interpret_community.mimic.models.lightgbm_model module

Defines an explainable lightgbm model.

class interpret_community.mimic.models.lightgbm_model.LGBMExplainableModel(multiclass=False, random_state=123, shap_values_output=<ShapValuesOutput.DEFAULT: 'default'>, classification=True, **kwargs)

Bases: interpret_community.mimic.models.explainable_model.BaseExplainableModel

available_explanations = ['global', 'local']
expected_values

Use TreeExplainer to get the expected values.

Returns:The expected values of the LightGBM tree model.
Return type:list
explain_global(**kwargs)

Call lightgbm feature importances to get the global feature importances from the explainable model.

Returns:The global explanation of feature importances.
Return type:numpy.ndarray
explain_local(evaluation_examples, probabilities=None, **kwargs)

Use TreeExplainer to get the local feature importances from the trained explainable model.

Parameters:
  • evaluation_examples (numpy.array or pandas.DataFrame or scipy.sparse.csr_matrix) – The evaluation examples to compute local feature importances for.
  • probabilities (numpy.ndarray) – If output_type is probability, can specify the teacher model’s probability for scaling the shap values.
Returns:

The local explanation of feature importances.
Return type:

Union[list, numpy.ndarray]
static explainable_model_type()

Retrieve the model type.

Returns:Tree explainable model type.
Return type:ExplainableModelType
explainer_type = 'model'

LightGBM (fast, high performance framework based on decision tree) explainable model.

Please see documentation for more details: https://github.com/Microsoft/LightGBM

Additional arguments to LightGBMClassifier and LightGBMRegressor can be passed through kwargs.

Parameters:
  • multiclass (bool) – Set to true to generate a multiclass model.
  • random_state (int) – Int to seed the model.
  • shap_values_output (interpret_community.common.constants.ShapValuesOutput) – The type of the output from explain_local when using TreeExplainer. Currently only types ‘default’, ‘probability’ and ‘teacher_probability’ are supported. If ‘probability’ is specified, then we approximately scale the raw log-odds values from the TreeExplainer to probabilities.
  • classification (bool) – Indicates if this is a classification or regression explanation.
fit(dataset, labels, **kwargs)

Call lightgbm fit to fit the explainable model.

Parameters:
model

Retrieve the underlying model.

Returns:The lightgbm model, either classifier or regressor.
Return type:Union[LGBMClassifier, LGBMRegressor]
predict(dataset, **kwargs)

Call lightgbm predict to predict labels using the explainable model.

Parameters:dataset (numpy.array or pandas.DataFrame or scipy.sparse.csr_matrix) – The dataset to predict on.
Returns:The predictions of the model.
Return type:list
predict_proba(dataset, **kwargs)

Call lightgbm predict_proba to predict probabilities using the explainable model.

Parameters:dataset (numpy.array or pandas.DataFrame or scipy.sparse.csr_matrix) – The dataset to predict probabilities on.
Returns:The predictions of the model.
Return type:list