{ "cells": [ { "cell_type": "markdown", "id": "b6bc96d0-3771-4e00-9b5a-fa98b56b52d8", "metadata": {}, "source": [ "# H2O Sonar Demo of Pickled Models\n", "This example demonstrates how to interpret a **pickled** Scikit-learn model using the H2O Sonar library." ] }, { "cell_type": "code", "execution_count": 1, "id": "8be6308d", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import pandas as pd\n", "import datatable as dt\n", "import webbrowser\n", "\n", "from h2o_sonar import interpret\n", "from h2o_sonar.lib.api.models import ExplainableModel, ExplainableModelType, ExplainableModelMeta\n", "from h2o_sonar.lib.api.datasets import ExplainableDataset" ] }, { "cell_type": "markdown", "id": "d0fa3a85-b11c-4768-85f5-cbb345371a20", "metadata": {}, "source": [ "**IMPORTANT**: make sure that you have the right version of `scikit-learn` compatible with demo model installed (`scikit-learn 1.8.0`) in order to ensure binary compability." ] }, { "cell_type": "code", "execution_count": 2, "id": "1c7f30b7-4ea0-4170-b6e2-beb1f115265e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "scikit-learn==1.8.0\n" ] } ], "source": [ "!pip freeze | grep scikit-learn" ] }, { "cell_type": "code", "execution_count": 3, "id": "4cce1315", "metadata": {}, "outputs": [], "source": [ "target_col = \"default payment next month\"\n", "\n", "# dataset\n", "dataset_path = \"../../data/predictive/creditcard.csv\"\n", "df = pd.read_csv(dataset_path)\n", "\n", "# pickled Sklearn model\n", "model_path = \"../../data/predictive/models/creditcard-binomial-sklearn-1.8.0-gbm.pkl\"\n", "\n", "results_location = \"../../results\"" ] }, { "cell_type": "code", "execution_count": 4, "id": "750d26ee", "metadata": {}, "outputs": [], "source": [ "(X, y) = df.drop(target_col,axis=1), df[target_col]" ] }, { "cell_type": "code", "execution_count": 5, "id": "c402130f", "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" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Preparing and checking DIA features (None): dataset= | PAY_AMT2 ID PAY_AMT5 BILL_AMT1 PAY_3 BILL_AMT6 EDUCATION LIMIT_BAL PAY_4 default payment next month … PAY_AMT6 SEX AGE PAY_AMT3 PAY_2\n", " | int32 int32 int32 int32 int32 int32 int32 int32 int32 int8 int32 int32 int32 int32 int32\n", "---- + -------- ----- -------- --------- ----- --------- --------- --------- ----- -------------------------- -------- ----- ----- -------- -----\n", " 0 | 689 1 0 3913 -1 0 2 20000 -1 1 … 0 2 24 0 2\n", " 1 | 1000 2 0 2682 0 3261 2 120000 0 1 … 2000 2 26 1000 2\n", " 2 | 1500 3 1000 29239 0 15549 2 90000 0 0 … 5000 2 34 1000 0\n", " 3 | 2019 4 1069 46990 0 29547 2 50000 0 0 … 1000 2 37 1200 0\n", " 4 | 36681 5 689 8617 -1 19131 2 50000 0 0 … 679 1 57 10000 0\n", " 5 | 1815 6 1000 64400 0 20024 1 50000 0 0 … 800 1 37 657 0\n", " 6 | 40000 7 13750 367965 0 473944 1 500000 0 0 … 13770 1 29 38000 0\n", " 7 | 601 8 1687 11876 -1 567 2 100000 0 0 … 1542 2 23 0 -1\n", " 8 | 0 9 1000 11285 2 3719 3 140000 0 0 … 1000 2 28 432 0\n", " 9 | 0 10 1122 0 -2 13912 3 20000 -2 0 … 0 1 35 0 -2\n", " 10 | 12 11 3738 11073 2 3731 3 200000 0 0 … 66 2 34 50 0\n", " 11 | 9966 12 0 12261 -1 13668 1 260000 -1 0 … 3640 2 51 8583 -1\n", " 12 | 6500 13 2870 12137 -1 2870 2 630000 -1 0 … 0 2 41 6500 0\n", " 13 | 0 14 1500 65802 2 36894 2 70000 0 1 … 0 1 30 3000 2\n", " 14 | 3000 15 3000 70887 0 55512 1 250000 0 0 … 3000 1 29 3000 0\n", " … | … … … … … … … … … … … … … … … …\n", "9995 | 0 9996 0 0 -2 0 1 140000 -2 0 … 1419 2 31 0 -2\n", "9996 | 0 9997 0 3946 -2 0 2 80000 -2 0 … 0 2 37 0 -2\n", "9997 | 5000 9998 10017 138877 0 168431 3 200000 0 0 … 4200 1 44 10000 0\n", "9998 | 0 9999 0 780 2 0 2 80000 -2 1 … 0 2 26 0 2\n", "9999 | 3000 10000 3000 19505 0 17479 2 230000 0 0 … 3000 1 36 3000 0\n", "[10000 rows x 25 columns]\n", " dataset_meta={\n", " \"shape\": \"(10000, 25)\",\n", " \"row_count\": 10000,\n", " \"column_names\": [\n", " \"ID\",\n", " \"LIMIT_BAL\",\n", " \"SEX\",\n", " \"EDUCATION\",\n", " \"MARRIAGE\",\n", " \"AGE\",\n", " \"PAY_0\",\n", " \"PAY_2\",\n", " \"PAY_3\",\n", " \"PAY_4\",\n", " \"PAY_5\",\n", " \"PAY_6\",\n", " \"BILL_AMT1\",\n", " \"BILL_AMT2\",\n", " \"BILL_AMT3\",\n", " \"BILL_AMT4\",\n", " \"BILL_AMT5\",\n", " \"BILL_AMT6\",\n", " \"PAY_AMT1\",\n", " \"PAY_AMT2\",\n", " \"PAY_AMT3\",\n", " \"PAY_AMT4\",\n", " \"PAY_AMT5\",\n", " \"PAY_AMT6\",\n", " \"default payment next month\"\n", " ],\n", " \"column_types\": [\n", " \"int\",\n", " \"int\",\n", " \"int\",\n", " \"int\",\n", " \"int\",\n", " \"int\",\n", " \"int\",\n", " \"int\",\n", " \"int\",\n", " \"int\",\n", " \"int\",\n", " \"int\",\n", " \"int\",\n", " \"int\",\n", " \"int\",\n", " \"int\",\n", " \"int\",\n", " \"int\",\n", " \"int\",\n", " \"int\",\n", " \"int\",\n", " \"int\",\n", " \"int\",\n", " \"int\",\n", " \"int\"\n", " ],\n", " \"column_uniques\": [\n", " 10000,\n", " 72,\n", " 2,\n", " 7,\n", " 4,\n", " 54,\n", " 11,\n", " 11,\n", " 11,\n", " 11,\n", " 10,\n", " 10,\n", " 8371,\n", " 8215,\n", " 8072,\n", " 7913,\n", " 7764,\n", " 7550,\n", " 3763,\n", " 3581,\n", " 3305,\n", " 3247,\n", " 3258,\n", " 3174,\n", " 2\n", " ],\n", " \"columns_cat\": [],\n", " \"columns_num\": [],\n", " \"file_path\": \"../../data/predictive/creditcard.csv\",\n", " \"file_name\": \"\",\n", " \"file_size\": 944719,\n", " \"missing_values\": [\n", " \"\",\n", " \"?\",\n", " \"None\",\n", " \"nan\",\n", " \"NA\",\n", " \"N/A\",\n", " \"unknown\",\n", " \"inf\",\n", " \"-inf\",\n", " \"1.7976931348623157e+308\",\n", " \"-1.7976931348623157e+308\"\n", " ],\n", " \"columns_meta\": [\n", " {\n", " \"name\": \"ID\",\n", " \"data_type\": \"int\",\n", " \"logical_types\": [],\n", " \"format\": \"\",\n", " \"is_id\": true,\n", " \"is_numeric\": true,\n", " \"is_categorical\": false,\n", " \"count\": 10000,\n", " \"frequency\": 0,\n", " \"unique\": 10000,\n", " \"max\": null,\n", " \"min\": null,\n", " \"mean\": null,\n", " \"std\": null,\n", " \"histogram_counts\": [],\n", " \"histogram_ticks\": []\n", " },\n", " {\n", " \"name\": \"LIMIT_BAL\",\n", " \"data_type\": \"int\",\n", " \"logical_types\": [],\n", " \"format\": \"\",\n", " \"is_id\": false,\n", " \"is_numeric\": true,\n", " \"is_categorical\": false,\n", " \"count\": 72,\n", " \"frequency\": 0,\n", " \"unique\": 72,\n", " \"max\": null,\n", " \"min\": null,\n", " \"mean\": null,\n", " \"std\": null,\n", " \"histogram_counts\": [],\n", " \"histogram_ticks\": []\n", " },\n", " {\n", " \"name\": \"SEX\",\n", " \"data_type\": \"int\",\n", " \"logical_types\": [],\n", " \"format\": \"\",\n", " \"is_id\": false,\n", " \"is_numeric\": true,\n", " \"is_categorical\": false,\n", " \"count\": 2,\n", " \"frequency\": 0,\n", " \"unique\": 2,\n", " \"max\": null,\n", " \"min\": null,\n", " \"mean\": null,\n", " \"std\": null,\n", " \"histogram_counts\": [],\n", " \"histogram_ticks\": []\n", " },\n", " {\n", " \"name\": \"EDUCATION\",\n", " \"data_type\": \"int\",\n", " \"logical_types\": [],\n", " \"format\": \"\",\n", " \"is_id\": false,\n", " \"is_numeric\": true,\n", " \"is_categorical\": false,\n", " \"count\": 7,\n", " \"frequency\": 0,\n", " \"unique\": 7,\n", " \"max\": null,\n", " \"min\": null,\n", " \"mean\": null,\n", " \"std\": null,\n", " \"histogram_counts\": [],\n", " \"histogram_ticks\": []\n", " },\n", " {\n", " \"name\": \"MARRIAGE\",\n", " \"data_type\": \"int\",\n", " \"logical_types\": [],\n", " \"format\": \"\",\n", " \"is_id\": false,\n", " \"is_numeric\": true,\n", " \"is_categorical\": false,\n", " \"count\": 4,\n", " \"frequency\": 0,\n", " \"unique\": 4,\n", " \"max\": null,\n", " \"min\": null,\n", " \"mean\": null,\n", " \"std\": null,\n", " \"histogram_counts\": [],\n", " \"histogram_ticks\": []\n", " },\n", " {\n", " \"name\": \"AGE\",\n", " \"data_type\": \"int\",\n", " \"logical_types\": [],\n", " \"format\": \"\",\n", " \"is_id\": false,\n", " \"is_numeric\": true,\n", " \"is_categorical\": false,\n", " \"count\": 54,\n", " \"frequency\": 0,\n", " \"unique\": 54,\n", " \"max\": null,\n", " \"min\": null,\n", " \"mean\": null,\n", " \"std\": null,\n", " \"histogram_counts\": [],\n", " \"histogram_ticks\": []\n", " },\n", " {\n", " \"name\": \"PAY_0\",\n", " \"data_type\": \"int\",\n", " \"logical_types\": [],\n", " \"format\": \"\",\n", " \"is_id\": false,\n", " \"is_numeric\": true,\n", " \"is_categorical\": false,\n", " \"count\": 11,\n", " \"frequency\": 0,\n", " \"unique\": 11,\n", " \"max\": null,\n", " \"min\": null,\n", " \"mean\": null,\n", " \"std\": null,\n", " \"histogram_counts\": [],\n", " \"histogram_ticks\": []\n", " },\n", " {\n", " \"name\": \"PAY_2\",\n", " \"data_type\": \"int\",\n", " \"logical_types\": [],\n", " \"format\": \"\",\n", " \"is_id\": false,\n", " \"is_numeric\": true,\n", " \"is_categorical\": false,\n", " \"count\": 11,\n", " \"frequency\": 0,\n", " \"unique\": 11,\n", " \"max\": null,\n", " \"min\": null,\n", " \"mean\": null,\n", " \"std\": null,\n", " \"histogram_counts\": [],\n", " \"histogram_ticks\": []\n", " },\n", " {\n", " \"name\": \"PAY_3\",\n", " \"data_type\": \"int\",\n", " \"logical_types\": [],\n", " \"format\": \"\",\n", " \"is_id\": false,\n", " \"is_numeric\": true,\n", " \"is_categorical\": false,\n", " \"count\": 11,\n", " \"frequency\": 0,\n", " \"unique\": 11,\n", " \"max\": null,\n", " \"min\": null,\n", " \"mean\": null,\n", " \"std\": null,\n", " \"histogram_counts\": [],\n", " \"histogram_ticks\": []\n", " },\n", " {\n", " \"name\": \"PAY_4\",\n", " \"data_type\": \"int\",\n", " \"logical_types\": [],\n", " \"format\": \"\",\n", " \"is_id\": false,\n", " \"is_numeric\": true,\n", " \"is_categorical\": false,\n", " \"count\": 11,\n", " \"frequency\": 0,\n", " \"unique\": 11,\n", " \"max\": null,\n", " \"min\": null,\n", " \"mean\": null,\n", " \"std\": null,\n", " \"histogram_counts\": [],\n", " \"histogram_ticks\": []\n", " },\n", " {\n", " \"name\": \"PAY_5\",\n", " \"data_type\": \"int\",\n", " \"logical_types\": [],\n", " \"format\": \"\",\n", " \"is_id\": false,\n", " \"is_numeric\": true,\n", " \"is_categorical\": false,\n", " \"count\": 10,\n", " \"frequency\": 0,\n", " \"unique\": 10,\n", " \"max\": null,\n", " \"min\": null,\n", " \"mean\": null,\n", " \"std\": null,\n", " \"histogram_counts\": [],\n", " \"histogram_ticks\": []\n", " },\n", " {\n", " \"name\": \"PAY_6\",\n", " \"data_type\": \"int\",\n", " \"logical_types\": [],\n", " \"format\": \"\",\n", " \"is_id\": false,\n", " \"is_numeric\": true,\n", " \"is_categorical\": false,\n", " \"count\": 10,\n", " \"frequency\": 0,\n", " \"unique\": 10,\n", " \"max\": null,\n", " \"min\": null,\n", " \"mean\": null,\n", " \"std\": null,\n", " \"histogram_counts\": [],\n", " \"histogram_ticks\": []\n", " },\n", " {\n", " \"name\": \"BILL_AMT1\",\n", " \"data_type\": \"int\",\n", " \"logical_types\": [],\n", " \"format\": \"\",\n", " \"is_id\": false,\n", " \"is_numeric\": true,\n", " \"is_categorical\": false,\n", " \"count\": 8371,\n", " \"frequency\": 0,\n", " \"unique\": 8371,\n", " \"max\": null,\n", " \"min\": null,\n", " \"mean\": null,\n", " \"std\": null,\n", " \"histogram_counts\": [],\n", " \"histogram_ticks\": []\n", " },\n", " {\n", " \"name\": \"BILL_AMT2\",\n", " \"data_type\": \"int\",\n", " \"logical_types\": [],\n", " \"format\": \"\",\n", " \"is_id\": false,\n", " \"is_numeric\": true,\n", " \"is_categorical\": false,\n", " \"count\": 8215,\n", " \"frequency\": 0,\n", " \"unique\": 8215,\n", " \"max\": null,\n", " \"min\": null,\n", " \"mean\": null,\n", " \"std\": null,\n", " \"histogram_counts\": [],\n", " \"histogram_ticks\": []\n", " },\n", " {\n", " \"name\": \"BILL_AMT3\",\n", " \"data_type\": \"int\",\n", " \"logical_types\": [],\n", " \"format\": \"\",\n", " \"is_id\": false,\n", " \"is_numeric\": true,\n", " \"is_categorical\": false,\n", " \"count\": 8072,\n", " \"frequency\": 0,\n", " \"unique\": 8072,\n", " \"max\": null,\n", " \"min\": null,\n", " \"mean\": null,\n", " \"std\": null,\n", " \"histogram_counts\": [],\n", " \"histogram_ticks\": []\n", " },\n", " {\n", " \"name\": \"BILL_AMT4\",\n", " \"data_type\": \"int\",\n", " \"logical_types\": [],\n", " \"format\": \"\",\n", " \"is_id\": false,\n", " \"is_numeric\": true,\n", " \"is_categorical\": false,\n", " \"count\": 7913,\n", " \"frequency\": 0,\n", " \"unique\": 7913,\n", " \"max\": null,\n", " \"min\": null,\n", " \"mean\": null,\n", " \"std\": null,\n", " \"histogram_counts\": [],\n", " \"histogram_ticks\": []\n", " },\n", " {\n", " \"name\": \"BILL_AMT5\",\n", " \"data_type\": \"int\",\n", " \"logical_types\": [],\n", " \"format\": \"\",\n", " \"is_id\": false,\n", " \"is_numeric\": true,\n", " \"is_categorical\": false,\n", " \"count\": 7764,\n", " \"frequency\": 0,\n", " \"unique\": 7764,\n", " \"max\": null,\n", " \"min\": null,\n", " \"mean\": null,\n", " \"std\": null,\n", " \"histogram_counts\": [],\n", " \"histogram_ticks\": []\n", " },\n", " {\n", " \"name\": \"BILL_AMT6\",\n", " \"data_type\": \"int\",\n", " \"logical_types\": [],\n", " \"format\": \"\",\n", " \"is_id\": false,\n", " \"is_numeric\": true,\n", " \"is_categorical\": false,\n", " \"count\": 7550,\n", " \"frequency\": 0,\n", " \"unique\": 7550,\n", " \"max\": null,\n", " \"min\": null,\n", " \"mean\": null,\n", " \"std\": null,\n", " \"histogram_counts\": [],\n", " \"histogram_ticks\": []\n", " },\n", " {\n", " \"name\": \"PAY_AMT1\",\n", " \"data_type\": \"int\",\n", " \"logical_types\": [],\n", " \"format\": \"\",\n", " \"is_id\": false,\n", " \"is_numeric\": true,\n", " \"is_categorical\": false,\n", " \"count\": 3763,\n", " \"frequency\": 0,\n", " \"unique\": 3763,\n", " \"max\": null,\n", " \"min\": null,\n", " \"mean\": null,\n", " \"std\": null,\n", " \"histogram_counts\": [],\n", " \"histogram_ticks\": []\n", " },\n", " {\n", " \"name\": \"PAY_AMT2\",\n", " \"data_type\": \"int\",\n", " \"logical_types\": [],\n", " \"format\": \"\",\n", " \"is_id\": false,\n", " \"is_numeric\": true,\n", " \"is_categorical\": false,\n", " \"count\": 3581,\n", " \"frequency\": 0,\n", " \"unique\": 3581,\n", " \"max\": null,\n", " \"min\": null,\n", " \"mean\": null,\n", " \"std\": null,\n", " \"histogram_counts\": [],\n", " \"histogram_ticks\": []\n", " },\n", " {\n", " \"name\": \"PAY_AMT3\",\n", " \"data_type\": \"int\",\n", " \"logical_types\": [],\n", " \"format\": \"\",\n", " \"is_id\": false,\n", " \"is_numeric\": true,\n", " \"is_categorical\": false,\n", " \"count\": 3305,\n", " \"frequency\": 0,\n", " \"unique\": 3305,\n", " \"max\": null,\n", " \"min\": null,\n", " \"mean\": null,\n", " \"std\": null,\n", " \"histogram_counts\": [],\n", " \"histogram_ticks\": []\n", " },\n", " {\n", " \"name\": \"PAY_AMT4\",\n", " \"data_type\": \"int\",\n", " \"logical_types\": [],\n", " \"format\": \"\",\n", " \"is_id\": false,\n", " \"is_numeric\": true,\n", " \"is_categorical\": false,\n", " \"count\": 3247,\n", " \"frequency\": 0,\n", " \"unique\": 3247,\n", " \"max\": null,\n", " \"min\": null,\n", " \"mean\": null,\n", " \"std\": null,\n", " \"histogram_counts\": [],\n", " \"histogram_ticks\": []\n", " },\n", " {\n", " \"name\": \"PAY_AMT5\",\n", " \"data_type\": \"int\",\n", " \"logical_types\": [],\n", " \"format\": \"\",\n", " \"is_id\": false,\n", " \"is_numeric\": true,\n", " \"is_categorical\": false,\n", " \"count\": 3258,\n", " \"frequency\": 0,\n", " \"unique\": 3258,\n", " \"max\": null,\n", " \"min\": null,\n", " \"mean\": null,\n", " \"std\": null,\n", " \"histogram_counts\": [],\n", " \"histogram_ticks\": []\n", " },\n", " {\n", " \"name\": \"PAY_AMT6\",\n", " \"data_type\": \"int\",\n", " \"logical_types\": [],\n", " \"format\": \"\",\n", " \"is_id\": false,\n", " \"is_numeric\": true,\n", " \"is_categorical\": false,\n", " \"count\": 3174,\n", " \"frequency\": 0,\n", " \"unique\": 3174,\n", " \"max\": null,\n", " \"min\": null,\n", " \"mean\": null,\n", " \"std\": null,\n", " \"histogram_counts\": [],\n", " \"histogram_ticks\": []\n", " },\n", " {\n", " \"name\": \"default payment next month\",\n", " \"data_type\": \"int\",\n", " \"logical_types\": [],\n", " \"format\": \"\",\n", " \"is_id\": false,\n", " \"is_numeric\": true,\n", " \"is_categorical\": false,\n", " \"count\": 2,\n", " \"frequency\": 0,\n", " \"unique\": 2,\n", " \"max\": null,\n", " \"min\": null,\n", " \"mean\": null,\n", " \"std\": null,\n", " \"histogram_counts\": [],\n", " \"histogram_ticks\": []\n", " }\n", " ],\n", " \"original_dataset_sampled\": false,\n", " \"original_dataset_path\": \"\",\n", " \"original_dataset_size\": 0,\n", " \"original_dataset_shape\": [\n", " 10000,\n", " 25\n", " ]\n", "}\n", "Using dataset ENTITY to prepare DIA features: column_names=['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', 'default payment next month'] column_uniques=[10000, 72, 2, 7, 4, 54, 11, 11, 11, 11, 10, 10, 8371, 8215, 8072, 7913, 7764, 7550, 3763, 3581, 3305, 3247, 3258, 3174, 2]\n", "DIA group columns prepared using dataset ENTITY: {'MARRIAGE', 'PAY_5', 'PAY_0', 'PAY_3', 'PAY_2', 'SEX', 'EDUCATION', 'PAY_6', 'PAY_4', 'default payment next month'}\n", "DIA group columns to SKIP: {'model_pred', 'default payment next month'}\n", "DIA group columns as BOOLs: [, , , , , , , , ]\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "X does not have valid feature names, but GradientBoostingClassifier was fitted with feature names\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Checking whether there is an H2O instance running at http://localhost:54321..... not found.\n", "Attempting to start a local H2O server...\n", " Java Version: openjdk version \"10\" 2018-03-20; OpenJDK Runtime Environment 18.3 (build 10+44); OpenJDK 64-Bit Server VM 18.3 (build 10+44, mixed mode)\n", " Starting server from /home/user/h/mli/git/h2o-sonar-FLOSS/.venv/lib/python3.11/site-packages/h2o/backend/bin/h2o.jar\n", " Ice root: /tmp/tmps5r447si\n", " JVM stdout: /tmp/tmps5r447si/h2o_user_started_from_python.out\n", " JVM stderr: /tmp/tmps5r447si/h2o_user_started_from_python.err\n", " Server is running at http://127.0.0.1:54321\n", " successful.o H2O server at http://127.0.0.1:54321 ...\n" ] }, { "data": { "text/html": [ "\n", " \n", "
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H2O_cluster_uptime:04 secs
H2O_cluster_timezone:Europe/Prague
H2O_data_parsing_timezone:UTC
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If you wish to train a binary model instead of a regression model, convert your target column to categorical before training.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "██████████████████████████████████████████████████████| (done) 100%\n", "████████████████████████████████████████████████████████████████| (done) 100%\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Converting H2O frame to pandas dataframe using single-thread. For faster conversion using multi-thread, install polars and pyarrow and use it as pandas_df = h2o_df.as_data_frame(use_multi_thread=True)\n", "\n", "More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). Consider using `matplotlib.pyplot.close()`.\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", "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", "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", "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" ] } ], "source": [ "# run Interpretation\n", "interpretation = interpret.run_interpretation(\n", " dataset=dataset_path,\n", " model=model_path,\n", " target_col=target_col,\n", " results_location=\"../../results\",\n", " used_features=list(X.columns),\n", ")" ] }, { "cell_type": "markdown", "id": "ec4e0892-99e2-429b-ba53-86a9337c23dd", "metadata": {}, "source": [ "---" ] }, { "cell_type": "code", "execution_count": 6, "id": "5a41f711-ce11-4688-89e9-110c8cc9f174", "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": "660e4b14-18df-4a5e-a6a2-8e3eb1b8922a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['h2o_sonar.explainers.dia_explainer.DiaExplainer',\n", " 'h2o_sonar.explainers.residual_dt_surrogate_explainer.ResidualDecisionTreeSurrogateExplainer',\n", " 'h2o_sonar.explainers.dt_surrogate_explainer.DecisionTreeSurrogateExplainer',\n", " 'h2o_sonar.explainers.summary_shap_explainer.SummaryShapleyExplainer',\n", " 'h2o_sonar.explainers.pd_ice_explainer.PdIceExplainer']" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "interpretation.get_scheduled_explainer_ids()" ] }, { "cell_type": "code", "execution_count": 8, "id": "47a77b90-c201-4b90-940d-714f9adfef10", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['h2o_sonar.explainers.dia_explainer.DiaExplainer',\n", " 'h2o_sonar.explainers.residual_dt_surrogate_explainer.ResidualDecisionTreeSurrogateExplainer',\n", " 'h2o_sonar.explainers.dt_surrogate_explainer.DecisionTreeSurrogateExplainer',\n", " 'h2o_sonar.explainers.summary_shap_explainer.SummaryShapleyExplainer',\n", " 'h2o_sonar.explainers.pd_ice_explainer.PdIceExplainer']" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "interpretation.get_finished_explainer_ids()" ] }, { "cell_type": "code", "execution_count": 9, "id": "cdf81e34-fa01-49ce-9091-4e52464275ab", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['h2o_sonar.explainers.dia_explainer.DiaExplainer',\n", " 'h2o_sonar.explainers.residual_dt_surrogate_explainer.ResidualDecisionTreeSurrogateExplainer',\n", " 'h2o_sonar.explainers.dt_surrogate_explainer.DecisionTreeSurrogateExplainer',\n", " 'h2o_sonar.explainers.summary_shap_explainer.SummaryShapleyExplainer',\n", " 'h2o_sonar.explainers.pd_ice_explainer.PdIceExplainer']" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "interpretation.get_successful_explainer_ids()" ] }, { "cell_type": "code", "execution_count": 10, "id": "6a17894f-93e2-4eb7-a9e8-e73b76e72f57", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "interpretation.get_failed_explainer_ids()" ] }, { "cell_type": "markdown", "id": "44081267-f227-4ece-9de1-e067bb1b5648", "metadata": {}, "source": [ "---\n", "\n", "Find **interpretation summary** in **HTML report** on the path below:" ] }, { "cell_type": "code", "execution_count": 11, "id": "67354270-31b7-4d61-a9f2-703c3254a01d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "../../results/h2o-sonar/mli_experiment_38874323-ea13-4123-90bb-48781ea45145/interpretation.html\n" ] } ], "source": [ "print(f\"{interpretation.result.html_location}\")" ] }, { "cell_type": "markdown", "id": "0a156602-28e4-45f5-9242-e67b1f68c874", "metadata": {}, "source": [ "---\n", "\n", "Check `results/` directory with artifacts created by explainers:" ] }, { "cell_type": "code", "execution_count": 12, "id": "66062b78", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[01;34m../../results/h2o-sonar/mli_experiment_38874323-ea13-4123-90bb-48781ea45145\u001b[00m\n", "├── \u001b[01;34mexplainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_ca3a2d95-dc70-43b6-8dc3-325c5a527682\u001b[00m\n", "│   ├── \u001b[01;34mglobal_disparate_impact_analysis\u001b[00m\n", "│   │   ├── \u001b[01;34mtext_plain\u001b[00m\n", "│   │   │   └── explanation.txt\n", "│   │   └── text_plain.meta\n", "│   ├── \u001b[01;34mglobal_html_fragment\u001b[00m\n", "│   │   ├── \u001b[01;34mtext_html\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdia-0-accuracy.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdia-0-adverse_impact.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdia-0-false_discovery_rate.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdia-0-false_negative_rate.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdia-0-false_omissions_rate.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdia-0-false_positive_rate.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdia-0-negative_predicted_value.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdia-0-n.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdia-0-precision.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdia-0-specificity.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdia-0-true_positive_rate.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdia-1-accuracy.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdia-1-adverse_impact.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdia-1-false_discovery_rate.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdia-1-false_negative_rate.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdia-1-false_omissions_rate.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdia-1-false_positive_rate.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdia-1-negative_predicted_value.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdia-1-n.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdia-1-precision.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdia-1-specificity.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdia-1-true_positive_rate.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdia-2-accuracy.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdia-2-adverse_impact.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdia-2-false_discovery_rate.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdia-2-false_negative_rate.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdia-2-false_omissions_rate.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdia-2-false_positive_rate.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdia-2-negative_predicted_value.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdia-2-n.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdia-2-precision.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdia-2-specificity.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdia-2-true_positive_rate.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdia-3-accuracy.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdia-3-adverse_impact.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdia-3-false_discovery_rate.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdia-3-false_negative_rate.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdia-3-false_omissions_rate.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdia-3-false_positive_rate.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdia-3-negative_predicted_value.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdia-3-n.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdia-3-precision.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdia-3-specificity.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdia-3-true_positive_rate.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdia-4-accuracy.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdia-4-adverse_impact.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdia-4-false_discovery_rate.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdia-4-false_negative_rate.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdia-4-false_omissions_rate.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdia-4-false_positive_rate.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdia-4-negative_predicted_value.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdia-4-n.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdia-4-precision.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdia-4-specificity.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdia-4-true_positive_rate.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdia-5-accuracy.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdia-5-adverse_impact.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdia-5-false_discovery_rate.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdia-5-false_negative_rate.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdia-5-false_omissions_rate.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdia-5-false_positive_rate.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdia-5-negative_predicted_value.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdia-5-n.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdia-5-precision.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdia-5-specificity.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdia-5-true_positive_rate.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdia-6-accuracy.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdia-6-adverse_impact.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdia-6-false_discovery_rate.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdia-6-false_negative_rate.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdia-6-false_omissions_rate.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdia-6-false_positive_rate.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdia-6-negative_predicted_value.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdia-6-n.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdia-6-precision.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdia-6-specificity.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdia-6-true_positive_rate.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdia-7-accuracy.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdia-7-adverse_impact.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdia-7-false_discovery_rate.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdia-7-false_negative_rate.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdia-7-false_omissions_rate.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdia-7-false_positive_rate.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdia-7-negative_predicted_value.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdia-7-n.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdia-7-precision.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdia-7-specificity.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdia-7-true_positive_rate.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdia-8-accuracy.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdia-8-adverse_impact.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdia-8-false_discovery_rate.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdia-8-false_negative_rate.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdia-8-false_omissions_rate.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdia-8-false_positive_rate.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdia-8-negative_predicted_value.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdia-8-n.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdia-8-precision.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdia-8-specificity.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdia-8-true_positive_rate.png\u001b[00m\n", "│   │   │   └── explanation.html\n", "│   │   └── text_html.meta\n", "│   ├── \u001b[01;34minsights\u001b[00m\n", "│   │   └── insights_and_actions.json\n", "│   ├── \u001b[01;34mlog\u001b[00m\n", "│   │   └── explainer_run_ca3a2d95-dc70-43b6-8dc3-325c5a527682.log\n", "│   ├── \u001b[01;34mproblems\u001b[00m\n", "│   │   └── problems_and_actions.json\n", "│   ├── result_descriptor.json\n", "│   └── \u001b[01;34mwork\u001b[00m\n", "│   ├── dia_entity.json\n", "│   ├── \u001b[01;34mEDUCATION\u001b[00m\n", "│   │   ├── \u001b[01;34m0\u001b[00m\n", "│   │   │   ├── cm.jay\n", "│   │   │   ├── disparity.jay\n", "│   │   │   ├── me_smd.jay\n", "│   │   │   └── parity.jay\n", "│   │   ├── \u001b[01;34m1\u001b[00m\n", "│   │   │   ├── cm.jay\n", "│   │   │   ├── disparity.jay\n", "│   │   │   ├── me_smd.jay\n", "│   │   │   └── parity.jay\n", "│   │   ├── \u001b[01;34m2\u001b[00m\n", "│   │   │   ├── cm.jay\n", "│   │   │   ├── disparity.jay\n", "│   │   │   ├── me_smd.jay\n", "│   │   │   └── parity.jay\n", "│   │   ├── \u001b[01;34m3\u001b[00m\n", "│   │   │   ├── cm.jay\n", "│   │   │   ├── disparity.jay\n", "│   │   │   ├── me_smd.jay\n", "│   │   │   └── parity.jay\n", "│   │   ├── \u001b[01;34m4\u001b[00m\n", "│   │   │   ├── cm.jay\n", "│   │   │   ├── disparity.jay\n", "│   │   │   ├── me_smd.jay\n", "│   │   │   └── parity.jay\n", "│   │   ├── \u001b[01;34m5\u001b[00m\n", "│   │   │   ├── cm.jay\n", "│   │   │   ├── disparity.jay\n", "│   │   │   ├── me_smd.jay\n", "│   │   │   └── parity.jay\n", "│   │   ├── \u001b[01;34m6\u001b[00m\n", "│   │   │   ├── cm.jay\n", "│   │   │   ├── disparity.jay\n", "│   │   │   ├── me_smd.jay\n", "│   │   │   └── parity.jay\n", "│   │   └── metrics.jay\n", "│   ├── \u001b[01;34mMARRIAGE\u001b[00m\n", "│   │   ├── \u001b[01;34m0\u001b[00m\n", "│   │   │   ├── cm.jay\n", "│   │   │   ├── disparity.jay\n", "│   │   │   ├── me_smd.jay\n", "│   │   │   └── parity.jay\n", "│   │   ├── \u001b[01;34m1\u001b[00m\n", "│   │   │   ├── cm.jay\n", "│   │   │   ├── disparity.jay\n", "│   │   │   ├── me_smd.jay\n", "│   │   │   └── parity.jay\n", "│   │   ├── \u001b[01;34m2\u001b[00m\n", "│   │   │   ├── cm.jay\n", "│   │   │   ├── disparity.jay\n", "│   │   │   ├── me_smd.jay\n", "│   │   │   └── parity.jay\n", "│   │   ├── \u001b[01;34m3\u001b[00m\n", "│   │   │   ├── cm.jay\n", "│   │   │   ├── disparity.jay\n", "│   │   │   ├── me_smd.jay\n", "│   │   │   └── parity.jay\n", "│   │   └── metrics.jay\n", "│   ├── \u001b[01;34mPAY_0\u001b[00m\n", "│   │   ├── \u001b[01;34m0\u001b[00m\n", "│   │   │   ├── cm.jay\n", "│   │   │   ├── disparity.jay\n", "│   │   │   ├── me_smd.jay\n", "│   │   │   └── parity.jay\n", "│   │   ├── \u001b[01;34m1\u001b[00m\n", "│   │   │   ├── cm.jay\n", "│   │   │   ├── disparity.jay\n", "│   │   │   ├── me_smd.jay\n", "│   │   │   └── parity.jay\n", "│   │   ├── \u001b[01;34m10\u001b[00m\n", "│   │   │   ├── cm.jay\n", "│   │   │   ├── disparity.jay\n", "│   │   │   ├── me_smd.jay\n", "│   │   │   └── parity.jay\n", "│   │   ├── \u001b[01;34m2\u001b[00m\n", "│   │   │   ├── cm.jay\n", "│   │   │   ├── disparity.jay\n", "│   │   │   ├── me_smd.jay\n", "│   │   │   └── parity.jay\n", "│   │   ├── \u001b[01;34m3\u001b[00m\n", "│   │   │   ├── cm.jay\n", "│   │   │   ├── disparity.jay\n", "│   │   │   ├── me_smd.jay\n", "│   │   │   └── parity.jay\n", "│   │   ├── \u001b[01;34m4\u001b[00m\n", "│   │   │   ├── cm.jay\n", "│   │   │   ├── disparity.jay\n", "│   │   │   ├── me_smd.jay\n", "│   │   │   └── parity.jay\n", "│   │   ├── \u001b[01;34m5\u001b[00m\n", "│   │   │   ├── cm.jay\n", "│   │   │   ├── disparity.jay\n", "│   │   │   ├── me_smd.jay\n", "│   │   │   └── parity.jay\n", "│   │   ├── \u001b[01;34m6\u001b[00m\n", "│   │   │   ├── cm.jay\n", "│   │   │   ├── disparity.jay\n", "│   │   │   ├── me_smd.jay\n", "│   │   │   └── parity.jay\n", "│   │   ├── \u001b[01;34m7\u001b[00m\n", "│   │   │   ├── cm.jay\n", "│   │   │   ├── disparity.jay\n", "│   │   │   ├── me_smd.jay\n", "│   │   │   └── parity.jay\n", "│   │   ├── \u001b[01;34m8\u001b[00m\n", "│   │   │   ├── cm.jay\n", "│   │   │   ├── disparity.jay\n", "│   │   │   ├── me_smd.jay\n", "│   │   │   └── parity.jay\n", "│   │   ├── \u001b[01;34m9\u001b[00m\n", "│   │   │   ├── cm.jay\n", "│   │   │   ├── disparity.jay\n", "│   │   │   ├── me_smd.jay\n", "│   │   │   └── parity.jay\n", "│   │   └── metrics.jay\n", "│   ├── \u001b[01;34mPAY_2\u001b[00m\n", "│   │   ├── \u001b[01;34m0\u001b[00m\n", "│   │   │   ├── cm.jay\n", "│   │   │   ├── disparity.jay\n", "│   │   │   ├── me_smd.jay\n", "│   │   │   └── parity.jay\n", "│   │   ├── \u001b[01;34m1\u001b[00m\n", "│   │   │   ├── cm.jay\n", "│   │   │   ├── disparity.jay\n", "│   │   │   ├── me_smd.jay\n", "│   │   │   └── parity.jay\n", "│   │   ├── \u001b[01;34m10\u001b[00m\n", "│   │   │   ├── cm.jay\n", "│   │   │   ├── disparity.jay\n", "│   │   │   ├── me_smd.jay\n", "│   │   │   └── parity.jay\n", "│   │   ├── \u001b[01;34m2\u001b[00m\n", "│   │   │   ├── cm.jay\n", "│   │   │   ├── disparity.jay\n", "│   │   │   ├── me_smd.jay\n", "│   │   │   └── parity.jay\n", "│   │   ├── \u001b[01;34m3\u001b[00m\n", "│   │   │   ├── cm.jay\n", "│   │   │   ├── disparity.jay\n", "│   │   │   ├── me_smd.jay\n", "│   │   │   └── parity.jay\n", "│   │   ├── \u001b[01;34m4\u001b[00m\n", "│   │   │   ├── cm.jay\n", "│   │   │   ├── disparity.jay\n", "│   │   │   ├── me_smd.jay\n", "│   │   │   └── parity.jay\n", "│   │   ├── \u001b[01;34m5\u001b[00m\n", "│   │   │   ├── cm.jay\n", "│   │   │   ├── disparity.jay\n", "│   │   │   ├── me_smd.jay\n", "│   │   │   └── parity.jay\n", "│   │   ├── \u001b[01;34m6\u001b[00m\n", "│   │   │   ├── cm.jay\n", "│   │   │   ├── disparity.jay\n", "│   │   │   ├── me_smd.jay\n", "│   │   │   └── parity.jay\n", "│   │   ├── \u001b[01;34m7\u001b[00m\n", "│   │   │   ├── cm.jay\n", "│   │   │   ├── disparity.jay\n", "│   │   │   ├── me_smd.jay\n", "│   │   │   └── parity.jay\n", "│   │   ├── \u001b[01;34m8\u001b[00m\n", "│   │   │   ├── cm.jay\n", "│   │   │   ├── disparity.jay\n", "│   │   │   ├── me_smd.jay\n", "│   │   │   └── parity.jay\n", "│   │   ├── \u001b[01;34m9\u001b[00m\n", "│   │   │   ├── cm.jay\n", "│   │   │   ├── disparity.jay\n", "│   │   │   ├── me_smd.jay\n", "│   │   │   └── parity.jay\n", "│   │   └── metrics.jay\n", "│   ├── \u001b[01;34mPAY_3\u001b[00m\n", "│   │   ├── \u001b[01;34m0\u001b[00m\n", "│   │   │   ├── cm.jay\n", "│   │   │   ├── disparity.jay\n", "│   │   │   ├── me_smd.jay\n", "│   │   │   └── parity.jay\n", "│   │   ├── \u001b[01;34m1\u001b[00m\n", "│   │   │   ├── cm.jay\n", "│   │   │   ├── disparity.jay\n", "│   │   │   ├── me_smd.jay\n", "│   │   │   └── parity.jay\n", "│   │   ├── \u001b[01;34m10\u001b[00m\n", "│   │   │   ├── cm.jay\n", "│   │   │   ├── disparity.jay\n", "│   │   │   ├── me_smd.jay\n", "│   │   │   └── parity.jay\n", "│   │   ├── \u001b[01;34m2\u001b[00m\n", "│   │   │   ├── cm.jay\n", "│   │   │   ├── disparity.jay\n", "│   │   │   ├── me_smd.jay\n", "│   │   │   └── parity.jay\n", "│   │   ├── \u001b[01;34m3\u001b[00m\n", "│   │   │   ├── cm.jay\n", "│   │   │   ├── disparity.jay\n", "│   │   │   ├── me_smd.jay\n", "│   │   │   └── parity.jay\n", "│   │   ├── \u001b[01;34m4\u001b[00m\n", "│   │   │   ├── cm.jay\n", "│   │   │   ├── disparity.jay\n", "│   │   │   ├── me_smd.jay\n", "│   │   │   └── parity.jay\n", "│   │   ├── \u001b[01;34m5\u001b[00m\n", "│   │   │   ├── cm.jay\n", "│   │   │   ├── disparity.jay\n", "│   │   │   ├── me_smd.jay\n", "│   │   │   └── parity.jay\n", "│   │   ├── \u001b[01;34m6\u001b[00m\n", "│   │   │   ├── cm.jay\n", "│   │   │   ├── disparity.jay\n", "│   │   │   ├── me_smd.jay\n", "│   │   │   └── parity.jay\n", "│   │   ├── \u001b[01;34m7\u001b[00m\n", "│   │   │   ├── cm.jay\n", "│   │   │   ├── disparity.jay\n", "│   │   │   ├── me_smd.jay\n", "│   │   │   └── parity.jay\n", "│   │   ├── \u001b[01;34m8\u001b[00m\n", "│   │   │   ├── cm.jay\n", "│   │   │   ├── disparity.jay\n", "│   │   │   ├── me_smd.jay\n", "│   │   │   └── parity.jay\n", "│   │   ├── \u001b[01;34m9\u001b[00m\n", "│   │   │   ├── cm.jay\n", "│   │   │   ├── disparity.jay\n", "│   │   │   ├── me_smd.jay\n", "│   │   │   └── parity.jay\n", "│   │   └── metrics.jay\n", "│   ├── \u001b[01;34mPAY_4\u001b[00m\n", "│   │   ├── \u001b[01;34m0\u001b[00m\n", "│   │   │   ├── cm.jay\n", "│   │   │   ├── disparity.jay\n", "│   │   │   ├── me_smd.jay\n", "│   │   │   └── parity.jay\n", "│   │   ├── \u001b[01;34m1\u001b[00m\n", "│   │   │   ├── cm.jay\n", "│   │   │   ├── disparity.jay\n", "│   │   │   ├── me_smd.jay\n", "│   │   │   └── parity.jay\n", "│   │   ├── \u001b[01;34m10\u001b[00m\n", "│   │   │   ├── cm.jay\n", "│   │   │   ├── disparity.jay\n", "│   │   │   ├── me_smd.jay\n", "│   │   │   └── parity.jay\n", "│   │   ├── \u001b[01;34m2\u001b[00m\n", "│   │   │   ├── cm.jay\n", "│   │   │   ├── disparity.jay\n", "│   │   │   ├── me_smd.jay\n", "│   │   │   └── parity.jay\n", "│   │   ├── \u001b[01;34m3\u001b[00m\n", "│   │   │   ├── cm.jay\n", "│   │   │   ├── disparity.jay\n", "│   │   │   ├── me_smd.jay\n", "│   │   │   └── parity.jay\n", "│   │   ├── \u001b[01;34m4\u001b[00m\n", "│   │   │   ├── cm.jay\n", "│   │   │   ├── disparity.jay\n", "│   │   │   ├── me_smd.jay\n", "│   │   │   └── parity.jay\n", "│   │   ├── \u001b[01;34m5\u001b[00m\n", "│   │   │   ├── cm.jay\n", "│   │   │   ├── disparity.jay\n", "│   │   │   ├── me_smd.jay\n", "│   │   │   └── parity.jay\n", "│   │   ├── \u001b[01;34m6\u001b[00m\n", "│   │   │   ├── cm.jay\n", "│   │   │   ├── disparity.jay\n", "│   │   │   ├── me_smd.jay\n", "│   │   │   └── parity.jay\n", "│   │   ├── \u001b[01;34m7\u001b[00m\n", "│   │   │   ├── cm.jay\n", "│   │   │   ├── disparity.jay\n", "│   │   │   ├── me_smd.jay\n", "│   │   │   └── parity.jay\n", "│   │   ├── \u001b[01;34m8\u001b[00m\n", "│   │   │   ├── cm.jay\n", "│   │   │   ├── disparity.jay\n", "│   │   │   ├── me_smd.jay\n", "│   │   │   └── parity.jay\n", "│   │   ├── \u001b[01;34m9\u001b[00m\n", "│   │   │   ├── cm.jay\n", "│   │   │   ├── disparity.jay\n", "│   │   │   ├── me_smd.jay\n", "│   │   │   └── parity.jay\n", "│   │   └── metrics.jay\n", "│   ├── \u001b[01;34mPAY_5\u001b[00m\n", "│   │   ├── \u001b[01;34m0\u001b[00m\n", "│   │   │   ├── cm.jay\n", "│   │   │   ├── disparity.jay\n", "│   │   │   ├── me_smd.jay\n", "│   │   │   └── parity.jay\n", "│   │   ├── \u001b[01;34m1\u001b[00m\n", "│   │   │   ├── cm.jay\n", "│   │   │   ├── disparity.jay\n", "│   │   │   ├── me_smd.jay\n", "│   │   │   └── parity.jay\n", "│   │   ├── \u001b[01;34m2\u001b[00m\n", "│   │   │   ├── cm.jay\n", "│   │   │   ├── disparity.jay\n", "│   │   │   ├── me_smd.jay\n", "│   │   │   └── parity.jay\n", "│   │   ├── \u001b[01;34m3\u001b[00m\n", "│   │   │   ├── cm.jay\n", "│   │   │   ├── disparity.jay\n", "│   │   │   ├── me_smd.jay\n", "│   │   │   └── parity.jay\n", "│   │   ├── \u001b[01;34m4\u001b[00m\n", "│   │   │   ├── cm.jay\n", "│   │   │   ├── disparity.jay\n", "│   │   │   ├── me_smd.jay\n", "│   │   │   └── parity.jay\n", "│   │   ├── \u001b[01;34m5\u001b[00m\n", "│   │   │   ├── cm.jay\n", "│   │   │   ├── disparity.jay\n", "│   │   │   ├── me_smd.jay\n", "│   │   │   └── parity.jay\n", "│   │   ├── \u001b[01;34m6\u001b[00m\n", "│   │   │   ├── cm.jay\n", "│   │   │   ├── disparity.jay\n", "│   │   │   ├── me_smd.jay\n", "│   │   │   └── parity.jay\n", "│   │   ├── \u001b[01;34m7\u001b[00m\n", "│   │   │   ├── cm.jay\n", "│   │   │   ├── disparity.jay\n", "│   │   │   ├── me_smd.jay\n", "│   │   │   └── parity.jay\n", "│   │   ├── \u001b[01;34m8\u001b[00m\n", "│   │   │   ├── cm.jay\n", "│   │   │   ├── disparity.jay\n", "│   │   │   ├── me_smd.jay\n", "│   │   │   └── parity.jay\n", "│   │   ├── \u001b[01;34m9\u001b[00m\n", "│   │   │   ├── cm.jay\n", "│   │   │   ├── disparity.jay\n", "│   │   │   ├── me_smd.jay\n", "│   │   │   └── parity.jay\n", "│   │   └── metrics.jay\n", "│   ├── \u001b[01;34mPAY_6\u001b[00m\n", "│   │   ├── \u001b[01;34m0\u001b[00m\n", "│   │   │   ├── cm.jay\n", "│   │   │   ├── disparity.jay\n", "│   │   │   ├── me_smd.jay\n", "│   │   │   └── parity.jay\n", "│   │   ├── \u001b[01;34m1\u001b[00m\n", "│   │   │   ├── cm.jay\n", "│   │   │   ├── disparity.jay\n", "│   │   │   ├── me_smd.jay\n", "│   │   │   └── parity.jay\n", "│   │   ├── \u001b[01;34m2\u001b[00m\n", "│   │   │   ├── cm.jay\n", "│   │   │   ├── disparity.jay\n", "│   │   │   ├── me_smd.jay\n", "│   │   │   └── parity.jay\n", "│   │   ├── \u001b[01;34m3\u001b[00m\n", "│   │   │   ├── cm.jay\n", "│   │   │   ├── disparity.jay\n", "│   │   │   ├── me_smd.jay\n", "│   │   │   └── parity.jay\n", "│   │   ├── \u001b[01;34m4\u001b[00m\n", "│   │   │   ├── cm.jay\n", "│   │   │   ├── disparity.jay\n", "│   │   │   ├── me_smd.jay\n", "│   │   │   └── parity.jay\n", "│   │   ├── \u001b[01;34m5\u001b[00m\n", "│   │   │   ├── cm.jay\n", "│   │   │   ├── disparity.jay\n", "│   │   │   ├── me_smd.jay\n", "│   │   │   └── parity.jay\n", "│   │   ├── \u001b[01;34m6\u001b[00m\n", "│   │   │   ├── cm.jay\n", "│   │   │   ├── disparity.jay\n", "│   │   │   ├── me_smd.jay\n", "│   │   │   └── parity.jay\n", "│   │   ├── \u001b[01;34m7\u001b[00m\n", "│   │   │   ├── cm.jay\n", "│   │   │   ├── disparity.jay\n", "│   │   │   ├── me_smd.jay\n", "│   │   │   └── parity.jay\n", "│   │   ├── \u001b[01;34m8\u001b[00m\n", "│   │   │   ├── cm.jay\n", "│   │   │   ├── disparity.jay\n", "│   │   │   ├── me_smd.jay\n", "│   │   │   └── parity.jay\n", "│   │   ├── \u001b[01;34m9\u001b[00m\n", "│   │   │   ├── cm.jay\n", "│   │   │   ├── disparity.jay\n", "│   │   │   ├── me_smd.jay\n", "│   │   │   └── parity.jay\n", "│   │   └── metrics.jay\n", "│   └── \u001b[01;34mSEX\u001b[00m\n", "│   ├── \u001b[01;34m0\u001b[00m\n", "│   │   ├── cm.jay\n", "│   │   ├── disparity.jay\n", "│   │   ├── me_smd.jay\n", "│   │   └── parity.jay\n", "│   ├── \u001b[01;34m1\u001b[00m\n", "│   │   ├── cm.jay\n", "│   │   ├── disparity.jay\n", "│   │   ├── me_smd.jay\n", "│   │   └── parity.jay\n", "│   └── metrics.jay\n", "├── \u001b[01;34mexplainer_h2o_sonar_explainers_dt_surrogate_explainer_DecisionTreeSurrogateExplainer_85f58120-d093-4917-8340-ebb430839da4\u001b[00m\n", "│   ├── \u001b[01;34mglobal_custom_archive\u001b[00m\n", "│   │   ├── \u001b[01;34mapplication_zip\u001b[00m\n", "│   │   │   └── \u001b[01;31mexplanation.zip\u001b[00m\n", "│   │   └── application_zip.meta\n", "│   ├── \u001b[01;34mglobal_decision_tree\u001b[00m\n", "│   │   ├── \u001b[01;34mapplication_json\u001b[00m\n", "│   │   │   ├── dt_class_0.json\n", "│   │   │   └── explanation.json\n", "│   │   └── application_json.meta\n", "│   ├── \u001b[01;34mglobal_html_fragment\u001b[00m\n", "│   │   ├── \u001b[01;34mtext_html\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdt-class-0.png\u001b[00m\n", "│   │   │   └── explanation.html\n", "│   │   └── text_html.meta\n", "│   ├── \u001b[01;34minsights\u001b[00m\n", "│   │   └── insights_and_actions.json\n", "│   ├── \u001b[01;34mlocal_decision_tree\u001b[00m\n", "│   │   ├── \u001b[01;34mapplication_json\u001b[00m\n", "│   │   │   └── explanation.json\n", "│   │   └── application_json.meta\n", "│   ├── \u001b[01;34mlog\u001b[00m\n", "│   │   └── explainer_run_85f58120-d093-4917-8340-ebb430839da4.log\n", "│   ├── \u001b[01;34mproblems\u001b[00m\n", "│   │   └── problems_and_actions.json\n", "│   ├── result_descriptor.json\n", "│   └── \u001b[01;34mwork\u001b[00m\n", "│   ├── dt-class-0.dot\n", "│   ├── dt-class-0.dot.pdf\n", "│   ├── dtModel.json\n", "│   ├── dtpaths_frame.bin\n", "│   ├── dtPathsFrame.csv\n", "│   ├── \u001b[01;31mdtsurr_mojo.zip\u001b[00m\n", "│   ├── dtSurrogate.json\n", "│   └── \u001b[01;31mdt_surrogate_rules.zip\u001b[00m\n", "├── \u001b[01;34mexplainer_h2o_sonar_explainers_pd_ice_explainer_PdIceExplainer_78cd7bae-55f0-4d31-a685-14c8781fdf70\u001b[00m\n", "│   ├── \u001b[01;34mglobal_html_fragment\u001b[00m\n", "│   │   ├── \u001b[01;34mtext_html\u001b[00m\n", "│   │   │   ├── explanation.html\n", "│   │   │   ├── \u001b[01;35mpd-feature-0-class-0.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mpd-feature-1-class-0.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mpd-feature-2-class-0.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mpd-feature-3-class-0.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mpd-feature-4-class-0.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mpd-feature-5-class-0.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mpd-feature-6-class-0.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mpd-feature-7-class-0.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mpd-feature-8-class-0.png\u001b[00m\n", "│   │   │   └── \u001b[01;35mpd-feature-9-class-0.png\u001b[00m\n", "│   │   └── text_html.meta\n", "│   ├── \u001b[01;34mglobal_partial_dependence\u001b[00m\n", "│   │   ├── \u001b[01;34mapplication_json\u001b[00m\n", "│   │   │   ├── explanation.json\n", "│   │   │   ├── pd_feature_0_class_0.json\n", "│   │   │   ├── pd_feature_1_class_0.json\n", "│   │   │   ├── pd_feature_2_class_0.json\n", "│   │   │   ├── pd_feature_3_class_0.json\n", "│   │   │   ├── pd_feature_4_class_0.json\n", "│   │   │   ├── pd_feature_5_class_0.json\n", "│   │   │   ├── pd_feature_6_class_0.json\n", "│   │   │   ├── pd_feature_7_class_0.json\n", "│   │   │   ├── pd_feature_8_class_0.json\n", "│   │   │   └── pd_feature_9_class_0.json\n", "│   │   └── application_json.meta\n", "│   ├── \u001b[01;34minsights\u001b[00m\n", "│   │   └── insights_and_actions.json\n", "│   ├── \u001b[01;34mlocal_individual_conditional_explanation\u001b[00m\n", "│   │   ├── \u001b[01;34mapplication_vnd_h2oai_json_datatable_jay\u001b[00m\n", "│   │   │   ├── explanation.json\n", "│   │   │   ├── ice_feature_0_class_0.jay\n", "│   │   │   ├── ice_feature_1_class_0.jay\n", "│   │   │   ├── ice_feature_2_class_0.jay\n", "│   │   │   ├── ice_feature_3_class_0.jay\n", "│   │   │   ├── ice_feature_4_class_0.jay\n", "│   │   │   ├── ice_feature_5_class_0.jay\n", "│   │   │   ├── ice_feature_6_class_0.jay\n", "│   │   │   ├── ice_feature_7_class_0.jay\n", "│   │   │   ├── ice_feature_8_class_0.jay\n", "│   │   │   ├── ice_feature_9_class_0.jay\n", "│   │   │   └── y_hat.jay\n", "│   │   └── application_vnd_h2oai_json_datatable_jay.meta\n", "│   ├── \u001b[01;34mlog\u001b[00m\n", "│   │   └── explainer_run_78cd7bae-55f0-4d31-a685-14c8781fdf70.log\n", "│   ├── \u001b[01;34mproblems\u001b[00m\n", "│   │   └── problems_and_actions.json\n", "│   ├── result_descriptor.json\n", "│   └── \u001b[01;34mwork\u001b[00m\n", "│   ├── h2o_sonar-ice-dai-model-10.jay\n", "│   ├── h2o_sonar-ice-dai-model-1.jay\n", "│   ├── h2o_sonar-ice-dai-model-2.jay\n", "│   ├── h2o_sonar-ice-dai-model-3.jay\n", "│   ├── h2o_sonar-ice-dai-model-4.jay\n", "│   ├── h2o_sonar-ice-dai-model-5.jay\n", "│   ├── h2o_sonar-ice-dai-model-6.jay\n", "│   ├── h2o_sonar-ice-dai-model-7.jay\n", "│   ├── h2o_sonar-ice-dai-model-8.jay\n", "│   ├── h2o_sonar-ice-dai-model-9.jay\n", "│   ├── h2o_sonar-ice-dai-model.json\n", "│   ├── h2o_sonar-pd-dai-model.json\n", "│   └── mli_dataset_y_hat.jay\n", "├── \u001b[01;34mexplainer_h2o_sonar_explainers_residual_dt_surrogate_explainer_ResidualDecisionTreeSurrogateExplainer_5ebdea59-f799-406d-aad3-aa090fce64b8\u001b[00m\n", "│   ├── \u001b[01;34mglobal_custom_archive\u001b[00m\n", "│   │   ├── \u001b[01;34mapplication_zip\u001b[00m\n", "│   │   │   └── \u001b[01;31mexplanation.zip\u001b[00m\n", "│   │   └── application_zip.meta\n", "│   ├── \u001b[01;34mglobal_decision_tree\u001b[00m\n", "│   │   ├── \u001b[01;34mapplication_json\u001b[00m\n", "│   │   │   ├── dt_class_0.json\n", "│   │   │   └── explanation.json\n", "│   │   └── application_json.meta\n", "│   ├── \u001b[01;34mglobal_html_fragment\u001b[00m\n", "│   │   ├── \u001b[01;34mtext_html\u001b[00m\n", "│   │   │   ├── \u001b[01;35mdt-class-0.png\u001b[00m\n", "│   │   │   └── explanation.html\n", "│   │   └── text_html.meta\n", "│   ├── \u001b[01;34minsights\u001b[00m\n", "│   │   └── insights_and_actions.json\n", "│   ├── \u001b[01;34mlocal_decision_tree\u001b[00m\n", "│   │   ├── \u001b[01;34mapplication_json\u001b[00m\n", "│   │   │   └── explanation.json\n", "│   │   └── application_json.meta\n", "│   ├── \u001b[01;34mlog\u001b[00m\n", "│   │   └── explainer_run_5ebdea59-f799-406d-aad3-aa090fce64b8.log\n", "│   ├── \u001b[01;34mproblems\u001b[00m\n", "│   │   └── problems_and_actions.json\n", "│   ├── result_descriptor.json\n", "│   └── \u001b[01;34mwork\u001b[00m\n", "│   ├── dt-class-0.dot\n", "│   ├── dt-class-0.dot.pdf\n", "│   ├── dtModel.json\n", "│   ├── dtpaths_frame.bin\n", "│   ├── dtPathsFrame.csv\n", "│   ├── \u001b[01;31mdtsurr_mojo.zip\u001b[00m\n", "│   ├── dtSurrogate.json\n", "│   └── \u001b[01;31mdt_surrogate_rules.zip\u001b[00m\n", "├── \u001b[01;34mexplainer_h2o_sonar_explainers_summary_shap_explainer_SummaryShapleyExplainer_c3010901-550e-4028-b471-ecba6036ff1c\u001b[00m\n", "│   ├── \u001b[01;34mglobal_html_fragment\u001b[00m\n", "│   │   ├── \u001b[01;34mtext_html\u001b[00m\n", "│   │   │   ├── explanation.html\n", "│   │   │   ├── \u001b[01;35mfeature_0_class_0.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mfeature_10_class_0.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mfeature_11_class_0.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mfeature_12_class_0.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mfeature_13_class_0.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mfeature_14_class_0.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mfeature_15_class_0.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mfeature_16_class_0.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mfeature_17_class_0.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mfeature_18_class_0.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mfeature_19_class_0.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mfeature_1_class_0.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mfeature_20_class_0.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mfeature_21_class_0.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mfeature_22_class_0.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mfeature_23_class_0.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mfeature_2_class_0.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mfeature_3_class_0.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mfeature_4_class_0.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mfeature_5_class_0.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mfeature_6_class_0.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mfeature_7_class_0.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mfeature_8_class_0.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mfeature_9_class_0.png\u001b[00m\n", "│   │   │   └── \u001b[01;35mshapley-class-0.png\u001b[00m\n", "│   │   └── text_html.meta\n", "│   ├── \u001b[01;34mglobal_summary_feature_importance\u001b[00m\n", "│   │   ├── \u001b[01;34mapplication_json\u001b[00m\n", "│   │   │   ├── explanation.json\n", "│   │   │   ├── \u001b[01;35mfeature_0_class_0.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mfeature_10_class_0.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mfeature_11_class_0.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mfeature_12_class_0.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mfeature_13_class_0.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mfeature_14_class_0.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mfeature_15_class_0.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mfeature_16_class_0.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mfeature_17_class_0.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mfeature_18_class_0.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mfeature_19_class_0.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mfeature_1_class_0.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mfeature_20_class_0.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mfeature_21_class_0.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mfeature_22_class_0.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mfeature_23_class_0.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mfeature_2_class_0.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mfeature_3_class_0.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mfeature_4_class_0.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mfeature_5_class_0.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mfeature_6_class_0.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mfeature_7_class_0.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mfeature_8_class_0.png\u001b[00m\n", "│   │   │   ├── \u001b[01;35mfeature_9_class_0.png\u001b[00m\n", "│   │   │   ├── summary_feature_importance_class_0_offset_0.json\n", "│   │   │   ├── summary_feature_importance_class_0_offset_1.json\n", "│   │   │   └── summary_feature_importance_class_0_offset_2.json\n", "│   │   ├── application_json.meta\n", "│   │   ├── \u001b[01;34mapplication_vnd_h2oai_json_datatable_jay\u001b[00m\n", "│   │   │   ├── explanation.json\n", "│   │   │   └── summary_feature_importance_class_0.jay\n", "│   │   ├── application_vnd_h2oai_json_datatable_jay.meta\n", "│   │   ├── \u001b[01;34mtext_markdown\u001b[00m\n", "│   │   │   ├── explanation.md\n", "│   │   │   └── \u001b[01;35mshapley-class-0.png\u001b[00m\n", "│   │   └── text_markdown.meta\n", "│   ├── \u001b[01;34minsights\u001b[00m\n", "│   │   └── insights_and_actions.json\n", "│   ├── \u001b[01;34mlog\u001b[00m\n", "│   │   └── explainer_run_c3010901-550e-4028-b471-ecba6036ff1c.log\n", "│   ├── \u001b[01;34mproblems\u001b[00m\n", "│   │   └── problems_and_actions.json\n", "│   ├── result_descriptor.json\n", "│   └── \u001b[01;34mwork\u001b[00m\n", "│   ├── raw_shapley_contribs_class_0.jay\n", "│   ├── raw_shapley_contribs_index.json\n", "│   ├── report.md\n", "│   └── \u001b[01;35mshapley-class-0.png\u001b[00m\n", "├── interpretation.html\n", "├── interpretation.json\n", "└── \u001b[01;34mtmp\u001b[00m\n", "\n", "144 directories, 595 files\n" ] } ], "source": [ "# View results directory\n", "!tree {interpretation.persistence.base_dir}" ] }, { "cell_type": "code", "execution_count": 13, "id": "0b04bbcc", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'model': '../../data/predictive/models/creditcard-binomial-sklearn-1.8.0-gbm.pkl',\n", " 'models': None,\n", " 'dataset': '../../data/predictive/creditcard.csv',\n", " 'validset': None,\n", " 'testset': None,\n", " 'use_raw_features': True,\n", " 'target_col': 'default payment next month',\n", " 'weight_col': '',\n", " 'prediction_col': '',\n", " 'drop_cols': [],\n", " 'sample_num_rows': 0,\n", " 'results_location': '../../results',\n", " 'extra_params': None,\n", " 'used_features': ['ID',\n", " 'LIMIT_BAL',\n", " 'SEX',\n", " 'EDUCATION',\n", " 'MARRIAGE',\n", " 'AGE',\n", " 'PAY_0',\n", " 'PAY_2',\n", " 'PAY_3',\n", " 'PAY_4',\n", " 'PAY_5',\n", " 'PAY_6',\n", " 'BILL_AMT1',\n", " 'BILL_AMT2',\n", " 'BILL_AMT3',\n", " 'BILL_AMT4',\n", " 'BILL_AMT5',\n", " 'BILL_AMT6',\n", " 'PAY_AMT1',\n", " 'PAY_AMT2',\n", " 'PAY_AMT3',\n", " 'PAY_AMT4',\n", " 'PAY_AMT5',\n", " 'PAY_AMT6'],\n", " 'cfg_items_dict': {'model': ,\n", " 'models': ,\n", " 'dataset': ,\n", " 'target_col': ,\n", " 'validset': ,\n", " 'testset': ,\n", " 'use_raw_features': ,\n", " 'weight_col': ,\n", " 'prediction_col': ,\n", " 'drop_cols': ,\n", " 'sample_num_rows': ,\n", " 'results_location': ,\n", " 'used_features': }}" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# view params passed into Interpretation job\n", "interpretation.common_params.dump()" ] }, { "cell_type": "code", "execution_count": null, "id": "4ed0f5d4-33d5-46e2-ad5b-7b8bb8476b65", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "H2O Sonar", "language": "python", "name": "h2o-sonar" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.11" } }, "nbformat": 4, "nbformat_minor": 5 }