{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6c4d4faf-ab84-4a72-a80e-535b211747cd",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Partial Dependence for 2 Features Explainer Demo\n",
    "\n",
    "This example demonstrates how to interpret a model using the H2O Sonar library and retrieve the data and **partial dependence plot for 2 featues**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "69f414e3-bc88-478b-bed5-890352b1041a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      ]
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     "metadata": {},
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   ],
   "source": [
    "import logging\n",
    "import os\n",
    "\n",
    "import daimojo\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_2_features_explainer import PdFor2FeaturesExplainer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bef37207-bd90-4a60-a927-bbc2c54ab149",
   "metadata": {},
   "outputs": [],
   "source": [
    "# dataset\n",
    "dataset_path = \"../../data/predictive/creditcard.csv\"\n",
    "target_col = \"default payment next month\"\n",
    "\n",
    "# model\n",
    "mojo_path = \"../../data/predictive/models/creditcard-binomial.mojo\"\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",
    "# results\n",
    "results_location = \"./results\"\n",
    "os.makedirs(results_location, exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bbe0ca51",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'id': 'h2o_sonar.explainers.pd_2_features_explainer.PdFor2FeaturesExplainer',\n",
       " 'name': 'PdFor2FeaturesExplainer',\n",
       " 'display_name': 'Partial Dependence Plot for Two Features',\n",
       " 'tagline': 'PdFor2FeaturesExplainer.',\n",
       " 'description': 'Partial dependence for 2 features portrays the average\\nprediction behavior of a model across the domains of two input variables\\ni.e. interaction of feature tuples with the prediction. While PD for one feature\\nproduces 2D plot, PD for two features produces 3D plots. This explainer plots PD for\\ntwo features using heatmap, contour 3D or surface 3D.\\n',\n",
       " 'brief_description': 'PdFor2FeaturesExplainer.',\n",
       " 'model_types': ['iid', 'time_series'],\n",
       " 'can_explain': ['regression', 'binomial'],\n",
       " 'explanation_scopes': ['global_scope'],\n",
       " 'explanations': [{'explanation_type': 'global-partial-dependence',\n",
       "   'name': 'PartialDependenceExplanation',\n",
       "   'category': '',\n",
       "   'scope': 'global',\n",
       "   'has_local': '',\n",
       "   'formats': []}],\n",
       " 'keywords': ['is_slow'],\n",
       " 'parameters': [{'name': 'sample_size',\n",
       "   'description': 'Sample size for Partial Dependence Plot of 2 features.',\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.',\n",
       "   'comment': '',\n",
       "   'type': 'int',\n",
       "   'val': 3,\n",
       "   'predefined': [],\n",
       "   'tags': [],\n",
       "   'min_': 0.0,\n",
       "   'max_': 0.0,\n",
       "   'category': ''},\n",
       "  {'name': 'features',\n",
       "   'description': 'List of features from which to choose pairs to compute PD for two features.',\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': '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': 10,\n",
       "   'predefined': [],\n",
       "   'tags': [],\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': 'plot_type',\n",
       "   'description': 'Plot type.',\n",
       "   'comment': '',\n",
       "   'type': 'str',\n",
       "   'val': 'heatmap',\n",
       "   'predefined': ['heatmap', 'contour-3d', 'surface-3d'],\n",
       "   'tags': [],\n",
       "   'min_': 0.0,\n",
       "   'max_': 0.0,\n",
       "   'category': ''}],\n",
       " 'metrics_meta': []}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# explainer description\n",
    "interpret.describe_explainer(PdFor2FeaturesExplainer)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90d401d2-14cd-4686-982f-3cac9e9f5eb7",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Interpretation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0ba8f0aa-2e0e-4a0a-93ab-77ce9e968fa0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/user/h/mli/git/h2o-sonar-FLOSS/.venv/lib/python3.11/site-packages/ragas/metrics/__init__.py:1: LangChainDeprecationWarning: As of langchain-core 0.3.0, LangChain uses pydantic v2 internally. The langchain_core.pydantic_v1 module was a compatibility shim for pydantic v1, and should no longer be used. Please update the code to import from Pydantic directly.\n",
      "\n",
      "For example, replace imports like: `from langchain_core.pydantic_v1 import BaseModel`\n",
      "with: `from pydantic import BaseModel`\n",
      "or the v1 compatibility namespace if you are working in a code base that has not been fully upgraded to pydantic 2 yet. \tfrom pydantic.v1 import BaseModel\n",
      "\n",
      "  from ragas.metrics._answer_correctness import AnswerCorrectness, answer_correctness\n",
      "/home/user/h/mli/git/h2o-sonar-FLOSS/.venv/lib/python3.11/site-packages/ragas/metrics/__init__.py:4: LangChainDeprecationWarning: As of langchain-core 0.3.0, LangChain uses pydantic v2 internally. The langchain.pydantic_v1 module was a compatibility shim for pydantic v1, and should no longer be used. Please update the code to import from Pydantic directly.\n",
      "\n",
      "For example, replace imports like: `from langchain.pydantic_v1 import BaseModel`\n",
      "with: `from pydantic import BaseModel`\n",
      "or the v1 compatibility namespace if you are working in a code base that has not been fully upgraded to pydantic 2 yet. \tfrom pydantic.v1 import BaseModel\n",
      "\n",
      "  from ragas.metrics._context_entities_recall import (\n",
      "set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n"
     ]
    }
   ],
   "source": [
    "interpretation = interpret.run_interpretation(\n",
    "    dataset=dataset_path,\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=PdFor2FeaturesExplainer.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": 5,
   "id": "25556ca5-8239-4201-8a23-1ace2b3a46d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# retrieve the result\n",
    "result = interpretation.get_explainer_result(PdFor2FeaturesExplainer.explainer_id())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "38c26ac9-df8e-480f-ab6c-c14b43860c5d",
   "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": "76c46623-6e24-4ac1-b6cc-66fc29b7ea0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'id': 'h2o_sonar.explainers.pd_2_features_explainer.PdFor2FeaturesExplainer',\n",
       " 'name': 'PdFor2FeaturesExplainer',\n",
       " 'display_name': 'Partial Dependence Plot for Two Features',\n",
       " 'tagline': 'PdFor2FeaturesExplainer.',\n",
       " 'description': 'Partial dependence for 2 features portrays the average\\nprediction behavior of a model across the domains of two input variables\\ni.e. interaction of feature tuples with the prediction. While PD for one feature\\nproduces 2D plot, PD for two features produces 3D plots. This explainer plots PD for\\ntwo features using heatmap, contour 3D or surface 3D.\\n',\n",
       " 'brief_description': 'PdFor2FeaturesExplainer.',\n",
       " 'model_types': ['iid', 'time_series'],\n",
       " 'can_explain': ['regression', 'binomial'],\n",
       " 'explanation_scopes': ['global_scope'],\n",
       " 'explanations': [{'explanation_type': 'global-report',\n",
       "   'name': 'Partial Dependence Plot for Two Features',\n",
       "   'category': 'DAI MODEL',\n",
       "   'scope': 'global',\n",
       "   'has_local': None,\n",
       "   'formats': ['text/markdown']},\n",
       "  {'explanation_type': 'global-3d-data',\n",
       "   'name': 'Partial Dependence Plot for Two Features',\n",
       "   'category': 'DAI MODEL',\n",
       "   'scope': 'global',\n",
       "   'has_local': None,\n",
       "   'formats': ['application/json', 'application/vnd.h2oai.json+csv']},\n",
       "  {'explanation_type': 'global-html-fragment',\n",
       "   'name': 'Partial Dependence Plot for Two Features',\n",
       "   'category': 'DAI MODEL',\n",
       "   'scope': 'global',\n",
       "   'has_local': None,\n",
       "   'formats': ['text/html']}],\n",
       " 'keywords': ['is_slow'],\n",
       " 'parameters': [{'name': 'sample_size',\n",
       "   'description': 'Sample size for Partial Dependence Plot of 2 features.',\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.',\n",
       "   'comment': '',\n",
       "   'type': 'int',\n",
       "   'val': 3,\n",
       "   'predefined': [],\n",
       "   'tags': [],\n",
       "   'min_': 0.0,\n",
       "   'max_': 0.0,\n",
       "   'category': ''},\n",
       "  {'name': 'features',\n",
       "   'description': 'List of features from which to choose pairs to compute PD for two features.',\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': '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': 10,\n",
       "   'predefined': [],\n",
       "   'tags': [],\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': 'plot_type',\n",
       "   'description': 'Plot type.',\n",
       "   'comment': '',\n",
       "   'type': 'str',\n",
       "   'val': 'heatmap',\n",
       "   'predefined': ['heatmap', 'contour-3d', 'surface-3d'],\n",
       "   'tags': [],\n",
       "   'min_': 0.0,\n",
       "   'max_': 0.0,\n",
       "   'category': ''}],\n",
       " 'metrics_meta': []}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# summary\n",
    "result.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e30e08f6-69b9-408f-8bd6-6dad14638694",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'sample_size': 25000,\n",
       " 'max_features': 3,\n",
       " 'features': None,\n",
       " 'grid_resolution': 10,\n",
       " 'oor_grid_resolution': 0,\n",
       " 'quantile-bin-grid-resolution': 0,\n",
       " 'plot_type': 'heatmap'}"
      ]
     },
     "execution_count": 8,
     "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": 9,
   "id": "2aa6274e-79d5-49b1-b29a-2263db5cb8a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'10000': {'6': 0.2552240192890167,\n",
       "  '5': 0.2552240192890167,\n",
       "  '4': 0.25266972184181213,\n",
       "  '3': 0.25266972184181213,\n",
       "  '2': 0.2251938432455063,\n",
       "  '1': 0.2251938432455063,\n",
       "  '0': 0.2251938432455063},\n",
       " '208000': {'6': 0.21158096194267273,\n",
       "  '5': 0.21158096194267273,\n",
       "  '4': 0.20898468792438507,\n",
       "  '3': 0.20898468792438507,\n",
       "  '2': 0.17519091069698334,\n",
       "  '1': 0.17519091069698334,\n",
       "  '0': 0.17519091069698334},\n",
       " '307000': {'6': 0.20750007033348083,\n",
       "  '5': 0.20750007033348083,\n",
       "  '4': 0.20484082400798798,\n",
       "  '3': 0.20484082400798798,\n",
       "  '2': 0.17081709206104279,\n",
       "  '1': 0.17081709206104279,\n",
       "  '0': 0.17081709206104279},\n",
       " '406000': {'6': 0.20645806193351746,\n",
       "  '5': 0.20645806193351746,\n",
       "  '4': 0.2037988305091858,\n",
       "  '3': 0.2037988305091858,\n",
       "  '2': 0.17081709206104279,\n",
       "  '1': 0.17081709206104279,\n",
       "  '0': 0.17081709206104279},\n",
       " '505000': {'6': 0.20645806193351746,\n",
       "  '5': 0.20645806193351746,\n",
       "  '4': 0.2037988305091858,\n",
       "  '3': 0.2037988305091858,\n",
       "  '2': 0.17081709206104279,\n",
       "  '1': 0.17081709206104279,\n",
       "  '0': 0.17081709206104279},\n",
       " '604000': {'6': 0.20645806193351746,\n",
       "  '5': 0.20645806193351746,\n",
       "  '4': 0.2037988305091858,\n",
       "  '3': 0.2037988305091858,\n",
       "  '2': 0.17081709206104279,\n",
       "  '1': 0.17081709206104279,\n",
       "  '0': 0.17081709206104279},\n",
       " '703000': {'6': 0.20645806193351746,\n",
       "  '5': 0.20645806193351746,\n",
       "  '4': 0.2037988305091858,\n",
       "  '3': 0.2037988305091858,\n",
       "  '2': 0.17081709206104279,\n",
       "  '1': 0.17081709206104279,\n",
       "  '0': 0.17081709206104279},\n",
       " '802000': {'6': 0.20645806193351746,\n",
       "  '5': 0.20645806193351746,\n",
       "  '4': 0.2037988305091858,\n",
       "  '3': 0.2037988305091858,\n",
       "  '2': 0.17081709206104279,\n",
       "  '1': 0.17081709206104279,\n",
       "  '0': 0.17081709206104279},\n",
       " '901000': {'6': 0.20645806193351746,\n",
       "  '5': 0.20645806193351746,\n",
       "  '4': 0.2037988305091858,\n",
       "  '3': 0.2037988305091858,\n",
       "  '2': 0.17081709206104279,\n",
       "  '1': 0.17081709206104279,\n",
       "  '0': 0.17081709206104279},\n",
       " '1000000': {'6': 0.20645806193351746,\n",
       "  '5': 0.20645806193351746,\n",
       "  '4': 0.2037988305091858,\n",
       "  '3': 0.2037988305091858,\n",
       "  '2': 0.17081709206104279,\n",
       "  '1': 0.17081709206104279,\n",
       "  '0': 0.17081709206104279}}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.data(feature_names=\"'LIMIT_BAL' and 'EDUCATION'\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df8a083b-3b88-4349-bb63-28551c24cc4f",
   "metadata": {},
   "source": [
    "### Plot PD Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5a9d8262-574e-4073-a282-567d4fd1209c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "result.plot(feature_names=\"'LIMIT_BAL' and 'EDUCATION'\")"
   ]
  },
  {
   "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",
    "result.log(path=\"./pd-2-features-demo.log\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f5d91240-09ff-4893-b652-b0259a8f222a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2026-01-29 16:11:38,082 INFO PD/ICE ea8d1116-5f12-4d93-b0c6-91453d509ffd/cba719fb-5061-483d-add5-79c4175d2b25 BEGIN calculation\n",
      "2026-01-29 16:11:38,082 INFO PD/ICE ea8d1116-5f12-4d93-b0c6-91453d509ffd/cba719fb-5061-483d-add5-79c4175d2b25 loading dataset\n",
      "2026-01-29 16:11:38,082 INFO PD/ICE ea8d1116-5f12-4d93-b0c6-91453d509ffd/cba719fb-5061-483d-add5-79c4175d2b25 loaded dataset has 10000 rows and 25 columns\n",
      "2026-01-29 16:11:38,082 INFO PD/ICE ea8d1116-5f12-4d93-b0c6-91453d509ffd/cba719fb-5061-483d-add5-79c4175d2b25 getting features list, importance and metadata\n",
      "2026-01-29 16:11:38,082 INFO PD/ICE ea8d1116-5f12-4d93-b0c6-91453d509ffd/cba719fb-5061-483d-add5-79c4175d2b25 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",
      "2026-01-29 16:11:38,082 INFO PD/ICE ea8d1116-5f12-4d93-b0c6-91453d509ffd/cba719fb-5061-483d-add5-79c4175d2b25 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",
      "2026-01-29 16:11:38,082 INFO PD/ICE ea8d1116-5f12-4d93-b0c6-91453d509ffd/cba719fb-5061-483d-add5-79c4175d2b25: calculating PD for features ['ID', 'LIMIT_BAL', 'SEX', 'EDUCATION', 'MARRIAGE', 'AGE', 'PAY_0', 'PAY_2', 'PAY_3', 'PAY_4']\n",
      "2026-01-29 16:11:38,082 INFO PD/ICE ea8d1116-5f12-4d93-b0c6-91453d509ffd/cba719fb-5061-483d-add5-79c4175d2b25 feature metadata: {'id': [], 'categorical': [], 'numeric': [], 'catnum': [], 'date': [], 'time': [], 'datetime': [], 'text': [], 'image': [], 'date-format': [], 'quantile-bin': {}}\n",
      "2026-01-29 16:11:38,082 INFO PD/ICE ea8d1116-5f12-4d93-b0c6-91453d509ffd/cba719fb-5061-483d-add5-79c4175d2b25 1 frame strategy: True\n",
      "2026-01-29 16:11:38,082 INFO PD/ICE ea8d1116-5f12-4d93-b0c6-91453d509ffd/cba719fb-5061-483d-add5-79c4175d2b25 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": 13,
   "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": 14,
   "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",
      "     4617  2026-01-29 16:22   explainer_h2o_sonar_explainers_pd_2_features_explainer_PdFor2FeaturesExplainer_c1f2af1f-eb25-42cc-9fad-d0290c557dce/result_descriptor.json\n",
      "        2  2026-01-29 16:22   explainer_h2o_sonar_explainers_pd_2_features_explainer_PdFor2FeaturesExplainer_c1f2af1f-eb25-42cc-9fad-d0290c557dce/problems/problems_and_actions.json\n",
      "      110  2026-01-29 16:22   explainer_h2o_sonar_explainers_pd_2_features_explainer_PdFor2FeaturesExplainer_c1f2af1f-eb25-42cc-9fad-d0290c557dce/global_html_fragment/text_html.meta\n",
      "    80253  2026-01-29 16:22   explainer_h2o_sonar_explainers_pd_2_features_explainer_PdFor2FeaturesExplainer_c1f2af1f-eb25-42cc-9fad-d0290c557dce/global_html_fragment/text_html/image-2.png\n",
      "     1247  2026-01-29 16:22   explainer_h2o_sonar_explainers_pd_2_features_explainer_PdFor2FeaturesExplainer_c1f2af1f-eb25-42cc-9fad-d0290c557dce/global_html_fragment/text_html/explanation.html\n",
      "    92250  2026-01-29 16:22   explainer_h2o_sonar_explainers_pd_2_features_explainer_PdFor2FeaturesExplainer_c1f2af1f-eb25-42cc-9fad-d0290c557dce/global_html_fragment/text_html/image-1.png\n",
      "    75680  2026-01-29 16:22   explainer_h2o_sonar_explainers_pd_2_features_explainer_PdFor2FeaturesExplainer_c1f2af1f-eb25-42cc-9fad-d0290c557dce/global_html_fragment/text_html/image-0.png\n",
      "      141  2026-01-29 16:22   explainer_h2o_sonar_explainers_pd_2_features_explainer_PdFor2FeaturesExplainer_c1f2af1f-eb25-42cc-9fad-d0290c557dce/global_3d_data/application_json.meta\n",
      "      161  2026-01-29 16:22   explainer_h2o_sonar_explainers_pd_2_features_explainer_PdFor2FeaturesExplainer_c1f2af1f-eb25-42cc-9fad-d0290c557dce/global_3d_data/application_vnd_h2oai_json_csv.meta\n",
      "     1503  2026-01-29 16:22   explainer_h2o_sonar_explainers_pd_2_features_explainer_PdFor2FeaturesExplainer_c1f2af1f-eb25-42cc-9fad-d0290c557dce/global_3d_data/application_json/explanation.json\n",
      "      498  2026-01-29 16:22   explainer_h2o_sonar_explainers_pd_2_features_explainer_PdFor2FeaturesExplainer_c1f2af1f-eb25-42cc-9fad-d0290c557dce/global_3d_data/application_json/data3d_feature_2_class_0.json\n",
      "     2583  2026-01-29 16:22   explainer_h2o_sonar_explainers_pd_2_features_explainer_PdFor2FeaturesExplainer_c1f2af1f-eb25-42cc-9fad-d0290c557dce/global_3d_data/application_json/data3d_feature_1_class_0.json\n",
      "      902  2026-01-29 16:22   explainer_h2o_sonar_explainers_pd_2_features_explainer_PdFor2FeaturesExplainer_c1f2af1f-eb25-42cc-9fad-d0290c557dce/global_3d_data/application_json/data3d_feature_0_class_0.json\n",
      "     1500  2026-01-29 16:22   explainer_h2o_sonar_explainers_pd_2_features_explainer_PdFor2FeaturesExplainer_c1f2af1f-eb25-42cc-9fad-d0290c557dce/global_3d_data/application_vnd_h2oai_json_csv/explanation.json\n",
      "      475  2026-01-29 16:22   explainer_h2o_sonar_explainers_pd_2_features_explainer_PdFor2FeaturesExplainer_c1f2af1f-eb25-42cc-9fad-d0290c557dce/global_3d_data/application_vnd_h2oai_json_csv/data3d_feature_0_class_0.csv\n",
      "      285  2026-01-29 16:22   explainer_h2o_sonar_explainers_pd_2_features_explainer_PdFor2FeaturesExplainer_c1f2af1f-eb25-42cc-9fad-d0290c557dce/global_3d_data/application_vnd_h2oai_json_csv/data3d_feature_2_class_0.csv\n",
      "     1466  2026-01-29 16:22   explainer_h2o_sonar_explainers_pd_2_features_explainer_PdFor2FeaturesExplainer_c1f2af1f-eb25-42cc-9fad-d0290c557dce/global_3d_data/application_vnd_h2oai_json_csv/data3d_feature_1_class_0.csv\n",
      "     2055  2026-01-29 16:22   explainer_h2o_sonar_explainers_pd_2_features_explainer_PdFor2FeaturesExplainer_c1f2af1f-eb25-42cc-9fad-d0290c557dce/log/explainer_run_c1f2af1f-eb25-42cc-9fad-d0290c557dce.log\n",
      "    80253  2026-01-29 16:22   explainer_h2o_sonar_explainers_pd_2_features_explainer_PdFor2FeaturesExplainer_c1f2af1f-eb25-42cc-9fad-d0290c557dce/work/image-2.png\n",
      "     1247  2026-01-29 16:22   explainer_h2o_sonar_explainers_pd_2_features_explainer_PdFor2FeaturesExplainer_c1f2af1f-eb25-42cc-9fad-d0290c557dce/work/explanation.html\n",
      "    92250  2026-01-29 16:22   explainer_h2o_sonar_explainers_pd_2_features_explainer_PdFor2FeaturesExplainer_c1f2af1f-eb25-42cc-9fad-d0290c557dce/work/image-1.png\n",
      "    40216  2026-01-29 16:22   explainer_h2o_sonar_explainers_pd_2_features_explainer_PdFor2FeaturesExplainer_c1f2af1f-eb25-42cc-9fad-d0290c557dce/work/mli_dataset_y_hat.jay\n",
      "    75680  2026-01-29 16:22   explainer_h2o_sonar_explainers_pd_2_features_explainer_PdFor2FeaturesExplainer_c1f2af1f-eb25-42cc-9fad-d0290c557dce/work/image-0.png\n",
      "     2642  2026-01-29 16:22   explainer_h2o_sonar_explainers_pd_2_features_explainer_PdFor2FeaturesExplainer_c1f2af1f-eb25-42cc-9fad-d0290c557dce/work/report.md\n",
      "        2  2026-01-29 16:22   explainer_h2o_sonar_explainers_pd_2_features_explainer_PdFor2FeaturesExplainer_c1f2af1f-eb25-42cc-9fad-d0290c557dce/insights/insights_and_actions.json\n",
      "      122  2026-01-29 16:22   explainer_h2o_sonar_explainers_pd_2_features_explainer_PdFor2FeaturesExplainer_c1f2af1f-eb25-42cc-9fad-d0290c557dce/global_report/text_markdown.meta\n",
      "     2642  2026-01-29 16:22   explainer_h2o_sonar_explainers_pd_2_features_explainer_PdFor2FeaturesExplainer_c1f2af1f-eb25-42cc-9fad-d0290c557dce/global_report/text_markdown/explanation.md\n",
      "    80253  2026-01-29 16:22   explainer_h2o_sonar_explainers_pd_2_features_explainer_PdFor2FeaturesExplainer_c1f2af1f-eb25-42cc-9fad-d0290c557dce/global_report/text_markdown/image-2.png\n",
      "    92250  2026-01-29 16:22   explainer_h2o_sonar_explainers_pd_2_features_explainer_PdFor2FeaturesExplainer_c1f2af1f-eb25-42cc-9fad-d0290c557dce/global_report/text_markdown/image-1.png\n",
      "    75680  2026-01-29 16:22   explainer_h2o_sonar_explainers_pd_2_features_explainer_PdFor2FeaturesExplainer_c1f2af1f-eb25-42cc-9fad-d0290c557dce/global_report/text_markdown/image-0.png\n",
      "---------                     -------\n",
      "   808965                     30 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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