{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6c4d4faf-ab84-4a72-a80e-535b211747cd",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Friedman H-statistic Explainer Demo\n",
    "\n",
    "This example demonstrates run [Friedman's H-statistic](https://christophm.github.io/interpretable-ml-book/interaction.html#theory-friedmans-h-statistic) explainer using\n",
    "the H2O Sonar library and retrieve the data and plot with original features interactions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "69f414e3-bc88-478b-bed5-890352b1041a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "import os\n",
    "import logging\n",
    "\n",
    "import datatable\n",
    "import daimojo\n",
    "import webbrowser\n",
    "\n",
    "from h2o_sonar import interpret\n",
    "from h2o_sonar.lib.api import commons\n",
    "from h2o_sonar.lib.api import explainers\n",
    "from h2o_sonar.explainers import friedman_h_statistic_explainer as explainer\n",
    "from h2o_sonar.lib.api.models import ModelApi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bbe0ca51",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'id': 'h2o_sonar.explainers.friedman_h_statistic_explainer.FriedmanHStatisticExplainer',\n",
       " 'name': 'FriedmanHStatisticExplainer',\n",
       " 'display_name': \"Friedman's H-statistic\",\n",
       " 'description': \"Friedman's H-statistic describes the amount of variance explained by the feature *pair*. It's expressed with a graph where most important original features are nodes and the interaction scores are edges.\\nWhen features interact with each other, then the influence of the features on the prediction does not have be additive, but more complex. For instance the contribution might be greater than the sum of contributions.\\nFriedman's H-statistic calculation is computationally intensive and typically requires long time to finish - calculation duration grows with the number of features and bins.\",\n",
       " 'model_types': ['iid'],\n",
       " 'can_explain': ['regression', 'binomial'],\n",
       " 'explanation_scopes': ['global_scope'],\n",
       " 'explanations': [{'explanation_type': 'global-report',\n",
       "   'name': 'ReportExplanation',\n",
       "   'category': None,\n",
       "   'scope': 'global',\n",
       "   'has_local': None,\n",
       "   'formats': []},\n",
       "  {'explanation_type': 'global-feature-importance',\n",
       "   'name': 'GlobalFeatImpExplanation',\n",
       "   'category': None,\n",
       "   'scope': 'global',\n",
       "   'has_local': None,\n",
       "   'formats': []}],\n",
       " 'parameters': [{'name': 'features_number',\n",
       "   'description': 'Number of features for which to calculate H-Statistic.',\n",
       "   'comment': '',\n",
       "   'type': 'int',\n",
       "   'val': 4,\n",
       "   'predefined': [],\n",
       "   'tags': [],\n",
       "   'min_': 2.0,\n",
       "   'max_': 0.0,\n",
       "   'category': ''},\n",
       "  {'name': 'grid_resolution',\n",
       "   'description': 'Observations per bin (number of equally spaced points used to create bins).',\n",
       "   'comment': '',\n",
       "   'type': 'int',\n",
       "   'val': 3,\n",
       "   'predefined': [],\n",
       "   'tags': [],\n",
       "   'min_': 1.0,\n",
       "   'max_': 0.0,\n",
       "   'category': ''},\n",
       "  {'name': 'features',\n",
       "   'description': 'Feature list - at least 2 features must be selected.',\n",
       "   'comment': '',\n",
       "   'type': 'multilist',\n",
       "   'val': None,\n",
       "   'predefined': [],\n",
       "   'tags': ['SOURCE_DATASET_COLUMN_NAMES'],\n",
       "   'min_': 0.0,\n",
       "   'max_': 0.0,\n",
       "   'category': ''},\n",
       "  {'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",
       " 'keywords': ['explains-feature-behavior', 'h2o-sonar']}"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# explainer description\n",
    "interpret.describe_explainer(explainer.FriedmanHStatisticExplainer)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90d401d2-14cd-4686-982f-3cac9e9f5eb7",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Interpretation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "15201d08-873b-45c3-82ad-052266f0526c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# dataset\n",
    "dataset_path = \"../../data/pd_ice_creditcard_10_rows.csv\"\n",
    "\n",
    "# Driverless AI MOJO model\n",
    "mojo_path = \"../../data/models/creditcard-regression.mojo\"\n",
    "target_col = \"LIMIT_BAL\"\n",
    "mojo_model = daimojo.model(mojo_path)\n",
    "model = ModelApi().create_model(\n",
    "    model_src=mojo_model,\n",
    "    target_col=target_col,\n",
    "    used_features=list(mojo_model.feature_names),\n",
    ")\n",
    "\n",
    "# scikit-learn model\n",
    "# mojo_path = \"../../data/models/creditcard-binomial-sklearn-gbm.pkl\"\n",
    "# target_col = \"default payment next month\"\n",
    "\n",
    "# results\n",
    "results_location = \"./results\"\n",
    "os.makedirs(results_location, exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0ba8f0aa-2e0e-4a0a-93ab-77ce9e968fa0",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/srasaratnam/projects/h2o-sonar/venv/lib/python3.8/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n",
      "2023-03-12 23:11:30,987 - h2o_sonar - DEBUG - ICE strategy: MANY predict method invocations\n",
      "2023-03-12 23:11:31,017 - h2o_sonar - DEBUG - ICE strategy: MANY predict method invocations\n",
      "2023-03-12 23:11:31,037 - h2o_sonar - DEBUG - ICE strategy: MANY predict method invocations\n",
      "2023-03-12 23:11:31,064 - h2o_sonar - DEBUG - ICE strategy: MANY predict method invocations\n",
      "2023-03-12 23:11:31,099 - h2o_sonar - DEBUG - ICE strategy: MANY predict method invocations\n",
      "2023-03-12 23:11:31,137 - h2o_sonar - DEBUG - ICE strategy: MANY predict method invocations\n",
      "2023-03-12 23:11:31,165 - h2o_sonar - DEBUG - ICE strategy: 1 predict method invocation\n",
      "2023-03-12 23:11:31,180 - h2o_sonar - DEBUG - ICE strategy: 1 predict method invocation\n",
      "2023-03-12 23:11:31,206 - h2o_sonar - DEBUG - ICE strategy: 1 predict method invocation\n",
      "2023-03-12 23:11:31,220 - h2o_sonar - DEBUG - ICE strategy: 1 predict method invocation\n"
     ]
    },
    {
     "data": {
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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": [
    "interpretation = interpret.run_interpretation(\n",
    "    dataset=dataset_path,\n",
    "    model=model,\n",
    "    target_col=target_col,\n",
    "    results_location=results_location,\n",
    "    explainers=[explainer.FriedmanHStatisticExplainer.explainer_id()],\n",
    "    log_level=logging.INFO,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff9df4be-d4da-44db-a479-7d8d7f45c29d",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Explainer Result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "25556ca5-8239-4201-8a23-1ace2b3a46d4",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# retrieve the result\n",
    "result = interpretation.get_explainer_result(\n",
    "    explainer.FriedmanHStatisticExplainer.explainer_id()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a3306cca-478d-4f5b-841a-6d305e247e13",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 6,
     "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": 7,
   "id": "510ff1d4-e248-4f04-bf72-9bf9b7973855",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'id': 'h2o_sonar.explainers.friedman_h_statistic_explainer.FriedmanHStatisticExplainer',\n",
       " 'name': 'FriedmanHStatisticExplainer',\n",
       " 'display_name': \"Friedman's H-statistic\",\n",
       " 'description': \"Friedman's H-statistic describes the amount of variance explained by the feature *pair*. It's expressed with a graph where most important original features are nodes and the interaction scores are edges.\\nWhen features interact with each other, then the influence of the features on the prediction does not have be additive, but more complex. For instance the contribution might be greater than the sum of contributions.\\nFriedman's H-statistic calculation is computationally intensive and typically requires long time to finish - calculation duration grows with the number of features and bins.\",\n",
       " 'model_types': ['iid'],\n",
       " 'can_explain': ['regression', 'binomial'],\n",
       " 'explanation_scopes': ['global_scope'],\n",
       " 'explanations': [{'explanation_type': 'global-feature-importance',\n",
       "   'name': \"Friedman's H-statistic\",\n",
       "   'category': 'DAI MODEL',\n",
       "   'scope': 'global',\n",
       "   'has_local': None,\n",
       "   'formats': ['application/vnd.h2oai.json+datatable.jay',\n",
       "    'application/vnd.h2oai.json+csv',\n",
       "    'application/json']},\n",
       "  {'explanation_type': 'global-report',\n",
       "   'name': \"Friedman's H-statistic report\",\n",
       "   'category': 'DAI MODEL',\n",
       "   'scope': 'global',\n",
       "   'has_local': None,\n",
       "   'formats': ['text/markdown']},\n",
       "  {'explanation_type': 'global-html-fragment',\n",
       "   'name': \"Friedman's H-statistic\",\n",
       "   'category': 'DAI MODEL',\n",
       "   'scope': 'global',\n",
       "   'has_local': None,\n",
       "   'formats': ['text/html']}],\n",
       " 'parameters': [{'name': 'features_number',\n",
       "   'description': 'Number of features for which to calculate H-Statistic.',\n",
       "   'comment': '',\n",
       "   'type': 'int',\n",
       "   'val': 4,\n",
       "   'predefined': [],\n",
       "   'tags': [],\n",
       "   'min_': 2.0,\n",
       "   'max_': 0.0,\n",
       "   'category': ''},\n",
       "  {'name': 'grid_resolution',\n",
       "   'description': 'Observations per bin (number of equally spaced points used to create bins).',\n",
       "   'comment': '',\n",
       "   'type': 'int',\n",
       "   'val': 3,\n",
       "   'predefined': [],\n",
       "   'tags': [],\n",
       "   'min_': 1.0,\n",
       "   'max_': 0.0,\n",
       "   'category': ''},\n",
       "  {'name': 'features',\n",
       "   'description': 'Feature list - at least 2 features must be selected.',\n",
       "   'comment': '',\n",
       "   'type': 'multilist',\n",
       "   'val': None,\n",
       "   'predefined': [],\n",
       "   'tags': ['SOURCE_DATASET_COLUMN_NAMES'],\n",
       "   'min_': 0.0,\n",
       "   'max_': 0.0,\n",
       "   'category': ''},\n",
       "  {'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",
       " 'keywords': ['explains-feature-behavior', 'h2o-sonar']}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# summary\n",
    "result.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "95d885c8-4435-431f-bf5e-741a9f1cb023",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'features_number': 4,\n",
       " 'grid_resolution': 3,\n",
       " 'features': None,\n",
       " 'sample_size': 25000}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Parameters\n",
    "result.params()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "490d132b-b7e2-48a2-8ec4-dbd71886edf9",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Display Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2aa6274e-79d5-49b1-b29a-2263db5cb8a8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class='datatable'>\n",
       "  <table class='frame'>\n",
       "  <thead>\n",
       "    <tr class='colnames'><td class='row_index'></td><th>feature</th><th>interactions</th></tr>\n",
       "    <tr class='coltypes'><td class='row_index'></td><td class='str' title='str32'>&#x25AA;&#x25AA;&#x25AA;&#x25AA;</td><td class='float' title='float64'>&#x25AA;&#x25AA;&#x25AA;&#x25AA;&#x25AA;&#x25AA;&#x25AA;&#x25AA;</td></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><td class='row_index'>0</td><td>'SEX' and 'MARRIAGE'</td><td>6.16078e-12</td></tr>\n",
       "    <tr><td class='row_index'>1</td><td>'SEX' and 'EDUCATION'</td><td>7.55627e-13</td></tr>\n",
       "    <tr><td class='row_index'>2</td><td>'EDUCATION' and 'MARRIAGE'</td><td>5.37272e-13</td></tr>\n",
       "    <tr><td class='row_index'>3</td><td>'EDUCATION' and 'AGE'</td><td>2.65952e-13</td></tr>\n",
       "    <tr><td class='row_index'>4</td><td>'MARRIAGE' and 'AGE'</td><td>2.57216e-13</td></tr>\n",
       "    <tr><td class='row_index'>5</td><td>'SEX' and 'AGE'</td><td>1.00249e-13</td></tr>\n",
       "  </tbody>\n",
       "  </table>\n",
       "  <div class='footer'>\n",
       "    <div class='frame_dimensions'>6 rows &times; 2 columns</div>\n",
       "  </div>\n",
       "</div>\n"
      ],
      "text/plain": [
       "<Frame#7fd64bc7d780 6x2>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.data()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df8a083b-3b88-4349-bb63-28551c24cc4f",
   "metadata": {},
   "source": [
    "### Plot Feature Interactions Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5a9d8262-574e-4073-a282-567d4fd1209c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a493b092-6236-419f-906c-16d52c47674f",
   "metadata": {},
   "source": [
    "### Save Explainer Log and Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7c638a2c-6b01-4228-aa0f-93fd8dd7feab",
   "metadata": {},
   "outputs": [],
   "source": [
    "# save the explainer log\n",
    "log_file_path = \"./feature-interactions-demo.log\"\n",
    "result.log(path=log_file_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f5d91240-09ff-4893-b652-b0259a8f222a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2023-03-12 23:11:30,982 INFO Friedman's H-statistic 48ac664c-3ef8-4494-baa3-49341aa2c100/7490c8b6-3ab0-4b7b-b401-e1efe8929fe5: getting features list, importance and metadata...\n",
      "2023-03-12 23:11:30,982 INFO Friedman's H-statistic 48ac664c-3ef8-4494-baa3-49341aa2c100/7490c8b6-3ab0-4b7b-b401-e1efe8929fe5 all most important model features: ['SEX', 'EDUCATION', 'MARRIAGE', 'AGE', 'PAY_1', '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 23:11:30,982 INFO Friedman's H-statistic 48ac664c-3ef8-4494-baa3-49341aa2c100/7490c8b6-3ab0-4b7b-b401-e1efe8929fe5: features used by model: ['SEX', 'EDUCATION', 'MARRIAGE', 'AGE', 'PAY_1', '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 23:11:30,982 INFO Friedman's H-statistic 48ac664c-3ef8-4494-baa3-49341aa2c100/7490c8b6-3ab0-4b7b-b401-e1efe8929fe5: final features list: ['SEX', 'EDUCATION', 'MARRIAGE', 'AGE']\n"
     ]
    }
   ],
   "source": [
    "!cat $log_file_path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "da4e2b28-96d7-440e-bfea-41cb694a52d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# save the explainer data\n",
    "result.zip(file_path=\"./feature-interactions-demo-archive.zip\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c0540819-f896-481a-b470-b9d53a243b0a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Archive:  feature-interactions-demo-archive.zip\n",
      "  Length      Date    Time    Name\n",
      "---------  ---------- -----   ----\n",
      "     3573  2023-03-12 23:11   explainer_h2o_sonar_explainers_friedman_h_statistic_explainer_FriedmanHStatisticExplainer_7490c8b6-3ab0-4b7b-b401-e1efe8929fe5/result_descriptor.json\n",
      "      122  2023-03-12 23:11   explainer_h2o_sonar_explainers_friedman_h_statistic_explainer_FriedmanHStatisticExplainer_7490c8b6-3ab0-4b7b-b401-e1efe8929fe5/global_report/text_markdown.meta\n",
      "      529  2023-03-12 23:11   explainer_h2o_sonar_explainers_friedman_h_statistic_explainer_FriedmanHStatisticExplainer_7490c8b6-3ab0-4b7b-b401-e1efe8929fe5/global_report/text_markdown/explanation.md\n",
      "   197234  2023-03-12 23:11   explainer_h2o_sonar_explainers_friedman_h_statistic_explainer_FriedmanHStatisticExplainer_7490c8b6-3ab0-4b7b-b401-e1efe8929fe5/global_report/text_markdown/network-chart.png\n",
      "   197234  2023-03-12 23:11   explainer_h2o_sonar_explainers_friedman_h_statistic_explainer_FriedmanHStatisticExplainer_7490c8b6-3ab0-4b7b-b401-e1efe8929fe5/work/network-chart.png\n",
      "      529  2023-03-12 23:11   explainer_h2o_sonar_explainers_friedman_h_statistic_explainer_FriedmanHStatisticExplainer_7490c8b6-3ab0-4b7b-b401-e1efe8929fe5/work/report.md\n",
      "      110  2023-03-12 23:11   explainer_h2o_sonar_explainers_friedman_h_statistic_explainer_FriedmanHStatisticExplainer_7490c8b6-3ab0-4b7b-b401-e1efe8929fe5/global_html_fragment/text_html.meta\n",
      "      399  2023-03-12 23:11   explainer_h2o_sonar_explainers_friedman_h_statistic_explainer_FriedmanHStatisticExplainer_7490c8b6-3ab0-4b7b-b401-e1efe8929fe5/global_html_fragment/text_html/explanation.html\n",
      "    26644  2023-03-12 23:11   explainer_h2o_sonar_explainers_friedman_h_statistic_explainer_FriedmanHStatisticExplainer_7490c8b6-3ab0-4b7b-b401-e1efe8929fe5/global_html_fragment/text_html/fi-class-0.png\n",
      "        2  2023-03-12 23:11   explainer_h2o_sonar_explainers_friedman_h_statistic_explainer_FriedmanHStatisticExplainer_7490c8b6-3ab0-4b7b-b401-e1efe8929fe5/model_problems/problems_and_actions.json\n",
      "     1165  2023-03-12 23:11   explainer_h2o_sonar_explainers_friedman_h_statistic_explainer_FriedmanHStatisticExplainer_7490c8b6-3ab0-4b7b-b401-e1efe8929fe5/log/explainer_run_7490c8b6-3ab0-4b7b-b401-e1efe8929fe5.log\n",
      "      143  2023-03-12 23:11   explainer_h2o_sonar_explainers_friedman_h_statistic_explainer_FriedmanHStatisticExplainer_7490c8b6-3ab0-4b7b-b401-e1efe8929fe5/global_feature_importance/application_json.meta\n",
      "      163  2023-03-12 23:11   explainer_h2o_sonar_explainers_friedman_h_statistic_explainer_FriedmanHStatisticExplainer_7490c8b6-3ab0-4b7b-b401-e1efe8929fe5/global_feature_importance/application_vnd_h2oai_json_csv.meta\n",
      "      185  2023-03-12 23:11   explainer_h2o_sonar_explainers_friedman_h_statistic_explainer_FriedmanHStatisticExplainer_7490c8b6-3ab0-4b7b-b401-e1efe8929fe5/global_feature_importance/application_vnd_h2oai_json_datatable_jay.meta\n",
      "      632  2023-03-12 23:11   explainer_h2o_sonar_explainers_friedman_h_statistic_explainer_FriedmanHStatisticExplainer_7490c8b6-3ab0-4b7b-b401-e1efe8929fe5/global_feature_importance/application_vnd_h2oai_json_datatable_jay/feature_importance_class_0.jay\n",
      "      808  2023-03-12 23:11   explainer_h2o_sonar_explainers_friedman_h_statistic_explainer_FriedmanHStatisticExplainer_7490c8b6-3ab0-4b7b-b401-e1efe8929fe5/global_feature_importance/application_vnd_h2oai_json_datatable_jay/explanation.json\n",
      "      530  2023-03-12 23:11   explainer_h2o_sonar_explainers_friedman_h_statistic_explainer_FriedmanHStatisticExplainer_7490c8b6-3ab0-4b7b-b401-e1efe8929fe5/global_feature_importance/application_json/feature_importance_class_0.json\n",
      "      747  2023-03-12 23:11   explainer_h2o_sonar_explainers_friedman_h_statistic_explainer_FriedmanHStatisticExplainer_7490c8b6-3ab0-4b7b-b401-e1efe8929fe5/global_feature_importance/application_json/explanation.json\n",
      "      331  2023-03-12 23:11   explainer_h2o_sonar_explainers_friedman_h_statistic_explainer_FriedmanHStatisticExplainer_7490c8b6-3ab0-4b7b-b401-e1efe8929fe5/global_feature_importance/application_vnd_h2oai_json_csv/feature_importance_class_0.csv\n",
      "      746  2023-03-12 23:11   explainer_h2o_sonar_explainers_friedman_h_statistic_explainer_FriedmanHStatisticExplainer_7490c8b6-3ab0-4b7b-b401-e1efe8929fe5/global_feature_importance/application_vnd_h2oai_json_csv/explanation.json\n",
      "---------                     -------\n",
      "   431826                     20 files\n"
     ]
    }
   ],
   "source": [
    "!unzip -l feature-interactions-demo-archive.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "72ae2b2f-5817-4ccc-a7d0-3cbc70d3eaa5",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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