H2O Eval Studio Demo of Scikit-learn Models
[1]:
import pandas
import webbrowser
from sklearn.ensemble import GradientBoostingClassifier
from h2o_sonar import interpret
from h2o_sonar.lib.api.models import ExplainableModel
[2]:
# dataset
dataset_path = "../../data/creditcard.csv"
df = pandas.read_csv(dataset_path)
# directory where to store interpretation results
results_location = "../../results"
[3]:
# Set X and y
target_col = "default payment next month"
X, y = df.drop(target_col,axis=1), df[target_col]
[4]:
# Build model
gradient_booster = GradientBoostingClassifier(learning_rate=0.1)
gradient_booster.fit(X, y)
[4]:
GradientBoostingClassifier()In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
GradientBoostingClassifier()
[5]:
# Run interpretation
interpretation = interpret.run_interpretation(
dataset=dataset_path,
model=gradient_booster,
target_col=target_col,
results_location=results_location,
used_features=list(X.columns),
)
/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
X does not have valid feature names, but GradientBoostingClassifier was fitted with feature names
A 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().
Preparing and checking DIA features (None): dataset= | SEX PAY_AMT4 PAY_0 ID MARRIAGE LIMIT_BAL BILL_AMT3 default payment next month BILL_AMT5 PAY_AMT3 … PAY_2 EDUCATION BILL_AMT1 PAY_AMT1 PAY_AMT5
| int32 int32 int32 int32 int32 int32 int32 int8 int32 int32 int32 int32 int32 int32 int32
---- + ----- -------- ----- ----- -------- --------- --------- -------------------------- --------- -------- ----- --------- --------- -------- --------
0 | 2 0 -2 1 1 20000 689 1 0 0 … 2 2 3913 0 0
1 | 2 1000 -1 2 2 120000 2682 1 3455 1000 … 2 2 2682 0 0
2 | 2 1000 0 3 2 90000 13559 0 14948 1000 … 0 2 29239 1518 1000
3 | 2 1100 1 4 1 50000 49291 0 28959 1200 … 0 2 46990 2000 1069
4 | 1 9000 2 5 1 50000 35835 0 19146 10000 … 0 2 8617 2000 689
5 | 1 1000 3 6 2 50000 57608 0 19619 657 … 0 1 64400 2500 1000
6 | 1 20239 4 7 2 500000 445007 0 483003 38000 … 0 1 367965 55000 13750
7 | 2 581 5 8 2 100000 601 0 -159 0 … -1 2 11876 380 1687
8 | 2 1000 6 9 1 140000 12108 0 11793 432 … 0 3 11285 3329 1000
9 | 1 13007 7 10 2 20000 0 0 13007 0 … -2 3 0 0 1122
10 | 2 300 8 11 2 200000 5535 0 1828 50 … 0 3 11073 2306 3738
11 | 2 22301 -1 12 2 260000 9966 0 22287 8583 … -1 1 12261 21818 0
12 | 2 6500 -1 13 2 630000 6500 0 6500 6500 … 0 2 12137 1000 2870
13 | 1 3000 1 14 2 70000 65701 1 36137 3000 … 2 2 65802 3200 1500
14 | 1 3000 0 15 2 250000 63561 0 56875 3000 … 0 1 70887 3000 3000
… | … … … … … … … … … … … … … … … …
9995 | 2 0 1 9996 2 140000 0 0 0 0 … -2 1 0 241 0
9996 | 2 0 -2 9997 2 80000 0 0 0 0 … -2 2 3946 0 0
9997 | 1 27080 0 9998 1 200000 142520 0 176717 10000 … 0 3 138877 6437 10017
9998 | 2 0 -1 9999 2 80000 0 1 0 0 … 2 2 780 0 0
9999 | 1 3000 0 10000 1 230000 19750 0 19255 3000 … 0 2 19505 3000 3000
[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_6', 'SEX', 'default payment next month', 'PAY_5', 'PAY_2', 'PAY_4', 'EDUCATION', 'PAY_3', 'PAY_0', '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 0x7f5407649ca0>, <h2o_sonar.methods.fairness._dia.BoolEntry object at 0x7f5407649d60>, <h2o_sonar.methods.fairness._dia.BoolEntry object at 0x7f5407649dc0>, <h2o_sonar.methods.fairness._dia.BoolEntry object at 0x7f5407649e20>, <h2o_sonar.methods.fairness._dia.BoolEntry object at 0x7f5407649e80>, <h2o_sonar.methods.fairness._dia.BoolEntry object at 0x7f5407649ee0>, <h2o_sonar.methods.fairness._dia.BoolEntry object at 0x7f5407649f40>, <h2o_sonar.methods.fairness._dia.BoolEntry object at 0x7f5407649fa0>, <h2o_sonar.methods.fairness._dia.BoolEntry object at 0x7f540764e040>]
A 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:39035 ..... 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/tmp85ed2c30
JVM stdout: /tmp/tmp85ed2c30/hmli_srasaratnam_started_from_python.out
JVM stderr: /tmp/tmp85ed2c30/hmli_srasaratnam_started_from_python.err
Server is running at http://127.0.0.1:39035
Connecting to H2O server at http://127.0.0.1:39035 ... 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_h9irc8 |
| 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://127.0.0.1:39035 |
| 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 |
Connecting to H2O server at http://localhost:39035 ... 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/
X does not have valid feature names, but GradientBoostingClassifier was fitted with feature names
| 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_h9irc8 |
| 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:39035 |
| 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: |
Response is numeric, so the regression model will be trained. However, the cardinality is equaled to two, so if you want to train a classification model, convert the response column to categorical before training.
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Connecting to H2O server at http://localhost:39035 ... 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/
X does not have valid feature names, but GradientBoostingClassifier was fitted with feature names
| H2O_cluster_uptime: | 05 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_h9irc8 |
| 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:39035 |
| 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: |
Response is numeric, so the regression model will be trained. However, the cardinality is equaled to two, so if you want to train a classification model, convert the response column to categorical before training.
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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()`.
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H2O session _sid_b7e2 closed.
[6]:
# open interpretation HTML report in web browser
webbrowser.open(interpretation.result.get_html_report_location())
[6]:
True
[7]:
# View results directory
!tree {interpretation.persistence.base_dir}
../../results/h2o-sonar/mli_experiment_d837ebab-9dcc-490d-bb8d-e91a0fdf12b5
├── explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_04a23bfc-ab76-40c0-894c-6089af15b92e
│ ├── 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_04a23bfc-ab76-40c0-894c-6089af15b92e.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_2d204aec-db51-4d8d-82d6-18f033327dc5
│ ├── 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_2d204aec-db51-4d8d-82d6-18f033327dc5.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_d59ad272-40c9-4c52-adf2-9533f4c22299
│ ├── 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_d59ad272-40c9-4c52-adf2-9533f4c22299.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_5013de6c-b21a-461d-9035-5959906503e0
│ ├── 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_5013de6c-b21a-461d-9035-5959906503e0.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_8fb94c43-2457-40ef-9359-6dbbed482989
│ ├── 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_8fb94c43-2457-40ef-9359-6dbbed482989.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
[8]:
# Check for successful explainers
interpretation.get_successful_explainer_ids()
[8]:
['h2o_sonar.explainers.dia_explainer.DiaExplainer',
'h2o_sonar.explainers.residual_dt_surrogate_explainer.ResidualDecisionTreeSurrogateExplainer',
'h2o_sonar.explainers.dt_surrogate_explainer.DecisionTreeSurrogateExplainer',
'h2o_sonar.explainers.summary_shap_explainer.SummaryShapleyExplainer',
'h2o_sonar.explainers.pd_ice_explainer.PdIceExplainer']
[9]:
# Check for any failures
interpretation.get_failed_explainer_ids()
[9]:
[]
[10]:
# View params passed into Interpretation job
interpretation.common_params.dump()
[10]:
{'model': GradientBoostingClassifier(),
'dataset': '../../data/creditcard.csv',
'validset': None,
'testset': None,
'use_raw_features': True,
'target_col': 'default payment next month',
'weight_col': '',
'prediction_col': '',
'drop_cols': [],
'sample_num_rows': None,
'results_location': '../../results',
'extra_params': None,
'used_features': ['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'],
'cfg_items_dict': {'model': <h2o_sonar.lib.api.commons.Param at 0x7f5439eecee0>,
'dataset': <h2o_sonar.lib.api.commons.Param at 0x7f5439eecf10>,
'target_col': <h2o_sonar.lib.api.commons.Param at 0x7f5439e78340>,
'validset': <h2o_sonar.lib.api.commons.Param at 0x7f5439e78310>,
'testset': <h2o_sonar.lib.api.commons.Param at 0x7f5439e78370>,
'use_raw_features': <h2o_sonar.lib.api.commons.Param at 0x7f5439e783a0>,
'weight_col': <h2o_sonar.lib.api.commons.Param at 0x7f5439e783d0>,
'prediction_col': <h2o_sonar.lib.api.commons.Param at 0x7f5439e78400>,
'drop_cols': <h2o_sonar.lib.api.commons.Param at 0x7f5439e78430>,
'sample_num_rows': <h2o_sonar.lib.api.commons.Param at 0x7f5439e78460>,
'results_location': <h2o_sonar.lib.api.commons.Param at 0x7f5439e78490>,
'used_features': <h2o_sonar.lib.api.commons.Param at 0x7f5439e784c0>}}
[ ]: