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, classification=True, **kwargs)¶

Bases: interpret_community.mimic.models.explainable_model.BaseExplainableModel

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

property 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.ndarray 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

dataset (numpy.ndarray or pandas.DataFrame or scipy.sparse.csr_matrix) – The dataset to train the model on.
labels (numpy.ndarray) – The labels to train the model on.

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