{ "cells": [ { "cell_type": "markdown", "id": "a89d1e71-a876-4189-b6ed-ee226dad679e", "metadata": {}, "source": [ "# Disparate Impact Analysis (DIA) Explainer Demo\n", "\n", "This example demonstrates how to interpret a **scikit-learn** model using\n", "the H2O Sonar library and retrieve the data and plot the **Disparate Impact Analysis**." ] }, { "cell_type": "code", "execution_count": 1, "id": "e4108f24-6e39-43a2-abda-456129eaf361", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import logging\n", "\n", "import pandas\n", "import webbrowser\n", "\n", "from h2o_sonar import interpret\n", "from h2o_sonar.lib.api import commons, explainers\n", "from h2o_sonar.explainers.dia_explainer import DiaExplainer\n", "from h2o_sonar.lib.api.models import ModelApi\n", "\n", "from sklearn.ensemble import GradientBoostingClassifier" ] }, { "cell_type": "code", "execution_count": 2, "id": "e8858dd3-64f5-4341-b3f7-c3a4115fdb24", "metadata": {}, "outputs": [], "source": [ "results_location = \"../../results\"\n", "\n", "# dataset\n", "dataset_path = \"../../data/predictive/creditcard.csv\"\n", "target_col = \"default payment next month\"\n", "df = pandas.read_csv(dataset_path)\n", "(X, y) = df.drop(target_col, axis=1), df[target_col]" ] }, { "cell_type": "code", "execution_count": 3, "id": "6eaeb369", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'id': 'h2o_sonar.explainers.dia_explainer.DiaExplainer',\n", " 'name': 'DiaExplainer',\n", " 'display_name': 'Disparate Impact Analysis',\n", " 'tagline': 'DiaExplainer.',\n", " 'description': 'Disparate Impact Analysis (DIA) is a technique that is used to evaluate fairness. Bias can be introduced to models during the process of collecting, processing, and labeling data as a result, it is important to determine whether a model is harming certain users by making a significant number of biased decisions. DIA typically works by comparing aggregate measurements of unprivileged groups to a privileged group. For instance, the proportion of the unprivileged group that receives the potentially harmful outcome is divided by the proportion of the privileged group that receives the same outcome - the resulting proportion is then used to determine whether the model is biased.',\n", " 'brief_description': 'DiaExplainer.',\n", " 'model_types': ['iid', 'time_series'],\n", " 'can_explain': ['regression', 'binomial'],\n", " 'explanation_scopes': ['global_scope'],\n", " 'explanations': [{'explanation_type': 'global-disparate-impact-analysis',\n", " 'name': 'DiaExplanation',\n", " 'category': '',\n", " 'scope': 'global',\n", " 'has_local': '',\n", " 'formats': []}],\n", " 'keywords': ['run-by-default', 'explains-fairness', 'h2o-sonar'],\n", " 'parameters': [{'name': 'dia_cols',\n", " 'description': 'List of features for which to compute DIA.',\n", " 'comment': '',\n", " 'type': 'list',\n", " 'val': None,\n", " 'predefined': [],\n", " 'tags': [],\n", " 'min_': 0.0,\n", " 'max_': 0.0,\n", " 'category': ''},\n", " {'name': 'cut_off',\n", " 'description': 'Cut off.',\n", " 'comment': '',\n", " 'type': 'float',\n", " 'val': 0.0,\n", " 'predefined': [],\n", " 'tags': [],\n", " 'min_': 0.0,\n", " 'max_': 0.0,\n", " 'category': ''},\n", " {'name': 'maximize_metric',\n", " 'description': 'Maximize metric.',\n", " 'comment': '',\n", " 'type': 'str',\n", " 'val': 'F1',\n", " 'predefined': ['F1', 'F05', 'F2', 'MCC'],\n", " 'tags': [],\n", " 'min_': 0.0,\n", " 'max_': 0.0,\n", " 'category': ''},\n", " {'name': 'max_cardinality',\n", " 'description': 'Max cardinality for categorical variables.',\n", " 'comment': '',\n", " 'type': 'int',\n", " 'val': 10,\n", " 'predefined': [],\n", " 'tags': [],\n", " 'min_': 0.0,\n", " 'max_': 0.0,\n", " 'category': ''},\n", " {'name': 'min_cardinality',\n", " 'description': 'Minimum cardinality for categorical variables.',\n", " 'comment': '',\n", " 'type': 'int',\n", " 'val': 2,\n", " 'predefined': [],\n", " 'tags': [],\n", " 'min_': 0.0,\n", " 'max_': 0.0,\n", " 'category': ''},\n", " {'name': 'num_card',\n", " 'description': 'Max cardinality for numeric variables to be considered categorical.',\n", " 'comment': '',\n", " 'type': 'int',\n", " 'val': 25,\n", " 'predefined': [],\n", " 'tags': [],\n", " 'min_': 0.0,\n", " 'max_': 0.0,\n", " 'category': ''}],\n", " 'metrics_meta': []}" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# parameters\n", "interpret.describe_explainer(DiaExplainer)" ] }, { "cell_type": "markdown", "id": "0a146156-5e48-4fe0-9f58-f37c305f89a6", "metadata": {}, "source": [ "## Interpret" ] }, { "cell_type": "code", "execution_count": 4, "id": "f9010a65-f211-4160-a7a6-2dca49618f06", "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", "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" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Preparing and checking DIA features (None): dataset= | PAY_0 PAY_4 BILL_AMT2 AGE PAY_3 EDUCATION BILL_AMT1 PAY_AMT2 SEX PAY_AMT5 … PAY_AMT4 PAY_AMT6 BILL_AMT3 LIMIT_BAL BILL_AMT4\n", " | int64 int64 int64 int64 int64 int64 int64 int64 int64 int64 int64 int64 int64 int64 int64\n", "---- + ----- ----- --------- ----- ----- --------- --------- -------- ----- -------- -------- -------- --------- --------- ---------\n", " 0 | -2 -1 3102 24 -1 2 3913 689 2 0 … 0 0 689 20000 0\n", " 1 | -1 0 1725 26 0 2 2682 1000 2 0 … 1000 2000 2682 120000 3272\n", " 2 | 0 0 14027 34 0 2 29239 1500 2 1000 … 1000 5000 13559 90000 14331\n", " 3 | 1 0 48233 37 0 2 46990 2019 2 1069 … 1100 1000 49291 50000 28314\n", " 4 | 2 0 5670 57 -1 2 8617 36681 1 689 … 9000 679 35835 50000 20940\n", " 5 | 3 0 57069 37 0 1 64400 1815 1 1000 … 1000 800 57608 50000 19394\n", " 6 | 4 0 412023 29 0 1 367965 40000 1 13750 … 20239 13770 445007 500000 542653\n", " 7 | 5 0 380 23 -1 2 11876 601 2 1687 … 581 1542 601 100000 221\n", " 8 | 6 0 14096 28 2 3 11285 0 2 1000 … 1000 1000 12108 140000 12211\n", " 9 | 7 -2 0 35 -2 3 0 0 1 1122 … 13007 0 0 20000 0\n", " 10 | 8 0 9787 34 2 3 11073 12 2 3738 … 300 66 5535 200000 2513\n", " 11 | -1 -1 21670 51 -1 1 12261 9966 2 0 … 22301 3640 9966 260000 8517\n", " 12 | -1 -1 6500 41 -1 2 12137 6500 2 2870 … 6500 0 6500 630000 6500\n", " 13 | 1 0 67369 30 2 2 65802 0 1 1500 … 3000 0 65701 70000 66782\n", " 14 | 0 0 67060 29 0 1 70887 3000 1 3000 … 3000 3000 63561 250000 59696\n", " … | … … … … … … … … … … … … … … … …\n", "9995 | 1 -2 241 31 -2 1 0 0 2 0 … 0 1419 0 140000 0\n", "9996 | -2 -2 0 37 -2 2 3946 0 2 0 … 0 0 0 80000 0\n", "9997 | 0 0 144085 44 0 3 138877 5000 1 10017 … 27080 4200 142520 200000 151078\n", "9998 | -1 -2 780 26 2 2 780 0 2 0 … 0 0 0 80000 0\n", "9999 | 0 0 20715 36 0 2 19505 3000 1 3000 … 3000 3000 19750 230000 19506\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\": \"\",\n", " \"file_name\": \"\",\n", " \"file_size\": 0,\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: {'PAY_5', 'PAY_6', 'default payment next month', 'SEX', 'PAY_0', 'MARRIAGE', 'PAY_4', 'PAY_3', 'EDUCATION', 'PAY_2'}\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": [ "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" ] } ], "source": [ "# scikit-learn model\n", "gradient_booster = GradientBoostingClassifier(learning_rate=0.1)\n", "gradient_booster.fit(X, y)\n", "\n", "# explainable model\n", "model = ModelApi().create_model(target_col=target_col, model_src=gradient_booster, used_features=X.columns.to_list())\n", "\n", "interpretation = interpret.run_interpretation(\n", " dataset=df,\n", " model=model,\n", " target_col=target_col,\n", " results_location=results_location,\n", " log_level=logging.INFO,\n", " explainers=[\n", " commons.ExplainerToRun(\n", " explainer_id=DiaExplainer.explainer_id(),\n", " params=\"\",\n", " )\n", " ]\n", ")" ] }, { "cell_type": "markdown", "id": "94378f1c-9778-42ad-a9c5-a3696266d90c", "metadata": {}, "source": [ "## Interact with the Explainer Result" ] }, { "cell_type": "code", "execution_count": 5, "id": "ecd0a2d7-c462-4b9e-aab7-ded94a73a278", "metadata": {}, "outputs": [], "source": [ "# retrieve the result\n", "result = interpretation.get_explainer_result(DiaExplainer.explainer_id())" ] }, { "cell_type": "code", "execution_count": 6, "id": "35e6147e-4e12-4faf-9584-4b3ad93d907b", "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": "324568e9-5495-4a5f-969b-ee9108b5e644", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'id': 'h2o_sonar.explainers.dia_explainer.DiaExplainer',\n", " 'name': 'DiaExplainer',\n", " 'display_name': 'Disparate Impact Analysis',\n", " 'tagline': 'DiaExplainer.',\n", " 'description': 'Disparate Impact Analysis (DIA) is a technique that is used to evaluate fairness. Bias can be introduced to models during the process of collecting, processing, and labeling data as a result, it is important to determine whether a model is harming certain users by making a significant number of biased decisions. DIA typically works by comparing aggregate measurements of unprivileged groups to a privileged group. For instance, the proportion of the unprivileged group that receives the potentially harmful outcome is divided by the proportion of the privileged group that receives the same outcome - the resulting proportion is then used to determine whether the model is biased.',\n", " 'brief_description': 'DiaExplainer.',\n", " 'model_types': ['iid', 'time_series'],\n", " 'can_explain': ['regression', 'binomial'],\n", " 'explanation_scopes': ['global_scope'],\n", " 'explanations': [{'explanation_type': 'global-disparate-impact-analysis',\n", " 'name': 'Disparate Impact Analysis',\n", " 'category': 'DAI MODEL',\n", " 'scope': 'global',\n", " 'has_local': None,\n", " 'formats': ['text/plain']},\n", " {'explanation_type': 'global-html-fragment',\n", " 'name': 'Disparate Impact Analysis',\n", " 'category': 'DAI MODEL',\n", " 'scope': 'global',\n", " 'has_local': None,\n", " 'formats': ['text/html']}],\n", " 'keywords': ['run-by-default', 'explains-fairness', 'h2o-sonar'],\n", " 'parameters': [{'name': 'dia_cols',\n", " 'description': 'List of features for which to compute DIA.',\n", " 'comment': '',\n", " 'type': 'list',\n", " 'val': None,\n", " 'predefined': [],\n", " 'tags': [],\n", " 'min_': 0.0,\n", " 'max_': 0.0,\n", " 'category': ''},\n", " {'name': 'cut_off',\n", " 'description': 'Cut off.',\n", " 'comment': '',\n", " 'type': 'float',\n", " 'val': 0.0,\n", " 'predefined': [],\n", " 'tags': [],\n", " 'min_': 0.0,\n", " 'max_': 0.0,\n", " 'category': ''},\n", " {'name': 'maximize_metric',\n", " 'description': 'Maximize metric.',\n", " 'comment': '',\n", " 'type': 'str',\n", " 'val': 'F1',\n", " 'predefined': ['F1', 'F05', 'F2', 'MCC'],\n", " 'tags': [],\n", " 'min_': 0.0,\n", " 'max_': 0.0,\n", " 'category': ''},\n", " {'name': 'max_cardinality',\n", " 'description': 'Max cardinality for categorical variables.',\n", " 'comment': '',\n", " 'type': 'int',\n", " 'val': 10,\n", " 'predefined': [],\n", " 'tags': [],\n", " 'min_': 0.0,\n", " 'max_': 0.0,\n", " 'category': ''},\n", " {'name': 'min_cardinality',\n", " 'description': 'Minimum cardinality for categorical variables.',\n", " 'comment': '',\n", " 'type': 'int',\n", " 'val': 2,\n", " 'predefined': [],\n", " 'tags': [],\n", " 'min_': 0.0,\n", " 'max_': 0.0,\n", " 'category': ''},\n", " {'name': 'num_card',\n", " 'description': 'Max cardinality for numeric variables to be considered categorical.',\n", " 'comment': '',\n", " 'type': 'int',\n", " 'val': 25,\n", " 'predefined': [],\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": "83be40a4-fe14-4fcd-919d-c0f5a2c84e31", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'dia_cols': ['PAY_5',\n", " 'PAY_6',\n", " 'SEX',\n", " 'PAY_0',\n", " 'MARRIAGE',\n", " 'PAY_4',\n", " 'PAY_3',\n", " 'EDUCATION',\n", " 'PAY_2'],\n", " 'cut_off': 0.0,\n", " 'maximize_metric': 'F1',\n", " 'max_cardinality': 10,\n", " 'min_cardinality': 2,\n", " 'num_card': 25}" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# parameters\n", "result.params()" ] }, { "cell_type": "markdown", "id": "9e780aa0-7682-47c6-a069-9ab28230aeab", "metadata": {}, "source": [ "### Display the DIA data" ] }, { "cell_type": "code", "execution_count": 9, "id": "77d2a9c5-1068-4e7c-9098-652b7eed6ec6", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'methods': {'data': {'doc': '',\n", " 'parameters': [{'default': '',\n", " 'doc': 'The name of the feature whose '\n", " 'data we want to retrieve.',\n", " 'name': 'feature_name',\n", " 'required': True,\n", " 'type': 'str'},\n", " {'default': '',\n", " 'doc': 'The category of data to be '\n", " 'retrieve. This can be one of the '\n", " \"following: 'metrics', 'cm', \"\n", " \"'me_smd', 'disparity' and \"\n", " \"'parity'\",\n", " 'name': 'category',\n", " 'required': True,\n", " 'type': 'str'},\n", " {'default': 'The first reference level '\n", " 'will be selected from the '\n", " 'set of available reference '\n", " 'levels.',\n", " 'doc': 'The reference levels for each '\n", " 'categorical data',\n", " 'name': 'ref_level',\n", " 'required': False,\n", " 'type': 'str | int'}]}}}\n" ] } ], "source": [ "# Print method help\n", "import pprint\n", "pprint.pprint(result.help())" ] }, { "cell_type": "code", "execution_count": 10, "id": "8d6a47ed-1208-4ea0-bd1f-c9a23e5eaf31", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
GroupsNAdverse ImpactAccuracyTrue Positive RatePrecisionSpecificityNegative Predicted ValueFalse Positive RateFalse Discovery RateFalse Negative RateFalse Omissions Rate
▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪
00170.98530.22440.9641430.2210490.008526030.448980.9914740.7789510.03585660.55102
1143900.9881550.2387240.9724860.2362840.006894480.4423080.9931060.7637160.02751420.557692
2254680.9829920.2134240.9566330.2093020.009785650.4516130.9902140.7906980.04336730.548387
331250.9840.2240.9642860.2195120.01030930.50.9896910.7804880.03571430.5
\n", "
\n", "
4 rows × 12 columns
\n", "
\n", "
\n" ], "text/plain": [ "" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "result.data(feature_name=\"MARRIAGE\", category=result.DiaCategory.DIA_METRICS)" ] }, { "cell_type": "code", "execution_count": 11, "id": "568925e0-a935-4d31-832e-990d752d6d99", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
GroupsNAdverse Impact DisparityMarginal ErrorStandardized Mean DifferenceAccuracy DisparityTrue Positive Rate DisparityPrecision DisparitySpecificity DisparityNegative Predicted Value DisparityFalse Positive Rate DisparityFalse Discovery Rate DisparityFalse Negative Rate DisparityFalse Omissions Rate Disparity
▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪
0031.00296−0.002910080.1463180.9078781.00220.9086290.7837390.9670331.002381.029370.9442231.02857
1137141.00778−0.007647750.06354330.8409691.012650.8440160.5317090.9896051.005151.050150.679571.00901
2245880.9999554.47035e-05−0.0003713060.9453140.9953220.9457770.917920.9306741.00091.017431.11851.06008
331590100111111111
44261.01793−0.01761010.1463180.1556081.039470.1580970NA1.0111.270660NA
55691.00317−0.003117320.02590090.3518091.039470.3022451.43632.153850.9952011.2243200
66101.01793−0.01761010.1463180NA00NA1.0111.32149NANA
\n", "
\n", "
7 rows × 14 columns
\n", "
\n", "
\n" ], "text/plain": [ "" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "result.data(feature_name=\"EDUCATION\", category=result.DiaCategory.DIA_CATEGORY_DISPARITY, ref_level=\"3\")" ] }, { "cell_type": "code", "execution_count": 12, "id": "a8ce85ad-30a5-497f-a2ed-29c4a4453245", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
actual1actual0
▪▪▪▪▪▪▪▪
03321640
144
\n", "
\n", "
2 rows × 2 columns
\n", "
\n", "
\n" ], "text/plain": [ "" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "result.data(feature_name=\"PAY_4\", category=result.DiaCategory.DIA_CATEGORY_CM, ref_level=-1)" ] }, { "cell_type": "code", "execution_count": 13, "id": "a7766faf-8bfb-45f3-9675-50250b19db07", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
GroupsNMarginal ErrorStandardized Mean Difference
▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪
01420600
1257940.00936753−0.0778322
\n", "
\n", "
2 rows × 4 columns
\n", "
\n", "
\n" ], "text/plain": [ "" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "result.data(feature_name=\"SEX\", category=result.DiaCategory.DIA_CATEGORY_ME_SMD)" ] }, { "cell_type": "code", "execution_count": 14, "id": "eaf6e65c-515a-4c37-a965-3e339cb97ab6", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
GroupsNAdverse Impact ParityAccuracy ParityTrue Positive Rate ParityPrecision ParitySpecificity ParityNegative Predicted Value ParityFalse Positive Rate ParityFalse Discovery Rate Parity…Type I ParityType II ParityEqualized OddsSupervised FairnessOverall Fairness
▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪
0-2121811110011…10100
1-1217111110011…10100
20514711110011…10100
31810000010…00000
42129410100010…00100
5310600000000…00000
642500000000…00000
751100000000…00000
86700000000…00000
971200000000…00000
108100000000…00000
11all1000000000000…00000
\n", "
\n", "
12 rows × 17 columns
\n", "
\n", "
\n" ], "text/plain": [ "" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "result.data(feature_name=\"PAY_2\", category=result.DiaCategory.DIA_CATEGORY_PARITY)" ] }, { "cell_type": "code", "execution_count": 15, "id": "78b2bde6-0cd3-4694-803a-c864ef3fcd94", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
GroupsNAdverse Impact DisparityMarginal ErrorStandardized Mean DifferenceAccuracy DisparityTrue Positive Rate DisparityPrecision DisparitySpecificity DisparityNegative Predicted Value DisparityFalse Positive Rate DisparityFalse Discovery Rate DisparityFalse Negative Rate DisparityFalse Omissions Rate Disparity
▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪▪
0017100111111111
1143901.0029−0.00285488−0.09841791.063831.008651.068920.808640.985141.001650.9804420.7673411.01211
2254680.9976570.00230807−0.1413160.9510850.992210.9468581.147741.005870.998731.015081.209470.995221
331250.9986810.00129998−0.132940.9982181.000150.9930461.209151.113640.9982011.001970.9960320.907407
\n", "
\n", "
4 rows × 14 columns
\n", "
\n", "
\n" ], "text/plain": [ "" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "result.data(feature_name=\"MARRIAGE\", category=result.DiaCategory.DIA_CATEGORY_DISPARITY)" ] }, { "cell_type": "markdown", "id": "fc15a1ba-c6c7-46a9-b60f-1f4308bcc448", "metadata": {}, "source": [ "### Plot the DIA Metrics" ] }, { "cell_type": "code", "execution_count": 16, "id": "3e903c66-05ad-492c-82f1-56ecd93dac47", "metadata": {}, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiMAAAHCCAYAAAA92skLAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjgsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvwVt1zgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAOMNJREFUeJzt3XtcVVX+//H3AQQERfESkKJ4VzTBRBQ1zcTUr5qmJZqF8Z2cmuSbhvab0Roxu6CZZuVtcsZLimKO2j2zUJqxNBUH77dMxEZBKQVFxYT9+8MHJxFQDpgL9fV8PPbj0Vl77bU/Z5/ovNt77X1slmVZAgAAMMTJdAEAAODORhgBAABGEUYAAIBRhBEAAGAUYQQAABhFGAEAAEYRRgAAgFGEEQAAYBRhBAAAGEUYAYAKICkpSTabTUlJSaZLAW46wghQSgsXLpTNZitx2bRpk73vle0uLi6qUaOG2rZtq1GjRmnPnj1Fxi74IvrnP/9Z7L6jo6Nls9mKtOfl5WnBggW6//77VaNGDbm5uSkgIEBRUVHaunVrsWPNnj1bNptN7du3L9QeEBBwzfdXsCxcuND+HqOjo4uM//PPP+uFF15Qs2bN5O7urho1aqhnz5769NNPi/RNTU21j7ty5coi6ydOnCibzabMzMxi30tZFBxrm82mJUuWFNunU6dOstlsatWqVZn2sXTpUs2YMaMcVQJ3FhfTBQC3mkmTJqlBgwZF2hs3blzodY8ePRQZGSnLspSVlaXt27dr0aJFmj17tqZMmaKYmJhy1XH+/HkNHDhQa9asUZcuXTR+/HjVqFFDqamp+uCDD7Ro0SKlpaWpbt26hbaLj49XQECANm/erB9++MFe94wZM3T27Fl7v88//1zLli3TW2+9pVq1atnbO3bsWGJN+/fvV/fu3XXy5ElFRUUpJCREp0+fVnx8vPr166exY8dq6tSpxW47adIkDRw4sNjQ9Xtwd3fX0qVL9fjjjxdqT01N1XfffSd3d/cyj7106VLt2rVLo0ePLvU2Xbp00fnz5+Xq6lrm/QK3LAtAqSxYsMCSZG3ZsuW6fSVZI0eOLNKemZlphYWFWZKszz77zN6+fv16S5K1YsWKYscbOXKkdfWfa0HbW2+9VaT/pUuXrKlTp1pHjx4t1P7jjz9akqxVq1ZZtWvXtiZOnFjie5g6daolyTp8+HCp3uPFixetVq1aWR4eHtamTZuK1BMREWFJshISEuzthw8ftiRZwcHBliRr5cqVhbaLjY21JFknT54ssU5HFRzrgQMHWi4uLkXGfu211ywfHx+rc+fOVsuWLcu0jz59+lj169cvVd/z589beXl5ZdoPcLvgMg1wE9WsWVMJCQlycXHRa6+9VuZxfvrpJ/3tb39Tjx49iv2/b2dnZ40dO7bYsyLe3t7q06ePHnnkEcXHx5e5hqutXLlSu3bt0l/+8pcil4CcnZ31t7/9TdWrV9fEiROLbDtkyBA1bdpUkyZNknWTfki8f//+cnNz04oVKwq1L126VIMHD5azs3Ox2y1ZskRt27ZV5cqVVaNGDQ0ZMkRHjx61r7///vv12Wef6ciRI/bLQQEBAZJ+u0SUkJCgl156SXXq1JGHh4eys7NLnDPy/fff63/+53/k7e0tT09PtW7dWm+//bZ9fXp6uqKiolS3bl25ubnJz89P/fv3V2pq6g05TsDNQBgBHJSVlaXMzMxCy88//1zq7evVq6euXbtq06ZNys7OLlMNX3zxhS5duqQnnnjCoe3i4+M1cOBAubq6aujQoTp48KC2bNlSphqu9sknn0iSIiMji11frVo19e/fX/v27dMPP/xQaJ2zs7Neeuklbd++XatXr74h9VyPh4eH+vfvr2XLltnbtm/frt27d+uxxx4rdpvXXntNkZGRatKkiaZPn67Ro0crMTFRXbp00enTpyVJL774ooKDg1WrVi0tXrxYixcvLjJ/5JVXXtFnn32msWPH6vXXXy/x0sxXX32lLl26aM+ePRo1apSmTZumbt26FZp/M2jQIK1evVpRUVGaPXu2nnvuOZ05c0ZpaWnlO0DATcScEcBB4eHhRdrc3Nx04cKFUo/RqlUrJSYmKjU1Va1bt3a4hr1790qS7rnnnlJvk5ycrH379undd9+VJHXu3Fl169ZVfHy82rVr53ANV9uzZ4+qVaum+vXrl9gnKChI0uX6r55j89hjj+mVV17RpEmT9PDDD9+UuSOPPfaY+vXrp6NHj8rf31/x8fFq2LChOnToUKTvkSNHFBsbq1dffVXjx4+3tw8cOFBt2rTR7NmzNX78ePXo0UN16tTRqVOnisxHKXDhwgVt3bpVlStXLrG2vLw8Pf300/Lz81NKSoqqV69uX1dw9uj06dP67rvvNHXqVI0dO9a+fty4cY4eCsAozowADpo1a5a++uqrQssXX3zh0BhVqlSRJJ05c6ZMNRScUalatWqpt4mPj5ePj4+6desm6fLdMBEREUpISFBeXl6Z6rjSmTNnrltPwfrizghdeXbkww8/LHc9pfHggw+qRo0aSkhIkGVZSkhI0NChQ4vtu2rVKuXn52vw4MGFzor5+vqqSZMmWr9+fan3O3z48GsGEUn6z3/+o8OHD2v06NGFgogke1CrXLmyXF1dlZSUpFOnTpV6/0BFw5kRwEGhoaEKCQkp1xgFd604Eiau5OXlJan0YSYvL08JCQnq1q2bDh8+bG9v3769pk2bpsTERD344INlqqVA1apVr3sLbkG9Jb3vYcOG2c+ODBgwoFT7vXjxon755ZdCbbVr1y5xzseVKlWqpEcffVRLly5VaGiojh49WuIlmoMHD8qyLDVp0qTEsUqruLuxrnbo0CFJuubtxW5ubpoyZYrGjBkjHx8fdejQQX379lVkZKR8fX1LXQ9gGmEEMGDXrl1ydna2fykV3EZ6/vz5YvufO3eu0K2mzZs3lyTt3LlTwcHB193funXrdPz4cSUkJCghIaHI+vj4+HKHkRYtWiglJUVpaWmqV69esX127NghSQoMDCx2fcHZkSeffFIfffRRqfb73Xff2c/2FDh8+LB90uj1PPbYY5o7d64mTpyooKCgEmvLz8+XzWbTF198UWzQKTjbVRrXOyviiNGjR6tfv3768MMP9eWXX+qvf/2r4uLitG7dOrVp0+aG7Qf4PRFGgJssLS1N33zzjcLCwuxnCArmWezfv7/Ybfbv319oLkbv3r3l7OysJUuWlGoSa3x8vO666y7NmjWryLpVq1Zp9erVmjt3brm+JPv27atly5bp/fff10svvVRkfXZ2tj766CM1b968yHyRKz3++ON69dVX9fLLL+uhhx667n6DgoL01VdfFWpz5KxA586dVa9ePSUlJWnKlCkl9mvUqJEsy1KDBg3UtGnTa455I+a7NGrUSNLl4FrcPKWr+44ZM0ZjxozRwYMHFRwcrGnTppX4UDegomHOCHAT/fLLLxo6dKjy8vL04osv2tv9/PwUHBysJUuW2O/KKJCcnKxNmzapd+/e9jZ/f3+NGDFCa9eutU9IvVJ+fr6mTZumn376SefPn9eqVavUt29fPfLII0WW6OhonTlzRh9//HG53tsjjzyiwMBATZ48ucjTX/Pz8/WnP/1Jp06dUmxs7DXHKTg7kpKSUqqavL29FR4eXmhx5IFlNptN77zzjmJjY68Z7AYOHChnZ2e9/PLLRW4/tiyr0B1Vnp6eysrKKnUNxbn33nvVoEEDzZgxo8i/EwX7P3fuXJGJ040aNVLVqlWVm5tbrv0DNxNnRgAHffHFF9q3b1+R9o4dO6phw4b21wcOHNCSJUtkWZays7O1fft2rVixQmfPntX06dPVq1evQttPnz5dPXv2VHBwsJ588kndfffd2rt3r9577z35+fkVuUNi2rRpOnTokJ577jl72PD29lZaWppWrFihffv2aciQIfr444915syZEs8ydOjQQbVr11Z8fLwiIiLKfFxcXV31z3/+U927d1fnzp0LPYF16dKl2rZtm8aMGaMhQ4Zcd6yCuSMpKSllrscR/fv3V//+/a/Zp1GjRnr11Vc1btw4paamasCAAapataoOHz6s1atX649//KP9jpa2bdtq+fLliomJUbt27VSlShX169fPoZqcnJw0Z84c9evXT8HBwYqKipKfn5/27dun3bt368svv9SBAwfUvXt3DR48WIGBgXJxcdHq1auVkZFRquMMVBjGHrcG3GIKnsBa0rJgwQJ73yvbnZycrOrVq1tt2rSxRo0aZe3evbvEfWzatMnq27ev5e3tbbm4uFh16tSxnnrqKeunn34qtv+lS5esv//979Z9991nVatWzapUqZJVv359KyoqyvrPf/5jWZZl9evXz3J3d7dycnJK3O+TTz5pVapUycrMzLS3OfoE1gInTpywYmJirMaNG1tubm5W9erVrfDwcOvjjz8u0rfgCaxTp04tsu7K4/17PIG1pKfdFujatWuxT2BduXKl1blzZ8vT09Py9PS0mjdvbo0cOdLav3+/vc/Zs2etxx57zKpevbolyf401mvtu2Dd+vXrC7Vv2LDB6tGjh1W1alXL09PTat26tfXuu+9alnX5ib4jR460mjdvbnl6elrVqlWz2rdvb33wwQcOHhXALJtl3aTHHQIAABSDOSMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMOqWeOhZfn6+jh07pqpVq96UnxUHAADlZ1mWzpw5o7vvvltOTiWf/7glwsixY8fk7+9vugwAAFAGR48eVd26dUtcf0uEkYIfEzt69Kj9p9MBAEDFlp2dLX9/f/v3eEluiTBScGnGy8uLMAIAwC3melMsmMAKAACMIowAAACjCCMAAMCoW2LOCABURHl5efr1119NlwEYU6lSJTk7O5d7HMIIADjIsiylp6fr9OnTpksBjKtevbp8fX3L9RwwwggAOKggiNx1113y8PDgYYy4I1mWpXPnzunEiROSJD8/vzKPRRgBAAfk5eXZg0jNmjVNlwMYVblyZUnSiRMndNddd5X5kg0TWAHAAQVzRDw8PAxXAlQMBX8L5Zk/RRgBgDLg0gxw2Y34WyCMAAAAowgjAIDrWrhwoapXr266jDJLTU2VzWZTSkpKucey2Wz68MMPyz0OfsMEVgC4QazNTW/q/myhBxzq/+STT2rRokVF2g8ePKjGjRvfqLLK7Pz585o8ebKWLVumI0eOqGrVqurWrZsmTpyoli1blmtsf39/HT9+XLVq1Sp3ncePH5e3t3e5xymrpKQkdevWzf66Vq1aateunaZMmaJ77rmn1OMsXLhQo0ePrhC3qHNmBADuIL169dLx48cLLQ0aNDBdlnJzcxUeHq758+fr1Vdf1YEDB/T555/r0qVLat++vTZt2lSu8Z2dneXr6ysXl/L/P7ivr6/c3NzKPU557d+/X8ePH9eXX36p3Nxc9enTRxcvXjRdVpkQRgDgDuLm5iZfX99Ci7Ozs6ZPn6577rlHnp6e8vf317PPPquzZ8+WOM727dvVrVs3Va1aVV5eXmrbtq22bt1qX79hwwbdd999qly5svz9/fXcc88pJyenxPFmzJihjRs36tNPP9XgwYNVv359hYaGauXKlWrRooX+8Ic/yLIsSZfP8AwYMECvv/66fHx8VL16dU2aNEmXLl3SCy+8oBo1aqhu3bpasGCBffyrL9OcOnVKw4YNU+3atVW5cmU1adLE3v/ixYuKjo6Wn5+f3N3dVb9+fcXFxdnHuvoyzc6dO/XAAw+ocuXKqlmzpv74xz8WOnYF9b755pvy8/NTzZo1NXLkyEJ3n8yePVtNmjSRu7u7fHx89Mgjj1znk5Tuuusu+fr66t5779Xo0aN19OhR7du3z77+Wp9pUlKSoqKilJWVJZvNJpvNpokTJ0q6HAzHjh2rOnXqyNPTU+3bt1dSUtJ16ykPwggAQE5OTnrnnXe0e/duLVq0SOvWrdP/+3//r8T+w4YNU926dbVlyxYlJyfrL3/5iypVqiRJOnTokHr16qVBgwZpx44dWr58uTZs2KDo6OgSx1u6dKl69OihoKCgInU9//zz2rNnj7Zv325vX7dunY4dO6Z//etfmj59umJjY9W3b195e3vr+++/1zPPPKOnn35aP/30U7H7++tf/6o9e/boiy++0N69ezVnzhz7JZx33nlHH3/8sT744APt379f8fHxCggIKHacnJwc9ezZU97e3tqyZYtWrFihr7/+ush7Xb9+vQ4dOqT169dr0aJFWrhwoRYuXChJ2rp1q5577jlNmjRJ+/fv15o1a9SlS5cSj9XVsrKylJCQIElydXUtdOxK+kw7duyoGTNmyMvLy36GbOzYsZKk6Ohobdy4UQkJCdqxY4ceffRR9erVSwcPHix1TY5izghwHTd7HsD1ODpPALjSp59+qipVqthf9+7dWytWrNDo0aPtbQEBAXr11Vf1zDPPaPbs2cWOk5aWphdeeEHNmzeXJDVp0sS+Li4uTsOGDbOP2aRJE73zzjvq2rWr5syZI3d39yLjHThwoNA8iCu1aNHC3ic4OFiSVKNGDb3zzjtycnJSs2bN9MYbb+jcuXMaP368JGncuHGaPHmyNmzYoCFDhhRbf5s2bRQSEmJ/z1eua9KkiTp37iybzab69esXW5d0OURduHBB77//vjw9PSVJM2fOVL9+/TRlyhT5+PhIkry9vTVz5kw5OzurefPm6tOnjxITEzVixAilpaXJ09NTffv2VdWqVVW/fn21adOmxH0WqFu3riTZzzg99NBD9s9D0jU/U1dXV1WrVk02m02+vr6F3vuCBQuUlpamu+++W5I0duxYrVmzRgsWLNDrr79+3brKgjACAHeQbt26ac6cOfbXBV+gX3/9teLi4rRv3z5lZ2fr0qVLunDhgs6dO1fsA95iYmL01FNPafHixQoPD9ejjz6qRo0aSbp8CWfHjh2Kj4+397csS/n5+Tp8+LA9XFyt4DJMabRs2VJOTr+d3Pfx8VGrVq3sr52dnVWzZk37o8qv9qc//UmDBg3Stm3b9OCDD2rAgAHq2LGjpMuXVXr06KFmzZqpV69e6tu3rx588MFix9m7d6+CgoLsx1GSOnXqpPz8fO3fv98eRlq2bFno6aR+fn7auXOnJKlHjx6qX7++GjZsqF69eqlXr156+OGHr/tgvX//+9/y8PDQpk2b9Prrr2vu3LmF1jv6mUqXLznl5eWpadPC/xOWm5v7uz5xmMs0AHAH8fT0VOPGje2Ln5+fUlNT1bdvX7Vu3VorV65UcnKyZs2aJUklToicOHGidu/erT59+mjdunUKDAzU6tWrJUlnz57V008/rZSUFPuyfft2HTx40B5Yrta0aVPt3bu32HUF7Vd+QRZcEipgs9mKbcvPzy92zN69e+vIkSN6/vnndezYMXXv3t1+meLee+/V4cOH9corr+j8+fMaPHhwqeZwXMu1aqtataq2bdumZcuWyc/PTxMmTFBQUNB173Jp0KCBmjVrpuHDh+upp55SRESEfV1ZPlPp8mfn7Oys5OTkQp/f3r179fbbb5fx3V8fYQQA7nDJycnKz8/XtGnT1KFDBzVt2lTHjh277nZNmzbV888/r7Vr12rgwIH2CaD33nuv9uzZUyj0FCxXzmm40pAhQ/T1118XmhciSfn5+XrrrbcUGBhYZD5JedWuXVvDhw/XkiVLNGPGDL333nv2dV5eXoqIiNC8efO0fPlyrVy5Ur/88kuRMVq0aKHt27cXmpz77bff2i8flZaLi4vCw8P1xhtvaMeOHUpNTdW6detKvf3IkSO1a9cueyAszWfq6uqqvLy8Qm1t2rRRXl6eTpw4UeSzu/Jyzo1GGAGAO1zjxo3166+/6t1339WPP/6oxYsXFznlf6Xz588rOjpaSUlJOnLkiL799ltt2bLFfvnlz3/+s7777jtFR0crJSVFBw8e1EcffXTNCazPP/+8QkND1a9fP61YsUJpaWnasmWLBg0apL179+of//jHDX0E/4QJE/TRRx/phx9+0O7du/Xpp5/a658+fbqWLVumffv26cCBA1qxYoV8fX2LfejbsGHD5O7uruHDh2vXrl1av369/u///k9PPPGE/RLN9Xz66ad65513lJKSoiNHjuj9999Xfn6+Q2HGw8NDI0aMUGxsrCzLKtVnGhAQoLNnzyoxMVGZmZk6d+6cmjZtqmHDhikyMlKrVq3S4cOHtXnzZsXFxemzzz4rdT2OIowAwB0uKChI06dP15QpU9SqVSvFx8cXupX1as7Ozvr5558VGRmppk2bavDgwerdu7defvllSVLr1q31zTff6MCBA7rvvvvUpk0bTZgwwT4hsjju7u5at26dIiMjNX78eDVu3Fi9evWSs7OzNm3apA4dOtzQ9+zq6qpx48apdevW6tKli5ydne13pFStWlVvvPGGQkJC1K5dO6Wmpurzzz8vNEelgIeHh7788kv98ssvateunR555BF1795dM2fOLHUt1atX16pVq/TAAw+oRYsWmjt3rpYtW+bwg96io6O1d+9erVixolSfaceOHfXMM88oIiJCtWvX1htvvCFJWrBggSIjIzVmzBg1a9ZMAwYM0JYtW1SvXj2H6nGEzXJkxpAh2dnZqlatmrKysuTl5WW6HNxhuJsGV7pw4YIOHz6sBg0aFHtXCHCnudbfRGm/vzkzAgAAjCKMAAAAowgjAADAqDvqoWdc+wcAoOLhzAgAADCKMAIAZVDSkz2BO82N+Fu4oy7TAEB5ubq6ysnJSceOHVPt2rXl6up6Qx/GBdwqLMvSxYsXdfLkSTk5OZX4dN3SIIwAgAOcnJzUoEEDHT9+vFSPTAdudx4eHqpXr16xD4UrLcIIADjI1dVV9erV06VLl4r8tgdwJ3F2dpaLi0u5zw4SRgCgDAp+JfbqX2MF4DjCCCRx2zMAwBzupgEAAEYRRgAAgFFcpgFQZhXp8h6X9oBbF2dGAACAUYQRAABgFGEEAAAYRRgBAABGEUYAAIBRhBEAAGAUYQQAABhFGAEAAEYRRgAAgFGEEQAAYBRhBAAAGEUYAQAARhFGAACAUYQRAABgFGEEAAAYRRgBAABGEUYAAIBRZQojs2bNUkBAgNzd3dW+fXtt3ry5xL4LFy6UzWYrtLi7u5e5YAAAcHtxOIwsX75cMTExio2N1bZt2xQUFKSePXvqxIkTJW7j5eWl48eP25cjR46Uq2gAAHD7cDiMTJ8+XSNGjFBUVJQCAwM1d+5ceXh4aP78+SVuY7PZ5Ovra198fHzKVTQAALh9OBRGLl68qOTkZIWHh/82gJOTwsPDtXHjxhK3O3v2rOrXry9/f3/1799fu3fvvuZ+cnNzlZ2dXWgBAAC3J4fCSGZmpvLy8oqc2fDx8VF6enqx2zRr1kzz58/XRx99pCVLlig/P18dO3bUTz/9VOJ+4uLiVK1aNfvi7+/vSJkAAOAW8rvfTRMWFqbIyEgFBwera9euWrVqlWrXrq2//e1vJW4zbtw4ZWVl2ZejR4/+3mUCAABDXBzpXKtWLTk7OysjI6NQe0ZGhnx9fUs1RqVKldSmTRv98MMPJfZxc3OTm5ubI6UBAIBblENnRlxdXdW2bVslJiba2/Lz85WYmKiwsLBSjZGXl6edO3fKz8/PsUoBAMBtyaEzI5IUExOj4cOHKyQkRKGhoZoxY4ZycnIUFRUlSYqMjFSdOnUUFxcnSZo0aZI6dOigxo0b6/Tp05o6daqOHDmip5566sa+EwAAcEtyOIxERETo5MmTmjBhgtLT0xUcHKw1a9bYJ7WmpaXJyem3Ey6nTp3SiBEjlJ6eLm9vb7Vt21bfffedAgMDb9y7AAAAtyybZVmW6SKuJzs7W9WqVVNWVpa8vLzKPI61uekNrKr8bKEHTJdgx7EpGcemZBXp2FSk4wLgstJ+f/PbNAAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACMIowAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACMIowAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACMIowAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACMIowAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCpTGJk1a5YCAgLk7u6u9u3ba/PmzaXaLiEhQTabTQMGDCjLbgEAwG3I4TCyfPlyxcTEKDY2Vtu2bVNQUJB69uypEydOXHO71NRUjR07Vvfdd1+ZiwUAALcfh8PI9OnTNWLECEVFRSkwMFBz586Vh4eH5s+fX+I2eXl5GjZsmF5++WU1bNiwXAUDAIDbi0Nh5OLFi0pOTlZ4ePhvAzg5KTw8XBs3bixxu0mTJumuu+7SH/7wh1LtJzc3V9nZ2YUWAABwe3IojGRmZiovL08+Pj6F2n18fJSenl7sNhs2bNA//vEPzZs3r9T7iYuLU7Vq1eyLv7+/I2UCAIBbyO96N82ZM2f0xBNPaN68eapVq1aptxs3bpyysrLsy9GjR3/HKgEAgEkujnSuVauWnJ2dlZGRUag9IyNDvr6+RfofOnRIqamp6tevn70tPz//8o5dXLR//341atSoyHZubm5yc3NzpDQAAHCLcujMiKurq9q2bavExER7W35+vhITExUWFlakf/PmzbVz506lpKTYl4ceekjdunVTSkoKl18AAIBjZ0YkKSYmRsOHD1dISIhCQ0M1Y8YM5eTkKCoqSpIUGRmpOnXqKC4uTu7u7mrVqlWh7atXry5JRdoBAMCdyeEwEhERoZMnT2rChAlKT09XcHCw1qxZY5/UmpaWJicnHuwKAABKx2ZZlmW6iOvJzs5WtWrVlJWVJS8vrzKPY21uegOrKj9b6AHTJdhxbErGsSlZRTo2Fem4AListN/fnMIAAABGEUYAAIBRhBEAAGAUYQQAABhFGAEAAEYRRgAAgFGEEQAAYBRhBAAAGEUYAQAARhFGAACAUYQRAABgFGEEAAAYRRgBAABGEUYAAIBRhBEAAGAUYQQAABhFGAEAAEYRRgAAgFGEEQAAYBRhBAAAGEUYAQAARhFGAACAUYQRAABgFGEEAAAYRRgBAABGEUYAAIBRhBEAAGAUYQQAABhFGAEAAEYRRgAAgFGEEQAAYBRhBAAAGEUYAQAARhFGAACAUYQRAABgFGEEAAAYRRgBAABGEUYAAIBRhBEAAGAUYQQAABhFGAEAAEYRRgAAgFGEEQAAYBRhBAAAGEUYAQAARhFGAACAUYQRAABgFGEEAAAYRRgBAABGEUYAAIBRZQojs2bNUkBAgNzd3dW+fXtt3ry5xL6rVq1SSEiIqlevLk9PTwUHB2vx4sVlLhgAANxeHA4jy5cvV0xMjGJjY7Vt2zYFBQWpZ8+eOnHiRLH9a9SooRdffFEbN27Ujh07FBUVpaioKH355ZflLh4AANz6HA4j06dP14gRIxQVFaXAwEDNnTtXHh4emj9/frH977//fj388MNq0aKFGjVqpFGjRql169basGFDuYsHAAC3PofCyMWLF5WcnKzw8PDfBnByUnh4uDZu3Hjd7S3LUmJiovbv368uXbo4Xi0AALjtuDjSOTMzU3l5efLx8SnU7uPjo3379pW4XVZWlurUqaPc3Fw5Oztr9uzZ6tGjR4n9c3NzlZuba3+dnZ3tSJkAAOAW4lAYKauqVasqJSVFZ8+eVWJiomJiYtSwYUPdf//9xfaPi4vTyy+/fDNKAwAAhjkURmrVqiVnZ2dlZGQUas/IyJCvr2+J2zk5Oalx48aSpODgYO3du1dxcXElhpFx48YpJibG/jo7O1v+/v6OlAoAAG4RDs0ZcXV1Vdu2bZWYmGhvy8/PV2JiosLCwko9Tn5+fqHLMFdzc3OTl5dXoQUAANyeHL5MExMTo+HDhyskJEShoaGaMWOGcnJyFBUVJUmKjIxUnTp1FBcXJ+nyJZeQkBA1atRIubm5+vzzz7V48WLNmTPnxr4TAABwS3I4jEREROjkyZOaMGGC0tPTFRwcrDVr1tgntaalpcnJ6bcTLjk5OXr22Wf1008/qXLlymrevLmWLFmiiIiIG/cuAADALctmWZZluojryc7OVrVq1ZSVlVWuSzbW5qY3sKrys4UeMF2CHcemZBybklWkY1ORjguAy0r7/c1v0wAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACMIowAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACMIowAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACMIowAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACMIowAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMKpMYWTWrFkKCAiQu7u72rdvr82bN5fYd968ebrvvvvk7e0tb29vhYeHX7M/AAC4szgcRpYvX66YmBjFxsZq27ZtCgoKUs+ePXXixIli+yclJWno0KFav369Nm7cKH9/fz344IP673//W+7iAQDArc/hMDJ9+nSNGDFCUVFRCgwM1Ny5c+Xh4aH58+cX2z8+Pl7PPvusgoOD1bx5c/39739Xfn6+EhMTy108AAC49TkURi5evKjk5GSFh4f/NoCTk8LDw7Vx48ZSjXHu3Dn9+uuvqlGjhmOVAgCA25KLI50zMzOVl5cnHx+fQu0+Pj7at29fqcb485//rLvvvrtQoLlabm6ucnNz7a+zs7MdKRMAANxCburdNJMnT1ZCQoJWr14td3f3EvvFxcWpWrVq9sXf3/8mVgkAAG4mh8JIrVq15OzsrIyMjELtGRkZ8vX1vea2b775piZPnqy1a9eqdevW1+w7btw4ZWVl2ZejR486UiYAALiFOBRGXF1d1bZt20KTTwsmo4aFhZW43RtvvKFXXnlFa9asUUhIyHX34+bmJi8vr0ILAAC4PTk0Z0SSYmJiNHz4cIWEhCg0NFQzZsxQTk6OoqKiJEmRkZGqU6eO4uLiJElTpkzRhAkTtHTpUgUEBCg9PV2SVKVKFVWpUuUGvhUAAHArcjiMRERE6OTJk5owYYLS09MVHBysNWvW2Ce1pqWlycnptxMuc+bM0cWLF/XII48UGic2NlYTJ04sX/UAAOCW53AYkaTo6GhFR0cXuy4pKanQ69TU1LLsAgAA3CH4bRoAAGAUYQQAABhFGAEAAEYRRgAAgFGEEQAAYBRhBAAAGEUYAQAARhFGAACAUYQRAABgFGEEAAAYRRgBAABGEUYAAIBRhBEAAGAUYQQAABhFGAEAAEYRRgAAgFGEEQAAYBRhBAAAGEUYAQAARhFGAACAUYQRAABgFGEEAAAYRRgBAABGEUYAAIBRhBEAAGAUYQQAABhFGAEAAEYRRgAAgFGEEQAAYBRhBAAAGEUYAQAARhFGAACAUYQRAABgFGEEAAAYRRgBAABGEUYAAIBRhBEAAGAUYQQAABhFGAEAAEYRRgAAgFGEEQAAYBRhBAAAGEUYAQAARhFGAACAUYQRAABgFGEEAAAYRRgBAABGEUYAAIBRhBEAAGAUYQQAABhFGAEAAEaVKYzMmjVLAQEBcnd3V/v27bV58+YS++7evVuDBg1SQECAbDabZsyYUdZaAQDAbcjhMLJ8+XLFxMQoNjZW27ZtU1BQkHr27KkTJ04U2//cuXNq2LChJk+eLF9f33IXDAAAbi8Oh5Hp06drxIgRioqKUmBgoObOnSsPDw/Nnz+/2P7t2rXT1KlTNWTIELm5uZW7YAAAcHtxKIxcvHhRycnJCg8P/20AJyeFh4dr48aNN7w4AABw+3NxpHNmZqby8vLk4+NTqN3Hx0f79u27YUXl5uYqNzfX/jo7O/uGjQ0AACqWCnk3TVxcnKpVq2Zf/P39TZcEAAB+Jw6FkVq1asnZ2VkZGRmF2jMyMm7o5NRx48YpKyvLvhw9evSGjQ0AACoWh8KIq6ur2rZtq8TERHtbfn6+EhMTFRYWdsOKcnNzk5eXV6EFAADcnhyaMyJJMTExGj58uEJCQhQaGqoZM2YoJydHUVFRkqTIyEjVqVNHcXFxki5Pet2zZ4/9n//73/8qJSVFVapUUePGjW/gWwEAALcih8NIRESETp48qQkTJig9PV3BwcFas2aNfVJrWlqanJx+O+Fy7NgxtWnTxv76zTff1JtvvqmuXbsqKSmp/O8AAADc0hwOI5IUHR2t6OjoYtddHTACAgJkWVZZdgMAAO4AFfJuGgAAcOcgjAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACMIowAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACMIowAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACMIowAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACMIowAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwKgyhZFZs2YpICBA7u7uat++vTZv3nzN/itWrFDz5s3l7u6ue+65R59//nmZigUAALcfh8PI8uXLFRMTo9jYWG3btk1BQUHq2bOnTpw4UWz/7777TkOHDtUf/vAH/ec//9GAAQM0YMAA7dq1q9zFAwCAW5/DYWT69OkaMWKEoqKiFBgYqLlz58rDw0Pz588vtv/bb7+tXr166YUXXlCLFi30yiuv6N5779XMmTPLXTwAALj1ORRGLl68qOTkZIWHh/82gJOTwsPDtXHjxmK32bhxY6H+ktSzZ88S+wMAgDuLiyOdMzMzlZeXJx8fn0LtPj4+2rdvX7HbpKenF9s/PT29xP3k5uYqNzfX/jorK0uSlJ2d7Ui5RVhn88q1/Y1mK+f7uZE4NiXj2JSsIh2binRcAFxW8L1tWdY1+zkURm6WuLg4vfzyy0Xa/f39DVTze6pmuoAKjGNTMo5N8TguQEV15swZVatW8t+oQ2GkVq1acnZ2VkZGRqH2jIwM+fr6FruNr6+vQ/0lady4cYqJibG/zs/P1y+//KKaNWvKZrM5UvINl52dLX9/fx09elReXl5Ga6loODYl49iUjGNTMo5N8TguJatox8ayLJ05c0Z33333Nfs5FEZcXV3Vtm1bJSYmasCAAZIuB4XExERFR0cXu01YWJgSExM1evRoe9tXX32lsLCwEvfj5uYmNze3Qm3Vq1d3pNTfnZeXV4X4oCsijk3JODYl49iUjGNTPI5LySrSsbnWGZECDl+miYmJ0fDhwxUSEqLQ0FDNmDFDOTk5ioqKkiRFRkaqTp06iouLkySNGjVKXbt21bRp09SnTx8lJCRo69ateu+99xzdNQAAuA05HEYiIiJ08uRJTZgwQenp6QoODtaaNWvsk1TT0tLk5PTbTTodO3bU0qVL9dJLL2n8+PFq0qSJPvzwQ7Vq1erGvQsAAHDLKtME1ujo6BIvyyQlJRVpe/TRR/Xoo4+WZVcVjpubm2JjY4tcRgLH5lo4NiXj2JSMY1M8jkvJbtVjY7Oud78NAADA74gfygMAAEYRRgAAgFGEEQAAYBRhBAAqKKb04U5RIR8HX5FkZmZq/vz52rhxo/33dHx9fdWxY0c9+eSTql27tuEKAdyu3NzctH37drVo0cJ0KcDvirtprmHLli3q2bOnPDw8FB4ebn+WSkZGhhITE3Xu3Dl9+eWXCgkJMVwpKprz588rOTlZNWrUUGBgYKF1Fy5c0AcffKDIyEhD1Zm1d+9ebdq0SWFhYWrevLn27dunt99+W7m5uXr88cf1wAMPmC7xprvy5y+u9Pbbb+vxxx9XzZo1JUnTp0+/mWVVSDk5Ofrggw/0ww8/yM/PT0OHDrUfH9y6CCPX0KFDBwUFBWnu3LlFfhPHsiw988wz2rFjhzZu3Giowort6NGjio2N1fz5802XclMdOHBADz74oNLS0mSz2dS5c2clJCTIz89P0uUwe/fddysvr+L84u3NsmbNGvXv319VqlTRuXPntHr1akVGRiooKEj5+fn65ptvtHbt2jsukDg5OSkoKKjIz1588803CgkJkaenp2w2m9atW2emQIMCAwO1YcMG1ahRQ0ePHlWXLl106tQpNW3aVIcOHZKLi4s2bdqkBg0amC71ptu2bZu8vb3t733x4sWaO3eu0tLSVL9+fUVHR2vIkCGGqywlCyVyd3e39u7dW+L6vXv3Wu7u7jexoltLSkqK5eTkZLqMm27AgAFWnz59rJMnT1oHDx60+vTpYzVo0MA6cuSIZVmWlZ6efkceF8uyrLCwMOvFF1+0LMuyli1bZnl7e1vjx4+3r//LX/5i9ejRw1R5xsTFxVkNGjSwEhMTC7W7uLhYu3fvNlRVxWCz2ayMjAzLsixr2LBhVseOHa3Tp09blmVZZ86cscLDw62hQ4eaLNGY1q1bW1999ZVlWZY1b948q3LlytZzzz1nzZkzxxo9erRVpUoV6x//+IfhKkuHMHINAQEB1qJFi0pcv2jRIqt+/fo3r6AK5qOPPrrm8tZbb92RX7p33XWXtWPHDvvr/Px865lnnrHq1atnHTp06I4OI15eXtbBgwcty7KsvLw8y8XFxdq2bZt9/c6dOy0fHx9T5Rm1efNmq2nTptaYMWOsixcvWpZFGLGswmGkYcOG1tq1awut//bbby1/f38TpRlXuXJlKzU11bIsy2rTpo313nvvFVofHx9vBQYGmijNYUxgvYaxY8fqj3/8o5KTk9W9e/cic0bmzZunN99803CV5gwYMEA2m+2aM/6vvrx1Jzh//rxcXH7707LZbJozZ46io6PVtWtXLV261GB15hX8O+Hk5CR3d/dCv+hZtWpVZWVlmSrNqHbt2ik5OVkjR45USEiI4uPj78i/n+IUHIcLFy7YL3cWqFOnjk6ePGmiLOM8PDyUmZmp+vXr67///a9CQ0MLrW/fvr0OHz5sqDrHcGvvNYwcOVKLFi3S999/r0GDBiksLExhYWEaNGiQvv/+ey1cuFDPPvus6TKN8fPz06pVq5Sfn1/ssm3bNtMlGtG8eXNt3bq1SPvMmTPVv39/PfTQQwaqqhgCAgJ08OBB++uNGzeqXr169tdpaWlFvmzuJFWqVNGiRYs0btw4hYeH35HziorTvXt33XvvvcrOztb+/fsLrTty5MgdO4G1d+/emjNnjiSpa9eu+uc//1lo/QcffKDGjRubKM1hnBm5joiICEVEROjXX39VZmamJKlWrVqqVKmS4crMa9u2rZKTk9W/f/9i11/vrMnt6uGHH9ayZcv0xBNPFFk3c+ZM5efna+7cuQYqM+9Pf/pToS/Yq3+9+4svvrjjJq8WZ8iQIercubOSk5NVv3590+UYFRsbW+h1lSpVCr3+5JNPdN99993MkiqMKVOmqFOnTuratatCQkI0bdo0JSUlqUWLFtq/f782bdqk1atXmy6zVLibBmX273//Wzk5OerVq1ex63NycrR161Z17dr1JlcGAHeG06dPa/Lkyfrkk0/0448/Kj8/X35+furUqZOef/75W+bRE4QRAABgFHNGAACAUYQRAABgFGEEAAAYRRgBAABGEUYAlFp6erpGjRqlxo0by93dXT4+PurUqZPmzJmjc+fOmS4PwC2K54wAKJUff/xRnTp1UvXq1fX666/rnnvukZubm3bu3Kn33ntPderUKfaBbr/++ivP5QFwTZwZAVAqzz77rFxcXLR161YNHjxYLVq0UMOGDdW/f3999tln6tevn6TfHn//0EMPydPTU6+99pokac6cOWrUqJFcXV3VrFkzLV682D52amqqbDabUlJS7G2nT5+WzWZTUlKSJCkpKUk2m02fffaZWrduLXd3d3Xo0EG7du2yb3PkyBH169dP3t7e8vT0VMuWLfX555///gcHQLkQRgBc188//6y1a9dq5MiR8vT0LLbPlb+jMnHiRD388MPauXOn/vd//1erV6/WqFGjNGbMGO3atUtPP/20oqKitH79eodreeGFFzRt2jRt2bJFtWvXVr9+/fTrr79KuvwTDrm5ufrXv/6lnTt3asqUKUWe2Amg4uEyDYDr+uGHH2RZlpo1a1aovVatWrpw4YKky0FgypQpkqTHHntMUVFR9n5Dhw7Vk08+af8tp5iYGG3atElvvvmmunXr5lAtsbGx6tGjhyRp0aJFqlu3rlavXq3BgwcrLS1NgwYN0j333CNJatiwYdneMICbijMjAMps8+bNSklJUcuWLZWbm2tvv/oR1Hv37lWnTp0KtXXq1El79+51eJ9hYWH2f65Ro4aaNWtmH+e5557Tq6++qk6dOik2NlY7duxweHwANx9hBMB1NW7cWDabrcgvpjZs2FCNGzdW5cqVC7WXdCmnJE5Ol/9TdOWvUxRcenHEU089pR9//FFPPPGEdu7cqZCQEL377rsOjwPg5iKMALiumjVrqkePHpo5c6ZycnIc3r5Fixb69ttvC7V9++23CgwMlCTVrl1bknT8+HH7+isns15p06ZN9n8+deqUDhw4oBYtWtjb/P399cwzz2jVqlUaM2aM5s2b53C9AG4u5owAKJXZs2erU6dOCgkJ0cSJE9W6dWs5OTlpy5Yt2rdvn9q2bVviti+88IIGDx6sNm3aKDw8XJ988olWrVqlr7/+WpJUuXJldejQQZMnT1aDBg104sQJvfTSS8WONWnSJNWsWVM+Pj568cUXVatWLQ0YMECSNHr0aPXu3VtNmzbVqVOntH79+kJBBUAFZQFAKR07dsyKjo62GjRoYFWqVMmqUqWKFRoaak2dOtXKycmxLMuyJFmrV68usu3s2bOthg0bWpUqVbKaNm1qvf/++4XW79mzxwoLC7MqV65sBQcHW2vXrrUkWevXr7csy7LWr19vSbI++eQTq2XLlparq6sVGhpqbd++3T5GdHS01ahRI8vNzc2qXbu29cQTT1iZmZm/2/EAcGPYLOuKi7QAUEElJSWpW7duOnXqlKpXr266HAA3EHNGAACAUYQRAABgFJdpAACAUZwZAQAARhFGAACAUYQRAABgFGEEAAAYRRgBAABGEUYAAIBRhBEAAGAUYQQAABhFGAEAAEb9f7FGbakslsI9AAAAAElFTkSuQmCC", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "[]" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "result.plot(feature_name=\"EDUCATION\")" ] }, { "cell_type": "code", "execution_count": 17, "id": "2dc2dd91-8bd1-4983-9abc-e0fea3a87954", "metadata": {}, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "[]" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Only plot metric for \"Specificity\"\n", "result.plot(feature_name=\"PAY_0\", metrics_of_interest=\"Specificity\")" ] }, { "cell_type": "code", "execution_count": 18, "id": "6814d3af-f6a1-4a40-b2d8-bbb7239afd3b", "metadata": {}, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "[]" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Only plot metrics for \"N\", \"Adverse Impact\" and \"True Positive Rate\"\n", "result.plot(feature_name=\"PAY_0\", metrics_of_interest=[\"N\", \"Adverse Impact\", \"True Positive Rate\"])" ] }, { "cell_type": "markdown", "id": "c3ebc2e5-0fe2-48e2-a70d-aa7ce8724bc2", "metadata": {}, "source": [ "### Save the explainer log and data" ] }, { "cell_type": "code", "execution_count": 19, "id": "499c0011-390a-4511-82bc-11aa371290d9", "metadata": {}, "outputs": [], "source": [ "# save the explainer log\n", "result.log(path=\"./dia-demo.log\")" ] }, { "cell_type": "code", "execution_count": 20, "id": "68f24660-e183-4163-b1ab-575462c24ec4", "metadata": {}, "outputs": [], "source": [ "!head dia-demo.log" ] }, { "cell_type": "code", "execution_count": 21, "id": "b7a4d9d7-4b91-4e2b-bef3-bdca9e81a39a", "metadata": {}, "outputs": [], "source": [ "# save the explainer data\n", "result.zip(file_path=\"./dia-demo-archive.zip\")" ] }, { "cell_type": "code", "execution_count": 22, "id": "a5b59db6-4ac9-40ea-b21f-be4d49a2ea82", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Archive: dia-demo-archive.zip\n", " Length Date Time Name\n", "--------- ---------- ----- ----\n", " 3988 2026-01-29 16:02 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/result_descriptor.json\n", " 117 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_disparate_impact_analysis/text_plain.meta\n", " 19 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_disparate_impact_analysis/text_plain/explanation.txt\n", " 2 2026-01-29 16:02 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/problems/problems_and_actions.json\n", " 110 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html.meta\n", " 15297 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-0-false_discovery_rate.png\n", " 14691 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-0-specificity.png\n", " 15195 2026-01-29 16:02 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-7-false_omissions_rate.png\n", " 11805 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-2-precision.png\n", " 14412 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-3-n.png\n", " 15132 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-4-false_negative_rate.png\n", " 15008 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-3-true_positive_rate.png\n", " 15456 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-5-accuracy.png\n", " 15423 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-5-precision.png\n", " 14760 2026-01-29 16:02 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-8-true_positive_rate.png\n", " 14829 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-0-true_positive_rate.png\n", " 13212 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-2-false_positive_rate.png\n", " 15257 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-3-false_omissions_rate.png\n", " 14901 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-1-true_positive_rate.png\n", " 12812 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-2-true_positive_rate.png\n", " 14404 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-6-n.png\n", " 14167 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-1-specificity.png\n", " 15684 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-6-false_omissions_rate.png\n", " 15118 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-5-false_positive_rate.png\n", " 15097 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-6-false_positive_rate.png\n", " 15736 2026-01-29 16:02 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-8-negative_predicted_value.png\n", " 14679 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-0-adverse_impact.png\n", " 14369 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-2-specificity.png\n", " 14472 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-4-true_positive_rate.png\n", " 14754 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-5-true_positive_rate.png\n", " 13556 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-7-n.png\n", " 16319 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-3-false_negative_rate.png\n", " 15996 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-4-specificity.png\n", " 15064 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-2-false_discovery_rate.png\n", " 13692 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-2-negative_predicted_value.png\n", " 13462 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-2-false_negative_rate.png\n", " 15723 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-5-negative_predicted_value.png\n", " 14267 2026-01-29 16:02 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-8-specificity.png\n", " 14324 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-4-accuracy.png\n", " 29111 2026-01-29 16:02 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/explanation.html\n", " 14997 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-5-false_negative_rate.png\n", " 14856 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-7-adverse_impact.png\n", " 14795 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-6-true_positive_rate.png\n", " 17011 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-1-false_discovery_rate.png\n", " 14870 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-4-false_positive_rate.png\n", " 15317 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-1-false_positive_rate.png\n", " 13481 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-4-precision.png\n", " 15341 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-6-false_discovery_rate.png\n", " 14723 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-7-precision.png\n", " 15039 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-3-false_discovery_rate.png\n", " 15311 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-3-false_positive_rate.png\n", " 13760 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-2-false_omissions_rate.png\n", " 14947 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-7-true_positive_rate.png\n", " 15800 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-1-negative_predicted_value.png\n", " 15282 2026-01-29 16:02 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-7-false_discovery_rate.png\n", " 16098 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-5-false_omissions_rate.png\n", " 15637 2026-01-29 16:02 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-8-false_negative_rate.png\n", " 14337 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-5-n.png\n", " 13925 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-4-n.png\n", " 13990 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-1-accuracy.png\n", " 12752 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-2-adverse_impact.png\n", " 15731 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-0-negative_predicted_value.png\n", " 15216 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-0-false_positive_rate.png\n", " 13905 2026-01-29 16:02 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-8-accuracy.png\n", " 14749 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-5-adverse_impact.png\n", " 14209 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-6-specificity.png\n", " 15075 2026-01-29 16:02 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-8-false_positive_rate.png\n", " 13832 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-6-precision.png\n", " 13947 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-6-accuracy.png\n", " 20218 2026-01-29 16:02 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-7-false_negative_rate.png\n", " 15723 2026-01-29 16:02 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-7-negative_predicted_value.png\n", " 15289 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-3-specificity.png\n", " 15339 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-1-false_negative_rate.png\n", " 14806 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-6-adverse_impact.png\n", " 19006 2026-01-29 16:02 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-7-specificity.png\n", " 15136 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-4-negative_predicted_value.png\n", " 13787 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-0-precision.png\n", " 15669 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-6-false_negative_rate.png\n", " 15312 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-5-false_discovery_rate.png\n", " 15316 2026-01-29 16:02 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-8-false_discovery_rate.png\n", " 14886 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-7-accuracy.png\n", " 15276 2026-01-29 16:02 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-7-false_positive_rate.png\n", " 14797 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-3-adverse_impact.png\n", " 14709 2026-01-29 16:02 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-8-adverse_impact.png\n", " 14398 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-0-n.png\n", " 13230 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-2-n.png\n", " 14787 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-1-adverse_impact.png\n", " 13877 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-1-precision.png\n", " 14400 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-4-adverse_impact.png\n", " 15738 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-6-negative_predicted_value.png\n", " 17518 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-0-false_negative_rate.png\n", " 15085 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-3-accuracy.png\n", " 14434 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-1-n.png\n", " 15812 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-1-false_omissions_rate.png\n", " 14905 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-3-precision.png\n", " 15802 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-5-specificity.png\n", " 13803 2026-01-29 16:02 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-8-precision.png\n", " 12658 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-2-accuracy.png\n", " 13918 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-0-accuracy.png\n", " 16628 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-4-false_discovery_rate.png\n", " 14407 2026-01-29 16:02 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-8-n.png\n", " 15816 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-3-negative_predicted_value.png\n", " 14744 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-4-false_omissions_rate.png\n", " 16209 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-0-false_omissions_rate.png\n", " 15166 2026-01-29 16:02 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-8-false_omissions_rate.png\n", " 0 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/log/explainer_run_2dc47eb0-225e-42cd-8b6a-b769177e05be.log\n", " 4076 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/dia_entity.json\n", " 2152 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_5/metrics.jay\n", " 2208 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_5/4/parity.jay\n", " 824 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_5/4/me_smd.jay\n", " 2648 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_5/4/disparity.jay\n", " 304 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_5/4/cm.jay\n", " 2208 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_5/2/parity.jay\n", " 824 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_5/2/me_smd.jay\n", " 2648 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_5/2/disparity.jay\n", " 304 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_5/2/cm.jay\n", " 2208 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_5/5/parity.jay\n", " 824 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_5/5/me_smd.jay\n", " 2648 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_5/5/disparity.jay\n", " 304 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_5/5/cm.jay\n", " 2208 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_5/0/parity.jay\n", " 824 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_5/0/me_smd.jay\n", " 2648 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_5/0/disparity.jay\n", " 304 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_5/0/cm.jay\n", " 2208 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_5/8/parity.jay\n", " 824 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_5/8/me_smd.jay\n", " 2648 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_5/8/disparity.jay\n", " 304 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_5/8/cm.jay\n", " 2208 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_5/7/parity.jay\n", " 824 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_5/7/me_smd.jay\n", " 2664 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_5/7/disparity.jay\n", " 304 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_5/7/cm.jay\n", " 2208 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_5/1/parity.jay\n", " 824 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_5/1/me_smd.jay\n", " 2648 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_5/1/disparity.jay\n", " 304 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_5/1/cm.jay\n", " 2208 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_5/6/parity.jay\n", " 824 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_5/6/me_smd.jay\n", " 2648 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_5/6/disparity.jay\n", " 304 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_5/6/cm.jay\n", " 2208 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_5/9/parity.jay\n", " 824 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_5/9/me_smd.jay\n", " 2656 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_5/9/disparity.jay\n", " 304 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_5/9/cm.jay\n", " 2208 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_5/3/parity.jay\n", " 824 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_5/3/me_smd.jay\n", " 2648 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_5/3/disparity.jay\n", " 304 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_5/3/cm.jay\n", " 1392 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/SEX/metrics.jay\n", " 1984 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/SEX/0/parity.jay\n", " 600 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/SEX/0/me_smd.jay\n", " 1792 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/SEX/0/disparity.jay\n", " 304 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/SEX/0/cm.jay\n", " 1984 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/SEX/1/parity.jay\n", " 600 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/SEX/1/me_smd.jay\n", " 1792 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/SEX/1/disparity.jay\n", " 304 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/SEX/1/cm.jay\n", " 2056 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_6/metrics.jay\n", " 2208 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_6/4/parity.jay\n", " 824 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_6/4/me_smd.jay\n", " 2552 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_6/4/disparity.jay\n", " 304 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_6/4/cm.jay\n", " 2208 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_6/2/parity.jay\n", " 824 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_6/2/me_smd.jay\n", " 2552 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_6/2/disparity.jay\n", " 304 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_6/2/cm.jay\n", " 2208 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_6/5/parity.jay\n", " 824 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_6/5/me_smd.jay\n", " 2552 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_6/5/disparity.jay\n", " 304 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_6/5/cm.jay\n", " 2208 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_6/0/parity.jay\n", " 824 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_6/0/me_smd.jay\n", " 2552 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_6/0/disparity.jay\n", " 304 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_6/0/cm.jay\n", " 2208 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_6/8/parity.jay\n", " 824 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_6/8/me_smd.jay\n", " 2560 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_6/8/disparity.jay\n", " 304 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_6/8/cm.jay\n", " 2208 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_6/7/parity.jay\n", " 824 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_6/7/me_smd.jay\n", " 2560 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_6/7/disparity.jay\n", " 304 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_6/7/cm.jay\n", " 2208 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_6/1/parity.jay\n", " 824 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_6/1/me_smd.jay\n", " 2552 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_6/1/disparity.jay\n", " 304 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_6/1/cm.jay\n", " 2208 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_6/6/parity.jay\n", " 824 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_6/6/me_smd.jay\n", " 2560 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_6/6/disparity.jay\n", " 304 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_6/6/cm.jay\n", " 2208 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_6/9/parity.jay\n", " 824 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_6/9/me_smd.jay\n", " 2568 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_6/9/disparity.jay\n", " 304 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_6/9/cm.jay\n", " 2208 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_6/3/parity.jay\n", " 824 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_6/3/me_smd.jay\n", " 2552 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_6/3/disparity.jay\n", " 304 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_6/3/cm.jay\n", " 1504 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/MARRIAGE/metrics.jay\n", " 2008 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/MARRIAGE/2/parity.jay\n", " 656 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/MARRIAGE/2/me_smd.jay\n", " 1928 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/MARRIAGE/2/disparity.jay\n", " 304 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/MARRIAGE/2/cm.jay\n", " 2008 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/MARRIAGE/0/parity.jay\n", " 656 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/MARRIAGE/0/me_smd.jay\n", " 1928 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/MARRIAGE/0/disparity.jay\n", " 304 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/MARRIAGE/0/cm.jay\n", " 2008 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/MARRIAGE/1/parity.jay\n", " 656 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/MARRIAGE/1/me_smd.jay\n", " 1928 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/MARRIAGE/1/disparity.jay\n", " 304 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/MARRIAGE/1/cm.jay\n", " 2008 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/MARRIAGE/3/parity.jay\n", " 656 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/MARRIAGE/3/me_smd.jay\n", " 1928 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/MARRIAGE/3/disparity.jay\n", " 304 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/MARRIAGE/3/cm.jay\n", " 2344 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_2/metrics.jay\n", " 2224 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_2/4/parity.jay\n", " 856 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_2/4/me_smd.jay\n", " 2856 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_2/4/disparity.jay\n", " 304 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_2/4/cm.jay\n", " 2224 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_2/10/parity.jay\n", " 856 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_2/10/me_smd.jay\n", " 2864 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_2/10/disparity.jay\n", " 304 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_2/10/cm.jay\n", " 2224 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_2/2/parity.jay\n", " 856 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_2/2/me_smd.jay\n", " 2856 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_2/2/disparity.jay\n", " 304 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_2/2/cm.jay\n", " 2224 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_2/5/parity.jay\n", " 856 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_2/5/me_smd.jay\n", " 2856 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_2/5/disparity.jay\n", " 304 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_2/5/cm.jay\n", " 2224 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_2/0/parity.jay\n", " 856 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_2/0/me_smd.jay\n", " 2856 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_2/0/disparity.jay\n", " 304 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_2/0/cm.jay\n", " 2224 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_2/8/parity.jay\n", " 856 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_2/8/me_smd.jay\n", " 2856 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_2/8/disparity.jay\n", " 304 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_2/8/cm.jay\n", " 2224 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_2/7/parity.jay\n", " 856 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_2/7/me_smd.jay\n", " 2856 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_2/7/disparity.jay\n", " 304 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_2/7/cm.jay\n", " 2224 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_2/1/parity.jay\n", " 856 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_2/1/me_smd.jay\n", " 2856 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_2/1/disparity.jay\n", " 304 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_2/1/cm.jay\n", " 2224 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_2/6/parity.jay\n", " 856 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_2/6/me_smd.jay\n", " 2856 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_2/6/disparity.jay\n", " 304 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_2/6/cm.jay\n", " 2224 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_2/9/parity.jay\n", " 856 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_2/9/me_smd.jay\n", " 2856 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_2/9/disparity.jay\n", " 304 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_2/9/cm.jay\n", " 2224 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_2/3/parity.jay\n", " 856 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_2/3/me_smd.jay\n", " 2864 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_2/3/disparity.jay\n", " 304 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_2/3/cm.jay\n", " 1936 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_0/metrics.jay\n", " 2224 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_0/4/parity.jay\n", " 856 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_0/4/me_smd.jay\n", " 2448 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_0/4/disparity.jay\n", " 304 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_0/4/cm.jay\n", " 2224 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_0/10/parity.jay\n", " 856 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_0/10/me_smd.jay\n", " 2448 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_0/10/disparity.jay\n", " 304 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_0/10/cm.jay\n", " 2224 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_0/2/parity.jay\n", " 856 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_0/2/me_smd.jay\n", " 2448 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_0/2/disparity.jay\n", " 304 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_0/2/cm.jay\n", " 2224 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_0/5/parity.jay\n", " 856 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_0/5/me_smd.jay\n", " 2448 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_0/5/disparity.jay\n", " 304 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_0/5/cm.jay\n", " 2224 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_0/0/parity.jay\n", " 856 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_0/0/me_smd.jay\n", " 2488 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_0/0/disparity.jay\n", " 304 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_0/0/cm.jay\n", " 2224 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_0/8/parity.jay\n", " 856 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_0/8/me_smd.jay\n", " 2488 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_0/8/disparity.jay\n", " 304 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_0/8/cm.jay\n", " 2224 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_0/7/parity.jay\n", " 856 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_0/7/me_smd.jay\n", " 2448 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_0/7/disparity.jay\n", " 304 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_0/7/cm.jay\n", " 2224 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_0/1/parity.jay\n", " 856 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_0/1/me_smd.jay\n", " 2448 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_0/1/disparity.jay\n", " 304 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_0/1/cm.jay\n", " 2224 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_0/6/parity.jay\n", " 856 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_0/6/me_smd.jay\n", " 2448 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_0/6/disparity.jay\n", " 304 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_0/6/cm.jay\n", " 2224 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_0/9/parity.jay\n", " 856 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_0/9/me_smd.jay\n", " 2488 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_0/9/disparity.jay\n", " 304 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_0/9/cm.jay\n", " 2224 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_0/3/parity.jay\n", " 856 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_0/3/me_smd.jay\n", " 2448 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_0/3/disparity.jay\n", " 304 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_0/3/cm.jay\n", " 2248 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_3/metrics.jay\n", " 2224 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_3/4/parity.jay\n", " 856 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_3/4/me_smd.jay\n", " 2760 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_3/4/disparity.jay\n", " 304 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_3/4/cm.jay\n", " 2224 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_3/10/parity.jay\n", " 856 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_3/10/me_smd.jay\n", " 2768 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_3/10/disparity.jay\n", " 304 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_3/10/cm.jay\n", " 2224 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_3/2/parity.jay\n", " 856 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_3/2/me_smd.jay\n", " 2760 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_3/2/disparity.jay\n", " 304 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_3/2/cm.jay\n", " 2224 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_3/5/parity.jay\n", " 856 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_3/5/me_smd.jay\n", " 2760 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_3/5/disparity.jay\n", " 304 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_3/5/cm.jay\n", " 2224 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_3/0/parity.jay\n", " 856 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_3/0/me_smd.jay\n", " 2760 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_3/0/disparity.jay\n", " 304 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_3/0/cm.jay\n", " 2224 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_3/8/parity.jay\n", " 856 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_3/8/me_smd.jay\n", " 2760 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_3/8/disparity.jay\n", " 304 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_3/8/cm.jay\n", " 2224 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_3/7/parity.jay\n", " 856 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_3/7/me_smd.jay\n", " 2768 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_3/7/disparity.jay\n", " 304 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_3/7/cm.jay\n", " 2224 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_3/1/parity.jay\n", " 856 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_3/1/me_smd.jay\n", " 2760 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_3/1/disparity.jay\n", " 304 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_3/1/cm.jay\n", " 2224 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_3/6/parity.jay\n", " 856 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_3/6/me_smd.jay\n", " 2760 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_3/6/disparity.jay\n", " 304 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_3/6/cm.jay\n", " 2224 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_3/9/parity.jay\n", " 856 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_3/9/me_smd.jay\n", " 2760 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_3/9/disparity.jay\n", " 304 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_3/9/cm.jay\n", " 2224 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_3/3/parity.jay\n", " 856 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_3/3/me_smd.jay\n", " 2776 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_3/3/disparity.jay\n", " 304 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_3/3/cm.jay\n", " 1864 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/EDUCATION/metrics.jay\n", " 2056 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/EDUCATION/4/parity.jay\n", " 744 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/EDUCATION/4/me_smd.jay\n", " 2336 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/EDUCATION/4/disparity.jay\n", " 304 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/EDUCATION/4/cm.jay\n", " 2056 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/EDUCATION/2/parity.jay\n", " 744 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/EDUCATION/2/me_smd.jay\n", " 2328 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/EDUCATION/2/disparity.jay\n", " 304 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/EDUCATION/2/cm.jay\n", " 2056 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/EDUCATION/5/parity.jay\n", " 744 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/EDUCATION/5/me_smd.jay\n", " 2328 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/EDUCATION/5/disparity.jay\n", " 304 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/EDUCATION/5/cm.jay\n", " 2056 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/EDUCATION/0/parity.jay\n", " 744 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/EDUCATION/0/me_smd.jay\n", " 2328 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/EDUCATION/0/disparity.jay\n", " 304 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/EDUCATION/0/cm.jay\n", " 2056 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/EDUCATION/1/parity.jay\n", " 744 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/EDUCATION/1/me_smd.jay\n", " 2328 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/EDUCATION/1/disparity.jay\n", " 304 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/EDUCATION/1/cm.jay\n", " 2056 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/EDUCATION/6/parity.jay\n", " 744 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/EDUCATION/6/me_smd.jay\n", " 2352 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/EDUCATION/6/disparity.jay\n", " 304 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/EDUCATION/6/cm.jay\n", " 2056 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/EDUCATION/3/parity.jay\n", " 744 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/EDUCATION/3/me_smd.jay\n", " 2328 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/EDUCATION/3/disparity.jay\n", " 304 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/EDUCATION/3/cm.jay\n", " 2152 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_4/metrics.jay\n", " 2224 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_4/4/parity.jay\n", " 856 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_4/4/me_smd.jay\n", " 2664 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_4/4/disparity.jay\n", " 304 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_4/4/cm.jay\n", " 2224 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_4/10/parity.jay\n", " 856 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_4/10/me_smd.jay\n", " 2672 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_4/10/disparity.jay\n", " 304 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_4/10/cm.jay\n", " 2224 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_4/2/parity.jay\n", " 856 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_4/2/me_smd.jay\n", " 2664 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_4/2/disparity.jay\n", " 304 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_4/2/cm.jay\n", " 2224 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_4/5/parity.jay\n", " 856 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_4/5/me_smd.jay\n", " 2664 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_4/5/disparity.jay\n", " 304 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_4/5/cm.jay\n", " 2224 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_4/0/parity.jay\n", " 856 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_4/0/me_smd.jay\n", " 2664 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_4/0/disparity.jay\n", " 304 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_4/0/cm.jay\n", " 2224 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_4/8/parity.jay\n", " 856 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_4/8/me_smd.jay\n", " 2664 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_4/8/disparity.jay\n", " 304 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_4/8/cm.jay\n", " 2224 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_4/7/parity.jay\n", " 856 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_4/7/me_smd.jay\n", " 2664 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_4/7/disparity.jay\n", " 304 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_4/7/cm.jay\n", " 2224 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_4/1/parity.jay\n", " 856 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_4/1/me_smd.jay\n", " 2664 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_4/1/disparity.jay\n", " 304 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_4/1/cm.jay\n", " 2224 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_4/6/parity.jay\n", " 856 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_4/6/me_smd.jay\n", " 2664 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_4/6/disparity.jay\n", " 304 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_4/6/cm.jay\n", " 2224 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_4/9/parity.jay\n", " 856 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_4/9/me_smd.jay\n", " 2664 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_4/9/disparity.jay\n", " 304 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_4/9/cm.jay\n", " 2224 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_4/3/parity.jay\n", " 856 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_4/3/me_smd.jay\n", " 2688 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_4/3/disparity.jay\n", " 304 2026-01-29 16:01 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_4/3/cm.jay\n", " 2 2026-01-29 16:02 explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/insights/insights_and_actions.json\n", "--------- -------\n", " 1984571 425 files\n" ] } ], "source": [ "!unzip -l dia-demo-archive.zip" ] }, { "cell_type": "code", "execution_count": null, "id": "2a6e2cac-c329-41a3-9523-325de194a546", "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 }