examples/rbm/boston-linear.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_boston\n",
    "boston = load_boston()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
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       "      <td>0.00632</td>\n",
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       "      <td>0.469</td>\n",
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       "      <td>78.9</td>\n",
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       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
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       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
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       "      <td>0.469</td>\n",
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       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
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       "      <td>2.18</td>\n",
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       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
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       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
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      ],
      "text/plain": [
       "      CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  \\\n",
       "0  0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900  1.0  296.0   \n",
       "1  0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671  2.0  242.0   \n",
       "2  0.02729   0.0   7.07   0.0  0.469  7.185  61.1  4.9671  2.0  242.0   \n",
       "3  0.03237   0.0   2.18   0.0  0.458  6.998  45.8  6.0622  3.0  222.0   \n",
       "4  0.06905   0.0   2.18   0.0  0.458  7.147  54.2  6.0622  3.0  222.0   \n",
       "\n",
       "   PTRATIO       B  LSTAT  \n",
       "0     15.3  396.90   4.98  \n",
       "1     17.8  396.90   9.14  \n",
       "2     17.8  392.83   4.03  \n",
       "3     18.7  394.63   2.94  \n",
       "4     18.7  396.90   5.33  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "boston_df = pd.DataFrame(boston.data, columns=boston.feature_names)\n",
    "boston_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(379, 13)\n",
      "(379,)\n",
      "(127, 13)\n",
      "(127,)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(boston_df, boston.target, test_size = 0.25, random_state = 31)\n",
    "print(X_train.shape)\n",
    "print(Y_train.shape)\n",
    "print(X_test.shape)\n",
    "print(Y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "operation       <=                                                        \\\n",
       "value     0.037314 0.059438 0.091626 0.141662 0.229270 0.489478 1.374682   \n",
       "90               0        1        1        1        1        1        1   \n",
       "434              0        0        0        0        0        0        0   \n",
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       "273              0        0        0        0        1        1        1   \n",
       "\n",
       "operation                          >                                      \\\n",
       "value     4.849966 9.842102 0.037314 0.059438 0.091626 0.141662 0.229270   \n",
       "90               1        1        1        0        0        0        0   \n",
       "434              0        0        1        1        1        1        1   \n",
       "384              0        0        1        1        1        1        1   \n",
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       "22               1        1        1        1        1        1        1   \n",
       "54               1        1        0        0        0        0        0   \n",
       "62               1        1        1        1        1        0        0   \n",
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       "199              1        1        0        0        0        0        0   \n",
       "273              1        1        1        1        1        1        0   \n",
       "\n",
       "operation                                      \n",
       "value     0.489478 1.374682 4.849966 9.842102  \n",
       "90               0        0        0        0  \n",
       "434              1        1        1        1  \n",
       "384              1        1        1        1  \n",
       "175              0        0        0        0  \n",
       "22               1        0        0        0  \n",
       "54               0        0        0        0  \n",
       "62               0        0        0        0  \n",
       "320              0        0        0        0  \n",
       "199              0        0        0        0  \n",
       "273              0        0        0        0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "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['CRIM'][:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from aix360.algorithms.rbm import GLRMExplainer, LinearRuleRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "linear_model = LinearRuleRegression()\n",
    "explainer = GLRMExplainer(linear_model)\n",
    "explainer.fit(X_train_fb, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_pred = explainer.predict(X_test_fb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 Score = 0.8249087941706609\n",
      "Explained Variance = 0.8289797066674642\n",
      "Mean abs. error = 2.754048495903457\n",
      "Max error = 11.399762504643611\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score, explained_variance_score, mean_absolute_error, max_error\n",
    "print(f'R2 Score = {r2_score(Y_test, Y_pred)}')\n",
    "print(f'Explained Variance = {explained_variance_score(Y_test, Y_pred)}')\n",
    "print(f'Mean abs. error = {mean_absolute_error(Y_test, Y_pred)}')\n",
    "print(f'Max error = {max_error(Y_test, Y_pred)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rule</th>\n",
       "      <th>coefficient</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(intercept)</td>\n",
       "      <td>21.944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NOX &lt;= 0.66</td>\n",
       "      <td>6.31439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RM &lt;= 7.16 AND DIS &gt; 1.62</td>\n",
       "      <td>-5.78581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LSTAT &lt;= 4.66</td>\n",
       "      <td>5.4635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DIS &lt;= 3.32 AND RAD &gt; 2.00 AND B &gt; 295.98 AND ...</td>\n",
       "      <td>4.80349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>CHAS not  AND PTRATIO &gt; 16.10</td>\n",
       "      <td>-3.9513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>RM &lt;= 7.16 AND RAD &lt;= 6.00</td>\n",
       "      <td>-3.35462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>TAX &gt; 293.00 AND LSTAT &gt; 4.66</td>\n",
       "      <td>-2.96268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>LSTAT &lt;= 15.03</td>\n",
       "      <td>2.84329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>INDUS &gt; 4.05 AND LSTAT &gt; 4.66</td>\n",
       "      <td>-2.56005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>DIS &lt;= 7.24 AND RAD &gt; 2.00 AND PTRATIO &lt;= 20.9...</td>\n",
       "      <td>2.51755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>LSTAT &lt;= 9.48</td>\n",
       "      <td>2.50064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>CRIM &lt;= 9.84 AND DIS &lt;= 4.64 AND RAD &gt; 1.00 AN...</td>\n",
       "      <td>2.28817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>LSTAT &lt;= 17.60</td>\n",
       "      <td>1.91807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>TAX &gt; 330.00 AND LSTAT &gt; 4.66</td>\n",
       "      <td>-1.80772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>CRIM &lt;= 9.84 AND CRIM &gt; 0.06 AND PTRATIO &lt;= 20.90</td>\n",
       "      <td>1.79055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>LSTAT &lt;= 6.25</td>\n",
       "      <td>1.66632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>RM &lt;= 7.16 AND B &gt; 380.27</td>\n",
       "      <td>-1.61764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>LSTAT &lt;= 11.12</td>\n",
       "      <td>1.59846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>RAD &gt; 2.00 AND LSTAT &lt;= 22.79</td>\n",
       "      <td>1.24117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>RM &lt;= 7.16</td>\n",
       "      <td>-1.20717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>CHAS not  AND RM &lt;= 7.16</td>\n",
       "      <td>1.19303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>RM &lt;= 6.51</td>\n",
       "      <td>-1.13602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>CRIM &lt;= 9.84 AND DIS &lt;= 3.95 AND TAX &lt;= 666.00...</td>\n",
       "      <td>1.0065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>CRIM &lt;= 9.84 AND RAD &gt; 1.00 AND LSTAT &lt;= 22.79</td>\n",
       "      <td>0.972651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>DIS &lt;= 3.95 AND LSTAT &lt;= 22.79</td>\n",
       "      <td>-0.919471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>RM &lt;= 6.74</td>\n",
       "      <td>-0.795374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>PTRATIO &lt;= 19.52</td>\n",
       "      <td>0.795212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>NOX &lt;= 0.66 AND PTRATIO &lt;= 20.90 AND LSTAT &lt;= ...</td>\n",
       "      <td>-0.775433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>RAD &gt; 4.00 AND LSTAT &lt;= 22.79</td>\n",
       "      <td>-0.632437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>B &lt;= 391.27 AND LSTAT &lt;= 22.79</td>\n",
       "      <td>0.479933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>LSTAT &lt;= 7.58</td>\n",
       "      <td>0.43504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>LSTAT &lt;= 13.14</td>\n",
       "      <td>0.178565</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 rule coefficient\n",
       "0                                         (intercept)      21.944\n",
       "1                                         NOX <= 0.66     6.31439\n",
       "2                           RM <= 7.16 AND DIS > 1.62    -5.78581\n",
       "3                                       LSTAT <= 4.66      5.4635\n",
       "4   DIS <= 3.32 AND RAD > 2.00 AND B > 295.98 AND ...     4.80349\n",
       "5                       CHAS not  AND PTRATIO > 16.10     -3.9513\n",
       "6                          RM <= 7.16 AND RAD <= 6.00    -3.35462\n",
       "7                       TAX > 293.00 AND LSTAT > 4.66    -2.96268\n",
       "8                                      LSTAT <= 15.03     2.84329\n",
       "9                       INDUS > 4.05 AND LSTAT > 4.66    -2.56005\n",
       "10  DIS <= 7.24 AND RAD > 2.00 AND PTRATIO <= 20.9...     2.51755\n",
       "11                                      LSTAT <= 9.48     2.50064\n",
       "12  CRIM <= 9.84 AND DIS <= 4.64 AND RAD > 1.00 AN...     2.28817\n",
       "13                                     LSTAT <= 17.60     1.91807\n",
       "14                      TAX > 330.00 AND LSTAT > 4.66    -1.80772\n",
       "15  CRIM <= 9.84 AND CRIM > 0.06 AND PTRATIO <= 20.90     1.79055\n",
       "16                                      LSTAT <= 6.25     1.66632\n",
       "17                          RM <= 7.16 AND B > 380.27    -1.61764\n",
       "18                                     LSTAT <= 11.12     1.59846\n",
       "19                      RAD > 2.00 AND LSTAT <= 22.79     1.24117\n",
       "20                                         RM <= 7.16    -1.20717\n",
       "21                           CHAS not  AND RM <= 7.16     1.19303\n",
       "22                                         RM <= 6.51    -1.13602\n",
       "23  CRIM <= 9.84 AND DIS <= 3.95 AND TAX <= 666.00...      1.0065\n",
       "24     CRIM <= 9.84 AND RAD > 1.00 AND LSTAT <= 22.79    0.972651\n",
       "25                     DIS <= 3.95 AND LSTAT <= 22.79   -0.919471\n",
       "26                                         RM <= 6.74   -0.795374\n",
       "27                                   PTRATIO <= 19.52    0.795212\n",
       "28  NOX <= 0.66 AND PTRATIO <= 20.90 AND LSTAT <= ...   -0.775433\n",
       "29                      RAD > 4.00 AND LSTAT <= 22.79   -0.632437\n",
       "30                     B <= 391.27 AND LSTAT <= 22.79    0.479933\n",
       "31                                      LSTAT <= 7.58     0.43504\n",
       "32                                     LSTAT <= 13.14    0.178565"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "explainer.explain()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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8dZ0P4obmNdefTRZW1e9tz4WrakPzeW+Si4BDgf4EsQE4oG97Mfa/GDvbOlVpv+POumq7nkJ8+pCGo0uC+EKz9Nuuv9ST7A48rqoeataPZMsRYi8B3pzkAnqN0w/a/jA/bc9TiE8f0vB0SRB7VdVH+wuSnDrdwR3tC1zUe5OVXYC/rKq/TXIKQFWdCVxK7xXX2+i95vo723lNjanteQrZ3rYPSdPrkiBOpNcfod9JLWWdVdXtwHNbys/sWy/gd7f1GpKk7TNTP4iVwPHAgUku6du1J/DAsAOTJI3WTE8Q36L3Suk+QP+81A8B1w8zKEnS6M3UD+JO4E7gsLkLR5I0LrpMGPSqJN9P8mCSTUkeSrJpLoKTJI1Ol0bqPwVeUVU3DzsYSdL46DJh0D0mB0na+XR5gliT5ELgi8AvJguramrnOUnSPNIlQTyZXke1I/vKii17V0uS5pFZE0RV2YNZknZCXd5iWpzkoiT3Nsvnkyyei+AkSaPTpZH6HHoD5+3fLF9qyiRJ81iXBLGwqs6pqs3Nci6wcMhxSZJGrEuCuD/JCUkWNMsJwP3DDkySNFpdEsTJwGuBu+mNzfRqHHpbkua9Lm8x3QkcOwexSJLGSJe3mM5Lslff9t5Jzh5uWJKkUetSxfScqvrp5EZV/QR43vBCkiSNgy4J4nFJ9p7cSPJUuvXAliTtwLr8ov8QcFWSzzXbrwHeN7yQJEnjoEsj9aeTrAFe3BS9qqrWDTcsSdKodaoqahKCSUGSdiJd2iAGKskBSS5Psi7JTUlObTnmiGYGu7XN8q65jlOSdnajaGzeDPx+VV2bZE/gmiSXtVRbfbOqXj6C+CRJjOAJoqo2VtW1zfpDwM3AormOQ5I0szlPEP2SLKXXp+Lqlt2HJfleki8nefYM37EqyZoka+67774hRSpJO5+RJYgkewCfB95SVZum7L4WeGZVPRf4GL3pTltV1eqqmqiqiYULHWRWkgZlJAkiya70ksNn2ua2rqpNVfVws34psGuSfeY4TEnaqY3iLaYAnwJurqrTpznmGc1xJDmUXpwOMS5Jc2gUbzG9APgPwA1J1jZl7wSWAFTVmfSGFH9Tks3APwGvq6oaQayStNOa8wRRVVcCmeWYM4Az5iYiSVIbB93TDm/dxk0cd9ZVA//eFcsWcfzyJQP/XmlHYYLQDm3FsuF0oVm3sfdinQlCOzMThHZoxy9fMpRf4sN4IpF2NCPtKCdJGl8mCElSKxOEJKmVCUKS1MoEIUlqZYKQJLUyQUiSWpkgJEmtTBCSpFYmCElSKxOEJKmVCUKS1MoEIUlqZYKQJLUyQUiSWpkgJEmtTBCSpFYmCElSq5EkiCRHJbk1yW1J3t6y//FJLmz2X51k6dxHKUk7tzlPEEkWAB8HjgYOBlYmOXjKYa8HflJV/wL4MPA/5zZKSdIuI7jmocBtVXU7QJILgBXAur5jVgDvbtb/CjgjSaqq5jJQ7dzWbdzEcWddNeowpFkdvP+TOe0Vzx74944iQSwCftS3vR5YPt0xVbU5yYPA04AfT/2yJKuAVQBLliwZRrzaCa1YtmjUIUgjN4oEMVBVtRpYDTAxMeEThgbi+OVLOH65f3Bo5zaKRuoNwAF924ubstZjkuwCPAW4f06ikyQBo0kQ3wUOSnJgkt2A1wGXTDnmEuDEZv3VwNdsf5CkuTXnVUxNm8Kbga8AC4Czq+qmJO8F1lTVJcCngL9IchvwAL0kIkmaQyNpg6iqS4FLp5S9q2/958Br5jouSdJj7EktSWplgpAktTJBSJJamSAkSa0yn94eTfIQcOuo4+hgH1p6hY8h4xws4xws4xyMZ1bVwrYdO3xP6iluraqJUQcxmyRrjHNwjHOwjHOwdpQ421jFJElqZYKQJLWabwli9agD6Mg4B8s4B8s4B2tHiXML86qRWpI0OPPtCUKSNCAmCElSq3mTIJIcleTWJLclefuo45lOkjuS3JBkbZI1o45nUpKzk9yb5Ma+sqcmuSzJ95vPvUcZYxNTW5zvTrKhuadrkxwzyhibmA5IcnmSdUluSnJqUz5W93SGOMfqniZ5QpLvJPleE+d7mvIDk1zd/Nxf2EwhMI5xnpvkh333c9ko4+xqXrRBJFkA/D3wUnpTmH4XWFlV62Y8cQSS3AFMVNVYdZxJ8kLgYeDTVXVIU/anwANV9YEm6e5dVW8bwzjfDTxcVR8cZWz9kuwH7FdV1ybZE7gGeCVwEmN0T2eI87WM0T1NEmD3qno4ya7AlcCpwFuBL1TVBUnOBL5XVZ8YwzhPAf66qv5qVLFti/nyBHEocFtV3V5VvwQuAFaMOKYdSlV9g97cG/1WAOc16+fR+8UxUtPEOXaqamNVXdusPwTcTG+u9bG6pzPEOVaq5+Fmc9dmKeDFwOQv3XG4n9PFuUOaLwliEfCjvu31jOF/8kYBX01yTZJVow5mFvtW1cZm/W5g31EGM4s3J7m+qYIaeVVYvyRLgecBVzPG93RKnDBm9zTJgiRrgXuBy4AfAD+tqs3NIWPxcz81zqqavJ/va+7nh5M8foQhdjZfEsSO5PCqej5wNPC7TZXJ2GumfB3Xv4Q+Afw6sAzYCHxotOE8JskewOeBt1TVpv5943RPW+Icu3taVY9U1TJ689gfCvzGiENqNTXOJIcA76AX728CTwVGWlXb1XxJEBuAA/q2FzdlY6eqNjSf9wIX0fuPPq7uaeqoJ+uq7x1xPK2q6p7mh/JR4JOMyT1t6qA/D3ymqr7QFI/dPW2Lc1zvKUBV/RS4HDgM2CvJ5JhyY/Vz3xfnUU1VXlXVL4BzGKP7OZP5kiC+CxzUvNGwG705rC8ZcUxbSLJ70xBIkt2BI4EbZz5rpC4BTmzWTwQuHmEs05r8hdv4LcbgnjaNlZ8Cbq6q0/t2jdU9nS7OcbunSRYm2atZfyK9F1JupvcL+NXNYeNwP9vivKXvj4LQaycZ+f/RLubFW0wAzWt4HwEWAGdX1ftGHNIWkvwavacG6I2k+5fjEmeS84Ej6A1NfA9wGvBF4LPAEuBO4LVVNdIG4mniPIJeVUgBdwBv7KvnH4kkhwPfBG4AHm2K30mvfn9s7ukMca5kjO5pkufQa4ReQO8P289W1Xubn6kL6FXbXAec0PyVPm5xfg1YCARYC5zS15g9tuZNgpAkDdZ8qWKSJA2YCUKS1MoEIUlqZYKQJLUyQUiSWpkgpK2QZItXE5M8K8kVzSidNydZneRlfSN3PpzeSMNrk3y677yPNCOmPq7Z/p2+c36Zx0b9/cBc/hulSb7mKm2FJA9X1R5Tyr4C/HlVXdxs/6uquqFv/xXAf62qNX1ljwN+SG8Yi3dU1eVTvvMOxnDUX+1cfIKQtt9+9AaKA6A/OczgCOAmemMerRxOWNL2MUFI2+/DwNeSfDnJf5kcamEWK4Hz6fWs/3fNeEjSWDFBSNupqs4B/iXwOXpPBt+eaTjnZrywY4AvNiOnXg28bA5ClbaKCUIagKq6q6rOrqoVwGbgkBkOfxmwF3BD09ZwOFYzaQyZIKTtlN586Ls2688AnsbMw06vBN5QVUurailwIPDSJE8aerDSVthl9kMk9XlSkvV926fTm4fgo0l+3pT9QVXd3XZykwSOojdHMQBV9bMkVwKvAC4cTtjS1vM1V0lSK6uYJEmtTBCSpFYmCElSKxOEJKmVCUKS1MoEIUlqZYKQJLX6/7/G4r5ly+0hAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "fb = FeatureBinarizer(negations=True)\n",
    "figs, _ = explainer.visualize(boston_df, fb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}