lecture_1_ebm_example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## License \n",
    "\n",
    "Copyright 2021 Patrick Hall (jphall@gwu.edu)\n",
    "\n",
    "Licensed under the Apache License, Version 2.0 (the \"License\");\n",
    "you may not use this file except in compliance with the License.\n",
    "You may obtain a copy of the License at\n",
    "\n",
    "    http://www.apache.org/licenses/LICENSE-2.0\n",
    "\n",
    "Unless required by applicable law or agreed to in writing, software\n",
    "distributed under the License is distributed on an \"AS IS\" BASIS,\n",
    "WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
    "See the License for the specific language governing permissions and\n",
    "limitations under the License."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**DISCLAIMER:** This notebook is not legal compliance advice."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lecture 1: EBM Example"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Python imports and inits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from interpret import show\n",
    "from interpret.glassbox import ExplainableBoostingClassifier\n",
    "import matplotlib.pyplot as plt \n",
    "%matplotlib inline \n",
    "import numpy as np                                                                                             \n",
    "import pandas as pd                                               \n",
    "import time                                             "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Global hyperpameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "SEED = 12345   # global random seed for better reproducibility\n",
    "np.random.seed(SEED)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Start global timer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "big_tic = time.time()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Download, Explore, and Prepare UCI Credit Card Default Data\n",
    "\n",
    "UCI credit card default data: https://archive.ics.uci.edu/ml/datasets/default+of+credit+card+clients\n",
    "\n",
    "The UCI credit card default data contains demographic and payment information about credit card customers in Taiwan in the year 2005. The data set contains 23 input features: \n",
    "\n",
    "* **`LIMIT_BAL`**: Amount of given credit (NT dollar)\n",
    "* **`SEX`**: 1 = male; 2 = female\n",
    "* **`EDUCATION`**: 1 = graduate school; 2 = university; 3 = high school; 4 = others \n",
    "* **`MARRIAGE`**: 1 = married; 2 = single; 3 = others\n",
    "* **`AGE`**: Age in years \n",
    "* **`PAY_0`, `PAY_2` - `PAY_6`**: History of past payment; `PAY_0` = the repayment status in September, 2005; `PAY_2` = the repayment status in August, 2005; ...; `PAY_6` = the repayment status in April, 2005. The measurement scale for the repayment status is: -1 = pay duly; 1 = payment delay for one month; 2 = payment delay for two months; ...; 8 = payment delay for eight months; 9 = payment delay for nine months and above. \n",
    "* **`BILL_AMT1` - `BILL_AMT6`**: Amount of bill statement (NT dollar). `BILL_AMNT1` = amount of bill statement in September, 2005; `BILL_AMT2` = amount of bill statement in August, 2005; ...; `BILL_AMT6` = amount of bill statement in April, 2005. \n",
    "* **`PAY_AMT1` - `PAY_AMT6`**: Amount of previous payment (NT dollar). `PAY_AMT1` = amount paid in September, 2005; `PAY_AMT2` = amount paid in August, 2005; ...; `PAY_AMT6` = amount paid in April, 2005. \n",
    "\n",
    "The steps below build several models to predict `DEFAULT_NEXT_MONTH` using payment, billing, and credit limit information in the UCI credit card default data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Import data and reformat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import XLS file\n",
    "path = 'default_of_credit_card_clients.xls'\n",
    "data = pd.read_excel(path,\n",
    "                     skiprows=1) # skip the first row of the spreadsheet\n",
    "\n",
    "# remove spaces from target column name \n",
    "data = data.rename(columns={'default payment next month': 'DEFAULT_NEXT_MONTH'}) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Assign modeling roles\n",
    "Notice that demographic features are not used as model inputs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y_name = DEFAULT_NEXT_MONTH\n",
      "x_names = ['LIMIT_BAL', 'PAY_0', 'PAY_2', 'PAY_3', 'PAY_4', 'PAY_5', 'PAY_6', 'BILL_AMT1', 'BILL_AMT2', 'BILL_AMT3', 'BILL_AMT4', 'BILL_AMT5', 'BILL_AMT6', 'PAY_AMT1', 'PAY_AMT2', 'PAY_AMT3', 'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6']\n"
     ]
    }
   ],
   "source": [
    "# assign target and inputs for models\n",
    "y_name = 'DEFAULT_NEXT_MONTH'\n",
    "x_names = [name for name in data.columns if name not in [y_name, 'ID', 'SEX', 'EDUCATION', 'MARRIAGE', 'AGE']]\n",
    "print('y_name =', y_name)\n",
    "print('x_names =', x_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Display descriptive statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LIMIT_BAL</th>\n",
       "      <th>PAY_0</th>\n",
       "      <th>PAY_2</th>\n",
       "      <th>PAY_3</th>\n",
       "      <th>PAY_4</th>\n",
       "      <th>PAY_5</th>\n",
       "      <th>PAY_6</th>\n",
       "      <th>BILL_AMT1</th>\n",
       "      <th>BILL_AMT2</th>\n",
       "      <th>BILL_AMT3</th>\n",
       "      <th>BILL_AMT4</th>\n",
       "      <th>BILL_AMT5</th>\n",
       "      <th>BILL_AMT6</th>\n",
       "      <th>PAY_AMT1</th>\n",
       "      <th>PAY_AMT2</th>\n",
       "      <th>PAY_AMT3</th>\n",
       "      <th>PAY_AMT4</th>\n",
       "      <th>PAY_AMT5</th>\n",
       "      <th>PAY_AMT6</th>\n",
       "      <th>DEFAULT_NEXT_MONTH</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>3.000000e+04</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>3.000000e+04</td>\n",
       "      <td>30000.00000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "      <td>30000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>167484.322667</td>\n",
       "      <td>-0.016700</td>\n",
       "      <td>-0.133767</td>\n",
       "      <td>-0.166200</td>\n",
       "      <td>-0.220667</td>\n",
       "      <td>-0.266200</td>\n",
       "      <td>-0.291100</td>\n",
       "      <td>51223.330900</td>\n",
       "      <td>49179.075167</td>\n",
       "      <td>4.701315e+04</td>\n",
       "      <td>43262.948967</td>\n",
       "      <td>40311.400967</td>\n",
       "      <td>38871.760400</td>\n",
       "      <td>5663.580500</td>\n",
       "      <td>5.921163e+03</td>\n",
       "      <td>5225.68150</td>\n",
       "      <td>4826.076867</td>\n",
       "      <td>4799.387633</td>\n",
       "      <td>5215.502567</td>\n",
       "      <td>0.221200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>129747.661567</td>\n",
       "      <td>1.123802</td>\n",
       "      <td>1.197186</td>\n",
       "      <td>1.196868</td>\n",
       "      <td>1.169139</td>\n",
       "      <td>1.133187</td>\n",
       "      <td>1.149988</td>\n",
       "      <td>73635.860576</td>\n",
       "      <td>71173.768783</td>\n",
       "      <td>6.934939e+04</td>\n",
       "      <td>64332.856134</td>\n",
       "      <td>60797.155770</td>\n",
       "      <td>59554.107537</td>\n",
       "      <td>16563.280354</td>\n",
       "      <td>2.304087e+04</td>\n",
       "      <td>17606.96147</td>\n",
       "      <td>15666.159744</td>\n",
       "      <td>15278.305679</td>\n",
       "      <td>17777.465775</td>\n",
       "      <td>0.415062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>10000.000000</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>-165580.000000</td>\n",
       "      <td>-69777.000000</td>\n",
       "      <td>-1.572640e+05</td>\n",
       "      <td>-170000.000000</td>\n",
       "      <td>-81334.000000</td>\n",
       "      <td>-339603.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>50000.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>3558.750000</td>\n",
       "      <td>2984.750000</td>\n",
       "      <td>2.666250e+03</td>\n",
       "      <td>2326.750000</td>\n",
       "      <td>1763.000000</td>\n",
       "      <td>1256.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>8.330000e+02</td>\n",
       "      <td>390.00000</td>\n",
       "      <td>296.000000</td>\n",
       "      <td>252.500000</td>\n",
       "      <td>117.750000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>140000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>22381.500000</td>\n",
       "      <td>21200.000000</td>\n",
       "      <td>2.008850e+04</td>\n",
       "      <td>19052.000000</td>\n",
       "      <td>18104.500000</td>\n",
       "      <td>17071.000000</td>\n",
       "      <td>2100.000000</td>\n",
       "      <td>2.009000e+03</td>\n",
       "      <td>1800.00000</td>\n",
       "      <td>1500.000000</td>\n",
       "      <td>1500.000000</td>\n",
       "      <td>1500.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>240000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>67091.000000</td>\n",
       "      <td>64006.250000</td>\n",
       "      <td>6.016475e+04</td>\n",
       "      <td>54506.000000</td>\n",
       "      <td>50190.500000</td>\n",
       "      <td>49198.250000</td>\n",
       "      <td>5006.000000</td>\n",
       "      <td>5.000000e+03</td>\n",
       "      <td>4505.00000</td>\n",
       "      <td>4013.250000</td>\n",
       "      <td>4031.500000</td>\n",
       "      <td>4000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1000000.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>964511.000000</td>\n",
       "      <td>983931.000000</td>\n",
       "      <td>1.664089e+06</td>\n",
       "      <td>891586.000000</td>\n",
       "      <td>927171.000000</td>\n",
       "      <td>961664.000000</td>\n",
       "      <td>873552.000000</td>\n",
       "      <td>1.684259e+06</td>\n",
       "      <td>896040.00000</td>\n",
       "      <td>621000.000000</td>\n",
       "      <td>426529.000000</td>\n",
       "      <td>528666.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            LIMIT_BAL         PAY_0         PAY_2         PAY_3         PAY_4  \\\n",
       "count    30000.000000  30000.000000  30000.000000  30000.000000  30000.000000   \n",
       "mean    167484.322667     -0.016700     -0.133767     -0.166200     -0.220667   \n",
       "std     129747.661567      1.123802      1.197186      1.196868      1.169139   \n",
       "min      10000.000000     -2.000000     -2.000000     -2.000000     -2.000000   \n",
       "25%      50000.000000     -1.000000     -1.000000     -1.000000     -1.000000   \n",
       "50%     140000.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "75%     240000.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "max    1000000.000000      8.000000      8.000000      8.000000      8.000000   \n",
       "\n",
       "              PAY_5         PAY_6      BILL_AMT1      BILL_AMT2     BILL_AMT3  \\\n",
       "count  30000.000000  30000.000000   30000.000000   30000.000000  3.000000e+04   \n",
       "mean      -0.266200     -0.291100   51223.330900   49179.075167  4.701315e+04   \n",
       "std        1.133187      1.149988   73635.860576   71173.768783  6.934939e+04   \n",
       "min       -2.000000     -2.000000 -165580.000000  -69777.000000 -1.572640e+05   \n",
       "25%       -1.000000     -1.000000    3558.750000    2984.750000  2.666250e+03   \n",
       "50%        0.000000      0.000000   22381.500000   21200.000000  2.008850e+04   \n",
       "75%        0.000000      0.000000   67091.000000   64006.250000  6.016475e+04   \n",
       "max        8.000000      8.000000  964511.000000  983931.000000  1.664089e+06   \n",
       "\n",
       "           BILL_AMT4      BILL_AMT5      BILL_AMT6       PAY_AMT1  \\\n",
       "count   30000.000000   30000.000000   30000.000000   30000.000000   \n",
       "mean    43262.948967   40311.400967   38871.760400    5663.580500   \n",
       "std     64332.856134   60797.155770   59554.107537   16563.280354   \n",
       "min   -170000.000000  -81334.000000 -339603.000000       0.000000   \n",
       "25%      2326.750000    1763.000000    1256.000000    1000.000000   \n",
       "50%     19052.000000   18104.500000   17071.000000    2100.000000   \n",
       "75%     54506.000000   50190.500000   49198.250000    5006.000000   \n",
       "max    891586.000000  927171.000000  961664.000000  873552.000000   \n",
       "\n",
       "           PAY_AMT2      PAY_AMT3       PAY_AMT4       PAY_AMT5  \\\n",
       "count  3.000000e+04   30000.00000   30000.000000   30000.000000   \n",
       "mean   5.921163e+03    5225.68150    4826.076867    4799.387633   \n",
       "std    2.304087e+04   17606.96147   15666.159744   15278.305679   \n",
       "min    0.000000e+00       0.00000       0.000000       0.000000   \n",
       "25%    8.330000e+02     390.00000     296.000000     252.500000   \n",
       "50%    2.009000e+03    1800.00000    1500.000000    1500.000000   \n",
       "75%    5.000000e+03    4505.00000    4013.250000    4031.500000   \n",
       "max    1.684259e+06  896040.00000  621000.000000  426529.000000   \n",
       "\n",
       "            PAY_AMT6  DEFAULT_NEXT_MONTH  \n",
       "count   30000.000000        30000.000000  \n",
       "mean     5215.502567            0.221200  \n",
       "std     17777.465775            0.415062  \n",
       "min         0.000000            0.000000  \n",
       "25%       117.750000            0.000000  \n",
       "50%      1500.000000            0.000000  \n",
       "75%      4000.000000            0.000000  \n",
       "max    528666.000000            1.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[x_names + [y_name]].describe() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Train Explainable Boosting Machine"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Split data into training and validation partitions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data rows = 20946, columns = 25\n",
      "Validation data rows = 9054, columns = 25\n"
     ]
    }
   ],
   "source": [
    "split_ratio = 0.7 # 70%/30% train/test split\n",
    "\n",
    "# execute split\n",
    "split = np.random.rand(len(data)) < split_ratio\n",
    "train = data[split]\n",
    "valid = data[~split]\n",
    "\n",
    "# summarize split\n",
    "print('Train data rows = %d, columns = %d' % (train.shape[0], train.shape[1]))\n",
    "print('Validation data rows = %d, columns = %d' % (valid.shape[0], valid.shape[1]))\n",
    "\n",
    "# convert to more standard scikit learn partitions\n",
    "X_train = train[x_names]\n",
    "X_valid = valid[x_names]\n",
    "y_train = train[y_name]\n",
    "y_valid = train[y_name]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Train EBM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ExplainableBoostingClassifier(feature_names=['LIMIT_BAL', 'PAY_0', 'PAY_2',\n",
       "                                             'PAY_3', 'PAY_4', 'PAY_5', 'PAY_6',\n",
       "                                             'BILL_AMT1', 'BILL_AMT2',\n",
       "                                             'BILL_AMT3', 'BILL_AMT4',\n",
       "                                             'BILL_AMT5', 'BILL_AMT6',\n",
       "                                             'PAY_AMT1', 'PAY_AMT2', 'PAY_AMT3',\n",
       "                                             'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6',\n",
       "                                             'PAY_0 x PAY_2', 'PAY_0 x PAY_3',\n",
       "                                             'PAY_0 x BILL_AMT1',\n",
       "                                             'PAY_0 x PAY_4',\n",
       "                                             'PAY_0 x PAY_AMT2',\n",
       "                                             'PAY_0 x PAY_AMT3',\n",
       "                                             'LIMIT_BAL x BI...\n",
       "                                             'continuous', 'continuous',\n",
       "                                             'continuous', 'continuous',\n",
       "                                             'continuous', 'continuous',\n",
       "                                             'continuous', 'continuous',\n",
       "                                             'continuous', 'continuous',\n",
       "                                             'continuous', 'continuous',\n",
       "                                             'continuous', 'continuous',\n",
       "                                             'continuous', 'interaction',\n",
       "                                             'interaction', 'interaction',\n",
       "                                             'interaction', 'interaction',\n",
       "                                             'interaction', 'interaction',\n",
       "                                             'interaction', 'interaction',\n",
       "                                             'interaction'],\n",
       "                              n_jobs=-1, random_state=12345)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ebm = ExplainableBoostingClassifier(random_state=SEED, n_jobs=-1)\n",
    "ebm.fit(X_train, y_train)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Show EBM feature plots - uncomment when running live for interactive output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#ebm_global = ebm.explain_global(name='EBM')\n",
    "#show(ebm_global)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Extract static values for explanatory content that can be rendered with static plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EBM contributions collected in 4.35 s.\n"
     ]
    }
   ],
   "source": [
    "# start local timer\n",
    "ebm_tic = time.time()\n",
    "\n",
    "# use EBM feature scores as local importance\n",
    "# use mean abs score as global importance\n",
    "ebm_contribs = ebm.explain_local(X_valid)\n",
    "names = ebm_contribs._internal_obj['specific'][0]['names']\n",
    "ebm_contribs_df = pd.DataFrame(columns=names)\n",
    "\n",
    "# extract scores from insane _internal_obj\n",
    "# new versions just have predict_and_explain() function\n",
    "# use that if you can\n",
    "for i in range(0, X_valid.shape[0]):\n",
    "    row_df = pd.DataFrame(np.array(ebm_contribs._internal_obj['specific'][i]['scores']).reshape(1, 29), columns=names)\n",
    "    ebm_contribs_df = ebm_contribs_df.append(row_df, ignore_index=True)\n",
    "    del row_df\n",
    "    \n",
    "# end local timer\n",
    "ebm_toc = time.time() - ebm_tic\n",
    "print('EBM contributions collected in %.2f s.' % (ebm_toc))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10,10))\n",
    "_ = np.abs(ebm_contribs_df).mean().sort_values().plot(kind='barh', ax=ax, title='EBM Global Feature Importance')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Utility function to manually plot partial dependence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def par_dep(x_name, valid, model, resolution=20, bins=None):\n",
    "\n",
    "    \"\"\" Creates Pandas DataFrame containing partial dependence or ICE\n",
    "        for a single input variable.\n",
    "        :param x_name: Variable for which to calculate partial dependence.\n",
    "        :param valid: Pandas validation frame.\n",
    "        :param model: Trained model object (assumes binary classifier).\n",
    "        :param resolution: The number of points across the domain of xs for which\n",
    "                           to calculate partial dependence, default 20.\n",
    "        :param bins: List of values at which to set xs, default 20 equally-spaced\n",
    "                     points between column minimum and maximum.\n",
    "        :return: Pandas DataFrame containing partial dependence values.\n",
    "    \"\"\"\n",
    "\n",
    "    # turn off pesky Pandas copy warning\n",
    "    pd.options.mode.chained_assignment = None\n",
    "\n",
    "    # determine values at which to calculate partial dependence\n",
    "    if bins is None:\n",
    "        min_ = valid[x_name].min()\n",
    "        max_ = valid[x_name].max()\n",
    "        by = (max_ - min_) / resolution\n",
    "        # modify max and by\n",
    "        # to preserve resolution and actually search up to max\n",
    "        bins = np.arange(min_, (max_ + by), (by + np.round((1. / resolution) * by, 3)))\n",
    "        \n",
    "    # cache original column values\n",
    "    col_cache = valid.loc[:, x_name].copy(deep=True)\n",
    "\n",
    "    # calculate partial dependence\n",
    "    # by setting column of interest to constant\n",
    "    # and scoring the altered data and taking the mean of the predictions\n",
    "    temp_df = valid.copy(deep=True)\n",
    "    temp_df.loc[:, x_name] = bins[0]\n",
    "    for j, _ in enumerate(bins):\n",
    "        if j + 1 < len(bins):\n",
    "            valid.loc[:, x_name] = bins[j + 1]\n",
    "            temp_df = temp_df.append(valid, ignore_index=True)\n",
    "\n",
    "    # return input frame to original cached state\n",
    "    valid.loc[:, x_name] = col_cache\n",
    "\n",
    "    # model predictions\n",
    "    # probably assumes binary classification\n",
    "    temp_df['partial_dependence'] = pd.DataFrame(model.predict_proba(temp_df)[:, 1], columns=['phat'])\n",
    "    \n",
    "    return pd.DataFrame(temp_df[[x_name, 'partial_dependence']].groupby([x_name]).mean()).reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Calculate partial dependence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculating partial dependence for LIMIT_BAL ...\n",
      "Calculating partial dependence for PAY_0 ...\n",
      "Calculating partial dependence for PAY_2 ...\n",
      "Calculating partial dependence for PAY_3 ...\n",
      "Calculating partial dependence for PAY_4 ...\n",
      "Calculating partial dependence for PAY_5 ...\n",
      "Calculating partial dependence for PAY_6 ...\n",
      "Calculating partial dependence for BILL_AMT1 ...\n",
      "Calculating partial dependence for BILL_AMT2 ...\n",
      "Calculating partial dependence for BILL_AMT3 ...\n",
      "Calculating partial dependence for BILL_AMT4 ...\n",
      "Calculating partial dependence for BILL_AMT5 ...\n",
      "Calculating partial dependence for BILL_AMT6 ...\n",
      "Calculating partial dependence for PAY_AMT1 ...\n",
      "Calculating partial dependence for PAY_AMT2 ...\n",
      "Calculating partial dependence for PAY_AMT3 ...\n",
      "Calculating partial dependence for PAY_AMT4 ...\n",
      "Calculating partial dependence for PAY_AMT5 ...\n",
      "Calculating partial dependence for PAY_AMT6 ...\n",
      "Done.\n",
      "Partial dependence calculated in 10.85 s.\n"
     ]
    }
   ],
   "source": [
    "# start local timer\n",
    "pd_tic = time.time()\n",
    "\n",
    "# init dict to hold results\n",
    "pd_dict = {}\n",
    "\n",
    "# cycle through names to calculate par dep\n",
    "for name in x_names:\n",
    "    \n",
    "    print('Calculating partial dependence for %s ...' % name)\n",
    "    pd_dict[name] = par_dep(name, X_valid, ebm)\n",
    "    \n",
    "print('Done.')\n",
    "\n",
    "# end local timer\n",
    "ebm_toc = time.time() - ebm_tic\n",
    "print('Partial dependence calculated in %.2f s.' % (ebm_toc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "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"
    },
    {
     "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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M4SQiRRHvTup6k+RM4SQiRRFLpNRXT3KmcBKRooglkjQrnCRHCicRKbhkXz9dPX265iQ5UziJSMHtDW7A1Wk9yZXCSUQKLtaV6Q6hkZPkRuEkIgWXafqqkZPkSuEkIgUXT2jkJCOjcBKRgoslNHKSkVE4iUjBxRNJaqsrqK+pDLsUKREKJxEpuHTrogmYWdilSIlQOIlIwcW6U7reJCOSUziZ2Qoze87M2s3smiHWX21mz5jZU2b2gJnNz1p3g5ltMLONZvYvpn86iZSdWFdSDxmUERk2nMysErgZuBBYAlxqZksGbbYeaHP3U4G7gBuCfd8MnAmcCpwCnAGcnbfqRaQkqOmrjFQuI6elQLu7b3H3FHA7cHH2Bu7+oLsfDF6uBuZkVgG1QA0wAagG9uSjcBEpDe6evuakkZOMQC7hNBvYnvV6R7DsaD4G3APg7v8DPAi8FHzd6+4bB+9gZlea2VozW9vZ2Zlr7SJSAg4c6qNvwNVXT0YkrxMizOwyoA24MXi9EFhMeiQ1GzjPzM4avJ+73+Lube7e1tzcnM+SRCRkncE9Ts0NGjlJ7nIJp53A3KzXc4Jlr2Jmy4FrgYvcPRksfg+w2t0T7p4gPaJ609hKFpFSEs/cgFuvcJLc5RJOa4BFZtZqZjXAJcCq7A3M7HTgu6SDqSNr1YvA2WZWZWbVpCdDHHFaT0TGr/jhjuQ6rSe5Gzac3L0PWAncSzpY7nD3DWZ2nZldFGx2IzAJuNPMnjCzTHjdBWwGngaeBJ5091/m+4cQkejKtC7SVHIZiapcNnL3u4G7By37Stb3y4+yXz/w8bEUKCKlLZZIYQaNddVhlyIlRB0iRKSg4okkjXU1VFXqz43kTr8tIlJQ6b56ut4kI6NwEpGCiiXUukhGTuEkIgUV705ppp6MmMJJRApKIycZDYWTiBRMsq+frp4+XXOSEVM4iUjBxBPpG3Cnq3WRjFBO9zmJFNrOfYd4OegkEBXzp9XRUKt7c8YiE04aOclIKZwkdPsP9nLujQ+R6h8Iu5RXecvC6fzoimVhl1HSYt1BXz1dc5IRUjhJ6J7b00Wqf4CrLziRxbMmh10OAP/5xE7u37CHnt5+aqsrwy6nZMW6go7kCicZIYWThK69IwHAn75+NnMa60Ku5hW/fuolnty+j2UnTAu7lJKlpq8yWpoQIaFr70gwsbqS46dMDLuUw85oaQRgzda9IVdS2uKJJLXVFdTVaPQpI6NwktBt6uhiwYx6Kios7FIOm1pXw8nHNfDYCwqnsUi3LpqAWXT+20ppUDhJ6DZ3JFjYPCnsMo6wtLWJddtepi9iEzVKSWciqWnkMioKJwlVItnHrv09LJrZEHYpRzijpYmDqX427DoQdiklK55IMV3TyGUUFE4Sqs3BZIgFER05ATyuU3ujFu9OajKEjIrCSUKVmam3cEb0wmnm5FpaptXxuCZFjMrAgKdHTppGLqOgcJJQtXcmqK405k+LzhTybEtbm1izdS8DAx52KSXnQE8vfQOuG3BlVBROEqpNexK0TKunOqJPSV3aOo19B3vZFIzwJHexTF89ndaTUYjmXwQpG5s7E5E8pZextCVz3SkeciWlJ54IWhfVa+QkI6dwktAk+/rZFu+OdDjNbZrIcZNrdb/TKBweOTVo5CQjp3CS0GyNHWTAozkZIsPMDl93ctd1p5GId2vkJKOncJLQbOroAqIdTpCeFLHnQJIX9x4Mu5SSEkukMIPGOj12REZO4SShae9IYBbNe5yyLQvud9KpvZGJJ5I01dVQFdHJLhJt+q2R0LR3JJjbWBf5R1IsnDGJxrpq3Yw7QrGEbsCV0VM4SWjaO6I9Uy/DzDijpUnhNEKZpq8io6FwklD0DzhbYtGeqZdtaWsTL+49yO79PWGXUjLi3SmNnGTUFE4Siu17D5LqG4hkN/KhLGtNP3BQrYxyF+tKqnWRjJrCSUJxuKfezNIIp8WzGpg0oUo34+aop7efrmSfukPIqCmcJBSbItzwdShVlRW8fn6jrjvlaO/hx7Nr5CSjo3CSULR3JJjRMIHJtaVzD8yy1iae35Pg5eAPrxxd/HBfPYWTjI7CSULR3plgUYmc0svIPN9pja47DSuW6aun03oySgonKTp3j+yj2Y/l1DlTqKmq0Km9HGTCabqmkssoKZyk6HYf6CGR7CuZ600ZE6oqOX3uVM3Yy0H88DUnjZxkdHIKJzNbYWbPmVm7mV0zxPqrzewZM3vKzB4ws/lZ6+aZ2X1mtjHYpiWP9UsJyszUW1Bi4QTpU3t/3LmfRLIv7FIiLZ5IMrG6kvoJVWGXIiVq2HAys0rgZuBCYAlwqZktGbTZeqDN3U8F7gJuyFr3Q+BGd18MLAU68lG4lK5MOC2a0RByJSO3tLWJAYd1214Ou5RIiyV0A66MTS4jp6VAu7tvcfcUcDtwcfYG7v6gu2daNq8G5gAEIVbl7vcH2yWytpMytakjwZSJ1SV5D8zr5zVSWWGs0XWnY0r31dP1Jhm9XMJpNrA96/WOYNnRfAy4J/j+RGCfmf3czNab2Y3BSOxVzOxKM1trZms7OztzrV1KVKannpmFXcqI1U+o4pTZUzQpYhjxRIrp9aX3jw+JjrxOiDCzy4A24MZgURVwFvAF4AzgBODywfu5+y3u3ububc3NzfksSSJoc0eCRSV4vSljWWsTT2zfR09vf9ilRFYsodZFMja5hNNOYG7W6znBslcxs+XAtcBF7p4MFu8AnghOCfYB/wm8fkwVS0nb250i3p0quZl62c5oaSLVP8CT2/eFXUokDQw4e9X0VcYol3BaAywys1YzqwEuAVZlb2BmpwPfJR1MHYP2nWpmmeHQecAzYy9bSlUpz9TLOKOlEUCn9o7iQE8vfQOua04yJsOGUzDiWQncC2wE7nD3DWZ2nZldFGx2IzAJuNPMnjCzVcG+/aRP6T1gZk8DBnyvAD+HlIhXZuqVbjhNravh5OMadL/TUcQOty7SyElGL6ebENz9buDuQcu+kvX98mPsez9w6mgLlPFlU0cXE6srOX7KxLBLGZOlrU3ctW4Hff0Degz5IIe7Q2jkJGOg/6ukqNo7EiyYUU9FRenN1Mu2tLWJg6l+Nuw6EHYpkZNp+qprTjIWCicpqlLsqTeUpS3pJrC67nSkeHfQ9FV99WQMFE5SNIlkH7v297BoZul1hhhsxuRaWqbV8ZjC6QixriRm0KT7nGQMFE5SNJszM/XGwcgJ0qf21m7by8CAh11KpMS6UzTV1VBZ4qduJVwKJyma9hJ7+u1wlrZOY9/B3sNP9ZW0eCKp600yZgonKZr2zgTVlcb8aXVhl5IXy1oz153iIVcSLfFESjP1ZMwUTlI0m/YkaJlWT/U4mXo9p3Eix02u1XWnQdT0VfJhfPyVkJKwuTMxbk7pAZgZS1ubePyFvbjrulNGPJFimiZDyBgpnKQokn39bIt3j6twgvSkiI6uJC/u1ZNgAHp6++lK9qk7hIyZwkmKYmvsIAM+fiZDZGSuO+nUXtre7kzrIp3Wk7FROElRbOroAsZfOC2cMYmm+hrdjBvItC7SNScZK4WTFEV7RwKz8XOPU4aZ0Ta/UeEUUOsiyReFkxRFe0eCuY111FYf8SDkkre0tYkX9x5k9/6esEsJXWbk1KyRk4yRwkmKIvNo9vFoWes0AD1Cg1cel6GRk4yVwkkKrn/A2RIbfzP1MhbPamDShCrdjEu6O8TE6krqanJ6Go/IUSmcpOC27z1Iqm9gXHQjH0pVZQVv0HUnAOJ6PLvkicJJCu5wT72Z4zOcIH3d6fk9icNTqctVLJHUNHLJC4WTFNymcdbwdShLg/ud1pT5dadYIqUbcCUvFE5ScO0dCWY0TGBybXXYpRTMqXOmUFNVwZoyP7UXTyT1kEHJC4WTFFx7Z4JF4/iUHsCEqkpOnzu1rGfsDQw4e7tTTG/QyEnGTuEkBeXu4+bR7MNZ1trEH3fuJ5HsC7uUUBzo6aVvwDVykrxQOElB7T7QQyLZN66vN2Wc0drEgMO6bS+HXUooXmldpJGTjJ3CSQrqlaffNoRcSeG9fl4jlRVWtvc7ZW7A1Ww9yQeFkxTUpj3jf6ZeRv2EKk6ZPYU1L5TnyCmucJI8UjhJQbV3Jpgysbpsphcva23iie376OntD7uUotNpPcknhZMUVKannpmFXUpRLG1pItU/wJPb94VdStHFE0nMoLFO4SRjp3CSgtrckWBRGZzSy2hraQQoy1ZGse4UTXU1VFaUxz9EpLAUTlIwe7tTxLtTZXG9KWNqXQ0nH9dQlvc7xdW6SPJI4SQFk5mpt6CMwgnSrYzWbXuZvv6BsEspqlhCTV8lfxROUjCZcCqn03qQDqeDqX427DoQdilFFU8k9Xh2yRuFkxTMpo4uJlZXcvyUiWGXUlRLW9JNYMvtulNcTV8ljxROUjDtHQkWzKinoswukM+YXEvLtDoeK6Nw6untpyvZp2tOkjcKJymYcumpN5SlrU2s2bqXgQEPu5SiiAfPsZpWr5GT5IfCSQoikexj1/4eFs0c/22LhrK0dRr7D/UefpbVeBc/fAOuRk6SHzmFk5mtMLPnzKzdzK4ZYv3VZvaMmT1lZg+Y2fxB6yeb2Q4zuylfhUu0bc7M1CvTkdOy1sx1p/Los/dK6yKNnCQ/hg0nM6sEbgYuBJYAl5rZkkGbrQfa3P1U4C7ghkHr/xfw8NjLlVLRXgZPvz2WOY0TmTWltmyuO3UGIyddc5J8yWXktBRod/ct7p4Cbgcuzt7A3R9094PBy9XAnMw6M3sDMBO4Lz8lSylo70xQXWnMn1YXdimhMDPOaGni8Rf24j7+rztlRk66z0nyJZdwmg1sz3q9I1h2NB8D7gEwswrg/wJfONYbmNmVZrbWzNZ2dnbmUJJE3aY9CVqm1VNdWb6XNZe2NtHRlWRb/ODwG5e4eCJJXU0ldTVVYZci40Re/3KY2WVAG3BjsOhTwN3uvuNY+7n7Le7e5u5tzc3N+SxJQrK5M1G2p/QyDl93KoNWRvFudYeQ/MolnHYCc7NezwmWvYqZLQeuBS5y92Sw+E3ASjPbCnwN+LCZfXVMFUvkJfv62RbvLvtwWjhjEk31NWVxM24skdTj2SWvchmDrwEWmVkr6VC6BPhA9gZmdjrwXWCFu3dklrv7B7O2uZz0pIkjZvvJ+LI1dpABL9/JEBnp606N/OwPO/jVU7vGfLwZDbWsWnkmUyP4SIpYIsXsqbVhlyHjyLDh5O59ZrYSuBeoBG519w1mdh2w1t1XkT6NNwm4M3huz4vuflEB65YI29TRBSicAD63/ERaptfDGOdEpPoH+MGjW/nBo1v5ywtOzE9xeRRPJHndnClhlyHjSE5XL939buDuQcu+kvX98hyOcRtw28jKk1LU3pHArHzvccq2eNZkFs+anJdj7Xz5ELf9fitXnNVKQ211Xo6ZDwMDrmtOknflO5VKCqa9I8HcxjpqqyvDLmVcWXneQvYf6uVHq18Mu5RX2X+ol/4B1zUnySuFk+Rd5tHskl+nzpnK2Sc28/3fbeFQqj/scg6Ldwc34DYonCR/FE6SV/0DzpaYZuoVyqfPW0i8O8VPH4/O6CmWaV2kpq+SRwonyavtew+S6htQOBVIW0sTy1qb+O7Dm0n2RWP0FFPTVykAhZPkVbn31CuGT5+3iD0Hkvxs3RG3G4ZCrYukEBROklebFE4Fd+bCaZw2dyrffqid3v6BsMshnkhSYdAYwfuvpHQpnCSv2jsSzGiYwOQITXUeb8yMT5+3kB0vH2LVE2O/uXesYt0pmuprqCyzJx5LYSmcJK/aOxMsmqlRU6Gdd/IMFs+azM0PtdMf8tN2Y11qXST5p3CSvHH3sn40ezGZGSvPXciWzm7+64+7Q60l3p1ieoNO6Ul+KZwkb3Yf6CGR7NP1piJZccpxLGiu51u/3RTqM6PiavoqBaBwkrx5ZaZeQ8iVlIfKCuOqcxfy7O4uHtjYMfwOBRJLqHWR5J/CSfJm0x7N1Cu2i153PHObJvKtB9tDGT319PaTSPbp8eySdwonyZv2zgRTJlYzXf+KLpqqygo+efZCnty+j0fb40V//3h30B1C/80lzxROkjeZnnrBY1OkSN77htkcN7mWb/12U9HfO57pDqFrTpJnCifJm80dCRbplF7RTaiq5ONnn8BjL+wt+lN3X2ldpJGT5JfCSfJib3eKeHdK15tCcskZ85hWX8NND7YX9X0PN33VNSfJM4WT5EVmpt4ChVMoJtZUcsVZJ/Dw8508tWNf0d5XffWkUBROkheZcNJpvfBc9sZ5TJlYzU2/Ld7oKZZIUldTSV1NTg/VFsmZwknyYlNHFxOrKzl+ysSwSylbDbXVfPTMFu57Zg/P7j5QlPeMJ5IaNUlBKJwkL9o7EiyYUU+Fmn+G6vI3tzBpQhU3P7i5KO8X707pepMUhMJJ8kI99aJhal0Nl71xPr9+ahdbOhMFf79YIqVp5FIQCicZs0Syj137e1g0U22LouCKs1qpqargOw8VfvQUSyR1A64UhMJJxmxzZqaeRk6RMH3SBC5dOo9frN/J9r0HC/Y+AwPO3m711ZPCUDjJmOnR7NFz5VtPoMKM7z5cuNHT/kO99A+4rjlJQSicZMzaOxNUVxrzp9WFXYoEZk2ZyHvfMIc71u5gz4GegrxHvDvTHULhJPmncJIx27QnQcu0eqor9esUJZ88ewH9A873Ht5SkON3dgXdIep1Wk/yT39NZMw2dyZ0Si+C5k2r4+LTjufHj714uEFrPmVGTtMbNHKS/FM4yZgk+/rZFu9WZ4iI+tQ5C+np6+fWR1/I+7EPty7SyEkKQOEkY7I1dpABV0+9qFo4YxJvP2UWP/z9NvYf6s3rsWOJJBWWvrdKJN8UTjImmzq6AM3Ui7Krzl1IV7KPH/5+a16PG0ukaKqvoVJdQaQAFE4yJu0dCcx0j1OULTl+MssXz+DfHn2B7mRf3o4bTyQ1jVwKRuEkY9LekWBuYx211ZVhlyLHcNW5C9l3sJcfP7Ytb8eM6wZcKSCFk4xJ5tHsEm2nz2vkLQunc8vDL9DT25+XY8YSSfXVk4JROMmo9Q84W2LdCqcSsfK8hcQSSe5Yuz0vx4sn1JFcCienJ4SZ2Qrgm0Al8H13/+qg9VcDVwB9QCfwF+6+zcxOA74DTAb6gevd/T/yV375eGn/IR7Y2MFzu7vCLuWwQ739pPoGFE4lYllrE2e0NPKdhzZTM8YbpvvdSST7dFpPCmbYcDKzSuBm4AJgB7DGzFa5+zNZm60H2tz9oJl9ErgBeD9wEPiwu28ys+OBdWZ2r7vvy/cPMt4MDDh/3LWf32zs4IGNe9iwK/3wuMm1VVRFqBPD7KkTWdrSFHYZkgMz43PLT+SD33+Ma37+dF6OqYkwUii5jJyWAu3uvgXAzG4HLgYOh5O7P5i1/WrgsmD581nb7DKzDqAZ2Dfmysehnt5+Hm2P8ZuNHfz22T3sOZC+j+QN8xu55sKTWb54BguaJ2GmqbsyOmcunM4f/vYCkn1jv+5UVVFBs7pDSIHkEk6zgeyT1DuAZcfY/mPAPYMXmtlSoAYoziM6S0RHVw+/3djBbzZ28Eh7Jz29A9TXVHL2Sc2cf/JMzj15Bk26A1/ySL9PUgpyuuaUKzO7DGgDzh60fBbw78BH3H1giP2uBK4EmDdvXj5Lihx3Z+NLXTywcQ+/ebaDJ7fvA9Knx97fNpfzF89k2QlNTKjS1GwRKV+5hNNOYG7W6znBslcxs+XAtcDZ7p7MWj4Z+DVwrbuvHuoN3P0W4BaAtrY2z7n6Auvp7edAHlq+OPDs7nQgPbCxg537DgHwurlT+cLbTuT8xTM5+bgGna4TEQnkEk5rgEVm1ko6lC4BPpC9gZmdDnwXWOHuHVnLa4BfAD9097vyVnWB7T/Uy62PvMCtj75AV0/+7qivra7grEXNfOb8hZx78gxmNNTm7dgiIuPJsOHk7n1mthK4l/RU8lvdfYOZXQesdfdVwI3AJODO4F//L7r7RcCfA28FppnZ5cEhL3f3J/L+k+TB4FC68JTjOHPhdPIxoDl+ykTetGCaOimIiOTA3CNzFg1In9Zbu3ZtUd9zqFD6zPmLWDxrclHrEBEpJ2a2zt3bhlqX1wkRpUahJCISTWUZTgolEZFoK6twGhxKK16TDqUlxyuURESipCzCSaEkIlJaxnU4KZRERErTuAwnhZKISGkbd+G0c98hVnzjYYWSiEgJG3fhdPyUWi5/cwsXnjJLoSQiUqLGXTiZGZ9/20lhlyEiImMQnafWiYiIBBROIiISOQonERGJHIWTiIhEjsJJREQiR+EkIiKRo3ASEZHIUTiJiEjkKJxERCRyIveYdjPrBLbl4VDTgVgejjOe6DM5kj6TI+kzOZI+k6GN9XOZ7+7NQ62IXDjli5mtPdqz6cuVPpMj6TM5kj6TI+kzGVohPxed1hMRkchROImISOSM53C6JewCIkifyZH0mRxJn8mR9JkMrWCfy7i95iQiIqVrPI+cRESkRCmcREQkcsZtOJnZjWb2rJk9ZWa/MLOpYdcUFjNbYWbPmVm7mV0Tdj1RYGZzzexBM3vGzDaY2WfDrikqzKzSzNab2a/CriUKzGyqmd0V/D3ZaGZvCrumsJnZXwb/3/zRzH5qZrX5fo9xG07A/cAp7n4q8DzwpZDrCYWZVQI3AxcCS4BLzWxJuFVFQh/weXdfArwRuEqfy2GfBTaGXUSEfBP4L3c/GXgdZf7ZmNls4DNAm7ufAlQCl+T7fcZtOLn7fe7eF7xcDcwJs54QLQXa3X2Lu6eA24GLQ64pdO7+krv/Ifi+i/QfnNnhVhU+M5sDvAP4fti1RIGZTQHeCvwbgLun3H1fqEVFQxUw0cyqgDpgV77fYNyG0yB/AdwTdhEhmQ1sz3q9A/0RfhUzawFOBx4LuZQo+Abw18BAyHVERSvQCfwgONX5fTOrD7uoMLn7TuBrwIvAS8B+d78v3+9T0uFkZr8JznkO/ro4a5trSZ/C+XF4lUpUmdkk4GfA59z9QNj1hMnM3gl0uPu6sGuJkCrg9cB33P10oBso6+u2ZtZI+uxLK3A8UG9ml+X7faryfcBicvflx1pvZpcD7wTO9/K9oWsnMDfr9ZxgWdkzs2rSwfRjd/952PVEwJnARWb2dqAWmGxmP3L3vP/hKSE7gB3unhlV30WZhxOwHHjB3TsBzOznwJuBH+XzTUp65HQsZraC9OmJi9z9YNj1hGgNsMjMWs2shvSFy1Uh1xQ6MzPS1xE2uvvXw64nCtz9S+4+x91bSP+e/LbMgwl33w1sN7OTgkXnA8+EWFIUvAi80czqgv+PzqcAk0RKeuQ0jJuACcD96c+P1e7+iXBLKj537zOzlcC9pGfV3OruG0IuKwrOBD4EPG1mTwTL/sbd7w6vJImoTwM/Dv5xtwX4aMj1hMrdHzOzu4A/kL5ksp4CtDFS+yIREYmccXtaT0RESpfCSUREIkfhJCIikaNwEhGRyFE4iYhI5CicREQkchROInlmZv1m9kTQSutOM6sLlleZWaeZfTV4fYGZ/U9wI2P2oyrefJTjXh7s/0TwdUXxfiqR4lI4ieTfIXc/LXicQArI3Px9AenHt7zPzMzd7we2AR8L1n8aWOvuvz/Gsf8jOPZp7q7O4TJuKZxECut3wMLg+0tJPxvoRSDzwLq/BL5kZq8BVgJfLHqFIhGkcBIpkOBZNxeSbpFUS7ph5i+Bn5IOKtz9JdKPqfgf4B/dfe8wh31v8HTnu8xs7jDbipQstS8SyTMz6weeDl7+Dvg8cBHwHnf/oJlNA54AWty938wqgC53P+ZzgoL9Eu6eNLOPA+939/MK9oOIhEjhJJJnZpZw90mDlv0MeAtwKFg0A7g4uO405D7DvEclsNfdp+SpbJFI0Wk9kQIzs8nAWcA8d28JHklxFcGpvREcZ1bWy4sowGMKRKJiPD8yQyQq3kP62UjJrGX/D7jBzCYMWn4snzGzi0g/pmAvcHl+yxSJDp3WExGRyNFpPRERiRyd1hOJGDO7FnjfoMV3uvv1YdQjEgad1hMRkcjRaT0REYkchZOIiESOwklERCJH4SQiIpHz/wFOeYy4wlhA1gAAAABJRU5ErkJggg==\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"
    },
    {
     "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"
    },
    {
     "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"
    },
    {
     "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"
    },
    {
     "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": [
    "# loop through names to calculate par dep \n",
    "for name in x_names:\n",
    "    \n",
    "    # init plot\n",
    "    fig, ax = plt.subplots()\n",
    "    plt.tight_layout()\n",
    "\n",
    "    # plot par dep from each model across frames\n",
    "    pd_dict[name].plot(kind='line', x=name, y='partial_dependence', ax=ax, title='Partial Dependence',\n",
    "                      legend=False)\n",
    "    ax.set_xlabel(name)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Display partial dependence"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### End global timer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All tasks completed in 18.61 s.\n"
     ]
    }
   ],
   "source": [
    "big_toc = time.time() - big_tic\n",
    "print('All tasks completed in %.2f s.' % (big_toc))"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "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": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}