fractal dimension error',\n",
              "       'worst radius', 'worst texture', 'worst perimeter', 'worst area',\n",
              "       'worst smoothness', 'worst compactness', 'worst concavity',\n",
              "       'worst concave points', 'worst symmetry', 'worst fractal dimension'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "12aXYhd-V4RO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 318
        },
        "outputId": "f678b569-b8df-4c71-c8e6-4e2027b843a2"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, Y_train, Y_test = train_test_split(bc_df, bc.target, test_size = 0.2, random_state = 31)\n",
        "from aix360.algorithms.rbm import FeatureBinarizer\n",
        "fb = FeatureBinarizer(negations=True)\n",
        "X_train_fb = fb.fit_transform(X_train)\n",
        "X_test_fb = fb.transform(X_test)\n",
        "X_train_fb['mean radius'][:8]\n"
      ],
      "execution_count": 6,
      "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 tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th>operation</th>\n",
              "      <th colspan=\"9\" halign=\"left\">&lt;=</th>\n",
              "      <th colspan=\"9\" halign=\"left\">&gt;</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>value</th>\n",
              "      <th>10.254</th>\n",
              "      <th>11.328</th>\n",
              "      <th>11.942</th>\n",
              "      <th>12.604</th>\n",
              "      <th>13.270</th>\n",
              "      <th>14.142</th>\n",
              "      <th>15.058</th>\n",
              "      <th>17.026</th>\n",
              "      <th>19.324</th>\n",
              "      <th>10.254</th>\n",
              "      <th>11.328</th>\n",
              "      <th>11.942</th>\n",
              "      <th>12.604</th>\n",
              "      <th>13.270</th>\n",
              "      <th>14.142</th>\n",
              "      <th>15.058</th>\n",
              "      <th>17.026</th>\n",
              "      <th>19.324</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>468</th>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>179</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</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",
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              "    <tr>\n",
              "      <th>114</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\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>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>88</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>507</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>213</th>\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>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>134</th>\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>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "operation     <=                              ...      >                            \n",
              "value     10.254 11.328 11.942 12.604 13.270  ... 13.270 14.142 15.058 17.026 19.324\n",
              "468            0      0      0      0      0  ...      1      1      1      1      0\n",
              "179            0      0      0      0      1  ...      0      0      0      0      0\n",
              "114            1      1      1      1      1  ...      0      0      0      0      0\n",
              "35             0      0      0      0      0  ...      1      1      1      0      0\n",
              "88             0      0      0      1      1  ...      0      0      0      0      0\n",
              "507            0      1      1      1      1  ...      0      0      0      0      0\n",
              "213            0      0      0      0      0  ...      1      1      1      1      0\n",
              "134            0      0      0      0      0  ...      1      1      1      1      0\n",
              "\n",
              "[8 rows x 18 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OvIxY9Qri6BC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 181
        },
        "outputId": "dcdbe19c-0bec-4bae-a3d6-660fea6cf9d1"
      },
      "source": [
        "from aix360.algorithms.rbm import BRCGExplainer, BooleanRuleCG\n",
        "boolean_model = BooleanRuleCG(silent=True)\n",
        "explainer = BRCGExplainer(boolean_model)\n",
        "explainer.fit(X_train_fb, Y_train)\n",
        "Y_pred = explainer.predict(X_test_fb)\n",
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
        "print(f'Accuracy = {accuracy_score(Y_test, Y_pred)}')\n",
        "print(f'Precision = {precision_score(Y_test, Y_pred)}')\n",
        "print(f'Recall = {recall_score(Y_test, Y_pred)}')\n",
        "print(f'F1 = {f1_score(Y_test, Y_pred)}')\n",
        "explainer.explain()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy = 0.9298245614035088\n",
            "Precision = 0.9538461538461539\n",
            "Recall = 0.9253731343283582\n",
            "F1 = 0.9393939393939394\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'isCNF': False,\n",
              " 'rules': ['compactness error > 0.01 AND worst concavity <= 0.22 AND worst symmetry <= 0.28',\n",
              "  'mean texture <= 15.46 AND mean concavity <= 0.15 AND area error <= 54.16',\n",
              "  'fractal dimension error > 0.00 AND worst area <= 680.60 AND worst concave points <= 0.18',\n",
              "  'mean concave points <= 0.05 AND perimeter error <= 3.80 AND worst area <= 930.88 AND worst smoothness <= 0.16']}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qYxGdKpxjBsi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 126
        },
        "outputId": "b7deaaff-4aa8-4219-e2fe-40b7598392d8"
      },
      "source": [
        "e = explainer.explain()\n",
        "isCNF = 'Predict Y=0 if ANY of the following rules are satisfied, otherwise Y=1:'\n",
        "notCNF = 'Predict Y=1 if ANY of the following rules are satisfied, otherwise Y=0:'\n",
        "print(isCNF if e['isCNF'] else notCNF)\n",
        "print()\n",
        "for rule in e['rules']:\n",
        "    print(f'  - {rule}')\n"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Predict Y=1 if ANY of the following rules are satisfied, otherwise Y=0:\n",
            "\n",
            "  - compactness error > 0.01 AND worst concavity <= 0.22 AND worst symmetry <= 0.28\n",
            "  - mean texture <= 15.46 AND mean concavity <= 0.15 AND area error <= 54.16\n",
            "  - fractal dimension error > 0.00 AND worst area <= 680.60 AND worst concave points <= 0.18\n",
            "  - mean concave points <= 0.05 AND perimeter error <= 3.80 AND worst area <= 930.88 AND worst smoothness <= 0.16\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sWVUSzjPlwHq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "from aix360.algorithms.protodash import ProtodashExplainer, get_Gaussian_Data"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jRGmjnK-lyHI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data=bc_df[[\"mean radius\",\"mean texture\",\"mean area\", \"area error\",\"worst area\",\"worst concavity\",\"concave points error\"]]\n",
        "data[\"target\"]=bc.target\n",
        "data.head()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QrNdGBAwl12J",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 274
        },
        "outputId": "8764749f-b138-4355-cc99-bc77cbc26857"
      },
      "source": [
        "data_numpy = data.to_numpy()\n",
        "explainer = ProtodashExplainer()\n",
        "(W, S, setValues) = explainer.explain(data_numpy,data_numpy, m=2, sigma=2)\n",
        "inc_prototypes =data.iloc[S, :].copy()\n",
        "# Compute normalized importance weights for prototypes\n",
        "inc_prototypes[\"Weights of Prototypes\"] = np.around(W/np.sum(W), 2) \n",
        "inc_prototypes"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/cvxopt/coneprog.py:2111: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
            "  if 'x' in initvals:\n",
            "/usr/local/lib/python3.6/dist-packages/cvxopt/coneprog.py:2116: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
            "  if 's' in initvals:\n",
            "/usr/local/lib/python3.6/dist-packages/cvxopt/coneprog.py:2131: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
            "  if 'y' in initvals:\n",
            "/usr/local/lib/python3.6/dist-packages/cvxopt/coneprog.py:2136: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
            "  if 'z' in initvals:\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mean radius</th>\n",
              "      <th>mean texture</th>\n",
              "      <th>mean area</th>\n",
              "      <th>area error</th>\n",
              "      <th>worst area</th>\n",
              "      <th>worst concavity</th>\n",
              "      <th>concave points error</th>\n",
              "      <th>target</th>\n",
              "      <th>Weights of Prototypes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>340</th>\n",
              "      <td>14.42</td>\n",
              "      <td>16.54</td>\n",
              "      <td>641.2</td>\n",
              "      <td>32.14</td>\n",
              "      <td>862.1</td>\n",
              "      <td>0.3755</td>\n",
              "      <td>0.01243</td>\n",
              "      <td>1</td>\n",
              "      <td>0.99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>212</th>\n",
              "      <td>28.11</td>\n",
              "      <td>18.47</td>\n",
              "      <td>2499.0</td>\n",
              "      <td>525.60</td>\n",
              "      <td>2499.0</td>\n",
              "      <td>0.3201</td>\n",
              "      <td>0.01407</td>\n",
              "      <td>0</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     mean radius  mean texture  ...  target  Weights of Prototypes\n",
              "340        14.42         16.54  ...       1                   0.99\n",
              "212        28.11         18.47  ...       0                   0.01\n",
              "\n",
              "[2 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V-MfXRGHmrRi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 78
        },
        "outputId": "4c325d0a-1306-4e12-ba9f-3eb4b1d6a14b"
      },
      "source": [
        "test=data[1:2]\n",
        "test"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "\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>mean radius</th>\n",
              "      <th>mean texture</th>\n",
              "      <th>mean area</th>\n",
              "      <th>area error</th>\n",
              "      <th>worst area</th>\n",
              "      <th>worst concavity</th>\n",
              "      <th>concave points error</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>20.57</td>\n",
              "      <td>17.77</td>\n",
              "      <td>1326.0</td>\n",
              "      <td>74.08</td>\n",
              "      <td>1956.0</td>\n",
              "      <td>0.2416</td>\n",
              "      <td>0.0134</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   mean radius  mean texture  ...  concave points error  target\n",
              "1        20.57         17.77  ...                0.0134       0\n",
              "\n",
              "[1 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I0pnr_FymAwB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 78
        },
        "outputId": "ca2ce82d-c74e-49b6-9c56-8c39d3ab9970"
      },
      "source": [
        "test_numpy=test.to_numpy()\n",
        "explain1 =data.drop([1])\n",
        "explain =data.drop([1]).to_numpy()\n",
        "explainer = ProtodashExplainer()\n",
        "(W, S, setValues) = explainer.explain(test_numpy,explain, m=1, sigma=2)\n",
        "inc_prototypes =explain1.iloc[S, :].copy()\n",
        "# Compute normalized importance weights for prototypes\n",
        "inc_prototypes[\"Weights of Prototypes\"] = np.around(W/np.sum(W), 2) \n",
        "inc_prototypes"
      ],
      "execution_count": 31,
      "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",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\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>mean radius</th>\n",
              "      <th>mean texture</th>\n",
              "      <th>mean area</th>\n",
              "      <th>area error</th>\n",
              "      <th>worst area</th>\n",
              "      <th>worst concavity</th>\n",
              "      <th>concave points error</th>\n",
              "      <th>target</th>\n",
              "      <th>Weights of Prototypes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>393</th>\n",
              "      <td>21.61</td>\n",
              "      <td>22.28</td>\n",
              "      <td>1407.0</td>\n",
              "      <td>80.99</td>\n",
              "      <td>2081.0</td>\n",
              "      <td>0.7053</td>\n",
              "      <td>0.01288</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     mean radius  mean texture  ...  target  Weights of Prototypes\n",
              "393        21.61         22.28  ...       0                    1.0\n",
              "\n",
              "[1 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    }
  ]
}