{ "cells": [ { "cell_type": "markdown", "id": "9514bb91-88a7-47d2-a662-5c9c8970e94b", "metadata": {}, "source": [ "# H2O Sonar Demo of H2O-3 Models\n", "This example demonstrates how to interpret a H2O-3 models using the H2O Sonar library." ] }, { "cell_type": "code", "execution_count": 1, "id": "843521fa-632c-4783-ac54-cab58f86df4f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: h2o in /home/user/h/mli/git/h2o-sonar-FLOSS/.venv/lib/python3.11/site-packages (3.46.0.9)\n", "Requirement already satisfied: requests in /home/user/h/mli/git/h2o-sonar-FLOSS/.venv/lib/python3.11/site-packages (from h2o) (2.32.5)\n", "Requirement already satisfied: tabulate in /home/user/h/mli/git/h2o-sonar-FLOSS/.venv/lib/python3.11/site-packages (from h2o) (0.9.0)\n", "Requirement already satisfied: charset_normalizer<4,>=2 in /home/user/h/mli/git/h2o-sonar-FLOSS/.venv/lib/python3.11/site-packages (from requests->h2o) (3.4.4)\n", "Requirement already satisfied: idna<4,>=2.5 in /home/user/h/mli/git/h2o-sonar-FLOSS/.venv/lib/python3.11/site-packages (from requests->h2o) (3.11)\n", "Requirement already satisfied: urllib3<3,>=1.21.1 in /home/user/h/mli/git/h2o-sonar-FLOSS/.venv/lib/python3.11/site-packages (from requests->h2o) (2.6.3)\n", "Requirement already satisfied: certifi>=2017.4.17 in /home/user/h/mli/git/h2o-sonar-FLOSS/.venv/lib/python3.11/site-packages (from requests->h2o) (2026.1.4)\n" ] } ], "source": [ "# install H2O-3 client\n", "!pip install h2o" ] }, { "cell_type": "code", "execution_count": 2, "id": "8be6308d", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import h2o\n", "import pandas\n", "import datatable\n", "import webbrowser\n", "\n", "from h2o.estimators.gbm import H2OGradientBoostingEstimator\n", "\n", "from h2o_sonar import interpret\n", "from h2o_sonar.lib.api.models import ExplainableModel, ExplainableModelType, ExplainableModelMeta\n", "from h2o_sonar.lib.api.datasets import ExplainableDataset\n", "from h2o_sonar.utils.sanitization import SanitizationMap" ] }, { "cell_type": "code", "execution_count": 3, "id": "2b3eb4b3", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Checking whether there is an H2O instance running at http://localhost:54321. connected.\n" ] }, { "data": { "text/html": [ "\n", " \n", "
\n", " \n", " \n", " \n", " \n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
H2O_cluster_uptime:17 mins 48 secs
H2O_cluster_timezone:Europe/Prague
H2O_data_parsing_timezone:UTC
H2O_cluster_version:3.46.0.9
H2O_cluster_version_age:2 months and 4 days
H2O_cluster_name:H2O_from_python_user_ornwkr
H2O_cluster_total_nodes:1
H2O_cluster_free_memory:4 Gb
H2O_cluster_total_cores:16
H2O_cluster_allowed_cores:16
H2O_cluster_status:locked, healthy
H2O_connection_url:http://localhost:54321
H2O_connection_proxy:{\"http\": null, \"https\": null}
H2O_internal_security:False
Python_version:3.11.11 final
\n", "
\n" ], "text/plain": [ "-------------------------- -----------------------------\n", "H2O_cluster_uptime: 17 mins 48 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_ornwkr\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", "-------------------------- -----------------------------" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "h2o.init()" ] }, { "cell_type": "code", "execution_count": 4, "id": "27613707", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%\n" ] } ], "source": [ "# dataset\n", "dataset_path = \"../../data/predictive/creditcard.csv\"\n", "target_col = \"default payment next month\"\n", "df = h2o.import_file(dataset_path)\n", "X = list(df.names)\n", "X.remove(target_col)" ] }, { "cell_type": "code", "execution_count": 5, "id": "f687b1e1-20ff-48a2-9031-3aeb8963a5b1", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['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']" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X" ] }, { "cell_type": "code", "execution_count": 6, "id": "119efe4d-2a94-4b9e-be83-d8f99b8dbd7f", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
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
1 20000 2 2 1 24 -2 2 -1 -1 -2 -2 3913 3102 689 0 0 0 0 689 0 0 0 0 1
2 120000 2 2 2 26 -1 2 0 0 0 2 2682 1725 2682 3272 3455 3261 0 1000 1000 1000 0 2000 1
3 90000 2 2 2 34 0 0 0 0 0 0 29239 14027 13559 14331 14948 15549 1518 1500 1000 1000 1000 5000 0
4 50000 2 2 1 37 1 0 0 0 0 0 46990 48233 49291 28314 28959 29547 2000 2019 1200 1100 1069 1000 0
5 50000 1 2 1 57 2 0 -1 0 0 0 8617 5670 35835 20940 19146 19131 2000 36681 10000 9000 689 679 0
6 50000 1 1 2 37 3 0 0 0 0 0 64400 57069 57608 19394 19619 20024 2500 1815 657 1000 1000 800 0
7 500000 1 1 2 29 4 0 0 0 0 0 367965 412023 445007 542653 483003 473944 55000 40000 38000 20239 13750 13770 0
8 100000 2 2 2 23 5 -1 -1 0 0 -1 11876 380 601 221 -159 567 380 601 0 581 1687 1542 0
9 140000 2 3 1 28 6 0 2 0 0 0 11285 14096 12108 12211 11793 3719 3329 0 432 1000 1000 1000 0
10 20000 1 3 2 35 7 -2 -2 -2 -1 -1 0 0 0 0 13007 13912 0 0 0 13007 1122 0 0
[10 rows x 25 columns]
" ], "text/plain": [ " 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\n", "---- ----------- ----- ----------- ---------- ----- ------- ------- ------- ------- ------- ------- ----------- ----------- ----------- ----------- ----------- ----------- ---------- ---------- ---------- ---------- ---------- ---------- ----------------------------\n", " 1 20000 2 2 1 24 -2 2 -1 -1 -2 -2 3913 3102 689 0 0 0 0 689 0 0 0 0 1\n", " 2 120000 2 2 2 26 -1 2 0 0 0 2 2682 1725 2682 3272 3455 3261 0 1000 1000 1000 0 2000 1\n", " 3 90000 2 2 2 34 0 0 0 0 0 0 29239 14027 13559 14331 14948 15549 1518 1500 1000 1000 1000 5000 0\n", " 4 50000 2 2 1 37 1 0 0 0 0 0 46990 48233 49291 28314 28959 29547 2000 2019 1200 1100 1069 1000 0\n", " 5 50000 1 2 1 57 2 0 -1 0 0 0 8617 5670 35835 20940 19146 19131 2000 36681 10000 9000 689 679 0\n", " 6 50000 1 1 2 37 3 0 0 0 0 0 64400 57069 57608 19394 19619 20024 2500 1815 657 1000 1000 800 0\n", " 7 500000 1 1 2 29 4 0 0 0 0 0 367965 412023 445007 542653 483003 473944 55000 40000 38000 20239 13750 13770 0\n", " 8 100000 2 2 2 23 5 -1 -1 0 0 -1 11876 380 601 221 -159 567 380 601 0 581 1687 1542 0\n", " 9 140000 2 3 1 28 6 0 2 0 0 0 11285 14096 12108 12211 11793 3719 3329 0 432 1000 1000 1000 0\n", " 10 20000 1 3 2 35 7 -2 -2 -2 -1 -1 0 0 0 0 13007 13912 0 0 0 13007 1122 0 0\n", "[10 rows x 25 columns]\n" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "code", "execution_count": 7, "id": "417e8a31", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "gbm Model Build progress: |" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/user/h/mli/git/h2o-sonar-FLOSS/.venv/lib/python3.11/site-packages/h2o/estimators/estimator_base.py:192: RuntimeWarning: 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", " warnings.warn(mesg[\"message\"], RuntimeWarning)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Scoring History for Model GBM_model_python_1769698393912_1 at 2026-01-29 16:11:05.484341\n", "Model Build is 0% done...\n", " timestamp duration number_of_trees training_rmse \\\n", "0 2026-01-29 16:11:05 0.026 sec 0.0 0.418174 \n", "\n", " training_mae training_deviance \n", "0 0.349738 0.174869 \n", "\n", "\n", "\n", "Scoring History for Model GBM_model_python_1769698393912_1 at 2026-01-29 16:11:05.716214\n", "Model Build is 99% done...\n", " timestamp duration number_of_trees training_rmse \\\n", "0 2026-01-29 16:11:05 0.026 sec 0.0 0.418174 \n", "1 2026-01-29 16:11:05 0.256 sec 1.0 0.410428 \n", "\n", " training_mae training_deviance \n", "0 0.349738 0.174869 \n", "1 0.342982 0.168451 \n", "\n", "\n", "██████████████████████████████████████████████████████| (done) 100%\n" ] }, { "data": { "text/html": [ "
Model Details\n",
       "=============\n",
       "H2OGradientBoostingEstimator : Gradient Boosting Machine\n",
       "Model Key: GBM_model_python_1769698393912_1\n",
       "
\n", "
\n", " \n", "
\n", " \n", " \n", " \n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", " \n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
Model Summary:
number_of_treesnumber_of_internal_treesmodel_size_in_bytesmin_depthmax_depthmean_depthmin_leavesmax_leavesmean_leaves
1.01.0385.05.05.05.026.026.026.0
\n", "
\n", "
\n", "
ModelMetricsRegression: gbm\n",
       "** Reported on train data. **\n",
       "\n",
       "MSE: 0.16845099602759814\n",
       "RMSE: 0.41042782072807654\n",
       "MAE: 0.3429823858305812\n",
       "RMSLE: 0.2875261825477124\n",
       "Mean Residual Deviance: 0.16845099602759814
\n", "
\n", " \n", "
\n", " \n", " \n", " \n", "\n", "\n", "\n", "\n", "\n", "\n", " \n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
Scoring History:
timestampdurationnumber_of_treestraining_rmsetraining_maetraining_deviance
2026-01-29 16:11:05 0.026 sec0.00.41817360.34973840.1748692
2026-01-29 16:11:05 0.256 sec1.00.41042780.34298240.1684510
\n", "
\n", "
\n", "
\n", " \n", "
\n", " \n", " \n", " \n", "\n", "\n", "\n", " \n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
Variable Importances:
variablerelative_importancescaled_importancepercentage
PAY_0258.51251221.00.7652836
PAY_324.08446500.09316560.0712981
PAY_AMT18.81467720.03409770.0260944
PAY_58.66350080.03351290.0256469
PAY_28.59064100.03323100.0254312
PAY_45.43343070.02101810.0160848
LIMIT_BAL4.95087430.01915140.0146562
ID4.80311200.01857980.0142188
BILL_AMT14.00652890.01549840.0118607
PAY_AMT53.99741320.01546310.0118337
------------
EDUCATION0.00.00.0
MARRIAGE0.00.00.0
PAY_60.00.00.0
BILL_AMT20.00.00.0
BILL_AMT30.00.00.0
BILL_AMT40.00.00.0
BILL_AMT50.00.00.0
PAY_AMT20.00.00.0
PAY_AMT40.00.00.0
PAY_AMT60.00.00.0
\n", "
\n", "
[24 rows x 4 columns]
\n",
       "\n",
       "[tips]\n",
       "Use `model.explain()` to inspect the model.\n",
       "--\n",
       "Use `h2o.display.toggle_user_tips()` to switch on/off this section.
" ], "text/plain": [ "Model Details\n", "=============\n", "H2OGradientBoostingEstimator : Gradient Boosting Machine\n", "Model Key: GBM_model_python_1769698393912_1\n", "\n", "\n", "Model Summary: \n", " number_of_trees number_of_internal_trees model_size_in_bytes min_depth max_depth mean_depth min_leaves max_leaves mean_leaves\n", "-- ----------------- -------------------------- --------------------- ----------- ----------- ------------ ------------ ------------ -------------\n", " 1 1 385 5 5 5 26 26 26\n", "\n", "ModelMetricsRegression: gbm\n", "** Reported on train data. **\n", "\n", "MSE: 0.16845099602759814\n", "RMSE: 0.41042782072807654\n", "MAE: 0.3429823858305812\n", "RMSLE: 0.2875261825477124\n", "Mean Residual Deviance: 0.16845099602759814\n", "\n", "Scoring History: \n", " timestamp duration number_of_trees training_rmse training_mae training_deviance\n", "-- ------------------- ---------- ----------------- --------------- -------------- -------------------\n", " 2026-01-29 16:11:05 0.026 sec 0 0.418174 0.349738 0.174869\n", " 2026-01-29 16:11:05 0.256 sec 1 0.410428 0.342982 0.168451\n", "\n", "Variable Importances: \n", "variable relative_importance scaled_importance percentage\n", "---------- --------------------- -------------------- --------------------\n", "PAY_0 258.51251220703125 1.0 0.7652835502800754\n", "PAY_3 24.08446502685547 0.09316556796897817 0.07129807661915928\n", "PAY_AMT1 8.814677238464355 0.03409768124262036 0.02609439455767084\n", "PAY_5 8.663500785827637 0.03351288768138782 0.025646861664949876\n", "PAY_2 8.590641021728516 0.03323104536947384 0.0254311723798692\n", "PAY_4 5.4334306716918945 0.021018056825584133 0.016084773147471396\n", "LIMIT_BAL 4.950874328613281 0.019151391498792695 0.01465624487899973\n", "ID 4.803112030029297 0.018579804857502203 0.014218819024860278\n", "BILL_AMT1 4.006528854370117 0.01549839433366175 0.011860666239305267\n", "PAY_AMT5 3.997413158416748 0.015463132226324876 0.011833680728612149\n", "--- --- --- ---\n", "EDUCATION 0.0 0.0 0.0\n", "MARRIAGE 0.0 0.0 0.0\n", "PAY_6 0.0 0.0 0.0\n", "BILL_AMT2 0.0 0.0 0.0\n", "BILL_AMT3 0.0 0.0 0.0\n", "BILL_AMT4 0.0 0.0 0.0\n", "BILL_AMT5 0.0 0.0 0.0\n", "PAY_AMT2 0.0 0.0 0.0\n", "PAY_AMT4 0.0 0.0 0.0\n", "PAY_AMT6 0.0 0.0 0.0\n", "[24 rows x 4 columns]\n", "\n", "\n", "[tips]\n", "Use `model.explain()` to inspect the model.\n", "--\n", "Use `h2o.display.toggle_user_tips()` to switch on/off this section." ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# h2o model\n", "gradient_booster = H2OGradientBoostingEstimator(ntrees=1, seed=1234)\n", "gradient_booster.train(\n", " x=X, \n", " y=target_col, \n", " training_frame=df,\n", " verbose=True,\n", ")" ] }, { "cell_type": "code", "execution_count": 8, "id": "89a36469", "metadata": {}, "outputs": [], "source": [ "mojo_path = gradient_booster.save_mojo(path=\"../../results\", force=True)" ] }, { "cell_type": "code", "execution_count": 9, "id": "6ea470f5", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "generic Model Build progress: |██████████████████████████████████████████████████| (done) 100%\n" ] } ], "source": [ "gradient_booster_mojo = h2o.import_mojo(mojo_path)" ] }, { "cell_type": "code", "execution_count": 10, "id": "c402130f", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/user/h/mli/git/h2o-sonar-FLOSS/.venv/lib/python3.11/site-packages/ragas/metrics/__init__.py:1: LangChainDeprecationWarning: As of langchain-core 0.3.0, LangChain uses pydantic v2 internally. The langchain_core.pydantic_v1 module was a compatibility shim for pydantic v1, and should no longer be used. Please update the code to import from Pydantic directly.\n", "\n", "For example, replace imports like: `from langchain_core.pydantic_v1 import BaseModel`\n", "with: `from pydantic import BaseModel`\n", "or the v1 compatibility namespace if you are working in a code base that has not been fully upgraded to pydantic 2 yet. \tfrom pydantic.v1 import BaseModel\n", "\n", " from ragas.metrics._answer_correctness import AnswerCorrectness, answer_correctness\n", "/home/user/h/mli/git/h2o-sonar-FLOSS/.venv/lib/python3.11/site-packages/ragas/metrics/__init__.py:4: LangChainDeprecationWarning: As of langchain-core 0.3.0, LangChain uses pydantic v2 internally. The langchain.pydantic_v1 module was a compatibility shim for pydantic v1, and should no longer be used. Please update the code to import from Pydantic directly.\n", "\n", "For example, replace imports like: `from langchain.pydantic_v1 import BaseModel`\n", "with: `from pydantic import BaseModel`\n", "or the v1 compatibility namespace if you are working in a code base that has not been fully upgraded to pydantic 2 yet. \tfrom pydantic.v1 import BaseModel\n", "\n", " from ragas.metrics._context_entities_recall import (\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Preparing and checking DIA features (None): dataset= | ID PAY_AMT6 PAY_5 PAY_AMT1 BILL_AMT3 PAY_AMT5 AGE MARRIAGE BILL_AMT2 PAY_4 … PAY_2 PAY_AMT4 BILL_AMT5 BILL_AMT1 EDUCATION\n", " | int32 int32 int32 int32 int32 int32 int32 int32 int32 int32 int32 int32 int32 int32 int32\n", "---- + ----- -------- ----- -------- --------- -------- ----- -------- --------- ----- ----- -------- --------- --------- ---------\n", " 0 | 1 0 -2 0 689 0 24 1 3102 -1 … 2 0 0 3913 2\n", " 1 | 2 2000 0 0 2682 0 26 2 1725 0 … 2 1000 3455 2682 2\n", " 2 | 3 5000 0 1518 13559 1000 34 2 14027 0 … 0 1000 14948 29239 2\n", " 3 | 4 1000 0 2000 49291 1069 37 1 48233 0 … 0 1100 28959 46990 2\n", " 4 | 5 679 0 2000 35835 689 57 1 5670 0 … 0 9000 19146 8617 2\n", " 5 | 6 800 0 2500 57608 1000 37 2 57069 0 … 0 1000 19619 64400 1\n", " 6 | 7 13770 0 55000 445007 13750 29 2 412023 0 … 0 20239 483003 367965 1\n", " 7 | 8 1542 0 380 601 1687 23 2 380 0 … -1 581 -159 11876 2\n", " 8 | 9 1000 0 3329 12108 1000 28 1 14096 0 … 0 1000 11793 11285 3\n", " 9 | 10 0 -1 0 0 1122 35 2 0 -2 … -2 13007 13007 0 3\n", " 10 | 11 66 0 2306 5535 3738 34 2 9787 0 … 0 300 1828 11073 3\n", " 11 | 12 3640 -1 21818 9966 0 51 2 21670 -1 … -1 22301 22287 12261 1\n", " 12 | 13 0 -1 1000 6500 2870 41 2 6500 -1 … 0 6500 6500 12137 2\n", " 13 | 14 0 0 3200 65701 1500 30 2 67369 0 … 2 3000 36137 65802 2\n", " 14 | 15 3000 0 3000 63561 3000 29 2 67060 0 … 0 3000 56875 70887 1\n", " … | … … … … … … … … … … … … … … … …\n", "9995 | 9996 1419 -2 241 0 0 31 2 241 -2 … -2 0 0 0 1\n", "9996 | 9997 0 -2 0 0 0 37 2 0 -2 … -2 0 0 3946 2\n", "9997 | 9998 4200 0 6437 142520 10017 44 1 144085 0 … 0 27080 176717 138877 3\n", "9998 | 9999 0 -2 0 0 0 26 2 780 -2 … 2 0 0 780 2\n", "9999 | 10000 3000 0 3000 19750 3000 36 1 20715 0 … 0 3000 19255 19505 2\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_3', 'PAY_0', 'MARRIAGE', 'PAY_6', 'PAY_5', 'default payment next month', 'PAY_2', 'PAY_4', 'EDUCATION'}\n", "DIA group columns to SKIP: {'default payment next month', 'model_pred'}\n", "DIA group columns as BOOLs: [, , , , , , , , ]\n", "Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%\n", "███████████████████████████████████████████████████████| (done) 100%\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Converting H2O frame to pandas dataframe using single-thread. For faster conversion using multi-thread, install polars and pyarrow and use it as pandas_df = h2o_df.as_data_frame(use_multi_thread=True)\n", "\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%\n", "███████████████████████████████████████████████████████| (done) 100%\n", "Connecting to H2O server at http://localhost:12349 ..." ] }, { "name": "stderr", "output_type": "stream", "text": [ "Converting H2O frame to pandas dataframe using single-thread. For faster conversion using multi-thread, install polars and pyarrow and use it as pandas_df = h2o_df.as_data_frame(use_multi_thread=True)\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ ".... failed.\n", "████████████████████████████████████████████████████████████████| (done) 100%\n", "███████████████████████████████████████████████████████| (done) 100%\n", "Connecting to H2O server at http://localhost:12349 ..." ] }, { "name": "stderr", "output_type": "stream", "text": [ "Converting H2O frame to pandas dataframe using single-thread. For faster conversion using multi-thread, install polars and pyarrow and use it as pandas_df = h2o_df.as_data_frame(use_multi_thread=True)\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ ".... failed.\n", "Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%\n", "████████████████████████████████████████████████████████| (done) 100%\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Converting H2O frame to pandas dataframe using single-thread. For faster conversion using multi-thread, install polars and pyarrow and use it as pandas_df = h2o_df.as_data_frame(use_multi_thread=True)\n", "\n", "More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). Consider using `matplotlib.pyplot.close()`.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%\n", "███████████████████████████████████████████████████████| (done) 100%\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Converting H2O frame to pandas dataframe using single-thread. For faster conversion using multi-thread, install polars and pyarrow and use it as pandas_df = h2o_df.as_data_frame(use_multi_thread=True)\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%\n", "███████████████████████████████████████████████████████| (done) 100%\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Converting H2O frame to pandas dataframe using single-thread. For faster conversion using multi-thread, install polars and pyarrow and use it as pandas_df = h2o_df.as_data_frame(use_multi_thread=True)\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%\n", "███████████████████████████████████████████████████████| (done) 100%\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Converting H2O frame to pandas dataframe using single-thread. For faster conversion using multi-thread, install polars and pyarrow and use it as pandas_df = h2o_df.as_data_frame(use_multi_thread=True)\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%\n", "███████████████████████████████████████████████████████| (done) 100%\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Converting H2O frame to pandas dataframe using single-thread. For faster conversion using multi-thread, install polars and pyarrow and use it as pandas_df = h2o_df.as_data_frame(use_multi_thread=True)\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%\n", "███████████████████████████████████████████████████████| (done) 100%\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Converting H2O frame to pandas dataframe using single-thread. For faster conversion using multi-thread, install polars and pyarrow and use it as pandas_df = h2o_df.as_data_frame(use_multi_thread=True)\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%\n", "███████████████████████████████████████████████████████| (done) 100%\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Converting H2O frame to pandas dataframe using single-thread. For faster conversion using multi-thread, install polars and pyarrow and use it as pandas_df = h2o_df.as_data_frame(use_multi_thread=True)\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%\n", "███████████████████████████████████████████████████████| (done) 100%\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Converting H2O frame to pandas dataframe using single-thread. For faster conversion using multi-thread, install polars and pyarrow and use it as pandas_df = h2o_df.as_data_frame(use_multi_thread=True)\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%\n", "███████████████████████████████████████████████████████| (done) 100%\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Converting H2O frame to pandas dataframe using single-thread. For faster conversion using multi-thread, install polars and pyarrow and use it as pandas_df = h2o_df.as_data_frame(use_multi_thread=True)\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%\n", "███████████████████████████████████████████████████████| (done) 100%\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Converting H2O frame to pandas dataframe using single-thread. For faster conversion using multi-thread, install polars and pyarrow and use it as pandas_df = h2o_df.as_data_frame(use_multi_thread=True)\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%\n", "███████████████████████████████████████████████████████| (done) 100%\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Converting H2O frame to pandas dataframe using single-thread. For faster conversion using multi-thread, install polars and pyarrow and use it as pandas_df = h2o_df.as_data_frame(use_multi_thread=True)\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%\n", "███████████████████████████████████████████████████████| (done) 100%\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Converting H2O frame to pandas dataframe using single-thread. For faster conversion using multi-thread, install polars and pyarrow and use it as pandas_df = h2o_df.as_data_frame(use_multi_thread=True)\n", "\n" ] } ], "source": [ "# H2O model\n", "results_location = \"../../results\"\n", "\n", "# run Interpretation\n", "interpretation = interpret.run_interpretation(\n", " dataset=dataset_path,\n", " model=gradient_booster,\n", " target_col=target_col,\n", " results_location=results_location,\n", " used_features=X,\n", ")\n", "\n", "# optionally make ExplainableModel() object to provide additional metadata\n", "# h2o_model = ExplainableModel(\n", "# predict_method=gradient_booster.predict,\n", "# model_src=gradient_booster,\n", "# model_type=ExplainableModelType.h2o3,\n", "# model_meta=ExplainableModelMeta(target_col=target_col)\n", "# )" ] }, { "cell_type": "code", "execution_count": 11, "id": "c1b1ff6f-ed3b-4300-83e4-ca7b774ad796", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 11, "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": 12, "id": "a8179382", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[01;34m../../results/h2o-sonar/mli_experiment_a640bd44-d444-49dc-beed-2f8790b9a666\u001b[00m\n", "├── \u001b[01;34mexplainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_bcf6d363-0695-4fba-9941-9982e3a949b8\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_bcf6d363-0695-4fba-9941-9982e3a949b8.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_6fae202c-d572-4bac-9bcd-2882f9be8bfb\u001b[00m\n", "│   ├── \u001b[01;34minsights\u001b[00m\n", "│   ├── \u001b[01;34mlog\u001b[00m\n", "│   │   └── explainer_run_6fae202c-d572-4bac-9bcd-2882f9be8bfb.log\n", "│   ├── \u001b[01;34mproblems\u001b[00m\n", "│   └── \u001b[01;34mwork\u001b[00m\n", "├── \u001b[01;34mexplainer_h2o_sonar_explainers_pd_ice_explainer_PdIceExplainer_7c4a99e7-2b20-46b2-98f0-7b48be024960\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_7c4a99e7-2b20-46b2-98f0-7b48be024960.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_fb644156-7c5d-4248-838d-48a6481f680c\u001b[00m\n", "│   ├── \u001b[01;34minsights\u001b[00m\n", "│   ├── \u001b[01;34mlog\u001b[00m\n", "│   │   └── explainer_run_fb644156-7c5d-4248-838d-48a6481f680c.log\n", "│   ├── \u001b[01;34mproblems\u001b[00m\n", "│   └── \u001b[01;34mwork\u001b[00m\n", "├── \u001b[01;34mexplainer_h2o_sonar_explainers_summary_shap_explainer_SummaryShapleyExplainer_347fbdd4-35b7-46f2-95e4-a68d41846ad2\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_347fbdd4-35b7-46f2-95e4-a68d41846ad2.log\n", "│   ├── \u001b[01;34mproblems\u001b[00m\n", "│   │   └── problems_and_actions.json\n", "│   ├── result_descriptor.json\n", "│   └── \u001b[01;34mwork\u001b[00m\n", "│   ├── raw_shapley_contribs_class_0.jay\n", "│   ├── raw_shapley_contribs_index.json\n", "│   ├── report.md\n", "│   └── \u001b[01;35mshapley-class-0.png\u001b[00m\n", "├── interpretation.html\n", "├── interpretation.json\n", "└── \u001b[01;34mtmp\u001b[00m\n", "\n", "128 directories, 553 files\n" ] } ], "source": [ "# View results directory\n", "!tree {interpretation.persistence.base_dir}" ] }, { "cell_type": "code", "execution_count": 13, "id": "b895811b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "H2O session _sid_9d04 closed.\n" ] } ], "source": [ "h2o.cluster().shutdown()" ] }, { "cell_type": "code", "execution_count": null, "id": "1923cb12-5f4f-422d-978a-473aaea4c7e0", "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 }