dt_surrogate_pd_ice.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# License\n",
    "\n",
    "***\n",
    "\n",
    "Copyright 2018-2019 Lingyao Meng (danielle@h2o.ai), J. Patrick Hall (phall@h2o.ai), and the H2O.ai team. \n",
    "\n",
    "Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:\n",
    "The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.\n",
    "\n",
    "THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n",
    "\n",
    "**DISCLAIMER:** This notebook is not legal compliance advice."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Partial Dependence, Individual Conditional Expectation, and Surrogate Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Python imports\n",
    "In general, NumPy and Pandas will be used for data manipulation purposes and H2o and XGBoost will be used for modeling tasks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for handling external processes to generate PNG file of decision tree\n",
    "import os\n",
    "import time\n",
    "import sys\n",
    "import re\n",
    "import subprocess\n",
    "\n",
    "# in-notebook display of decision tree\n",
    "from IPython.display import Image\n",
    "from IPython.display import display\n",
    "\n",
    "# to generate synthetic data w/ known interactions\n",
    "from data_maker_and_getter import DataMakerAndGetter\n",
    "\n",
    "import h2o                                                        # Python API for h2o library and server \n",
    "from h2o.estimators.random_forest import H2ORandomForestEstimator # h2o, for single tree\n",
    "from h2o.backend import H2OLocalServer                            # h2o, for plotting local tree in-notebook\n",
    "\n",
    "import matplotlib.pyplot as plt                                   # basic plotting\n",
    "%matplotlib inline\n",
    "\n",
    "import numpy as np                                                # basic math and array and matrix handling \n",
    "import pandas as pd                                               # basic Dataframe handling\n",
    "import xgboost as xgb                                             # for training gradient boosting machines "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Start H2O server"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Checking whether there is an H2O instance running at http://localhost:54321 ..... not found.\n",
      "Attempting to start a local H2O server...\n",
      "  Java Version: java version \"1.8.0_201\"; Java(TM) SE Runtime Environment (build 1.8.0_201-b09); Java HotSpot(TM) 64-Bit Server VM (build 25.201-b09, mixed mode)\n",
      "  Starting server from /home/patrickh/anaconda3/lib/python3.6/site-packages/h2o/backend/bin/h2o.jar\n",
      "  Ice root: /tmp/tmpk0snj6eo\n",
      "  JVM stdout: /tmp/tmpk0snj6eo/h2o_patrickh_started_from_python.out\n",
      "  JVM stderr: /tmp/tmpk0snj6eo/h2o_patrickh_started_from_python.err\n",
      "  Server is running at http://127.0.0.1:54321\n",
      "Connecting to H2O server at http://127.0.0.1:54321 ... successful.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"overflow:auto\"><table style=\"width:50%\"><tr><td>H2O cluster uptime:</td>\n",
       "<td>01 secs</td></tr>\n",
       "<tr><td>H2O cluster timezone:</td>\n",
       "<td>America/New_York</td></tr>\n",
       "<tr><td>H2O data parsing timezone:</td>\n",
       "<td>UTC</td></tr>\n",
       "<tr><td>H2O cluster version:</td>\n",
       "<td>3.26.0.3</td></tr>\n",
       "<tr><td>H2O cluster version age:</td>\n",
       "<td>15 days </td></tr>\n",
       "<tr><td>H2O cluster name:</td>\n",
       "<td>H2O_from_python_patrickh_s01bpc</td></tr>\n",
       "<tr><td>H2O cluster total nodes:</td>\n",
       "<td>1</td></tr>\n",
       "<tr><td>H2O cluster free memory:</td>\n",
       "<td>1.778 Gb</td></tr>\n",
       "<tr><td>H2O cluster total cores:</td>\n",
       "<td>8</td></tr>\n",
       "<tr><td>H2O cluster allowed cores:</td>\n",
       "<td>8</td></tr>\n",
       "<tr><td>H2O cluster status:</td>\n",
       "<td>accepting new members, healthy</td></tr>\n",
       "<tr><td>H2O connection url:</td>\n",
       "<td>http://127.0.0.1:54321</td></tr>\n",
       "<tr><td>H2O connection proxy:</td>\n",
       "<td>None</td></tr>\n",
       "<tr><td>H2O internal security:</td>\n",
       "<td>False</td></tr>\n",
       "<tr><td>H2O API Extensions:</td>\n",
       "<td>Amazon S3, XGBoost, Algos, AutoML, Core V3, Core V4</td></tr>\n",
       "<tr><td>Python version:</td>\n",
       "<td>3.6.4 final</td></tr></table></div>"
      ],
      "text/plain": [
       "--------------------------  ---------------------------------------------------\n",
       "H2O cluster uptime:         01 secs\n",
       "H2O cluster timezone:       America/New_York\n",
       "H2O data parsing timezone:  UTC\n",
       "H2O cluster version:        3.26.0.3\n",
       "H2O cluster version age:    15 days\n",
       "H2O cluster name:           H2O_from_python_patrickh_s01bpc\n",
       "H2O cluster total nodes:    1\n",
       "H2O cluster free memory:    1.778 Gb\n",
       "H2O cluster total cores:    8\n",
       "H2O cluster allowed cores:  8\n",
       "H2O cluster status:         accepting new members, healthy\n",
       "H2O connection url:         http://127.0.0.1:54321\n",
       "H2O connection proxy:\n",
       "H2O internal security:      False\n",
       "H2O API Extensions:         Amazon S3, XGBoost, Algos, AutoML, Core V3, Core V4\n",
       "Python version:             3.6.4 final\n",
       "--------------------------  ---------------------------------------------------"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "h2o.init(max_mem_size='2G') # start server, allocate RAM\n",
    "h2o.remove_all()            # delete any existing data structures in server, best practice"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "#### Dataset with Known Signal Generating Functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create dataset with a known signal generating function with noise: \n",
    "\n",
    "$$ y = x_1 * x_4 + |x_8| * (x_9)^2 + e $$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Generation and XGBoost Training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generate synthetic data with:\n",
    "* 200,000 rows\n",
    "* Binary target\n",
    "* A single, known signal-generating function (see above)\n",
    "* With noise via label switching"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parse progress: |█████████████████████████████████████████████████████████| 100%\n",
      "Parse progress: |█████████████████████████████████████████████████████████| 100%\n"
     ]
    }
   ],
   "source": [
    "ds_ = DataMakerAndGetter(nrows=200000, target='binary', one_function=True, noise=True)\n",
    "rson = ds_.make_random_with_signal()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Histogram of important variable in generated data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f717ca7d198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rson_pd = rson.as_data_frame()\n",
    "_ = rson_pd['num9'].plot(kind='hist', bins=30, title='Histogram: num9')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Assign modeling roles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['num1', 'num2', 'num3', 'num4', 'num5', 'num6', 'num7', 'num8', 'num9', 'num10', 'num11', 'num12']\n"
     ]
    }
   ],
   "source": [
    "y = 'target'\n",
    "X = [name for name in rson.columns if name not in [y,'row_id','function','cat1','cat2','cat3']]\n",
    "print(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Split data into training and validation set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(80049, 18)\n",
      "(59947, 18)\n"
     ]
    }
   ],
   "source": [
    "rson[y] = rson[y].asfactor()\n",
    "train, valid, _ = rson.split_frame([0.4, 0.3], seed = 12345)\n",
    "print(train.shape)\n",
    "print(valid.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Convert h2o frame into Pandas Dataframe for various activites below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "rsontrain_pd = train.as_data_frame()\n",
    "rsonvalid_pd = valid.as_data_frame()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Convert Pandas Dataframes into XGBoost DMatrices (LightSVM format), required for training XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "rsontrain_dm = xgb.DMatrix(rsontrain_pd[X],\n",
    "                           rsontrain_pd[y])\n",
    "rsonvalid_dm = xgb.DMatrix(rsonvalid_pd[X],\n",
    "                           rsonvalid_pd[y])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Use average of y as XGBoost null model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.42154180564404303\n"
     ]
    }
   ],
   "source": [
    "ave_y = rsontrain_pd['target'].mean()\n",
    "print(ave_y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Train XGBoost GBM\n",
    "Hyperparameters selected by Cartesian grid search: https://gist.github.com/jphall663/705595e3bc72e8fdfee8fa56220503a5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\ttrain-auc:0.662655\teval-auc:0.637728\n",
      "Multiple eval metrics have been passed: 'eval-auc' will be used for early stopping.\n",
      "\n",
      "Will train until eval-auc hasn't improved in 50 rounds.\n",
      "[1]\ttrain-auc:0.701792\teval-auc:0.669751\n",
      "[2]\ttrain-auc:0.756935\teval-auc:0.722574\n",
      "[3]\ttrain-auc:0.792702\teval-auc:0.758416\n",
      "[4]\ttrain-auc:0.814924\teval-auc:0.782046\n",
      "[5]\ttrain-auc:0.81665\teval-auc:0.782009\n",
      "[6]\ttrain-auc:0.826115\teval-auc:0.79378\n",
      "[7]\ttrain-auc:0.829857\teval-auc:0.797381\n",
      "[8]\ttrain-auc:0.831158\teval-auc:0.798757\n",
      "[9]\ttrain-auc:0.832994\teval-auc:0.801017\n",
      "[10]\ttrain-auc:0.83283\teval-auc:0.799187\n",
      "[11]\ttrain-auc:0.836437\teval-auc:0.80342\n",
      "[12]\ttrain-auc:0.836729\teval-auc:0.802991\n",
      "[13]\ttrain-auc:0.839226\teval-auc:0.806378\n",
      "[14]\ttrain-auc:0.840314\teval-auc:0.808367\n",
      "[15]\ttrain-auc:0.841832\teval-auc:0.81027\n",
      "[16]\ttrain-auc:0.843173\teval-auc:0.8109\n",
      "[17]\ttrain-auc:0.843384\teval-auc:0.810746\n",
      "[18]\ttrain-auc:0.844492\teval-auc:0.812145\n",
      "[19]\ttrain-auc:0.845608\teval-auc:0.813533\n",
      "[20]\ttrain-auc:0.845638\teval-auc:0.81319\n",
      "[21]\ttrain-auc:0.846479\teval-auc:0.814112\n",
      "[22]\ttrain-auc:0.846693\teval-auc:0.813886\n",
      "[23]\ttrain-auc:0.846448\teval-auc:0.813307\n",
      "[24]\ttrain-auc:0.846257\teval-auc:0.812858\n",
      "[25]\ttrain-auc:0.847116\teval-auc:0.813745\n",
      "[26]\ttrain-auc:0.846877\teval-auc:0.813469\n",
      "[27]\ttrain-auc:0.848038\teval-auc:0.814094\n",
      "[28]\ttrain-auc:0.848931\teval-auc:0.81473\n",
      "[29]\ttrain-auc:0.849569\teval-auc:0.815095\n",
      "[30]\ttrain-auc:0.850246\teval-auc:0.815687\n",
      "[31]\ttrain-auc:0.850532\teval-auc:0.816101\n",
      "[32]\ttrain-auc:0.851083\teval-auc:0.816858\n",
      "[33]\ttrain-auc:0.851538\teval-auc:0.817414\n",
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      "[308]\ttrain-auc:0.886458\teval-auc:0.822812\n",
      "[309]\ttrain-auc:0.886494\teval-auc:0.822802\n",
      "[310]\ttrain-auc:0.886564\teval-auc:0.822806\n",
      "[311]\ttrain-auc:0.886667\teval-auc:0.822847\n",
      "[312]\ttrain-auc:0.886798\teval-auc:0.822845\n",
      "[313]\ttrain-auc:0.886945\teval-auc:0.822896\n",
      "[314]\ttrain-auc:0.887018\teval-auc:0.822907\n",
      "[315]\ttrain-auc:0.887107\teval-auc:0.822894\n",
      "[316]\ttrain-auc:0.887227\teval-auc:0.822922\n",
      "[317]\ttrain-auc:0.887318\teval-auc:0.822903\n",
      "[318]\ttrain-auc:0.887352\teval-auc:0.822889\n",
      "[319]\ttrain-auc:0.887543\teval-auc:0.822908\n",
      "[320]\ttrain-auc:0.887607\teval-auc:0.822889\n",
      "[321]\ttrain-auc:0.887686\teval-auc:0.82286\n",
      "[322]\ttrain-auc:0.887809\teval-auc:0.822885\n",
      "[323]\ttrain-auc:0.887897\teval-auc:0.822897\n",
      "[324]\ttrain-auc:0.887996\teval-auc:0.822939\n",
      "[325]\ttrain-auc:0.888061\teval-auc:0.82296\n",
      "[326]\ttrain-auc:0.888158\teval-auc:0.82298\n",
      "[327]\ttrain-auc:0.88826\teval-auc:0.822996\n",
      "[328]\ttrain-auc:0.888364\teval-auc:0.822968\n",
      "[329]\ttrain-auc:0.888406\teval-auc:0.822976\n",
      "[330]\ttrain-auc:0.888493\teval-auc:0.82299\n",
      "[331]\ttrain-auc:0.888495\teval-auc:0.822999\n",
      "[332]\ttrain-auc:0.888644\teval-auc:0.823041\n",
      "[333]\ttrain-auc:0.888735\teval-auc:0.823046\n",
      "[334]\ttrain-auc:0.88879\teval-auc:0.823021\n",
      "[335]\ttrain-auc:0.888869\teval-auc:0.823018\n",
      "[336]\ttrain-auc:0.888981\teval-auc:0.823006\n",
      "[337]\ttrain-auc:0.888996\teval-auc:0.823023\n",
      "[338]\ttrain-auc:0.889089\teval-auc:0.823055\n",
      "[339]\ttrain-auc:0.88917\teval-auc:0.823087\n",
      "[340]\ttrain-auc:0.8892\teval-auc:0.823078\n",
      "[341]\ttrain-auc:0.889233\teval-auc:0.82307\n",
      "[342]\ttrain-auc:0.88937\teval-auc:0.82305\n",
      "[343]\ttrain-auc:0.889542\teval-auc:0.823098\n",
      "[344]\ttrain-auc:0.889589\teval-auc:0.823056\n",
      "[345]\ttrain-auc:0.889713\teval-auc:0.823089\n",
      "[346]\ttrain-auc:0.889758\teval-auc:0.8231\n",
      "[347]\ttrain-auc:0.889837\teval-auc:0.823125\n",
      "[348]\ttrain-auc:0.889907\teval-auc:0.823134\n",
      "[349]\ttrain-auc:0.889941\teval-auc:0.823128\n",
      "[350]\ttrain-auc:0.890005\teval-auc:0.82313\n",
      "[351]\ttrain-auc:0.890097\teval-auc:0.823132\n",
      "[352]\ttrain-auc:0.890118\teval-auc:0.823132\n",
      "[353]\ttrain-auc:0.890167\teval-auc:0.823157\n",
      "[354]\ttrain-auc:0.890184\teval-auc:0.823144\n",
      "[355]\ttrain-auc:0.890283\teval-auc:0.823143\n",
      "[356]\ttrain-auc:0.890321\teval-auc:0.823136\n",
      "[357]\ttrain-auc:0.890503\teval-auc:0.823148\n",
      "[358]\ttrain-auc:0.890633\teval-auc:0.823146\n",
      "[359]\ttrain-auc:0.890762\teval-auc:0.823135\n",
      "[360]\ttrain-auc:0.890829\teval-auc:0.823136\n",
      "[361]\ttrain-auc:0.890978\teval-auc:0.823197\n",
      "[362]\ttrain-auc:0.890995\teval-auc:0.823186\n",
      "[363]\ttrain-auc:0.891028\teval-auc:0.823216\n",
      "[364]\ttrain-auc:0.891092\teval-auc:0.823207\n",
      "[365]\ttrain-auc:0.891106\teval-auc:0.823229\n",
      "[366]\ttrain-auc:0.89117\teval-auc:0.8232\n",
      "[367]\ttrain-auc:0.891234\teval-auc:0.823205\n",
      "[368]\ttrain-auc:0.891308\teval-auc:0.823236\n",
      "[369]\ttrain-auc:0.891375\teval-auc:0.823245\n",
      "[370]\ttrain-auc:0.891446\teval-auc:0.823246\n",
      "[371]\ttrain-auc:0.891485\teval-auc:0.823267\n",
      "[372]\ttrain-auc:0.891548\teval-auc:0.823282\n",
      "[373]\ttrain-auc:0.891682\teval-auc:0.823286\n",
      "[374]\ttrain-auc:0.891774\teval-auc:0.823297\n",
      "[375]\ttrain-auc:0.891808\teval-auc:0.8233\n",
      "[376]\ttrain-auc:0.891856\teval-auc:0.82331\n",
      "[377]\ttrain-auc:0.891933\teval-auc:0.823308\n",
      "[378]\ttrain-auc:0.891987\teval-auc:0.823289\n",
      "[379]\ttrain-auc:0.892029\teval-auc:0.823286\n",
      "[380]\ttrain-auc:0.89209\teval-auc:0.823263\n",
      "[381]\ttrain-auc:0.892129\teval-auc:0.823256\n",
      "[382]\ttrain-auc:0.892161\teval-auc:0.823222\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[383]\ttrain-auc:0.892321\teval-auc:0.823225\n",
      "[384]\ttrain-auc:0.89238\teval-auc:0.823224\n",
      "[385]\ttrain-auc:0.892507\teval-auc:0.823215\n",
      "[386]\ttrain-auc:0.892617\teval-auc:0.82319\n",
      "[387]\ttrain-auc:0.892649\teval-auc:0.82321\n",
      "[388]\ttrain-auc:0.892674\teval-auc:0.823212\n",
      "[389]\ttrain-auc:0.89271\teval-auc:0.823205\n",
      "[390]\ttrain-auc:0.892739\teval-auc:0.823216\n",
      "[391]\ttrain-auc:0.892819\teval-auc:0.823202\n",
      "[392]\ttrain-auc:0.892898\teval-auc:0.823223\n",
      "[393]\ttrain-auc:0.893005\teval-auc:0.823223\n",
      "[394]\ttrain-auc:0.893051\teval-auc:0.823198\n",
      "[395]\ttrain-auc:0.893092\teval-auc:0.823163\n",
      "[396]\ttrain-auc:0.893113\teval-auc:0.823163\n",
      "[397]\ttrain-auc:0.893189\teval-auc:0.823159\n",
      "[398]\ttrain-auc:0.893237\teval-auc:0.823173\n",
      "[399]\ttrain-auc:0.893294\teval-auc:0.823183\n"
     ]
    }
   ],
   "source": [
    "params = {\n",
    "     'base_score': ave_y,\n",
    "     'booster': 'gbtree',\n",
    "     'colsample_bytree': 0.9,\n",
    "     'eta': 0.01,\n",
    "     'eval_metric': 'auc',\n",
    "     'max_depth': 12,\n",
    "     'nthread': 4,\n",
    "     'objective': 'binary:logistic',\n",
    "     'reg_alpha': 0.001,\n",
    "     'reg_lambda': 0.01,\n",
    "     'seed': 12345,\n",
    "     'silent': 0,\n",
    "     'subsample': 0.1}\n",
    "\n",
    "watchlist = [(rsontrain_dm, 'train'), (rsonvalid_dm, 'eval')]\n",
    "\n",
    "rson_model = xgb.train(params, \n",
    "                       rsontrain_dm, \n",
    "                       400,\n",
    "                       early_stopping_rounds=50,\n",
    "                       evals=watchlist, \n",
    "                       verbose_eval=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate and Display Partial Dependence and ICE Curves"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Function for calculating partial dependence and ICE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def par_dep(xs, frame, model, resolution=20, bins=None):\n",
    "    \n",
    "    \"\"\" Creates Pandas DataFrame containing partial dependence for a \n",
    "        single variable.\n",
    "    \n",
    "    Args:\n",
    "        xs: Variable for which to calculate partial dependence.\n",
    "        frame: Pandas DataFrame for which to calculate partial dependence.\n",
    "        model: XGBoost model for which to calculate partial dependence.\n",
    "        resolution: The number of points across the domain of xs for which \n",
    "                    to calculate partial dependence, default 20.\n",
    "        bins: List of values at which to set xs, default 20 equally-spaced \n",
    "              points between column minimum and maximum.\n",
    "    \n",
    "    Returns:\n",
    "        Pandas DataFrame containing partial dependence values.\n",
    "        \n",
    "    \"\"\"\n",
    "    \n",
    "    # turn off pesky Pandas copy warning\n",
    "    pd.options.mode.chained_assignment = None\n",
    "    \n",
    "    # initialize empty Pandas DataFrame with correct column names\n",
    "    par_dep_frame = pd.DataFrame(columns=[xs, 'partial_dependence'])\n",
    "    \n",
    "    # cache original column values \n",
    "    col_cache = frame.loc[:, xs].copy(deep=True)\n",
    "  \n",
    "    # determine values at which to calculate partial dependence\n",
    "    if bins == None:\n",
    "        min_ = frame[xs].min()\n",
    "        max_ = frame[xs].max()\n",
    "        by = (max_ - min_)/resolution\n",
    "        bins = np.arange(min_, max_, by)\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",
    "    for j in bins:\n",
    "        frame.loc[:, xs] = j\n",
    "        dframe = xgb.DMatrix(frame)\n",
    "        par_dep_i = pd.DataFrame(model.predict(dframe))\n",
    "        par_dep_j = par_dep_i.mean()[0]\n",
    "        par_dep_frame = par_dep_frame.append({xs:j,\n",
    "                                              'partial_dependence': par_dep_j}, \n",
    "                                              ignore_index=True)\n",
    "        \n",
    "    # return input frame to original cached state    \n",
    "    frame.loc[:, xs] = col_cache\n",
    "\n",
    "    return par_dep_frame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Calculate partial dependence for the most important input variables in the GBM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "par_dep_num9 = par_dep('num9', rsonvalid_pd[X], rson_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Display partial dependence for important variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num9</th>\n",
       "      <th>partial_dependence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-4.080806</td>\n",
       "      <td>0.764284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-3.682671</td>\n",
       "      <td>0.764284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-3.284536</td>\n",
       "      <td>0.764284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-2.886401</td>\n",
       "      <td>0.766352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-2.488266</td>\n",
       "      <td>0.758818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-2.090131</td>\n",
       "      <td>0.706026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-1.691996</td>\n",
       "      <td>0.679998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-1.293861</td>\n",
       "      <td>0.575561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-0.895725</td>\n",
       "      <td>0.450925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-0.497590</td>\n",
       "      <td>0.297198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>-0.099455</td>\n",
       "      <td>0.276268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.298680</td>\n",
       "      <td>0.286923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.696815</td>\n",
       "      <td>0.353933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1.094950</td>\n",
       "      <td>0.535525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.493085</td>\n",
       "      <td>0.640867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1.891220</td>\n",
       "      <td>0.710100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2.289355</td>\n",
       "      <td>0.760286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2.687490</td>\n",
       "      <td>0.771724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>3.085625</td>\n",
       "      <td>0.758185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>3.483760</td>\n",
       "      <td>0.758185</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        num9  partial_dependence\n",
       "0  -4.080806            0.764284\n",
       "1  -3.682671            0.764284\n",
       "2  -3.284536            0.764284\n",
       "3  -2.886401            0.766352\n",
       "4  -2.488266            0.758818\n",
       "5  -2.090131            0.706026\n",
       "6  -1.691996            0.679998\n",
       "7  -1.293861            0.575561\n",
       "8  -0.895725            0.450925\n",
       "9  -0.497590            0.297198\n",
       "10 -0.099455            0.276268\n",
       "11  0.298680            0.286923\n",
       "12  0.696815            0.353933\n",
       "13  1.094950            0.535525\n",
       "14  1.493085            0.640867\n",
       "15  1.891220            0.710100\n",
       "16  2.289355            0.760286\n",
       "17  2.687490            0.771724\n",
       "18  3.085625            0.758185\n",
       "19  3.483760            0.758185"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "par_dep_num9"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Bind XGBoost predictions to training data and display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parse progress: |█████████████████████████████████████████████████████████| 100%\n"
     ]
    },
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num1</th>\n",
       "      <th>num2</th>\n",
       "      <th>num3</th>\n",
       "      <th>num4</th>\n",
       "      <th>num5</th>\n",
       "      <th>num6</th>\n",
       "      <th>num7</th>\n",
       "      <th>num8</th>\n",
       "      <th>num9</th>\n",
       "      <th>num10</th>\n",
       "      <th>num11</th>\n",
       "      <th>num12</th>\n",
       "      <th>cat1</th>\n",
       "      <th>cat2</th>\n",
       "      <th>cat3</th>\n",
       "      <th>target</th>\n",
       "      <th>row_id</th>\n",
       "      <th>function</th>\n",
       "      <th>predict</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.274992</td>\n",
       "      <td>0.228913</td>\n",
       "      <td>1.352917</td>\n",
       "      <td>0.886429</td>\n",
       "      <td>-2.001637</td>\n",
       "      <td>-0.371843</td>\n",
       "      <td>1.669025</td>\n",
       "      <td>-0.438570</td>\n",
       "      <td>-0.539741</td>\n",
       "      <td>0.476985</td>\n",
       "      <td>3.248944</td>\n",
       "      <td>-1.021228</td>\n",
       "      <td>GGGGGGGG</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0.290944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.331286</td>\n",
       "      <td>1.349742</td>\n",
       "      <td>0.069877</td>\n",
       "      <td>0.246674</td>\n",
       "      <td>-0.011862</td>\n",
       "      <td>1.004812</td>\n",
       "      <td>1.327195</td>\n",
       "      <td>-0.919262</td>\n",
       "      <td>-1.549106</td>\n",
       "      <td>0.022185</td>\n",
       "      <td>0.758363</td>\n",
       "      <td>-0.660524</td>\n",
       "      <td>DDDDDDDD</td>\n",
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       "      <td>1</td>\n",
       "      <td>4</td>\n",
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       "      <td>0.826447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.862580</td>\n",
       "      <td>-0.010032</td>\n",
       "      <td>0.050009</td>\n",
       "      <td>0.670216</td>\n",
       "      <td>0.852965</td>\n",
       "      <td>-0.955869</td>\n",
       "      <td>-0.023493</td>\n",
       "      <td>-2.304234</td>\n",
       "      <td>-0.652469</td>\n",
       "      <td>-1.218302</td>\n",
       "      <td>-1.332610</td>\n",
       "      <td>1.074623</td>\n",
       "      <td>BBBBBBBB</td>\n",
       "      <td>CCCCCCCC</td>\n",
       "      <td>AAAAAAAA</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0.633627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.723642</td>\n",
       "      <td>0.690002</td>\n",
       "      <td>1.001543</td>\n",
       "      <td>-0.503087</td>\n",
       "      <td>-0.622274</td>\n",
       "      <td>-0.921169</td>\n",
       "      <td>-0.726213</td>\n",
       "      <td>0.222896</td>\n",
       "      <td>0.051316</td>\n",
       "      <td>-1.157719</td>\n",
       "      <td>0.816707</td>\n",
       "      <td>0.433610</td>\n",
       "      <td>CCCCCCCC</td>\n",
       "      <td>GGGGGGGG</td>\n",
       "      <td>FFFFFFFF</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0.143136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-2.252797</td>\n",
       "      <td>-1.166832</td>\n",
       "      <td>0.353607</td>\n",
       "      <td>0.702110</td>\n",
       "      <td>-0.274569</td>\n",
       "      <td>-0.139142</td>\n",
       "      <td>0.107657</td>\n",
       "      <td>-0.606545</td>\n",
       "      <td>-0.417064</td>\n",
       "      <td>-0.017007</td>\n",
       "      <td>-1.224145</td>\n",
       "      <td>-1.800840</td>\n",
       "      <td>FFFFFFFF</td>\n",
       "      <td>DDDDDDDD</td>\n",
       "      <td>CCCCCCCC</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>0.157180</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       num1      num2      num3      num4      num5      num6      num7  \\\n",
       "0  0.274992  0.228913  1.352917  0.886429 -2.001637 -0.371843  1.669025   \n",
       "1  0.331286  1.349742  0.069877  0.246674 -0.011862  1.004812  1.327195   \n",
       "2  0.862580 -0.010032  0.050009  0.670216  0.852965 -0.955869 -0.023493   \n",
       "3  0.723642  0.690002  1.001543 -0.503087 -0.622274 -0.921169 -0.726213   \n",
       "4 -2.252797 -1.166832  0.353607  0.702110 -0.274569 -0.139142  0.107657   \n",
       "\n",
       "       num8      num9     num10     num11     num12      cat1      cat2  \\\n",
       "0 -0.438570 -0.539741  0.476985  3.248944 -1.021228  GGGGGGGG  CCCCCCCC   \n",
       "1 -0.919262 -1.549106  0.022185  0.758363 -0.660524  DDDDDDDD  AAAAAAAA   \n",
       "2 -2.304234 -0.652469 -1.218302 -1.332610  1.074623  BBBBBBBB  CCCCCCCC   \n",
       "3  0.222896  0.051316 -1.157719  0.816707  0.433610  CCCCCCCC  GGGGGGGG   \n",
       "4 -0.606545 -0.417064 -0.017007 -1.224145 -1.800840  FFFFFFFF  DDDDDDDD   \n",
       "\n",
       "       cat3  target  row_id  function   predict  \n",
       "0  BBBBBBBB       0       1         1  0.290944  \n",
       "1  DDDDDDDD       1       4         1  0.826447  \n",
       "2  AAAAAAAA       1       5         1  0.633627  \n",
       "3  FFFFFFFF       0       6         1  0.143136  \n",
       "4  CCCCCCCC       1      10         1  0.157180  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rson_preds = pd.DataFrame(rson_model.predict(rsonvalid_dm))\n",
    "rson_decile_frame = pd.concat([rsonvalid_pd, rson_preds], axis=1)\n",
    "rson_decile_frame = rson_decile_frame.rename(columns={0: 'predict'})\n",
    "rson_decile_hframe = h2o.H2OFrame(rson_decile_frame)\n",
    "rson_decile_frame.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Find percentiles of XGBoost predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "rson_percentile_dict = ds_.get_percentile_dict('predict', 'row_id', rson_decile_hframe)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Display percentiles with row identifiers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 137359,\n",
       " 10: 95964,\n",
       " 20: 148660,\n",
       " 30: 73527,\n",
       " 40: 127436,\n",
       " 50: 95138,\n",
       " 60: 190656,\n",
       " 70: 162259,\n",
       " 80: 101443,\n",
       " 90: 55938,\n",
       " 99: 191654}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rson_percentile_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Calculate ICE curve values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# retreive bins from original partial dependence calculation\n",
    "bins_num9 = list(par_dep_num9['num9'])\n",
    "\n",
    "# for each percentile in percentile_dict\n",
    "# create a new column in the par_dep frame \n",
    "# representing the ICE curve for that percentile\n",
    "# and the variables of interest\n",
    "for i in sorted(rson_percentile_dict.keys()):\n",
    "    \n",
    "    col_name = 'Percentile_' + str(i)\n",
    "    \n",
    "    # ICE curves for num11 across percentiles at bins_num11 intervals\n",
    "    par_dep_num9[col_name] = par_dep('num9', \n",
    "                                     rsonvalid_pd[rsonvalid_pd['row_id'] == int(rson_percentile_dict[i])][X], \n",
    "                                     rson_model, \n",
    "                                     bins=bins_num9)['partial_dependence']\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Display partial dependence and ICE for num9 -- all calculated DIRECTLY from the XGBoost model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>num9</th>\n",
       "      <th>partial_dependence</th>\n",
       "      <th>Percentile_0</th>\n",
       "      <th>Percentile_10</th>\n",
       "      <th>Percentile_20</th>\n",
       "      <th>Percentile_30</th>\n",
       "      <th>Percentile_40</th>\n",
       "      <th>Percentile_50</th>\n",
       "      <th>Percentile_60</th>\n",
       "      <th>Percentile_70</th>\n",
       "      <th>Percentile_80</th>\n",
       "      <th>Percentile_90</th>\n",
       "      <th>Percentile_99</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-4.080806</td>\n",
       "      <td>0.764284</td>\n",
       "      <td>0.515088</td>\n",
       "      <td>0.458600</td>\n",
       "      <td>0.810318</td>\n",
       "      <td>0.839874</td>\n",
       "      <td>0.727913</td>\n",
       "      <td>0.733273</td>\n",
       "      <td>0.890809</td>\n",
       "      <td>0.759960</td>\n",
       "      <td>0.841190</td>\n",
       "      <td>0.849099</td>\n",
       "      <td>0.923298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-3.682671</td>\n",
       "      <td>0.764284</td>\n",
       "      <td>0.515088</td>\n",
       "      <td>0.458600</td>\n",
       "      <td>0.810318</td>\n",
       "      <td>0.839874</td>\n",
       "      <td>0.727913</td>\n",
       "      <td>0.733273</td>\n",
       "      <td>0.890809</td>\n",
       "      <td>0.759960</td>\n",
       "      <td>0.841190</td>\n",
       "      <td>0.849099</td>\n",
       "      <td>0.923298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-3.284536</td>\n",
       "      <td>0.764284</td>\n",
       "      <td>0.515088</td>\n",
       "      <td>0.458600</td>\n",
       "      <td>0.810318</td>\n",
       "      <td>0.839874</td>\n",
       "      <td>0.727913</td>\n",
       "      <td>0.733273</td>\n",
       "      <td>0.890809</td>\n",
       "      <td>0.759960</td>\n",
       "      <td>0.841190</td>\n",
       "      <td>0.849099</td>\n",
       "      <td>0.923298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-2.886401</td>\n",
       "      <td>0.766352</td>\n",
       "      <td>0.520794</td>\n",
       "      <td>0.464279</td>\n",
       "      <td>0.810370</td>\n",
       "      <td>0.842923</td>\n",
       "      <td>0.732415</td>\n",
       "      <td>0.737994</td>\n",
       "      <td>0.893012</td>\n",
       "      <td>0.764360</td>\n",
       "      <td>0.844218</td>\n",
       "      <td>0.849204</td>\n",
       "      <td>0.924900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-2.488266</td>\n",
       "      <td>0.758818</td>\n",
       "      <td>0.490112</td>\n",
       "      <td>0.446488</td>\n",
       "      <td>0.820998</td>\n",
       "      <td>0.847313</td>\n",
       "      <td>0.711169</td>\n",
       "      <td>0.733383</td>\n",
       "      <td>0.884938</td>\n",
       "      <td>0.762031</td>\n",
       "      <td>0.835098</td>\n",
       "      <td>0.853508</td>\n",
       "      <td>0.923788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-2.090131</td>\n",
       "      <td>0.706026</td>\n",
       "      <td>0.333122</td>\n",
       "      <td>0.345124</td>\n",
       "      <td>0.806049</td>\n",
       "      <td>0.758585</td>\n",
       "      <td>0.570403</td>\n",
       "      <td>0.678371</td>\n",
       "      <td>0.880654</td>\n",
       "      <td>0.716486</td>\n",
       "      <td>0.836961</td>\n",
       "      <td>0.837111</td>\n",
       "      <td>0.894585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-1.691996</td>\n",
       "      <td>0.679998</td>\n",
       "      <td>0.238474</td>\n",
       "      <td>0.276057</td>\n",
       "      <td>0.809211</td>\n",
       "      <td>0.726572</td>\n",
       "      <td>0.561841</td>\n",
       "      <td>0.691951</td>\n",
       "      <td>0.887216</td>\n",
       "      <td>0.694028</td>\n",
       "      <td>0.848665</td>\n",
       "      <td>0.860497</td>\n",
       "      <td>0.900010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-1.293861</td>\n",
       "      <td>0.575561</td>\n",
       "      <td>0.162722</td>\n",
       "      <td>0.163089</td>\n",
       "      <td>0.757918</td>\n",
       "      <td>0.455597</td>\n",
       "      <td>0.329458</td>\n",
       "      <td>0.714196</td>\n",
       "      <td>0.871064</td>\n",
       "      <td>0.474803</td>\n",
       "      <td>0.787196</td>\n",
       "      <td>0.870774</td>\n",
       "      <td>0.889968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-0.895725</td>\n",
       "      <td>0.450925</td>\n",
       "      <td>0.123221</td>\n",
       "      <td>0.183517</td>\n",
       "      <td>0.371279</td>\n",
       "      <td>0.290782</td>\n",
       "      <td>0.246867</td>\n",
       "      <td>0.568430</td>\n",
       "      <td>0.791420</td>\n",
       "      <td>0.428179</td>\n",
       "      <td>0.760115</td>\n",
       "      <td>0.865656</td>\n",
       "      <td>0.849518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-0.497590</td>\n",
       "      <td>0.297198</td>\n",
       "      <td>0.097429</td>\n",
       "      <td>0.148200</td>\n",
       "      <td>0.164114</td>\n",
       "      <td>0.156815</td>\n",
       "      <td>0.203267</td>\n",
       "      <td>0.301832</td>\n",
       "      <td>0.528376</td>\n",
       "      <td>0.255190</td>\n",
       "      <td>0.774756</td>\n",
       "      <td>0.814984</td>\n",
       "      <td>0.813392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>-0.099455</td>\n",
       "      <td>0.276268</td>\n",
       "      <td>0.088391</td>\n",
       "      <td>0.140099</td>\n",
       "      <td>0.161738</td>\n",
       "      <td>0.126305</td>\n",
       "      <td>0.189583</td>\n",
       "      <td>0.259490</td>\n",
       "      <td>0.435990</td>\n",
       "      <td>0.224022</td>\n",
       "      <td>0.791923</td>\n",
       "      <td>0.815694</td>\n",
       "      <td>0.773794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.298680</td>\n",
       "      <td>0.286923</td>\n",
       "      <td>0.100431</td>\n",
       "      <td>0.143395</td>\n",
       "      <td>0.179885</td>\n",
       "      <td>0.128954</td>\n",
       "      <td>0.201908</td>\n",
       "      <td>0.252408</td>\n",
       "      <td>0.437592</td>\n",
       "      <td>0.281610</td>\n",
       "      <td>0.812805</td>\n",
       "      <td>0.787094</td>\n",
       "      <td>0.802685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.696815</td>\n",
       "      <td>0.353933</td>\n",
       "      <td>0.119176</td>\n",
       "      <td>0.155513</td>\n",
       "      <td>0.217710</td>\n",
       "      <td>0.144802</td>\n",
       "      <td>0.222661</td>\n",
       "      <td>0.531005</td>\n",
       "      <td>0.718564</td>\n",
       "      <td>0.418190</td>\n",
       "      <td>0.790759</td>\n",
       "      <td>0.810187</td>\n",
       "      <td>0.850838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1.094950</td>\n",
       "      <td>0.535525</td>\n",
       "      <td>0.116738</td>\n",
       "      <td>0.171764</td>\n",
       "      <td>0.525957</td>\n",
       "      <td>0.287840</td>\n",
       "      <td>0.243541</td>\n",
       "      <td>0.763368</td>\n",
       "      <td>0.847758</td>\n",
       "      <td>0.617243</td>\n",
       "      <td>0.788250</td>\n",
       "      <td>0.815270</td>\n",
       "      <td>0.905777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.493085</td>\n",
       "      <td>0.640867</td>\n",
       "      <td>0.361788</td>\n",
       "      <td>0.184537</td>\n",
       "      <td>0.800341</td>\n",
       "      <td>0.690465</td>\n",
       "      <td>0.409667</td>\n",
       "      <td>0.749745</td>\n",
       "      <td>0.847365</td>\n",
       "      <td>0.654774</td>\n",
       "      <td>0.812988</td>\n",
       "      <td>0.825142</td>\n",
       "      <td>0.919811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1.891220</td>\n",
       "      <td>0.710100</td>\n",
       "      <td>0.515434</td>\n",
       "      <td>0.263159</td>\n",
       "      <td>0.827429</td>\n",
       "      <td>0.774268</td>\n",
       "      <td>0.561791</td>\n",
       "      <td>0.813286</td>\n",
       "      <td>0.879870</td>\n",
       "      <td>0.689945</td>\n",
       "      <td>0.826707</td>\n",
       "      <td>0.840979</td>\n",
       "      <td>0.930708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2.289355</td>\n",
       "      <td>0.760286</td>\n",
       "      <td>0.619072</td>\n",
       "      <td>0.370477</td>\n",
       "      <td>0.831445</td>\n",
       "      <td>0.842531</td>\n",
       "      <td>0.692365</td>\n",
       "      <td>0.835827</td>\n",
       "      <td>0.885941</td>\n",
       "      <td>0.713575</td>\n",
       "      <td>0.840609</td>\n",
       "      <td>0.841567</td>\n",
       "      <td>0.950628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2.687490</td>\n",
       "      <td>0.771724</td>\n",
       "      <td>0.677100</td>\n",
       "      <td>0.430045</td>\n",
       "      <td>0.837354</td>\n",
       "      <td>0.852141</td>\n",
       "      <td>0.714199</td>\n",
       "      <td>0.833966</td>\n",
       "      <td>0.881968</td>\n",
       "      <td>0.734441</td>\n",
       "      <td>0.811607</td>\n",
       "      <td>0.838029</td>\n",
       "      <td>0.946800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>3.085625</td>\n",
       "      <td>0.758185</td>\n",
       "      <td>0.670126</td>\n",
       "      <td>0.410701</td>\n",
       "      <td>0.819641</td>\n",
       "      <td>0.839703</td>\n",
       "      <td>0.707620</td>\n",
       "      <td>0.824520</td>\n",
       "      <td>0.866181</td>\n",
       "      <td>0.716048</td>\n",
       "      <td>0.794770</td>\n",
       "      <td>0.808861</td>\n",
       "      <td>0.936947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>3.483760</td>\n",
       "      <td>0.758185</td>\n",
       "      <td>0.670126</td>\n",
       "      <td>0.410701</td>\n",
       "      <td>0.819641</td>\n",
       "      <td>0.839703</td>\n",
       "      <td>0.707620</td>\n",
       "      <td>0.824520</td>\n",
       "      <td>0.866181</td>\n",
       "      <td>0.716048</td>\n",
       "      <td>0.794770</td>\n",
       "      <td>0.808861</td>\n",
       "      <td>0.936947</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        num9  partial_dependence  Percentile_0  Percentile_10  Percentile_20  \\\n",
       "0  -4.080806            0.764284      0.515088       0.458600       0.810318   \n",
       "1  -3.682671            0.764284      0.515088       0.458600       0.810318   \n",
       "2  -3.284536            0.764284      0.515088       0.458600       0.810318   \n",
       "3  -2.886401            0.766352      0.520794       0.464279       0.810370   \n",
       "4  -2.488266            0.758818      0.490112       0.446488       0.820998   \n",
       "5  -2.090131            0.706026      0.333122       0.345124       0.806049   \n",
       "6  -1.691996            0.679998      0.238474       0.276057       0.809211   \n",
       "7  -1.293861            0.575561      0.162722       0.163089       0.757918   \n",
       "8  -0.895725            0.450925      0.123221       0.183517       0.371279   \n",
       "9  -0.497590            0.297198      0.097429       0.148200       0.164114   \n",
       "10 -0.099455            0.276268      0.088391       0.140099       0.161738   \n",
       "11  0.298680            0.286923      0.100431       0.143395       0.179885   \n",
       "12  0.696815            0.353933      0.119176       0.155513       0.217710   \n",
       "13  1.094950            0.535525      0.116738       0.171764       0.525957   \n",
       "14  1.493085            0.640867      0.361788       0.184537       0.800341   \n",
       "15  1.891220            0.710100      0.515434       0.263159       0.827429   \n",
       "16  2.289355            0.760286      0.619072       0.370477       0.831445   \n",
       "17  2.687490            0.771724      0.677100       0.430045       0.837354   \n",
       "18  3.085625            0.758185      0.670126       0.410701       0.819641   \n",
       "19  3.483760            0.758185      0.670126       0.410701       0.819641   \n",
       "\n",
       "    Percentile_30  Percentile_40  Percentile_50  Percentile_60  Percentile_70  \\\n",
       "0        0.839874       0.727913       0.733273       0.890809       0.759960   \n",
       "1        0.839874       0.727913       0.733273       0.890809       0.759960   \n",
       "2        0.839874       0.727913       0.733273       0.890809       0.759960   \n",
       "3        0.842923       0.732415       0.737994       0.893012       0.764360   \n",
       "4        0.847313       0.711169       0.733383       0.884938       0.762031   \n",
       "5        0.758585       0.570403       0.678371       0.880654       0.716486   \n",
       "6        0.726572       0.561841       0.691951       0.887216       0.694028   \n",
       "7        0.455597       0.329458       0.714196       0.871064       0.474803   \n",
       "8        0.290782       0.246867       0.568430       0.791420       0.428179   \n",
       "9        0.156815       0.203267       0.301832       0.528376       0.255190   \n",
       "10       0.126305       0.189583       0.259490       0.435990       0.224022   \n",
       "11       0.128954       0.201908       0.252408       0.437592       0.281610   \n",
       "12       0.144802       0.222661       0.531005       0.718564       0.418190   \n",
       "13       0.287840       0.243541       0.763368       0.847758       0.617243   \n",
       "14       0.690465       0.409667       0.749745       0.847365       0.654774   \n",
       "15       0.774268       0.561791       0.813286       0.879870       0.689945   \n",
       "16       0.842531       0.692365       0.835827       0.885941       0.713575   \n",
       "17       0.852141       0.714199       0.833966       0.881968       0.734441   \n",
       "18       0.839703       0.707620       0.824520       0.866181       0.716048   \n",
       "19       0.839703       0.707620       0.824520       0.866181       0.716048   \n",
       "\n",
       "    Percentile_80  Percentile_90  Percentile_99  \n",
       "0        0.841190       0.849099       0.923298  \n",
       "1        0.841190       0.849099       0.923298  \n",
       "2        0.841190       0.849099       0.923298  \n",
       "3        0.844218       0.849204       0.924900  \n",
       "4        0.835098       0.853508       0.923788  \n",
       "5        0.836961       0.837111       0.894585  \n",
       "6        0.848665       0.860497       0.900010  \n",
       "7        0.787196       0.870774       0.889968  \n",
       "8        0.760115       0.865656       0.849518  \n",
       "9        0.774756       0.814984       0.813392  \n",
       "10       0.791923       0.815694       0.773794  \n",
       "11       0.812805       0.787094       0.802685  \n",
       "12       0.790759       0.810187       0.850838  \n",
       "13       0.788250       0.815270       0.905777  \n",
       "14       0.812988       0.825142       0.919811  \n",
       "15       0.826707       0.840979       0.930708  \n",
       "16       0.840609       0.841567       0.950628  \n",
       "17       0.811607       0.838029       0.946800  \n",
       "18       0.794770       0.808861       0.936947  \n",
       "19       0.794770       0.808861       0.936947  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "par_dep_num9"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot partial dependence and ICE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f717918bcc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_par_dep_ICE(xs, par_dep_frame):\n",
    "\n",
    "    \n",
    "    \"\"\" Plots ICE overlayed onto partial dependence for a single variable.\n",
    "    \n",
    "    Args: \n",
    "        xs: Name of variable for which to plot ICE and partial dependence.\n",
    "        par_dep_frame: Name of Pandas DataFrame containing ICE and partial\n",
    "                       dependence values.\n",
    "    \n",
    "    \"\"\"\n",
    "    \n",
    "    # initialize figure and axis\n",
    "    fig, ax = plt.subplots()\n",
    "    \n",
    "    # plot ICE curves\n",
    "    par_dep_frame.drop('partial_dependence', axis=1).plot(x=xs, \n",
    "                                                          colormap='gnuplot',\n",
    "                                                          ax=ax)\n",
    "\n",
    "    # overlay partial dependence, annotate plot\n",
    "    par_dep_frame.plot(title='Partial Dependence and ICE for ' + str(xs),\n",
    "                       x=xs, \n",
    "                       y='partial_dependence',\n",
    "                       style='r-', \n",
    "                       linewidth=3, \n",
    "                       ax=ax)\n",
    "\n",
    "    # add legend\n",
    "    _ = plt.legend(bbox_to_anchor=(1.05, 0),\n",
    "                   loc=3, \n",
    "                   borderaxespad=0.)\n",
    "\n",
    "plot_par_dep_ICE('num9', par_dep_num9)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice the divergence of the ICE curves from the red partial dependence curve for `~-1 < num9 < ~1`. This divergence can be indicative of an interaction between input variables. The surrogate tree at the bottom of the notebook can provide further insight into which input variables are driving the detected interaction."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train and Display Surrogate Decision Tree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Train single h2o decision tree as surrogate between the XGBoost predictions and inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "drf Model Build progress: |███████████████████████████████████████████████| 100%\n",
      "Generated MOJO path:\n",
      " /home/patrickh/workspace/xai_guidelines/dt_surrogate_mojo.zip\n"
     ]
    }
   ],
   "source": [
    "model_id = 'dt_surrogate_mojo' # gives MOJO artifact a recognizable name\n",
    "\n",
    "# initialize single tree surrogate model\n",
    "surrogate = H2ORandomForestEstimator(ntrees=1,          # use only one tree\n",
    "                                     sample_rate=1,     # use all rows in that tree\n",
    "                                     mtries=-2,         # use all columns in that tree\n",
    "                                     max_depth=5,       # shallow trees are easier to understand\n",
    "                                     seed=12345,        # random seed for reproducibility\n",
    "                                     model_id=model_id) # gives MOJO artifact a recognizable name\n",
    "\n",
    "# train single tree surrogate model\n",
    "surrogate.train(x=X, y='predict', training_frame=rson_decile_hframe)\n",
    "\n",
    "# persist MOJO (compiled, representation of trained model)\n",
    "# from which to generate plot of surrogate\n",
    "mojo_path = surrogate.download_mojo(path='.')\n",
    "print('Generated MOJO path:\\n', mojo_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create GraphViz dot file of tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Discovered H2O jar path:\n",
      " /home/patrickh/anaconda3/lib/python3.6/site-packages/h2o/backend/bin/h2o.jar\n",
      "\n",
      "Calling external process ...\n",
      "java -cp /home/patrickh/anaconda3/lib/python3.6/site-packages/h2o/backend/bin/h2o.jar hex.genmodel.tools.PrintMojo --tree 0 -i /home/patrickh/workspace/xai_guidelines/dt_surrogate_mojo.zip -o dt_surrogate_mojo.gv --title Known Signal Data (with Validation) Decision Tree Surrogate\n"
     ]
    }
   ],
   "source": [
    "# title for plot\n",
    "title = 'Known Signal Data (with Validation) Decision Tree Surrogate'  \n",
    "\n",
    "# locate h2o jar\n",
    "hs = H2OLocalServer()\n",
    "h2o_jar_path = hs._find_jar()\n",
    "print('Discovered H2O jar path:\\n', h2o_jar_path)\n",
    "\n",
    "# construct command line call to generate graphviz version of \n",
    "# surrogate tree see for more information: \n",
    "# http://docs.h2o.ai/h2o/latest-stable/h2o-genmodel/javadoc/index.html\n",
    "gv_file_name = model_id + '.gv'\n",
    "gv_args = str('-cp ' + h2o_jar_path +\n",
    "              ' hex.genmodel.tools.PrintMojo --tree 0 -i '\n",
    "              + mojo_path + ' -o').split()\n",
    "gv_args.insert(0, 'java')\n",
    "gv_args.append(gv_file_name)\n",
    "if title is not None:\n",
    "    gv_args = gv_args + ['--title', title]\n",
    "    \n",
    "# call \n",
    "print()\n",
    "print('Calling external process ...')\n",
    "print(' '.join(gv_args))\n",
    "_ = subprocess.call(gv_args)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create PNG from GraphViz dot file and display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calling external process ...\n",
      "dot -Tpng dt_surrogate_mojo.gv -o dt_surrogate_mojo.png\n"
     ]
    }
   ],
   "source": [
    "# construct call to generate PNG from \n",
    "# graphviz representation of the tree\n",
    "png_file_name = model_id + '.png'\n",
    "png_args = str('dot -Tpng ' + gv_file_name + ' -o ' + png_file_name)\n",
    "png_args = png_args.split()\n",
    "\n",
    "# call\n",
    "print('Calling external process ...')\n",
    "print(' '.join(png_args))\n",
    "_ = subprocess.call(png_args)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Display surrogate tree in-notebook\n",
    "Double click to zoom."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HH32k6tWrq2/fvrpw4YLdIQEAAAC2y8zMVExMjNq0aaNz587p66+/1qxZs1S2bFm7QwMAoMS5/iXiNm3a6IEHHlB4eDjfaAwAAAAgx6+//qoHH3xQTz75ZM7LzW3btrU7LAAAUIBatWrlLKaSkJCgVq1aKSYmRllZWXaHBgAAALjsl19+0cSJE/Xcc8+pR48edodj2V/+8hfdc889+sMf/qCzZ8/aHQ4AAPCwMmXKKCIiQocPH9bLL7+s2bNnq0mTJlq4cCH9dQAAn8SigQAAn5OSkqLo6GjVr19fr732ml566SUlJiYqMjKSF5cBALBZfHy8du7cqRkzZtgdimW33nqr1q1bp4sXL+rRRx9Venq63SEBAAAAtklISFD37t01efJkPfvss9qxY4c6dOhgd1gAAJR4tWvXVnx8vFatWqVvv/1WTZs2VUxMjLKzs+0ODQAAAICNPvzwQ7Vq1Ur79+/XZ599plmzZikwMNDusAAAgEUDBgxQQkKCJkyYoMjISHXq1Elff/213WEBAAAALhk3bpwqV66smTNn2h2KU/z8/LR06VI5HA4NHjyYxYEAACilKlasqMjISB04cED9+vXTuHHj1Lp1a8XHx9sdGgAATmHRQACAz8jIyNDChQt15513asaMGYqIiNCRI0cUGRmpChUq2B0eAAClXlZWlqKiojRo0CC1bdvW7nCcUqdOHa1cuVI7duzQM888Y3c4AAAAgMdlZmYqOjpaHTp0kMPh0N69e3kBGQAAG4SGhur777/X+PHj9cILL6hz587auXOn3WEBAAAA8LCLFy9qzJgxCgsLU9++ffXdd9/pnnvusTssAADggvLlyysqKkr79u1TUFCQunfvrmHDhul///uf3aEBAAAAln3wwQdasWKFFi5cqEqVKtkdjtOqV6+uZcuW6csvv9SMGTPsDgcAANioVq1aWrBggfbt26eWLVsqPDxcISEh2r17t92hAQBgCYsGAgC8XnZ2tuLj49W8eXM999xzCg0N1eHDhzVr1ixVqVLF7vAAAMD/s3jxYh0+fFjTpk2zOxSXtG/fXsuXL9eSJUv0l7/8xe5wAAAAAI/57rvv1KVLF0VFRWnatGn66quv1KJFC7vDAgCg1KpQoYKioqK0Y8cOBQYGqmvXrho/frwuXbpkd2gAAAAAPGDz5s1q1aqVVq5cqVWrVik2NtYnX8QGAAC5NW7cWBs3btSyZcu0ceNGtWrVSrGxsTLG2B0aAAAAUKCzZ89qwoQJGjt2rHr37m13OC67++67NXv2bM2YMUPr1q2zOxwAAGCzZs2aKS4uTtu2bdPVq1fVsWNHhYd+EqIQAAAgAElEQVSH66effrI7NAAACsSigQAAr2WM0erVq9WuXTsNGTJE3bp104EDB7RgwQLVrFnT7vAAAMB1rl69qhkzZmj06NFq1KiR3eG4rF+/fnr77bf18ssv6/3337c7HAAAAKBYZWRkKDo6Wp06dVLZsmW1Z88eRUZGyt/f3+7QAACApNatW+urr77Su+++q6VLl6pZs2aKjY21OywAAAAAxSQ1NVVTpkxRnz591LVrVyUkJCg0NNTusAAAgJuFhYXpwIEDCg8P15NPPqmePXvq+++/tzssAAAAIF9jx45V+fLlNWvWLLtDKbJnnnlGw4YN0xNPPMGCQAAAQJLUtWtXbdmyRStXrtSePXvUvHlzjRkzRklJSXaHBgBAnlg0EADglTZv3qxOnTrpoYceUtOmTZWQkKDY2Fg1aNDA7tAAAEAe5syZo7Nnz+qll16yO5Qii4iI0KRJkzRq1Ch9+umndocDAAAAFIu9e/fq7rvv1vTp0zV9+nR9+eWXatasmd1hAQCAGzgcDg0bNkwHDx7UgAEDNGLECIWGhioxMdHu0AAAAAC40bfffqt27dpp/vz5eueddxQXF6fq1avbHRYAACgmVapUUUxMjL799lulpqaqbdu2Gj9+vK5cuWJ3aAAAAEAuK1eu1EcffaQFCxbolltusTsct3jnnXfUoEEDPfroo0pJSbE7HAAA4CVCQ0P1/fffa86cOVq5cqWaNm2q6OhoXb161e7QAADIhUUDAQBeZfv27erVq5dCQkJUtWpV7dq1S3FxcWrcuLHdoQEAgHxcvHhR0dHRmjhxomrXrm13OG7x17/+VWFhYfrDH/6g/fv32x0OAAAA4DZXr17VlClT1LFjR1WqVEl79+5VZGSk/P397Q4NAAAUoFq1alqwYIE+//xz/fTTT2rZsqWioqKUnp5ud2gAAAAAiiAzM1PR0dEKDg5WvXr1tH//fkVERNgdFgAA8JD27dtr+/btmjt3rt577z21bt1a69atszus/8venUdVVS7+H/8cFOcybXLKTFREcARNnDWsHLBSOdTVKFPBmVQSbFBQU9AcMFOhHKJyAK/mkJpDaqEYzgyCA45dS0lvSk4o8PvjfvN3u5U5AM8B3q+1zrqrOHvv91mtdfdznr3PswEAAABJ0vnz5+Xn56d+/frpueeeM52Ta0qVKqV//vOf+uGHH+Tn52c6BwAA2BB7e3v5+vrq6NGjCgwM1IQJE1SnTh1FRkYqKyvLdB4AAJJYNBAAYCOSkpJktVrVokULZWZmatu2bdq4caMaN25sOg0AAPyNDz74QFlZWRo5cqTplFxjsVg0b948OTs7y9PTU2fPnjWdBAAAANy3uLg4NW7cWHPmzNHUqVO1detWHtYBAEAB06ZNG+3fv18TJ07UBx98oAYNGuibb74xnQUAAADgHqSkpMjd3V0hISEaP3681q9fr2rVqpnOAgAA+czOzk6+vr5KTU1Vy5Yt1aVLF3l6eurUqVOm0wAAAFDEDR06VMWKFdPkyZNNp+S6GjVqaOHChVq0aJEiIyNN5wAAABtTrlw5BQYGKjU1VZ06ddKgQYPUsGFDffXVV6bTAABg0UAAgFmpqany8fFRw4YNdfLkSa1atUqxsbFq06aN6TQAAHAH0tPTFR4erqCgIFWsWNF0Tq4qVaqUVq1apRIlSqhLly66fPmy6SQAAADgnly9elVBQUFq3bq1nnzySSUmJsrf3192dlwmAgCgILK3t5e/v78SEhL01FNPycPDQz4+PkpPTzedBgAAAOAO5OTkKDIyUm5ubrKzs9O+ffsUGBjIfB0AAEVc5cqVFRUVpS1btigtLU1OTk4KDg5WZmam6TQAAAAUQWvWrNGSJUv0ySef6KGHHjKdkye6du2qt99+W8OGDdOuXbtM5wAAABtUtWpVRUREKDExUfXq1VPXrl3VsWNH7d+/33QaAKAI4+4SAIARp0+flp+fn+rXr689e/ZoyZIl2rlzpzw9PU2nAQCAuzBu3DiVK1dOQ4cONZ2SJx5++GGtW7dOp0+flre3t7KyskwnAQAAAHclNjZWjRo10ty5czV79mytW7dO1atXN50FAAByQc2aNbVu3TqtXLlSW7duVd26dRUZGamcnBzTaQAAAAD+wqlTp+Th4aHBgwdr6NChio2NlaOjo+ksAABgQ9q1a6cDBw5ozJgxCgsLU9OmTbVjxw7TWQAAAChCLl68qAEDBui1115Tp06dTOfkqZCQELVv3149evTgQX0AAOAvOTk5KTo6Wtu3b9eVK1fk6uoqq9Wq48ePm04DABRBLBoIAMhXP//8s4KCglSnTh19/fXX+uijj5SQkCAvLy9ZLBbTeQAA4C6cOHFCH3/8scaOHasyZcqYzskzNWvW1OrVq7VlyxYNHjzYdA4AAABwR65cuaKgoCC1bdtWtWrVUlJSknx9fZmDAwCgEPL09FRiYqJ69+6tQYMGqW3btkpOTjadBQAAAOB/xMTEqFGjRvrxxx+1c+dOhYaGyt7e3nQWAACwQfb29goMDFRSUpKqVKmiVq1aycfHh0VMAAAAkC+GDRum7OxsTZs2zXRKnrOzs9MXX3yh4sWL65VXXlFWVpbpJAAAYMNatGih2NhYLVmyRHv37lXdunXl7++vX375xXQaAKAIYdFAAEC+yMjIUFhYmBwcHLRgwQIFBwfr0KFD8vX1VbFixUznAQCAezBmzBhVr15db7zxhumUPNesWTMtXLhQH3/8sWbMmGE6BwAAALitr7/+Wk5OToqMjNScOXP01VdfqVq1aqazAABAHipfvrzCw8O1a9cuZWZmqnHjxvL399evv/5qOg0AAAAo8tLT09WjRw95e3vLy8tLu3fvlqurq+ksAABQADg4OGjdunVauXKltm7dqrp16yo8PFzZ2dmm0wAAAFBIrV27VlFRUfroo49UoUIF0zn5omLFilq+fLl27NihsWPHms4BAAA2zmKxyMvLSykpKfrwww+1ZMkSOTg4KCwsTNeuXTOdBwAoAlg0EACQpy5fvqywsDA9+eSTmjJlit5++22dOHFCgYGBKlmypOk8AABwj5KSkvTFF19o3Lhxsre3N52TL7y8vDRp0iSNHDlSy5cvN50DAAAA/MHFixfl5+enTp06qUGDBkpKSpKvr6/pLAAAkI8aN26sHTt2aNasWVq4cKEaNGigtWvXms4CAAAAiqz169erUaNG2r17tzZv3qyIiAiVKVPGdBYAAChgPD09lZKSov79+2vkyJFq27atkpKSTGcBAACgkLl06ZIGDBigXr166aWXXjKdk68aNWqk8PBwTZw4kd+LAACAO2Jvby9fX1+lpaVp6NChCgkJkaOjoyIjI3noBwAgT7FoIAAgT2RmZioyMlK1a9fWhAkTbn3hCQwMVOnSpU3nAQCA+/T222/LxcVFVqvVdEq+GjVqlAYNGqTevXsrLi7OdA4AAABwy9q1a+Xi4qKVK1cqOjpaq1evVpUqVUxnAQAAA+zs7OTr66tDhw6pVatW6tKlizw9PXXq1CnTaQAAAECRkZGRcesBHy1bttT+/fvVvn1701kAAKAAK1u2rEJDQ7Vnzx5lZWWpcePG8vf3V0ZGhuk0AAAAFBIjRozQtWvXNH36dNMpRvTv319vvPGGXn/9daWmpprOAQAABUS5cuUUHBysw4cP6/nnn9egQYP09NNPa8uWLabTAACFFIsGAgByVXZ2tmJiYuTk5KShQ4fK09NTR48eVWhoqMqXL286DwAA5IL4+HitWbNGoaGhsrMrel8rZ8yYoWeffVbdunXTkSNHTOcAAACgiPvll1/k5+enLl26yN3dXUlJSerZs6fpLAAAYAMqVaqkqKgoffPNNzp69Kjq1aunsLAwZWVlmU4DAAAACrW4uDg1adJEK1as0PLlyxUdHa0KFSqYzgIAAIVEw4YNtX37ds2bN0+LFi2Sk5OToqKiTGcBAACggPvmm280f/58zZkzR48++qjpHGNmzZolR0dHWa1WXb582XQOAAAoQKpVq6aIiAglJCToqaeeUocOHdSxY0cdOHDAdBoAoJApeqs7AADyRE5OjlavXq1GjRqpV69e8vDw0LFjxxQREaHHH3/cdB4AAMhFQUFBatWqlTp16mQ6xYhixYpp0aJFcnBwUKdOnZSenm46CQAAAEXU6tWr5ezsrFWrVmnFihWKjo7WI488YjoLAADYmPbt22vv3r0KCAjQ2LFj5erqqp07d5rOAgAAAAqdGzduKDg4WK1bt1atWrW0f/9+vfTSS6azAABAIWSxWOTj46NDhw6pS5cuev311+Xp6akTJ06YTgMAAEABdPnyZfXv319eXl7q0aOH6RyjSpUqpX/+85/68ccf1b9/f9M5AACgAKpXr56io6O1adMmXbhwQU2aNJHVamXuDgCQa1g0EABw3zZt2qSmTZvqxRdfVN26dZWcnKyIiAhVrVrVdBoAAMhl69at05YtWxQaGmo6xagyZcpoxYoVunnzpnr06KFr166ZTgIAAEARcu7cOVmtVnXr1k0tW7ZUcnKyXnzxRdNZAADAhpUuXVrBwcFKSkrSY489phYtWsjHx0fnz583nQYAAAAUCklJSWrWrJmmT5+u2bNna926dapSpYrpLAAAUMhVrFhRERER2rp1q44fP6569eopODhY169fN50GAACAAiQgIECXLl3SzJkzTafYhOrVq2vx4sWKjo7WRx99ZDoHAAAUUM8884x2796tJUuWaM+ePXJ2dlZQUJAuXrxoOg0AUMCxaCAA4J7t2LFD7du3V8eOHVWhQgXt2bNH0dHRql27tuk0AACQB3JycvTuu++qW7duatGihekc4ypXrqy1a9cqKSlJr732mrKzs00nAQAAoAiIiYmRs7Oz4uPjtWHDBkVHR6tixYqmswAAQAFRq1YtbdiwQUuXLtXXX38tFxcXRUVFKScnx3QaAAAAUCBlZ2crPDxcbm5uKlOmjPbs2SNfX1/TWQAAoIhp06aN9u3bp0mTJmnq1KmqX7++Nm3aZDoLAAAABcDWrVsVERGhWbNm6fHHHzedYzM8PDz03nvvafjw4YqNjTWdAwAACiiLxSIvLy+lpKRo+vTpmj9/vhwcHBQWFsaDPwAA94xFAwEAdy0xMVFWq1UtW7bUjRs39O2332rjxo1q1KiR6bQiyWKxyM7OTqNGjVJoaKjGjx+vhg0bymKxyNnZWVeuXPnd+zdt2qTnnntOFotFbm5uWrp06Z/uNysrS+7u7rp27dqf/v3w4cMKDQ3V0KFDZbFYZLFYcv2zAQBsy5IlS7R//36NGzfOdIrNqFevnlasWKGVK1fqnXfeMZ0DAACAQuynn35S9+7d5e3tre7duysxMVEdO3b8w/uYKwIAAHfCy8tLhw4dktVqVZ8+fdShQwelpKSYzsoXjJcAAACQW44fP6727dsrKChIISEh+vbbb1WrVi3TWQAAoIiyt7eXv7+/UlJS1LBhQ3Xs2FFWq1Xnzp0znQYAAAAbdeXKFfXv319du3aVt7e36RybM2bMGHXp0kVeXl768ccfTecAAIACrESJEvL19dWhQ4fUr18/BQcHy9HRkQf+AgDuiSXnPs4eVqtVkhQdHZ1rQQAA25WamqoxY8Zo2bJlatasmd5//30988wzprOKpOjoaHl7eysnJ0cWi0UODg46evTorb9funRJ5cuXlyT1799fkZGRv9v+xIkTeuqpp5SamipHR8c/PcaXX36pl156SR9//LH69et3254aNWro5MmTfCkFgELs5s2bcnFx0dNPP61PP/3UdI7NWbx4sXr16qVZs2Zp0KBBpnMAAABQyMTExGjAgAEqX768PvnkE3Xo0OEv38tc0e9ZLBYtXbr01jUtAEDh8t/XS3Dv9uzZowEDBujAgQMaMWKEgoODVapUKdNZeYbxEgAAAHJDVFSUBg8erBo1auizzz7jgbu3wf3mAIC7xfWd3LF69WoNHTpUFy9eVHBwsIYMGaJixYqZzgIAAIANGTZsmD777DMlJSWpatWqpnNs0r///W81bdpU1atX14YNG1S8eHHTSQAA5Amu5+Sv06dPa8KECZo3b56aNGmiyZMnq127dqazAAD5IDfuf7fLxR4AQCF16tQp+fn5qX79+kpOTtbSpUsVFxfHgoE25H8nmx988EFJUuvWrfXxxx9r6dKlv/v7b5P4Tz311F/uc/78+XriiSc0bdo0ZWdn3/b4hfmHYwCA//jkk0907NgxjR071nSKTXrllVc0ZswYDRs2TKtWrTKdAwAAgELizJkzeuGFF+Tt7a2ePXsqISHhtgsG/oa5IgAAcDdcXV0VFxenKVOmaPbs2XJxcdHXX39tOitPMV4CAADAvTp79qy6deumN954Q4MHD9aePXtYMBAAANgkT09PHTx4UP7+/ho1apSaNWumXbt2mc4CAACAjYiLi9NHH32kDz/8kAUDb6NChQpavny5vv/+e73zzjumcwAAQCHxxBNPKCIiQgcOHNDjjz+u9u3bq2PHjkpISDCdBgAoAFg0EADwl9LT0xUUFCRHR0d9/fXX+uijj5SQkCAvLy9ZLBbTebgDS5cuVaVKleTr66tjx47d+vf29vaSpBIlSvzpdgcOHFCtWrU0cuRIpaSkaP369fnSCwCwTVevXtX777+vAQMGqGbNmqZzbNbYsWP16quvqnfv3tq3b5/pHAAAABRgOTk5ioyMVN26dZWcnKwtW7YoIiJC5cqVu6/9MlcEAAD+SvHixeXv76/U1FS1aNFCzz//vDw9PfXDDz+YTstXjJcAAABwO8uWLZOLi4uSkpL0zTffKDQ09C/HiAAAALagTJkyCg4OVkJCgipUqKDmzZvLz89Ply5dMp0GAAAAg65du6a+ffuqU6dO6t27t+kcm9egQQNFRkZqypQpWrZsmekcAABQiDg7O2v16tXauHGjzp8/r8aNG8vHx0dnzpwxnQYAsGEsGggA+IMLFy4oODhYDg4O+uKLLxQaGqpDhw7J19dXxYoVM52Hu1C5cmVFR0fr8uXLevnll5WZmXlH282ePVtvvvmm+vbtqwoVKmjq1Kl5XAoAsGUzZ87Uv//9b7399tumU2yaxWJRZGSkmjdvri5duujUqVOmkwAAAFAAHT9+XB07dtTgwYPVp08fJSQkqG3btrmyb+aKAADA36lSpYqioqK0evVqJSUlqX79+goPD1dWVpbptHzBeAkAAAB/5uLFi/Lz85OXl5c6deqkhIQEtWnTxnQWAADAHXN0dNTGjRu1YMECrVixQnXr1lVUVJTpLAAAABjyzjvv6MyZM5o7d67plAKjV69e8vX1VZ8+fXTw4EHTOQAAoJDx8PDQ7t27tWTJEsXGxqp27doKCgrSxYsXTacBAGwQiwYCAG65fPmywsLC5ODgoFmzZumdd97R4cOH5e/vr5IlS5rOwz1q3bq1pkyZol27dt3RYk/p6enKyspS9erVVa5cOQ0cOFDffPON9u/fnw+1AABb88svv2jy5MkaPny4KlWqZDrH5tnb22vZsmV69NFH1blzZ/3yyy+mkwAAAFBA5OTkKDIyUg0aNNDZs2e1fft2hYeHq0yZMrl6HOaKAADAnejatauSk5Pl7++vUaNGqVmzZtq1a5fprHzBeAkAAAD/bdOmTXJxcdHKlSu1atUqRUVFqVy5cqazAAAA7prFYpGPj48OHTokLy8v9enTRx06dFBqaqrpNAAAAOSj77//XuHh4Zo2bZqqVatmOqdA+fDDD9WwYUN1795dly5dMp0DAAAKGTs7O3l5eSk1NVUTJ05URESEHBwcFBYWpuvXr5vOAwDYEBYNBAAoMzNTkZGRqlWrliZMmCA/Pz+lpaUpMDBQpUuXNp2HXPDmm2+qZ8+emjp1qtauXXvb90ZGRmrIkCG3/nno0KEqWbKkpk6dmteZAAAbNHnyZOXk5GjEiBGmUwqMBx98UGvXrtXFixf10ksvKTMz03QSAAAAbFxaWpo6dOigwYMHa/Dgwdq9e7eaNWuWZ8djrggAANyJMmXKKDg4WImJiXrooYfUvHlz+fn5FYkfPzBeAgAAwNWrVxUUFKTnnntO7u7uSk5Olqenp+ksAACA+1ahQgWFh4fr+++/V0ZGhho2bKigoCBdu3bNdBoAAADy2PXr19W3b1+1a9dOffr0MZ1T4Njb22vx4sW6cOGCfH19TecAAIBCqkSJEvL391daWpr69eun4OBgNWjQQDExMcrJyTGdBwCwASwaCABFWHZ2tmJiYuTk5KShQ4eqW7duOnr0qEJDQ1W+fHnTechFFotF8+fPV926dfXaa6/pzJkzf/q+zMxMffTRR2rcuLEsFossFosqV66s69eva8mSJfrhhx/yuRwAYNKPP/6omTNn6u2331aFChVM5xQoVatW1cqVK7V7924NGDDAdA4AAABsVFZWlsLDw9WwYUNduHBBO3fuVGhoqEqWLJmnx2WuCAAA3I06depo06ZNWrBggVasWKG6desqKirKdFaeYrwEAABQtMXHx6tx48aaO3eu5syZo+joaD388MOmswAAAHKVm5ub4uLiNHnyZM2ePVsuLi76+uuvTWcBAAAgDwUHB+v06dOaP3++LBaL6ZwC6YknntCSJUu0bNkyzZgxw3QOAAAoxCpWrKjQ0FAdPnxY7dq108svv6zmzZtr27ZtptMAAIaxaCAAFEE5OTm3Fgvs1auXPDw8dOzYMUVEROjxxx83nYc88sADD+if//ynrl69ql69ev3pe2JiYhQQEKCcnJzfvT7//HPdvHlTH374YT5XAwBMmjBhgsqXL69BgwaZTimQmjRpoujoaH322WcaP3686RwAAADYmOTkZLVo0UJvvfWWhgwZol27dsnV1TXfjs9cEQAAuBsWi0U+Pj5KTU2Vl5eX+vTpo65du+r48eOm0/IM4yUAAICi5+bNmwoLC1OrVq1UvXp1JSUlydfX13QWAABAnilevLj8/f2VmpqqFi1a6Pnnn5enp6dOnz5tOg0AAAC5bN++fZo6daomT56s6tWrm84p0Dp06KBx48bprbfe0rfffms6BwAAFHJPPPGEIiIiFB8fr3Llyqldu3bq2LGjEhMTTacBAAxh0UAAKGI2bdokNzc3vfzyy2rYsKEOHjyoiIgIVa1a1XQaclFWVtbv/vc39erV0/z587V169Y/3WbKlCny8fH5w9969uypRx99VBEREcrIyMiTZgCAbTl+/Lg++eQTBQcHq0yZMqZzCqxOnTpp9uzZGjt2rKKiokznAAAAwAb89sNjV1dXZWZmKj4+XqGhoSpRokSeHZO5IgAAkFsqVqyo8PBwbd26VSdOnJCzs7OCg4N1/fp102n3hfESAAAAUlJS5O7urpCQEI0fP17r169XtWrVTGcBAADkiypVqigqKkqbN2/WkSNH5OTkpLCwsD/MlwEAAKBgyszM1GuvvaZWrVrxkIxcMnr0aL3wwguyWq06c+aM6RwAAFAEuLq6avPmzdq4caPS09PVpEkT+fn56ccffzSdBgDIZywaCABFxI4dO26tGl6xYkXt2bNH0dHRqlWrluk05IFz585Jks6ePfuHv1mtVr355pt/+PeLFi3Sww8/rEceeeQPfytZsqS6du2qixcvatq0abkfDACwOe+++65q1KihPn36mE4p8Pr376+AgAD169dPmzZtMp0DAAAAgxISEtS8eXMFBwcrJCREu3fvVqNGjfL8uMwVAQCA3Na6dWvt27dPkyZN0tSpU1W/fv0CPffFeAkAAKDoysnJUWRkpNzc3GRnZ6d9+/YpMDBQdnbcYg0AAIqeDh06aN++fQoICNDYsWPl5uamnTt3ms4CAADAfZowYYLS0tL08ccfy2KxmM4pFCwWixYsWKAKFSqoZ8+eyszMNJ0EAACKCA8PD+3du1eLFi3Shg0bVLt2bQUFBenSpUum0wAA+YQ7WgCgkEtMTJTValXLli118+ZNffvtt9q4cWO+/BgZZixfvlx9+/aVJPn6+uq77777w3smT56sli1b/m6bgIAA7d+/X3PmzPnD+1esWKEDBw5IkkJDQxUWFpZH9QAAW5CYmKglS5ZowoQJKl68uOmcQiEsLExWq1U9evRQYmKi6RwAAADksxs3bigsLExNmzZVyZIlb/3wuFixYnl+bOaKAABAXrG3t5e/v78SEhJUu3ZtPfvss/Lx8VF6errptLvCeAkAAKDoOnXqlDw8PDR48GANHTpUsbGxcnR0NJ0FAABgVOnSpRUcHKykpCQ99thjatGihXx8fHT+/HnTaQAAALgHBw4cuHXN0sHBwXROofLAAw8oOjpaCQkJCgoKMp0DAACKEDs7O3l5eengwYN67733FBERIQcHB4WFhbGYMQAUAZacnJyce93YarVKkqKjo3MtCACQO1JSUjR27FgtW7ZMzZo10/vvv69nnnnGdNYtcXFxWrNmjd5//33TKQVSdHS0vL29lZOTI4vFIkdHR6WmphrrqVu3rg4dOqT7GFYAAGxI165d9cMPP2jv3r2ys7v3teY53//etWvX5OHhoX/961+Ki4tTpUqVcnX/ixcvVmZmpl555RWVKFEiV/cNAACAe7d//3698cYbSk1N1dixYxUQEJCniwUyV/R7FotFw4YNU5MmTRgrA0Ah9N/XS/4Kc1T5Z/Xq1RoyZIgyMjI0duxYDR069L7mF/MK4yUAAABIUkxMjPz8/FSpUiV99tlncnV1zbdjF7XvKVarVadOndLAgQOZowMA3BGLxaKlS5fe+s3Snylq51OTVq9erUGDBunKlSuaNGmS+vfvL4vFYjrrd7h3DgAA4M/dvHlTzZs3V8mSJfXdd9/x+5A8snjxYv3jH/9QVFSUXn311VzZH+NbAEB+u5P1gxgP2K4LFy5o8uTJmjFjhmrUqKHx48erZ8+eNjeP978Y9wAoiu7k/ve/Y3t3ZwMA7supU6fk5yxEuoAAACAASURBVOen+vXr6+DBg1q6dKni4uJsYsHA7OxsrVmzRm3atFGrVq106dIl00mFxvXr140e/+bNm0aPDwDIPbGxsfrqq680efLke7ogzPn+r5UqVUorV65UyZIl1bVrV/3666+5uv+yZctq+PDhqlmzpqZNm6aMjIxc3T8AAADuzrVr1xQUFCQ3NzeVK1dOBw4cUGBgYJ4uGPgb5op+r2TJkoyVAaCIYY7KDE9PTx08eFC+vr4KCAhQmzZtlJSUZDrrTzFeAgAAKLrS09PVo0cPeXt7y8vLS7t3786XBQOL+veU4sWLM0cHALhvRf18aoqnp6cSExPVu3dvDRo0SO3atVNycrLprN/h3jkAAIA/N2nSJKWkpGjhwoX8PiQPvfLKKxoyZIgGDhyYK2NlxrcAAFvCeKBgqFixokJDQ3X48GE1a9ZM3t7ecnd313fffWc67bYY9wDAvbHk3MeSg3eyUjAAIH+kp6dr6tSpmjFjhipXrqzRo0erb9+++fIj5L+TmZmpxYsXa/LkyUpLS9Prr7+ugIAA1apVy3RagfXfKwf/tsL70KFDVaVKFXXv3l116tTJ84bDhw9r+fLlunDhgqZMmSJJ97WSMQDANrRv317Z2dnatm3bXW3H+f7OHTt2TO7u7nJzc9PKlStVvHjxXNt3RkaGPv74Y02fPl0ZGRkaNGiQhg0bpkqVKv3ufefPn1d6evpt91W6dGk9+eSTudYGAABQlMTFxemNN97QmTNnNG7cOA0dOvS+ntJ8N5gr+j2LxaKlS5eqU6dOjJUBoBD63yctMkdlOw4cOKABAwZo9+7dGjRokN5//32VK1fOdJYkxksAAABF2fr169W3b18VL15cCxcuVPv27fP8mHxP+f/3m8+bN485OgDAHfnt+s5v5xDOp7Zj3759GjhwoPbu3auBAwfa1Lwf984BAAD8XkpKipo0aaKJEydq+PDhd7UtY/C7d+PGDT3zzDM6e/as4uPjVb58+fvaH+NbAEB++9/1gxgPFGy7du1SYGCgtmzZIg8PD82YMUPOzs6ms/4U4x4ARc3/3v9+L1g0EAAKuAsXLmjmzJmaNm2aypcvr4CAAA0cOFAlSpQwnaaMjAxFRkZq+vTp+vXXXzVw4ED5+/v/YYCOu5cbgwAAAP7XmjVr5OnpqR07dsjd3f2OtuF8f2927dqldu3aqXfv3oqIiMj1/WdmZmrRokWaMmWK0tLS9Nprr2nkyJG3fvz8wQcf6K233rrtPlq2bKnY2NhcbwMAACjMrl69qpCQEH3wwQfy8PBQZGSkqlevbjqrSPuzH5UxVgaAwuO36yWXLl1ijsoG5eTk6LPPPtOIESNUqlQpzZgxQz179jSdBQAAgCIoIyNDAQEBioyMlJeXlyIiIlShQoU8PybfU/7jz35kxhwdAOB2/vuhUJxPbU92drY+//xzDR8+XOXLl9eHH36oLl26mM66hbEGAACAdPPmTbVo0ULFihVTbGysihUrdkfbMad1f3766Sc1adJEzZo104oVK2490O5+ML4FAOSX/34IFOOBwmPTpk0aOXKkUlJS1KdPH4WEhNjsf0vGPQCKChYNBIAi7PLly5o1a5ZCQ0NVrFgxvfXWWxo2bJhKly5tOk2S9OWXX+r1119X2bJl9eabb8rPz08PPvig6axCg0UDAQC5LTs7W66urnrqqae0fPnyO9qG8/39WbZsmby9vTVlyhSNGDEiT46RnZ2tNWvWKCwsTHFxcRowYIBmz56dJ8cCAAAo6mJjY9W3b1+dPXtWkydPVv/+/XPlpj/cn/9dNPA3jJUBoHD47XpJ+fLlmaOyYT/99JNGjRqlzz//XF26dNGsWbN4mi8AAADyTVxcnHx8fHTx4kVFRETopZdeyvNjci399/7qfnPm6AAAf8VisSggIEAff/wx51MbZuvzfow1AABAURYaGqrg4GDt3btX9erVu6NtmNPKHVu3blXHjh01ceLEv13U5m4wvgUA5DWr1ap//etfSk5OZjxQyPz2EJDRo0fr4sWLGjJkiN555x098MADptP+FOMeAIVdbqwXZJeLPQCAfJCZmanIyEjVqlVLEyZMkJ+fn9LS0hQYGGgzCwZK0rlz53Tx4kXVrl1bjRo1stkvDQAA4D8WL16sxMREjR8//o634Xx/f3r27KnJkycrICBAixYtypNj2NnZycXFRY0bN5adnZ2OHTuWJ8cBAAAoyq5cuaKgoCC1bdtWtWrVUlJSknx9fVkw0MYxVgaAwoU5KttWqVIlRUVFacuWLUpLS1O9evUUHByszMxM02kAAAAoxG7cuKHg4GC1bt1atWrV0v79+/NlwUCJa+l3ijk6AMDtXLx4kfOpjftt3u+bb76xyXk/xhoAAKCoOnTokEJCQjRu3Lg7XjBQYk4rt7Rr104TJ07U6NGjtXHjxlzbL+NbAEB+uH79OuOBQsjOzk4+Pj46cuSI3nvvPc2dO1cODg4KDw/XzZs3Tef9AeMeAPh7LBoIAAXEzZs3FRUVpbp162r48OGyWq1KS0tTaGioypcvbzrvD3x9fZWUlKQaNWqoc+fOcnV11dKlS23yiwMAAEXdjRs3NHbsWL366qtydna+4+0439+/kSNHasiQIerbt6927NiRq/veu3evXnnlFdWuXVt79+7VsmXLtHbtWknS+fPnlZqaetvXyZMnc7UHAACgMPr666/l5OSkyMhIzZkzR1999ZWqVatmOgt/g7EyABQ+zFEVDG3bttWBAwc0ZswYhYWFqWnTpoqLizOdBQAAgEIoKSlJzZo10/Tp0zV79mytW7dOVapUybfjcy397zFHBwD4Ox4eHpxPC4h27dr9Yd5v+/btRpsYawAAgKIqOztb/fr1k5OTk4YPH35X2zKnlXsCAgLUvXt39e7dWz/88MN974/xLQAgv9SsWZPxQCFWpkwZBQYGKi0tTW+88YYCAwNVv359xcTEmE67hXEPANwZFg0EABuXk5OjmJgYOTs7q1+/furYsaOOHDmi8PBwPfbYY6bzbsvZ2VkLFy7U8ePH1aFDB/Xr10916tTR7NmzdeXKFdN5AADg/0RGRur06dN677337npbzvf3b8aMGXr++efVrVs3HT58+L72lZOTo02bNunZZ5+Vq6urfv31V23btk3bt2/Xiy++KDu7/0wDLFiwQE5OTrd99erVKzc+HgAAQKF08eJF+fn5qVOnTmrQoIGSkpLk6+trOgu3wVgZAAo35qgKDnt7ewUGBiopKUmVK1dWy5Yt5ePjo59//tl0GgAAAAqB7OxshYeHy83NTWXKlNGePXuMzdvxPeWPmKMDANwtzqcFx2/zfsnJyapatapat24tHx8fpaen51sDYw0AAABp+vTpio+PV1RUlOzt7e96e8bgucNisWj+/Pl65JFH1LNnT2VmZt71PhjfAgBMYTxQ+D388MMKDQ3VoUOH1LRpU3l7e8vd3V2xsbFGehj3AMDdY9FAALBhmzZtkpubm15++WU1bNhQBw8eVERERL4+9Tg3VKtWTR988IFOnz6t/v37a9y4cXryyScVHh5uOg0AgCLv6tWrmjRpkgYOHKiaNWve83443987Ozs7LVq0SLVr11bnzp117ty5e9pPXFyc3Nzc1LlzZ1WtWlXJyclavXq1WrVqJYvF8rv3BgQEKCcn57YvU5O8AAAAtm7t2rVycXHRypUrFR0drdWrVxe4+bqi5vDhw4yVAaCIYI6q4HBwcND69eu1cuVKbdmyRY6OjoqMjFROTo7pNAAAABRQx48fV/v27RUUFKSQkBB9++23qlWrluksvqf8n/PnzzNHBwC4Z5xPC46aNWtq7dq1WrlypbZt2yZHR0eFh4crOzs7T4/LvXMAAADSsWPHNHbsWI0ZM0YuLi73tS/G4PevXLlyWr58uVJSUjRy5Mi72pbxLQDAFjAeKPyefPJJRUVFaefOnSpZsqRat24tT09PHT16NN8aGPcAwL1h0UAAsEHbt29Xu3bt1LFjR1WsWFF79+5VdHS0TdzEeD8eeughjR49WidOnNDEiRO1Y8cO00kAABR506dP18WLFzV69Ohc2R/n+3tTunRprVq1ShaLRZ6envf01J34+Hi1b99ex44d04IFC1SvXr08KAUAACi6fvnlF/n5+alLly5yd3dXUlKSevbsaToLd+Do0aOMlQGgiGGOquDw9PRUUlKSevfurUGDBqldu3Y6ePCg6SwAAAAUMFFRUWrQoIEuXLiguLg4BQYGqlixYqazfqeof0+5cOECc3QAgPtW1M+nBYmnp6cOHjwoX19fBQQEqE2bNkpMTMyz43HvHAAAKOqys7PVp08f1apVS6NGjcq1/TIGvz+Ojo769NNP9dFHH2nhwoV3vB3jWwCALWE8UPg1a9ZMW7du1caNG3Xy5EnVq1dPfn5+Onv2bJ4fm3EPANwbS859PKbdarVKkqKjo3MtCACKsl27dmncuHFas2aNPDw8NHHiRDVt2tR0FmxQdHS0vL29dbvT+OLFi5WZmalXXnlFJUqUyMc6AEBB8csvv8jBwUFDhw5VcHCw6RxISklJUcuWLeXh4aElS5bIzo61/gEAAGzB6tWrNWDAAGVnZ2v27Nl66aWXTCfli7i4OK1Zs0bvv/++6ZR7ZrFYtHTp0lvXtAAAhcudXC9BwbF3714NGDBA+/fv18CBAzVx4kSVLVs2347PtTUAAICC5+zZs+rfv7/Wrl2rgIAAjRs3jrGcDeJ+cwDA3eL6TuFy4MABDRw4ULt27dKgQYM0YcIEPfDAA6azAAAACpWZM2dq5MiR+v7779WkSRPTOfgfI0eO1OzZs7Vjxw41btzYdA4AAH+K6zmQ/rMY9eeff66goCBlZGRo8ODBeuedd5jPA4BclBv3v7P6AADYgJSUFFmtVj399NP6+eeftXnzZm3cuJEFA3FfypYtq+HDh6tmzZqaNm2aMjIyTCcBAGzMpEmTZGdnpxEjRphOwf9xcnLSl19+qVWrVmn06NGmcwAAAIq8c+fOyWq1qlu3bmrZsqWSk5ML/YKB2dnZWrNmjdq0aaNWrVrp0qVLppMAAEAR0aRJE+3cuVOzZs3SwoUL1aBBA61fvz7fjs+1NQAAgIJl2bJlcnZ2VlJSkr755huFhoayYCAAAIANatiwobZv36558+Zp0aJFqlu3rqKiokxnAQAAFBonTpzQO++8o3feeYcFA21UWFiYnn76aVmtVv3yyy+mcwAAAP6SnZ2dfHx8dOTIEb377ruaM2eOnJycFBkZqaysLNN5AID/w6KBAGDQqVOn5Ofnp/r16+vgwYNaunSpduzYoQ4dOphOQyHQrVs3nTx5UiNGjND06dP1xBNP6O2339ZPP/30h/eeP39eqampt32dPHnSwKcAAOSVH3/8UbNmzdLo0aP14IMPms7Bf2nTpo0WLlyoKVOmaNasWaZzAAAAiqyYmBg5OzsrPj5eGzZsUHR0tCpWrGg6K89kZmbq008/Vf369dWzZ0/Vq1dPhw4d0ocffmg6DQAAFCF2dnby9fVVamqqWrZsqU6dOsnT01OnT5/O82NzbQ0AAKBguHjxovz8/OTl5aXOnTsrISFBbdq0MZ0FAACA27BYLPLx8dGhQ4fUtWtXvf766+ratauOHz9uOg0AAKBAy8nJka+vr2rWrKm3337bdA7+QvHixbV06VJdvXpVr776qrKzs00nAQAA3FbZsmUVGBiotLQ09e7dW0OHDlX9+vUVExNjOg0AIBYNBAAj0tPTFRQUpDp16mjDhg2aPXu2Dhw4IC8vL1ksFtN5KEQeeOABjRgxQmlpaZoxY4ZWrlypGjVqyM/PT4cPH771vgULFsjJyem2r169ehn8JACA3BYSEqIKFSpo4MCBplPwJ15++WWFhITozTff1MqVK03nAAAAFCk//fSTunfvLm9vb3Xv3l2JiYnq2LGj6aw8k5GRoalTp6pmzZry9/dXt27ddOLECc2dO1e1atUynQcAAIqoypUrKyoqSps3b9aRI0dUv359hYeH5/nTirm2BgAAYNs2bdokFxcXrVy5UqtWrVJUVJTKlStnOgsAAAB3qGLFioqIiNC2bdt08uRJOTs7Kzg4WNevXzedBgAAUCDNnTtXW7Zs0bx581SiRAnTObiNxx9/XDExMdqwYYNCQ0NN5wAAANyRRx55RKGhoUpMTJSLi4u8vb3VsmVLbd++3XQaABRpLBoIAPnowoULCgoKUo0aNfTFF19o5syZOnLkiHx9fVWsWDHTeSjESpQooddff12JiYmKjo5WUlKS6tatq0GDBkmSAgIClJOTc9tXbGys4U8BAMgtR44c0fz58zVu3DiVLl3adA7+wnvvvaf+/fvrH//4h+Lj403nAAAAFAkxMTFycXHR/v37tWnTJkVEROiBBx4wnZVnvvzySz3xxBOaNm2a/P39derUKU2aNEmVKlUynQYAACBJ6tChg/bt26c333xTgYGBatq0qb7//vs8Py7X1gAAAGzL1atXFRQUpOeee07u7u5KTk6Wp6en6SwAAADco9atW2vv3r2aNGmSpk6dqvr162vjxo2mswAAAAqUkydPKjAwUKNGjZKbm5vpHNwBd3d3TZ48We+9957Wr19vOgcAAOCO1alTR9HR0YqLi1Px4sXVunVrWa1WpaWlmU4DgCKJRQMBIB9cvnxZYWFhcnBw0Lx58zRmzJhbiwUWL17cdB6KEDs7O7m4uKhx48ays7PTsWPHTCcBAAwYM2aMnnrqKfn4+JhOwd+YOXOmWrVqpRdffFEnT540nQMAAFBonTlzRi+88IK8vb3Vo0cPJSQkqEOHDqaz8ty5c+d08eJF1a5dW40aNSrUCyQCAICCq3Tp0goODlZiYqIefvhhtWjRQn5+frp06VKeH5trawAAAObFx8ercePGmjt3rubMmaPo6Gg9/PDDprMAAABwn+zt7eXv76/U1FQ1bNhQzz77rKxWq86ePWs6DQAAwObl5OTIz89PVatW1XvvvWc6B3fB399fvXv3Vq9evXT8+HHTOQAAAHfl6aef1rZt27RhwwalpKTIyclJfn5+OnfunOk0AChSWDQQAPJQZmamIiMjVatWLU2YMEF+fn5KS0tTYGCgSpUqZToPRczevXv1yiuvqHbt2tq7d6+WLVumtWvXSpLOnz+v1NTU275YqAgACoeEhARFR0dr4sSJLF5cANjb22vZsmV67LHH1LlzZ/373/82nQQAAFCo5OTkKDIyUnXr1lVycrK2bNmiiIgIlStXznRavvD19VVSUpJq1Kihzp07y9XVVUuXLtXNmzdNpwEAAPxB7dq1tWHDBi1YsEBffvmlHB0dFRUVlWfH49oaAACAWTdv3lRYWJhatWql6tWrKykpSb6+vqazAAAAkMuqVq2qmJgYrVq1Srt27VLdunUVHh6urKws02kAAAA2a968edq4caM++eQTfqdaAM2ePVtVqlRR9+7ddfXqVdM5AAAAd83Dw0P79u3TrFmztGrVKjk4OCg4OJixDQDkExYNBIA8cOPGDUVFRalu3boaPny4rFar0tLSFBoaqgcffNB0HoqQnJwcbdq0Sc8++6xcXV3166+/atu2bdq+fbtefPFF2dn9ZyiwYMECOTk53fbVq1cvw58GAJAbAgMD1aRJE3Xv3t10Cu7QAw88oLVr1yojI0MvvfSSrl+/bjoJAACgUDh+/Lg6duyowYMHq0+fPkpISFDbtm1NZ+U7Z2dnLVy4UMePH1eHDh3Ur18/1alTR7Nnz9aVK1dM5wEAAPyOxWKRj4+PUlNTZbVa1adPH3Xo0EGpqam5sn+urQEAANiGlJQUubu7KyQkROPHj9f69etVrVo101kAAADIQ56enkpOTpa/v79GjRqlZs2aKT4+3nQWAACAzTlz5oxGjRqlkSNHqmXLlqZzcA/Kli2r5cuX6/jx4xo+fLjpHAAAgHtSvHhx+fr66ujRo3r33Xc1ffp01alTR5GRkTwQBADyGIsGAkAuysnJUUxMjJydndWvXz917NhRR44cUXh4uB577DHTeShi4uLi5Obmps6dO6tq1apKTk7W6tWr1apVK1kslt+9NyAgQDk5Obd9xcbGGvokAIDc8t1332n9+vUKDQ39w7kAtq1KlSpau3at9u/frz59+ignJ8d0EgAAQIGVk5OjyMhINWjQQD/99JO2b9+u8PBwlSlTxnSaUdWqVdMHH3yg06dPq3///ho3bpyefPJJhYeHm04DAAD4gwoVKig8PFzffvut0tPT1aRJEwUHB9/XAze4tgYAAGDeb3N3bm5usrOz0759+xQYGHhr8WYAAAAUbmXKlFFwcLASExNVsWJFubu7y8/PT5cuXTKdBgAAYDMGDRqkRx99VCEhIaZTcB9q166tqKgoRUZGav78+aZzAAAA7lnZsmUVGBiotLQ0de/eXYMHD1aDBg0UExNjOg0ACi3uogGAXLJp0ya5urrq5ZdfVqNGjZSSkqKIiAhVqVLFdBqKqPj4eLVv317Hjh3TggULVK9ePdNJAADDgoKC1LZtWz3zzDOmU3APXFxctGTJEsXExGjcuHGmcwAAAAqktLQ0dejQQYMHD9bgwYO1Z88eNWvWzHSWTXnooYc0evRonThxQhMnTtSOHTtMJwEAAPylli1bat++fZo0aZKmTp0qFxcXbdy48Z72xbU1AAAAs06dOiUPDw8NHjxYQ4cOVWxsrBwdHU1nAQAAwIA6depow4YNWrBggb788ks5OjoqKirKdBYAAIBxn376qVavXq1PPvlEpUuXNp2D+9StWzeNGjXq1r2MAAAABdkjjzyi8PBwJSUlydnZWVarVR4eHtq7d6/pNAAodFg0EADu0/bt29W2bVt17NhRDz/8sPbu3avo6Gg5ODiYTkMR5+/vrw8++EDVqlUznQIAsAGrVq1SXFycQkNDTafgPjz//POaO3euQkJCtHDhQtM5AAAABUZ2drYiIyPVsGFDXbhwQTt37lRoaKhKlixpOs1mlSpVSv3799fSpUtNpwAAANxW8eLF5e/vr9TUVDVq1EjPPvusrFarzp07d1f74doaAACAOTExMWrUqJF++umnW3N39vb2prMAAABgkMVikY+Pjw4dOiSr1ao+ffqoffv2SklJMZ0GAABgxI8//qgRI0Zo2LBhat26tekc5JKJEyeqTZs26tGjh37++WfTOQAAAPfN0dFR0dHRiouL0/Xr1+Xm5iar1apjx46ZTgOAQoNFAwHgHsXHx8vT01OtWrVSiRIltGvXLm3cuFENGzY0nQYAAPA72dnZGjNmjLp3767mzZubzsF96tu3rwIDA+Xr66uNGzeazgEAALB5ycnJcnd315AhQzRkyBDt2rVLrq6uprMAAACQy6pWraqYmBitWrVK8fHxcnR0VHh4uLKzs02nAQAA4C+kp6ere/fu8vb2lpeXF3N3AAAA+IOHHnpI4eHhio+P1+XLl9WwYUMFBQXp2rVrptMAAADy1eDBg1W+fHmNHz/edApykZ2dnT777DNlZWXpH//4h7KyskwnAQAA5IrmzZvru+++08qVK7V//345OTnJz89P6enpptMAoMBj0UAAuEsHDx6U1WpV8+bN9fPPP2vz5s3auHGj3NzcTKcBAAD8qc8//1xJSUkaN26c6RTkkokTJ8rb21s9e/ZUQkKC6RwAAACbdPPmTYWFhcnV1VWZmZmKj49XaGioSpQoYToNAAAAecjT01MHDx6Uv7+/Ro0apWbNmmn37t2mswAAAPA/1q9fr0aNGmnPnj3avHmzIiIiVKZMGdNZAAAAsFGurq7asWOHpkyZojlz5sjFxUXr1683nQUAAJAvFi1apC+//FKRkZEqV66c6Rzksscee0zLli3Tt99+qwkTJpjOAQAAyFWenp5KTk7Whx9+qJUrV8rR0VFhYWE8FAQA7gOLBgLAHTp58qT8/PzUoEEDpaSkaOnSpYqLi1OHDh1MpwEAAPylGzduKCQkRK+//rrq1atnOge5xGKxaN68eXJzc1Pnzp31ww8/mE4CAACwKQkJCXJ3d1dwcLBCQkK0e/duNWrUyHQWAAAA8kmZMmUUHBysXbt2qWTJknJ3d5e/v78yMjJMpwEAABR5GRkZ8vPzU6dOndSyZUvt379f7du3N50FAACAAqB48eLy9/dXamqqWrRooU6dOsnT01OnT582nQYAAJBnfv75Zw0fPlwDBw6Uh4eH6RzkkaefflrTpk3TuHHjtHbtWtM5AAAAucre3l6+vr46evSoAgMD9f7776t27dqKjIxUVlaW6TwAKHBYNBAA/sa5c+cUFBQkR0dHbdu2TfPnz9eBAwfk5eVlOg0AAOBvzZ07V2fOnNGYMWNMpyCXlShRQjExMSpXrpxeeOEF/frrr6aTAAAAjLtx44bCwsLUtGlTlShRQvv27VNgYKCKFStmOg0AAAAGNGjQQLGxsZo3b54WLVqkunXrKioqynQWAABAkRUXF6cmTZpoxYoVWr58uaKjo1WhQgXTWQAAAChgKleurKioKG3evFlHjx6Vk5OTwsLCdPPmTdNpAAAAuW7gwIEqXbq0QkNDTacgjw0aNEg+Pj7q3bu3jh07ZjoHAAAg15UrV06BgYFKSUlR586dNXjwYDVs2FBr1qwxnQYABQqLBgLAXzh//ryCgoJUo0YNLVq0SDNnzlRSUpJ8fHxkZ8f/fQIAANt3+fJlTZw4UYMGDVL16tVN5yAPVKxYUevWrdO//vUvWa1WbnoEAABF2v79+/X/2Lvz8Ciq7P/jn87CJohiUEQUZV8UQRBFWYZNBYzbkLCDCyIuiMtocEFAUUERxAUlgiPMT1ZHBAR1RERFEARFCEpAFHRE2SFAgKTT9/cH32QI6aSrO5Wu6s779Tw+z9Cprj5dU6fu6XNvV19xxRUaNWqUnn76aX355Zdq0KCB02EBAADAYR6PR/3791d6erquv/56Yk4hBQAAIABJREFU3XrrrUpMTNS2bducDg0AAKDUyM7O1siRI9WmTRvVrVtX69at08033+x0WAAAAIhwHTp00Hfffad//OMfGjFihFq0aKGVK1c6HRYAAIBt5s+fr3//+9+aPHmyKlWq5HQ4CIM33nhDF110kW655RZlZmY6HQ4AAECJOO+88zR58mRt2LBBjRo1UmJiojp37qzvv//e6dAAICJw1ysAOMXhw4c1duxY1a5dW1OnTtWIESO0efNmDRo0SHFxcU6HBwAAYNmECRN0+PBhpaSkOB0KStBFF12kDz/8UF9++aXuuecep8MBAAAIu2PHjmnYsGFq0aKFKlasqB9++EEpKSmKjY11OjQAAAC4SJUqVTR58mQtW7ZMv/zyixo3bqyRI0cqKyvL6dAAAACiWlpamlq2bKkJEyZo0qRJWrx4sapXr+50WAAAAIgS5cuX18iRI5WWlqZq1arp6quvVv/+/bVnzx6nQwMAACiWvXv36q677tLAgQN17bXXOh0OwqRcuXL697//rf/+97+66667nA4HAACgRDVo0EBz5szRihUrdPToUbVo0ULJycn69ddfnQ4NAFyNmwYCwP/JyspSamqq6tSpo2effVaDBw/W1q1blZKSonLlyjkdHgAAQFD279+v8ePH65FHHtHZZ5/tdDgoYS1atNCsWbP09ttv68UXX3Q6HAAAgLBZuXKlmjVrpjfeeEMvvfSSli1bprp16zodFgAAAFysbdu2WrdunZ577jmNGzdOl1xyiZYuXep0WAAAAFHH5/Np4sSJatGihSpUqKC1a9dq0KBBTocFAACAKFWnTh19/PHHmj9/vj7//HPVr19fqampMsY4HRoAAEBIhgwZotjYWL3wwgtOh4Iwu/DCC/XOO+9oxowZSk1NdTocAACAEteqVSt99dVXmjVrlr777js1aNBAQ4cO1YEDB5wODQBciZsGAij1srOzlZqaqlq1aunBBx/Urbfequ3bt2vMmDE6/fTTnQ4PAAAgJM8995xiY2P1wAMPOB0KwuT666/Xiy++qJSUFL377rtOhwMAAFCijh49qmHDhqlNmzaqWbOmNmzYoKFDhyomhmkPAAAABBYfH6+hQ4dq/fr1ql27tjp16qT+/ftr9+7dTocGAAAQFX799Ve1b99ew4YN06hRo/Tll1+qTp06TocFAACAUiAxMVFpaWnq27ev7rnnHrVr104bN250OiwAAICgfPjhh5o1a5amTJmiM844w+lw4IDrr79ejz/+uO6//359++23TocDAABQ4jwej5KSkvTTTz/p1Vdf1axZs1S7dm2NHTtWx44dczo8AHAVvj0HoNTy+XyaO3euGjdurPvuu0/dunXTzz//rDFjxujMM890OjwAAICQ7dixQ5MmTdKTTz7JTZBLmQcffFBDhgzRwIED9fXXXzsdDgAAQIlYvny5mjZtqjfffFOTJk3SRx99pAsuuMDpsAAAABCBatWqpcWLF2v+/PlatmyZGjRooIkTJ8rn8zkdGgAAQMSaPn26mjRpon379mnlypVKSUlRbGys02EBAACgFKlcubImTpyob7/9VllZWWrWrJmGDh2qw4cPOx0aAABAQAcPHtTgwYM1YMAAdenSxelw4KBRo0apffv2+vvf/84P4AEAgFIjPj5egwYN0tatWzVkyBCNGjVK9erVU2pqKuv6AOD/cNNAAKXSkiVL1Lx5c/Xs2VNNmzbVTz/9pMmTJ+vcc891OjQAAIBiGzFihKpWrarBgwc7HQocMGHCBHXp0kU33nij0tPTnQ4HAADANpmZmRo2bJjatWunOnXqKC0tTYMGDZLH43E6NAAAAES4xMRE/fTTT7rzzjv18MMP629/+5s2btzodFgAAAARZefOnbrhhht0++23695779XatWvVtGlTp8MCAABAKdasWTOtWLFCU6ZM0bvvvqsGDRrovffeczosAACAIt1///3y+XwaP36806HAYTExMXr33XcVFxenXr16KScnx+mQAAAAwqZixYoaOXKktmzZoi5duuiee+5Ry5YttXTpUqdDAwDHcdNAAKXK8uXL1bZtW11zzTWqUaOGvv/+e82ZM0e1a9d2OjQAAABbbN68We+8845GjhypsmXLOh0OHJA7MVyvXj117dpVu3btcjokAACAYvvkk0/UsGFDpaam6o033tCiRYtUo0YNp8MCAABAFDnttNM0ZswYrVmzRllZWWrWrJmGDh2qw4cPOx0aAACA67333ntq3Lix0tLS9Pnnn2vMmDEqU6aM02EBAAAAiomJUf/+/ZWWlqYOHTooOTlZiYmJ2r59u9OhAQAAFLB48WJNnz5dr7/+us4880ynw4ELVKlSRe+//75WrFihESNGOB0OAABA2J133nmaPHmyNmzYoFq1aqljx47q3Lmz1q1b53RoAOAYbhoIoFRYtWqVOnXqpDZt2qhs2bL69ttvtXDhQjVp0sTp0AAAAGz15JNPqnbt2urbt6/TocBB5cuX14IFCxQTE6Prr79emZmZTocEAAAQkoMHD+quu+5Sly5d1KRJE6WlpWnQoEFOhwUAAIAo1rRpU61YsUKvvfaapk2bpiZNmmjx4sVOhwUAAOBKuf27pKQkde3aVevXr1ebNm2cDgsAAAAooFq1apo+fbo+//xz/fLLL2rUqJFGjhyprKwsp0MDAACQJGVkZGjw4MHq06ePbr75ZqfDgYs0bdpUEydO1HPPPaf333/f6XAAAAAc0bBhQ82ZM0dLlizRvn371Lx5cyUnJ2vbtm1OhwYAYcdNAwFEtR9//FHJyclq1aqVMjMztXTpUn366adq3ry506EBAADYbu3atXrvvfc0ZswYxcXFOR0OHJaQkKCFCxdq69atGjBggHw+n9MhAQAABGXx4sW6+OKLNX/+fM2ZM0cLFy5U9erVnQ4LAAAApUBMTIwGDRqkTZs2qXXr1urWrZsSExP122+/OR0aAACAayxZsiSvf7dgwQJNnz5dFStWdDosAAAAoEjt2rXTunXr9Nxzz2ncuHG65JJL9NlnnzkdFgAAgB566CEdO3ZMEyZMcDoUuNCdd96p22+/Xbfeeqs2bdrkdDgAAACO6dixo9asWaNZs2Zp7dq1atSokYYNG6YDBw44HRoAhA13kQAQlbZv367nnntOU6dOVcOGDTV79mwlJSU5HRYQsnXr1mnPnj15/96wYYOkE4tvT9aoUSO+PA8ApcDx48d12WWXqUuXLnrsscd01llnSZIef/xxtWjRQjfeeKPDEcItGjRooA8++ECdO3fWo48+qnHjxhXYJjs7W/Hx8Q5EBwAA4N+BAweUkpKi1NRUJSUladKkSUpISHA6LESAHTt26Mcffyzw+IYNG1SlSpW8fyckJKhp06bhDA0AYBPmSxBu1apV0/Tp03XbbbfpnnvuUaNGjTR8+HA9/PDD/HALAAAotY4ePapRo0bpxRdf1N///ne98cYbeXPWQHZ2tr766qt8P2q3c+dOSfk/u8XExKhNmzbMVQMAmN+BI+Lj4zV06FAlJiZqyJAh6ty5s/r27atx48bp7LPPdjo8AAAQxXJycvSPf/xDvXv31uWXX573+NKlS/X2229r7ty5qlq1qoMRws1ee+01/fDDD0pOTtbKlSt12mmn5f3NGKMvvvhCbdu2VUxMjINRAgAiAfM5iHQej0dJSUm68cYb9c4772j48OF666239Oijj+qBBx5Q2bJlnQ4RAEqUxxhjQn1ycnKyJGnOnDm2BQQAxfHHH3/ohRde0OTJk3XRRRfpscceU9++fWlyIeKdccYZOnjwYMDtBg0apMmTJ4chIgCAkzZu3KiLL75YsbGxKleunIYNG6bLL79c1113nT777DN16NDB6RDhMrNnz1avXr00ceJEDRkyJO/x1atX6/rrr9ejjz6qf/zjHw5GCAAAcMLChQs1ePBg+Xw+TZo0STfffLPTISGC3HXXXUpNTQ24XeXKlfklQQCIUMyXwEnHjh3TmDFjNGbMGDVo0EBvvvmmrrzySqfDAgAACKvVq1erf//++uuvv/TCCy9o0KBBTocEl1myZIk6d+5sadtPP/1UnTp1KuGIAABux/wO3GDhwoW67777dOjQIY0YMUJDhgzhOygAAKBErF69WldccYViYmL08MMP6+mnn1ZOTo6aNGmiFi1aaPbs2U6HCJf77bff1Lx5c3Xu3FkzZsyQJGVkZKhv375auHCh5syZo6SkJIejBAC4HfM5iDb79+/X2LFj9corr6hq1ap64oknNHDgQHp8AFxpzpw56tGjh4px2z9xdQMQFfbu3athw4apbt26mjdvnl555RVt2LBB/fv3p5BDVOjWrZulu/B36dIlDNEAAJyWnp4uj8ejnJwcHTlyRCNHjlRycrK6deumq666yunw4EI9evTQM888owcffFAffPCBJGn+/Plq27at9uzZo5dffjnfLwMBAADYLTs7u8i/79q1S8nJybrhhht09dVXa+PGjdwwEEGz0huLi4tT165dwxANAKAkMF8CJ5UrV04jR45UWlqazjnnHF111VXq37+/9u7d63RoAAAAxZaZmakpU6YoJyfH79+9Xq/Gjh2r1q1b64ILLlBaWho3DIRfV199tSpUqBBwuwoVKujqq68OQ0QAALdjfgdukJiYqJ9++kn333+/Hn30UbVs2VJr1qxxOiwAABCFli1bpvj4ePl8Po0fP16NGzdWSkqKMjIy9OqrrzodHiLABRdcoJkzZ2rOnDl6/fXXlZaWpmbNmunjjz9WTExM3o0EAQAoCvM5iDZnnnmmxowZo82bN+u6667TPffcoyuuuEKff/6506EBQIngTloAXOnw4cM6evSope3Gjh2r2rVra+rUqRoxYoQ2b96sQYMGKS4uLgyRAuHRu3fvgF+ur1SpEl+CA4BSIj09Pd+Xo3NycpSRkaGPPvpItWrVUmpqaqFf5kDp9cQTT+i2225T37599cgjj+iWW25Rdna2jDH6448/9OmnnzodIgAAiFJjx47VOeecox07dvj9+9y5c9W4cWOtXr1an3zyiebMmaMqVaqEOUpEg65du6pSpUpFbuP1etW3b98wRQQAsBvzJXCDOnXq6JNPPtHs2bP1ySef6OKLL9b06dML/cXPTZs2qXPnzvrjjz/CHCkAAIB19913n+68806NGzeuwN9++ukntWrVSqNGjdIzzzyjjz/+WDVq1HAgSkSC8uXLq3v37kXe8D0+Pl7du3dX+fLlwxgZAMCtmN+BW1SoUEEjR47Ut99+q7Jly6pVq1YaOnSoDh06VOhz7rnnHv3rX/8KY5QAACDSLVmyJO+7Hjk5Odq+fbsmTZqk9u3bW7pxDyBJnTp10vDhwzVz5kxdfvnl+u2335SdnS2fz6fFixcrIyPD6RABAC7HfA6iVY0aNTR58mStX79e1apVU4cOHdS5c2etX78+4HO//fbbInuBAOAm3DQQgOvs2LFDjRo1Uo8ePQrdJisrS6mpqapTp46effZZDR48WFu3blVKSorKlSsXxmiB8LjmmmtUuXLlQv8eHx+v5ORklS1bNoxRAQCcsnnzZvl8vgKP+3w+/fXXXxo8eLAaNWqk77//3oHo4GaTJk1S8+bNNW7cOPl8vrzzKD4+Xm+++abD0QEAgGi0bNkyPf744zpw4IAGDRqU729//fWXbrnlFvXo0UO33HKLNmzYoGuuucahSBENypQpo6SkpCIXsJx++unq1KlTGKMCANiJ+RK4SVJSktLT05WcnKzbb79d7du3108//ZRvG2OMBg4cqCVLligxMVHHjh1zKFoAAIDCzZo1S//85z8lScOHD8/7woQxRqmpqWrRooViYmL0/fffKyUlRTExLD1G0QLd8D07O1t9+vQJY0QAADdjfgdu06RJEy1fvlxTp07VzJkz1aBBA02fPr3Adv/+97/1xhtv6Pbbb9fy5csdiBQAAEQar9er5cuX5/suSE5Ojowxmjdvnho2bKilS5c6GCEihdfrVWZmpr7++msdP35cXq8372/Z2dlatGiRg9EBACIF8zmIZo0aNdLChQv16aefau/evWrWrJmSk5O1fft2v9v//PPPuuqqq9S1a1fW+AGICKzcAeAqe/fuVYcOHbRjxw4tXLhQ33zzTb6/Z2dnKzU1VbVq1dJDDz2kW2+9Vdu3b9eYMWN0+umnOxQ1UPLi4+PVq1cvlSlTxu/fs7Oz1bt37zBHBQBwyoYNG/JN6p3MGCNjjLZs2aKtW7eGOTK42bFjx9SnTx+/CxSzs7O1cOFC7dixw4HIAABAtNq5c6eSk5Pl8XhkjNGiRYs0Y8YMSdLcuXN18cUXa926dVqyZIkmT56sSpUqORwxokFRC1ji4+PVp0+fQntsAAD3Y74EbnPGGWdo4sSJWrVqlY4cOaJLL71Uw4YNy1s4OG3aNK1YsULSib7uwIEDnQwXAACggF9++UUDBw6Ux+ORdGK+uXv37tqyZYs6deqke++9V0OGDNHy5ctVv359h6NFpOjYsaOqVKlS6N/POOMMdejQIYwRAQDcjvkduI3H41H//v21adMmde/eXbfddps6duyo9PR0SdKhQ4d07733KiYmRsYYJSYm6tdff3U4agAA4HZr1qzR0aNH/f7N6/Xqzz//VKdOnTR06NAwR4ZIsmvXLnXo0EEvvfSSpBM93ZPFxsZq5syZToQGAIgwzOegNOjUqZPWrl2rWbNmac2aNWrUqJGGDRumgwcP5tsuJSVFkvTNN9+oV69eysnJcSJcALCMmwYCcI3MzEx17dpVv/zyi3JychQXF6eHH35YkuTz+TR37lw1atRIQ4YMUbdu3bRlyxaNGTNGZ555psORA+HRq1cvZWVl+f1bQkKC2rVrF+aIAABO2bx5c6F/83g8iomJ0ZQpU9S9e/cwRgU327t3r/72t79p3rx5+X6Z8GQej0fTpk0Lc2QAACBa5eTkqGfPnjpw4EDehKnH49Hdd9+tDh06qGfPnurXr5/S0tJYTABbtW/fXlWrVvX7t+zsbPXq1SvMEQEA7MZ8CdyoefPmWrlypV588UVNmjRJF198sd577z099NBDedt4vV7NmDFDL7/8soORAgAA/I/X61XPnj11/PjxvC+Wer1e/fLLL+revbt27typVatWacyYMYqPj3c4WkSSuLg49e7d2+/NncqUKaN+/fopLi7OgcgAAG7F/A7cqkqVKpo4caK++OIL7dq1S82aNdPIkSP1xBNPaO/evfL5fMrJydHhw4eVmJioI0eOOB0yAABwsWXLlhXZZ8vJyZHH49FHH33ETUrg14YNG9SoUSN98803hZ4jXq9XH3/8sTIyMsIcHQAg0jCfg9LC4/EoKSlJmzZt0oQJE/T222+rdu3aGjt2rI4fP67Vq1dr3rx58nq98nq9Wrhwoe655x6nwwaAInHTQACukJWVpRtuuEHfffdd3q8Eer1erVixQqNGjVKTJk3Uu3dvtW3bVlu2bNHkyZN17rnnOhw1EF5t2rTxe97Hx8erX79+io2NdSAqAEC47d69W4cOHfL7t5iYGMXFxWnu3Lm6/fbbwxwZ3Oy6667TqlWr5PV6C93G6/XqjTfeKPSmggAAAMF48skn9dVXX+X1+qQTv2h79OhR/fHHH/rqq680YcIEVahQwcEoEY1iYmLUp08fvwtsq1WrpquvvtqBqAAAdmK+BG4VFxenoUOHatOmTbrqqqv06KOP6vDhw3k34JFO1MQPP/ywPv74YwcjBQAAOOHJJ5/Ud999V2AOMScnRxs2bNCECRN02WWXORQdIl1hN3zPysrixk8AgAKY34HbtW7dWt9//72ef/55vfHGG5o0aVK+Otrr9So9PV39+vXL1w8EAAA42ZIlS4q8GaDH41G3bt20evVq5r3h18GDB3Xs2LGA2+Xk5Gj+/PlhiAgAEOmYz0FpUqZMGQ0aNEibNm3SgAEDNGLECF1yySW6++6789XfOTk5mjJlikaPHu1gtABQNG4aCMBxOTk56tmzp7744osCCxBjY2M1fvx4XXTRRVq7dq2mTp2qCy64wKFIAWd5PB716dOnwB37+QVNAChd0tPT/T4eGxursmXLavHixbrlllvCHBXc7qmnntJ5550X8Nd9fv/9d3322WdhigoAAESrRYsWaezYsX4XOGZnZ2vz5s36888/HYgMpUWvXr3y3bBS+t8vXsbEMDUGAJGO+RK4XfXq1XXvvfdq27ZtBWqSXD169NDPP/8c5sgAAAD+Z9myZXrxxRcL/ZJyTEyMbr/99kJ/0A4IpFWrVqpRo0aBx6tXr64rr7zSgYgAAG7H/A7cLi4uTkOGDNEFF1wgj8dT4O9er1cffPCBxo4d60B0AADA7bxer1asWCGfz1fgb7GxsYqNjdXzzz+vBQsW6IwzznAgQkSC1q1b6+eff1a3bt0kqdDPSh6PRzNnzgxnaACACMV8DkqjKlWq6KWXXtKmTZtUq1Ytvz+05/P5NHz4cL3++usORQkARWPmDICjjDEaOHCgFixYUKCQkk7cUPDQoUO644471KRJEwciBNzF3x37zz//fLVo0cKhiAAA4Zaenl7gV+Pi4uJ0+umn6+uvv1anTp0cigxulpiYqF9++UXjxo1ThQoV/P4qt3TiXHrzzTfDHB0AAIgmv/32m/r06eP3SxK5YmJidOedd2r37t1hjAylScuWLVWzZs18j/GLlwAQXZgvgZt5vV7dfvvtBfq4uXw+n44ePaouXbooIyMjzNEBAABIu3fvVnJycpE9vJycHO3cuVMPPvhgGCNDNPF4POrbt2++uekyZcpowIABRZ57AIDSi/kdRIKpU6f6/RJxLmOMHn/8cX344YdhjgwAALjd6tWrdfTo0QKPx8fHq2rVqvrqq6+UkpLiQGSINNWqVdO8efO0YMECJSQk+P1uSE5Ojv7zn/9o3759DkQIAIgkzOegNDv//PP166+/FrrOT5Luv/9+vf/++2GMCgCs4aaBABz10EMPafr06YX+YrF04sNGSkqK319RAUqbyy67THXq1Mn7d3x8vG677TY+eANAKbJ582bFxcXl/Ts+Pl5nn322Vq5cqWbNmjkYGdyuTJkyGjp0qLZv367BgwcrJiamwASx1+vV/Pnz9eeffzoUJQAAiGTHjx/XjTfeqMzMzCJ7eT6fT4cPH9YDDzwQxuhQ2vTv3z9fvVurVi0+MwFAFGG+BG42fvx4bdq0qdAvDktSdna2tm3bpr59+8oYE8boAABAaWeMUZ8+fXTgwIEi1+xJJ2qWqVOn6ssvvwxTdIg2vXr1UnZ2dt6/ufETACAQ5nfgZnv37tUjjzwS8HstHo9HPXr00MaNG8MUGQAAiASff/55gbX7MTEx6tixozZu3KhWrVo5FBkiVWJiotLT0/Nu6uTvZjcLFixwIDIAQKRhPgel1ZQpU/Tzzz8XOW9ujFGvXr20fPnyMEYGAIFx00AAjhk+fLgmTpwYcNLU5/Pp559/1owZM8IUGeBuJ9+xPzs7Wz169HA4IgBAOP3000/KysqSdOLL0DVr1tQ333yj+vXrOxwZIkVCQoJeeeUVrVmzRi1btpR0YsFBLo/Ho+nTpzsVHgAAiGAPPfSQNmzYkG/RgD9lypSR1+vVvHnzdOzYsTBFh9Kmd+/eeedi7i9eAgCiC/MlcKOMjAyNHDnS0o0AvV6vFi1apNGjR4chMgAAgBPGjx+vzz77rNAensfjyfsRuypVqqhfv36qVatWOENEFGnSpEm+tQx169bVJZdc4mBEAAC3Y34Hbvb000/r4MGDAbfz+XzKyspSYmKiDhw4EIbIAABAJPjss8/yfnQsNjZWMTExGj58uBYtWqQqVao4HB0i1RlnnKG33npLy5YtU82aNfN6u9KJG9zwnWwAgBXM56A0OnLkiJ588smA6/yMMcrJyVG3bt30448/hik6AAiMmwYCcMT48eM1evRoS1+WyMWXJYATTl4Q07hxYzVq1MjhiAAA4bRx40YZYxQfH6+LL75Y33zzjc4//3ynw0IEatasmZYvX64FCxaoRo0aeb8s5/V69frrrwe8uTcAAMDJZs6cqUmTJvn9lbX4+Hh5PB55PB41atRI99xzj+bNm6c//vhD5cqVcyBalAYNGjRQ48aNJfGLlwAQrZgvgRtVrFhRzz77rG644QYlJCRIOnHjncLqXp/PpxEjRmj+/PnhDBMAAJRSa9eu1bBhwwrMA5YtW1bSiRvztGvXTqNHj9aaNWu0Z88eTZ8+XTVq1HAiXESJ/v37Kz4+XvHx8br11ludDgcA4HLM78DNbr75Zg0YMEC1a9eWx+ORJJUrVy7fD/bm8nq9+v3335WUlOR3Dh0AAJQuWVlZWrFihYwxiouL05lnnqmlS5dq5MiRfmsJIFht27ZVWlqaHn74YcXExCguLk4+n09Lly7V3r17nQ4PABABmM9BafPRRx9pz549io2NVZkyZYrcNicnR5mZmerYsaN+//33MEUIAEXzmGDu2HWK5ORkSdKcOXNsCwhA9Hv77bc1cODAAjcMjIuLk8fjyftyT2xsrM477zw1bNhQDRs2VJs2bXTLLbc4ETLgOk2bNtUPP/ygF154QY888ojT4QAAwsTr9ap8+fLyer1q3bq1Fi1apNNPP93psBAFjh8/rokTJ2rUqFE6evSojDH69NNP1alTJ6dDAwAAEWDTpk267LLLdOzYsbwbXOf+KnLDhg3VuXNntW/fXm3atOFXkRFWL7zwglJSUnTppZdq3bp1TocDACgBzJfA7bZt26aVK1dq1apV+uqrr7R+/Xp5vV7Fx8fL5/MpJycn76aCa9as4eaXAACgxBw6dEiXXHKJtm/frtjYWBljZIxRgwYN1K1bN11zzTVq06YNP/IB223fvl0XXXSRJGnr1q15/xsAgMIwv4NIsH//fq1atUqrVq3SypUr9c033+jgwYOKiYlRfHy8jh8Xf7xiAAAgAElEQVQ/nrftww8/rHHjxjkYLQAAcNry5cvVpk0bSdLVV1+tuXPn6txzz3U4KkSrNWvW6NZbb9WPP/4oY4zeeustDRw40OmwAAAux3wOShuv16tvvvlGGzdu1I8//qj169dr48aN2r17t6QT976Jj4/P+46MJMXExKhOnTpauXIl34sBUCxz5sxRjx49Ctx3KxjcNBCusG3bNj322GP8glYpsG/fPi1dujTfhatcuXKqVKmSTj/9dFWsWFGVKlVSpUqVVKFChbD8UkpSUpKSkpJK/HVKO/LcXps3b9b69evVtWtXVahQwelwokKNGjU0fvx4p8MAIsrcuXM1d+5cp8MoVTIzM7Vo0SJVr15drVq14lfl/k9sbKyef/55XXjhhSWy/4ceekj//e9/S2TfbnPs2DGlpaVp27ZtuvDCC9WiRQunQ8JJqFcARDrqx+i1ZMkS7d+/X5J0+umn65xzztHZZ5+thISEgL+65iZu6xPSTyu+zMxMLV68WE2aNFG9evWcDieiuS0/gEjG9d1ezJfYL1r6D279/OHz+bR//37t3btXe/fu1Z49e3Ts2DFJUqVKlXTdddc5HCHsVtL9awAIF+rYyJeenq7169erTJkyqlatmqpVq6ZzzjknIm4S6La+BPkQvCVLlkgSP1oXBOpIIPKUpvVFJY35HftF27ji1nw7dOiQ9u3bp3379mn37t3KyMjI+95M27Ztdc455zgcIewWLf10ANGBfoW7bd68WT/88IMaNmyoxo0by+PxOB2SK/G9EPv4fD5t3rxZGzduVLVq1XT11Vc7HVKpRt0IlIzSdF0PF+ZzSgbjQGBuyufs7GxlZGTk/Xf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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(Image((png_file_name)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the decision tree above, there are parent-child node relationships between num9 and several other input variables for the range `~-1 < num9 < ~1`. These parent-child relationships can be indicative of the variables driving the interactions detected in the partial dependence and ICE plot above.\n",
    "\n",
    "\n",
    "**Conclusion**: In this notebook, direct and indirect model explanation techniques were combined to gain insights into the behavior of a complex ML model. "
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
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   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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 },
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