examples/gam_coach_extract_model.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generate Models to Use in GAM Coach UI\n",
    "\n",
    "In this notebook, we show how to generate model JSON files to set up your own GAM Coach UI."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import gamcoach as coach\n",
    "import pickle\n",
    "import re\n",
    "\n",
    "from tqdm import tqdm\n",
    "from io import StringIO\n",
    "from collections import Counter\n",
    "from matplotlib import pyplot as plt\n",
    "from interpret.glassbox import ExplainableBoostingClassifier\n",
    "from interpret import show\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import metrics\n",
    "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
    "from sklearn import linear_model\n",
    "from time import time\n",
    "from json import load, dump\n",
    "\n",
    "SEED = 922"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>77516</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2174</td>\n",
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       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>83311</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>215646</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>234721</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>338409</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   0                  1       2           3   4                    5   \\\n",
       "0  39          State-gov   77516   Bachelors  13        Never-married   \n",
       "1  50   Self-emp-not-inc   83311   Bachelors  13   Married-civ-spouse   \n",
       "2  38            Private  215646     HS-grad   9             Divorced   \n",
       "3  53            Private  234721        11th   7   Married-civ-spouse   \n",
       "4  28            Private  338409   Bachelors  13   Married-civ-spouse   \n",
       "\n",
       "                   6               7       8        9     10  11  12  \\\n",
       "0        Adm-clerical   Not-in-family   White     Male  2174   0  40   \n",
       "1     Exec-managerial         Husband   White     Male     0   0  13   \n",
       "2   Handlers-cleaners   Not-in-family   White     Male     0   0  40   \n",
       "3   Handlers-cleaners         Husband   Black     Male     0   0  40   \n",
       "4      Prof-specialty            Wife   Black   Female     0   0  40   \n",
       "\n",
       "               13      14  \n",
       "0   United-States   <=50K  \n",
       "1   United-States   <=50K  \n",
       "2   United-States   <=50K  \n",
       "3   United-States   <=50K  \n",
       "4            Cuba   <=50K  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adult_data = pd.read_csv('./data/adult.data', header=None)\n",
    "adult_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "      <td>25</td>\n",
       "      <td>Private</td>\n",
       "      <td>226802</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Own-child</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>89814</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Farming-fishing</td>\n",
       "      <td>Husband</td>\n",
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       "      <td>Male</td>\n",
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       "      <td>United-States</td>\n",
       "      <td>&lt;=50K.</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>Local-gov</td>\n",
       "      <td>336951</td>\n",
       "      <td>Assoc-acdm</td>\n",
       "      <td>12</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Protective-serv</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>44</td>\n",
       "      <td>Private</td>\n",
       "      <td>160323</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>7688</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18</td>\n",
       "      <td>?</td>\n",
       "      <td>103497</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>?</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K.</td>\n",
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       "   0           1       2              3   4                    5   \\\n",
       "0  25     Private  226802           11th   7        Never-married   \n",
       "1  38     Private   89814        HS-grad   9   Married-civ-spouse   \n",
       "2  28   Local-gov  336951     Assoc-acdm  12   Married-civ-spouse   \n",
       "3  44     Private  160323   Some-college  10   Married-civ-spouse   \n",
       "4  18           ?  103497   Some-college  10        Never-married   \n",
       "\n",
       "                   6           7       8        9     10  11  12  \\\n",
       "0   Machine-op-inspct   Own-child   Black     Male     0   0  40   \n",
       "1     Farming-fishing     Husband   White     Male     0   0  50   \n",
       "2     Protective-serv     Husband   White     Male     0   0  40   \n",
       "3   Machine-op-inspct     Husband   Black     Male  7688   0  40   \n",
       "4                   ?   Own-child   White   Female     0   0  30   \n",
       "\n",
       "               13       14  \n",
       "0   United-States   <=50K.  \n",
       "1   United-States   <=50K.  \n",
       "2   United-States    >50K.  \n",
       "3   United-States    >50K.  \n",
       "4   United-States   <=50K.  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adult_data_test = pd.read_csv('./data/adult.test', header=None)\n",
    "adult_data_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(32561, 15)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adult_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# From https://github.com/amirhk/mace/blob/01e6a405ff74e24dc3438a005cd60892154d189d/_data_main/fair_adult_data.py\n",
    "adult_attrs = [\n",
    "    \"age\",\n",
    "    \"workclass\",\n",
    "    \"fnlwgt\",\n",
    "    \"education\",\n",
    "    \"education_num\",\n",
    "    \"marital_status\",\n",
    "    \"occupation\",\n",
    "    \"relationship\",\n",
    "    \"race\",\n",
    "    \"sex\",\n",
    "    \"capital_gain\",\n",
    "    \"capital_loss\",\n",
    "    \"hours_per_week\",\n",
    "    \"native_country\",\n",
    "]\n",
    "\n",
    "selected_features = [0, 1, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13]\n",
    "\n",
    "x_train = adult_data.to_numpy()[:, selected_features]\n",
    "x_train[:, [8, 9]] = np.log10(x_train[:, [8, 9]].astype(float) + 1e-6)\n",
    "\n",
    "y_train_ints = list(map(lambda x: 0 if x == ' <=50K' else 1, adult_data.iloc[:, -1].tolist()))\n",
    "y_train = np.array(y_train_ints)\n",
    "\n",
    "x_test = adult_data_test.to_numpy()[:, selected_features]\n",
    "x_test[:, [8, 9]] = np.log10(x_test[:, [8, 9]].astype(float) + 1e-6)\n",
    "\n",
    "y_test_ints = list(map(lambda x: 0 if x == ' <=50K.' else 1, adult_data_test.iloc[:, -1].tolist()))\n",
    "y_test = np.array(y_test_ints)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "adult_feature_names = np.array(adult_attrs)[selected_features]\n",
    "adult_feature_names\n",
    "adult_cont_indexes = [0, 3, 8, 9, 10]\n",
    "adult_feature_types = [\n",
    "    \"continuous\" if i in adult_cont_indexes else \"nominal\"\n",
    "    for i in range(len(adult_feature_names))\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "       &#x27;occupation&#x27;, &#x27;relationship&#x27;, &#x27;sex&#x27;, &#x27;capital_gain&#x27;,\n",
       "       &#x27;capital_loss&#x27;, &#x27;hours_per_week&#x27;, &#x27;native_country&#x27;], dtype=&#x27;&lt;U14&#x27;),\n",
       "                              feature_types=[&#x27;continuous&#x27;, &#x27;nominal&#x27;, &#x27;nominal&#x27;,\n",
       "                                             &#x27;continuous&#x27;, &#x27;nominal&#x27;, &#x27;nominal&#x27;,\n",
       "                                             &#x27;nominal&#x27;, &#x27;nominal&#x27;, &#x27;continuous&#x27;,\n",
       "                                             &#x27;continuous&#x27;, &#x27;continuous&#x27;,\n",
       "                                             &#x27;nominal&#x27;],\n",
       "                              random_state=922)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ExplainableBoostingClassifier</label><div class=\"sk-toggleable__content\"><pre>ExplainableBoostingClassifier(feature_names=array([&#x27;age&#x27;, &#x27;workclass&#x27;, &#x27;education&#x27;, &#x27;education_num&#x27;, &#x27;marital_status&#x27;,\n",
       "       &#x27;occupation&#x27;, &#x27;relationship&#x27;, &#x27;sex&#x27;, &#x27;capital_gain&#x27;,\n",
       "       &#x27;capital_loss&#x27;, &#x27;hours_per_week&#x27;, &#x27;native_country&#x27;], dtype=&#x27;&lt;U14&#x27;),\n",
       "                              feature_types=[&#x27;continuous&#x27;, &#x27;nominal&#x27;, &#x27;nominal&#x27;,\n",
       "                                             &#x27;continuous&#x27;, &#x27;nominal&#x27;, &#x27;nominal&#x27;,\n",
       "                                             &#x27;nominal&#x27;, &#x27;nominal&#x27;, &#x27;continuous&#x27;,\n",
       "                                             &#x27;continuous&#x27;, &#x27;continuous&#x27;,\n",
       "                                             &#x27;nominal&#x27;],\n",
       "                              random_state=922)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "ExplainableBoostingClassifier(feature_names=array(['age', 'workclass', 'education', 'education_num', 'marital_status',\n",
       "       'occupation', 'relationship', 'sex', 'capital_gain',\n",
       "       'capital_loss', 'hours_per_week', 'native_country'], dtype='<U14'),\n",
       "                              feature_types=['continuous', 'nominal', 'nominal',\n",
       "                                             'continuous', 'nominal', 'nominal',\n",
       "                                             'nominal', 'nominal', 'continuous',\n",
       "                                             'continuous', 'continuous',\n",
       "                                             'nominal'],\n",
       "                              random_state=922)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train an EBM classifier\n",
    "adult_ebm = ExplainableBoostingClassifier(\n",
    "    feature_names=adult_feature_names,\n",
    "    feature_types=adult_feature_types,\n",
    "    random_state=SEED,\n",
    ")\n",
    "adult_ebm.fit(x_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy 0.8736564093114674\n"
     ]
    }
   ],
   "source": [
    "y_predict = adult_ebm.predict(x_test)\n",
    "print('accuracy', metrics.accuracy_score(y_test, y_predict))\n",
    "pickle.dump(adult_ebm, open('./model_pickles/adult-ebm-ca.pickle', 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Users can provide a partial dictionary to add descriptions for some features/levels\n",
    "feature_info = {\n",
    "    \"age\": [\"Age\", \"Age (years)\"],\n",
    "    \"workclass\": [\"Workclass\", \"Types of workclass\"],\n",
    "    \"education\": [\"Education\", \"Types of education level\"],\n",
    "    \"education_num\": [\"Education Years\", \"Years of education\"],\n",
    "    \"marital_status\": [\"Marital Status\", \"Marital status\"],\n",
    "    \"occupation\": [\"Occupation\", \"Occupation\"],\n",
    "    \"relationship\": [\"Relationship\", \"Relationship\"],\n",
    "    \"sex\": [\"Sex\", \"Sex\"],\n",
    "    \"capital_gain\": [\"Capital gain\", \"Capital gain in a year\"],\n",
    "    \"capital_loss\": [\"Capital loss\", \"Capital loss in a year\"],\n",
    "    \"hours_per_week\": [\"Work Hours\", \"Hours to work every week\"],\n",
    "    \"native_country\": [\"Native Country\", \"Native Country\"],\n",
    "}\n",
    "\n",
    "feature_level_info = {\n",
    "    \"workclass\": {\n",
    "        1: [\"Unknown\", \"\"],\n",
    "        2: [\"Federal government\", \"\"],\n",
    "        3: [\"Local government\", \"\"],\n",
    "        4: [\"Never worked\", \"\"],\n",
    "        5: [\"Private\", \"\"],\n",
    "        6: [\"Self-employed (inc)\", \"\"],\n",
    "        7: [\"Self-employed\", \"\"],\n",
    "        8: [\"State government\", \"\"],\n",
    "        9: [\"Without pay\", \"\"],\n",
    "    },\n",
    "    \"education\": {\n",
    "        1: [\"10th\", \"\"],\n",
    "        2: [\"11th\", \"\"],\n",
    "        3: [\"12th\", \"\"],\n",
    "        4: [\"1st-4th\", \"\"],\n",
    "        5: [\"5th-6th\", \"\"],\n",
    "        6: [\"7th-8th\", \"\"],\n",
    "        7: [\"9th\", \"\"],\n",
    "        8: [\"Assoc academy\", \"\"],\n",
    "        9: [\"Assoc voc\", \"\"],\n",
    "        10: [\"Bachelors\", \"\"],\n",
    "        11: [\"Doctorate\", \"\"],\n",
    "        12: [\"High school\", \"\"],\n",
    "        13: [\"Masters\", \"\"],\n",
    "        14: [\"Preschool\", \"\"],\n",
    "        15: [\"Professional school\", \"\"],\n",
    "        16: [\"Some college\", \"\"],\n",
    "    },\n",
    "    \"marital_status\": {\n",
    "        1: [\"Divorced\", \"\"],\n",
    "        2: [\"Married (armed force spouse)\", \"\"],\n",
    "        3: [\"Married (civilian spouse)\", \"\"],\n",
    "        4: [\"Married (absent spouse)\", \"\"],\n",
    "        5: [\"Never married\", \"\"],\n",
    "        6: [\"Separated\", \"\"],\n",
    "        7: [\"Widowed\", \"\"],\n",
    "    },\n",
    "    \"occupation\": {\n",
    "        1: [\"Unknown\", \"\"],\n",
    "        2: [\"Adm-clerical\", \"\"],\n",
    "        3: [\"Armed-Forces\", \"\"],\n",
    "        4: [\"Craft-repair\", \"\"],\n",
    "        5: [\"Exec-managerial\", \"\"],\n",
    "        6: [\"Farming-fishing\", \"\"],\n",
    "        7: [\"Handlers-cleaners\", \"\"],\n",
    "        8: [\"Machine-op-inspct\", \"\"],\n",
    "        9: [\"Other-service\", \"\"],\n",
    "        10: [\"Priv-house-serv\", \"\"],\n",
    "        11: [\"Prof-specialty\", \"\"],\n",
    "        12: [\"Protective-serv\", \"\"],\n",
    "        13: [\"Sales\", \"\"],\n",
    "        14: [\"Tech-support\", \"\"],\n",
    "        15: [\"Transport-moving\", \"\"],\n",
    "    },\n",
    "    \"relationship\": {\n",
    "        1: [\"Husband\", \"\"],\n",
    "        2: [\"Not-in-family\", \"\"],\n",
    "        3: [\"Other-relative\", \"\"],\n",
    "        4: [\"Own-child\", \"\"],\n",
    "        5: [\"Unmarried\", \"\"],\n",
    "        6: [\"Wife\", \"\"],\n",
    "    },\n",
    "    \"sex\": {1: [\"Female\", \"\"], 2: [\"Male\", \"\"]},\n",
    "    \"native_country\": {\n",
    "        1: [\"Unknown\", \"\"],\n",
    "        2: [\"Cambodia\", \"\"],\n",
    "        3: [\"Canada\", \"\"],\n",
    "        4: [\"China\", \"\"],\n",
    "        5: [\"Columbia\", \"\"],\n",
    "        6: [\"Cuba\", \"\"],\n",
    "        7: [\"Dominican-Republic\", \"\"],\n",
    "        8: [\"Ecuador\", \"\"],\n",
    "        9: [\"El-Salvador\", \"\"],\n",
    "        10: [\"England\", \"\"],\n",
    "        11: [\"France\", \"\"],\n",
    "        12: [\"Germany\", \"\"],\n",
    "        13: [\"Greece\", \"\"],\n",
    "        14: [\"Guatemala\", \"\"],\n",
    "        15: [\"Haiti\", \"\"],\n",
    "        16: [\"Holand-Netherlands\", \"\"],\n",
    "        17: [\"Honduras\", \"\"],\n",
    "        18: [\"Hong\", \"\"],\n",
    "        19: [\"Hungary\", \"\"],\n",
    "        20: [\"India\", \"\"],\n",
    "        21: [\"Iran\", \"\"],\n",
    "        22: [\"Ireland\", \"\"],\n",
    "        23: [\"Italy\", \"\"],\n",
    "        24: [\"Jamaica\", \"\"],\n",
    "        25: [\"Japan\", \"\"],\n",
    "        26: [\"Laos\", \"\"],\n",
    "        27: [\"Mexico\", \"\"],\n",
    "        28: [\"Nicaragua\", \"\"],\n",
    "        29: [\"Outlying-US(Guam-USVI-etc)\", \"\"],\n",
    "        30: [\"Peru\", \"\"],\n",
    "        31: [\"Philippines\", \"\"],\n",
    "        32: [\"Poland\", \"\"],\n",
    "        33: [\"Portugal\", \"\"],\n",
    "        34: [\"Puerto-Rico\", \"\"],\n",
    "        35: [\"Scotland\", \"\"],\n",
    "        36: [\"South\", \"\"],\n",
    "        37: [\"Taiwan\", \"\"],\n",
    "        38: [\"Thailand\", \"\"],\n",
    "        39: [\"Trinadad&Tobago\", \"\"],\n",
    "        40: [\"United-States\", \"\"],\n",
    "        41: [\"Vietnam\", \"\"],\n",
    "        42: [\"Yugoslavia\", \"\"],\n",
    "    },\n",
    "}\n",
    "\n",
    "# Developers can provide initial configurations for individual features. The\n",
    "# configuration includes difficulty, requireInt, and search range. If a\n",
    "# configuration is not given for a feature, it uses the default value\n",
    "# The difficulty is an integer between 1 and 6: 1 (very easy to change), 2 (easy)\n",
    "# 3 (default), 4 (hard), 5 (very hard), 6 (impossible to change)\n",
    "\n",
    "feature_config = {\n",
    "    \"age\": {\"requiresInt\": True, \"requiresIncreasing\": True},\n",
    "    # \"workclass\": {\"requiresInt\": True},\n",
    "    # \"education\": {\"requiresInt\": True},\n",
    "    \"education_num\": {\"requiresInt\": True},\n",
    "    # \"marital_status\": {\"requiresInt\": True},\n",
    "    # \"occupation\": {\"requiresInt\": True},\n",
    "    # \"relationship\": {\"requiresInt\": True},\n",
    "    \"sex\": {\"difficulty\": 5, \"requiresInt\": True},\n",
    "    \"capital_gain\": {\"requiresInt\": True, \"usesTransform\": 'log10'},\n",
    "    \"capital_loss\": {\"requiresInt\": True, \"usesTransform\": 'log10'},\n",
    "    \"hours_per_week\": {\"requiresInt\": True},\n",
    "    \"native_country\": {\"difficulty\": 5, \"requiresInt\": True},\n",
    "}\n",
    "\n",
    "model_info = {\"classes\": [\"loan rejection\", \"loan approval\"]}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 22/22 [00:00<00:00, 32.50it/s]\n"
     ]
    }
   ],
   "source": [
    "model_data = coach.get_model_data(\n",
    "    adult_ebm,\n",
    "    x_train,\n",
    "    feature_info=feature_info,\n",
    "    feature_level_info=feature_level_info,\n",
    "    model_info=model_info,\n",
    "    feature_config=feature_config,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def dump_rejected_samples(cur_ebm, name, x_test, cont_indexes):\n",
    "    # Find all examples that are rejected\n",
    "    x_pred_prob = cur_ebm.predict_proba(x_test)[:, 1]\n",
    "    interested_indexes = np.where(\n",
    "        np.logical_and(\n",
    "            x_pred_prob > 0.4,\n",
    "            x_pred_prob < 0.45\n",
    "        )\n",
    "    )[0]\n",
    "\n",
    "    # Generate 500 random samples\n",
    "    np.random.seed(SEED)\n",
    "    random_indexes = interested_indexes[np.random.choice(range(len(interested_indexes)), 500)]\n",
    "\n",
    "    random_samples = []\n",
    "    for i in random_indexes:\n",
    "        cur_row = x_test[i, :].tolist()\n",
    "        cur_row_with_string = [x if i in cont_indexes else str(x) for (i, x) in enumerate(cur_row)]\n",
    "        random_samples.append(cur_row_with_string)\n",
    "\n",
    "    dump(random_samples, open(f'./outputs/{name}-classifier-random-samples.json', 'w'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "dump(model_data, open('./outputs/adult-classifier.json', 'w'))\n",
    "dump_rejected_samples(adult_ebm, 'adult', x_test, adult_cont_indexes)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Put JSON Files in the Right Folders\n",
    "\n",
    "Finally, put `adult-classifier-random-samples.json` to `gamcoach-ui/src/config/data` and put `adult-classifier.json` to `gamcoach-ui/public/data`. You will see GAM Coach running with this model and data file!"
   ]
  }
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