{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6c4d4faf-ab84-4a72-a80e-535b211747cd",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Partial dependence / Individual Conditional Expectation (PD/ICE) Explainer Demo\n",
    "\n",
    "This example demonstrates how to interpret a **scikit-learn** model using\n",
    "the H2O Sonar library and retrieve the data and **partial dependence plot**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "69f414e3-bc88-478b-bed5-890352b1041a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "import os\n",
    "\n",
    "import pandas\n",
    "import datatable\n",
    "import webbrowser\n",
    "\n",
    "from h2o_sonar import interpret\n",
    "from h2o_sonar.lib.api import commons, explainers\n",
    "from h2o_sonar.lib.api.models import ModelApi\n",
    "from h2o_sonar.explainers.pd_ice_explainer import PdIceExplainer\n",
    "\n",
    "from sklearn.ensemble import GradientBoostingClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "bef37207-bd90-4a60-a927-bbc2c54ab149",
   "metadata": {},
   "outputs": [],
   "source": [
    "# dataset\n",
    "dataset_path = \"../../data/creditcard.csv\"\n",
    "target_col = \"default payment next month\"\n",
    "df = pandas.read_csv(dataset_path)\n",
    "(X, y) = df.drop(target_col, axis=1), df[target_col]\n",
    "\n",
    "# results\n",
    "results_location = \"../../results\"\n",
    "os.makedirs(results_location, exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "bbe0ca51",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'id': 'h2o_sonar.explainers.pd_ice_explainer.PdIceExplainer',\n",
       " 'name': 'PdIceExplainer',\n",
       " 'display_name': 'Partial Dependence Plot',\n",
       " 'description': 'Partial dependence plot (PDP) portrays the average prediction\\nbehavior of the model across the domain of an input variable along with +/- 1\\nstandard deviation bands. Individual Conditional Expectations plot (ICE) displays\\nthe prediction behavior for an individual row of data when an input variable is\\ntoggled across its domain.\\n\\nPD binning:\\n\\n**Integer** feature:\\n\\n* bins in **numeric** mode:\\n    * bins are integers\\n    * (at most) `grid_resolution` integer values in between minimum and maximum\\n      of feature values\\n    * bin values are created as evenly as possible\\n    * minimum and maximum is included in bins\\n      (if `grid_resolution` is bigger or equal to 2)\\n* bins in **categorical** mode:\\n    * bins are integers\\n    * top `grid_resolution` values from feature values ordered by frequency\\n      (int values are converted to strings and most frequent values are used\\n      as bins)\\n* quantile bins in **numeric** mode:\\n    * bins are integers\\n    * bin values are created with the aim that there will be the same number of\\n      observations per bin\\n    * q-quantile used to created ``q`` bins where ``q`` is specified by PD parameter\\n* quantile bins in **categorical** mode:\\n    * not supported\\n\\n**Float** feature:\\n\\n* bins in **numeric** mode:\\n    * bins are floats\\n    * `grid_resolution` float values in between minimum and maximum of feature\\n       values\\n    * bin values are created as evenly as possible\\n    * minimum and maximum is included in bins\\n      (if `grid_resolution` is bigger or equal to 2)\\n* bins in **categorical** mode:\\n    * bins are floats\\n    * top `grid_resolution` values from feature values ordered by frequency\\n      (float values are converted to strings and most frequent values are used\\n      as bins)\\n* quantile bins in **numeric** mode:\\n    * bins are floats\\n    * bin values are created with the aim that there will be the same number of\\n      observations per bin\\n    * q-quantile used to created ``q`` bins where ``q`` is specified by PD parameter\\n* quantile bins in **categorical** mode:\\n    * not supported\\n\\n**String** feature:\\n\\n* bins in **numeric** mode:\\n    * not supported\\n* bins in **categorical** mode:\\n    * bins are strings\\n    * top `grid_resolution` values from feature values ordered by frequency\\n* quantile bins:\\n    * not supported\\n\\n**Date/datetime** feature:\\n\\n* bins in **numeric** mode:\\n    * bins are dates\\n    * `grid_resolution` date values in between minimum and maximum of feature\\n      values\\n    * bin values are created as evenly as possible:\\n        1. dates are parsed and converted to epoch timestamps i.e integers\\n        2. bins are created as in case of numeric integer binning\\n        3. integer bins are converted back to original date format\\n    * minimum and maximum is included in bins\\n      (if `grid_resolution` is bigger or equal to 2)\\n* bins in **categorical** mode:\\n    * bins are dates\\n    * top `grid_resolution` values from feature values ordered by frequency\\n      (dates are handled as opaque strings and most frequent values are used\\n      as bins)\\n* quantile bins:\\n    * not supported\\n\\nPD out of range binning:\\n\\n**Integer** feature:\\n\\n* OOR bins in **numeric** mode:\\n    * OOR bins are integers\\n    * (at most) `oor_grid_resolution` integer values are added below minimum and\\n      above maximum\\n    * bin values are created by adding/substracting rounded standard deviation\\n      (of feature values) above and below maximum and minimum `oor_grid_resolution`\\n      times\\n        * 1 used used if rounded standard deviation would be 0\\n    * if feature is of unsigned integer type, then bins below 0\\n      are not created\\n        * if rounded standard deviation and/or `oor_grid_resolution` is so high\\n          that it would cause lower OOR bins to be negative numbers, then standard\\n          deviation of size 1 is tried instead\\n* OOR bins in **categorical** mode:\\n    * same as numeric mode\\n\\n**Float** feature:\\n\\n* OOR bins in **numeric** mode:\\n    * OOR bins are floats\\n    * `oor_grid_resolution` float values are added below minimum and above maximum\\n    * bin values are created by adding/substracting standard deviation\\n      (of feature values) above and below maximum and minimum `oor_grid_resolution`\\n      times\\n* OOR bins in **categorical** mode:\\n    * same as numeric mode\\n\\n**String** feature:\\n\\n* bins in **numeric** mode:\\n    * not supported\\n* bins in **categorical** mode:\\n    * OOR bins are strings\\n    * value `UNSEEN` is added as OOR bin\\n\\n**Date** feature:\\n\\n* bins in **numeric** mode:\\n    * not supported\\n* bins in **categorical** mode:\\n    * OOR bins are strings\\n    * value `UNSEEN` is added as OOR bin\\n\\n',\n",
       " 'model_types': ['iid', 'time_series'],\n",
       " 'can_explain': ['regression', 'binomial', 'multinomial'],\n",
       " 'explanation_scopes': ['global_scope', 'local_scope'],\n",
       " 'explanations': [{'explanation_type': 'global-partial-dependence',\n",
       "   'name': 'PartialDependenceExplanation',\n",
       "   'category': None,\n",
       "   'scope': 'global',\n",
       "   'has_local': None,\n",
       "   'formats': []},\n",
       "  {'explanation_type': 'local-individual-conditional-explanation',\n",
       "   'name': 'IndividualConditionalExplanation',\n",
       "   'category': None,\n",
       "   'scope': 'local',\n",
       "   'has_local': None,\n",
       "   'formats': []}],\n",
       " 'parameters': [{'name': 'sample_size',\n",
       "   'description': 'Sample size for Partial Dependence Plot.',\n",
       "   'comment': '',\n",
       "   'type': 'int',\n",
       "   'val': 25000,\n",
       "   'predefined': [],\n",
       "   'tags': [],\n",
       "   'min_': 0.0,\n",
       "   'max_': 0.0,\n",
       "   'category': ''},\n",
       "  {'name': 'max_features',\n",
       "   'description': 'Partial Dependence Plot number of features (to see all features used by model set to -1).',\n",
       "   'comment': '',\n",
       "   'type': 'int',\n",
       "   'val': 10,\n",
       "   'predefined': [],\n",
       "   'tags': [],\n",
       "   'min_': 0.0,\n",
       "   'max_': 0.0,\n",
       "   'category': ''},\n",
       "  {'name': 'features',\n",
       "   'description': 'Partial Dependence Plot feature list.',\n",
       "   'comment': '',\n",
       "   'type': 'list',\n",
       "   'val': None,\n",
       "   'predefined': [],\n",
       "   'tags': ['SOURCE_DATASET_COLUMN_NAMES'],\n",
       "   'min_': 0.0,\n",
       "   'max_': 0.0,\n",
       "   'category': ''},\n",
       "  {'name': 'oor_grid_resolution',\n",
       "   'description': 'Partial Dependence Plot number of out of range bins.',\n",
       "   'comment': '',\n",
       "   'type': 'int',\n",
       "   'val': 0,\n",
       "   'predefined': [],\n",
       "   'tags': [],\n",
       "   'min_': 0.0,\n",
       "   'max_': 0.0,\n",
       "   'category': ''},\n",
       "  {'name': 'quantile-bin-grid-resolution',\n",
       "   'description': 'Partial Dependence Plot quantile binning (total quantile points used to create bins).',\n",
       "   'comment': '',\n",
       "   'type': 'int',\n",
       "   'val': 0,\n",
       "   'predefined': [],\n",
       "   'tags': [],\n",
       "   'min_': 0.0,\n",
       "   'max_': 0.0,\n",
       "   'category': ''},\n",
       "  {'name': 'grid_resolution',\n",
       "   'description': 'Partial Dependence Plot observations per bin (number of equally spaced points used to create bins).',\n",
       "   'comment': '',\n",
       "   'type': 'int',\n",
       "   'val': 20,\n",
       "   'predefined': [],\n",
       "   'tags': [],\n",
       "   'min_': 0.0,\n",
       "   'max_': 0.0,\n",
       "   'category': ''},\n",
       "  {'name': 'center',\n",
       "   'description': 'Center Partial Dependence Plot using ICE centered at 0.',\n",
       "   'comment': '',\n",
       "   'type': 'bool',\n",
       "   'val': False,\n",
       "   'predefined': [],\n",
       "   'tags': [],\n",
       "   'min_': 0.0,\n",
       "   'max_': 0.0,\n",
       "   'category': ''},\n",
       "  {'name': 'sort_bins',\n",
       "   'description': 'Ensure bin values sorting.',\n",
       "   'comment': '',\n",
       "   'type': 'bool',\n",
       "   'val': True,\n",
       "   'predefined': [],\n",
       "   'tags': [],\n",
       "   'min_': 0.0,\n",
       "   'max_': 0.0,\n",
       "   'category': ''},\n",
       "  {'name': 'histograms',\n",
       "   'description': 'Enable histograms.',\n",
       "   'comment': '',\n",
       "   'type': 'bool',\n",
       "   'val': True,\n",
       "   'predefined': [],\n",
       "   'tags': [],\n",
       "   'min_': 0.0,\n",
       "   'max_': 0.0,\n",
       "   'category': ''},\n",
       "  {'name': 'quantile-bins',\n",
       "   'description': 'Per-feature quantile binning (Example: if choosing features\\n                 F1 and F2, this parameter is \\'{\"F1\": 2,\"F2\": 5}\\'. Note, you can\\n                 set all features to use the same quantile binning with the\\n                 `Partial Dependence Plot quantile binning` parameter and then\\n                 adjust the quantile binning for a subset of PDP features with\\n                 this parameter).',\n",
       "   'comment': '',\n",
       "   'type': 'str',\n",
       "   'val': '',\n",
       "   'predefined': [],\n",
       "   'tags': [],\n",
       "   'min_': 0.0,\n",
       "   'max_': 0.0,\n",
       "   'category': ''},\n",
       "  {'name': 'numcat_num_chart',\n",
       "   'description': 'Unique feature values count driven Partial Dependence Plot binning and chart selection.',\n",
       "   'comment': '',\n",
       "   'type': 'bool',\n",
       "   'val': True,\n",
       "   'predefined': [],\n",
       "   'tags': [],\n",
       "   'min_': 0.0,\n",
       "   'max_': 0.0,\n",
       "   'category': ''},\n",
       "  {'name': 'numcat_threshold',\n",
       "   'description': 'Threshold for Partial Dependence Plot binning and chart selection (<=threshold categorical, >threshold numeric).',\n",
       "   'comment': '',\n",
       "   'type': 'int',\n",
       "   'val': 11,\n",
       "   'predefined': [],\n",
       "   'tags': [],\n",
       "   'min_': 0.0,\n",
       "   'max_': 0.0,\n",
       "   'category': ''},\n",
       "  {'name': 'debug_residuals',\n",
       "   'description': 'Debug model residuals.',\n",
       "   'comment': '',\n",
       "   'type': 'bool',\n",
       "   'val': False,\n",
       "   'predefined': [],\n",
       "   'tags': [],\n",
       "   'min_': 0.0,\n",
       "   'max_': 0.0,\n",
       "   'category': ''}],\n",
       " 'keywords': ['run-by-default',\n",
       "  'can-add-feature',\n",
       "  'explains-feature-behavior',\n",
       "  'h2o-sonar']}"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# explainer description\n",
    "interpret.describe_explainer(PdIceExplainer)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90d401d2-14cd-4686-982f-3cac9e9f5eb7",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Interpretation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0ba8f0aa-2e0e-4a0a-93ab-77ce9e968fa0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-03-12 23:27:53,358 - h2o_sonar.explainers.pd_ice_explainer.PdIceExplainerLogger - INFO - PD/ICE a30a4f57-9c78-4c5f-9c8e-85198b523c1c/745668cc-bbc8-42be-9b39-47487112a885 BEGIN calculation\n",
      "2023-03-12 23:27:53,359 - h2o_sonar.explainers.pd_ice_explainer.PdIceExplainerLogger - INFO - PD/ICE a30a4f57-9c78-4c5f-9c8e-85198b523c1c/745668cc-bbc8-42be-9b39-47487112a885 loading dataset\n",
      "2023-03-12 23:27:53,360 - h2o_sonar.explainers.pd_ice_explainer.PdIceExplainerLogger - INFO - PD/ICE a30a4f57-9c78-4c5f-9c8e-85198b523c1c/745668cc-bbc8-42be-9b39-47487112a885 loaded dataset has 10000 rows and 25 columns\n",
      "2023-03-12 23:27:53,360 - h2o_sonar.explainers.pd_ice_explainer.PdIceExplainerLogger - INFO - PD/ICE a30a4f57-9c78-4c5f-9c8e-85198b523c1c/745668cc-bbc8-42be-9b39-47487112a885 getting features list, importanceand metadata\n",
      "2023-03-12 23:27:53,361 - h2o_sonar.explainers.pd_ice_explainer.PdIceExplainerLogger - INFO - PD/ICE a30a4f57-9c78-4c5f-9c8e-85198b523c1c/745668cc-bbc8-42be-9b39-47487112a885 all most important model features: ['ID', 'LIMIT_BAL', 'SEX', 'EDUCATION', 'MARRIAGE', 'AGE', 'PAY_0', 'PAY_2', 'PAY_3', 'PAY_4', 'PAY_5', 'PAY_6', 'BILL_AMT1', 'BILL_AMT2', 'BILL_AMT3', 'BILL_AMT4', 'BILL_AMT5', 'BILL_AMT6', 'PAY_AMT1', 'PAY_AMT2', 'PAY_AMT3', 'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6']\n",
      "2023-03-12 23:27:53,362 - h2o_sonar.explainers.pd_ice_explainer.PdIceExplainerLogger - INFO - PD/ICE a30a4f57-9c78-4c5f-9c8e-85198b523c1c/745668cc-bbc8-42be-9b39-47487112a885 features used by model: ['ID', 'LIMIT_BAL', 'SEX', 'EDUCATION', 'MARRIAGE', 'AGE', 'PAY_0', 'PAY_2', 'PAY_3', 'PAY_4', 'PAY_5', 'PAY_6', 'BILL_AMT1', 'BILL_AMT2', 'BILL_AMT3', 'BILL_AMT4', 'BILL_AMT5', 'BILL_AMT6', 'PAY_AMT1', 'PAY_AMT2', 'PAY_AMT3', 'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6']\n",
      "2023-03-12 23:27:53,362 - h2o_sonar.explainers.pd_ice_explainer.PdIceExplainerLogger - INFO - PD/ICE a30a4f57-9c78-4c5f-9c8e-85198b523c1c/745668cc-bbc8-42be-9b39-47487112a885: calculating PD for features ['ID', 'LIMIT_BAL', 'SEX', 'EDUCATION', 'MARRIAGE', 'AGE', 'PAY_0', 'PAY_2', 'PAY_3', 'PAY_4']\n",
      "2023-03-12 23:27:53,363 - h2o_sonar.explainers.pd_ice_explainer.PdIceExplainerLogger - INFO - PD/ICE a30a4f57-9c78-4c5f-9c8e-85198b523c1c/745668cc-bbc8-42be-9b39-47487112a885 feature metadata: {'id': [], 'categorical': [], 'numeric': [], 'catnum': [], 'date': [], 'time': [], 'datetime': [], 'text': [], 'image': [], 'date-format': [], 'quantile-bin': {}}\n",
      "2023-03-12 23:27:53,363 - h2o_sonar.explainers.pd_ice_explainer.PdIceExplainerLogger - INFO - PD/ICE a30a4f57-9c78-4c5f-9c8e-85198b523c1c/745668cc-bbc8-42be-9b39-47487112a885 1 frame strategy: True\n",
      "2023-03-12 23:27:53,364 - h2o_sonar.explainers.pd_ice_explainer.PdIceExplainerLogger - INFO - PD/ICE a30a4f57-9c78-4c5f-9c8e-85198b523c1c/745668cc-bbc8-42be-9b39-47487112a885 residual PD/ICE should NOT be calculated, but y has been specified - setting it None\n",
      "X does not have valid feature names, but GradientBoostingClassifier was fitted with feature names\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "h2o_sonar.explainers.pd_ice_explainer.PdIceExplainer: progress 10.0%\n",
      "h2o_sonar.explainers.pd_ice_explainer.PdIceExplainer: progress 20.0%\n",
      "h2o_sonar.explainers.pd_ice_explainer.PdIceExplainer: progress 20.0%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "X does not have valid feature names, but GradientBoostingClassifier was fitted with feature names\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "h2o_sonar.explainers.pd_ice_explainer.PdIceExplainer: progress 20.0%\n",
      "h2o_sonar.explainers.pd_ice_explainer.PdIceExplainer: progress 30.0%\n",
      "h2o_sonar.explainers.pd_ice_explainer.PdIceExplainer: progress 30.0%\n",
      "h2o_sonar.explainers.pd_ice_explainer.PdIceExplainer: progress 30.0%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "X does not have valid feature names, but GradientBoostingClassifier was fitted with feature names\n",
      "X does not have valid feature names, but GradientBoostingClassifier was fitted with feature names\n",
      "X does not have valid feature names, but GradientBoostingClassifier was fitted with feature names\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "h2o_sonar.explainers.pd_ice_explainer.PdIceExplainer: progress 40.0%\n",
      "h2o_sonar.explainers.pd_ice_explainer.PdIceExplainer: progress 40.0%\n",
      "h2o_sonar.explainers.pd_ice_explainer.PdIceExplainer: progress 50.0%\n",
      "h2o_sonar.explainers.pd_ice_explainer.PdIceExplainer: progress 50.0%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "X does not have valid feature names, but GradientBoostingClassifier was fitted with feature names\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "h2o_sonar.explainers.pd_ice_explainer.PdIceExplainer: progress 60.0%\n",
      "h2o_sonar.explainers.pd_ice_explainer.PdIceExplainer: progress 60.0%\n",
      "h2o_sonar.explainers.pd_ice_explainer.PdIceExplainer: progress 60.0%\n",
      "h2o_sonar.explainers.pd_ice_explainer.PdIceExplainer: progress 70.0%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "X does not have valid feature names, but GradientBoostingClassifier was fitted with feature names\n",
      "X does not have valid feature names, but GradientBoostingClassifier was fitted with feature names\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "h2o_sonar.explainers.pd_ice_explainer.PdIceExplainer: progress 70.0%\n",
      "h2o_sonar.explainers.pd_ice_explainer.PdIceExplainer: progress 70.0%\n",
      "h2o_sonar.explainers.pd_ice_explainer.PdIceExplainer: progress 80.0%\n",
      "h2o_sonar.explainers.pd_ice_explainer.PdIceExplainer: progress 80.0%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "X does not have valid feature names, but GradientBoostingClassifier was fitted with feature names\n",
      "X does not have valid feature names, but GradientBoostingClassifier was fitted with feature names\n",
      "X does not have valid feature names, but GradientBoostingClassifier was fitted with feature names\n",
      "2023-03-12 23:27:55,425 - h2o_sonar.explainers.pd_ice_explainer.PdIceExplainerLogger - INFO - PD/ICE a30a4f57-9c78-4c5f-9c8e-85198b523c1c/745668cc-bbc8-42be-9b39-47487112a885 saving PD to ../../results/h2o-sonar/mli_experiment_a30a4f57-9c78-4c5f-9c8e-85198b523c1c/explainer_h2o_sonar_explainers_pd_ice_explainer_PdIceExplainer_745668cc-bbc8-42be-9b39-47487112a885/work/h2o_sonar-pd-dai-model.json\n",
      "2023-03-12 23:27:55,428 - h2o_sonar.explainers.pd_ice_explainer.PdIceExplainerLogger - INFO - PD/ICE a30a4f57-9c78-4c5f-9c8e-85198b523c1c/745668cc-bbc8-42be-9b39-47487112a885 computation finished & stored to: ../../results/h2o-sonar/mli_experiment_a30a4f57-9c78-4c5f-9c8e-85198b523c1c/explainer_h2o_sonar_explainers_pd_ice_explainer_PdIceExplainer_745668cc-bbc8-42be-9b39-47487112a885/work/h2o_sonar-pd-dai-model.json\n",
      "2023-03-12 23:27:55,431 - h2o_sonar.explainers.pd_ice_explainer.PdIceExplainerLogger - INFO - PD/ICE a30a4f57-9c78-4c5f-9c8e-85198b523c1c/745668cc-bbc8-42be-9b39-47487112a885 creating histogram: ID/True\n",
      "2023-03-12 23:27:55,441 - h2o_sonar.explainers.pd_ice_explainer.PdIceExplainerLogger - INFO - PD/ICE a30a4f57-9c78-4c5f-9c8e-85198b523c1c/745668cc-bbc8-42be-9b39-47487112a885 creating histogram: ID/False\n",
      "2023-03-12 23:27:55,452 - h2o_sonar.explainers.pd_ice_explainer.PdIceExplainerLogger - INFO - PD/ICE a30a4f57-9c78-4c5f-9c8e-85198b523c1c/745668cc-bbc8-42be-9b39-47487112a885 creating histogram: LIMIT_BAL/True\n",
      "2023-03-12 23:27:55,453 - h2o_sonar.explainers.pd_ice_explainer.PdIceExplainerLogger - INFO - PD/ICE a30a4f57-9c78-4c5f-9c8e-85198b523c1c/745668cc-bbc8-42be-9b39-47487112a885 creating histogram: LIMIT_BAL/False\n",
      "2023-03-12 23:27:55,456 - h2o_sonar.explainers.pd_ice_explainer.PdIceExplainerLogger - INFO - PD/ICE a30a4f57-9c78-4c5f-9c8e-85198b523c1c/745668cc-bbc8-42be-9b39-47487112a885 creating histogram: SEX/True\n",
      "2023-03-12 23:27:55,458 - h2o_sonar.explainers.pd_ice_explainer.PdIceExplainerLogger - INFO - PD/ICE a30a4f57-9c78-4c5f-9c8e-85198b523c1c/745668cc-bbc8-42be-9b39-47487112a885 creating histogram: SEX/False\n",
      "2023-03-12 23:27:55,462 - h2o_sonar.explainers.pd_ice_explainer.PdIceExplainerLogger - INFO - PD/ICE a30a4f57-9c78-4c5f-9c8e-85198b523c1c/745668cc-bbc8-42be-9b39-47487112a885 creating histogram: EDUCATION/True\n",
      "2023-03-12 23:27:55,468 - h2o_sonar.explainers.pd_ice_explainer.PdIceExplainerLogger - INFO - PD/ICE a30a4f57-9c78-4c5f-9c8e-85198b523c1c/745668cc-bbc8-42be-9b39-47487112a885 creating histogram: EDUCATION/False\n",
      "2023-03-12 23:27:55,472 - h2o_sonar.explainers.pd_ice_explainer.PdIceExplainerLogger - INFO - PD/ICE a30a4f57-9c78-4c5f-9c8e-85198b523c1c/745668cc-bbc8-42be-9b39-47487112a885 creating histogram: MARRIAGE/True\n",
      "2023-03-12 23:27:55,480 - h2o_sonar.explainers.pd_ice_explainer.PdIceExplainerLogger - INFO - PD/ICE a30a4f57-9c78-4c5f-9c8e-85198b523c1c/745668cc-bbc8-42be-9b39-47487112a885 creating histogram: MARRIAGE/False\n",
      "2023-03-12 23:27:55,483 - h2o_sonar.explainers.pd_ice_explainer.PdIceExplainerLogger - INFO - PD/ICE a30a4f57-9c78-4c5f-9c8e-85198b523c1c/745668cc-bbc8-42be-9b39-47487112a885 creating histogram: AGE/True\n",
      "2023-03-12 23:27:55,485 - h2o_sonar.explainers.pd_ice_explainer.PdIceExplainerLogger - INFO - PD/ICE a30a4f57-9c78-4c5f-9c8e-85198b523c1c/745668cc-bbc8-42be-9b39-47487112a885 creating histogram: AGE/False\n",
      "2023-03-12 23:27:55,488 - h2o_sonar.explainers.pd_ice_explainer.PdIceExplainerLogger - INFO - PD/ICE a30a4f57-9c78-4c5f-9c8e-85198b523c1c/745668cc-bbc8-42be-9b39-47487112a885 creating histogram: PAY_0/True\n",
      "2023-03-12 23:27:55,492 - h2o_sonar.explainers.pd_ice_explainer.PdIceExplainerLogger - INFO - PD/ICE a30a4f57-9c78-4c5f-9c8e-85198b523c1c/745668cc-bbc8-42be-9b39-47487112a885 creating histogram: PAY_0/False\n",
      "2023-03-12 23:27:55,502 - h2o_sonar.explainers.pd_ice_explainer.PdIceExplainerLogger - INFO - PD/ICE a30a4f57-9c78-4c5f-9c8e-85198b523c1c/745668cc-bbc8-42be-9b39-47487112a885 creating histogram: PAY_2/True\n",
      "2023-03-12 23:27:55,505 - h2o_sonar.explainers.pd_ice_explainer.PdIceExplainerLogger - INFO - PD/ICE a30a4f57-9c78-4c5f-9c8e-85198b523c1c/745668cc-bbc8-42be-9b39-47487112a885 creating histogram: PAY_2/False\n",
      "2023-03-12 23:27:55,516 - h2o_sonar.explainers.pd_ice_explainer.PdIceExplainerLogger - INFO - PD/ICE a30a4f57-9c78-4c5f-9c8e-85198b523c1c/745668cc-bbc8-42be-9b39-47487112a885 creating histogram: PAY_3/True\n",
      "2023-03-12 23:27:55,520 - h2o_sonar.explainers.pd_ice_explainer.PdIceExplainerLogger - INFO - PD/ICE a30a4f57-9c78-4c5f-9c8e-85198b523c1c/745668cc-bbc8-42be-9b39-47487112a885 creating histogram: PAY_3/False\n",
      "2023-03-12 23:27:55,532 - h2o_sonar.explainers.pd_ice_explainer.PdIceExplainerLogger - INFO - PD/ICE a30a4f57-9c78-4c5f-9c8e-85198b523c1c/745668cc-bbc8-42be-9b39-47487112a885 creating histogram: PAY_4/True\n",
      "2023-03-12 23:27:55,534 - h2o_sonar.explainers.pd_ice_explainer.PdIceExplainerLogger - INFO - PD/ICE a30a4f57-9c78-4c5f-9c8e-85198b523c1c/745668cc-bbc8-42be-9b39-47487112a885 creating histogram: PAY_4/False\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "h2o_sonar.explainers.pd_ice_explainer.PdIceExplainer: progress 90.0%\n",
      "h2o_sonar.explainers.pd_ice_explainer.PdIceExplainer: progress 90.0%\n",
      "h2o_sonar.explainers.pd_ice_explainer.PdIceExplainer: progress 90.0%\n",
      "h2o_sonar.explainers.pd_ice_explainer.PdIceExplainer: progress 90.0%\n",
      "h2o_sonar.explainers.pd_ice_explainer.PdIceExplainer: progress 100.0%\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ktLQ0zZ8/Xz169FBKSor279+vGTNmKC0tzREu99xzj9LS0nTuuefq6NGjmjVrlqxWq4YNG+bChwp4H4IFABrG6WAZOnSoCgoKNHPmTOXm5uqSSy7Rli1bHBfiHjp0qNoRlYceekgWi0UPPfSQfvzxR7Vt21ZpaWl67LHHHOscOXJEw4YN07Fjx9S2bVv16dNHn376qdq2beuChwh4L4IFABrGYtR1XsaHlJSUKDIyUsXFxYqIiDB7HKBB7Ha7bDab7Ha7fvzxR8XHx5s9EgB4lDPP33yWEGCSY8eOyW63y2KxcDQRAM6CYAFMUnU6KCoqSkFBQSZPAwDejWABTML1KwDQcAQLYBKCBQAajmABTEKwAEDDESyASQgWAGg4ggUwCcECAA1HsAAmIVgAoOEIFsAkBAsANBzBApiEYAGAhiNYABNUVFTol19+kUSwAEBDECyACfLy8iRJQUFBat26tcnTAID3I1gAE/z2dJDFYjF5GgDwfgQLYAKuXwEA5xAsgAkIFgBwDsECmIBgAQDnECyACQgWAHAOwQKYgGABAOcQLIAJCBYAcA7BApiAYAEA5xAsgIcZhkGwAICTCBbAw44fP66TJ09KkmJiYkyeBgB8A8ECeFjV0ZXw8HC1aNHC5GkAwDcQLICHcToIAJxHsAAeRrAAgPMIFsDDCBYAcB7BAngYwQIAziNYAA8jWADAeQQL4GEECwA4j2ABPIxgAQDnESyAhxEsAOA8ggXwILvdrvz8fEkECwA4g2ABPKiwsFB2u10Wi0XR0dFmjwMAPoNgATyo6nRQ27ZtFRgYaPI0AOA7CBbAg7h+BQAah2ABPIhgAYDGIVgADyJYAKBxCBbAgwgWAGgcggXwIIIFABqHYAE8iGABgMYhWAAPIlgAoHEIFsCDCBYAaByCBfCQ8vJyFRUVSSJYAMBZBAvgIXl5eZKk4OBgtWrVytxhAMDHECyAh/z2dJDFYjF5GgDwLQQL4CFcvwIAjUewAB5CsABA4xEsgIcQLADQeAQL4CEECwA0HsECeAjBAgCNR7AAHkKwAEDjESyAhxAsANB4BAvgAYZhECwA0AQEC+ABJSUlKi8vlyTFxMSYPA0A+B6CBfCAqqMrERERCgsLM3kaAPA9BAvgAZwOAoCmIVgADyBYAKBpCBbAAwgWAGgaggXwAIIFAJqGYAE8gGABgKYhWAAPIFgAoGkIFsADCBYAaBqCBfAAggUAmoZgAdzMbrcrPz9fEsECAI1FsABuVlhYqMrKSlksFrVt29bscQDAJzUqWBYtWqTExESFhIQoJSVF2dnZ9a6fmZmppKQkhYaGKiEhQXfddZfjc1Uau03AV1SdDmrbtq0CAwNNngYAfJPTwfLKK68oIyNDs2bN0o4dO9S9e3f169fPccj7P61Zs0b333+/Zs2apT179mj58uV65ZVX9OCDDzZ6m4Av4foVAGg6p4Nl/vz5mjBhgsaMGaMLLrhAS5YsUVhYmFasWFHr+p988ol69+6t4cOHKzExUTfccIOGDRtW7QiKs9usqKhQSUlJtRvgrQgWAGg6p4Ll1KlTysnJUWpq6r83EBCg1NRUbdu2rdb7XHnllcrJyXEEyoEDB7R582YNGDCg0ducO3euIiMjHbeEhARnHgbgUQQLADSdU8FSWFgou92umJiYastjYmIcv5T/0/DhwzVnzhz16dNHQUFB6tSpk/r27es4JdSYbT7wwAMqLi523A4fPuzMwwA8imABgKZz+6uEtm7dqscff1zPP/+8duzYoQ0bNmjTpk165JFHGr1Nm82miIiIajfAWxEsANB0Tr1kISoqSlarVXl5edWW5+Xl1fnLeMaMGRoxYoTGjx8vSerWrZvKysqUnp6u6dOnN2qbgC8hWACg6Zw6whIcHKyePXsqKyvLsayyslJZWVnq1atXrfc5ceKEAgKq/xir1SpJMgyjUdsEfAnBAgBN5/SbQmRkZGjUqFG67LLLlJycrMzMTJWVlWnMmDGSpJEjR6pdu3aaO3euJCktLU3z589Xjx49lJKSov3792vGjBlKS0tzhMvZtgn4MoIFAJrO6WAZOnSoCgoKNHPmTOXm5uqSSy7Rli1bHBfNHjp0qNoRlYceekgWi0UPPfSQfvzxR7Vt21ZpaWl67LHHGrxNwFeVl5erqKhIEsECAE1hMQzDMHuIpiopKVFkZKSKi4u5ABde5YcfflBiYqKCg4NVXl4ui8Vi9kgA4DWcef7ms4QAN/rt6SBiBQAaj2AB3KgqWOLi4kyeBAB8G8ECuBEX3AKAaxAsgBsRLADgGgQL4EYECwC4BsECuBHBAgCuQbAAbkSwAIBrECyAGxEsAOAaBAvgJoZhECwA4CIEC+AmxcXFKi8vlyQ+ZgIAmohgAdyk6uhKZGSkQkNDTZ4GAHwbwQK4CaeDAMB1CBbATQgWAHAdggVwE4IFAFyHYAHchGABANchWAA3IVgAwHUIFsBNCBYAcB2CBXATggUAXIdgAdyEYAEA1yFYADew2+0qKCiQRLAAgCsQLIAbFBQUqLKyUgEBAWrbtq3Z4wCAzyNYADeoOh3Utm1bWa1Wk6cBAN9HsABuwPUrAOBaBAvgBgQLALgWwQK4AcECAK5FsABuQLAAgGsRLIAbECwA4FoEC+AGBAsAuBbBArgBwQIArkWwAG5AsACAaxEsgIudPHlSxcXFkggWAHAVggVwsby8PEmSzWZTZGSkydMAgH8gWAAX++3pIIvFYvI0AOAfCBbAxbh+BQBcj2ABXIxgAQDXI1gAFyNYAMD1CBbAxQgWAHA9ggVwMYIFAFyPYAFcjGABANcjWAAXI1gAwPUIFsCFDMMgWADADQgWwIWKi4tVUVEhSYqJiTF5GgDwHwQL4EJVR1ciIyMVGhpq8jQA4D8IFsCFOB0EAO5BsAAuVBUscXFxJk8CAP6FYAFciCMsAOAeBAvgQgQLALgHwQK4EMECAO5BsAAuRLAAgHsQLIALESwA4B4EC+BCBAsAuAfBAriI3W5XQUGBJIIFAFyNYAFcpKCgQJWVlQoICFBUVJTZ4wCAXyFYABepOh0UHR0tq9Vq8jQA4F8IFsBFuH4FANyHYAFchGABAPchWAAXIVgAwH0IFsBFCBYAcB+CBXARggUA3IdgAVyEYAEA9yFYABchWADAfQgWwEUIFgBwH4IFcIGTJ0+quLhYEsECAO5AsAAuUHV0JSQkRBERESZPAwD+p1HBsmjRIiUmJiokJEQpKSnKzs6uc92+ffvKYrHUuA0cONCxzujRo2t8v3///o0ZDTDFb08HWSwWk6cBAP8T6OwdXnnlFWVkZGjJkiVKSUlRZmam+vXrp7179yo6OrrG+hs2bNCpU6ccXx87dkzdu3fXzTffXG29/v37a+XKlY6vbTabs6MBpuH6FQBwL6eDZf78+ZowYYLGjBkjSVqyZIk2bdqkFStW6P7776+xfps2bap9vW7dOoWFhdUIFpvN1uBf9hUVFaqoqHB8XVJS4uzDAFyKYAEA93LqlNCpU6eUk5Oj1NTUf28gIECpqanatm1bg7axfPly3XrrrWrRokW15Vu3blV0dLSSkpJ055136tixY3VuY+7cuYqMjHTcEhISnHkYgMsRLADgXk4FS2Fhoex2u2JiYqotj4mJcfzCrk92drZ2796t8ePHV1vev39/vfTSS8rKytKTTz6pf/zjH7rxxhtlt9tr3c4DDzyg4uJix+3w4cPOPAzA5QgWAHAvp08JNcXy5cvVrVs3JScnV1t+6623Ov7crVs3XXzxxerUqZO2bt2q6667rsZ2bDYb17jAqxAsAOBeTh1hiYqKktVqVV5eXrXleXl5Z/1FXVZWpnXr1mncuHFn/TkdO3ZUVFSU9u/f78x4gGkIFgBwL6eCJTg4WD179lRWVpZjWWVlpbKystSrV69677t+/XpVVFTo9ttvP+vPOXLkiI4dO6a4uDhnxgNMQ7AAgHs5/T4sGRkZWrp0qV588UXt2bNHd955p8rKyhyvGho5cqQeeOCBGvdbvny5hgwZonPOOafa8tLSUt1777369NNP9f333ysrK0uDBw/Weeedp379+jXyYQGeYxgGwQIAbub0NSxDhw5VQUGBZs6cqdzcXF1yySXasmWL40LcQ4cOKSCgegft3btXH330kd55550a27Nardq1a5defPFFFRUVKT4+XjfccIMeeeQRrlOBTygqKnK819B/XpAOAHANi2EYhtlDNFVJSYkiIyNVXFzM26LD4/bs2aMLLrhArVq10i+//GL2OADgM5x5/uazhIAm4nQQALgfwQI0EcECAO5HsABNRLAAgPsRLEATESwA4H4EC9BEBAsAuB/BAjQRwQIA7kewAE1EsACA+xEsQBMRLADgfgQL0ARnzpxRQUGBJIIFANyJYAGaoKCgQIZhKCAgQFFRUWaPAwB+i2ABmqDqdFB0dLSsVqvJ0wCA/yJYgCbg+hUA8AyCBWgCggUAPINgAZqgKlji4uJMngQA/BvBAjQBR1gAwDMIFqAJCBYA8AyCBWgCggUAPINgAZqAYAEAzyBYgCYgWADAMwgWoJFOnDihkpISSQQLALgbwQI0Ul5eniQpNDRU4eHhJk8DAP6NYAEa6bengywWi8nTAIB/I1iARuL6FQDwHIIFaCSCBQA8h2ABGolgAQDPIViARiJYAMBzAs0eAL5j165dmjRpkn766SezR/EKVa8SIlgAwP0IFjTITz/9pIEDB+rIkSNmj+J1Lr30UrNHAAC/R7DgrE6ePKnBgwfryJEjSkpK0rJly2S1Ws0eyyvExsaqQ4cOZo8BAH6PYEG9KisrNWrUKH322Wdq06aNNm7cqPPOO8/ssQAAzQwX3aJeDz/8sNavX6+goCBt2LCBWAEAmIJgQZ3WrFmjRx55RJK0ZMkSXXPNNSZPBABorggW1Grbtm0aO3asJOnee+91/BkAADMQLKjhhx9+0JAhQ1RRUaGbbrpJc+fONXskAEAzR7CgmuPHj2vQoEHKz89X9+7dtXr1al4RBAAwHcECB7vdrmHDhmn37t2KjY3Vm2++qZYtW5o9FgAABAv+7d5779WmTZsUEhKiv//970pISDB7JAAAJBEs+H9Lly7VggULJEkvvviikpOTTZ4IAIB/I1ig999/X5MmTZIkzZ49W7fccovJEwEAUB3B0sx9++23+q//+i+dOXNGw4YN04wZM8weCQCAGgiWZuznn3/WoEGDVFRUpCuuuEIrVqyQxWIxeywAAGogWJqp06dP609/+pP27dun9u3b6/XXX1dISIjZYwEAUCuCpRkyDEOTJ0/WBx98oJYtW2rjxo2KiYkxeywAAOpEsDRDmZmZWrp0qSwWi9auXatu3bqZPRIAAPUiWJqZjRs36u6775YkzZs3T4MGDTJ5IgAAzo5gaUZ27dqlYcOGyTAMTZgwQdOmTTN7JAAAGoRgaSby8vKUlpam0tJS/f73v9eiRYt4RRAAwGcQLM1AeXm5hgwZokOHDqlz585av369goKCzB4LAIAGI1j8nGEYGjt2rD799FO1bt1aGzduVJs2bcweCwAApxAsfu7RRx/V2rVrFRgYqNdee01dunQxeyQAAJxGsPixV199VTNnzpQkPf/887r22mtNnggAgMYhWPxUdna2Ro0aJUnKyMjQhAkTTJ4IAIDGI1j80OHDh3XTTTepvLxcgwYN0lNPPWX2SAAANAnB4mdOnz6tP/zhD8rLy1O3bt20Zs0aWa1Ws8cCAKBJCBY/89RTTyknJ0dt2rTRm2++qfDwcLNHAgCgyQgWP/L1119rzpw5kqSFCxfq3HPPNXkiAABcg2DxE3a7XWPHjtWpU6c0aNAgDR8+3OyRAABwGYLFTyxcuFDbt29XRESElixZwtvuAwD8CsHiB/bv36/p06dL+vUTmNu1a2fyRAAAuBbB4uMqKys1YcIEnTx5Utddd53GjRtn9kgAALgcweLjXnjhBW3dulVhYWFaunQpp4IAAH6JYPFhhw8f1n333SdJmjt3rjp06GDyRAAAuAfB4qMMw9DEiRN1/PhxXXnllZoyZYrZIwEA4DYEi4/661//qrfeeks2m03Lly9XQAD/KQEA/qtRz3KLFi1SYmKiQkJClJKSouzs7DrX7du3rywWS43bwIEDHesYhqGZM2cqLi5OoaGhSk1N1b59+xozWrOQm5uradOmSZIefvhhde3a1dyBAABwM6eD5ZVXXlFGRoZmzZqlHTt2qHv37urXr5/y8/NrXX/Dhg366aefHLfdu3fLarXq5ptvdqzz1FNPaeHChVqyZIm2b9+uFi1aqF+/fiovL2/8I/NjkydP1i+//KJLL71U99xzj9njAADgfoaTkpOTjcmTJzu+ttvtRnx8vDF37twG3X/BggVGeHi4UVpaahiGYVRWVhqxsbHG008/7VinqKjIsNlsxtq1axu0zeLiYkOSUVxc7MQj8U3r1683JBmBgYHGzp07zR4HAIBGc+b526kjLKdOnVJOTo5SU1MdywICApSamqpt27Y1aBvLly/XrbfeqhYtWkiSDh48qNzc3GrbjIyMVEpKSp3brKioUElJSbVbc3Ds2DFNnjxZkvTAAw+oe/fuJk8EAIBnOBUshYWFstvtiomJqbY8JiZGubm5Z71/dna2du/erfHjxzuWVd3PmW3OnTtXkZGRjltCQoIzD8NnTZs2Tfn5+brwwgsd72wLAEBz4NGXlixfvlzdunVTcnJyk7bzwAMPqLi42HE7fPiwiyb0Xps2bdLLL7+sgIAArVixQjabzeyRAADwGKeCJSoqSlarVXl5edWW5+XlKTY2tt77lpWVad26dTXeOr7qfs5s02azKSIiotrNnxUXF2vixImSpIyMjCYHHwAAvsapYAkODlbPnj2VlZXlWFZZWamsrCz16tWr3vuuX79eFRUVuv3226st79Chg2JjY6tts6SkRNu3bz/rNpuL++67Tz/++KPOO+88zZ492+xxAADwuEBn75CRkaFRo0bpsssuU3JysjIzM1VWVqYxY8ZIkkaOHKl27dpp7ty51e63fPlyDRkyROecc0615RaLRdOmTdOjjz6qzp07q0OHDpoxY4bi4+M1ZMiQxj8yP/H+++/rhRdekPTrPgwLCzN5IgAAPM/pYBk6dKgKCgo0c+ZM5ebm6pJLLtGWLVscF80eOnSoxruu7t27Vx999JHeeeedWrd53333qaysTOnp6SoqKlKfPn20ZcsWhYSENOIh+Y+ysjLHBcqTJk3S1VdfbfJEAACYw2IYhmH2EE1VUlKiyMhIFRcX+9X1LNOmTdOzzz6r9u3ba/fu3QoPDzd7JAAAXMaZ528+gMZLffLJJ1q4cKEk6YUXXiBWAADNGsHihcrLyzV27FgZhqHRo0erX79+Zo8EAICpCBYvNGfOHO3du1exsbGaP3++2eMAAGA6gsXL7NixQ0899ZQkafHixWrdurXJEwEAYD6CxYucPn1aY8eOld1u1y233MLLugEA+H8Eixd58skn9a9//UvnnHOOnnvuObPHAQDAaxAsXuKrr77SnDlzJEkLFy5UdHS0yRMBAOA9CBYvYLfbNXbsWJ0+fVppaWkaNmyY2SMBAOBVCBYvkJmZqezsbEVERGjx4sWyWCxmjwQAgFchWEy2b98+PfTQQ5KkefPmqV27diZPBACA9yFYTFRZWanx48ervLxc1113ncaNG2f2SAAAeCWCxUT/8z//ow8//FBhYWFaunQpp4IAAKgDwWKSQ4cO6b777pMkPfHEE+rQoYPJEwEA4L0IFhMYhqH09HSVlpaqd+/emjx5stkjAQDg1QgWE7z00kt6++23ZbPZtHz5cgUE8J8BAID68EzpYT/99JOmTZsmSZo9e7aSkpLMHQgAAB9AsHiQYRiaNGmSioqK1LNnT919991mjwQAgE8gWDxo/fr1ev311xUYGKgVK1YoMDDQ7JEAAPAJBIuHFBYWasqUKZKkBx98UBdffLHJEwEA4DsIFg+ZNm2aCgoKdNFFF2n69OlmjwMAgE8hWDxg48aNWr16tQICArRixQoFBwebPRIAAD6FYHGzoqIiTZw4UZKUkZGhyy+/3OSJAADwPQSLm9177706evSoOnfurDlz5pg9DgAAPolgcaP33ntPy5YtkyQtW7ZMoaGhJk8EAIBvIljcpLS0VBMmTJAkTZ48WVdffbXJEwEA4LsIFjeZPn26vv/+e7Vv315z5841exwAAHwaweIGH3/8sZ577jlJ0tKlSxUeHm7yRAAA+DaCxcXKy8s1btw4GYahMWPG6IYbbjB7JAAAfB7B4mKzZ8/W3r17FRsbq3nz5pk9DgAAfoFgcaGcnBw9/fTTkqQlS5aodevWJk8EAIB/IFhc5NSpUxo7dqzsdruGDh2qwYMHmz0SAAB+g2BxkSeeeEK7du1SVFSU44JbAADgGgSLC+zevVuPPvqoJGnhwoVq27atyRMBAOBfCJYmOnPmjMaOHavTp08rLS1Nt956q9kjAQDgdwiWJsrMzNRnn32myMhILV68WBaLxeyRAADwOwRLE3z77beaMWOGJGnevHlq166dyRMBAOCfCJZGqqys1Pjx41VeXq7U1FSNHTvW7JEAAPBbBEsjLVmyRP/85z/VokULLV26lFNBAAC4EcHSCD/88IP+8pe/SPr15cyJiYnmDgQAgJ8jWJxkGIbS09NVWlqqPn36aNKkSWaPBACA3yNYnLRq1Sq98847stlsWrZsmQIC2IUAALgbz7ZOOHr0qDIyMiRJc+bMUVJSkskTAQDQPBAsDWQYhiZNmqSioiL17NnTES4AAMD9CJYGevXVV/X3v/9dQUFBWrlypQIDA80eCQCAZoNgaYCCggL993//tyTpwQcfVLdu3UyeCACA5oVgaYCpU6eqoKBAF110kR588EGzxwEAoNkhWM7ijTfe0Nq1axUQEKAVK1YoODjY7JEAAGh2CJZ6FBUV6Y477pAk3X333br88stNnggAgOaJK0frUVhYqOjoaLVs2VKzZ882exwAAJotgqUe5513nj777DMdOXJEoaGhZo8DAECzxSmhswgKClKHDh3MHgMAgGaNYAEAAF6PYAEAAF6PYAEAAF6PYAEAAF6PYAEAAF6PYAEAAF6PYAEAAF6PYAEAAF6PYAEAAF6PYAEAAF6PYAEAAF6PYAEAAF6PYAEAAF4v0OwBXMEwDElSSUmJyZMAAICGqnrernoer49fBMvx48clSQkJCSZPAgAAnHX8+HFFRkbWu47FaEjWeLnKykodPXpU4eHhslgsjd5OSUmJEhISdPjwYUVERLhwQtSG/e057GvPYV97Dvvac9y1rw3D0PHjxxUfH6+AgPqvUvGLIywBAQH63e9+57LtRURE8Jffg9jfnsO+9hz2teewrz3HHfv6bEdWqnDRLQAA8HoECwAA8HoEy2/YbDbNmjVLNpvN7FGaBfa357CvPYd97Tnsa8/xhn3tFxfdAgAA/8YRFgAA4PUIFgAA4PUIFgAA4PUIFgAA4PWaXbAsWrRIiYmJCgkJUUpKirKzs+tdf/369eratatCQkLUrVs3bd682UOT+j5n9vXSpUt11VVXqXXr1mrdurVSU1PP+t8G1Tn7d7vKunXrZLFYNGTIEPcO6Eec3ddFRUWaPHmy4uLiZLPZ1KVLF36XNJCz+zozM1NJSUkKDQ1VQkKC7rrrLpWXl3toWt/04YcfKi0tTfHx8bJYLHr99dfPep+tW7fq0ksvlc1m03nnnadVq1a5fU4Zzci6deuM4OBgY8WKFcZXX31lTJgwwWjVqpWRl5dX6/off/yxYbVajaeeesr4+uuvjYceesgICgoyvvzySw9P7nuc3dfDhw83Fi1aZHzxxRfGnj17jNGjRxuRkZHGkSNHPDy5b3J2f1c5ePCg0a5dO+Oqq64yBg8e7JlhfZyz+7qiosK47LLLjAEDBhgfffSRcfDgQWPr1q3Gzp07PTy573F2X69evdqw2WzG6tWrjYMHDxpvv/22ERcXZ9x1110enty3bN682Zg+fbqxYcMGQ5Lxt7/9rd71Dxw4YISFhRkZGRnG119/bTz33HOG1Wo1tmzZ4tY5m1WwJCcnG5MnT3Z8bbfbjfj4eGPu3Lm1rn/LLbcYAwcOrLYsJSXFmDhxolvn9AfO7uv/dObMGSM8PNx48cUX3TWiX2nM/j5z5oxx5ZVXGsuWLTNGjRpFsDSQs/t68eLFRseOHY1Tp055akS/4ey+njx5svH73/++2rKMjAyjd+/ebp3TnzQkWO677z7jwgsvrLZs6NChRr9+/dw4mWE0m1NCp06dUk5OjlJTUx3LAgIClJqaqm3bttV6n23btlVbX5L69etX5/r4VWP29X86ceKETp8+rTZt2rhrTL/R2P09Z84cRUdHa9y4cZ4Y0y80Zl+/8cYb6tWrlyZPnqyYmBhddNFFevzxx2W32z01tk9qzL6+8sorlZOT4zhtdODAAW3evFkDBgzwyMzNhVnPjX7x4YcNUVhYKLvdrpiYmGrLY2Ji9M0339R6n9zc3FrXz83Ndduc/qAx+/o//eUvf1F8fHyN/ylQU2P290cffaTly5dr586dHpjQfzRmXx84cEDvv/++brvtNm3evFn79+/XpEmTdPr0ac2aNcsTY/ukxuzr4cOHq7CwUH369JFhGDpz5ozuuOMOPfjgg54Yudmo67mxpKREJ0+eVGhoqFt+brM5wgLf8cQTT2jdunX629/+ppCQELPH8TvHjx/XiBEjtHTpUkVFRZk9jt+rrKxUdHS0XnjhBfXs2VNDhw7V9OnTtWTJErNH8ztbt27V448/rueff147duzQhg0btGnTJj3yyCNmjwYXaDZHWKKiomS1WpWXl1dteV5enmJjY2u9T2xsrFPr41eN2ddVnnnmGT3xxBN67733dPHFF7tzTL/h7P7+7rvv9P333ystLc2xrLKyUpIUGBiovXv3qlOnTu4d2kc15u92XFycgoKCZLVaHcvOP/985ebm6tSpUwoODnbrzL6qMft6xowZGjFihMaPHy9J6tatm8rKypSenq7p06crIIB/o7tCXc+NERERbju6IjWjIyzBwcHq2bOnsrKyHMsqKyuVlZWlXr161XqfXr16VVtfkt59990618evGrOvJempp57SI488oi1btuiyyy7zxKh+wdn93bVrV3355ZfauXOn43bTTTfp2muv1c6dO5WQkODJ8X1KY/5u9+7dW/v373dEoSR9++23iouLI1bq0Zh9feLEiRpRUhWKBh+b5zKmPTe69ZJeL7Nu3TrDZrMZq1atMr7++msjPT3daNWqlZGbm2sYhmGMGDHCuP/++x3rf/zxx0ZgYKDxzDPPGHv27DFmzZrFy5obyNl9/cQTTxjBwcHG//7v/xo//fST43b8+HGzHoJPcXZ//ydeJdRwzu7rQ4cOGeHh4caUKVOMvXv3Ghs3bjSio6ONRx991KyH4DOc3dezZs0ywsPDjbVr1xoHDhww3nnnHaNTp07GLbfcYtZD8AnHjx83vvjiC+OLL74wJBnz5883vvjiC+OHH34wDMMw7r//fmPEiBGO9ate1nzvvfcae/bsMRYtWsTLmt3hueeeM9q3b28EBwcbycnJxqeffur43jXXXGOMGjWq2vqvvvqq0aVLFyM4ONi48MILjU2bNnl4Yt/lzL4+99xzDUk1brNmzfL84D7K2b/bv0WwOMfZff3JJ58YKSkphs1mMzp27Gg89thjxpkzZzw8tW9yZl+fPn3aePjhh41OnToZISEhRkJCgjFp0iTjl19+8fzgPuSDDz6o9fdv1b4dNWqUcc0119S4zyWXXGIEBwcbHTt2NFauXOn2OS2GwXEyAADg3ZrNNSwAAMB3ESwAAMDrESwAAMDrESwAAMDrESwAAMDrESwAAMDrESwAAMDrESwAAMDrESwATNW3b19Nmzatzu8nJiYqMzPTY/MAqO7DDz9UWlqa4uPjZbFY9Prrrzu9DcMw9Mwzz6hLly6y2Wxq166dHnvsMae20Ww+rRmAb/rss8/UokULs8cAmq2ysjJ1795dY8eO1R//+MdGbWPq1Kl655139Mwzz6hbt276+eef9fPPPzu1DYIFgFdr27at2SMAzdqNN96oG2+8sc7vV1RUaPr06Vq7dq2Kiop00UUX6cknn1Tfvn0lSXv27NHixYu1e/duJSUlSZI6dOjg9BycEgJgujNnzmjKlCmKjIxUVFSUZsyYoaqPOfvPU0IWi0XLli3TH/7wB4WFhalz58564403TJocwJQpU7Rt2zatW7dOu3bt0s0336z+/ftr3759kqQ333xTHTt21MaNG9WhQwclJiZq/PjxTh9hIVgAmO7FF19UYGCgsrOz9eyzz2r+/PlatmxZnevPnj1bt9xyi3bt2qUBAwbotttuc/qXH4CmO3TokFauXKn169frqquuUqdOnXTPPfeoT58+WrlypSTpwIED+uGHH7R+/Xq99NJLWrVqlXJycvSnP/3JqZ/FKSEApktISNCCBQtksViUlJSkL7/8UgsWLNCECRNqXX/06NEaNmyYJOnxxx/XwoULlZ2drf79+3tybKDZ+/LLL2W329WlS5dqyysqKnTOOedIkiorK1VRUaGXXnrJsd7y5cvVs2dP7d2713Ga6GwIFgCmu+KKK2SxWBxf9+rVS/PmzZPdbq91/Ysvvtjx5xYtWigiIkL5+flunxNAdaWlpbJarcrJyZHVaq32vZYtW0qS4uLiFBgYWC1qzj//fEm/HqEhWAD4raCgoGpfWywWVVZWmjQN0Hz16NFDdrtd+fn5uuqqq2pdp3fv3jpz5oy+++47derUSZL07bffSpLOPffcBv8sggWA6bZv317t608//VSdO3eu8S82AJ5XWlqq/fv3O74+ePCgdu7cqTZt2qhLly667bbbNHLkSM2bN089evRQQUGBsrKydPHFF2vgwIFKTU3VpZdeqrFjxyozM1OVlZWaPHmyrr/++hqnkurDRbcATHfo0CFlZGRo7969Wrt2rZ577jlNnTrV7LEASPr888/Vo0cP9ejRQ5KUkZGhHj16aObMmZKklStXauTIkbr77ruVlJSkIUOG6LPPPlP79u0lSQEBAXrzzTcVFRWlq6++WgMHDtT555+vdevWOTUHR1gAmG7kyJE6efKkkpOTZbVaNXXqVKWnp5s9FgD9+m7UVW8zUJugoCDNnj1bs2fPrnOd+Ph4vfbaa02aw2LUNwUAAIAX4JQQAADwegQLAADwegQLAADwegQLAADwegQLAADwegQLAADwegQLAADwegQLAADwegQLAADwegQLAADwegQLAADwev8HHyArgWXFevIAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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5GjJkSJs1b7nlFuXm5uqss87S2WefrbKyMr388svasGFD9/YKAAD0KX4HltzcXO3Zs0eFhYWqq6tTcnKyysrKvBfi1tbWKijI95Ommpoabdq0SevWrWt3zUsvvVRLlixRUVGRbrjhBiUmJmrVqlWaMGFCN3YJAAD0NTarO7fAM5DL5ZLD4VBTU5OioqICXQ4AoB/7/vvvtWPHDo0cOZKboKrz49HV929+SwgAABiPwAIAQA/pIx9i/Gy/xHEgsAAA8AsLCQmR9ONvCOGn43DouHSH3xfdAgCAztntdg0aNMj7K8YRERE+v83TX1iWpQMHDqihoUGDBg2S3W7v9loEFgAAesChG6oeCi392aBBgw57g9nDIbAAANADbDab4uLiNHToUB08eDDQ5QRMSEjIzzqzcgiBBQCAHmS323+RN+z+jotuAQCA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYr1uBpaSkRAkJCQoPD1daWpoqKio6HJuRkSGbzdamZWdne8dcffXVbbaff/753SkNAAD0QcH+TlixYoXy8/O1ZMkSpaWlqbi4WFlZWaqpqdHQoUPbjF+9erVaWlq8f3/99ddKSkrS5MmTfcadf/75Wrp0qffvsLAwf0sDAAB9lN9nWB588EFdc801mjFjhsaMGaMlS5YoIiJCTzzxRLvjBw8erNjYWG9bv369IiIi2gSWsLAwn3FHH3109/YIAAD0OX4FlpaWFlVWViozM/OnBYKClJmZqc2bN3dpDafTqby8PEVGRvr0b9iwQUOHDlViYqKuv/56ff31152u09zcLJfL5dMAAEDf5FdgaWxsVGtrq2JiYnz6Y2JiVFdXd9j5FRUVqq6u1qxZs3z6zz//fP3f//2fysvLtXDhQm3cuFEXXHCBWltbO1yrqKhIDofD2+Lj4/3ZFQAAcATx+xqWn8PpdGrs2LFKTU316c/Ly/P+e+zYsTrttNN0wgknaMOGDTrnnHPaXaugoED5+fnev10uF6EFAIA+yq8zLNHR0bLb7aqvr/fpr6+vV2xsbKdz3W63li9frpkzZx72cY4//nhFR0frk08+6XBMWFiYoqKifBoAAOib/AosoaGhSklJUXl5ubfP4/GovLxc6enpnc5duXKlmpubNXXq1MM+zpdffqmvv/5acXFx/pQHAAD6KL+/JZSfn6/HHntMTz75pLZv367rr79ebrdbM2bMkCRNmzZNBQUFbeY5nU7l5ORoyJAhPv379+/XLbfcoi1btujzzz9XeXm5Jk2apFGjRikrK6ubuwUAAPoSv69hyc3N1Z49e1RYWKi6ujolJyerrKzMeyFubW2tgoJ8c1BNTY02bdqkdevWtVnPbrfr/fff15NPPqm9e/dq2LBhOu+883TXXXdxLxYAACBJslmWZQW6iF+Cy+WSw+FQU1MT17MAAHCE6Or7N78lBAAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjdSuwlJSUKCEhQeHh4UpLS1NFRUWHYzMyMmSz2dq07Ozsdsf/9re/lc1mU3FxcXdKAwAAfZDfgWXFihXKz8/XggULVFVVpaSkJGVlZamhoaHd8atXr9bu3bu9rbq6Wna7XZMnT24z9vnnn9eWLVs0bNgw//cEAAD0WX4HlgcffFDXXHONZsyYoTFjxmjJkiWKiIjQE0880e74wYMHKzY21tvWr1+viIiINoHlq6++0ty5c1VaWqqQkJDu7Q0AAOiT/AosLS0tqqysVGZm5k8LBAUpMzNTmzdv7tIaTqdTeXl5ioyM9PZ5PB5dddVVuuWWW3TKKad0aZ3m5ma5XC6fBgAA+ia/AktjY6NaW1sVExPj0x8TE6O6urrDzq+oqFB1dbVmzZrl079w4UIFBwfrhhtu6HItRUVFcjgc3hYfH9/luQAA4MjSq98ScjqdGjt2rFJTU719lZWVeuihh7Rs2TLZbLYur1VQUKCmpiZv27lzZ0+UDAAADOBXYImOjpbdbld9fb1Pf319vWJjYzud63a7tXz5cs2cOdOn/6233lJDQ4OOO+44BQcHKzg4WF988YVuvvlmJSQkdLheWFiYoqKifBoAAOib/AosoaGhSklJUXl5ubfP4/GovLxc6enpnc5duXKlmpubNXXqVJ/+q666Su+//77ee+89bxs2bJhuueUWrV271p/yAABAHxXs74T8/HxNnz5d48aNU2pqqoqLi+V2uzVjxgxJ0rRp0zR8+HAVFRX5zHM6ncrJydGQIUN8+ocMGdKmLyQkRLGxsUpMTPS3PAAA0Af5HVhyc3O1Z88eFRYWqq6uTsnJySorK/NeiFtbW6ugIN8TNzU1Ndq0aZPWrVv3y1QNAAD6FZtlWVagi/gluFwuORwONTU1cT0LAABHiK6+f/NbQgAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYr1uBpaSkRAkJCQoPD1daWpoqKio6HJuRkSGbzdamZWdne8fccccdOvnkkxUZGamjjz5amZmZ2rp1a3dKAwAAfZDfgWXFihXKz8/XggULVFVVpaSkJGVlZamhoaHd8atXr9bu3bu9rbq6Wna7XZMnT/aOOemkk/TII49o27Zt2rRpkxISEnTeeedpz5493d8zAADQZ9gsy7L8mZCWlqbx48frkUcekSR5PB7Fx8dr7ty5mj9//mHnFxcXq7CwULt371ZkZGS7Y1wulxwOh15//XWdc845Xarr0JympiZFRUV1fYcAAEDAdPX9268zLC0tLaqsrFRmZuZPCwQFKTMzU5s3b+7SGk6nU3l5eR2GlZaWFj366KNyOBxKSkrqcJ3m5ma5XC6fBgAA+ia/AktjY6NaW1sVExPj0x8TE6O6urrDzq+oqFB1dbVmzZrVZtuaNWt01FFHKTw8XIsWLdL69esVHR3d4VpFRUVyOBzeFh8f78+uAACAI0ivfkvI6XRq7NixSk1NbbPt7LPP1nvvvae3335b559/vi6//PIOr4uRpIKCAjU1NXnbzp07e7J0AAAQQH4FlujoaNntdtXX1/v019fXKzY2ttO5brdby5cv18yZM9vdHhkZqVGjRun000+X0+lUcHCwnE5nh+uFhYUpKirKpwEAgL7Jr8ASGhqqlJQUlZeXe/s8Ho/Ky8uVnp7e6dyVK1equblZU6dO7dJjeTweNTc3+1MeAADoo4L9nZCfn6/p06dr3LhxSk1NVXFxsdxut2bMmCFJmjZtmoYPH66ioiKfeU6nUzk5ORoyZIhPv9vt1h//+EddcskliouLU2Njo0pKSvTVV1/5fPUZAAD0X34HltzcXO3Zs0eFhYWqq6tTcnKyysrKvBfi1tbWKijI98RNTU2NNm3apHXr1rVZz26368MPP9STTz6pxsZGDRkyROPHj9dbb72lU045pZu7BQAA+hK/78NiKu7DAgDAkadH7sMCAAAQCAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMFB7oAk1mWpQMHDgS6DAAAjBARESGbzRaQxyawdOLAgQM66qijAl0GAABG2L9/vyIjIwPy2HwkBAAAjMcZlk5ERERo//79gS4DAAAjREREBOyxCSydsNlsATv1BQAAfsJHQgAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjdSuwlJSUKCEhQeHh4UpLS1NFRUWHYzMyMmSz2dq07OxsSdLBgwd12223aezYsYqMjNSwYcM0bdo07dq1q3t7BAAA+hy/A8uKFSuUn5+vBQsWqKqqSklJScrKylJDQ0O741evXq3du3d7W3V1tex2uyZPnixJOnDggKqqqnT77berqqpKq1evVk1NjS655JKft2cAAKDPsFmWZfkzIS0tTePHj9cjjzwiSfJ4PIqPj9fcuXM1f/78w84vLi5WYWGhdu/ercjIyHbHvPPOO0pNTdUXX3yh4447rkt1uVwuORwONTU1KSoqqus7BAAAAqar799+nWFpaWlRZWWlMjMzf1ogKEiZmZnavHlzl9ZwOp3Ky8vrMKxIUlNTk2w2mwYNGtThmObmZrlcLp8GAAD6Jr8CS2Njo1pbWxUTE+PTHxMTo7q6usPOr6ioUHV1tWbNmtXhmO+//1633XabpkyZ0mnSKioqksPh8Lb4+Piu7wgAADii9Oq3hJxOp8aOHavU1NR2tx88eFCXX365LMvS4sWLO12roKBATU1N3rZz586eKBkAABgg2J/B0dHRstvtqq+v9+mvr69XbGxsp3PdbreWL1+uP/zhD+1uPxRWvvjiC73xxhuHvQ4lLCxMYWFh/pQPAACOUH6dYQkNDVVKSorKy8u9fR6PR+Xl5UpPT+907sqVK9Xc3KypU6e22XYorHz88cd6/fXXNWTIEH/KAgAAfZxfZ1gkKT8/X9OnT9e4ceOUmpqq4uJiud1uzZgxQ5I0bdo0DR8+XEVFRT7znE6ncnJy2oSRgwcP6te//rWqqqq0Zs0atba2eq+HGTx4sEJDQ7u7bwAAoI/wO7Dk5uZqz549KiwsVF1dnZKTk1VWVua9ELe2tlZBQb4nbmpqarRp0yatW7euzXpfffWVXnrpJUlScnKyz7Y333xTGRkZ/pYIAAD6GL/vw2Iq7sMCAMCRp0fuwwIAABAIBBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMF63AktJSYkSEhIUHh6utLQ0VVRUdDg2IyNDNputTcvOzvaOWb16tc477zwNGTJENptN7733XnfKAgAAfZTfgWXFihXKz8/XggULVFVVpaSkJGVlZamhoaHd8atXr9bu3bu9rbq6Wna7XZMnT/aOcbvdmjBhghYuXNj9PQEAAH2WzbIsy58JaWlpGj9+vB555BFJksfjUXx8vObOnav58+cfdn5xcbEKCwu1e/duRUZG+mz7/PPPNXLkSL377rtKTk72pyy5XC45HA41NTUpKirKr7kAACAwuvr+7dcZlpaWFlVWViozM/OnBYKClJmZqc2bN3dpDafTqby8vDZhBQAAoCPB/gxubGxUa2urYmJifPpjYmL04YcfHnZ+RUWFqqur5XQ6/auyHc3NzWpubvb+7XK5fvaaAADATL36LSGn06mxY8cqNTX1Z69VVFQkh8PhbfHx8b9AhQAAwER+BZbo6GjZ7XbV19f79NfX1ys2NrbTuW63W8uXL9fMmTP9r7IdBQUFampq8radO3f+IusCAADz+BVYQkNDlZKSovLycm+fx+NReXm50tPTO527cuVKNTc3a+rUqd2r9N+EhYUpKirKpwEAgL7Jr2tYJCk/P1/Tp0/XuHHjlJqaquLiYrndbs2YMUOSNG3aNA0fPlxFRUU+85xOp3JycjRkyJA2a37zzTeqra3Vrl27JEk1NTWSpNjY2MOeuQEAAH2f34ElNzdXe/bsUWFhoerq6pScnKyysjLvhbi1tbUKCvI9cVNTU6NNmzZp3bp17a750ksveQOPJOXl5UmSFixYoDvuuMPfEgEAQB/j931YTMV9WAAAOPL0yH1YAAAAAoHAAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjdSuwlJSUKCEhQeHh4UpLS1NFRUWHYzMyMmSz2dq07Oxs7xjLslRYWKi4uDgNGDBAmZmZ+vjjj7tTGgAA6IP8DiwrVqxQfn6+FixYoKqqKiUlJSkrK0sNDQ3tjl+9erV2797tbdXV1bLb7Zo8ebJ3zH333ac//elPWrJkibZu3arIyEhlZWXp+++/7/6eAQCAPsNmWZblz4S0tDSNHz9ejzzyiCTJ4/EoPj5ec+fO1fz58w87v7i4WIWFhdq9e7ciIyNlWZaGDRumm2++Wb/73e8kSU1NTYqJidGyZcuUl5fXpbpcLpccDoeampoUFRXlzy4BAIAA6er7t19nWFpaWlRZWanMzMyfFggKUmZmpjZv3tylNZxOp/Ly8hQZGSlJ2rFjh+rq6nzWdDgcSktL63TN5uZmuVwunwYAAPomvwJLY2OjWltbFRMT49MfExOjurq6w86vqKhQdXW1Zs2a5e07NM/fNYuKiuRwOLwtPj7en10BAABHkF79lpDT6dTYsWOVmpr6s9cqKChQU1OTt+3cufMXqBAAAJjIr8ASHR0tu92u+vp6n/76+nrFxsZ2Otftdmv58uWaOXOmT/+hef6uGRYWpqioKJ8GAAD6Jr8CS2hoqFJSUlReXu7t83g8Ki8vV3p6eqdzV65cqebmZk2dOtWnf+TIkYqNjfVZ0+VyaevWrYddEwAA9A/B/k7Iz8/X9OnTNW7cOKWmpqq4uFhut1szZsyQJE2bNk3Dhw9XUVGRzzyn06mcnBwNGTLEp99ms+nGG2/U3XffrRNPPFEjR47U7bffrmHDhiknJ6f7ewYAAPoMvwNLbm6u9uzZo8LCQtXV1Sk5OVllZWXei2Zra2sVFOR74qampkabNm3SunXr2l3z1ltvldvt1rXXXqu9e/dqwoQJKisrU3h4eDd2CQAA9DV+34fFVNyHBQCAI0+P3IcFAAAgEAgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGC8bgWWkpISJSQkKDw8XGlpaaqoqOh0/N69ezV79mzFxcUpLCxMJ510kl599VXv9n379unGG2/UiBEjNGDAAJ1xxhl65513ulMaAADog/wOLCtWrFB+fr4WLFigqqoqJSUlKSsrSw0NDe2Ob2lp0bnnnqvPP/9czz33nGpqavTYY49p+PDh3jGzZs3S+vXr9dRTT2nbtm0677zzlJmZqa+++qr7ewYAAPoMm2VZlj8T0tLSNH78eD3yyCOSJI/Ho/j4eM2dO1fz589vM37JkiW6//779eGHHyokJKTN9u+++04DBw7Uiy++qOzsbG9/SkqKLrjgAt19991dqsvlcsnhcKipqUlRUVH+7BIAAAiQrr5/+3WGpaWlRZWVlcrMzPxpgaAgZWZmavPmze3Oeemll5Senq7Zs2crJiZGp556qu655x61trZKkn744Qe1trYqPDzcZ96AAQO0adOmDmtpbm6Wy+XyaQAAoG/yK7A0NjaqtbVVMTExPv0xMTGqq6trd85nn32m5557Tq2trXr11Vd1++2364EHHvCeORk4cKDS09N11113adeuXWptbdXTTz+tzZs3a/fu3R3WUlRUJIfD4W3x8fH+7AoAADiC9Pi3hDwej4YOHapHH31UKSkpys3N1e9//3stWbLEO+app56SZVkaPny4wsLC9Kc//UlTpkxRUFDH5RUUFKipqcnbdu7c2dO7AgAAAiTYn8HR0dGy2+2qr6/36a+vr1dsbGy7c+Li4hQSEiK73e7tGz16tOrq6tTS0qLQ0FCdcMIJ2rhxo9xut1wul+Li4pSbm6vjjz++w1rCwsIUFhbmT/kAAOAI5dcZltDQUKWkpKi8vNzb5/F4VF5ervT09HbnnHnmmfrkk0/k8Xi8fR999JHi4uIUGhrqMzYyMlJxcXH69ttvtXbtWk2aNMmf8gAAQB/l90dC+fn5euyxx/Tkk09q+/btuv766+V2uzVjxgxJ0rRp01RQUOAdf/311+ubb77RvHnz9NFHH+mVV17RPffco9mzZ3vHrF27VmVlZdqxY4fWr1+vs88+WyeffLJ3TQAA0L/59ZGQJOXm5mrPnj0qLCxUXV2dkpOTVVZW5r0Qt7a21ufak/j4eK1du1Y33XSTTjvtNA0fPlzz5s3Tbbfd5h3T1NSkgoICffnllxo8eLD+67/+S3/84x/b/Ro0AADof/y+D4upuA8LAABHnh65DwsAAEAgEFgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxgsOdAG/FMuyJEkulyvAlQAAgK469L596H28I30msOzbt0+SFB8fH+BKAACAv/bt2yeHw9Hhdpt1uEhzhPB4PNq1a5cGDhwom832i63rcrkUHx+vnTt3Kioq6hdbty/iWHUdx8o/HK+u41h1Hceq63ryWFmWpX379mnYsGEKCur4SpU+c4YlKChIxx57bI+tHxUVxRO6izhWXcex8g/Hq+s4Vl3Hseq6njpWnZ1ZOYSLbgEAgPEILAAAwHgElsMICwvTggULFBYWFuhSjMex6jqOlX84Xl3Hseo6jlXXmXCs+sxFtwAAoO/iDAsAADAegQUAABiPwAIAAIxHYAEAAMYjsEgqKSlRQkKCwsPDlZaWpoqKik7Hr1y5UieffLLCw8M1duxYvfrqq71UaeD5c6yWLVsmm83m08LDw3ux2sD529/+posvvljDhg2TzWbTCy+8cNg5GzZs0H/8x38oLCxMo0aN0rJly3q8ThP4e6w2bNjQ5nlls9lUV1fXOwUHUFFRkcaPH6+BAwdq6NChysnJUU1NzWHn9cfXrO4cq/76mrV48WKddtpp3pvCpaen67XXXut0TiCeU/0+sKxYsUL5+flasGCBqqqqlJSUpKysLDU0NLQ7/u2339aUKVM0c+ZMvfvuu8rJyVFOTo6qq6t7ufLe5++xkn68K+Lu3bu97YsvvujFigPH7XYrKSlJJSUlXRq/Y8cOZWdn6+yzz9Z7772nG2+8UbNmzdLatWt7uNLA8/dYHVJTU+Pz3Bo6dGgPVWiOjRs3avbs2dqyZYvWr1+vgwcP6rzzzpPb7e5wTn99zerOsZL652vWscceq3vvvVeVlZX6xz/+of/8z//UpEmT9MEHH7Q7PmDPKaufS01NtWbPnu39u7W11Ro2bJhVVFTU7vjLL7/cys7O9ulLS0uzrrvuuh6t0wT+HqulS5daDoejl6ozlyTr+eef73TMrbfeap1yyik+fbm5uVZWVlYPVmaerhyrN99805Jkffvtt71Sk8kaGhosSdbGjRs7HNOfX7P+VVeOFa9ZPzn66KOtxx9/vN1tgXpO9eszLC0tLaqsrFRmZqa3LygoSJmZmdq8eXO7czZv3uwzXpKysrI6HN9XdOdYSdL+/fs1YsQIxcfHd5rY+7v++rz6OZKTkxUXF6dzzz1Xf//73wNdTkA0NTVJkgYPHtzhGJ5bP+rKsZJ4zWptbdXy5cvldruVnp7e7phAPaf6dWBpbGxUa2urYmJifPpjYmI6/Dy8rq7Or/F9RXeOVWJiop544gm9+OKLevrpp+XxeHTGGWfoyy+/7I2SjygdPa9cLpe+++67AFVlpri4OC1ZskSrVq3SqlWrFB8fr4yMDFVVVQW6tF7l8Xh044036swzz9Spp57a4bj++pr1r7p6rPrza9a2bdt01FFHKSwsTL/97W/1/PPPa8yYMe2ODdRzqs/8WjPMk56e7pPQzzjjDI0ePVp/+ctfdNdddwWwMhzJEhMTlZiY6P37jDPO0KeffqpFixbpqaeeCmBlvWv27Nmqrq7Wpk2bAl2K8bp6rPrza1ZiYqLee+89NTU16bnnntP06dO1cePGDkNLIPTrMyzR0dGy2+2qr6/36a+vr1dsbGy7c2JjY/0a31d051j9u5CQEP3qV7/SJ5980hMlHtE6el5FRUVpwIABAarqyJGamtqvnldz5szRmjVr9Oabb+rYY4/tdGx/fc06xJ9j9e/602tWaGioRo0apZSUFBUVFSkpKUkPPfRQu2MD9Zzq14ElNDRUKSkpKi8v9/Z5PB6Vl5d3+Nldenq6z3hJWr9+fYfj+4ruHKt/19raqm3btikuLq6nyjxi9dfn1S/lvffe6xfPK8uyNGfOHD3//PN64403NHLkyMPO6a/Pre4cq3/Xn1+zPB6Pmpub290WsOdUj17SewRYvny5FRYWZi1btsz65z//aV177bXWoEGDrLq6OsuyLOuqq66y5s+f7x3/97//3QoODrb+93//19q+fbu1YMECKyQkxNq2bVugdqHX+Hus7rzzTmvt2rXWp59+alVWVlp5eXlWeHi49cEHHwRqF3rNvn37rHfffdd69913LUnWgw8+aL377rvWF198YVmWZc2fP9+66qqrvOM/++wzKyIiwrrlllus7du3WyUlJZbdbrfKysoCtQu9xt9jtWjRIuuFF16wPv74Y2vbtm3WvHnzrKCgIOv1118P1C70muuvv95yOBzWhg0brN27d3vbgQMHvGN4zfpRd45Vf33Nmj9/vrVx40Zrx44d1vvvv2/Nnz/fstls1rp16yzLMuc51e8Di2VZ1sMPP2wdd9xxVmhoqJWammpt2bLFu23ixInW9OnTfcb/9a9/tU466SQrNDTUOuWUU6xXXnmllysOHH+O1Y033ugdGxMTY1144YVWVVVVAKrufYe+evvv7dDxmT59ujVx4sQ2c5KTk63Q0FDr+OOPt5YuXdrrdQeCv8dq4cKF1gknnGCFh4dbgwcPtjIyMqw33ngjMMX3svaOkySf5wqvWT/qzrHqr69Zv/nNb6wRI0ZYoaGh1jHHHGOdc8453rBiWeY8p2yWZVk9ew4HAADg5+nX17AAAIAjA4EFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAZWRkaEbb7yxw+0JCQkqLi7utXoAmIlfawZgtHfeeUeRkZGBLgNAgBFYABjtmGOOCXQJAAzAR0IAAu6HH37QnDlz5HA4FB0drdtvv12HfjXk3z8Sstlsevzxx3XppZcqIiJCJ554ol566aUAVQ6gtxBYAATck08+qeDgYFVUVOihhx7Sgw8+qMcff7zD8Xfeeacuv/xyvf/++7rwwgt15ZVX6ptvvunFigH0NgILgICLj4/XokWLlJiYqCuvvFJz587VokWLOhx/9dVXa8qUKRo1apTuuece7d+/XxUVFb1YMYDeRmABEHCnn366bDab9+/09HR9/PHHam1tbXf8aaed5v13ZGSkoqKi1NDQ0ON1AggcAguAI05ISIjP3zabTR6PJ0DVAOgNBBYAAbd161afv7ds2aITTzxRdrs9QBUBMA2BBUDA1dbWKj8/XzU1NXr22Wf18MMPa968eYEuC4BBuA8LgICbNm2avvvuO6Wmpsput2vevHm69tprA10WAIPYrEM3OwAAADAUHwkBAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYLz/B2ptMp/1qRbxAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# scikit-learn model\n",
    "gradient_booster = GradientBoostingClassifier(learning_rate=0.1)\n",
    "gradient_booster.fit(X, y)\n",
    "\n",
    "# explainable model\n",
    "model = ModelApi().create_model(target_col=target_col, model_src=gradient_booster, used_features=X.columns.to_list())\n",
    "\n",
    "interpretation = interpret.run_interpretation(\n",
    "    dataset=df,\n",
    "    model=model,\n",
    "    target_col=target_col,\n",
    "    results_location=results_location,\n",
    "    log_level=logging.INFO,\n",
    "    explainers=[\n",
    "        commons.ExplainerToRun(\n",
    "            explainer_id=PdIceExplainer.explainer_id(),\n",
    "            params=\"\",\n",
    "        )\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff9df4be-d4da-44db-a479-7d8d7f45c29d",
   "metadata": {},
   "source": [
    "## Explainer Result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "25556ca5-8239-4201-8a23-1ace2b3a46d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# retrieve the result\n",
    "result = interpretation.get_explainer_result(PdIceExplainer.explainer_id())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "38c26ac9-df8e-480f-ab6c-c14b43860c5d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# open interpretation HTML report in web browser\n",
    "webbrowser.open(interpretation.result.get_html_report_location())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "76c46623-6e24-4ac1-b6cc-66fc29b7ea0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'id': 'h2o_sonar.explainers.pd_ice_explainer.PdIceExplainer',\n",
       " 'name': 'PdIceExplainer',\n",
       " 'display_name': 'Partial Dependence Plot',\n",
       " 'description': 'Partial dependence plot (PDP) portrays the average prediction\\nbehavior of the model across the domain of an input variable along with +/- 1\\nstandard deviation bands. Individual Conditional Expectations plot (ICE) displays\\nthe prediction behavior for an individual row of data when an input variable is\\ntoggled across its domain.\\n\\nPD binning:\\n\\n**Integer** feature:\\n\\n* bins in **numeric** mode:\\n    * bins are integers\\n    * (at most) `grid_resolution` integer values in between minimum and maximum\\n      of feature values\\n    * bin values are created as evenly as possible\\n    * minimum and maximum is included in bins\\n      (if `grid_resolution` is bigger or equal to 2)\\n* bins in **categorical** mode:\\n    * bins are integers\\n    * top `grid_resolution` values from feature values ordered by frequency\\n      (int values are converted to strings and most frequent values are used\\n      as bins)\\n* quantile bins in **numeric** mode:\\n    * bins are integers\\n    * bin values are created with the aim that there will be the same number of\\n      observations per bin\\n    * q-quantile used to created ``q`` bins where ``q`` is specified by PD parameter\\n* quantile bins in **categorical** mode:\\n    * not supported\\n\\n**Float** feature:\\n\\n* bins in **numeric** mode:\\n    * bins are floats\\n    * `grid_resolution` float values in between minimum and maximum of feature\\n       values\\n    * bin values are created as evenly as possible\\n    * minimum and maximum is included in bins\\n      (if `grid_resolution` is bigger or equal to 2)\\n* bins in **categorical** mode:\\n    * bins are floats\\n    * top `grid_resolution` values from feature values ordered by frequency\\n      (float values are converted to strings and most frequent values are used\\n      as bins)\\n* quantile bins in **numeric** mode:\\n    * bins are floats\\n    * bin values are created with the aim that there will be the same number of\\n      observations per bin\\n    * q-quantile used to created ``q`` bins where ``q`` is specified by PD parameter\\n* quantile bins in **categorical** mode:\\n    * not supported\\n\\n**String** feature:\\n\\n* bins in **numeric** mode:\\n    * not supported\\n* bins in **categorical** mode:\\n    * bins are strings\\n    * top `grid_resolution` values from feature values ordered by frequency\\n* quantile bins:\\n    * not supported\\n\\n**Date/datetime** feature:\\n\\n* bins in **numeric** mode:\\n    * bins are dates\\n    * `grid_resolution` date values in between minimum and maximum of feature\\n      values\\n    * bin values are created as evenly as possible:\\n        1. dates are parsed and converted to epoch timestamps i.e integers\\n        2. bins are created as in case of numeric integer binning\\n        3. integer bins are converted back to original date format\\n    * minimum and maximum is included in bins\\n      (if `grid_resolution` is bigger or equal to 2)\\n* bins in **categorical** mode:\\n    * bins are dates\\n    * top `grid_resolution` values from feature values ordered by frequency\\n      (dates are handled as opaque strings and most frequent values are used\\n      as bins)\\n* quantile bins:\\n    * not supported\\n\\nPD out of range binning:\\n\\n**Integer** feature:\\n\\n* OOR bins in **numeric** mode:\\n    * OOR bins are integers\\n    * (at most) `oor_grid_resolution` integer values are added below minimum and\\n      above maximum\\n    * bin values are created by adding/substracting rounded standard deviation\\n      (of feature values) above and below maximum and minimum `oor_grid_resolution`\\n      times\\n        * 1 used used if rounded standard deviation would be 0\\n    * if feature is of unsigned integer type, then bins below 0\\n      are not created\\n        * if rounded standard deviation and/or `oor_grid_resolution` is so high\\n          that it would cause lower OOR bins to be negative numbers, then standard\\n          deviation of size 1 is tried instead\\n* OOR bins in **categorical** mode:\\n    * same as numeric mode\\n\\n**Float** feature:\\n\\n* OOR bins in **numeric** mode:\\n    * OOR bins are floats\\n    * `oor_grid_resolution` float values are added below minimum and above maximum\\n    * bin values are created by adding/substracting standard deviation\\n      (of feature values) above and below maximum and minimum `oor_grid_resolution`\\n      times\\n* OOR bins in **categorical** mode:\\n    * same as numeric mode\\n\\n**String** feature:\\n\\n* bins in **numeric** mode:\\n    * not supported\\n* bins in **categorical** mode:\\n    * OOR bins are strings\\n    * value `UNSEEN` is added as OOR bin\\n\\n**Date** feature:\\n\\n* bins in **numeric** mode:\\n    * not supported\\n* bins in **categorical** mode:\\n    * OOR bins are strings\\n    * value `UNSEEN` is added as OOR bin\\n\\n',\n",
       " 'model_types': ['iid', 'time_series'],\n",
       " 'can_explain': ['regression', 'binomial', 'multinomial'],\n",
       " 'explanation_scopes': ['global_scope', 'local_scope'],\n",
       " 'explanations': [{'explanation_type': 'global-partial-dependence',\n",
       "   'name': 'Partial Dependence Plot (PDP)',\n",
       "   'category': 'DAI MODEL',\n",
       "   'scope': 'global',\n",
       "   'has_local': 'local-individual-conditional-explanation',\n",
       "   'formats': ['application/json']},\n",
       "  {'explanation_type': 'local-individual-conditional-explanation',\n",
       "   'name': 'Individual Conditional Expectations (ICE)',\n",
       "   'category': 'DAI MODEL',\n",
       "   'scope': 'local',\n",
       "   'has_local': None,\n",
       "   'formats': ['application/vnd.h2oai.json+datatable.jay']},\n",
       "  {'explanation_type': 'global-html-fragment',\n",
       "   'name': 'Partial Dependence Plot (PDP)',\n",
       "   'category': 'DAI MODEL',\n",
       "   'scope': 'global',\n",
       "   'has_local': None,\n",
       "   'formats': ['text/html']}],\n",
       " 'parameters': [{'name': 'sample_size',\n",
       "   'description': 'Sample size for Partial Dependence Plot.',\n",
       "   'comment': '',\n",
       "   'type': 'int',\n",
       "   'val': 25000,\n",
       "   'predefined': [],\n",
       "   'tags': [],\n",
       "   'min_': 0.0,\n",
       "   'max_': 0.0,\n",
       "   'category': ''},\n",
       "  {'name': 'max_features',\n",
       "   'description': 'Partial Dependence Plot number of features (to see all features used by model set to -1).',\n",
       "   'comment': '',\n",
       "   'type': 'int',\n",
       "   'val': 10,\n",
       "   'predefined': [],\n",
       "   'tags': [],\n",
       "   'min_': 0.0,\n",
       "   'max_': 0.0,\n",
       "   'category': ''},\n",
       "  {'name': 'features',\n",
       "   'description': 'Partial Dependence Plot feature list.',\n",
       "   'comment': '',\n",
       "   'type': 'list',\n",
       "   'val': None,\n",
       "   'predefined': [],\n",
       "   'tags': ['SOURCE_DATASET_COLUMN_NAMES'],\n",
       "   'min_': 0.0,\n",
       "   'max_': 0.0,\n",
       "   'category': ''},\n",
       "  {'name': 'oor_grid_resolution',\n",
       "   'description': 'Partial Dependence Plot number of out of range bins.',\n",
       "   'comment': '',\n",
       "   'type': 'int',\n",
       "   'val': 0,\n",
       "   'predefined': [],\n",
       "   'tags': [],\n",
       "   'min_': 0.0,\n",
       "   'max_': 0.0,\n",
       "   'category': ''},\n",
       "  {'name': 'quantile-bin-grid-resolution',\n",
       "   'description': 'Partial Dependence Plot quantile binning (total quantile points used to create bins).',\n",
       "   'comment': '',\n",
       "   'type': 'int',\n",
       "   'val': 0,\n",
       "   'predefined': [],\n",
       "   'tags': [],\n",
       "   'min_': 0.0,\n",
       "   'max_': 0.0,\n",
       "   'category': ''},\n",
       "  {'name': 'grid_resolution',\n",
       "   'description': 'Partial Dependence Plot observations per bin (number of equally spaced points used to create bins).',\n",
       "   'comment': '',\n",
       "   'type': 'int',\n",
       "   'val': 20,\n",
       "   'predefined': [],\n",
       "   'tags': [],\n",
       "   'min_': 0.0,\n",
       "   'max_': 0.0,\n",
       "   'category': ''},\n",
       "  {'name': 'center',\n",
       "   'description': 'Center Partial Dependence Plot using ICE centered at 0.',\n",
       "   'comment': '',\n",
       "   'type': 'bool',\n",
       "   'val': False,\n",
       "   'predefined': [],\n",
       "   'tags': [],\n",
       "   'min_': 0.0,\n",
       "   'max_': 0.0,\n",
       "   'category': ''},\n",
       "  {'name': 'sort_bins',\n",
       "   'description': 'Ensure bin values sorting.',\n",
       "   'comment': '',\n",
       "   'type': 'bool',\n",
       "   'val': True,\n",
       "   'predefined': [],\n",
       "   'tags': [],\n",
       "   'min_': 0.0,\n",
       "   'max_': 0.0,\n",
       "   'category': ''},\n",
       "  {'name': 'histograms',\n",
       "   'description': 'Enable histograms.',\n",
       "   'comment': '',\n",
       "   'type': 'bool',\n",
       "   'val': True,\n",
       "   'predefined': [],\n",
       "   'tags': [],\n",
       "   'min_': 0.0,\n",
       "   'max_': 0.0,\n",
       "   'category': ''},\n",
       "  {'name': 'quantile-bins',\n",
       "   'description': 'Per-feature quantile binning (Example: if choosing features\\n                 F1 and F2, this parameter is \\'{\"F1\": 2,\"F2\": 5}\\'. Note, you can\\n                 set all features to use the same quantile binning with the\\n                 `Partial Dependence Plot quantile binning` parameter and then\\n                 adjust the quantile binning for a subset of PDP features with\\n                 this parameter).',\n",
       "   'comment': '',\n",
       "   'type': 'str',\n",
       "   'val': '',\n",
       "   'predefined': [],\n",
       "   'tags': [],\n",
       "   'min_': 0.0,\n",
       "   'max_': 0.0,\n",
       "   'category': ''},\n",
       "  {'name': 'numcat_num_chart',\n",
       "   'description': 'Unique feature values count driven Partial Dependence Plot binning and chart selection.',\n",
       "   'comment': '',\n",
       "   'type': 'bool',\n",
       "   'val': True,\n",
       "   'predefined': [],\n",
       "   'tags': [],\n",
       "   'min_': 0.0,\n",
       "   'max_': 0.0,\n",
       "   'category': ''},\n",
       "  {'name': 'numcat_threshold',\n",
       "   'description': 'Threshold for Partial Dependence Plot binning and chart selection (<=threshold categorical, >threshold numeric).',\n",
       "   'comment': '',\n",
       "   'type': 'int',\n",
       "   'val': 11,\n",
       "   'predefined': [],\n",
       "   'tags': [],\n",
       "   'min_': 0.0,\n",
       "   'max_': 0.0,\n",
       "   'category': ''},\n",
       "  {'name': 'debug_residuals',\n",
       "   'description': 'Debug model residuals.',\n",
       "   'comment': '',\n",
       "   'type': 'bool',\n",
       "   'val': False,\n",
       "   'predefined': [],\n",
       "   'tags': [],\n",
       "   'min_': 0.0,\n",
       "   'max_': 0.0,\n",
       "   'category': ''}],\n",
       " 'keywords': ['run-by-default',\n",
       "  'can-add-feature',\n",
       "  'explains-feature-behavior',\n",
       "  'h2o-sonar']}"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# summary\n",
    "result.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e30e08f6-69b9-408f-8bd6-6dad14638694",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'sample_size': 25000,\n",
       " 'max_features': 10,\n",
       " 'features': None,\n",
       " 'oor_grid_resolution': 0,\n",
       " 'quantile-bin-grid-resolution': 0,\n",
       " 'grid_resolution': 20,\n",
       " 'center': False,\n",
       " 'sort_bins': True,\n",
       " 'histograms': True,\n",
       " 'quantile-bins': '',\n",
       " 'numcat_num_chart': True,\n",
       " 'numcat_threshold': 11,\n",
       " 'debug_residuals': False}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# parameters\n",
    "result.params()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "490d132b-b7e2-48a2-8ec4-dbd71886edf9",
   "metadata": {},
   "source": [
    "### Display PD Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "2aa6274e-79d5-49b1-b29a-2263db5cb8a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class='datatable'>\n",
       "  <table class='frame'>\n",
       "  <thead>\n",
       "    <tr class='colnames'><td class='row_index'></td><th>bin</th><th>pd</th><th>sd</th><th>oor</th></tr>\n",
       "    <tr class='coltypes'><td class='row_index'></td><td class='int' title='int32'>&#x25AA;&#x25AA;&#x25AA;&#x25AA;</td><td class='float' title='float64'>&#x25AA;&#x25AA;&#x25AA;&#x25AA;&#x25AA;&#x25AA;&#x25AA;&#x25AA;</td><td class='float' title='float64'>&#x25AA;&#x25AA;&#x25AA;&#x25AA;&#x25AA;&#x25AA;&#x25AA;&#x25AA;</td><td class='bool' title='bool8'>&#x25AA;</td></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><td class='row_index'>0</td><td>0</td><td>0.7639</td><td>0.424706</td><td>0</td></tr>\n",
       "    <tr><td class='row_index'>1</td><td>1</td><td>0.7248</td><td>0.446637</td><td>0</td></tr>\n",
       "    <tr><td class='row_index'>2</td><td>2</td><td>0.7248</td><td>0.446637</td><td>0</td></tr>\n",
       "    <tr><td class='row_index'>3</td><td>3</td><td>0.7248</td><td>0.446637</td><td>0</td></tr>\n",
       "    <tr><td class='row_index'>4</td><td>4</td><td>0.7248</td><td>0.446637</td><td>0</td></tr>\n",
       "    <tr><td class='row_index'>5</td><td>5</td><td>0.7248</td><td>0.446637</td><td>0</td></tr>\n",
       "    <tr><td class='row_index'>6</td><td>6</td><td>0.7248</td><td>0.446637</td><td>0</td></tr>\n",
       "  </tbody>\n",
       "  </table>\n",
       "  <div class='footer'>\n",
       "    <div class='frame_dimensions'>7 rows &times; 4 columns</div>\n",
       "  </div>\n",
       "</div>\n"
      ],
      "text/plain": [
       "<Frame#7febe86a0240 7x4>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.data(feature_name=\"EDUCATION\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "9519fbef-d7b7-417a-85f2-2450ae9c1ddf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class='datatable'>\n",
       "  <table class='frame'>\n",
       "  <thead>\n",
       "    <tr class='colnames'><td class='row_index'></td><th>bin</th><th>pd</th><th>sd</th><th>oor</th></tr>\n",
       "    <tr class='coltypes'><td class='row_index'></td><td class='int' title='int32'>&#x25AA;&#x25AA;&#x25AA;&#x25AA;</td><td class='float' title='float64'>&#x25AA;&#x25AA;&#x25AA;&#x25AA;&#x25AA;&#x25AA;&#x25AA;&#x25AA;</td><td class='float' title='float64'>&#x25AA;&#x25AA;&#x25AA;&#x25AA;&#x25AA;&#x25AA;&#x25AA;&#x25AA;</td><td class='bool' title='bool8'>&#x25AA;</td></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><td class='row_index'>0</td><td>&minus;2</td><td>0.7248</td><td>0.446637</td><td>0</td></tr>\n",
       "    <tr><td class='row_index'>1</td><td>&minus;1</td><td>0.7248</td><td>0.446637</td><td>0</td></tr>\n",
       "    <tr><td class='row_index'>2</td><td>0</td><td>0.7248</td><td>0.446637</td><td>0</td></tr>\n",
       "    <tr><td class='row_index'>3</td><td>1</td><td>0.7248</td><td>0.446637</td><td>0</td></tr>\n",
       "    <tr><td class='row_index'>4</td><td>2</td><td>0.7248</td><td>0.446637</td><td>0</td></tr>\n",
       "    <tr><td class='row_index'>5</td><td>3</td><td>0.7248</td><td>0.446637</td><td>0</td></tr>\n",
       "    <tr><td class='row_index'>6</td><td>4</td><td>0.7248</td><td>0.446637</td><td>0</td></tr>\n",
       "    <tr><td class='row_index'>7</td><td>5</td><td>0.7248</td><td>0.446637</td><td>0</td></tr>\n",
       "    <tr><td class='row_index'>8</td><td>6</td><td>0.7248</td><td>0.446637</td><td>0</td></tr>\n",
       "    <tr><td class='row_index'>9</td><td>7</td><td>0.7248</td><td>0.446637</td><td>0</td></tr>\n",
       "    <tr><td class='row_index'>10</td><td>8</td><td>0.7248</td><td>0.446637</td><td>0</td></tr>\n",
       "  </tbody>\n",
       "  </table>\n",
       "  <div class='footer'>\n",
       "    <div class='frame_dimensions'>11 rows &times; 4 columns</div>\n",
       "  </div>\n",
       "</div>\n"
      ],
      "text/plain": [
       "<Frame#7febe865fb40 11x4>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.data(feature_name=\"PAY_3\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df8a083b-3b88-4349-bb63-28551c24cc4f",
   "metadata": {},
   "source": [
    "### Plot PD Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5a9d8262-574e-4073-a282-567d4fd1209c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result.plot(feature_name=\"EDUCATION\", override_feature_type=result.format.KEY_CATEGORICAL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "146130b2-74df-4af5-a63f-d17363a05462",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result.plot(feature_name=\"PAY_3\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a493b092-6236-419f-906c-16d52c47674f",
   "metadata": {},
   "source": [
    "### Save Explainer Log and Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "7c638a2c-6b01-4228-aa0f-93fd8dd7feab",
   "metadata": {},
   "outputs": [],
   "source": [
    "# save the explainer log\n",
    "result.log(path=\"./pd-ice-demo.log\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f5d91240-09ff-4893-b652-b0259a8f222a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2023-03-12 23:27:53,358 INFO PD/ICE a30a4f57-9c78-4c5f-9c8e-85198b523c1c/745668cc-bbc8-42be-9b39-47487112a885 BEGIN calculation\n",
      "2023-03-12 23:27:53,359 INFO PD/ICE a30a4f57-9c78-4c5f-9c8e-85198b523c1c/745668cc-bbc8-42be-9b39-47487112a885 loading dataset\n",
      "2023-03-12 23:27:53,360 INFO PD/ICE a30a4f57-9c78-4c5f-9c8e-85198b523c1c/745668cc-bbc8-42be-9b39-47487112a885 loaded dataset has 10000 rows and 25 columns\n",
      "2023-03-12 23:27:53,360 INFO PD/ICE a30a4f57-9c78-4c5f-9c8e-85198b523c1c/745668cc-bbc8-42be-9b39-47487112a885 getting features list, importanceand metadata\n",
      "2023-03-12 23:27:53,361 INFO PD/ICE a30a4f57-9c78-4c5f-9c8e-85198b523c1c/745668cc-bbc8-42be-9b39-47487112a885 all most important model features: ['ID', 'LIMIT_BAL', 'SEX', 'EDUCATION', 'MARRIAGE', 'AGE', 'PAY_0', 'PAY_2', 'PAY_3', 'PAY_4', 'PAY_5', 'PAY_6', 'BILL_AMT1', 'BILL_AMT2', 'BILL_AMT3', 'BILL_AMT4', 'BILL_AMT5', 'BILL_AMT6', 'PAY_AMT1', 'PAY_AMT2', 'PAY_AMT3', 'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6']\n",
      "2023-03-12 23:27:53,362 INFO PD/ICE a30a4f57-9c78-4c5f-9c8e-85198b523c1c/745668cc-bbc8-42be-9b39-47487112a885 features used by model: ['ID', 'LIMIT_BAL', 'SEX', 'EDUCATION', 'MARRIAGE', 'AGE', 'PAY_0', 'PAY_2', 'PAY_3', 'PAY_4', 'PAY_5', 'PAY_6', 'BILL_AMT1', 'BILL_AMT2', 'BILL_AMT3', 'BILL_AMT4', 'BILL_AMT5', 'BILL_AMT6', 'PAY_AMT1', 'PAY_AMT2', 'PAY_AMT3', 'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6']\n",
      "2023-03-12 23:27:53,362 INFO PD/ICE a30a4f57-9c78-4c5f-9c8e-85198b523c1c/745668cc-bbc8-42be-9b39-47487112a885: calculating PD for features ['ID', 'LIMIT_BAL', 'SEX', 'EDUCATION', 'MARRIAGE', 'AGE', 'PAY_0', 'PAY_2', 'PAY_3', 'PAY_4']\n",
      "2023-03-12 23:27:53,363 INFO PD/ICE a30a4f57-9c78-4c5f-9c8e-85198b523c1c/745668cc-bbc8-42be-9b39-47487112a885 feature metadata: {'id': [], 'categorical': [], 'numeric': [], 'catnum': [], 'date': [], 'time': [], 'datetime': [], 'text': [], 'image': [], 'date-format': [], 'quantile-bin': {}}\n",
      "2023-03-12 23:27:53,363 INFO PD/ICE a30a4f57-9c78-4c5f-9c8e-85198b523c1c/745668cc-bbc8-42be-9b39-47487112a885 1 frame strategy: True\n",
      "2023-03-12 23:27:53,364 INFO PD/ICE a30a4f57-9c78-4c5f-9c8e-85198b523c1c/745668cc-bbc8-42be-9b39-47487112a885 residual PD/ICE should NOT be calculated, but y has been specified - setting it None\n"
     ]
    }
   ],
   "source": [
    "!head pd-ice-demo.log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "da4e2b28-96d7-440e-bfea-41cb694a52d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# save the explainer data\n",
    "result.zip(file_path=\"./pd-ice-demo-archive.zip\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c0540819-f896-481a-b470-b9d53a243b0a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Archive:  pd-ice-demo-archive.zip\n",
      "  Length      Date    Time    Name\n",
      "---------  ---------- -----   ----\n",
      "    11434  2023-03-12 23:27   explainer_h2o_sonar_explainers_pd_ice_explainer_PdIceExplainer_745668cc-bbc8-42be-9b39-47487112a885/result_descriptor.json\n",
      "   881024  2023-03-12 23:27   explainer_h2o_sonar_explainers_pd_ice_explainer_PdIceExplainer_745668cc-bbc8-42be-9b39-47487112a885/work/h2o_sonar-ice-dai-model-8.jay\n",
      "   160272  2023-03-12 23:27   explainer_h2o_sonar_explainers_pd_ice_explainer_PdIceExplainer_745668cc-bbc8-42be-9b39-47487112a885/work/h2o_sonar-ice-dai-model-3.jay\n",
      "    13658  2023-03-12 23:27   explainer_h2o_sonar_explainers_pd_ice_explainer_PdIceExplainer_745668cc-bbc8-42be-9b39-47487112a885/work/h2o_sonar-pd-dai-model.json\n",
      "  1601936  2023-03-12 23:27   explainer_h2o_sonar_explainers_pd_ice_explainer_PdIceExplainer_745668cc-bbc8-42be-9b39-47487112a885/work/h2o_sonar-ice-dai-model-1.jay\n",
      "     2349  2023-03-12 23:27   explainer_h2o_sonar_explainers_pd_ice_explainer_PdIceExplainer_745668cc-bbc8-42be-9b39-47487112a885/work/h2o_sonar-ice-dai-model.json\n",
      "   320440  2023-03-12 23:27   explainer_h2o_sonar_explainers_pd_ice_explainer_PdIceExplainer_745668cc-bbc8-42be-9b39-47487112a885/work/h2o_sonar-ice-dai-model-5.jay\n",
      "   881024  2023-03-12 23:27   explainer_h2o_sonar_explainers_pd_ice_explainer_PdIceExplainer_745668cc-bbc8-42be-9b39-47487112a885/work/h2o_sonar-ice-dai-model-9.jay\n",
      "   560688  2023-03-12 23:27   explainer_h2o_sonar_explainers_pd_ice_explainer_PdIceExplainer_745668cc-bbc8-42be-9b39-47487112a885/work/h2o_sonar-ice-dai-model-4.jay\n",
      "  1601944  2023-03-12 23:27   explainer_h2o_sonar_explainers_pd_ice_explainer_PdIceExplainer_745668cc-bbc8-42be-9b39-47487112a885/work/h2o_sonar-ice-dai-model-2.jay\n",
      "   881024  2023-03-12 23:27   explainer_h2o_sonar_explainers_pd_ice_explainer_PdIceExplainer_745668cc-bbc8-42be-9b39-47487112a885/work/h2o_sonar-ice-dai-model-10.jay\n",
      "    80184  2023-03-12 23:27   explainer_h2o_sonar_explainers_pd_ice_explainer_PdIceExplainer_745668cc-bbc8-42be-9b39-47487112a885/work/mli_dataset_y_hat.jay\n",
      "  1601784  2023-03-12 23:27   explainer_h2o_sonar_explainers_pd_ice_explainer_PdIceExplainer_745668cc-bbc8-42be-9b39-47487112a885/work/h2o_sonar-ice-dai-model-6.jay\n",
      "   881024  2023-03-12 23:27   explainer_h2o_sonar_explainers_pd_ice_explainer_PdIceExplainer_745668cc-bbc8-42be-9b39-47487112a885/work/h2o_sonar-ice-dai-model-7.jay\n",
      "      110  2023-03-12 23:27   explainer_h2o_sonar_explainers_pd_ice_explainer_PdIceExplainer_745668cc-bbc8-42be-9b39-47487112a885/global_html_fragment/text_html.meta\n",
      "    21396  2023-03-12 23:27   explainer_h2o_sonar_explainers_pd_ice_explainer_PdIceExplainer_745668cc-bbc8-42be-9b39-47487112a885/global_html_fragment/text_html/pd-feature-2-class-0.png\n",
      "    12565  2023-03-12 23:27   explainer_h2o_sonar_explainers_pd_ice_explainer_PdIceExplainer_745668cc-bbc8-42be-9b39-47487112a885/global_html_fragment/text_html/pd-feature-5-class-0.png\n",
      "    14256  2023-03-12 23:27   explainer_h2o_sonar_explainers_pd_ice_explainer_PdIceExplainer_745668cc-bbc8-42be-9b39-47487112a885/global_html_fragment/text_html/pd-feature-1-class-0.png\n",
      "    12616  2023-03-12 23:27   explainer_h2o_sonar_explainers_pd_ice_explainer_PdIceExplainer_745668cc-bbc8-42be-9b39-47487112a885/global_html_fragment/text_html/pd-feature-8-class-0.png\n",
      "    19038  2023-03-12 23:27   explainer_h2o_sonar_explainers_pd_ice_explainer_PdIceExplainer_745668cc-bbc8-42be-9b39-47487112a885/global_html_fragment/text_html/pd-feature-3-class-0.png\n",
      "     4027  2023-03-12 23:27   explainer_h2o_sonar_explainers_pd_ice_explainer_PdIceExplainer_745668cc-bbc8-42be-9b39-47487112a885/global_html_fragment/text_html/explanation.html\n",
      "    14358  2023-03-12 23:27   explainer_h2o_sonar_explainers_pd_ice_explainer_PdIceExplainer_745668cc-bbc8-42be-9b39-47487112a885/global_html_fragment/text_html/pd-feature-0-class-0.png\n",
      "    12571  2023-03-12 23:27   explainer_h2o_sonar_explainers_pd_ice_explainer_PdIceExplainer_745668cc-bbc8-42be-9b39-47487112a885/global_html_fragment/text_html/pd-feature-7-class-0.png\n",
      "    13577  2023-03-12 23:27   explainer_h2o_sonar_explainers_pd_ice_explainer_PdIceExplainer_745668cc-bbc8-42be-9b39-47487112a885/global_html_fragment/text_html/pd-feature-6-class-0.png\n",
      "    18249  2023-03-12 23:27   explainer_h2o_sonar_explainers_pd_ice_explainer_PdIceExplainer_745668cc-bbc8-42be-9b39-47487112a885/global_html_fragment/text_html/pd-feature-9-class-0.png\n",
      "    13688  2023-03-12 23:27   explainer_h2o_sonar_explainers_pd_ice_explainer_PdIceExplainer_745668cc-bbc8-42be-9b39-47487112a885/global_html_fragment/text_html/pd-feature-4-class-0.png\n",
      "      165  2023-03-12 23:27   explainer_h2o_sonar_explainers_pd_ice_explainer_PdIceExplainer_745668cc-bbc8-42be-9b39-47487112a885/local_individual_conditional_explanation/application_vnd_h2oai_json_datatable_jay.meta\n",
      "   881024  2023-03-12 23:27   explainer_h2o_sonar_explainers_pd_ice_explainer_PdIceExplainer_745668cc-bbc8-42be-9b39-47487112a885/local_individual_conditional_explanation/application_vnd_h2oai_json_datatable_jay/ice_feature_8_class_0.jay\n",
      "   881024  2023-03-12 23:27   explainer_h2o_sonar_explainers_pd_ice_explainer_PdIceExplainer_745668cc-bbc8-42be-9b39-47487112a885/local_individual_conditional_explanation/application_vnd_h2oai_json_datatable_jay/ice_feature_6_class_0.jay\n",
      "  1601936  2023-03-12 23:27   explainer_h2o_sonar_explainers_pd_ice_explainer_PdIceExplainer_745668cc-bbc8-42be-9b39-47487112a885/local_individual_conditional_explanation/application_vnd_h2oai_json_datatable_jay/ice_feature_0_class_0.jay\n",
      "  1601944  2023-03-12 23:27   explainer_h2o_sonar_explainers_pd_ice_explainer_PdIceExplainer_745668cc-bbc8-42be-9b39-47487112a885/local_individual_conditional_explanation/application_vnd_h2oai_json_datatable_jay/ice_feature_1_class_0.jay\n",
      "  1601784  2023-03-12 23:27   explainer_h2o_sonar_explainers_pd_ice_explainer_PdIceExplainer_745668cc-bbc8-42be-9b39-47487112a885/local_individual_conditional_explanation/application_vnd_h2oai_json_datatable_jay/ice_feature_5_class_0.jay\n",
      "   560688  2023-03-12 23:27   explainer_h2o_sonar_explainers_pd_ice_explainer_PdIceExplainer_745668cc-bbc8-42be-9b39-47487112a885/local_individual_conditional_explanation/application_vnd_h2oai_json_datatable_jay/ice_feature_3_class_0.jay\n",
      "   160272  2023-03-12 23:27   explainer_h2o_sonar_explainers_pd_ice_explainer_PdIceExplainer_745668cc-bbc8-42be-9b39-47487112a885/local_individual_conditional_explanation/application_vnd_h2oai_json_datatable_jay/ice_feature_2_class_0.jay\n",
      "   320440  2023-03-12 23:27   explainer_h2o_sonar_explainers_pd_ice_explainer_PdIceExplainer_745668cc-bbc8-42be-9b39-47487112a885/local_individual_conditional_explanation/application_vnd_h2oai_json_datatable_jay/ice_feature_4_class_0.jay\n",
      "     7096  2023-03-12 23:27   explainer_h2o_sonar_explainers_pd_ice_explainer_PdIceExplainer_745668cc-bbc8-42be-9b39-47487112a885/local_individual_conditional_explanation/application_vnd_h2oai_json_datatable_jay/explanation.json\n",
      "   881024  2023-03-12 23:27   explainer_h2o_sonar_explainers_pd_ice_explainer_PdIceExplainer_745668cc-bbc8-42be-9b39-47487112a885/local_individual_conditional_explanation/application_vnd_h2oai_json_datatable_jay/ice_feature_9_class_0.jay\n",
      "    80184  2023-03-12 23:27   explainer_h2o_sonar_explainers_pd_ice_explainer_PdIceExplainer_745668cc-bbc8-42be-9b39-47487112a885/local_individual_conditional_explanation/application_vnd_h2oai_json_datatable_jay/y_hat.jay\n",
      "   881024  2023-03-12 23:27   explainer_h2o_sonar_explainers_pd_ice_explainer_PdIceExplainer_745668cc-bbc8-42be-9b39-47487112a885/local_individual_conditional_explanation/application_vnd_h2oai_json_datatable_jay/ice_feature_7_class_0.jay\n",
      "      151  2023-03-12 23:27   explainer_h2o_sonar_explainers_pd_ice_explainer_PdIceExplainer_745668cc-bbc8-42be-9b39-47487112a885/global_partial_dependence/application_json.meta\n",
      "      924  2023-03-12 23:27   explainer_h2o_sonar_explainers_pd_ice_explainer_PdIceExplainer_745668cc-bbc8-42be-9b39-47487112a885/global_partial_dependence/application_json/pd_feature_3_class_0.json\n",
      "     2751  2023-03-12 23:27   explainer_h2o_sonar_explainers_pd_ice_explainer_PdIceExplainer_745668cc-bbc8-42be-9b39-47487112a885/global_partial_dependence/application_json/pd_feature_1_class_0.json\n",
      "     1412  2023-03-12 23:27   explainer_h2o_sonar_explainers_pd_ice_explainer_PdIceExplainer_745668cc-bbc8-42be-9b39-47487112a885/global_partial_dependence/application_json/pd_feature_9_class_0.json\n",
      "     2660  2023-03-12 23:27   explainer_h2o_sonar_explainers_pd_ice_explainer_PdIceExplainer_745668cc-bbc8-42be-9b39-47487112a885/global_partial_dependence/application_json/pd_feature_0_class_0.json\n",
      "      563  2023-03-12 23:27   explainer_h2o_sonar_explainers_pd_ice_explainer_PdIceExplainer_745668cc-bbc8-42be-9b39-47487112a885/global_partial_dependence/application_json/pd_feature_4_class_0.json\n",
      "     1414  2023-03-12 23:27   explainer_h2o_sonar_explainers_pd_ice_explainer_PdIceExplainer_745668cc-bbc8-42be-9b39-47487112a885/global_partial_dependence/application_json/pd_feature_6_class_0.json\n",
      "     2557  2023-03-12 23:27   explainer_h2o_sonar_explainers_pd_ice_explainer_PdIceExplainer_745668cc-bbc8-42be-9b39-47487112a885/global_partial_dependence/application_json/pd_feature_5_class_0.json\n",
      "     1413  2023-03-12 23:27   explainer_h2o_sonar_explainers_pd_ice_explainer_PdIceExplainer_745668cc-bbc8-42be-9b39-47487112a885/global_partial_dependence/application_json/pd_feature_8_class_0.json\n",
      "     1413  2023-03-12 23:27   explainer_h2o_sonar_explainers_pd_ice_explainer_PdIceExplainer_745668cc-bbc8-42be-9b39-47487112a885/global_partial_dependence/application_json/pd_feature_7_class_0.json\n",
      "     7156  2023-03-12 23:27   explainer_h2o_sonar_explainers_pd_ice_explainer_PdIceExplainer_745668cc-bbc8-42be-9b39-47487112a885/global_partial_dependence/application_json/explanation.json\n",
      "      322  2023-03-12 23:27   explainer_h2o_sonar_explainers_pd_ice_explainer_PdIceExplainer_745668cc-bbc8-42be-9b39-47487112a885/global_partial_dependence/application_json/pd_feature_2_class_0.json\n",
      "        2  2023-03-12 23:27   explainer_h2o_sonar_explainers_pd_ice_explainer_PdIceExplainer_745668cc-bbc8-42be-9b39-47487112a885/model_problems/problems_and_actions.json\n",
      "     5746  2023-03-12 23:27   explainer_h2o_sonar_explainers_pd_ice_explainer_PdIceExplainer_745668cc-bbc8-42be-9b39-47487112a885/log/explainer_run_745668cc-bbc8-42be-9b39-47487112a885.log\n",
      "---------                     -------\n",
      " 19122325                     53 files\n"
     ]
    }
   ],
   "source": [
    "!unzip -l pd-ice-demo-archive.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "584ad6ad-9989-46e4-9c65-aae4bff14df6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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