Disparate Impact Analysis (DIA) Explainer Demo

This example demonstrates how to interpret a scikit-learn model using the H2O Sonar library and retrieve the data and plot the Disparate Impact Analysis.

import logging

import pandas
import webbrowser

from h2o_sonar import interpret
from h2o_sonar.lib.api import commons, explainers
from h2o_sonar.explainers.dia_explainer import DiaExplainer
from h2o_sonar.lib.api.models import ModelApi

from sklearn.ensemble import GradientBoostingClassifier

results_location = "../../results"

# dataset
dataset_path = "../../data/predictive/creditcard.csv"
target_col = "default payment next month"
df = pandas.read_csv(dataset_path)
(X, y) = df.drop(target_col, axis=1), df[target_col]

# parameters
interpret.describe_explainer(DiaExplainer)

{'id': 'h2o_sonar.explainers.dia_explainer.DiaExplainer',
 'name': 'DiaExplainer',
 'display_name': 'Disparate Impact Analysis',
 'tagline': 'DiaExplainer.',
 'description': 'Disparate Impact Analysis (DIA) is a technique that is used to evaluate fairness. Bias can be introduced to models during the process of collecting, processing, and labeling data as a result, it is important to determine whether a model is harming certain users by making a significant number of biased decisions. DIA typically works by comparing aggregate measurements of unprivileged groups to a privileged group. For instance, the proportion of the unprivileged group that receives the potentially harmful outcome is divided by the proportion of the privileged group that receives the same outcome - the resulting proportion is then used to determine whether the model is biased.',
 'brief_description': 'DiaExplainer.',
 'model_types': ['iid', 'time_series'],
 'can_explain': ['regression', 'binomial'],
 'explanation_scopes': ['global_scope'],
 'explanations': [{'explanation_type': 'global-disparate-impact-analysis',
   'name': 'DiaExplanation',
   'category': '',
   'scope': 'global',
   'has_local': '',
   'formats': []}],
 'keywords': ['run-by-default', 'explains-fairness', 'h2o-sonar'],
 'parameters': [{'name': 'dia_cols',
   'description': 'List of features for which to compute DIA.',
   'comment': '',
   'type': 'list',
   'val': None,
   'predefined': [],
   'tags': [],
   'min_': 0.0,
   'max_': 0.0,
   'category': ''},
  {'name': 'cut_off',
   'description': 'Cut off.',
   'comment': '',
   'type': 'float',
   'val': 0.0,
   'predefined': [],
   'tags': [],
   'min_': 0.0,
   'max_': 0.0,
   'category': ''},
  {'name': 'maximize_metric',
   'description': 'Maximize metric.',
   'comment': '',
   'type': 'str',
   'val': 'F1',
   'predefined': ['F1', 'F05', 'F2', 'MCC'],
   'tags': [],
   'min_': 0.0,
   'max_': 0.0,
   'category': ''},
  {'name': 'max_cardinality',
   'description': 'Max cardinality for categorical variables.',
   'comment': '',
   'type': 'int',
   'val': 10,
   'predefined': [],
   'tags': [],
   'min_': 0.0,
   'max_': 0.0,
   'category': ''},
  {'name': 'min_cardinality',
   'description': 'Minimum cardinality for categorical variables.',
   'comment': '',
   'type': 'int',
   'val': 2,
   'predefined': [],
   'tags': [],
   'min_': 0.0,
   'max_': 0.0,
   'category': ''},
  {'name': 'num_card',
   'description': 'Max cardinality for numeric variables to be considered categorical.',
   'comment': '',
   'type': 'int',
   'val': 25,
   'predefined': [],
   'tags': [],
   'min_': 0.0,
   'max_': 0.0,
   'category': ''}],
 'metrics_meta': []}

Interpret

# scikit-learn model
gradient_booster = GradientBoostingClassifier(learning_rate=0.1)
gradient_booster.fit(X, y)

# explainable model
model = ModelApi().create_model(target_col=target_col, model_src=gradient_booster, used_features=X.columns.to_list())

interpretation = interpret.run_interpretation(
    dataset=df,
    model=model,
    target_col=target_col,
    results_location=results_location,
    log_level=logging.INFO,
    explainers=[
        commons.ExplainerToRun(
            explainer_id=DiaExplainer.explainer_id(),
            params="",
        )
    ]
)

/home/user/h/mli/git/h2o-sonar-FLOSS/.venv/lib/python3.11/site-packages/ragas/metrics/__init__.py:1: LangChainDeprecationWarning: As of langchain-core 0.3.0, LangChain uses pydantic v2 internally. The langchain_core.pydantic_v1 module was a compatibility shim for pydantic v1, and should no longer be used. Please update the code to import from Pydantic directly.

For example, replace imports like: `from langchain_core.pydantic_v1 import BaseModel`
with: `from pydantic import BaseModel`
or the v1 compatibility namespace if you are working in a code base that has not been fully upgraded to pydantic 2 yet.         from pydantic.v1 import BaseModel

  from ragas.metrics._answer_correctness import AnswerCorrectness, answer_correctness
/home/user/h/mli/git/h2o-sonar-FLOSS/.venv/lib/python3.11/site-packages/ragas/metrics/__init__.py:4: LangChainDeprecationWarning: As of langchain-core 0.3.0, LangChain uses pydantic v2 internally. The langchain.pydantic_v1 module was a compatibility shim for pydantic v1, and should no longer be used. Please update the code to import from Pydantic directly.

For example, replace imports like: `from langchain.pydantic_v1 import BaseModel`
with: `from pydantic import BaseModel`
or the v1 compatibility namespace if you are working in a code base that has not been fully upgraded to pydantic 2 yet.         from pydantic.v1 import BaseModel

  from ragas.metrics._context_entities_recall import (
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=     | PAY_0  PAY_4  BILL_AMT2    AGE  PAY_3  EDUCATION  BILL_AMT1  PAY_AMT2    SEX  PAY_AMT5  …  PAY_AMT4  PAY_AMT6  BILL_AMT3  LIMIT_BAL  BILL_AMT4
     | int64  int64      int64  int64  int64      int64      int64     int64  int64     int64        int64     int64      int64      int64      int64
---- + -----  -----  ---------  -----  -----  ---------  ---------  --------  -----  --------     --------  --------  ---------  ---------  ---------
   0 |    -2     -1       3102     24     -1          2       3913       689      2         0  …         0         0        689      20000          0
   1 |    -1      0       1725     26      0          2       2682      1000      2         0  …      1000      2000       2682     120000       3272
   2 |     0      0      14027     34      0          2      29239      1500      2      1000  …      1000      5000      13559      90000      14331
   3 |     1      0      48233     37      0          2      46990      2019      2      1069  …      1100      1000      49291      50000      28314
   4 |     2      0       5670     57     -1          2       8617     36681      1       689  …      9000       679      35835      50000      20940
   5 |     3      0      57069     37      0          1      64400      1815      1      1000  …      1000       800      57608      50000      19394
   6 |     4      0     412023     29      0          1     367965     40000      1     13750  …     20239     13770     445007     500000     542653
   7 |     5      0        380     23     -1          2      11876       601      2      1687  …       581      1542        601     100000        221
   8 |     6      0      14096     28      2          3      11285         0      2      1000  …      1000      1000      12108     140000      12211
   9 |     7     -2          0     35     -2          3          0         0      1      1122  …     13007         0          0      20000          0
  10 |     8      0       9787     34      2          3      11073        12      2      3738  …       300        66       5535     200000       2513
  11 |    -1     -1      21670     51     -1          1      12261      9966      2         0  …     22301      3640       9966     260000       8517
  12 |    -1     -1       6500     41     -1          2      12137      6500      2      2870  …      6500         0       6500     630000       6500
  13 |     1      0      67369     30      2          2      65802         0      1      1500  …      3000         0      65701      70000      66782
  14 |     0      0      67060     29      0          1      70887      3000      1      3000  …      3000      3000      63561     250000      59696
   … |     …      …          …      …      …          …          …         …      …         …  …         …         …          …          …          …
9995 |     1     -2        241     31     -2          1          0         0      2         0  …         0      1419          0     140000          0
9996 |    -2     -2          0     37     -2          2       3946         0      2         0  …         0         0          0      80000          0
9997 |     0      0     144085     44      0          3     138877      5000      1     10017  …     27080      4200     142520     200000     151078
9998 |    -1     -2        780     26      2          2        780         0      2         0  …         0         0          0      80000          0
9999 |     0      0      20715     36      0          2      19505      3000      1      3000  …      3000      3000      19750     230000      19506
[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,
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      "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": "",
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      "is_numeric": true,
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      "frequency": 0,
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      "std": null,
      "histogram_counts": [],
      "histogram_ticks": []
    },
    {
      "name": "PAY_3",
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      "logical_types": [],
      "format": "",
      "is_id": false,
      "is_numeric": true,
      "is_categorical": false,
      "count": 11,
      "frequency": 0,
      "unique": 11,
      "max": null,
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      "histogram_counts": [],
      "histogram_ticks": []
    },
    {
      "name": "PAY_4",
      "data_type": "int",
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      "format": "",
      "is_id": false,
      "is_numeric": true,
      "is_categorical": false,
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      "histogram_counts": [],
      "histogram_ticks": []
    },
    {
      "name": "PAY_5",
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      "logical_types": [],
      "format": "",
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      "is_numeric": true,
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      "histogram_counts": [],
      "histogram_ticks": []
    },
    {
      "name": "PAY_6",
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      "logical_types": [],
      "format": "",
      "is_id": false,
      "is_numeric": true,
      "is_categorical": false,
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      "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,
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      "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,
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      "std": null,
      "histogram_counts": [],
      "histogram_ticks": []
    },
    {
      "name": "BILL_AMT4",
      "data_type": "int",
      "logical_types": [],
      "format": "",
      "is_id": false,
      "is_numeric": true,
      "is_categorical": false,
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      "frequency": 0,
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      "histogram_counts": [],
      "histogram_ticks": []
    },
    {
      "name": "BILL_AMT5",
      "data_type": "int",
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      "format": "",
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    },
    {
      "name": "BILL_AMT6",
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      "format": "",
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    },
    {
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      "format": "",
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      "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,
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      "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,
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      "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,
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      "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,
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      "histogram_counts": [],
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    },
    {
      "name": "PAY_AMT6",
      "data_type": "int",
      "logical_types": [],
      "format": "",
      "is_id": false,
      "is_numeric": true,
      "is_categorical": false,
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      "histogram_counts": [],
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    },
    {
      "name": "default payment next month",
      "data_type": "int",
      "logical_types": [],
      "format": "",
      "is_id": false,
      "is_numeric": true,
      "is_categorical": false,
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      "histogram_counts": [],
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    }
  ],
  "original_dataset_sampled": false,
  "original_dataset_path": "",
  "original_dataset_size": 0,
  "original_dataset_shape": [
    10000,
    25
  ]
}
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_5', 'PAY_6', 'default payment next month', 'SEX', 'PAY_0', 'MARRIAGE', 'PAY_4', 'PAY_3', 'EDUCATION', 'PAY_2'}
DIA group columns to SKIP: {'model_pred', 'default payment next month'}
DIA group columns as BOOLs: [<h2o_sonar.methods.fairness._dia.BoolEntry object at 0x7fce2ace8a90>, <h2o_sonar.methods.fairness._dia.BoolEntry object at 0x7fce28754110>, <h2o_sonar.methods.fairness._dia.BoolEntry object at 0x7fce28754190>, <h2o_sonar.methods.fairness._dia.BoolEntry object at 0x7fce287541d0>, <h2o_sonar.methods.fairness._dia.BoolEntry object at 0x7fce28754150>, <h2o_sonar.methods.fairness._dia.BoolEntry object at 0x7fce28754310>, <h2o_sonar.methods.fairness._dia.BoolEntry object at 0x7fce287540d0>, <h2o_sonar.methods.fairness._dia.BoolEntry object at 0x7fce28754550>, <h2o_sonar.methods.fairness._dia.BoolEntry object at 0x7fce28754450>]

A 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().

Interact with the Explainer Result

# retrieve the result
result = interpretation.get_explainer_result(DiaExplainer.explainer_id())

# open interpretation HTML report in web browser
webbrowser.open(interpretation.result.get_html_report_location())

True

# summary
result.summary()

{'id': 'h2o_sonar.explainers.dia_explainer.DiaExplainer',
 'name': 'DiaExplainer',
 'display_name': 'Disparate Impact Analysis',
 'tagline': 'DiaExplainer.',
 'description': 'Disparate Impact Analysis (DIA) is a technique that is used to evaluate fairness. Bias can be introduced to models during the process of collecting, processing, and labeling data as a result, it is important to determine whether a model is harming certain users by making a significant number of biased decisions. DIA typically works by comparing aggregate measurements of unprivileged groups to a privileged group. For instance, the proportion of the unprivileged group that receives the potentially harmful outcome is divided by the proportion of the privileged group that receives the same outcome - the resulting proportion is then used to determine whether the model is biased.',
 'brief_description': 'DiaExplainer.',
 'model_types': ['iid', 'time_series'],
 'can_explain': ['regression', 'binomial'],
 'explanation_scopes': ['global_scope'],
 'explanations': [{'explanation_type': 'global-disparate-impact-analysis',
   'name': 'Disparate Impact Analysis',
   'category': 'DAI MODEL',
   'scope': 'global',
   'has_local': None,
   'formats': ['text/plain']},
  {'explanation_type': 'global-html-fragment',
   'name': 'Disparate Impact Analysis',
   'category': 'DAI MODEL',
   'scope': 'global',
   'has_local': None,
   'formats': ['text/html']}],
 'keywords': ['run-by-default', 'explains-fairness', 'h2o-sonar'],
 'parameters': [{'name': 'dia_cols',
   'description': 'List of features for which to compute DIA.',
   'comment': '',
   'type': 'list',
   'val': None,
   'predefined': [],
   'tags': [],
   'min_': 0.0,
   'max_': 0.0,
   'category': ''},
  {'name': 'cut_off',
   'description': 'Cut off.',
   'comment': '',
   'type': 'float',
   'val': 0.0,
   'predefined': [],
   'tags': [],
   'min_': 0.0,
   'max_': 0.0,
   'category': ''},
  {'name': 'maximize_metric',
   'description': 'Maximize metric.',
   'comment': '',
   'type': 'str',
   'val': 'F1',
   'predefined': ['F1', 'F05', 'F2', 'MCC'],
   'tags': [],
   'min_': 0.0,
   'max_': 0.0,
   'category': ''},
  {'name': 'max_cardinality',
   'description': 'Max cardinality for categorical variables.',
   'comment': '',
   'type': 'int',
   'val': 10,
   'predefined': [],
   'tags': [],
   'min_': 0.0,
   'max_': 0.0,
   'category': ''},
  {'name': 'min_cardinality',
   'description': 'Minimum cardinality for categorical variables.',
   'comment': '',
   'type': 'int',
   'val': 2,
   'predefined': [],
   'tags': [],
   'min_': 0.0,
   'max_': 0.0,
   'category': ''},
  {'name': 'num_card',
   'description': 'Max cardinality for numeric variables to be considered categorical.',
   'comment': '',
   'type': 'int',
   'val': 25,
   'predefined': [],
   'tags': [],
   'min_': 0.0,
   'max_': 0.0,
   'category': ''}],
 'metrics_meta': []}

# parameters
result.params()

{'dia_cols': ['PAY_5',
  'PAY_6',
  'SEX',
  'PAY_0',
  'MARRIAGE',
  'PAY_4',
  'PAY_3',
  'EDUCATION',
  'PAY_2'],
 'cut_off': 0.0,
 'maximize_metric': 'F1',
 'max_cardinality': 10,
 'min_cardinality': 2,
 'num_card': 25}

Display the DIA data

# Print method help
import pprint
pprint.pprint(result.help())

{'methods': {'data': {'doc': '',
                      'parameters': [{'default': '',
                                      'doc': 'The name of the feature whose '
                                             'data we want to retrieve.',
                                      'name': 'feature_name',
                                      'required': True,
                                      'type': 'str'},
                                     {'default': '',
                                      'doc': 'The category of data to be '
                                             'retrieve. This can be one of the '
                                             "following: 'metrics', 'cm', "
                                             "'me_smd', 'disparity' and "
                                             "'parity'",
                                      'name': 'category',
                                      'required': True,
                                      'type': 'str'},
                                     {'default': 'The first reference level '
                                                 'will be selected from the '
                                                 'set of available reference '
                                                 'levels.',
                                      'doc': 'The reference levels for each '
                                             'categorical data',
                                      'name': 'ref_level',
                                      'required': False,
                                      'type': 'str | int'}]}}}

result.data(feature_name="MARRIAGE", category=result.DiaCategory.DIA_METRICS)

	Groups	N	Adverse Impact	Accuracy	True Positive Rate	Precision	Specificity	Negative Predicted Value	False Positive Rate	False Discovery Rate	False Negative Rate	False Omissions Rate
	▪▪▪▪▪▪▪▪	▪▪▪▪▪▪▪▪	▪▪▪▪	▪▪▪▪	▪▪▪▪	▪▪▪▪	▪▪▪▪	▪▪▪▪	▪▪▪▪	▪▪▪▪	▪▪▪▪	▪▪▪▪
0	0	17	0.9853	0.2244	0.964143	0.221049	0.00852603	0.44898	0.991474	0.778951	0.0358566	0.55102
1	1	4390	0.988155	0.238724	0.972486	0.236284	0.00689448	0.442308	0.993106	0.763716	0.0275142	0.557692
2	2	5468	0.982992	0.213424	0.956633	0.209302	0.00978565	0.451613	0.990214	0.790698	0.0433673	0.548387
3	3	125	0.984	0.224	0.964286	0.219512	0.0103093	0.5	0.989691	0.780488	0.0357143	0.5

4 rows × 12 columns

result.data(feature_name="EDUCATION", category=result.DiaCategory.DIA_CATEGORY_DISPARITY, ref_level="3")

	Groups	N	Adverse Impact Disparity	Marginal Error	Standardized Mean Difference	Accuracy Disparity	True Positive Rate Disparity	Precision Disparity	Specificity Disparity	Negative Predicted Value Disparity	False Positive Rate Disparity	False Discovery Rate Disparity	False Negative Rate Disparity	False Omissions Rate Disparity
	▪▪▪▪▪▪▪▪	▪▪▪▪▪▪▪▪	▪▪▪▪	▪▪▪▪	▪▪▪▪▪▪▪▪	▪▪▪▪	▪▪▪▪▪▪▪▪	▪▪▪▪	▪▪▪▪	▪▪▪▪▪▪▪▪	▪▪▪▪	▪▪▪▪	▪▪▪▪▪▪▪▪	▪▪▪▪▪▪▪▪
0	0	3	1.00296	−0.00291008	0.146318	0.907878	1.0022	0.908629	0.783739	0.967033	1.00238	1.02937	0.944223	1.02857
1	1	3714	1.00778	−0.00764775	0.0635433	0.840969	1.01265	0.844016	0.531709	0.989605	1.00515	1.05015	0.67957	1.00901
2	2	4588	0.999955	4.47035e-05	−0.000371306	0.945314	0.995322	0.945777	0.91792	0.930674	1.0009	1.01743	1.1185	1.06008
3	3	1590	1	0	0	1	1	1	1	1	1	1	1	1
4	4	26	1.01793	−0.0176101	0.146318	0.155608	1.03947	0.158097	0	NA	1.011	1.27066	0	NA
5	5	69	1.00317	−0.00311732	0.0259009	0.351809	1.03947	0.302245	1.4363	2.15385	0.995201	1.22432	0	0
6	6	10	1.01793	−0.0176101	0.146318	0	NA	0	0	NA	1.011	1.32149	NA	NA

7 rows × 14 columns

result.data(feature_name="PAY_4", category=result.DiaCategory.DIA_CATEGORY_CM, ref_level=-1)

	actual1	actual0
	▪▪▪▪	▪▪▪▪
0	332	1640
1	4	4

2 rows × 2 columns

result.data(feature_name="SEX", category=result.DiaCategory.DIA_CATEGORY_ME_SMD)

	Groups	N	Marginal Error	Standardized Mean Difference
	▪▪▪▪▪▪▪▪	▪▪▪▪▪▪▪▪	▪▪▪▪	▪▪▪▪▪▪▪▪
0	1	4206	0	0
1	2	5794	0.00936753	−0.0778322

2 rows × 4 columns

result.data(feature_name="PAY_2", category=result.DiaCategory.DIA_CATEGORY_PARITY)

	Groups	N	Adverse Impact Parity	Accuracy Parity	True Positive Rate Parity	Precision Parity	Specificity Parity	Negative Predicted Value Parity	False Positive Rate Parity	False Discovery Rate Parity	…	Type I Parity	Type II Parity	Equalized Odds	Supervised Fairness	Overall Fairness
	▪▪▪▪	▪▪▪▪▪▪▪▪	▪	▪	▪	▪	▪	▪	▪	▪		▪	▪	▪	▪	▪
0	-2	1218	1	1	1	1	0	0	1	1	…	1	0	1	0	0
1	-1	2171	1	1	1	1	0	0	1	1	…	1	0	1	0	0
2	0	5147	1	1	1	1	0	0	1	1	…	1	0	1	0	0
3	1	8	1	0	0	0	0	0	1	0	…	0	0	0	0	0
4	2	1294	1	0	1	0	0	0	1	0	…	0	0	1	0	0
5	3	106	0	0	0	0	0	0	0	0	…	0	0	0	0	0
6	4	25	0	0	0	0	0	0	0	0	…	0	0	0	0	0
7	5	11	0	0	0	0	0	0	0	0	…	0	0	0	0	0
8	6	7	0	0	0	0	0	0	0	0	…	0	0	0	0	0
9	7	12	0	0	0	0	0	0	0	0	…	0	0	0	0	0
10	8	1	0	0	0	0	0	0	0	0	…	0	0	0	0	0
11	all	10000	0	0	0	0	0	0	0	0	…	0	0	0	0	0

12 rows × 17 columns

result.data(feature_name="MARRIAGE", category=result.DiaCategory.DIA_CATEGORY_DISPARITY)

	Groups	N	Adverse Impact Disparity	Marginal Error	Standardized Mean Difference	Accuracy Disparity	True Positive Rate Disparity	Precision Disparity	Specificity Disparity	Negative Predicted Value Disparity	False Positive Rate Disparity	False Discovery Rate Disparity	False Negative Rate Disparity	False Omissions Rate Disparity
	▪▪▪▪▪▪▪▪	▪▪▪▪▪▪▪▪	▪▪▪▪	▪▪▪▪	▪▪▪▪▪▪▪▪	▪▪▪▪	▪▪▪▪	▪▪▪▪	▪▪▪▪	▪▪▪▪	▪▪▪▪	▪▪▪▪	▪▪▪▪	▪▪▪▪
0	0	17	1	0	0	1	1	1	1	1	1	1	1	1
1	1	4390	1.0029	−0.00285488	−0.0984179	1.06383	1.00865	1.06892	0.80864	0.98514	1.00165	0.980442	0.767341	1.01211
2	2	5468	0.997657	0.00230807	−0.141316	0.951085	0.99221	0.946858	1.14774	1.00587	0.99873	1.01508	1.20947	0.995221
3	3	125	0.998681	0.00129998	−0.13294	0.998218	1.00015	0.993046	1.20915	1.11364	0.998201	1.00197	0.996032	0.907407

4 rows × 14 columns

Plot the DIA Metrics

result.plot(feature_name="EDUCATION")

[]

# Only plot metric for "Specificity"
result.plot(feature_name="PAY_0", metrics_of_interest="Specificity")

[]

# Only plot metrics for "N", "Adverse Impact" and "True Positive Rate"
result.plot(feature_name="PAY_0", metrics_of_interest=["N", "Adverse Impact", "True Positive Rate"])

[]

Save the explainer log and data

# save the explainer log
result.log(path="./dia-demo.log")

!head dia-demo.log

# save the explainer data
result.zip(file_path="./dia-demo-archive.zip")

!unzip -l dia-demo-archive.zip

Archive:  dia-demo-archive.zip
  Length      Date    Time    Name
---------  ---------- -----   ----
     3988  2026-01-29 16:02   explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/result_descriptor.json
      117  2026-01-29 16:01   explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_disparate_impact_analysis/text_plain.meta
       19  2026-01-29 16:01   explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_disparate_impact_analysis/text_plain/explanation.txt
        2  2026-01-29 16:02   explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/problems/problems_and_actions.json
      110  2026-01-29 16:01   explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html.meta
    15297  2026-01-29 16:01   explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-0-false_discovery_rate.png
    14691  2026-01-29 16:01   explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-0-specificity.png
    15195  2026-01-29 16:02   explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-7-false_omissions_rate.png
    11805  2026-01-29 16:01   explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-2-precision.png
    14412  2026-01-29 16:01   explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/global_html_fragment/text_html/dia-3-n.png
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     2560  2026-01-29 16:01   explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_6/8/disparity.jay
      304  2026-01-29 16:01   explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_6/8/cm.jay
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     2560  2026-01-29 16:01   explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_6/7/disparity.jay
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      304  2026-01-29 16:01   explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_6/9/cm.jay
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      304  2026-01-29 16:01   explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_6/3/cm.jay
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     1928  2026-01-29 16:01   explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/MARRIAGE/1/disparity.jay
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      304  2026-01-29 16:01   explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_0/4/cm.jay
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      304  2026-01-29 16:01   explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_0/10/cm.jay
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      304  2026-01-29 16:01   explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_0/2/cm.jay
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     2448  2026-01-29 16:01   explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_0/5/disparity.jay
      304  2026-01-29 16:01   explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_0/5/cm.jay
     2224  2026-01-29 16:01   explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_0/0/parity.jay
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     2488  2026-01-29 16:01   explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_0/0/disparity.jay
      304  2026-01-29 16:01   explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_0/0/cm.jay
     2224  2026-01-29 16:01   explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_0/8/parity.jay
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      304  2026-01-29 16:01   explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_0/8/cm.jay
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      304  2026-01-29 16:01   explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_0/7/cm.jay
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      304  2026-01-29 16:01   explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_3/4/cm.jay
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     2664  2026-01-29 16:01   explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_4/9/disparity.jay
      304  2026-01-29 16:01   explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_4/9/cm.jay
     2224  2026-01-29 16:01   explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_4/3/parity.jay
      856  2026-01-29 16:01   explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_4/3/me_smd.jay
     2688  2026-01-29 16:01   explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_4/3/disparity.jay
      304  2026-01-29 16:01   explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/work/PAY_4/3/cm.jay
        2  2026-01-29 16:02   explainer_h2o_sonar_explainers_dia_explainer_DiaExplainer_2dc47eb0-225e-42cd-8b6a-b769177e05be/insights/insights_and_actions.json
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