{
"cells": [
{
"cell_type": "markdown",
"id": "fa61e99e-9c07-4bcc-842f-f722cfc5b463",
"metadata": {},
"source": [
"# Summary Shapley Explainer Demo\n",
"\n",
"This example demonstrates how to interpret a **scikit-learn** model using\n",
"the H2O Sonar library and retrieve the data and plot the **summary Shapley**."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "051f5c55-5471-4017-86c9-ad7bd39b8fc6",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import logging\n",
"\n",
"import pandas\n",
"import webbrowser\n",
"\n",
"from h2o_sonar import interpret\n",
"from h2o_sonar.lib.api import commons\n",
"from h2o_sonar.lib.api import explainers\n",
"from h2o_sonar.lib.api.models import ModelApi\n",
"from h2o_sonar.explainers.summary_shap_explainer import SummaryShapleyExplainer\n",
"\n",
"from sklearn.ensemble import GradientBoostingClassifier"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "e8858dd3-64f5-4341-b3f7-c3a4115fdb24",
"metadata": {},
"outputs": [],
"source": [
"results_location = \"../../results\"\n",
"\n",
"# dataset\n",
"dataset_path = \"../../data/predictive/creditcard.csv\"\n",
"target_col = \"default payment next month\"\n",
"df = pandas.read_csv(dataset_path)\n",
"(X, y) = df.drop(target_col, axis=1), df[target_col]"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "4cf5be8c",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'id': 'h2o_sonar.explainers.summary_shap_explainer.SummaryShapleyExplainer',\n",
" 'name': 'SummaryShapleyExplainer',\n",
" 'display_name': 'Shapley Summary Plot for Original Features (Kernel SHAP Method)',\n",
" 'tagline': 'SummaryShapleyExplainer.',\n",
" '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',\n",
" 'brief_description': 'SummaryShapleyExplainer.',\n",
" 'model_types': ['iid', 'time_series'],\n",
" 'can_explain': ['regression', 'binomial', 'multinomial'],\n",
" 'explanation_scopes': ['global_scope'],\n",
" 'explanations': [{'explanation_type': 'global-summary-feature-importance',\n",
" 'name': 'GlobalSummaryFeatImpExplanation',\n",
" 'category': '',\n",
" 'scope': 'global',\n",
" 'has_local': '',\n",
" 'formats': []}],\n",
" 'keywords': ['run-by-default', 'explains-feature-behavior', 'h2o-sonar'],\n",
" 'parameters': [{'name': 'max_features',\n",
" 'description': 'Maximum number of features to be shown in the plot.',\n",
" 'comment': '',\n",
" 'type': 'int',\n",
" 'val': 50,\n",
" 'predefined': [],\n",
" 'tags': [],\n",
" 'min_': 0.0,\n",
" 'max_': 0.0,\n",
" 'category': ''},\n",
" {'name': 'sample_size',\n",
" 'description': 'Sample size.',\n",
" 'comment': '',\n",
" 'type': 'int',\n",
" 'val': 20000,\n",
" 'predefined': [],\n",
" 'tags': [],\n",
" 'min_': 100,\n",
" 'max_': 0.0,\n",
" 'category': ''},\n",
" {'name': 'x_shapley_resolution',\n",
" 'description': 'x-axis resolution (number of Shapley values bins).',\n",
" 'comment': '',\n",
" 'type': 'int',\n",
" 'val': 500,\n",
" 'predefined': [],\n",
" 'tags': [],\n",
" 'min_': 100,\n",
" 'max_': 0.0,\n",
" 'category': ''},\n",
" {'name': 'enable_drilldown_charts',\n",
" 'description': 'Enable creation of per-feature Shapley/feature value scatter plots.',\n",
" 'comment': '',\n",
" 'type': 'bool',\n",
" 'val': True,\n",
" 'predefined': [],\n",
" 'tags': [],\n",
" 'min_': 0.0,\n",
" 'max_': 0.0,\n",
" 'category': ''},\n",
" {'name': 'fast_approx_contribs',\n",
" 'description': 'Speed up predictions with fast predictions and contributions approximations.',\n",
" 'comment': '',\n",
" 'type': 'bool',\n",
" 'val': True,\n",
" 'predefined': [],\n",
" 'tags': [],\n",
" 'min_': 0.0,\n",
" 'max_': 0.0,\n",
" 'category': ''}],\n",
" 'metrics_meta': []}"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# parameters\n",
"interpret.describe_explainer(SummaryShapleyExplainer)"
]
},
{
"cell_type": "markdown",
"id": "b3ce4251-2fe6-4f04-a6df-9cbf2141ad65",
"metadata": {},
"source": [
"## Interpretation"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "8f313bd8-394e-4e26-af04-e72bfece1862",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"As of langchain-core 0.3.0, LangChain uses pydantic v2 internally. The langchain_core.pydantic_v1 module was a compatibility shim for pydantic v1, and should no longer be used. Please update the code to import from Pydantic directly.\n",
"\n",
"For example, replace imports like: `from langchain_core.pydantic_v1 import BaseModel`\n",
"with: `from pydantic import BaseModel`\n",
"or the v1 compatibility namespace if you are working in a code base that has not been fully upgraded to pydantic 2 yet. \tfrom pydantic.v1 import BaseModel\n",
"\n",
"As of langchain-core 0.3.0, LangChain uses pydantic v2 internally. The langchain.pydantic_v1 module was a compatibility shim for pydantic v1, and should no longer be used. Please update the code to import from Pydantic directly.\n",
"\n",
"For example, replace imports like: `from langchain.pydantic_v1 import BaseModel`\n",
"with: `from pydantic import BaseModel`\n",
"or the v1 compatibility namespace if you are working in a code base that has not been fully upgraded to pydantic 2 yet. \tfrom pydantic.v1 import BaseModel\n",
"\n",
"More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). Consider using `matplotlib.pyplot.close()`.\n"
]
}
],
"source": [
"# scikit-learn model\n",
"gradient_booster = GradientBoostingClassifier(learning_rate=0.1)\n",
"gradient_booster.fit(X, y)\n",
"\n",
"# explainable model\n",
"explainable_model = ModelApi().create_model(\n",
" target_col=target_col,\n",
" model_src=gradient_booster,\n",
" used_features=X.columns.to_list()\n",
")\n",
"\n",
"interpretation = interpret.run_interpretation(\n",
" dataset=df,\n",
" model=explainable_model,\n",
" target_col=target_col,\n",
" results_location=results_location,\n",
" log_level=logging.INFO,\n",
" explainers=[\n",
" commons.ExplainerToRun(\n",
" explainer_id=SummaryShapleyExplainer.explainer_id(),\n",
" params=\"\",\n",
" )\n",
" ]\n",
")"
]
},
{
"cell_type": "markdown",
"id": "cc191c38-95f1-4637-80db-72ff792b3b37",
"metadata": {},
"source": [
"## Interact with the Explainer Result"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "1794bc18-61af-491a-9c19-9613e851731a",
"metadata": {},
"outputs": [],
"source": [
"# retrieve the result\n",
"result = interpretation.get_explainer_result(SummaryShapleyExplainer.explainer_id())"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "97158dce-3414-4c08-8ac8-b9c791155a52",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# open interpretation HTML report in web browser\n",
"webbrowser.open(interpretation.result.get_html_report_location())"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "9bdab895-2a8c-4ed4-b3cd-48750c1147d9",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'id': 'h2o_sonar.explainers.summary_shap_explainer.SummaryShapleyExplainer',\n",
" 'name': 'SummaryShapleyExplainer',\n",
" 'display_name': 'Shapley Summary Plot for Original Features (Kernel SHAP Method)',\n",
" 'tagline': 'SummaryShapleyExplainer.',\n",
" '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',\n",
" 'brief_description': 'SummaryShapleyExplainer.',\n",
" 'model_types': ['iid', 'time_series'],\n",
" 'can_explain': ['regression', 'binomial', 'multinomial'],\n",
" 'explanation_scopes': ['global_scope'],\n",
" 'explanations': [{'explanation_type': 'global-summary-feature-importance',\n",
" 'name': 'Shapley Summary Plot for Original Features',\n",
" 'category': 'DAI MODEL',\n",
" 'scope': 'global',\n",
" 'has_local': None,\n",
" 'formats': ['application/vnd.h2oai.json+datatable.jay',\n",
" 'application/json',\n",
" 'text/markdown']},\n",
" {'explanation_type': 'global-html-fragment',\n",
" 'name': 'Shapley Summary Plot for Original Features',\n",
" 'category': 'DAI MODEL',\n",
" 'scope': 'global',\n",
" 'has_local': None,\n",
" 'formats': ['text/html']}],\n",
" 'keywords': ['run-by-default', 'explains-feature-behavior', 'h2o-sonar'],\n",
" 'parameters': [{'name': 'max_features',\n",
" 'description': 'Maximum number of features to be shown in the plot.',\n",
" 'comment': '',\n",
" 'type': 'int',\n",
" 'val': 50,\n",
" 'predefined': [],\n",
" 'tags': [],\n",
" 'min_': 0.0,\n",
" 'max_': 0.0,\n",
" 'category': ''},\n",
" {'name': 'sample_size',\n",
" 'description': 'Sample size.',\n",
" 'comment': '',\n",
" 'type': 'int',\n",
" 'val': 20000,\n",
" 'predefined': [],\n",
" 'tags': [],\n",
" 'min_': 100,\n",
" 'max_': 0.0,\n",
" 'category': ''},\n",
" {'name': 'x_shapley_resolution',\n",
" 'description': 'x-axis resolution (number of Shapley values bins).',\n",
" 'comment': '',\n",
" 'type': 'int',\n",
" 'val': 500,\n",
" 'predefined': [],\n",
" 'tags': [],\n",
" 'min_': 100,\n",
" 'max_': 0.0,\n",
" 'category': ''},\n",
" {'name': 'enable_drilldown_charts',\n",
" 'description': 'Enable creation of per-feature Shapley/feature value scatter plots.',\n",
" 'comment': '',\n",
" 'type': 'bool',\n",
" 'val': True,\n",
" 'predefined': [],\n",
" 'tags': [],\n",
" 'min_': 0.0,\n",
" 'max_': 0.0,\n",
" 'category': ''},\n",
" {'name': 'fast_approx_contribs',\n",
" 'description': 'Speed up predictions with fast predictions and contributions approximations.',\n",
" 'comment': '',\n",
" 'type': 'bool',\n",
" 'val': True,\n",
" 'predefined': [],\n",
" 'tags': [],\n",
" 'min_': 0.0,\n",
" 'max_': 0.0,\n",
" 'category': ''}],\n",
" 'metrics_meta': []}"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# summary\n",
"result.summary()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "9a9bebe0-18e2-496f-ac27-798cbd63c6b4",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'max_features': 50,\n",
" 'sample_size': 20000,\n",
" 'x_shapley_resolution': 500,\n",
" 'enable_drilldown_charts': True,\n",
" 'fast_approx_contribs': True}"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# parameters\n",
"result.params()"
]
},
{
"cell_type": "markdown",
"id": "3c15b17b-2941-488c-8c13-b3e962baef41",
"metadata": {},
"source": [
"### Display the Shapley Shapley"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "1ce467a4-e7e0-4354-8d7b-db2ff517ff8f",
"metadata": {},
"outputs": [
{
"data": {
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|---|
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" | 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 |
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" | 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 |
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\n",
" | 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 |
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" | 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 |
\n",
" | 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 |
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" | 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 |
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" | 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 |
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" | 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 |
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" | 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 |
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" | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋱ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |
\n",
" | 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 |
\n",
" | 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 |
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" | 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 |
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" | 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 |
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" | 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 |
\n",
" \n",
"
\n",
" \n",
"
\n",
"
\n",
" \n",
" | PAY_0 | bias |
|---|
\n",
" | ▪▪▪▪▪▪▪▪ | ▪▪▪▪▪▪▪▪ |
\n",
" \n",
" \n",
" | 0 | −0.0769926 | −1.44588 |
\n",
" | 1 | −0.224273 | −1.44588 |
\n",
" | 2 | −0.257663 | −1.44588 |
\n",
" | 3 | −0.257663 | −1.44588 |
\n",
" | 4 | −0.23324 | −1.44588 |
\n",
" | 5 | −0.257663 | −1.44588 |
\n",
" | 6 | −0.17116 | −1.44588 |
\n",
" | 7 | −0.236441 | −1.44588 |
\n",
" | 8 | −0.23324 | −1.44588 |
\n",
" | 9 | 0.0326562 | −1.44588 |
\n",
" | 10 | −0.236432 | −1.44588 |
\n",
" | 11 | −0.23322 | −1.44588 |
\n",
" | 12 | −0.232394 | −1.44588 |
\n",
" | 13 | −0.256149 | −1.44588 |
\n",
" | 14 | −0.256149 | −1.44588 |
\n",
" | ⋮ | ⋮ | ⋮ |
\n",
" | 9995 | −0.0612512 | −1.44588 |
\n",
" | 9996 | −0.0612512 | −1.44588 |
\n",
" | 9997 | −0.171181 | −1.44588 |
\n",
" | 9998 | −0.0794263 | −1.44588 |
\n",
" | 9999 | −0.230307 | −1.44588 |
\n",
" \n",
"
\n",
" \n",
"
\n",
"
\n",
" \n",
" | PAY_0 | AGE | EDUCATION | bias |
|---|
\n",
" | ▪▪▪▪▪▪▪▪ | ▪▪▪▪▪▪▪▪ | ▪▪▪▪▪▪▪▪ | ▪▪▪▪▪▪▪▪ |
\n",
" \n",
" \n",
" | 0 | −0.0769926 | 0.525014 | 0.459352 | −1.44588 |
\n",
" | 1 | −0.224273 | 0.311988 | 0.0425741 | −1.44588 |
\n",
" | 2 | −0.257663 | 0.25219 | 0.0711456 | −1.44588 |
\n",
" | 3 | −0.257663 | 0.252297 | 0.0711456 | −1.44588 |
\n",
" | 4 | −0.23324 | 0.248932 | 0.0711456 | −1.44588 |
\n",
" | 5 | −0.257663 | 0.25219 | 0.0711456 | −1.44588 |
\n",
" | 6 | −0.17116 | 0.149483 | 0.11591 | −1.44588 |
\n",
" | 7 | −0.236441 | 0.304604 | 0.363289 | −1.44588 |
\n",
" | 8 | −0.23324 | 0.321885 | 0.105382 | −1.44588 |
\n",
" | 9 | 0.0326562 | 0.113788 | 0.376821 | −1.44588 |
\n",
" | 10 | −0.236432 | 0.361627 | 0.180256 | −1.44588 |
\n",
" | 11 | −0.23322 | 0.35988 | 0.0754547 | −1.44588 |
\n",
" | 12 | −0.232394 | 0.317515 | 0.100958 | −1.44588 |
\n",
" | 13 | −0.256149 | 0.342016 | 0.0555813 | −1.44588 |
\n",
" | 14 | −0.256149 | 0.30161 | 0.105271 | −1.44588 |
\n",
" | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |
\n",
" | 9995 | −0.0612512 | 0.122733 | 0.458607 | −1.44588 |
\n",
" | 9996 | −0.0612512 | 0.735138 | 0.363868 | −1.44588 |
\n",
" | 9997 | −0.171181 | 0.190154 | 0.103827 | −1.44588 |
\n",
" | 9998 | −0.0794263 | 0.537259 | 0.345937 | −1.44588 |
\n",
" | 9999 | −0.230307 | 0.330204 | 0.103938 | −1.44588 |
\n",
" \n",
"
\n",
" \n",
"