{ "cells": [ { "cell_type": "markdown", "id": "bf925586-9e47-4588-b544-0faa8bc4cf7b", "metadata": {}, "source": [ "# H2O Sonar Pickled Model CLI demo\n", "\n", "This example demonstrates how to interpret a pickled model using H2O Sonar from the **command line**." ] }, { "cell_type": "code", "execution_count": 6, "id": "0da787e4-964b-486b-a67e-19efdc8c48cd", "metadata": {}, "outputs": [], "source": [ "import os\n", "import time" ] }, { "cell_type": "markdown", "id": "94fa1d12-1fed-446c-b153-9ae4a4eb2f99", "metadata": {}, "source": [ "Get **H2O Sonar** command line interface help:" ] }, { "cell_type": "code", "execution_count": 7, "id": "7259cacf-44f5-4c81-934b-ec68ac759e8e", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "usage: h2o-sonar [-h] [--dataset DATASET] [--target-col TARGET_COL]\n", " [--results-location RESULTS_LOCATION]\n", " [--results-formats RESULTS_FORMATS] [--model MODEL]\n", " [--validset VALIDSET] [--testset TESTSET]\n", " [--use_raw-features USE_RAW_FEATURES]\n", " [--weight-col WEIGHT_COL] [--drop-cols DROP_COLS]\n", " [--sample-num-rows SAMPLE_NUM_ROWS]\n", " [--used-features USED_FEATURES] [--model-type {pickle,mojo}]\n", " [--explainer EXPLAINER] [--explainers EXPLAINERS]\n", " [--all-explainers] [--explainers-pars EXPLAINERS_PARS]\n", " [--config-path CONFIG_PATH] [--config-type CONFIG_TYPE]\n", " [--config-value CONFIG_VALUE]\n", " [--encryption-key ENCRYPTION_KEY] [-d]\n", " [--interpretation INTERPRETATION]\n", " [--collection-name COLLECTION_NAME] [--upload-to UPLOAD_TO]\n", " [--args-as-json-location ARGS_AS_JSON_LOCATION]\n", " [--log-level {error,warning,info,debug}]\n", " action entity\n", "\n", "H2O Sonar Python library for Responsible AI.\n", "\n", "H2O Sonar is Python package that enables a holistic, low-risk, human-interpretable,\n", "fair, and trustable approach to machine learning by implementing various facets of\n", "Responsible AI.\n", "\n", "model, dataset, validset or testset handle schema:\n", "\n", " resource:connection:\"\":key:\n", " [:version:]\n", "\n", "optional arguments per action and entity:\n", "\n", " show version:\n", " show H2O Sonar version\n", "\n", " add config:\n", " --config-path path to JSon or TOML file with H2O Sonar config to be changed\n", " --config-type config item type: 'CONNECTION' or 'LICENSE'\n", " --config-value config item value (serialized as JSon) to add to the config file\n", " --encryption-key secret key to encrypt config fields with sensitive data\n", " (alternatively set H2O_SONAR_ENCRYPTION_KEY environment variable)\n", "\n", " show config:\n", " --config-path path to JSon or TOML file with H2O Sonar config\n", " --encryption-key optional secret key to decrypt config fields with sensitive data\n", " (alternatively set H2O_SONAR_ENCRYPTION_KEY environment variable)\n", "\n", " list explainers:\n", " --detailed show detailed descriptors (only IDs are shown by default)\n", " --args-as-json-location\n", " optional JSon file which overrides filtering CLI arguments\n", "\n", " describe explainer:\n", " --explainer explainer ID\n", "\n", " run interpretation:\n", " --dataset path to dataset\n", " --target-col target column\n", " --model path to the serialized model, URL or locator\n", " --results-location\n", " optional path to the interpretation results location (directory)\n", " --validset optional path to validation dataset\n", " --testset optional path to test dataset\n", " --use_raw_features\n", " force the use of transformed features in surrogate models\n", " with 'false', by default the original (raw) features are used\n", " --weight-col optional dataset column name with examples weights\n", " --drop-cols optional list of dataset columns to drop\n", " --sample-num-rows\n", " optional number of rows to sample from dataset (default: sample\n", " based on the RAM size, 0 do not sample, >0 sample to the specified\n", " number of rows)\n", " --all-explainers run all explainers (only the most important are run by default)\n", " --used-features optional comma separated list of features used by the model\n", " --model-type optional model type: 'pickle' or 'mojo'\n", " --explainers optional comma separated list of explainer IDs to be run\n", " --explainers-pars optional dictionary with explainer parameters\n", " --config-path path to JSon or TOML file with H2O Sonar config to be changed\n", " --encryption-key secret key to encrypt config fields with sensitive data\n", " (alternatively set H2O_SONAR_ENCRYPTION_KEY environment variable)\n", " --upload-to optional (h2oGPT Enterprise) connection key from the configuration\n", " where the report is to be uploaded in order to talk to it\n", " --args-as-json-location\n", " optional JSon file which overrides CLI arguments\n", " --log-level optional log level: 'error', 'warning', 'info', 'debug'\n", "\n", " list interpretations:\n", " --results-location\n", " path to directory, URL, location of interpretation results\n", " --log-level optional log level: 'error', 'warning', 'info', 'debug'\n", "\n", " upload interpretations:\n", " --interpretation path to the interpretation report PDF or HTML file\n", " --upload-to (h2oGPT Enterprise) connection key from the configuration\n", " where the report is to be uploaded in order to talk to it\n", " --collection-name optional name of the collection where the report is to be uploaded\n", " --config-path path to JSon or TOML file with H2O Sonar config to be changed\n", " --encryption-key secret key to encrypt config fields with sensitive data\n", " (alternatively set H2O_SONAR_ENCRYPTION_KEY environment variable)\n", "\n", "positional arguments:\n", " action action to take: 'list', 'run' or 'describe'\n", " entity entity on which to perform the action:\n", " 'interpretation'(s) or 'explainer'(s)\n", "\n", "options:\n", " -h, --help show this help message and exit\n", " --dataset DATASET location of the dataset\n", " --target-col TARGET_COL\n", " target column\n", " --results-location RESULTS_LOCATION\n", " location where to store the interpretation results\n", " --results-formats RESULTS_FORMATS\n", " comma separated list of MIME types of the\n", " interpretation results to create\n", " --model MODEL location of the model\n", " --validset VALIDSET location of the validation dataset\n", " --testset TESTSET location of the test dataset\n", " --use_raw-features USE_RAW_FEATURES\n", " force the use of transformed features in surrogate\n", " models with `false`\n", " --weight-col WEIGHT_COL\n", " optional dataset column name with examples weights\n", " --drop-cols DROP_COLS\n", " optional list of dataset columns to drop\n", " --sample-num-rows SAMPLE_NUM_ROWS\n", " optional number of rows to sample from the dataset\n", " --used-features USED_FEATURES\n", " optional comma separated list of features used by the\n", " model\n", " --model-type {pickle,mojo}\n", " model type: 'pickle' (.pkl) or 'mojo' (.mojo)\n", " --explainer EXPLAINER\n", " ID of the explainer to describe\n", " --explainers EXPLAINERS\n", " comma separated list of explainer IDs to be run (only\n", " the most important explainers are run by default)\n", " --all-explainers run all explainers (only the most important explainers\n", " are run by default)\n", " --explainers-pars EXPLAINERS_PARS\n", " optional dictionary with explainer parameters - the\n", " dictionary key is explainer ID and value is dictionary\n", " with parameters; parameter dictionary has parameter\n", " name as the key and parameter value as the value\n", " --config-path CONFIG_PATH\n", " path to JSon or TOML file with H2O Sonar configuration\n", " to be used to override defaults - specify only items\n", " you want to change (please refer to\n", " h2o_sonar.config.H2oSonarConfig for more details)\n", " --config-type CONFIG_TYPE\n", " configuration item type - 'CONNECTION' or 'LICENSE'\n", " --config-value CONFIG_VALUE\n", " configuration item value represented either as\n", " dictionary or as string with JSon serialization of the\n", " configuration item - it is expected that the config\n", " item is NOT encrypted\n", " --encryption-key ENCRYPTION_KEY\n", " encryption key to be used for encrypting/decrypting\n", " sensitive data in the configuration. If not specified,\n", " shell environment variable H2O_SONAR_ENCRYPTION_KEY\n", " with the encryption key is used.\n", " -d, --detailed show detailed descriptors (only IDs are shown by\n", " default)\n", " --interpretation INTERPRETATION\n", " path to the interpretation report (PDF or HTML) to be\n", " uploaded\n", " --collection-name COLLECTION_NAME\n", " optional (h2oGPT Enterprise) collection name where to\n", " upload the interpretation report\n", " --upload-to UPLOAD_TO\n", " optional (h2oGPT Enterprise) connection key from the\n", " configuration where the report is to be uploaded in\n", " order to talk to it\n", " --args-as-json-location ARGS_AS_JSON_LOCATION\n", " location of the JSon file with all command arguments\n", " (replacing command line arguments) allowing to load\n", " them from the filesystem\n", " --log-level {error,warning,info,debug}\n", " log level\n", "\n", "examples:\n", "\n", " h2o-sonar --help\n", " h2o-sonar show version\n", " h2o-sonar list explainers\n", " h2o-sonar list explainers --detailed\n", " h2o-sonar describe explainer\n", " --explainer=h2o_sonar.explainers.dia_explainer.DiaExplainer\n", " h2o-sonar run interpretation\n", " --dataset=dataset.csv\n", " --target-col=PROFIT\n", " --results-location=/home/user/results\n", " --model=model.pickle\n", " --all-explainers\n", " h2o-sonar run interpretation\n", " --dataset=dataset.csv\n", " --target-col=PROFIT\n", " --results-location=/home/user/results\n", " --model=model.pickle\n", " --used-features=FEATURE_1,FEATURE_2,FEATURE_3\n", " --explainers=h2o_sonar.explainers.dia_explainer.DiaExplainer\n", " --explainers-pars=\n", " \"{'h2o_sonar.explainers.dia_explainer.DiaExplainer':{'cut_off': 0.5}}\"\n", " --drop_cols=COLUMN_1,COLUMN_2,COLUMN_3\n", " --config-path=h2o-sonar-config.json\n", " --upload-to=4cff6fc9-f49a-4dda-aeb5-2c42e9f12807\n", " h2o-sonar run interpretation\n", " --args-as-json-location=h2o-sonar-args.json\n", " h2o-sonar list interpretations --results-location=/home/user/results\n", " h2o-sonar upload interpretation\n", " --interpretation=./results/h2o-sonar/mli-experiment/interpretation-detailed.html\n", " --upload-to=4cff6fc9-f49a-4dda-aeb5-2c42e9f12807\n", "\n", "H2O Sonar JSon configuration example:\n", " {\n", " \"h2o_host\": \"192.168.0.1\",\n", " \"h2o_port\": 57561,\n", " \"h2o_auto_start\": true,\n", " \"connections\": [\n", " {\n", " \"key\": \"4cff6fc9-f49a-4dda-aeb5-2c42e9f12807\",\n", " \"connection_type\": \"H2O_GPT_E\",\n", " \"name\": \"H2O GPT Enterprise\",\n", " \"description\": \"H2O GPT Enterprise service.\",\n", " \"server_url\": \"https://h2ogpte.h2o.ai\",\n", " \"token\": {\n", " \"encrypted\": \"gAAA3LcKQ7x_X...gnKsBqVdNydYTlk8nyQ==\"\n", " },\n", " \"token_use_type\": \"API_KEY\"\n", " }\n", " ]\n", " }\n", "\n", "Interpretation arguments JSon file example - see interpret.py::run_interpretation():\n", " {\n", " \"dataset\": \"dataset.csv\",\n", " \"model\": \"model.pickle\",\n", " \"target_col\": \"PROFIT\",\n", " \"results_location\": \"./results\"\n", " }\n", "\n", "Explainer listing arguments JSon file example - see interpret.py::list_explainers():\n", " {\n", " \"experiment_types\": [\"regression\"],\n", " \"explanation_scopes\": [\"local_scope\"],\n", " \"keywords\": [\"explains-fairness\"],\n", " \"explainer_filter\": [{\"filter_by\": \"filter-name\", \"value\": \"v\"}]\n", " }\n" ] } ], "source": [ "!h2o-sonar --help" ] }, { "cell_type": "markdown", "id": "7e593ebe-33aa-4a58-b6d1-7c35767690e7", "metadata": {}, "source": [ "**Pickled** model to be interpreted by the library:" ] }, { "cell_type": "code", "execution_count": 8, "id": "7934b7a8-f86a-44ca-8ce7-f0b5be4b696e", "metadata": {}, "outputs": [], "source": [ "dataset_path = \"../../data/predictive/creditcard.csv\"\n", "target_column = \"\\\"default payment next month\\\"\"\n", "\n", "# path to sklearn GBM model - update model to match your Scikit learn version\n", "model_path = \"../../data/predictive/models/creditcard-binomial-sklearn-1.8.0-gbm.pkl\"\n", "\n", "results_path = f\"/tmp/{time.time()}\"\n", "os.mkdir(results_path)" ] }, { "cell_type": "markdown", "id": "308379a8-c1cd-48ca-ab71-38c51e897935", "metadata": {}, "source": [ "Run new interpretation using command line interface:" ] }, { "cell_type": "code", "execution_count": 9, "id": "c8697b39-06b0-4bf5-a481-f17fc7baaf6d", "metadata": {}, "outputs": [ { "name": "stdout", "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", "Stratified/random sampler: loading the original dataset '../../data/predictive/creditcard.csv' for sampling...\n", "Stratified/random sampler: -> did NO sampling as the sampling limit is smaller than the number of rows in the dataset: 10000 <= 25000\n", "Preparing and checking DIA features (None): dataset= | SEX ID AGE PAY_5 BILL_AMT2 PAY_AMT1 MARRIAGE PAY_0 PAY_AMT2 BILL_AMT3 … PAY_AMT3 PAY_4 BILL_AMT6 EDUCATION BILL_AMT4\n", " | int32 int32 int32 int32 int32 int32 int32 int32 int32 int32 int32 int32 int32 int32 int32\n", "---- + ----- ----- ----- ----- --------- -------- -------- ----- -------- --------- -------- ----- --------- --------- ---------\n", " 0 | 2 1 24 -2 3102 0 1 -2 689 689 … 0 -1 0 2 0\n", " 1 | 2 2 26 0 1725 0 2 -1 1000 2682 … 1000 0 3261 2 3272\n", " 2 | 2 3 34 0 14027 1518 2 0 1500 13559 … 1000 0 15549 2 14331\n", " 3 | 2 4 37 0 48233 2000 1 1 2019 49291 … 1200 0 29547 2 28314\n", " 4 | 1 5 57 0 5670 2000 1 2 36681 35835 … 10000 0 19131 2 20940\n", " 5 | 1 6 37 0 57069 2500 2 3 1815 57608 … 657 0 20024 1 19394\n", " 6 | 1 7 29 0 412023 55000 2 4 40000 445007 … 38000 0 473944 1 542653\n", " 7 | 2 8 23 0 380 380 2 5 601 601 … 0 0 567 2 221\n", " 8 | 2 9 28 0 14096 3329 1 6 0 12108 … 432 0 3719 3 12211\n", " 9 | 1 10 35 -1 0 0 2 7 0 0 … 0 -2 13912 3 0\n", " 10 | 2 11 34 0 9787 2306 2 8 12 5535 … 50 0 3731 3 2513\n", " 11 | 2 12 51 -1 21670 21818 2 -1 9966 9966 … 8583 -1 13668 1 8517\n", " 12 | 2 13 41 -1 6500 1000 2 -1 6500 6500 … 6500 -1 2870 2 6500\n", " 13 | 1 14 30 0 67369 3200 2 1 0 65701 … 3000 0 36894 2 66782\n", " 14 | 1 15 29 0 67060 3000 2 0 3000 63561 … 3000 0 55512 1 59696\n", " … | … … … … … … … … … … … … … … … …\n", "9995 | 2 9996 31 -2 241 241 2 1 0 0 … 0 -2 0 1 0\n", "9996 | 2 9997 37 -2 0 0 2 -2 0 0 … 0 -2 0 2 0\n", "9997 | 1 9998 44 0 144085 6437 1 0 5000 142520 … 10000 0 168431 3 151078\n", "9998 | 2 9999 26 -2 780 0 2 -1 0 0 … 0 -2 0 2 0\n", "9999 | 1 10000 36 0 20715 3000 1 0 3000 19750 … 3000 0 17479 2 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\": \"../../data/predictive/creditcard.csv\",\n", " \"file_name\": \"\",\n", " \"file_size\": 944719,\n", " \"missing_values\": [\n", " \"\",\n", " \"?\",\n", " \"None\",\n", " \"nan\",\n", " \"NA\",\n", " \"N/A\",\n", " \"unknown\",\n", " \"inf\",\n", " \"-inf\",\n", " \"1.7976931348623157e+308\",\n", " \"-1.7976931348623157e+308\"\n", " ],\n", " \"columns_meta\": [\n", " {\n", " \"name\": \"ID\",\n", " \"data_type\": \"int\",\n", " \"logical_types\": [],\n", " \"format\": \"\",\n", " \"is_id\": true,\n", " \"is_numeric\": true,\n", " \"is_categorical\": false,\n", " \"count\": 10000,\n", " \"frequency\": 0,\n", " \"unique\": 10000,\n", " \"max\": null,\n", " \"min\": null,\n", " \"mean\": null,\n", " \"std\": null,\n", " \"histogram_counts\": [],\n", " \"histogram_ticks\": []\n", " },\n", " {\n", " \"name\": \"LIMIT_BAL\",\n", " \"data_type\": \"int\",\n", " \"logical_types\": [],\n", " \"format\": \"\",\n", " \"is_id\": false,\n", " \"is_numeric\": true,\n", " \"is_categorical\": false,\n", " \"count\": 72,\n", " \"frequency\": 0,\n", " \"unique\": 72,\n", " \"max\": null,\n", " \"min\": null,\n", " \"mean\": null,\n", " \"std\": null,\n", " \"histogram_counts\": [],\n", " \"histogram_ticks\": []\n", " },\n", " {\n", " \"name\": \"SEX\",\n", " \"data_type\": \"int\",\n", " \"logical_types\": [],\n", " \"format\": \"\",\n", " \"is_id\": false,\n", " \"is_numeric\": true,\n", " \"is_categorical\": false,\n", " \"count\": 2,\n", " \"frequency\": 0,\n", " \"unique\": 2,\n", " \"max\": null,\n", " \"min\": null,\n", " \"mean\": null,\n", " \"std\": null,\n", " \"histogram_counts\": [],\n", " \"histogram_ticks\": []\n", " },\n", " {\n", " \"name\": \"EDUCATION\",\n", " \"data_type\": \"int\",\n", " \"logical_types\": [],\n", " \"format\": \"\",\n", " \"is_id\": false,\n", " \"is_numeric\": true,\n", " \"is_categorical\": false,\n", " \"count\": 7,\n", " \"frequency\": 0,\n", " \"unique\": 7,\n", " \"max\": null,\n", " \"min\": null,\n", " \"mean\": null,\n", " \"std\": null,\n", " \"histogram_counts\": [],\n", " \"histogram_ticks\": []\n", " },\n", " {\n", " \"name\": \"MARRIAGE\",\n", " \"data_type\": \"int\",\n", " \"logical_types\": [],\n", " \"format\": \"\",\n", " \"is_id\": false,\n", " \"is_numeric\": true,\n", " \"is_categorical\": false,\n", " \"count\": 4,\n", " \"frequency\": 0,\n", " \"unique\": 4,\n", " \"max\": null,\n", " \"min\": null,\n", " \"mean\": null,\n", " \"std\": null,\n", " \"histogram_counts\": [],\n", " \"histogram_ticks\": []\n", " },\n", " {\n", " \"name\": \"AGE\",\n", " \"data_type\": \"int\",\n", " \"logical_types\": [],\n", " \"format\": \"\",\n", " \"is_id\": false,\n", " \"is_numeric\": true,\n", " \"is_categorical\": false,\n", " \"count\": 54,\n", " \"frequency\": 0,\n", " \"unique\": 54,\n", " \"max\": null,\n", " \"min\": null,\n", " \"mean\": null,\n", " \"std\": null,\n", " \"histogram_counts\": [],\n", " \"histogram_ticks\": []\n", " },\n", " {\n", " \"name\": \"PAY_0\",\n", " \"data_type\": \"int\",\n", " \"logical_types\": [],\n", " \"format\": \"\",\n", " \"is_id\": false,\n", " \"is_numeric\": true,\n", " \"is_categorical\": false,\n", " \"count\": 11,\n", " \"frequency\": 0,\n", " \"unique\": 11,\n", " \"max\": null,\n", " \"min\": null,\n", " \"mean\": null,\n", " \"std\": null,\n", " \"histogram_counts\": [],\n", " \"histogram_ticks\": []\n", " },\n", " {\n", " \"name\": \"PAY_2\",\n", " \"data_type\": \"int\",\n", " \"logical_types\": [],\n", " \"format\": \"\",\n", " \"is_id\": false,\n", " \"is_numeric\": true,\n", " \"is_categorical\": false,\n", " \"count\": 11,\n", " \"frequency\": 0,\n", " \"unique\": 11,\n", " \"max\": null,\n", " \"min\": null,\n", " \"mean\": null,\n", " \"std\": null,\n", " \"histogram_counts\": [],\n", " \"histogram_ticks\": []\n", " },\n", " {\n", " \"name\": \"PAY_3\",\n", " \"data_type\": \"int\",\n", " \"logical_types\": [],\n", " \"format\": \"\",\n", " \"is_id\": false,\n", " \"is_numeric\": true,\n", " \"is_categorical\": false,\n", " \"count\": 11,\n", " \"frequency\": 0,\n", " \"unique\": 11,\n", " \"max\": null,\n", " \"min\": null,\n", " \"mean\": null,\n", " \"std\": null,\n", " \"histogram_counts\": [],\n", " \"histogram_ticks\": []\n", " },\n", " {\n", " \"name\": \"PAY_4\",\n", " \"data_type\": \"int\",\n", " \"logical_types\": [],\n", " \"format\": \"\",\n", " \"is_id\": false,\n", " \"is_numeric\": true,\n", " \"is_categorical\": false,\n", " \"count\": 11,\n", " \"frequency\": 0,\n", " \"unique\": 11,\n", " \"max\": null,\n", " \"min\": null,\n", " \"mean\": null,\n", " \"std\": null,\n", " \"histogram_counts\": [],\n", " \"histogram_ticks\": []\n", " },\n", " {\n", " \"name\": \"PAY_5\",\n", " \"data_type\": \"int\",\n", " \"logical_types\": [],\n", " \"format\": \"\",\n", " \"is_id\": false,\n", " \"is_numeric\": true,\n", " \"is_categorical\": false,\n", " \"count\": 10,\n", " \"frequency\": 0,\n", " \"unique\": 10,\n", " \"max\": null,\n", " \"min\": null,\n", " \"mean\": null,\n", " \"std\": null,\n", " \"histogram_counts\": [],\n", " \"histogram_ticks\": []\n", " },\n", " {\n", " \"name\": \"PAY_6\",\n", " \"data_type\": \"int\",\n", " \"logical_types\": [],\n", " \"format\": \"\",\n", " \"is_id\": false,\n", " \"is_numeric\": true,\n", " \"is_categorical\": false,\n", " \"count\": 10,\n", " \"frequency\": 0,\n", " \"unique\": 10,\n", " \"max\": null,\n", " \"min\": null,\n", " \"mean\": null,\n", " \"std\": null,\n", " \"histogram_counts\": [],\n", " \"histogram_ticks\": []\n", " },\n", " {\n", " \"name\": \"BILL_AMT1\",\n", " \"data_type\": \"int\",\n", " \"logical_types\": [],\n", " \"format\": \"\",\n", " \"is_id\": false,\n", " \"is_numeric\": true,\n", " \"is_categorical\": false,\n", " \"count\": 8371,\n", " \"frequency\": 0,\n", " \"unique\": 8371,\n", " \"max\": null,\n", " \"min\": null,\n", " \"mean\": null,\n", " \"std\": null,\n", " \"histogram_counts\": [],\n", " \"histogram_ticks\": []\n", " },\n", " {\n", " \"name\": \"BILL_AMT2\",\n", " \"data_type\": \"int\",\n", " \"logical_types\": [],\n", " \"format\": \"\",\n", " \"is_id\": false,\n", " \"is_numeric\": true,\n", " \"is_categorical\": false,\n", " \"count\": 8215,\n", " \"frequency\": 0,\n", " \"unique\": 8215,\n", " \"max\": null,\n", " \"min\": null,\n", " \"mean\": null,\n", " \"std\": null,\n", " \"histogram_counts\": [],\n", " \"histogram_ticks\": []\n", " },\n", " {\n", " \"name\": \"BILL_AMT3\",\n", " \"data_type\": \"int\",\n", " \"logical_types\": [],\n", " \"format\": \"\",\n", " \"is_id\": false,\n", " \"is_numeric\": true,\n", " \"is_categorical\": false,\n", " \"count\": 8072,\n", " \"frequency\": 0,\n", " \"unique\": 8072,\n", " \"max\": null,\n", " \"min\": null,\n", " \"mean\": null,\n", " \"std\": null,\n", " \"histogram_counts\": [],\n", " \"histogram_ticks\": []\n", " },\n", " {\n", " \"name\": \"BILL_AMT4\",\n", " \"data_type\": \"int\",\n", " \"logical_types\": [],\n", " \"format\": \"\",\n", " \"is_id\": false,\n", " \"is_numeric\": true,\n", " \"is_categorical\": false,\n", " \"count\": 7913,\n", " \"frequency\": 0,\n", " \"unique\": 7913,\n", " \"max\": null,\n", " \"min\": null,\n", " \"mean\": null,\n", " \"std\": null,\n", " \"histogram_counts\": [],\n", " \"histogram_ticks\": []\n", " },\n", " {\n", " \"name\": \"BILL_AMT5\",\n", " \"data_type\": \"int\",\n", " \"logical_types\": [],\n", " \"format\": \"\",\n", " \"is_id\": false,\n", " \"is_numeric\": true,\n", " \"is_categorical\": false,\n", " \"count\": 7764,\n", " \"frequency\": 0,\n", " \"unique\": 7764,\n", " \"max\": null,\n", " \"min\": null,\n", " \"mean\": null,\n", " \"std\": null,\n", " \"histogram_counts\": [],\n", " \"histogram_ticks\": []\n", " },\n", " {\n", " \"name\": \"BILL_AMT6\",\n", " \"data_type\": \"int\",\n", " \"logical_types\": [],\n", " \"format\": \"\",\n", " \"is_id\": false,\n", " \"is_numeric\": true,\n", " \"is_categorical\": false,\n", " \"count\": 7550,\n", " \"frequency\": 0,\n", " \"unique\": 7550,\n", " \"max\": null,\n", " \"min\": null,\n", " \"mean\": null,\n", " \"std\": null,\n", " \"histogram_counts\": [],\n", " \"histogram_ticks\": []\n", " },\n", " {\n", " \"name\": \"PAY_AMT1\",\n", " \"data_type\": \"int\",\n", " \"logical_types\": [],\n", " \"format\": \"\",\n", " \"is_id\": false,\n", " \"is_numeric\": true,\n", " \"is_categorical\": false,\n", " \"count\": 3763,\n", " \"frequency\": 0,\n", " \"unique\": 3763,\n", " \"max\": null,\n", " \"min\": null,\n", " \"mean\": null,\n", " \"std\": null,\n", " \"histogram_counts\": [],\n", " \"histogram_ticks\": []\n", " },\n", " {\n", " \"name\": \"PAY_AMT2\",\n", " \"data_type\": \"int\",\n", " \"logical_types\": [],\n", " \"format\": \"\",\n", " \"is_id\": false,\n", " \"is_numeric\": true,\n", " \"is_categorical\": false,\n", " \"count\": 3581,\n", " \"frequency\": 0,\n", " \"unique\": 3581,\n", " \"max\": null,\n", " \"min\": null,\n", " \"mean\": null,\n", " \"std\": null,\n", " \"histogram_counts\": [],\n", " \"histogram_ticks\": []\n", " },\n", " {\n", " \"name\": \"PAY_AMT3\",\n", " \"data_type\": \"int\",\n", " \"logical_types\": [],\n", " \"format\": \"\",\n", " \"is_id\": false,\n", " \"is_numeric\": true,\n", " \"is_categorical\": false,\n", " \"count\": 3305,\n", " \"frequency\": 0,\n", " \"unique\": 3305,\n", " \"max\": null,\n", " \"min\": null,\n", " \"mean\": null,\n", " \"std\": null,\n", " \"histogram_counts\": [],\n", " \"histogram_ticks\": []\n", " },\n", " {\n", " \"name\": \"PAY_AMT4\",\n", " \"data_type\": \"int\",\n", " \"logical_types\": [],\n", " \"format\": \"\",\n", " \"is_id\": false,\n", " \"is_numeric\": true,\n", " \"is_categorical\": false,\n", " \"count\": 3247,\n", " \"frequency\": 0,\n", " \"unique\": 3247,\n", " \"max\": null,\n", " \"min\": null,\n", " \"mean\": null,\n", " \"std\": null,\n", " \"histogram_counts\": [],\n", " \"histogram_ticks\": []\n", " },\n", " {\n", " \"name\": \"PAY_AMT5\",\n", " \"data_type\": \"int\",\n", " \"logical_types\": [],\n", " \"format\": \"\",\n", " \"is_id\": false,\n", " \"is_numeric\": true,\n", " \"is_categorical\": false,\n", " \"count\": 3258,\n", " \"frequency\": 0,\n", " \"unique\": 3258,\n", " \"max\": null,\n", " \"min\": null,\n", " \"mean\": null,\n", " \"std\": null,\n", " \"histogram_counts\": [],\n", " \"histogram_ticks\": []\n", " },\n", " {\n", " \"name\": \"PAY_AMT6\",\n", " \"data_type\": \"int\",\n", " \"logical_types\": [],\n", " \"format\": \"\",\n", " \"is_id\": false,\n", " \"is_numeric\": true,\n", " \"is_categorical\": false,\n", " \"count\": 3174,\n", " \"frequency\": 0,\n", " \"unique\": 3174,\n", " \"max\": null,\n", " \"min\": null,\n", " \"mean\": null,\n", " \"std\": null,\n", " \"histogram_counts\": [],\n", " \"histogram_ticks\": []\n", " },\n", " {\n", " \"name\": \"default payment next month\",\n", " \"data_type\": \"int\",\n", " \"logical_types\": [],\n", " \"format\": \"\",\n", " \"is_id\": false,\n", " \"is_numeric\": true,\n", " \"is_categorical\": false,\n", " \"count\": 2,\n", " \"frequency\": 0,\n", " \"unique\": 2,\n", " \"max\": null,\n", " \"min\": null,\n", " \"mean\": null,\n", " \"std\": null,\n", " \"histogram_counts\": [],\n", " \"histogram_ticks\": []\n", " }\n", " ],\n", " \"original_dataset_sampled\": false,\n", " \"original_dataset_path\": \"\",\n", " \"original_dataset_size\": 0,\n", " \"original_dataset_shape\": [\n", " 10000,\n", " 25\n", " ]\n", "}\n", "Using dataset ENTITY to prepare DIA features: column_names=['ID', 'LIMIT_BAL', 'SEX', 'EDUCATION', 'MARRIAGE', 'AGE', 'PAY_0', 'PAY_2', 'PAY_3', 'PAY_4', 'PAY_5', 'PAY_6', 'BILL_AMT1', 'BILL_AMT2', 'BILL_AMT3', 'BILL_AMT4', 'BILL_AMT5', 'BILL_AMT6', 'PAY_AMT1', 'PAY_AMT2', 'PAY_AMT3', 'PAY_AMT4', 'PAY_AMT5', 'PAY_AMT6', 'default payment next month'] column_uniques=[10000, 72, 2, 7, 4, 54, 11, 11, 11, 11, 10, 10, 8371, 8215, 8072, 7913, 7764, 7550, 3763, 3581, 3305, 3247, 3258, 3174, 2]\n", "DIA group columns prepared using dataset ENTITY: {'SEX', 'PAY_0', 'PAY_3', 'PAY_4', 'PAY_5', 'PAY_2', 'PAY_6', 'default payment next month', 'MARRIAGE', 'EDUCATION'}\n", "DIA group columns to SKIP: {'model_pred', 'default payment next month'}\n", "DIA group columns as BOOLs: [, , , , , , , , ]\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", "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", "Checking whether there is an H2O instance running at http://localhost:54321..... not found.\n", "Attempting to start a local H2O server...\n", " Java Version: openjdk version \"10\" 2018-03-20; OpenJDK Runtime Environment 18.3 (build 10+44); OpenJDK 64-Bit Server VM 18.3 (build 10+44, mixed mode)\n", " Starting server from /home/user/h/mli/git/h2o-sonar-FLOSS/.venv/lib/python3.11/site-packages/h2o/backend/bin/h2o.jar\n", " Ice root: /tmp/tmphs6w297_\n", " JVM stdout: /tmp/tmphs6w297_/h2o_user_started_from_python.out\n", " JVM stderr: /tmp/tmphs6w297_/h2o_user_started_from_python.err\n", " Server is running at http://127.0.0.1:54321\n", " successful.o H2O server at http://127.0.0.1:54321 ...\n", "-------------------------- -----------------------------\n", "H2O_cluster_uptime: 01 secs\n", "H2O_cluster_timezone: Europe/Prague\n", "H2O_data_parsing_timezone: UTC\n", "H2O_cluster_version: 3.46.0.9\n", "H2O_cluster_version_age: 2 months and 4 days\n", "H2O_cluster_name: H2O_from_python_user_oilgyy\n", "H2O_cluster_total_nodes: 1\n", "H2O_cluster_free_memory: 4 Gb\n", "H2O_cluster_total_cores: 16\n", "H2O_cluster_allowed_cores: 16\n", "H2O_cluster_status: locked, healthy\n", "H2O_connection_url: http://127.0.0.1:54321\n", "H2O_connection_proxy: {\"http\": null, \"https\": null}\n", "H2O_internal_security: False\n", "Python_version: 3.11.11 final\n", "-------------------------- -----------------------------\n", " successful.o H2O server at http://localhost:54321 ...\n", "-------------------------- -----------------------------\n", "H2O_cluster_uptime: 01 secs\n", "H2O_cluster_timezone: Europe/Prague\n", "H2O_data_parsing_timezone: UTC\n", "H2O_cluster_version: 3.46.0.9\n", "H2O_cluster_version_age: 2 months and 4 days\n", "H2O_cluster_name: H2O_from_python_user_oilgyy\n", "H2O_cluster_total_nodes: 1\n", "H2O_cluster_free_memory: 4 Gb\n", "H2O_cluster_total_cores: 16\n", "H2O_cluster_allowed_cores: 16\n", "H2O_cluster_status: locked, healthy\n", "H2O_connection_url: http://localhost:54321\n", "H2O_connection_proxy: {\"http\": null, \"https\": null}\n", "H2O_internal_security: False\n", "Python_version: 3.11.11 final\n", "-------------------------- -----------------------------\n", "X does not have valid feature names, but GradientBoostingClassifier was fitted with feature names\n", "Connecting to H2O server at http://localhost:54321 ... successful.\n", "-------------------------- -----------------------------\n", "H2O_cluster_uptime: 01 secs\n", "H2O_cluster_timezone: Europe/Prague\n", "H2O_data_parsing_timezone: UTC\n", "H2O_cluster_version: 3.46.0.9\n", "H2O_cluster_version_age: 2 months and 4 days\n", "H2O_cluster_name: H2O_from_python_user_oilgyy\n", "H2O_cluster_total_nodes: 1\n", "H2O_cluster_free_memory: 4 Gb\n", "H2O_cluster_total_cores: 16\n", "H2O_cluster_allowed_cores: 16\n", "H2O_cluster_status: locked, healthy\n", "H2O_connection_url: http://localhost:54321\n", "H2O_connection_proxy: {\"http\": null, \"https\": null}\n", "H2O_internal_security: False\n", "Python_version: 3.11.11 final\n", "-------------------------- -----------------------------\n", "Parse progress: |████████████████████████████████████████████████████████████████ (done)| 100%\n", "Parse progress: |████████████████████████████████████████████████████████████████ (done)| 100%\n", "drf Model Build progress: |██████████████████████████████████████████████████████ (done)| 100%\n", "Parse progress: |████████████████████████████████████████████████████████████████ (done)| 100%\n", "Converting H2O frame to pandas dataframe using single-thread. For faster conversion using multi-thread, install polars and pyarrow and use it as pandas_df = h2o_df.as_data_frame(use_multi_thread=True)\n", "\n", "X does not have valid feature names, but GradientBoostingClassifier was fitted with feature names\n", "Connecting to H2O server at http://localhost:54321 ... successful.\n", "-------------------------- -----------------------------\n", "H2O_cluster_uptime: 04 secs\n", "H2O_cluster_timezone: Europe/Prague\n", "H2O_data_parsing_timezone: UTC\n", "H2O_cluster_version: 3.46.0.9\n", "H2O_cluster_version_age: 2 months and 4 days\n", "H2O_cluster_name: H2O_from_python_user_oilgyy\n", "H2O_cluster_total_nodes: 1\n", "H2O_cluster_free_memory: 4 Gb\n", "H2O_cluster_total_cores: 16\n", "H2O_cluster_allowed_cores: 16\n", "H2O_cluster_status: locked, healthy\n", "H2O_connection_url: http://localhost:54321\n", "H2O_connection_proxy: {\"http\": null, \"https\": null}\n", "H2O_internal_security: False\n", "Python_version: 3.11.11 final\n", "-------------------------- -----------------------------\n", "Parse progress: |████████████████████████████████████████████████████████████████ (done)| 100%\n", "Parse progress: |████████████████████████████████████████████████████████████████ (done)| 100%\n", "We have detected that your response column has only 2 unique values (0/1). If you wish to train a binary model instead of a regression model, convert your target column to categorical before training.\n", "drf Model Build progress: |██████████████████████████████████████████████████████ (done)| 100%\n", "Parse progress: |████████████████████████████████████████████████████████████████ (done)| 100%\n", "Converting H2O frame to pandas dataframe using single-thread. For faster conversion using multi-thread, install polars and pyarrow and use it as pandas_df = h2o_df.as_data_frame(use_multi_thread=True)\n", "\n", "More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). Consider using `matplotlib.pyplot.close()`.\n", "X does not have valid feature names, but GradientBoostingClassifier was fitted with feature names\n", "X does not have valid feature names, but GradientBoostingClassifier was fitted with feature names\n", "X does not have valid feature names, but GradientBoostingClassifier was fitted with feature names\n", "X does not have valid feature names, but GradientBoostingClassifier was fitted with feature names\n", "X does not have valid feature names, but GradientBoostingClassifier was fitted with feature names\n", "X does not have valid feature names, but GradientBoostingClassifier was fitted with feature names\n", "X does not have valid feature names, but GradientBoostingClassifier was fitted with feature names\n", "X does not have valid feature names, but GradientBoostingClassifier was fitted with feature names\n", "X does not have valid feature names, but GradientBoostingClassifier was fitted with feature names\n", "X does not have valid feature names, but GradientBoostingClassifier was fitted with feature names\n", "X does not have valid feature names, but GradientBoostingClassifier was fitted with feature names\n", "\n", "Interpretation FINISHED.\n", " Results directory:\n", " file:///tmp/1769698076.7775896\n", " Interpretations index:\n", " file:///tmp/1769698076.7775896/h2o-sonar.html\n", " HTML report:\n", " file:///tmp/1769698076.7775896/h2o-sonar/mli_experiment_ffbb1cd8-c442-40ef-95db-234cc7ba82c2/interpretation.html\n", "Closing connection _sid_8b32 at exit\n", "H2O session _sid_8b32 closed.\n", "Closing connection _sid_b813 at exit\n", "H2O session _sid_b813 closed.\n", "Closing connection _sid_b3ae at exit\n", "H2O session _sid_b3ae closed.\n" ] } ], "source": [ "!h2o-sonar run interpretation \\\n", " --dataset={dataset_path} \\\n", " --model={model_path} \\\n", " --target-col={target_column} \\\n", " --results-location={results_path} " ] }, { "cell_type": "code", "execution_count": 10, "id": "feef75bc-eb9c-4d24-8fd2-10c7dedee5a7", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[01;34m/tmp/1769698076.7775896\u001b[00m\n", "├── \u001b[01;34mh2o-sonar\u001b[00m\n", "│   └── \u001b[01;34mmli_experiment_ffbb1cd8-c442-40ef-95db-234cc7ba82c2\u001b[00m\n", "│   ├── \u001b[01;34mexplainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_86f4e7c7-37d1-47a2-b1cc-0c16f03ec1d7\u001b[00m\n", "│   │   ├── \u001b[01;34mglobal_disparate_impact_analysis\u001b[00m\n", "│   │   │   ├── \u001b[01;34mtext_plain\u001b[00m\n", "│   │   │   │   └── explanation.txt\n", "│   │   │   └── text_plain.meta\n", "│   │   ├── \u001b[01;34mglobal_html_fragment\u001b[00m\n", "│   │   │   ├── \u001b[01;34mtext_html\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdia-0-accuracy.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdia-0-adverse_impact.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdia-0-false_discovery_rate.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdia-0-false_negative_rate.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdia-0-false_omissions_rate.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdia-0-false_positive_rate.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdia-0-negative_predicted_value.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdia-0-n.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdia-0-precision.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdia-0-specificity.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdia-0-true_positive_rate.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdia-1-accuracy.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdia-1-adverse_impact.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdia-1-false_discovery_rate.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdia-1-false_negative_rate.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdia-1-false_omissions_rate.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdia-1-false_positive_rate.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdia-1-negative_predicted_value.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdia-1-n.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdia-1-precision.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdia-1-specificity.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdia-1-true_positive_rate.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdia-2-accuracy.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdia-2-adverse_impact.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdia-2-false_discovery_rate.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdia-2-false_negative_rate.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdia-2-false_omissions_rate.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdia-2-false_positive_rate.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdia-2-negative_predicted_value.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdia-2-n.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdia-2-precision.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdia-2-specificity.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdia-2-true_positive_rate.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdia-3-accuracy.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdia-3-adverse_impact.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdia-3-false_discovery_rate.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdia-3-false_negative_rate.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdia-3-false_omissions_rate.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdia-3-false_positive_rate.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdia-3-negative_predicted_value.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdia-3-n.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdia-3-precision.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdia-3-specificity.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdia-3-true_positive_rate.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdia-4-accuracy.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdia-4-adverse_impact.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdia-4-false_discovery_rate.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdia-4-false_negative_rate.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdia-4-false_omissions_rate.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdia-4-false_positive_rate.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdia-4-negative_predicted_value.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdia-4-n.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdia-4-precision.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdia-4-specificity.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdia-4-true_positive_rate.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdia-5-accuracy.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdia-5-adverse_impact.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdia-5-false_discovery_rate.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdia-5-false_negative_rate.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdia-5-false_omissions_rate.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdia-5-false_positive_rate.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdia-5-negative_predicted_value.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdia-5-n.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdia-5-precision.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdia-5-specificity.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdia-5-true_positive_rate.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdia-6-accuracy.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdia-6-adverse_impact.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdia-6-false_discovery_rate.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdia-6-false_negative_rate.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdia-6-false_omissions_rate.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdia-6-false_positive_rate.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdia-6-negative_predicted_value.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdia-6-n.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdia-6-precision.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdia-6-specificity.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdia-6-true_positive_rate.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdia-7-accuracy.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdia-7-adverse_impact.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdia-7-false_discovery_rate.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdia-7-false_negative_rate.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdia-7-false_omissions_rate.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdia-7-false_positive_rate.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdia-7-negative_predicted_value.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdia-7-n.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdia-7-precision.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdia-7-specificity.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdia-7-true_positive_rate.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdia-8-accuracy.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdia-8-adverse_impact.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdia-8-false_discovery_rate.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdia-8-false_negative_rate.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdia-8-false_omissions_rate.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdia-8-false_positive_rate.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdia-8-negative_predicted_value.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdia-8-n.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdia-8-precision.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdia-8-specificity.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdia-8-true_positive_rate.png\u001b[00m\n", "│   │   │   │   └── explanation.html\n", "│   │   │   └── text_html.meta\n", "│   │   ├── \u001b[01;34minsights\u001b[00m\n", "│   │   │   └── insights_and_actions.json\n", "│   │   ├── \u001b[01;34mlog\u001b[00m\n", "│   │   │   └── explainer_run_86f4e7c7-37d1-47a2-b1cc-0c16f03ec1d7.log\n", "│   │   ├── \u001b[01;34mproblems\u001b[00m\n", "│   │   │   └── problems_and_actions.json\n", "│   │   ├── result_descriptor.json\n", "│   │   └── \u001b[01;34mwork\u001b[00m\n", "│   │   ├── dia_entity.json\n", "│   │   ├── \u001b[01;34mEDUCATION\u001b[00m\n", "│   │   │   ├── \u001b[01;34m0\u001b[00m\n", "│   │   │   │   ├── cm.jay\n", "│   │   │   │   ├── disparity.jay\n", "│   │   │   │   ├── me_smd.jay\n", "│   │   │   │   └── parity.jay\n", "│   │   │   ├── \u001b[01;34m1\u001b[00m\n", "│   │   │   │   ├── cm.jay\n", "│   │   │   │   ├── disparity.jay\n", "│   │   │   │   ├── me_smd.jay\n", "│   │   │   │   └── parity.jay\n", "│   │   │   ├── \u001b[01;34m2\u001b[00m\n", "│   │   │   │   ├── cm.jay\n", "│   │   │   │   ├── disparity.jay\n", "│   │   │   │   ├── me_smd.jay\n", "│   │   │   │   └── parity.jay\n", "│   │   │   ├── \u001b[01;34m3\u001b[00m\n", "│   │   │   │   ├── cm.jay\n", "│   │   │   │   ├── disparity.jay\n", "│   │   │   │   ├── me_smd.jay\n", "│   │   │   │   └── parity.jay\n", "│   │   │   ├── \u001b[01;34m4\u001b[00m\n", "│   │   │   │   ├── cm.jay\n", "│   │   │   │   ├── disparity.jay\n", "│   │   │   │   ├── me_smd.jay\n", "│   │   │   │   └── parity.jay\n", "│   │   │   ├── \u001b[01;34m5\u001b[00m\n", "│   │   │   │   ├── cm.jay\n", "│   │   │   │   ├── disparity.jay\n", "│   │   │   │   ├── me_smd.jay\n", "│   │   │   │   └── parity.jay\n", "│   │   │   ├── \u001b[01;34m6\u001b[00m\n", "│   │   │   │   ├── cm.jay\n", "│   │   │   │   ├── disparity.jay\n", "│   │   │   │   ├── me_smd.jay\n", "│   │   │   │   └── parity.jay\n", "│   │   │   └── metrics.jay\n", "│   │   ├── \u001b[01;34mMARRIAGE\u001b[00m\n", "│   │   │   ├── \u001b[01;34m0\u001b[00m\n", "│   │   │   │   ├── cm.jay\n", "│   │   │   │   ├── disparity.jay\n", "│   │   │   │   ├── me_smd.jay\n", "│   │   │   │   └── parity.jay\n", "│   │   │   ├── \u001b[01;34m1\u001b[00m\n", "│   │   │   │   ├── cm.jay\n", "│   │   │   │   ├── disparity.jay\n", "│   │   │   │   ├── me_smd.jay\n", "│   │   │   │   └── parity.jay\n", "│   │   │   ├── \u001b[01;34m2\u001b[00m\n", "│   │   │   │   ├── cm.jay\n", "│   │   │   │   ├── disparity.jay\n", "│   │   │   │   ├── me_smd.jay\n", "│   │   │   │   └── parity.jay\n", "│   │   │   ├── \u001b[01;34m3\u001b[00m\n", "│   │   │   │   ├── cm.jay\n", "│   │   │   │   ├── disparity.jay\n", "│   │   │   │   ├── me_smd.jay\n", "│   │   │   │   └── parity.jay\n", "│   │   │   └── metrics.jay\n", "│   │   ├── \u001b[01;34mPAY_0\u001b[00m\n", "│   │   │   ├── \u001b[01;34m0\u001b[00m\n", "│   │   │   │   ├── cm.jay\n", "│   │   │   │   ├── disparity.jay\n", "│   │   │   │   ├── me_smd.jay\n", "│   │   │   │   └── parity.jay\n", "│   │   │   ├── \u001b[01;34m1\u001b[00m\n", "│   │   │   │   ├── cm.jay\n", "│   │   │   │   ├── disparity.jay\n", "│   │   │   │   ├── me_smd.jay\n", "│   │   │   │   └── parity.jay\n", "│   │   │   ├── \u001b[01;34m10\u001b[00m\n", "│   │   │   │   ├── cm.jay\n", "│   │   │   │   ├── disparity.jay\n", "│   │   │   │   ├── me_smd.jay\n", "│   │   │   │   └── parity.jay\n", "│   │   │   ├── \u001b[01;34m2\u001b[00m\n", "│   │   │   │   ├── cm.jay\n", "│   │   │   │   ├── disparity.jay\n", "│   │   │   │   ├── me_smd.jay\n", "│   │   │   │   └── parity.jay\n", "│   │   │   ├── \u001b[01;34m3\u001b[00m\n", "│   │   │   │   ├── cm.jay\n", "│   │   │   │   ├── disparity.jay\n", "│   │   │   │   ├── me_smd.jay\n", "│   │   │   │   └── parity.jay\n", "│   │   │   ├── \u001b[01;34m4\u001b[00m\n", "│   │   │   │   ├── cm.jay\n", "│   │   │   │   ├── disparity.jay\n", "│   │   │   │   ├── me_smd.jay\n", "│   │   │   │   └── parity.jay\n", "│   │   │   ├── \u001b[01;34m5\u001b[00m\n", "│   │   │   │   ├── cm.jay\n", "│   │   │   │   ├── disparity.jay\n", "│   │   │   │   ├── me_smd.jay\n", "│   │   │   │   └── parity.jay\n", "│   │   │   ├── \u001b[01;34m6\u001b[00m\n", "│   │   │   │   ├── cm.jay\n", "│   │   │   │   ├── disparity.jay\n", "│   │   │   │   ├── me_smd.jay\n", "│   │   │   │   └── parity.jay\n", "│   │   │   ├── \u001b[01;34m7\u001b[00m\n", "│   │   │   │   ├── cm.jay\n", "│   │   │   │   ├── disparity.jay\n", "│   │   │   │   ├── me_smd.jay\n", "│   │   │   │   └── parity.jay\n", "│   │   │   ├── \u001b[01;34m8\u001b[00m\n", "│   │   │   │   ├── cm.jay\n", "│   │   │   │   ├── disparity.jay\n", "│   │   │   │   ├── me_smd.jay\n", "│   │   │   │   └── parity.jay\n", "│   │   │   ├── \u001b[01;34m9\u001b[00m\n", "│   │   │   │   ├── cm.jay\n", "│   │   │   │   ├── disparity.jay\n", "│   │   │   │   ├── me_smd.jay\n", "│   │   │   │   └── parity.jay\n", "│   │   │   └── metrics.jay\n", "│   │   ├── \u001b[01;34mPAY_2\u001b[00m\n", "│   │   │   ├── \u001b[01;34m0\u001b[00m\n", "│   │   │   │   ├── cm.jay\n", "│   │   │   │   ├── disparity.jay\n", "│   │   │   │   ├── me_smd.jay\n", "│   │   │   │   └── parity.jay\n", "│   │   │   ├── \u001b[01;34m1\u001b[00m\n", "│   │   │   │   ├── cm.jay\n", "│   │   │   │   ├── disparity.jay\n", "│   │   │   │   ├── me_smd.jay\n", "│   │   │   │   └── parity.jay\n", "│   │   │   ├── \u001b[01;34m10\u001b[00m\n", "│   │   │   │   ├── cm.jay\n", "│   │   │   │   ├── disparity.jay\n", "│   │   │   │   ├── me_smd.jay\n", "│   │   │   │   └── parity.jay\n", "│   │   │   ├── \u001b[01;34m2\u001b[00m\n", "│   │   │   │   ├── cm.jay\n", "│   │   │   │   ├── disparity.jay\n", "│   │   │   │   ├── me_smd.jay\n", "│   │   │   │   └── parity.jay\n", "│   │   │   ├── \u001b[01;34m3\u001b[00m\n", "│   │   │   │   ├── cm.jay\n", "│   │   │   │   ├── disparity.jay\n", "│   │   │   │   ├── me_smd.jay\n", "│   │   │   │   └── parity.jay\n", "│   │   │   ├── \u001b[01;34m4\u001b[00m\n", "│   │   │   │   ├── cm.jay\n", "│   │   │   │   ├── disparity.jay\n", "│   │   │   │   ├── me_smd.jay\n", "│   │   │   │   └── parity.jay\n", "│   │   │   ├── \u001b[01;34m5\u001b[00m\n", "│   │   │   │   ├── cm.jay\n", "│   │   │   │   ├── disparity.jay\n", "│   │   │   │   ├── me_smd.jay\n", "│   │   │   │   └── parity.jay\n", "│   │   │   ├── \u001b[01;34m6\u001b[00m\n", "│   │   │   │   ├── cm.jay\n", "│   │   │   │   ├── disparity.jay\n", "│   │   │   │   ├── me_smd.jay\n", "│   │   │   │   └── parity.jay\n", "│   │   │   ├── \u001b[01;34m7\u001b[00m\n", "│   │   │   │   ├── cm.jay\n", "│   │   │   │   ├── disparity.jay\n", "│   │   │   │   ├── me_smd.jay\n", "│   │   │   │   └── parity.jay\n", "│   │   │   ├── \u001b[01;34m8\u001b[00m\n", "│   │   │   │   ├── cm.jay\n", "│   │   │   │   ├── disparity.jay\n", "│   │   │   │   ├── me_smd.jay\n", "│   │   │   │   └── parity.jay\n", "│   │   │   ├── \u001b[01;34m9\u001b[00m\n", "│   │   │   │   ├── cm.jay\n", "│   │   │   │   ├── disparity.jay\n", "│   │   │   │   ├── me_smd.jay\n", "│   │   │   │   └── parity.jay\n", "│   │   │   └── metrics.jay\n", "│   │   ├── \u001b[01;34mPAY_3\u001b[00m\n", "│   │   │   ├── \u001b[01;34m0\u001b[00m\n", "│   │   │   │   ├── cm.jay\n", "│   │   │   │   ├── disparity.jay\n", "│   │   │   │   ├── me_smd.jay\n", "│   │   │   │   └── parity.jay\n", "│   │   │   ├── \u001b[01;34m1\u001b[00m\n", "│   │   │   │   ├── cm.jay\n", "│   │   │   │   ├── disparity.jay\n", "│   │   │   │   ├── me_smd.jay\n", "│   │   │   │   └── parity.jay\n", "│   │   │   ├── \u001b[01;34m10\u001b[00m\n", "│   │   │   │   ├── cm.jay\n", "│   │   │   │   ├── disparity.jay\n", "│   │   │   │   ├── me_smd.jay\n", "│   │   │   │   └── parity.jay\n", "│   │   │   ├── \u001b[01;34m2\u001b[00m\n", "│   │   │   │   ├── cm.jay\n", "│   │   │   │   ├── disparity.jay\n", "│   │   │   │   ├── me_smd.jay\n", "│   │   │   │   └── parity.jay\n", "│   │   │   ├── \u001b[01;34m3\u001b[00m\n", "│   │   │   │   ├── cm.jay\n", "│   │   │   │   ├── disparity.jay\n", "│   │   │   │   ├── me_smd.jay\n", "│   │   │   │   └── parity.jay\n", "│   │   │   ├── \u001b[01;34m4\u001b[00m\n", "│   │   │   │   ├── cm.jay\n", "│   │   │   │   ├── disparity.jay\n", "│   │   │   │   ├── me_smd.jay\n", "│   │   │   │   └── parity.jay\n", "│   │   │   ├── \u001b[01;34m5\u001b[00m\n", "│   │   │   │   ├── cm.jay\n", "│   │   │   │   ├── disparity.jay\n", "│   │   │   │   ├── me_smd.jay\n", "│   │   │   │   └── parity.jay\n", "│   │   │   ├── \u001b[01;34m6\u001b[00m\n", "│   │   │   │   ├── cm.jay\n", "│   │   │   │   ├── disparity.jay\n", "│   │   │   │   ├── me_smd.jay\n", "│   │   │   │   └── parity.jay\n", "│   │   │   ├── \u001b[01;34m7\u001b[00m\n", "│   │   │   │   ├── cm.jay\n", "│   │   │   │   ├── disparity.jay\n", "│   │   │   │   ├── me_smd.jay\n", "│   │   │   │   └── parity.jay\n", "│   │   │   ├── \u001b[01;34m8\u001b[00m\n", "│   │   │   │   ├── cm.jay\n", "│   │   │   │   ├── disparity.jay\n", "│   │   │   │   ├── me_smd.jay\n", "│   │   │   │   └── parity.jay\n", "│   │   │   ├── \u001b[01;34m9\u001b[00m\n", "│   │   │   │   ├── cm.jay\n", "│   │   │   │   ├── disparity.jay\n", "│   │   │   │   ├── me_smd.jay\n", "│   │   │   │   └── parity.jay\n", "│   │   │   └── metrics.jay\n", "│   │   ├── \u001b[01;34mPAY_4\u001b[00m\n", "│   │   │   ├── \u001b[01;34m0\u001b[00m\n", "│   │   │   │   ├── cm.jay\n", "│   │   │   │   ├── disparity.jay\n", "│   │   │   │   ├── me_smd.jay\n", "│   │   │   │   └── parity.jay\n", "│   │   │   ├── \u001b[01;34m1\u001b[00m\n", "│   │   │   │   ├── cm.jay\n", "│   │   │   │   ├── disparity.jay\n", "│   │   │   │   ├── me_smd.jay\n", "│   │   │   │   └── parity.jay\n", "│   │   │   ├── \u001b[01;34m10\u001b[00m\n", "│   │   │   │   ├── cm.jay\n", "│   │   │   │   ├── disparity.jay\n", "│   │   │   │   ├── me_smd.jay\n", "│   │   │   │   └── parity.jay\n", "│   │   │   ├── \u001b[01;34m2\u001b[00m\n", "│   │   │   │   ├── cm.jay\n", "│   │   │   │   ├── disparity.jay\n", "│   │   │   │   ├── me_smd.jay\n", "│   │   │   │   └── parity.jay\n", "│   │   │   ├── \u001b[01;34m3\u001b[00m\n", "│   │   │   │   ├── cm.jay\n", "│   │   │   │   ├── disparity.jay\n", "│   │   │   │   ├── me_smd.jay\n", "│   │   │   │   └── parity.jay\n", "│   │   │   ├── \u001b[01;34m4\u001b[00m\n", "│   │   │   │   ├── cm.jay\n", "│   │   │   │   ├── disparity.jay\n", "│   │   │   │   ├── me_smd.jay\n", "│   │   │   │   └── parity.jay\n", "│   │   │   ├── \u001b[01;34m5\u001b[00m\n", "│   │   │   │   ├── cm.jay\n", "│   │   │   │   ├── disparity.jay\n", "│   │   │   │   ├── me_smd.jay\n", "│   │   │   │   └── parity.jay\n", "│   │   │   ├── \u001b[01;34m6\u001b[00m\n", "│   │   │   │   ├── cm.jay\n", "│   │   │   │   ├── disparity.jay\n", "│   │   │   │   ├── me_smd.jay\n", "│   │   │   │   └── parity.jay\n", "│   │   │   ├── \u001b[01;34m7\u001b[00m\n", "│   │   │   │   ├── cm.jay\n", "│   │   │   │   ├── disparity.jay\n", "│   │   │   │   ├── me_smd.jay\n", "│   │   │   │   └── parity.jay\n", "│   │   │   ├── \u001b[01;34m8\u001b[00m\n", "│   │   │   │   ├── cm.jay\n", "│   │   │   │   ├── disparity.jay\n", "│   │   │   │   ├── me_smd.jay\n", "│   │   │   │   └── parity.jay\n", "│   │   │   ├── \u001b[01;34m9\u001b[00m\n", "│   │   │   │   ├── cm.jay\n", "│   │   │   │   ├── disparity.jay\n", "│   │   │   │   ├── me_smd.jay\n", "│   │   │   │   └── parity.jay\n", "│   │   │   └── metrics.jay\n", "│   │   ├── \u001b[01;34mPAY_5\u001b[00m\n", "│   │   │   ├── \u001b[01;34m0\u001b[00m\n", "│   │   │   │   ├── cm.jay\n", "│   │   │   │   ├── disparity.jay\n", "│   │   │   │   ├── me_smd.jay\n", "│   │   │   │   └── parity.jay\n", "│   │   │   ├── \u001b[01;34m1\u001b[00m\n", "│   │   │   │   ├── cm.jay\n", "│   │   │   │   ├── disparity.jay\n", "│   │   │   │   ├── me_smd.jay\n", "│   │   │   │   └── parity.jay\n", "│   │   │   ├── \u001b[01;34m2\u001b[00m\n", "│   │   │   │   ├── cm.jay\n", "│   │   │   │   ├── disparity.jay\n", "│   │   │   │   ├── me_smd.jay\n", "│   │   │   │   └── parity.jay\n", "│   │   │   ├── \u001b[01;34m3\u001b[00m\n", "│   │   │   │   ├── cm.jay\n", "│   │   │   │   ├── disparity.jay\n", "│   │   │   │   ├── me_smd.jay\n", "│   │   │   │   └── parity.jay\n", "│   │   │   ├── \u001b[01;34m4\u001b[00m\n", "│   │   │   │   ├── cm.jay\n", "│   │   │   │   ├── disparity.jay\n", "│   │   │   │   ├── me_smd.jay\n", "│   │   │   │   └── parity.jay\n", "│   │   │   ├── \u001b[01;34m5\u001b[00m\n", "│   │   │   │   ├── cm.jay\n", "│   │   │   │   ├── disparity.jay\n", "│   │   │   │   ├── me_smd.jay\n", "│   │   │   │   └── parity.jay\n", "│   │   │   ├── \u001b[01;34m6\u001b[00m\n", "│   │   │   │   ├── cm.jay\n", "│   │   │   │   ├── disparity.jay\n", "│   │   │   │   ├── me_smd.jay\n", "│   │   │   │   └── parity.jay\n", "│   │   │   ├── \u001b[01;34m7\u001b[00m\n", "│   │   │   │   ├── cm.jay\n", "│   │   │   │   ├── disparity.jay\n", "│   │   │   │   ├── me_smd.jay\n", "│   │   │   │   └── parity.jay\n", "│   │   │   ├── \u001b[01;34m8\u001b[00m\n", "│   │   │   │   ├── cm.jay\n", "│   │   │   │   ├── disparity.jay\n", "│   │   │   │   ├── me_smd.jay\n", "│   │   │   │   └── parity.jay\n", "│   │   │   ├── \u001b[01;34m9\u001b[00m\n", "│   │   │   │   ├── cm.jay\n", "│   │   │   │   ├── disparity.jay\n", "│   │   │   │   ├── me_smd.jay\n", "│   │   │   │   └── parity.jay\n", "│   │   │   └── metrics.jay\n", "│   │   ├── \u001b[01;34mPAY_6\u001b[00m\n", "│   │   │   ├── \u001b[01;34m0\u001b[00m\n", "│   │   │   │   ├── cm.jay\n", "│   │   │   │   ├── disparity.jay\n", "│   │   │   │   ├── me_smd.jay\n", "│   │   │   │   └── parity.jay\n", "│   │   │   ├── \u001b[01;34m1\u001b[00m\n", "│   │   │   │   ├── cm.jay\n", "│   │   │   │   ├── disparity.jay\n", "│   │   │   │   ├── me_smd.jay\n", "│   │   │   │   └── parity.jay\n", "│   │   │   ├── \u001b[01;34m2\u001b[00m\n", "│   │   │   │   ├── cm.jay\n", "│   │   │   │   ├── disparity.jay\n", "│   │   │   │   ├── me_smd.jay\n", "│   │   │   │   └── parity.jay\n", "│   │   │   ├── \u001b[01;34m3\u001b[00m\n", "│   │   │   │   ├── cm.jay\n", "│   │   │   │   ├── disparity.jay\n", "│   │   │   │   ├── me_smd.jay\n", "│   │   │   │   └── parity.jay\n", "│   │   │   ├── \u001b[01;34m4\u001b[00m\n", "│   │   │   │   ├── cm.jay\n", "│   │   │   │   ├── disparity.jay\n", "│   │   │   │   ├── me_smd.jay\n", "│   │   │   │   └── parity.jay\n", "│   │   │   ├── \u001b[01;34m5\u001b[00m\n", "│   │   │   │   ├── cm.jay\n", "│   │   │   │   ├── disparity.jay\n", "│   │   │   │   ├── me_smd.jay\n", "│   │   │   │   └── parity.jay\n", "│   │   │   ├── \u001b[01;34m6\u001b[00m\n", "│   │   │   │   ├── cm.jay\n", "│   │   │   │   ├── disparity.jay\n", "│   │   │   │   ├── me_smd.jay\n", "│   │   │   │   └── parity.jay\n", "│   │   │   ├── \u001b[01;34m7\u001b[00m\n", "│   │   │   │   ├── cm.jay\n", "│   │   │   │   ├── disparity.jay\n", "│   │   │   │   ├── me_smd.jay\n", "│   │   │   │   └── parity.jay\n", "│   │   │   ├── \u001b[01;34m8\u001b[00m\n", "│   │   │   │   ├── cm.jay\n", "│   │   │   │   ├── disparity.jay\n", "│   │   │   │   ├── me_smd.jay\n", "│   │   │   │   └── parity.jay\n", "│   │   │   ├── \u001b[01;34m9\u001b[00m\n", "│   │   │   │   ├── cm.jay\n", "│   │   │   │   ├── disparity.jay\n", "│   │   │   │   ├── me_smd.jay\n", "│   │   │   │   └── parity.jay\n", "│   │   │   └── metrics.jay\n", "│   │   └── \u001b[01;34mSEX\u001b[00m\n", "│   │   ├── \u001b[01;34m0\u001b[00m\n", "│   │   │   ├── cm.jay\n", "│   │   │   ├── disparity.jay\n", "│   │   │   ├── me_smd.jay\n", "│   │   │   └── parity.jay\n", "│   │   ├── \u001b[01;34m1\u001b[00m\n", "│   │   │   ├── cm.jay\n", "│   │   │   ├── disparity.jay\n", "│   │   │   ├── me_smd.jay\n", "│   │   │   └── parity.jay\n", "│   │   └── metrics.jay\n", "│   ├── \u001b[01;34mexplainer_h2o_sonar_explainers_dt_surrogate_explainer_DecisionTreeSurrogateExplainer_f03ade5e-92e1-475b-93fc-b52a2b5b3385\u001b[00m\n", "│   │   ├── \u001b[01;34mglobal_custom_archive\u001b[00m\n", "│   │   │   ├── \u001b[01;34mapplication_zip\u001b[00m\n", "│   │   │   │   └── \u001b[01;31mexplanation.zip\u001b[00m\n", "│   │   │   └── application_zip.meta\n", "│   │   ├── \u001b[01;34mglobal_decision_tree\u001b[00m\n", "│   │   │   ├── \u001b[01;34mapplication_json\u001b[00m\n", "│   │   │   │   ├── dt_class_0.json\n", "│   │   │   │   └── explanation.json\n", "│   │   │   └── application_json.meta\n", "│   │   ├── \u001b[01;34mglobal_html_fragment\u001b[00m\n", "│   │   │   ├── \u001b[01;34mtext_html\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdt-class-0.png\u001b[00m\n", "│   │   │   │   └── explanation.html\n", "│   │   │   └── text_html.meta\n", "│   │   ├── \u001b[01;34minsights\u001b[00m\n", "│   │   │   └── insights_and_actions.json\n", "│   │   ├── \u001b[01;34mlocal_decision_tree\u001b[00m\n", "│   │   │   ├── \u001b[01;34mapplication_json\u001b[00m\n", "│   │   │   │   └── explanation.json\n", "│   │   │   └── application_json.meta\n", "│   │   ├── \u001b[01;34mlog\u001b[00m\n", "│   │   │   └── explainer_run_f03ade5e-92e1-475b-93fc-b52a2b5b3385.log\n", "│   │   ├── \u001b[01;34mproblems\u001b[00m\n", "│   │   │   └── problems_and_actions.json\n", "│   │   ├── result_descriptor.json\n", "│   │   └── \u001b[01;34mwork\u001b[00m\n", "│   │   ├── dt-class-0.dot\n", "│   │   ├── dt-class-0.dot.pdf\n", "│   │   ├── dtModel.json\n", "│   │   ├── dtpaths_frame.bin\n", "│   │   ├── dtPathsFrame.csv\n", "│   │   ├── \u001b[01;31mdtsurr_mojo.zip\u001b[00m\n", "│   │   ├── dtSurrogate.json\n", "│   │   └── \u001b[01;31mdt_surrogate_rules.zip\u001b[00m\n", "│   ├── \u001b[01;34mexplainer_h2o_sonar_explainers_pd_ice_explainer_PdIceExplainer_a60801bf-aae6-479d-9af8-11dc089aa988\u001b[00m\n", "│   │   ├── \u001b[01;34mglobal_html_fragment\u001b[00m\n", "│   │   │   ├── \u001b[01;34mtext_html\u001b[00m\n", "│   │   │   │   ├── explanation.html\n", "│   │   │   │   ├── \u001b[01;35mpd-feature-0-class-0.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mpd-feature-1-class-0.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mpd-feature-2-class-0.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mpd-feature-3-class-0.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mpd-feature-4-class-0.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mpd-feature-5-class-0.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mpd-feature-6-class-0.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mpd-feature-7-class-0.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mpd-feature-8-class-0.png\u001b[00m\n", "│   │   │   │   └── \u001b[01;35mpd-feature-9-class-0.png\u001b[00m\n", "│   │   │   └── text_html.meta\n", "│   │   ├── \u001b[01;34mglobal_partial_dependence\u001b[00m\n", "│   │   │   ├── \u001b[01;34mapplication_json\u001b[00m\n", "│   │   │   │   ├── explanation.json\n", "│   │   │   │   ├── pd_feature_0_class_0.json\n", "│   │   │   │   ├── pd_feature_1_class_0.json\n", "│   │   │   │   ├── pd_feature_2_class_0.json\n", "│   │   │   │   ├── pd_feature_3_class_0.json\n", "│   │   │   │   ├── pd_feature_4_class_0.json\n", "│   │   │   │   ├── pd_feature_5_class_0.json\n", "│   │   │   │   ├── pd_feature_6_class_0.json\n", "│   │   │   │   ├── pd_feature_7_class_0.json\n", "│   │   │   │   ├── pd_feature_8_class_0.json\n", "│   │   │   │   └── pd_feature_9_class_0.json\n", "│   │   │   └── application_json.meta\n", "│   │   ├── \u001b[01;34minsights\u001b[00m\n", "│   │   │   └── insights_and_actions.json\n", "│   │   ├── \u001b[01;34mlocal_individual_conditional_explanation\u001b[00m\n", "│   │   │   ├── \u001b[01;34mapplication_vnd_h2oai_json_datatable_jay\u001b[00m\n", "│   │   │   │   ├── explanation.json\n", "│   │   │   │   ├── ice_feature_0_class_0.jay\n", "│   │   │   │   ├── ice_feature_1_class_0.jay\n", "│   │   │   │   ├── ice_feature_2_class_0.jay\n", "│   │   │   │   ├── ice_feature_3_class_0.jay\n", "│   │   │   │   ├── ice_feature_4_class_0.jay\n", "│   │   │   │   ├── ice_feature_5_class_0.jay\n", "│   │   │   │   ├── ice_feature_6_class_0.jay\n", "│   │   │   │   ├── ice_feature_7_class_0.jay\n", "│   │   │   │   ├── ice_feature_8_class_0.jay\n", "│   │   │   │   ├── ice_feature_9_class_0.jay\n", "│   │   │   │   └── y_hat.jay\n", "│   │   │   └── application_vnd_h2oai_json_datatable_jay.meta\n", "│   │   ├── \u001b[01;34mlog\u001b[00m\n", "│   │   │   └── explainer_run_a60801bf-aae6-479d-9af8-11dc089aa988.log\n", "│   │   ├── \u001b[01;34mproblems\u001b[00m\n", "│   │   │   └── problems_and_actions.json\n", "│   │   ├── result_descriptor.json\n", "│   │   └── \u001b[01;34mwork\u001b[00m\n", "│   │   ├── h2o_sonar-ice-dai-model-10.jay\n", "│   │   ├── h2o_sonar-ice-dai-model-1.jay\n", "│   │   ├── h2o_sonar-ice-dai-model-2.jay\n", "│   │   ├── h2o_sonar-ice-dai-model-3.jay\n", "│   │   ├── h2o_sonar-ice-dai-model-4.jay\n", "│   │   ├── h2o_sonar-ice-dai-model-5.jay\n", "│   │   ├── h2o_sonar-ice-dai-model-6.jay\n", "│   │   ├── h2o_sonar-ice-dai-model-7.jay\n", "│   │   ├── h2o_sonar-ice-dai-model-8.jay\n", "│   │   ├── h2o_sonar-ice-dai-model-9.jay\n", "│   │   ├── h2o_sonar-ice-dai-model.json\n", "│   │   ├── h2o_sonar-pd-dai-model.json\n", "│   │   └── mli_dataset_y_hat.jay\n", "│   ├── \u001b[01;34mexplainer_h2o_sonar_explainers_residual_dt_surrogate_explainer_ResidualDecisionTreeSurrogateExplainer_2a0a6169-0847-4f2e-aebd-2c81be908b69\u001b[00m\n", "│   │   ├── \u001b[01;34mglobal_custom_archive\u001b[00m\n", "│   │   │   ├── \u001b[01;34mapplication_zip\u001b[00m\n", "│   │   │   │   └── \u001b[01;31mexplanation.zip\u001b[00m\n", "│   │   │   └── application_zip.meta\n", "│   │   ├── \u001b[01;34mglobal_decision_tree\u001b[00m\n", "│   │   │   ├── \u001b[01;34mapplication_json\u001b[00m\n", "│   │   │   │   ├── dt_class_0.json\n", "│   │   │   │   └── explanation.json\n", "│   │   │   └── application_json.meta\n", "│   │   ├── \u001b[01;34mglobal_html_fragment\u001b[00m\n", "│   │   │   ├── \u001b[01;34mtext_html\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mdt-class-0.png\u001b[00m\n", "│   │   │   │   └── explanation.html\n", "│   │   │   └── text_html.meta\n", "│   │   ├── \u001b[01;34minsights\u001b[00m\n", "│   │   │   └── insights_and_actions.json\n", "│   │   ├── \u001b[01;34mlocal_decision_tree\u001b[00m\n", "│   │   │   ├── \u001b[01;34mapplication_json\u001b[00m\n", "│   │   │   │   └── explanation.json\n", "│   │   │   └── application_json.meta\n", "│   │   ├── \u001b[01;34mlog\u001b[00m\n", "│   │   │   └── explainer_run_2a0a6169-0847-4f2e-aebd-2c81be908b69.log\n", "│   │   ├── \u001b[01;34mproblems\u001b[00m\n", "│   │   │   └── problems_and_actions.json\n", "│   │   ├── result_descriptor.json\n", "│   │   └── \u001b[01;34mwork\u001b[00m\n", "│   │   ├── dt-class-0.dot\n", "│   │   ├── dt-class-0.dot.pdf\n", "│   │   ├── dtModel.json\n", "│   │   ├── dtpaths_frame.bin\n", "│   │   ├── dtPathsFrame.csv\n", "│   │   ├── \u001b[01;31mdtsurr_mojo.zip\u001b[00m\n", "│   │   ├── dtSurrogate.json\n", "│   │   └── \u001b[01;31mdt_surrogate_rules.zip\u001b[00m\n", "│   ├── \u001b[01;34mexplainer_h2o_sonar_explainers_summary_shap_explainer_SummaryShapleyExplainer_9db2bad1-f7b2-42a1-85de-6a5e1b8f8510\u001b[00m\n", "│   │   ├── \u001b[01;34mglobal_html_fragment\u001b[00m\n", "│   │   │   ├── \u001b[01;34mtext_html\u001b[00m\n", "│   │   │   │   ├── explanation.html\n", "│   │   │   │   ├── \u001b[01;35mfeature_0_class_0.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mfeature_10_class_0.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mfeature_11_class_0.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mfeature_12_class_0.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mfeature_13_class_0.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mfeature_14_class_0.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mfeature_15_class_0.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mfeature_16_class_0.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mfeature_17_class_0.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mfeature_18_class_0.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mfeature_19_class_0.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mfeature_1_class_0.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mfeature_20_class_0.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mfeature_21_class_0.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mfeature_22_class_0.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mfeature_23_class_0.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mfeature_2_class_0.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mfeature_3_class_0.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mfeature_4_class_0.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mfeature_5_class_0.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mfeature_6_class_0.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mfeature_7_class_0.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mfeature_8_class_0.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mfeature_9_class_0.png\u001b[00m\n", "│   │   │   │   └── \u001b[01;35mshapley-class-0.png\u001b[00m\n", "│   │   │   └── text_html.meta\n", "│   │   ├── \u001b[01;34mglobal_summary_feature_importance\u001b[00m\n", "│   │   │   ├── \u001b[01;34mapplication_json\u001b[00m\n", "│   │   │   │   ├── explanation.json\n", "│   │   │   │   ├── \u001b[01;35mfeature_0_class_0.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mfeature_10_class_0.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mfeature_11_class_0.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mfeature_12_class_0.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mfeature_13_class_0.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mfeature_14_class_0.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mfeature_15_class_0.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mfeature_16_class_0.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mfeature_17_class_0.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mfeature_18_class_0.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mfeature_19_class_0.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mfeature_1_class_0.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mfeature_20_class_0.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mfeature_21_class_0.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mfeature_22_class_0.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mfeature_23_class_0.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mfeature_2_class_0.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mfeature_3_class_0.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mfeature_4_class_0.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mfeature_5_class_0.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mfeature_6_class_0.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mfeature_7_class_0.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mfeature_8_class_0.png\u001b[00m\n", "│   │   │   │   ├── \u001b[01;35mfeature_9_class_0.png\u001b[00m\n", "│   │   │   │   ├── summary_feature_importance_class_0_offset_0.json\n", "│   │   │   │   ├── summary_feature_importance_class_0_offset_1.json\n", "│   │   │   │   └── summary_feature_importance_class_0_offset_2.json\n", "│   │   │   ├── application_json.meta\n", "│   │   │   ├── \u001b[01;34mapplication_vnd_h2oai_json_datatable_jay\u001b[00m\n", "│   │   │   │   ├── explanation.json\n", "│   │   │   │   └── summary_feature_importance_class_0.jay\n", "│   │   │   ├── application_vnd_h2oai_json_datatable_jay.meta\n", "│   │   │   ├── \u001b[01;34mtext_markdown\u001b[00m\n", "│   │   │   │   ├── explanation.md\n", "│   │   │   │   └── \u001b[01;35mshapley-class-0.png\u001b[00m\n", "│   │   │   └── text_markdown.meta\n", "│   │   ├── \u001b[01;34minsights\u001b[00m\n", "│   │   │   └── insights_and_actions.json\n", "│   │   ├── \u001b[01;34mlog\u001b[00m\n", "│   │   │   └── explainer_run_9db2bad1-f7b2-42a1-85de-6a5e1b8f8510.log\n", "│   │   ├── \u001b[01;34mproblems\u001b[00m\n", "│   │   │   └── problems_and_actions.json\n", "│   │   ├── result_descriptor.json\n", "│   │   └── \u001b[01;34mwork\u001b[00m\n", "│   │   ├── raw_shapley_contribs_class_0.jay\n", "│   │   ├── raw_shapley_contribs_index.json\n", "│   │   ├── report.md\n", "│   │   └── \u001b[01;35mshapley-class-0.png\u001b[00m\n", "│   ├── interpretation.html\n", "│   ├── interpretation.json\n", "│   └── \u001b[01;34mtmp\u001b[00m\n", "├── h2o-sonar.html\n", "└── h2o-sonar.log\n", "\n", "146 directories, 597 files\n" ] } ], "source": [ "!tree $results_path" ] }, { "cell_type": "markdown", "id": "8524e3ac-caf6-4844-9cd3-60ae757022e2", "metadata": {}, "source": [ "See `interpretation.html` ^ for interpretation **results**." ] }, { "cell_type": "code", "execution_count": null, "id": "6ac8b24a-7468-4d7e-b2f3-1e62bfff0e72", "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 }