H2O Eval Studio Demo of H2O-3 Models
This example demonstrates how to interpret a H2O-3 models using the H2O Eval Studio library.
[1]:
# install H2O-3 client
!pip install h2o
Collecting h2o
Downloading h2o-3.40.0.2.tar.gz (177.6 MB)
|████████████████████████████████| 177.6 MB 71 kB/s s eta 0:00:01
Requirement already satisfied: requests in /home/srasaratnam/minicondadai_py38/lib/python3.8/site-packages (from h2o) (2.24.0)
Collecting tabulate
Using cached tabulate-0.9.0-py3-none-any.whl (35 kB)
Collecting future
Using cached future-0.18.3.tar.gz (840 kB)
Requirement already satisfied: idna<3,>=2.5 in /home/srasaratnam/minicondadai_py38/lib/python3.8/site-packages (from requests->h2o) (2.10)
Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /home/srasaratnam/minicondadai_py38/lib/python3.8/site-packages (from requests->h2o) (1.25.11)
Requirement already satisfied: chardet<4,>=3.0.2 in /home/srasaratnam/minicondadai_py38/lib/python3.8/site-packages (from requests->h2o) (3.0.4)
Requirement already satisfied: certifi>=2017.4.17 in /home/srasaratnam/minicondadai_py38/lib/python3.8/site-packages (from requests->h2o) (2022.12.7)
Building wheels for collected packages: h2o, future
Building wheel for h2o (setup.py) ... done
Created wheel for h2o: filename=h2o-3.40.0.2-py2.py3-none-any.whl size=177693441 sha256=1615886e6fa85c8f01380f428e68e9d37f413b4d157aaa0ae79f1d0d0866e947
Stored in directory: /home/srasaratnam/.cache/pip/wheels/6f/65/34/dd63b76ae6dfc27df6237662b2b6cc1a1c8b3dbe9a38b8b704
Building wheel for future (setup.py) ... done
Created wheel for future: filename=future-0.18.3-py3-none-any.whl size=492026 sha256=1a5162fbb121247b8a51c04cb82eddc9491d4e0f92a96e2fc569bc5a04ed3c9e
Stored in directory: /home/srasaratnam/.cache/pip/wheels/a0/0b/ee/e6994fadb42c1354dcccb139b0bf2795271bddfe6253ccdf11
Successfully built h2o future
Installing collected packages: tabulate, future, h2o
ERROR: After October 2020 you may experience errors when installing or updating packages. This is because pip will change the way that it resolves dependency conflicts.
We recommend you use --use-feature=2020-resolver to test your packages with the new resolver before it becomes the default.
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h2oaicore 1.10.5+local.dev.placeholder.15282.g7c65ae8a9f.dirty requires tifffile, which is not installed.
h2oaicore 1.10.5+local.dev.placeholder.15282.g7c65ae8a9f.dirty requires timm, which is not installed.
h2oaicore 1.10.5+local.dev.placeholder.15282.g7c65ae8a9f.dirty requires tokenizers, which is not installed.
h2oaicore 1.10.5+local.dev.placeholder.15282.g7c65ae8a9f.dirty requires toml, which is not installed.
h2oaicore 1.10.5+local.dev.placeholder.15282.g7c65ae8a9f.dirty requires toolz, which is not installed.
h2oaicore 1.10.5+local.dev.placeholder.15282.g7c65ae8a9f.dirty requires torch, which is not installed.
h2oaicore 1.10.5+local.dev.placeholder.15282.g7c65ae8a9f.dirty requires torchaudio, which is not installed.
h2oaicore 1.10.5+local.dev.placeholder.15282.g7c65ae8a9f.dirty requires torchmetrics, which is not installed.
h2oaicore 1.10.5+local.dev.placeholder.15282.g7c65ae8a9f.dirty requires torchvision, which is not installed.
h2oaicore 1.10.5+local.dev.placeholder.15282.g7c65ae8a9f.dirty requires transformers, which is not installed.
h2oaicore 1.10.5+local.dev.placeholder.15282.g7c65ae8a9f.dirty requires treelite, which is not installed.
h2oaicore 1.10.5+local.dev.placeholder.15282.g7c65ae8a9f.dirty requires treelite-runtime, which is not installed.
h2oaicore 1.10.5+local.dev.placeholder.15282.g7c65ae8a9f.dirty requires trio, which is not installed.
h2oaicore 1.10.5+local.dev.placeholder.15282.g7c65ae8a9f.dirty requires trio-websocket, which is not installed.
h2oaicore 1.10.5+local.dev.placeholder.15282.g7c65ae8a9f.dirty requires tritonclient, which is not installed.
h2oaicore 1.10.5+local.dev.placeholder.15282.g7c65ae8a9f.dirty requires typesentry, which is not installed.
h2oaicore 1.10.5+local.dev.placeholder.15282.g7c65ae8a9f.dirty requires typing-extensions, which is not installed.
h2oaicore 1.10.5+local.dev.placeholder.15282.g7c65ae8a9f.dirty requires umap-learn, which is not installed.
h2oaicore 1.10.5+local.dev.placeholder.15282.g7c65ae8a9f.dirty requires virtualenv, which is not installed.
h2oaicore 1.10.5+local.dev.placeholder.15282.g7c65ae8a9f.dirty requires werkzeug, which is not installed.
h2oaicore 1.10.5+local.dev.placeholder.15282.g7c65ae8a9f.dirty requires wsproto, which is not installed.
h2oaicore 1.10.5+local.dev.placeholder.15282.g7c65ae8a9f.dirty requires xarray, which is not installed.
h2oaicore 1.10.5+local.dev.placeholder.15282.g7c65ae8a9f.dirty requires xlrd, which is not installed.
h2oaicore 1.10.5+local.dev.placeholder.15282.g7c65ae8a9f.dirty requires yapf, which is not installed.
h2oaicore 1.10.5+local.dev.placeholder.15282.g7c65ae8a9f.dirty requires yarl, which is not installed.
h2oaicore 1.10.5+local.dev.placeholder.15282.g7c65ae8a9f.dirty requires zict, which is not installed.
Successfully installed future-0.18.3 h2o-3.40.0.2 tabulate-0.9.0
[2]:
import h2o
import pandas
import datatable
import webbrowser
from h2o.estimators.gbm import H2OGradientBoostingEstimator
from h2o_sonar import interpret
from h2o_sonar.lib.api.models import ExplainableModel, ExplainableModelType, ExplainableModelMeta
from h2o_sonar.lib.api.datasets import ExplainableDataset
from h2o_sonar.utils.sanitization import SanitizationMap
[3]:
h2o.init()
Checking whether there is an H2O instance running at http://localhost:54321..... not found.
Attempting to start a local H2O server...
Java Version: openjdk version "11.0.18" 2023-01-17; OpenJDK Runtime Environment (build 11.0.18+10-post-Ubuntu-0ubuntu120.04.1); OpenJDK 64-Bit Server VM (build 11.0.18+10-post-Ubuntu-0ubuntu120.04.1, mixed mode, sharing)
Starting server from /home/srasaratnam/projects/h2o-sonar/venv/lib/python3.8/site-packages/h2o/backend/bin/h2o.jar
Ice root: /tmp/tmpya82k42b
JVM stdout: /tmp/tmpya82k42b/h2o_srasaratnam_started_from_python.out
JVM stderr: /tmp/tmpya82k42b/h2o_srasaratnam_started_from_python.err
Server is running at http://127.0.0.1:54321
Connecting to H2O server at http://127.0.0.1:54321 ... successful.
| H2O_cluster_uptime: | 01 secs |
| H2O_cluster_timezone: | America/Toronto |
| H2O_data_parsing_timezone: | UTC |
| H2O_cluster_version: | 3.40.0.2 |
| H2O_cluster_version_age: | 3 days |
| H2O_cluster_name: | H2O_from_python_srasaratnam_re6lv3 |
| H2O_cluster_total_nodes: | 1 |
| H2O_cluster_free_memory: | 7.775 Gb |
| H2O_cluster_total_cores: | 12 |
| H2O_cluster_allowed_cores: | 12 |
| H2O_cluster_status: | locked, healthy |
| H2O_connection_url: | http://127.0.0.1:54321 |
| H2O_connection_proxy: | {"http": null, "https": null} |
| H2O_internal_security: | False |
| Python_version: | 3.8.10 final |
[4]:
# dataset
dataset_path = "../../data/creditcard.csv"
target_col = "default payment next month"
df = h2o.import_file(dataset_path)
X = list(df.names)
X.remove(target_col)
Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%
[5]:
X
[5]:
['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']
[6]:
df.head()
[6]:
| 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]
[7]:
# h2o model
gradient_booster = H2OGradientBoostingEstimator(ntrees=1, seed=1234)
gradient_booster.train(
x=X,
y=target_col,
training_frame=df,
verbose=True,
)
gbm Model Build progress: |
/home/srasaratnam/projects/h2o-sonar/venv/lib/python3.8/site-packages/h2o/estimators/estimator_base.py:193: 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.
warnings.warn(mesg["message"], RuntimeWarning)
Scoring History for Model GBM_model_python_1678677213382_1 at 2023-03-12 23:14:01.124124
Model Build is 0% done...
timestamp duration number_of_trees training_rmse \
0 2023-03-12 23:14:00 0.026 sec 0.0 0.418174
training_mae training_deviance
0 0.349738 0.174869
██████████████████████████████████████████████████████| (done) 100%
[7]:
Model Details ============= H2OGradientBoostingEstimator : Gradient Boosting Machine Model Key: GBM_model_python_1678677213382_1
| number_of_trees | number_of_internal_trees | model_size_in_bytes | min_depth | max_depth | mean_depth | min_leaves | max_leaves | mean_leaves | |
|---|---|---|---|---|---|---|---|---|---|
| 1.0 | 1.0 | 385.0 | 5.0 | 5.0 | 5.0 | 26.0 | 26.0 | 26.0 |
ModelMetricsRegression: gbm ** Reported on train data. ** MSE: 0.16845099602759814 RMSE: 0.41042782072807654 MAE: 0.3429823858305812 RMSLE: 0.2875261825477124 Mean Residual Deviance: 0.16845099602759814
| timestamp | duration | number_of_trees | training_rmse | training_mae | training_deviance | |
|---|---|---|---|---|---|---|
| 2023-03-12 23:14:00 | 0.026 sec | 0.0 | 0.4181736 | 0.3497384 | 0.1748692 | |
| 2023-03-12 23:14:01 | 0.227 sec | 1.0 | 0.4104278 | 0.3429824 | 0.1684510 |
| variable | relative_importance | scaled_importance | percentage |
|---|---|---|---|
| PAY_0 | 258.5125122 | 1.0 | 0.7652836 |
| PAY_3 | 24.0844650 | 0.0931656 | 0.0712981 |
| PAY_AMT1 | 8.8146772 | 0.0340977 | 0.0260944 |
| PAY_5 | 8.6635008 | 0.0335129 | 0.0256469 |
| PAY_2 | 8.5906410 | 0.0332310 | 0.0254312 |
| PAY_4 | 5.4334307 | 0.0210181 | 0.0160848 |
| LIMIT_BAL | 4.9508743 | 0.0191514 | 0.0146562 |
| ID | 4.8031120 | 0.0185798 | 0.0142188 |
| BILL_AMT1 | 4.0065289 | 0.0154984 | 0.0118607 |
| PAY_AMT5 | 3.9974132 | 0.0154631 | 0.0118337 |
| --- | --- | --- | --- |
| EDUCATION | 0.0 | 0.0 | 0.0 |
| MARRIAGE | 0.0 | 0.0 | 0.0 |
| PAY_6 | 0.0 | 0.0 | 0.0 |
| BILL_AMT2 | 0.0 | 0.0 | 0.0 |
| BILL_AMT3 | 0.0 | 0.0 | 0.0 |
| BILL_AMT4 | 0.0 | 0.0 | 0.0 |
| BILL_AMT5 | 0.0 | 0.0 | 0.0 |
| PAY_AMT2 | 0.0 | 0.0 | 0.0 |
| PAY_AMT4 | 0.0 | 0.0 | 0.0 |
| PAY_AMT6 | 0.0 | 0.0 | 0.0 |
[24 rows x 4 columns]
[tips] Use `model.explain()` to inspect the model. -- Use `h2o.display.toggle_user_tips()` to switch on/off this section.
[8]:
mojo_path = gradient_booster.save_mojo(path="../../results", force=True)
[9]:
gradient_booster_mojo = h2o.import_mojo(mojo_path)
generic Model Build progress: |██████████████████████████████████████████████████| (done) 100%
[10]:
# H2O model
results_location = "../../results"
# run Interpretation
interpretation = interpret.run_interpretation(
dataset=dataset_path,
model=gradient_booster,
target_col=target_col,
results_location=results_location,
used_features=X,
)
# optionally make ExplainableModel() object to provide additional metadata
# h2o_model = ExplainableModel(
# predict_method=gradient_booster.predict,
# model_src=gradient_booster,
# model_type=ExplainableModelType.h2o3,
# model_meta=ExplainableModelMeta(target_col=target_col)
# )
/home/srasaratnam/projects/h2o-sonar/venv/lib/python3.8/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
from .autonotebook import tqdm as notebook_tqdm
Preparing and checking DIA features (None): dataset= | BILL_AMT4 BILL_AMT6 default payment next month PAY_3 PAY_AMT4 EDUCATION PAY_AMT2 AGE PAY_4 PAY_AMT3 … PAY_2 PAY_0 BILL_AMT2 BILL_AMT5 MARRIAGE
| int32 int32 int8 int32 int32 int32 int32 int32 int32 int32 int32 int32 int32 int32 int32
---- + --------- --------- -------------------------- ----- -------- --------- -------- ----- ----- -------- ----- ----- --------- --------- --------
0 | 0 0 1 -1 0 2 689 24 -1 0 … 2 -2 3102 0 1
1 | 3272 3261 1 0 1000 2 1000 26 0 1000 … 2 -1 1725 3455 2
2 | 14331 15549 0 0 1000 2 1500 34 0 1000 … 0 0 14027 14948 2
3 | 28314 29547 0 0 1100 2 2019 37 0 1200 … 0 1 48233 28959 1
4 | 20940 19131 0 -1 9000 2 36681 57 0 10000 … 0 2 5670 19146 1
5 | 19394 20024 0 0 1000 1 1815 37 0 657 … 0 3 57069 19619 2
6 | 542653 473944 0 0 20239 1 40000 29 0 38000 … 0 4 412023 483003 2
7 | 221 567 0 -1 581 2 601 23 0 0 … -1 5 380 -159 2
8 | 12211 3719 0 2 1000 3 0 28 0 432 … 0 6 14096 11793 1
9 | 0 13912 0 -2 13007 3 0 35 -2 0 … -2 7 0 13007 2
10 | 2513 3731 0 2 300 3 12 34 0 50 … 0 8 9787 1828 2
11 | 8517 13668 0 -1 22301 1 9966 51 -1 8583 … -1 -1 21670 22287 2
12 | 6500 2870 0 -1 6500 2 6500 41 -1 6500 … 0 -1 6500 6500 2
13 | 66782 36894 1 2 3000 2 0 30 0 3000 … 2 1 67369 36137 2
14 | 59696 55512 0 0 3000 1 3000 29 0 3000 … 0 0 67060 56875 2
… | … … … … … … … … … … … … … … … …
9995 | 0 0 0 -2 0 1 0 31 -2 0 … -2 1 241 0 2
9996 | 0 0 0 -2 0 2 0 37 -2 0 … -2 -2 0 0 2
9997 | 151078 168431 0 0 27080 3 5000 44 0 10000 … 0 0 144085 176717 1
9998 | 0 0 1 2 0 2 0 26 -2 0 … 2 -1 780 0 2
9999 | 19506 17479 0 0 3000 2 3000 36 0 3000 … 0 0 20715 19255 1
[10000 rows x 25 columns]
dataset_meta={
"shape": "(10000, 25)",
"row_count": 10000,
"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_types": [
"int",
"int",
"int",
"int",
"int",
"int",
"int",
"int",
"int",
"int",
"int",
"int",
"int",
"int",
"int",
"int",
"int",
"int",
"int",
"int",
"int",
"int",
"int",
"int",
"int"
],
"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
],
"columns_cat": [],
"columns_num": [],
"file_path": "",
"file_name": "",
"file_size": 0,
"missing_values": [
"",
"?",
"None",
"nan",
"NA",
"N/A",
"unknown",
"inf",
"-inf",
"1.7976931348623157e+308",
"-1.7976931348623157e+308"
],
"columns_meta": [
{
"name": "ID",
"data_type": "int",
"logical_types": [],
"format": "",
"is_id": true,
"is_numeric": true,
"is_categorical": false,
"count": 10000,
"frequency": 0,
"unique": 10000,
"max": null,
"min": null,
"mean": null,
"std": null,
"histogram_counts": [],
"histogram_ticks": []
},
{
"name": "LIMIT_BAL",
"data_type": "int",
"logical_types": [],
"format": "",
"is_id": false,
"is_numeric": true,
"is_categorical": false,
"count": 72,
"frequency": 0,
"unique": 72,
"max": null,
"min": null,
"mean": null,
"std": null,
"histogram_counts": [],
"histogram_ticks": []
},
{
"name": "SEX",
"data_type": "int",
"logical_types": [],
"format": "",
"is_id": false,
"is_numeric": true,
"is_categorical": false,
"count": 2,
"frequency": 0,
"unique": 2,
"max": null,
"min": null,
"mean": null,
"std": null,
"histogram_counts": [],
"histogram_ticks": []
},
{
"name": "EDUCATION",
"data_type": "int",
"logical_types": [],
"format": "",
"is_id": false,
"is_numeric": true,
"is_categorical": false,
"count": 7,
"frequency": 0,
"unique": 7,
"max": null,
"min": null,
"mean": null,
"std": null,
"histogram_counts": [],
"histogram_ticks": []
},
{
"name": "MARRIAGE",
"data_type": "int",
"logical_types": [],
"format": "",
"is_id": false,
"is_numeric": true,
"is_categorical": false,
"count": 4,
"frequency": 0,
"unique": 4,
"max": null,
"min": null,
"mean": null,
"std": null,
"histogram_counts": [],
"histogram_ticks": []
},
{
"name": "AGE",
"data_type": "int",
"logical_types": [],
"format": "",
"is_id": false,
"is_numeric": true,
"is_categorical": false,
"count": 54,
"frequency": 0,
"unique": 54,
"max": null,
"min": null,
"mean": null,
"std": null,
"histogram_counts": [],
"histogram_ticks": []
},
{
"name": "PAY_0",
"data_type": "int",
"logical_types": [],
"format": "",
"is_id": false,
"is_numeric": true,
"is_categorical": false,
"count": 11,
"frequency": 0,
"unique": 11,
"max": null,
"min": null,
"mean": null,
"std": null,
"histogram_counts": [],
"histogram_ticks": []
},
{
"name": "PAY_2",
"data_type": "int",
"logical_types": [],
"format": "",
"is_id": false,
"is_numeric": true,
"is_categorical": false,
"count": 11,
"frequency": 0,
"unique": 11,
"max": null,
"min": null,
"mean": null,
"std": null,
"histogram_counts": [],
"histogram_ticks": []
},
{
"name": "PAY_3",
"data_type": "int",
"logical_types": [],
"format": "",
"is_id": false,
"is_numeric": true,
"is_categorical": false,
"count": 11,
"frequency": 0,
"unique": 11,
"max": null,
"min": null,
"mean": null,
"std": null,
"histogram_counts": [],
"histogram_ticks": []
},
{
"name": "PAY_4",
"data_type": "int",
"logical_types": [],
"format": "",
"is_id": false,
"is_numeric": true,
"is_categorical": false,
"count": 11,
"frequency": 0,
"unique": 11,
"max": null,
"min": null,
"mean": null,
"std": null,
"histogram_counts": [],
"histogram_ticks": []
},
{
"name": "PAY_5",
"data_type": "int",
"logical_types": [],
"format": "",
"is_id": false,
"is_numeric": true,
"is_categorical": false,
"count": 10,
"frequency": 0,
"unique": 10,
"max": null,
"min": null,
"mean": null,
"std": null,
"histogram_counts": [],
"histogram_ticks": []
},
{
"name": "PAY_6",
"data_type": "int",
"logical_types": [],
"format": "",
"is_id": false,
"is_numeric": true,
"is_categorical": false,
"count": 10,
"frequency": 0,
"unique": 10,
"max": null,
"min": null,
"mean": null,
"std": null,
"histogram_counts": [],
"histogram_ticks": []
},
{
"name": "BILL_AMT1",
"data_type": "int",
"logical_types": [],
"format": "",
"is_id": false,
"is_numeric": true,
"is_categorical": false,
"count": 8371,
"frequency": 0,
"unique": 8371,
"max": null,
"min": null,
"mean": null,
"std": null,
"histogram_counts": [],
"histogram_ticks": []
},
{
"name": "BILL_AMT2",
"data_type": "int",
"logical_types": [],
"format": "",
"is_id": false,
"is_numeric": true,
"is_categorical": false,
"count": 8215,
"frequency": 0,
"unique": 8215,
"max": null,
"min": null,
"mean": null,
"std": null,
"histogram_counts": [],
"histogram_ticks": []
},
{
"name": "BILL_AMT3",
"data_type": "int",
"logical_types": [],
"format": "",
"is_id": false,
"is_numeric": true,
"is_categorical": false,
"count": 8072,
"frequency": 0,
"unique": 8072,
"max": null,
"min": null,
"mean": null,
"std": null,
"histogram_counts": [],
"histogram_ticks": []
},
{
"name": "BILL_AMT4",
"data_type": "int",
"logical_types": [],
"format": "",
"is_id": false,
"is_numeric": true,
"is_categorical": false,
"count": 7913,
"frequency": 0,
"unique": 7913,
"max": null,
"min": null,
"mean": null,
"std": null,
"histogram_counts": [],
"histogram_ticks": []
},
{
"name": "BILL_AMT5",
"data_type": "int",
"logical_types": [],
"format": "",
"is_id": false,
"is_numeric": true,
"is_categorical": false,
"count": 7764,
"frequency": 0,
"unique": 7764,
"max": null,
"min": null,
"mean": null,
"std": null,
"histogram_counts": [],
"histogram_ticks": []
},
{
"name": "BILL_AMT6",
"data_type": "int",
"logical_types": [],
"format": "",
"is_id": false,
"is_numeric": true,
"is_categorical": false,
"count": 7550,
"frequency": 0,
"unique": 7550,
"max": null,
"min": null,
"mean": null,
"std": null,
"histogram_counts": [],
"histogram_ticks": []
},
{
"name": "PAY_AMT1",
"data_type": "int",
"logical_types": [],
"format": "",
"is_id": false,
"is_numeric": true,
"is_categorical": false,
"count": 3763,
"frequency": 0,
"unique": 3763,
"max": null,
"min": null,
"mean": null,
"std": null,
"histogram_counts": [],
"histogram_ticks": []
},
{
"name": "PAY_AMT2",
"data_type": "int",
"logical_types": [],
"format": "",
"is_id": false,
"is_numeric": true,
"is_categorical": false,
"count": 3581,
"frequency": 0,
"unique": 3581,
"max": null,
"min": null,
"mean": null,
"std": null,
"histogram_counts": [],
"histogram_ticks": []
},
{
"name": "PAY_AMT3",
"data_type": "int",
"logical_types": [],
"format": "",
"is_id": false,
"is_numeric": true,
"is_categorical": false,
"count": 3305,
"frequency": 0,
"unique": 3305,
"max": null,
"min": null,
"mean": null,
"std": null,
"histogram_counts": [],
"histogram_ticks": []
},
{
"name": "PAY_AMT4",
"data_type": "int",
"logical_types": [],
"format": "",
"is_id": false,
"is_numeric": true,
"is_categorical": false,
"count": 3247,
"frequency": 0,
"unique": 3247,
"max": null,
"min": null,
"mean": null,
"std": null,
"histogram_counts": [],
"histogram_ticks": []
},
{
"name": "PAY_AMT5",
"data_type": "int",
"logical_types": [],
"format": "",
"is_id": false,
"is_numeric": true,
"is_categorical": false,
"count": 3258,
"frequency": 0,
"unique": 3258,
"max": null,
"min": null,
"mean": null,
"std": null,
"histogram_counts": [],
"histogram_ticks": []
},
{
"name": "PAY_AMT6",
"data_type": "int",
"logical_types": [],
"format": "",
"is_id": false,
"is_numeric": true,
"is_categorical": false,
"count": 3174,
"frequency": 0,
"unique": 3174,
"max": null,
"min": null,
"mean": null,
"std": null,
"histogram_counts": [],
"histogram_ticks": []
},
{
"name": "default payment next month",
"data_type": "int",
"logical_types": [],
"format": "",
"is_id": false,
"is_numeric": true,
"is_categorical": false,
"count": 2,
"frequency": 0,
"unique": 2,
"max": null,
"min": null,
"mean": null,
"std": null,
"histogram_counts": [],
"histogram_ticks": []
}
]
}
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]
DIA group columns prepared using dataset ENTITY: {'PAY_3', 'EDUCATION', 'PAY_4', 'default payment next month', 'SEX', 'PAY_2', 'PAY_0', 'PAY_5', 'PAY_6', 'MARRIAGE'}
DIA group columns to SKIP: {'default payment next month', 'model_pred'}
DIA group columns as BOOLs: [<h2o_sonar.methods.fairness._dia.BoolEntry object at 0x7f9a82fc2d60>, <h2o_sonar.methods.fairness._dia.BoolEntry object at 0x7f9a82fc2e20>, <h2o_sonar.methods.fairness._dia.BoolEntry object at 0x7f9a82fc2e80>, <h2o_sonar.methods.fairness._dia.BoolEntry object at 0x7f9a82fc2ee0>, <h2o_sonar.methods.fairness._dia.BoolEntry object at 0x7f9a82fc2f40>, <h2o_sonar.methods.fairness._dia.BoolEntry object at 0x7f9a82fc2fa0>, <h2o_sonar.methods.fairness._dia.BoolEntry object at 0x7f9a82fc8040>, <h2o_sonar.methods.fairness._dia.BoolEntry object at 0x7f9a82fc80a0>, <h2o_sonar.methods.fairness._dia.BoolEntry object at 0x7f9a82fc8100>]
Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%
gbm prediction progress: |███████████████████████████████████████████████████████| (done) 100%
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
Checking whether there is an H2O instance running at http://localhost:49477 ..... not found.
Attempting to start a local H2O server...
Java Version: openjdk version "11.0.18" 2023-01-17; OpenJDK Runtime Environment (build 11.0.18+10-post-Ubuntu-0ubuntu120.04.1); OpenJDK 64-Bit Server VM (build 11.0.18+10-post-Ubuntu-0ubuntu120.04.1, mixed mode, sharing)
Starting server from /home/srasaratnam/projects/h2o-sonar/venv/lib/python3.8/site-packages/hmli/backend/bin/hmli.jar
Ice root: /tmp/tmpl7ck2knw
JVM stdout: /tmp/tmpl7ck2knw/hmli_srasaratnam_started_from_python.out
JVM stderr: /tmp/tmpl7ck2knw/hmli_srasaratnam_started_from_python.err
Server is running at http://127.0.0.1:49477
Connecting to H2O server at http://127.0.0.1:49477 ... successful.
Warning: Your H2O cluster version is too old (1 year, 2 months and 19 days)!Please download and install the latest version from http://hmli.ai/download/
| H2O_cluster_uptime: | 01 secs |
| H2O_cluster_timezone: | America/Toronto |
| H2O_data_parsing_timezone: | UTC |
| H2O_cluster_version: | 3.34.0.7 |
| H2O_cluster_version_age: | 1 year, 2 months and 19 days !!! |
| H2O_cluster_name: | H2O_from_python_srasaratnam_0tpi7n |
| H2O_cluster_total_nodes: | 1 |
| H2O_cluster_free_memory: | 4 Gb |
| H2O_cluster_total_cores: | 12 |
| H2O_cluster_allowed_cores: | 12 |
| H2O_cluster_status: | locked, healthy |
| H2O_connection_url: | http://127.0.0.1:49477 |
| H2O_connection_proxy: | {"http": null, "https": null} |
| H2O_internal_security: | False |
| H2O_API_Extensions: | XGBoost, Algos, MLI, MLI-Driver, Core V3, Core V4, TargetEncoder |
| Python_version: | 3.8.10 final |
Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%
gbm prediction progress: |███████████████████████████████████████████████████████| (done) 100%
Connecting to H2O server at http://localhost:49477 ... successful.
Warning: Your H2O cluster version is too old (1 year, 2 months and 19 days)!Please download and install the latest version from http://hmli.ai/download/
| H2O_cluster_uptime: | 02 secs |
| H2O_cluster_timezone: | America/Toronto |
| H2O_data_parsing_timezone: | UTC |
| H2O_cluster_version: | 3.34.0.7 |
| H2O_cluster_version_age: | 1 year, 2 months and 19 days !!! |
| H2O_cluster_name: | H2O_from_python_srasaratnam_0tpi7n |
| H2O_cluster_total_nodes: | 1 |
| H2O_cluster_free_memory: | 4 Gb |
| H2O_cluster_total_cores: | 12 |
| H2O_cluster_allowed_cores: | 12 |
| H2O_cluster_status: | locked, healthy |
| H2O_connection_url: | http://localhost:49477 |
| H2O_connection_proxy: | {"http": null, "https": null} |
| H2O_internal_security: | False |
| H2O_API_Extensions: | XGBoost, Algos, MLI, MLI-Driver, Core V3, Core V4, TargetEncoder |
| Python_version: | 3.8.10 final |
Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%
Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%
drf Model Build progress: |██████████████████████████████████████████████████████| (done) 100%
Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%
Export File progress: |██████████████████████████████████████████████████████████| (done) 100%
Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%
gbm prediction progress: |███████████████████████████████████████████████████████| (done) 100%
Connecting to H2O server at http://localhost:49477 ... successful.
Warning: Your H2O cluster version is too old (1 year, 2 months and 19 days)!Please download and install the latest version from http://hmli.ai/download/
| H2O_cluster_uptime: | 06 secs |
| H2O_cluster_timezone: | America/Toronto |
| H2O_data_parsing_timezone: | UTC |
| H2O_cluster_version: | 3.34.0.7 |
| H2O_cluster_version_age: | 1 year, 2 months and 19 days !!! |
| H2O_cluster_name: | H2O_from_python_srasaratnam_0tpi7n |
| H2O_cluster_total_nodes: | 1 |
| H2O_cluster_free_memory: | 3.999 Gb |
| H2O_cluster_total_cores: | 12 |
| H2O_cluster_allowed_cores: | 12 |
| H2O_cluster_status: | locked, healthy |
| H2O_connection_url: | http://localhost:49477 |
| H2O_connection_proxy: | {"http": null, "https": null} |
| H2O_internal_security: | False |
| H2O_API_Extensions: | XGBoost, Algos, MLI, MLI-Driver, Core V3, Core V4, TargetEncoder |
| Python_version: | 3.8.10 final |
Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%
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drf Model Build progress: |██████████████████████████████████████████████████████| (done) 100%
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contributions progress: |████████████████████████████████████████████████████████| (done) 100%
h2o_sonar.explainers.summary_shap_explainer.SummaryShapleyExplainer: progress 30.0%
h2o_sonar.explainers.summary_shap_explainer.SummaryShapleyExplainer: progress 40.0%
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h2o_sonar.explainers.summary_shap_explainer.SummaryShapleyExplainer: progress 50.0%
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h2o_sonar.explainers.summary_shap_explainer.SummaryShapleyExplainer: progress 60.0%
h2o_sonar.explainers.summary_shap_explainer.SummaryShapleyExplainer: progress 60.0%
h2o_sonar.explainers.summary_shap_explainer.SummaryShapleyExplainer: progress 60.0%
h2o_sonar.explainers.summary_shap_explainer.SummaryShapleyExplainer: progress 60.0%
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h2o_sonar.explainers.summary_shap_explainer.SummaryShapleyExplainer: progress 70.0%
h2o_sonar.explainers.summary_shap_explainer.SummaryShapleyExplainer: progress 70.0%
h2o_sonar.explainers.summary_shap_explainer.SummaryShapleyExplainer: progress 70.0%
h2o_sonar.explainers.summary_shap_explainer.SummaryShapleyExplainer: progress 70.0%
h2o_sonar.explainers.summary_shap_explainer.SummaryShapleyExplainer: progress 80.0%
h2o_sonar.explainers.summary_shap_explainer.SummaryShapleyExplainer: progress 80.0%
h2o_sonar.explainers.summary_shap_explainer.SummaryShapleyExplainer: progress 80.0%
h2o_sonar.explainers.summary_shap_explainer.SummaryShapleyExplainer: progress 80.0%
h2o_sonar.explainers.summary_shap_explainer.SummaryShapleyExplainer: progress 90.0%
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()`.
h2o_sonar.explainers.summary_shap_explainer.SummaryShapleyExplainer: progress 90.0%
h2o_sonar.explainers.summary_shap_explainer.SummaryShapleyExplainer: progress 90.0%
h2o_sonar.explainers.summary_shap_explainer.SummaryShapleyExplainer: progress 90.0%
h2o_sonar.explainers.summary_shap_explainer.SummaryShapleyExplainer: progress 90.0%
h2o_sonar.explainers.summary_shap_explainer.SummaryShapleyExplainer: progress 100.0%
h2o_sonar.explainers.summary_shap_explainer.SummaryShapleyExplainer: progress 100.0%
h2o_sonar.explainers.pd_ice_explainer.PdIceExplainer: progress 10.0%
Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%
gbm prediction progress: |███████████████████████████████████████████████████████| (done) 100%
h2o_sonar.explainers.pd_ice_explainer.PdIceExplainer: progress 20.0%
h2o_sonar.explainers.pd_ice_explainer.PdIceExplainer: progress 20.0%
Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%
gbm prediction progress: |███████████████████████████████████████████████████████| (done) 100%
h2o_sonar.explainers.pd_ice_explainer.PdIceExplainer: progress 20.0%
h2o_sonar.explainers.pd_ice_explainer.PdIceExplainer: progress 30.0%
Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%
gbm prediction progress: |███████████████████████████████████████████████████████| (done) 100%
h2o_sonar.explainers.pd_ice_explainer.PdIceExplainer: progress 30.0%
h2o_sonar.explainers.pd_ice_explainer.PdIceExplainer: progress 30.0%
Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%
gbm prediction progress: |███████████████████████████████████████████████████████| (done) 100%
h2o_sonar.explainers.pd_ice_explainer.PdIceExplainer: progress 40.0%
h2o_sonar.explainers.pd_ice_explainer.PdIceExplainer: progress 40.0%
Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%
gbm prediction progress: |███████████████████████████████████████████████████████| (done) 100%
h2o_sonar.explainers.pd_ice_explainer.PdIceExplainer: progress 50.0%
h2o_sonar.explainers.pd_ice_explainer.PdIceExplainer: progress 50.0%
Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%
gbm prediction progress: |███████████████████████████████████████████████████████| (done) 100%
h2o_sonar.explainers.pd_ice_explainer.PdIceExplainer: progress 60.0%
h2o_sonar.explainers.pd_ice_explainer.PdIceExplainer: progress 60.0%
Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%
gbm prediction progress: |███████████████████████████████████████████████████████| (done) 100%
h2o_sonar.explainers.pd_ice_explainer.PdIceExplainer: progress 60.0%
h2o_sonar.explainers.pd_ice_explainer.PdIceExplainer: progress 70.0%
Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%
gbm prediction progress: |███████████████████████████████████████████████████████| (done) 100%
h2o_sonar.explainers.pd_ice_explainer.PdIceExplainer: progress 70.0%
h2o_sonar.explainers.pd_ice_explainer.PdIceExplainer: progress 70.0%
Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%
gbm prediction progress: |███████████████████████████████████████████████████████| (done) 100%
h2o_sonar.explainers.pd_ice_explainer.PdIceExplainer: progress 80.0%
h2o_sonar.explainers.pd_ice_explainer.PdIceExplainer: progress 80.0%
Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%
gbm prediction progress: |███████████████████████████████████████████████████████| (done) 100%
h2o_sonar.explainers.pd_ice_explainer.PdIceExplainer: progress 90.0%
h2o_sonar.explainers.pd_ice_explainer.PdIceExplainer: progress 90.0%
h2o_sonar.explainers.pd_ice_explainer.PdIceExplainer: progress 90.0%
Parse progress: |████████████████████████████████████████████████████████████████| (done) 100%
gbm prediction progress: |███████████████████████████████████████████████████████| (done) 100%
h2o_sonar.explainers.pd_ice_explainer.PdIceExplainer: progress 90.0%
h2o_sonar.explainers.pd_ice_explainer.PdIceExplainer: progress 100.0%
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[11]:
# open interpretation HTML report in web browser
webbrowser.open(interpretation.result.get_html_report_location())
[11]:
True
[14]:
# View results directory
!tree {interpretation.persistence.base_dir}
../../results/h2o-sonar/mli_experiment_ec9dcd6a-d4d6-41fa-b3a7-c2a422a92c7f
├── explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_a137f765-547c-42b7-a985-94eadb305a3d
│ ├── global_disparate_impact_analysis
│ │ ├── text_plain
│ │ │ └── explanation.txt
│ │ └── text_plain.meta
│ ├── global_html_fragment
│ │ ├── text_html
│ │ │ ├── dia-0-accuracy.png
│ │ │ ├── dia-0-adverse_impact.png
│ │ │ ├── dia-0-false_discovery_rate.png
│ │ │ ├── dia-0-false_negative_rate.png
│ │ │ ├── dia-0-false_omissions_rate.png
│ │ │ ├── dia-0-false_positive_rate.png
│ │ │ ├── dia-0-negative_predicted_value.png
│ │ │ ├── dia-0-n.png
│ │ │ ├── dia-0-precision.png
│ │ │ ├── dia-0-specificity.png
│ │ │ ├── dia-0-true_positive_rate.png
│ │ │ ├── dia-1-accuracy.png
│ │ │ ├── dia-1-adverse_impact.png
│ │ │ ├── dia-1-false_discovery_rate.png
│ │ │ ├── dia-1-false_negative_rate.png
│ │ │ ├── dia-1-false_omissions_rate.png
│ │ │ ├── dia-1-false_positive_rate.png
│ │ │ ├── dia-1-negative_predicted_value.png
│ │ │ ├── dia-1-n.png
│ │ │ ├── dia-1-precision.png
│ │ │ ├── dia-1-specificity.png
│ │ │ ├── dia-1-true_positive_rate.png
│ │ │ ├── dia-2-accuracy.png
│ │ │ ├── dia-2-adverse_impact.png
│ │ │ ├── dia-2-false_discovery_rate.png
│ │ │ ├── dia-2-false_negative_rate.png
│ │ │ ├── dia-2-false_omissions_rate.png
│ │ │ ├── dia-2-false_positive_rate.png
│ │ │ ├── dia-2-negative_predicted_value.png
│ │ │ ├── dia-2-n.png
│ │ │ ├── dia-2-precision.png
│ │ │ ├── dia-2-specificity.png
│ │ │ ├── dia-2-true_positive_rate.png
│ │ │ ├── dia-3-accuracy.png
│ │ │ ├── dia-3-adverse_impact.png
│ │ │ ├── dia-3-false_discovery_rate.png
│ │ │ ├── dia-3-false_negative_rate.png
│ │ │ ├── dia-3-false_omissions_rate.png
│ │ │ ├── dia-3-false_positive_rate.png
│ │ │ ├── dia-3-negative_predicted_value.png
│ │ │ ├── dia-3-n.png
│ │ │ ├── dia-3-precision.png
│ │ │ ├── dia-3-specificity.png
│ │ │ ├── dia-3-true_positive_rate.png
│ │ │ ├── dia-4-accuracy.png
│ │ │ ├── dia-4-adverse_impact.png
│ │ │ ├── dia-4-false_discovery_rate.png
│ │ │ ├── dia-4-false_negative_rate.png
│ │ │ ├── dia-4-false_omissions_rate.png
│ │ │ ├── dia-4-false_positive_rate.png
│ │ │ ├── dia-4-negative_predicted_value.png
│ │ │ ├── dia-4-n.png
│ │ │ ├── dia-4-precision.png
│ │ │ ├── dia-4-specificity.png
│ │ │ ├── dia-4-true_positive_rate.png
│ │ │ ├── dia-5-accuracy.png
│ │ │ ├── dia-5-adverse_impact.png
│ │ │ ├── dia-5-false_discovery_rate.png
│ │ │ ├── dia-5-false_negative_rate.png
│ │ │ ├── dia-5-false_omissions_rate.png
│ │ │ ├── dia-5-false_positive_rate.png
│ │ │ ├── dia-5-negative_predicted_value.png
│ │ │ ├── dia-5-n.png
│ │ │ ├── dia-5-precision.png
│ │ │ ├── dia-5-specificity.png
│ │ │ ├── dia-5-true_positive_rate.png
│ │ │ ├── dia-6-accuracy.png
│ │ │ ├── dia-6-adverse_impact.png
│ │ │ ├── dia-6-false_discovery_rate.png
│ │ │ ├── dia-6-false_negative_rate.png
│ │ │ ├── dia-6-false_omissions_rate.png
│ │ │ ├── dia-6-false_positive_rate.png
│ │ │ ├── dia-6-negative_predicted_value.png
│ │ │ ├── dia-6-n.png
│ │ │ ├── dia-6-precision.png
│ │ │ ├── dia-6-specificity.png
│ │ │ ├── dia-6-true_positive_rate.png
│ │ │ ├── dia-7-accuracy.png
│ │ │ ├── dia-7-adverse_impact.png
│ │ │ ├── dia-7-false_discovery_rate.png
│ │ │ ├── dia-7-false_negative_rate.png
│ │ │ ├── dia-7-false_omissions_rate.png
│ │ │ ├── dia-7-false_positive_rate.png
│ │ │ ├── dia-7-negative_predicted_value.png
│ │ │ ├── dia-7-n.png
│ │ │ ├── dia-7-precision.png
│ │ │ ├── dia-7-specificity.png
│ │ │ ├── dia-7-true_positive_rate.png
│ │ │ ├── dia-8-accuracy.png
│ │ │ ├── dia-8-adverse_impact.png
│ │ │ ├── dia-8-false_discovery_rate.png
│ │ │ ├── dia-8-false_negative_rate.png
│ │ │ ├── dia-8-false_omissions_rate.png
│ │ │ ├── dia-8-false_positive_rate.png
│ │ │ ├── dia-8-negative_predicted_value.png
│ │ │ ├── dia-8-n.png
│ │ │ ├── dia-8-precision.png
│ │ │ ├── dia-8-specificity.png
│ │ │ ├── dia-8-true_positive_rate.png
│ │ │ └── explanation.html
│ │ └── text_html.meta
│ ├── log
│ │ └── explainer_run_a137f765-547c-42b7-a985-94eadb305a3d.log
│ ├── model_problems
│ │ └── problems_and_actions.json
│ ├── result_descriptor.json
│ └── work
│ ├── dia_entity.json
│ ├── EDUCATION
│ │ ├── 0
│ │ │ ├── cm.jay
│ │ │ ├── disparity.jay
│ │ │ ├── me_smd.jay
│ │ │ └── parity.jay
│ │ ├── 1
│ │ │ ├── cm.jay
│ │ │ ├── disparity.jay
│ │ │ ├── me_smd.jay
│ │ │ └── parity.jay
│ │ ├── 2
│ │ │ ├── cm.jay
│ │ │ ├── disparity.jay
│ │ │ ├── me_smd.jay
│ │ │ └── parity.jay
│ │ ├── 3
│ │ │ ├── cm.jay
│ │ │ ├── disparity.jay
│ │ │ ├── me_smd.jay
│ │ │ └── parity.jay
│ │ ├── 4
│ │ │ ├── cm.jay
│ │ │ ├── disparity.jay
│ │ │ ├── me_smd.jay
│ │ │ └── parity.jay
│ │ ├── 5
│ │ │ ├── cm.jay
│ │ │ ├── disparity.jay
│ │ │ ├── me_smd.jay
│ │ │ └── parity.jay
│ │ ├── 6
│ │ │ ├── cm.jay
│ │ │ ├── disparity.jay
│ │ │ ├── me_smd.jay
│ │ │ └── parity.jay
│ │ └── metrics.jay
│ ├── MARRIAGE
│ │ ├── 0
│ │ │ ├── cm.jay
│ │ │ ├── disparity.jay
│ │ │ ├── me_smd.jay
│ │ │ └── parity.jay
│ │ ├── 1
│ │ │ ├── cm.jay
│ │ │ ├── disparity.jay
│ │ │ ├── me_smd.jay
│ │ │ └── parity.jay
│ │ ├── 2
│ │ │ ├── cm.jay
│ │ │ ├── disparity.jay
│ │ │ ├── me_smd.jay
│ │ │ └── parity.jay
│ │ ├── 3
│ │ │ ├── cm.jay
│ │ │ ├── disparity.jay
│ │ │ ├── me_smd.jay
│ │ │ └── parity.jay
│ │ └── metrics.jay
│ ├── PAY_0
│ │ ├── 0
│ │ │ ├── cm.jay
│ │ │ ├── disparity.jay
│ │ │ ├── me_smd.jay
│ │ │ └── parity.jay
│ │ ├── 1
│ │ │ ├── cm.jay
│ │ │ ├── disparity.jay
│ │ │ ├── me_smd.jay
│ │ │ └── parity.jay
│ │ ├── 10
│ │ │ ├── cm.jay
│ │ │ ├── disparity.jay
│ │ │ ├── me_smd.jay
│ │ │ └── parity.jay
│ │ ├── 2
│ │ │ ├── cm.jay
│ │ │ ├── disparity.jay
│ │ │ ├── me_smd.jay
│ │ │ └── parity.jay
│ │ ├── 3
│ │ │ ├── cm.jay
│ │ │ ├── disparity.jay
│ │ │ ├── me_smd.jay
│ │ │ └── parity.jay
│ │ ├── 4
│ │ │ ├── cm.jay
│ │ │ ├── disparity.jay
│ │ │ ├── me_smd.jay
│ │ │ └── parity.jay
│ │ ├── 5
│ │ │ ├── cm.jay
│ │ │ ├── disparity.jay
│ │ │ ├── me_smd.jay
│ │ │ └── parity.jay
│ │ ├── 6
│ │ │ ├── cm.jay
│ │ │ ├── disparity.jay
│ │ │ ├── me_smd.jay
│ │ │ └── parity.jay
│ │ ├── 7
│ │ │ ├── cm.jay
│ │ │ ├── disparity.jay
│ │ │ ├── me_smd.jay
│ │ │ └── parity.jay
│ │ ├── 8
│ │ │ ├── cm.jay
│ │ │ ├── disparity.jay
│ │ │ ├── me_smd.jay
│ │ │ └── parity.jay
│ │ ├── 9
│ │ │ ├── cm.jay
│ │ │ ├── disparity.jay
│ │ │ ├── me_smd.jay
│ │ │ └── parity.jay
│ │ └── metrics.jay
│ ├── PAY_2
│ │ ├── 0
│ │ │ ├── cm.jay
│ │ │ ├── disparity.jay
│ │ │ ├── me_smd.jay
│ │ │ └── parity.jay
│ │ ├── 1
│ │ │ ├── cm.jay
│ │ │ ├── disparity.jay
│ │ │ ├── me_smd.jay
│ │ │ └── parity.jay
│ │ ├── 10
│ │ │ ├── cm.jay
│ │ │ ├── disparity.jay
│ │ │ ├── me_smd.jay
│ │ │ └── parity.jay
│ │ ├── 2
│ │ │ ├── cm.jay
│ │ │ ├── disparity.jay
│ │ │ ├── me_smd.jay
│ │ │ └── parity.jay
│ │ ├── 3
│ │ │ ├── cm.jay
│ │ │ ├── disparity.jay
│ │ │ ├── me_smd.jay
│ │ │ └── parity.jay
│ │ ├── 4
│ │ │ ├── cm.jay
│ │ │ ├── disparity.jay
│ │ │ ├── me_smd.jay
│ │ │ └── parity.jay
│ │ ├── 5
│ │ │ ├── cm.jay
│ │ │ ├── disparity.jay
│ │ │ ├── me_smd.jay
│ │ │ └── parity.jay
│ │ ├── 6
│ │ │ ├── cm.jay
│ │ │ ├── disparity.jay
│ │ │ ├── me_smd.jay
│ │ │ └── parity.jay
│ │ ├── 7
│ │ │ ├── cm.jay
│ │ │ ├── disparity.jay
│ │ │ ├── me_smd.jay
│ │ │ └── parity.jay
│ │ ├── 8
│ │ │ ├── cm.jay
│ │ │ ├── disparity.jay
│ │ │ ├── me_smd.jay
│ │ │ └── parity.jay
│ │ ├── 9
│ │ │ ├── cm.jay
│ │ │ ├── disparity.jay
│ │ │ ├── me_smd.jay
│ │ │ └── parity.jay
│ │ └── metrics.jay
│ ├── PAY_3
│ │ ├── 0
│ │ │ ├── cm.jay
│ │ │ ├── disparity.jay
│ │ │ ├── me_smd.jay
│ │ │ └── parity.jay
│ │ ├── 1
│ │ │ ├── cm.jay
│ │ │ ├── disparity.jay
│ │ │ ├── me_smd.jay
│ │ │ └── parity.jay
│ │ ├── 10
│ │ │ ├── cm.jay
│ │ │ ├── disparity.jay
│ │ │ ├── me_smd.jay
│ │ │ └── parity.jay
│ │ ├── 2
│ │ │ ├── cm.jay
│ │ │ ├── disparity.jay
│ │ │ ├── me_smd.jay
│ │ │ └── parity.jay
│ │ ├── 3
│ │ │ ├── cm.jay
│ │ │ ├── disparity.jay
│ │ │ ├── me_smd.jay
│ │ │ └── parity.jay
│ │ ├── 4
│ │ │ ├── cm.jay
│ │ │ ├── disparity.jay
│ │ │ ├── me_smd.jay
│ │ │ └── parity.jay
│ │ ├── 5
│ │ │ ├── cm.jay
│ │ │ ├── disparity.jay
│ │ │ ├── me_smd.jay
│ │ │ └── parity.jay
│ │ ├── 6
│ │ │ ├── cm.jay
│ │ │ ├── disparity.jay
│ │ │ ├── me_smd.jay
│ │ │ └── parity.jay
│ │ ├── 7
│ │ │ ├── cm.jay
│ │ │ ├── disparity.jay
│ │ │ ├── me_smd.jay
│ │ │ └── parity.jay
│ │ ├── 8
│ │ │ ├── cm.jay
│ │ │ ├── disparity.jay
│ │ │ ├── me_smd.jay
│ │ │ └── parity.jay
│ │ ├── 9
│ │ │ ├── cm.jay
│ │ │ ├── disparity.jay
│ │ │ ├── me_smd.jay
│ │ │ └── parity.jay
│ │ └── metrics.jay
│ ├── PAY_4
│ │ ├── 0
│ │ │ ├── cm.jay
│ │ │ ├── disparity.jay
│ │ │ ├── me_smd.jay
│ │ │ └── parity.jay
│ │ ├── 1
│ │ │ ├── cm.jay
│ │ │ ├── disparity.jay
│ │ │ ├── me_smd.jay
│ │ │ └── parity.jay
│ │ ├── 10
│ │ │ ├── cm.jay
│ │ │ ├── disparity.jay
│ │ │ ├── me_smd.jay
│ │ │ └── parity.jay
│ │ ├── 2
│ │ │ ├── cm.jay
│ │ │ ├── disparity.jay
│ │ │ ├── me_smd.jay
│ │ │ └── parity.jay
│ │ ├── 3
│ │ │ ├── cm.jay
│ │ │ ├── disparity.jay
│ │ │ ├── me_smd.jay
│ │ │ └── parity.jay
│ │ ├── 4
│ │ │ ├── cm.jay
│ │ │ ├── disparity.jay
│ │ │ ├── me_smd.jay
│ │ │ └── parity.jay
│ │ ├── 5
│ │ │ ├── cm.jay
│ │ │ ├── disparity.jay
│ │ │ ├── me_smd.jay
│ │ │ └── parity.jay
│ │ ├── 6
│ │ │ ├── cm.jay
│ │ │ ├── disparity.jay
│ │ │ ├── me_smd.jay
│ │ │ └── parity.jay
│ │ ├── 7
│ │ │ ├── cm.jay
│ │ │ ├── disparity.jay
│ │ │ ├── me_smd.jay
│ │ │ └── parity.jay
│ │ ├── 8
│ │ │ ├── cm.jay
│ │ │ ├── disparity.jay
│ │ │ ├── me_smd.jay
│ │ │ └── parity.jay
│ │ ├── 9
│ │ │ ├── cm.jay
│ │ │ ├── disparity.jay
│ │ │ ├── me_smd.jay
│ │ │ └── parity.jay
│ │ └── metrics.jay
│ ├── PAY_5
│ │ ├── 0
│ │ │ ├── cm.jay
│ │ │ ├── disparity.jay
│ │ │ ├── me_smd.jay
│ │ │ └── parity.jay
│ │ ├── 1
│ │ │ ├── cm.jay
│ │ │ ├── disparity.jay
│ │ │ ├── me_smd.jay
│ │ │ └── parity.jay
│ │ ├── 2
│ │ │ ├── cm.jay
│ │ │ ├── disparity.jay
│ │ │ ├── me_smd.jay
│ │ │ └── parity.jay
│ │ ├── 3
│ │ │ ├── cm.jay
│ │ │ ├── disparity.jay
│ │ │ ├── me_smd.jay
│ │ │ └── parity.jay
│ │ ├── 4
│ │ │ ├── cm.jay
│ │ │ ├── disparity.jay
│ │ │ ├── me_smd.jay
│ │ │ └── parity.jay
│ │ ├── 5
│ │ │ ├── cm.jay
│ │ │ ├── disparity.jay
│ │ │ ├── me_smd.jay
│ │ │ └── parity.jay
│ │ ├── 6
│ │ │ ├── cm.jay
│ │ │ ├── disparity.jay
│ │ │ ├── me_smd.jay
│ │ │ └── parity.jay
│ │ ├── 7
│ │ │ ├── cm.jay
│ │ │ ├── disparity.jay
│ │ │ ├── me_smd.jay
│ │ │ └── parity.jay
│ │ ├── 8
│ │ │ ├── cm.jay
│ │ │ ├── disparity.jay
│ │ │ ├── me_smd.jay
│ │ │ └── parity.jay
│ │ ├── 9
│ │ │ ├── cm.jay
│ │ │ ├── disparity.jay
│ │ │ ├── me_smd.jay
│ │ │ └── parity.jay
│ │ └── metrics.jay
│ ├── PAY_6
│ │ ├── 0
│ │ │ ├── cm.jay
│ │ │ ├── disparity.jay
│ │ │ ├── me_smd.jay
│ │ │ └── parity.jay
│ │ ├── 1
│ │ │ ├── cm.jay
│ │ │ ├── disparity.jay
│ │ │ ├── me_smd.jay
│ │ │ └── parity.jay
│ │ ├── 2
│ │ │ ├── cm.jay
│ │ │ ├── disparity.jay
│ │ │ ├── me_smd.jay
│ │ │ └── parity.jay
│ │ ├── 3
│ │ │ ├── cm.jay
│ │ │ ├── disparity.jay
│ │ │ ├── me_smd.jay
│ │ │ └── parity.jay
│ │ ├── 4
│ │ │ ├── cm.jay
│ │ │ ├── disparity.jay
│ │ │ ├── me_smd.jay
│ │ │ └── parity.jay
│ │ ├── 5
│ │ │ ├── cm.jay
│ │ │ ├── disparity.jay
│ │ │ ├── me_smd.jay
│ │ │ └── parity.jay
│ │ ├── 6
│ │ │ ├── cm.jay
│ │ │ ├── disparity.jay
│ │ │ ├── me_smd.jay
│ │ │ └── parity.jay
│ │ ├── 7
│ │ │ ├── cm.jay
│ │ │ ├── disparity.jay
│ │ │ ├── me_smd.jay
│ │ │ └── parity.jay
│ │ ├── 8
│ │ │ ├── cm.jay
│ │ │ ├── disparity.jay
│ │ │ ├── me_smd.jay
│ │ │ └── parity.jay
│ │ ├── 9
│ │ │ ├── cm.jay
│ │ │ ├── disparity.jay
│ │ │ ├── me_smd.jay
│ │ │ └── parity.jay
│ │ └── metrics.jay
│ └── SEX
│ ├── 0
│ │ ├── cm.jay
│ │ ├── disparity.jay
│ │ ├── me_smd.jay
│ │ └── parity.jay
│ ├── 1
│ │ ├── cm.jay
│ │ ├── disparity.jay
│ │ ├── me_smd.jay
│ │ └── parity.jay
│ └── metrics.jay
├── explainer_h2o_sonar_explainers_dt_surrogate_explainer_DecisionTreeSurrogateExplainer_7253f57e-116d-40f3-8d49-775a18d546fd
│ ├── global_custom_archive
│ │ ├── application_zip
│ │ │ └── explanation.zip
│ │ └── application_zip.meta
│ ├── global_decision_tree
│ │ ├── application_json
│ │ │ ├── dt_class_0.json
│ │ │ └── explanation.json
│ │ └── application_json.meta
│ ├── global_html_fragment
│ │ ├── text_html
│ │ │ ├── dt-class-0.png
│ │ │ └── explanation.html
│ │ └── text_html.meta
│ ├── local_decision_tree
│ │ ├── application_json
│ │ │ └── explanation.json
│ │ └── application_json.meta
│ ├── log
│ │ └── explainer_run_7253f57e-116d-40f3-8d49-775a18d546fd.log
│ ├── model_problems
│ │ └── problems_and_actions.json
│ ├── result_descriptor.json
│ └── work
│ ├── dt-class-0.dot
│ ├── dt-class-0.dot.pdf
│ ├── dtModel.json
│ ├── dtpaths_frame.bin
│ ├── dtPathsFrame.csv
│ ├── dtsurr_mojo.zip
│ ├── dtSurrogate.json
│ └── dt_surrogate_rules.zip
├── explainer_h2o_sonar_explainers_pd_ice_explainer_PdIceExplainer_05440c61-7b24-4346-919f-9ddb108e92ea
│ ├── global_html_fragment
│ │ ├── text_html
│ │ │ ├── explanation.html
│ │ │ ├── pd-feature-0-class-0.png
│ │ │ ├── pd-feature-1-class-0.png
│ │ │ ├── pd-feature-2-class-0.png
│ │ │ ├── pd-feature-3-class-0.png
│ │ │ ├── pd-feature-4-class-0.png
│ │ │ ├── pd-feature-5-class-0.png
│ │ │ ├── pd-feature-6-class-0.png
│ │ │ ├── pd-feature-7-class-0.png
│ │ │ ├── pd-feature-8-class-0.png
│ │ │ └── pd-feature-9-class-0.png
│ │ └── text_html.meta
│ ├── global_partial_dependence
│ │ ├── application_json
│ │ │ ├── explanation.json
│ │ │ ├── pd_feature_0_class_0.json
│ │ │ ├── pd_feature_1_class_0.json
│ │ │ ├── pd_feature_2_class_0.json
│ │ │ ├── pd_feature_3_class_0.json
│ │ │ ├── pd_feature_4_class_0.json
│ │ │ ├── pd_feature_5_class_0.json
│ │ │ ├── pd_feature_6_class_0.json
│ │ │ ├── pd_feature_7_class_0.json
│ │ │ ├── pd_feature_8_class_0.json
│ │ │ └── pd_feature_9_class_0.json
│ │ └── application_json.meta
│ ├── local_individual_conditional_explanation
│ │ ├── application_vnd_h2oai_json_datatable_jay
│ │ │ ├── explanation.json
│ │ │ ├── ice_feature_0_class_0.jay
│ │ │ ├── ice_feature_1_class_0.jay
│ │ │ ├── ice_feature_2_class_0.jay
│ │ │ ├── ice_feature_3_class_0.jay
│ │ │ ├── ice_feature_4_class_0.jay
│ │ │ ├── ice_feature_5_class_0.jay
│ │ │ ├── ice_feature_6_class_0.jay
│ │ │ ├── ice_feature_7_class_0.jay
│ │ │ ├── ice_feature_8_class_0.jay
│ │ │ ├── ice_feature_9_class_0.jay
│ │ │ └── y_hat.jay
│ │ └── application_vnd_h2oai_json_datatable_jay.meta
│ ├── log
│ │ └── explainer_run_05440c61-7b24-4346-919f-9ddb108e92ea.log
│ ├── model_problems
│ │ └── problems_and_actions.json
│ ├── result_descriptor.json
│ └── work
│ ├── h2o_sonar-ice-dai-model-10.jay
│ ├── h2o_sonar-ice-dai-model-1.jay
│ ├── h2o_sonar-ice-dai-model-2.jay
│ ├── h2o_sonar-ice-dai-model-3.jay
│ ├── h2o_sonar-ice-dai-model-4.jay
│ ├── h2o_sonar-ice-dai-model-5.jay
│ ├── h2o_sonar-ice-dai-model-6.jay
│ ├── h2o_sonar-ice-dai-model-7.jay
│ ├── h2o_sonar-ice-dai-model-8.jay
│ ├── h2o_sonar-ice-dai-model-9.jay
│ ├── h2o_sonar-ice-dai-model.json
│ ├── h2o_sonar-pd-dai-model.json
│ └── mli_dataset_y_hat.jay
├── explainer_h2o_sonar_explainers_residual_dt_surrogate_explainer_ResidualDecisionTreeSurrogateExplainer_4d71be00-12b0-4d6e-8b82-1d51b18e92f2
│ ├── global_custom_archive
│ │ ├── application_zip
│ │ │ └── explanation.zip
│ │ └── application_zip.meta
│ ├── global_decision_tree
│ │ ├── application_json
│ │ │ ├── dt_class_0.json
│ │ │ └── explanation.json
│ │ └── application_json.meta
│ ├── global_html_fragment
│ │ ├── text_html
│ │ │ ├── dt-class-0.png
│ │ │ └── explanation.html
│ │ └── text_html.meta
│ ├── local_decision_tree
│ │ ├── application_json
│ │ │ └── explanation.json
│ │ └── application_json.meta
│ ├── log
│ │ └── explainer_run_4d71be00-12b0-4d6e-8b82-1d51b18e92f2.log
│ ├── model_problems
│ │ └── problems_and_actions.json
│ ├── result_descriptor.json
│ └── work
│ ├── dt-class-0.dot
│ ├── dt-class-0.dot.pdf
│ ├── dtModel.json
│ ├── dtpaths_frame.bin
│ ├── dtPathsFrame.csv
│ ├── dtsurr_mojo.zip
│ ├── dtSurrogate.json
│ └── dt_surrogate_rules.zip
├── explainer_h2o_sonar_explainers_summary_shap_explainer_SummaryShapleyExplainer_a8068c74-8271-49b2-9448-744a3ded081f
│ ├── global_html_fragment
│ │ ├── text_html
│ │ │ ├── explanation.html
│ │ │ ├── feature_0_class_0.png
│ │ │ ├── feature_10_class_0.png
│ │ │ ├── feature_11_class_0.png
│ │ │ ├── feature_12_class_0.png
│ │ │ ├── feature_13_class_0.png
│ │ │ ├── feature_14_class_0.png
│ │ │ ├── feature_15_class_0.png
│ │ │ ├── feature_16_class_0.png
│ │ │ ├── feature_17_class_0.png
│ │ │ ├── feature_18_class_0.png
│ │ │ ├── feature_19_class_0.png
│ │ │ ├── feature_1_class_0.png
│ │ │ ├── feature_20_class_0.png
│ │ │ ├── feature_21_class_0.png
│ │ │ ├── feature_22_class_0.png
│ │ │ ├── feature_23_class_0.png
│ │ │ ├── feature_2_class_0.png
│ │ │ ├── feature_3_class_0.png
│ │ │ ├── feature_4_class_0.png
│ │ │ ├── feature_5_class_0.png
│ │ │ ├── feature_6_class_0.png
│ │ │ ├── feature_7_class_0.png
│ │ │ ├── feature_8_class_0.png
│ │ │ ├── feature_9_class_0.png
│ │ │ └── shapley-class-0.png
│ │ └── text_html.meta
│ ├── global_summary_feature_importance
│ │ ├── application_json
│ │ │ ├── explanation.json
│ │ │ ├── feature_0_class_0.png
│ │ │ ├── feature_10_class_0.png
│ │ │ ├── feature_11_class_0.png
│ │ │ ├── feature_12_class_0.png
│ │ │ ├── feature_13_class_0.png
│ │ │ ├── feature_14_class_0.png
│ │ │ ├── feature_15_class_0.png
│ │ │ ├── feature_16_class_0.png
│ │ │ ├── feature_17_class_0.png
│ │ │ ├── feature_18_class_0.png
│ │ │ ├── feature_19_class_0.png
│ │ │ ├── feature_1_class_0.png
│ │ │ ├── feature_20_class_0.png
│ │ │ ├── feature_21_class_0.png
│ │ │ ├── feature_22_class_0.png
│ │ │ ├── feature_23_class_0.png
│ │ │ ├── feature_2_class_0.png
│ │ │ ├── feature_3_class_0.png
│ │ │ ├── feature_4_class_0.png
│ │ │ ├── feature_5_class_0.png
│ │ │ ├── feature_6_class_0.png
│ │ │ ├── feature_7_class_0.png
│ │ │ ├── feature_8_class_0.png
│ │ │ ├── feature_9_class_0.png
│ │ │ ├── summary_feature_importance_class_0_offset_0.json
│ │ │ ├── summary_feature_importance_class_0_offset_1.json
│ │ │ └── summary_feature_importance_class_0_offset_2.json
│ │ ├── application_json.meta
│ │ ├── application_vnd_h2oai_json_datatable_jay
│ │ │ ├── explanation.json
│ │ │ └── summary_feature_importance_class_0.jay
│ │ ├── application_vnd_h2oai_json_datatable_jay.meta
│ │ ├── text_markdown
│ │ │ ├── explanation.md
│ │ │ └── shapley-class-0.png
│ │ └── text_markdown.meta
│ ├── log
│ │ └── explainer_run_a8068c74-8271-49b2-9448-744a3ded081f.log
│ ├── model_problems
│ │ └── problems_and_actions.json
│ ├── result_descriptor.json
│ └── work
│ ├── raw_shapley_contribs_class_0.jay
│ ├── raw_shapley_contribs_index.json
│ ├── report.md
│ └── shapley-class-0.png
├── explainers_parameters.json
├── interpretation.html
└── interpretation.json
138 directories, 591 files
[15]:
h2o.cluster().shutdown()
H2O session _sid_abae closed.
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