Summary Shapley Explainer Demo

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

[1]:

import logging

import pandas
import webbrowser

from h2o_sonar import interpret
from h2o_sonar.lib.api import commons
from h2o_sonar.lib.api import explainers
from h2o_sonar.lib.api.models import ModelApi
from h2o_sonar.explainers.summary_shap_explainer import SummaryShapleyExplainer

from sklearn.ensemble import GradientBoostingClassifier

[2]:

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]

[3]:

# parameters
interpret.describe_explainer(SummaryShapleyExplainer)

[3]:

{'id': 'h2o_sonar.explainers.summary_shap_explainer.SummaryShapleyExplainer',
 'name': 'SummaryShapleyExplainer',
 'display_name': 'Shapley Summary Plot for Original Features (Kernel SHAP Method)',
 'tagline': 'SummaryShapleyExplainer.',
 'description': 'Shapley explanations are a technique with credible theoretical support that presents consistent global and local feature contributions.\n\nThe Shapley Summary Plot shows original features versus their local Shapley values on a sample of the dataset. Feature values are binned by Shapley values and the average normalized feature value for each bin is plotted. The legend corresponds to numeric features and maps to their normalized value - yellow is the lowest value and deep orange is the highest. You can also get a scatter plot of the actual numeric features values versus their corresponding Shapley values. Categorical features are shown in grey and do not provide an actual-value scatter plot.\n\nNotes:\n\n* The Shapley Summary Plot only shows original features that are used in the model.\n* The dataset sample size and the number of bins can be updated in the interpretation settings.\n\n',
 'brief_description': 'SummaryShapleyExplainer.',
 'model_types': ['iid', 'time_series'],
 'can_explain': ['regression', 'binomial', 'multinomial'],
 'explanation_scopes': ['global_scope'],
 'explanations': [{'explanation_type': 'global-summary-feature-importance',
   'name': 'GlobalSummaryFeatImpExplanation',
   'category': '',
   'scope': 'global',
   'has_local': '',
   'formats': []}],
 'keywords': ['run-by-default', 'explains-feature-behavior', 'h2o-sonar'],
 'parameters': [{'name': 'max_features',
   'description': 'Maximum number of features to be shown in the plot.',
   'comment': '',
   'type': 'int',
   'val': 50,
   'predefined': [],
   'tags': [],
   'min_': 0.0,
   'max_': 0.0,
   'category': ''},
  {'name': 'sample_size',
   'description': 'Sample size.',
   'comment': '',
   'type': 'int',
   'val': 20000,
   'predefined': [],
   'tags': [],
   'min_': 100,
   'max_': 0.0,
   'category': ''},
  {'name': 'x_shapley_resolution',
   'description': 'x-axis resolution (number of Shapley values bins).',
   'comment': '',
   'type': 'int',
   'val': 500,
   'predefined': [],
   'tags': [],
   'min_': 100,
   'max_': 0.0,
   'category': ''},
  {'name': 'enable_drilldown_charts',
   'description': 'Enable creation of per-feature Shapley/feature value scatter plots.',
   'comment': '',
   'type': 'bool',
   'val': True,
   'predefined': [],
   'tags': [],
   'min_': 0.0,
   'max_': 0.0,
   'category': ''},
  {'name': 'fast_approx_contribs',
   'description': 'Speed up predictions with fast predictions and contributions approximations.',
   'comment': '',
   'type': 'bool',
   'val': True,
   'predefined': [],
   'tags': [],
   'min_': 0.0,
   'max_': 0.0,
   'category': ''}],
 'metrics_meta': []}

Interpretation

[4]:

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

# explainable model
explainable_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=explainable_model,
    target_col=target_col,
    results_location=results_location,
    log_level=logging.INFO,
    explainers=[
        commons.ExplainerToRun(
            explainer_id=SummaryShapleyExplainer.explainer_id(),
            params="",
        )
    ]
)

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

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

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()`.

Interact with the Explainer Result

[5]:

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

[6]:

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

[6]:

True

[7]:

# summary
result.summary()

[7]:

{'id': 'h2o_sonar.explainers.summary_shap_explainer.SummaryShapleyExplainer',
 'name': 'SummaryShapleyExplainer',
 'display_name': 'Shapley Summary Plot for Original Features (Kernel SHAP Method)',
 'tagline': 'SummaryShapleyExplainer.',
 'description': 'Shapley explanations are a technique with credible theoretical support that presents consistent global and local feature contributions.\n\nThe Shapley Summary Plot shows original features versus their local Shapley values on a sample of the dataset. Feature values are binned by Shapley values and the average normalized feature value for each bin is plotted. The legend corresponds to numeric features and maps to their normalized value - yellow is the lowest value and deep orange is the highest. You can also get a scatter plot of the actual numeric features values versus their corresponding Shapley values. Categorical features are shown in grey and do not provide an actual-value scatter plot.\n\nNotes:\n\n* The Shapley Summary Plot only shows original features that are used in the model.\n* The dataset sample size and the number of bins can be updated in the interpretation settings.\n\n',
 'brief_description': 'SummaryShapleyExplainer.',
 'model_types': ['iid', 'time_series'],
 'can_explain': ['regression', 'binomial', 'multinomial'],
 'explanation_scopes': ['global_scope'],
 'explanations': [{'explanation_type': 'global-summary-feature-importance',
   'name': 'Shapley Summary Plot for Original Features',
   'category': 'DAI MODEL',
   'scope': 'global',
   'has_local': None,
   'formats': ['application/vnd.h2oai.json+datatable.jay',
    'application/json',
    'text/markdown']},
  {'explanation_type': 'global-html-fragment',
   'name': 'Shapley Summary Plot for Original Features',
   'category': 'DAI MODEL',
   'scope': 'global',
   'has_local': None,
   'formats': ['text/html']}],
 'keywords': ['run-by-default', 'explains-feature-behavior', 'h2o-sonar'],
 'parameters': [{'name': 'max_features',
   'description': 'Maximum number of features to be shown in the plot.',
   'comment': '',
   'type': 'int',
   'val': 50,
   'predefined': [],
   'tags': [],
   'min_': 0.0,
   'max_': 0.0,
   'category': ''},
  {'name': 'sample_size',
   'description': 'Sample size.',
   'comment': '',
   'type': 'int',
   'val': 20000,
   'predefined': [],
   'tags': [],
   'min_': 100,
   'max_': 0.0,
   'category': ''},
  {'name': 'x_shapley_resolution',
   'description': 'x-axis resolution (number of Shapley values bins).',
   'comment': '',
   'type': 'int',
   'val': 500,
   'predefined': [],
   'tags': [],
   'min_': 100,
   'max_': 0.0,
   'category': ''},
  {'name': 'enable_drilldown_charts',
   'description': 'Enable creation of per-feature Shapley/feature value scatter plots.',
   'comment': '',
   'type': 'bool',
   'val': True,
   'predefined': [],
   'tags': [],
   'min_': 0.0,
   'max_': 0.0,
   'category': ''},
  {'name': 'fast_approx_contribs',
   'description': 'Speed up predictions with fast predictions and contributions approximations.',
   'comment': '',
   'type': 'bool',
   'val': True,
   'predefined': [],
   'tags': [],
   'min_': 0.0,
   'max_': 0.0,
   'category': ''}],
 'metrics_meta': []}

[8]:

# parameters
result.params()

[8]:

{'max_features': 50,
 'sample_size': 20000,
 'x_shapley_resolution': 500,
 'enable_drilldown_charts': True,
 'fast_approx_contribs': True}

Display the Shapley Shapley

[9]:

# get the data for all features
result.data()

[9]:

	MARRIAGE	BILL_AMT2	AGE	PAY_0	PAY_AMT3	EDUCATION	BILL_AMT3	BILL_AMT5	PAY_AMT5	LIMIT_BAL	…	PAY_AMT6	PAY_4	BILL_AMT6	PAY_AMT1	bias
	▪▪▪▪▪▪▪▪	▪▪▪▪▪▪▪▪	▪▪▪▪▪▪▪▪	▪▪▪▪▪▪▪▪	▪▪▪▪▪▪▪▪	▪▪▪▪▪▪▪▪	▪▪▪▪▪▪▪▪	▪▪▪▪▪▪▪▪	▪▪▪▪▪▪▪▪	▪▪▪▪▪▪▪▪		▪▪▪▪▪▪▪▪	▪▪▪▪▪▪▪▪	▪▪▪▪▪▪▪▪	▪▪▪▪▪▪▪▪	▪▪▪▪▪▪▪▪
0	1.66392	0.754229	0.525014	−0.0769926	0.343486	0.459352	0.173675	0.0163271	−0.00919648	−0.0213322	…	−0.0672808	0.0943492	−0.106023	0.010494	−1.44588
1	1.75075	0.365596	0.311988	−0.224273	−0.119805	0.0425741	0.141538	0.26145	−0.00507203	0.00920574	…	−0.0162608	0.0213093	0.00764593	0.00946223	−1.44588
2	1.79185	0.379105	0.25219	−0.257663	−0.144234	0.0711456	0.153144	0.190386	0.207591	−0.0191975	…	−0.0408922	0.0126469	0.00341155	−0.0158366	−1.44588
3	1.79185	0.34528	0.252297	−0.257663	−0.144234	0.0711456	0.169791	0.190446	0.207591	−0.0191975	…	−0.0408922	0.0126469	0.00432853	−0.0168906	−1.44588
4	1.79242	0.433088	0.248932	−0.23324	−0.128322	0.0711456	0.153144	0.181755	0.207591	−0.0193396	…	−0.0163037	0.0126469	0.00780872	−0.0122551	−1.44588
5	1.79185	0.379105	0.25219	−0.257663	−0.144234	0.0711456	0.153144	0.190386	0.207591	−0.0191975	…	−0.0408922	0.0126469	0.00341155	−0.0158366	−1.44588
6	1.84325	0.378151	0.149483	−0.17116	−0.144735	0.11591	0.0972286	−0.263704	0.197469	0.18672	…	−0.0407482	0.0126469	0.0289509	−0.089523	−1.44588
7	1.7705	0.430811	0.304604	−0.236441	0.345056	0.363289	0.139994	−0.0422748	0.130124	0.00971828	…	−0.0124586	0.0943492	0.0151488	−0.0012627	−1.44588
8	1.78661	0.380776	0.321885	−0.23324	−0.128322	0.105382	0.153144	0.153698	0.187062	0.0740197	…	−0.0163037	0.0126469	0.00461817	−0.00961812	−1.44588
9	0.887399	−0.0137737	0.113788	0.0326562	0.242886	0.376821	0.0401564	0.0463024	0.196936	−0.0188685	…	−0.0025913	0.101591	0.00734594	−0.0108075	−1.44588
10	1.8608	0.375309	0.361627	−0.236432	−0.128322	0.180256	0.142784	0.362827	0.187062	0.255124	…	0.00253912	0.0126469	0.00504963	0.00127701	−1.44588
11	1.83266	0.372649	0.35988	−0.23322	−0.130309	0.0754547	0.151898	−0.0514293	−0.00507203	0.258542	…	−0.0163037	0.0126469	−0.0121005	−0.14393	−1.44588
12	1.86076	0.31364	0.317515	−0.232394	−0.128322	0.100958	0.153144	0.27184	0.207591	0.42002	…	−0.0711689	0.0126469	0.00485099	0.00127695	−1.44588
13	1.99488	0.285282	0.342016	−0.256149	−0.144234	0.0555813	0.169791	0.175341	0.0838827	−0.0178721	…	−0.0957574	0.0126469	0.00432853	−0.0133385	−1.44588
14	1.83875	0.34528	0.30161	−0.256149	−0.144234	0.105271	0.169791	0.0872774	0.207591	0.154961	…	−0.0408922	0.0126469	0.00456134	−0.0133385	−1.44588
⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋱	⋮	⋮	⋮	⋮	⋮
9995	0.720147	0.980218	0.122733	−0.0612512	0.228076	0.458607	0.0390142	0.0681344	−0.00685849	0.152334	…	−0.0129881	0.103012	−0.0398354	0.00129276	−1.44588
9996	0.608979	−0.013273	0.735138	−0.0612512	0.208421	0.363868	0.00175616	−0.0213594	−0.00607075	−0.0215157	…	−0.0025913	0.0929286	−0.0388194	0.010494	−1.44588
9997	1.83875	0.372146	0.190154	−0.171181	−0.143942	0.103827	0.169405	−0.18818	0.188581	0.154961	…	−0.0408922	0.0126469	0.00935096	−0.0133385	−1.44588
9998	0.635687	0.920219	0.537259	−0.0794263	0.228076	0.345937	0.00276488	0.0131017	−0.00336023	−0.0200068	…	−0.0678533	0.103012	−0.040204	0.00947798	−1.44588
9999	1.83874	0.405971	0.330204	−0.230307	−0.139331	0.103938	0.152757	0.15433	0.198703	0.154961	…	−0.0400925	0.0126469	0.00416723	−0.0122845	−1.44588

[10]:

# get the data for only feature "PAY_0"
result.data(feature_names="PAY_0")

[10]:

	PAY_0	bias
	▪▪▪▪▪▪▪▪	▪▪▪▪▪▪▪▪
0	−0.0769926	−1.44588
1	−0.224273	−1.44588
2	−0.257663	−1.44588
3	−0.257663	−1.44588
4	−0.23324	−1.44588
5	−0.257663	−1.44588
6	−0.17116	−1.44588
7	−0.236441	−1.44588
8	−0.23324	−1.44588
9	0.0326562	−1.44588
10	−0.236432	−1.44588
11	−0.23322	−1.44588
12	−0.232394	−1.44588
13	−0.256149	−1.44588
14	−0.256149	−1.44588
⋮	⋮	⋮
9995	−0.0612512	−1.44588
9996	−0.0612512	−1.44588
9997	−0.171181	−1.44588
9998	−0.0794263	−1.44588
9999	−0.230307	−1.44588

[11]:

# get the data for "PAY_0", "AGE and "EDUCATION"
result.data(feature_names=["PAY_0", "AGE", "EDUCATION"])

[11]:

	PAY_0	AGE	EDUCATION	bias
	▪▪▪▪▪▪▪▪	▪▪▪▪▪▪▪▪	▪▪▪▪▪▪▪▪	▪▪▪▪▪▪▪▪
0	−0.0769926	0.525014	0.459352	−1.44588
1	−0.224273	0.311988	0.0425741	−1.44588
2	−0.257663	0.25219	0.0711456	−1.44588
3	−0.257663	0.252297	0.0711456	−1.44588
4	−0.23324	0.248932	0.0711456	−1.44588
5	−0.257663	0.25219	0.0711456	−1.44588
6	−0.17116	0.149483	0.11591	−1.44588
7	−0.236441	0.304604	0.363289	−1.44588
8	−0.23324	0.321885	0.105382	−1.44588
9	0.0326562	0.113788	0.376821	−1.44588
10	−0.236432	0.361627	0.180256	−1.44588
11	−0.23322	0.35988	0.0754547	−1.44588
12	−0.232394	0.317515	0.100958	−1.44588
13	−0.256149	0.342016	0.0555813	−1.44588
14	−0.256149	0.30161	0.105271	−1.44588
⋮	⋮	⋮	⋮	⋮
9995	−0.0612512	0.122733	0.458607	−1.44588
9996	−0.0612512	0.735138	0.363868	−1.44588
9997	−0.171181	0.190154	0.103827	−1.44588
9998	−0.0794263	0.537259	0.345937	−1.44588
9999	−0.230307	0.330204	0.103938	−1.44588

Plot the Summary Shapley

[12]:

# plot summary shap for all features
result.plot()

invalid value encountered in divide

../_images/notebooks_h2o-sonar-summary-shap-explainer_16_1.png

[13]:

# plot summary shap for "PAY_0", "AGE and "EDUCATION"
result.plot(feature_names=["PAY_0", "AGE", "EDUCATION"])

invalid value encountered in divide

../_images/notebooks_h2o-sonar-summary-shap-explainer_17_1.png

Save the explainer log and data

[14]:

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

[15]:

!head summary-shapley-demo.log

2026-01-29 16:28:24,384 INFO Summary Shapley explainer b33e9469-d080-404e-891c-4e2159d64a4c/a1b410cf-2b5d-473a-8295-ec5310f7277f raw MEANs (1)
2026-01-29 16:28:24,384 INFO Summary Shapley explainer b33e9469-d080-404e-891c-4e2159d64a4c/a1b410cf-2b5d-473a-8295-ec5310f7277f raw CONTRIBs (1)

[16]:

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

[17]:

!unzip -l summary-shapley-demo-archive.zip

Archive:  summary-shapley-demo-archive.zip
  Length      Date    Time    Name
---------  ---------- -----   ----
     4129  2026-01-29 16:28   explainer_h2o_sonar_explainers_summary_shap_explainer_SummaryShapleyExplainer_a1b410cf-2b5d-473a-8295-ec5310f7277f/result_descriptor.json
        2  2026-01-29 16:28   explainer_h2o_sonar_explainers_summary_shap_explainer_SummaryShapleyExplainer_a1b410cf-2b5d-473a-8295-ec5310f7277f/problems/problems_and_actions.json
      110  2026-01-29 16:28   explainer_h2o_sonar_explainers_summary_shap_explainer_SummaryShapleyExplainer_a1b410cf-2b5d-473a-8295-ec5310f7277f/global_html_fragment/text_html.meta
   142756  2026-01-29 16:28   explainer_h2o_sonar_explainers_summary_shap_explainer_SummaryShapleyExplainer_a1b410cf-2b5d-473a-8295-ec5310f7277f/global_html_fragment/text_html/feature_1_class_0.png
    95488  2026-01-29 16:28   explainer_h2o_sonar_explainers_summary_shap_explainer_SummaryShapleyExplainer_a1b410cf-2b5d-473a-8295-ec5310f7277f/global_html_fragment/text_html/feature_5_class_0.png
    87457  2026-01-29 16:28   explainer_h2o_sonar_explainers_summary_shap_explainer_SummaryShapleyExplainer_a1b410cf-2b5d-473a-8295-ec5310f7277f/global_html_fragment/text_html/feature_23_class_0.png
   118236  2026-01-29 16:28   explainer_h2o_sonar_explainers_summary_shap_explainer_SummaryShapleyExplainer_a1b410cf-2b5d-473a-8295-ec5310f7277f/global_html_fragment/text_html/feature_12_class_0.png
     8171  2026-01-29 16:28   explainer_h2o_sonar_explainers_summary_shap_explainer_SummaryShapleyExplainer_a1b410cf-2b5d-473a-8295-ec5310f7277f/global_html_fragment/text_html/explanation.html
   113752  2026-01-29 16:28   explainer_h2o_sonar_explainers_summary_shap_explainer_SummaryShapleyExplainer_a1b410cf-2b5d-473a-8295-ec5310f7277f/global_html_fragment/text_html/feature_14_class_0.png
    63184  2026-01-29 16:28   explainer_h2o_sonar_explainers_summary_shap_explainer_SummaryShapleyExplainer_a1b410cf-2b5d-473a-8295-ec5310f7277f/global_html_fragment/text_html/feature_18_class_0.png
    60161  2026-01-29 16:28   explainer_h2o_sonar_explainers_summary_shap_explainer_SummaryShapleyExplainer_a1b410cf-2b5d-473a-8295-ec5310f7277f/global_html_fragment/text_html/feature_16_class_0.png
   515472  2026-01-29 16:28   explainer_h2o_sonar_explainers_summary_shap_explainer_SummaryShapleyExplainer_a1b410cf-2b5d-473a-8295-ec5310f7277f/global_html_fragment/text_html/feature_2_class_0.png
    46324  2026-01-29 16:28   explainer_h2o_sonar_explainers_summary_shap_explainer_SummaryShapleyExplainer_a1b410cf-2b5d-473a-8295-ec5310f7277f/global_html_fragment/text_html/feature_21_class_0.png
   141161  2026-01-29 16:28   explainer_h2o_sonar_explainers_summary_shap_explainer_SummaryShapleyExplainer_a1b410cf-2b5d-473a-8295-ec5310f7277f/global_html_fragment/text_html/feature_7_class_0.png
    67801  2026-01-29 16:28   explainer_h2o_sonar_explainers_summary_shap_explainer_SummaryShapleyExplainer_a1b410cf-2b5d-473a-8295-ec5310f7277f/global_html_fragment/text_html/feature_11_class_0.png
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