interpret_community.shap.linear_explainer module

Defines the LinearExplainer for returning explanations for linear models.

class interpret_community.shap.linear_explainer.LinearExplainer(model, initialization_examples, explain_subset=None, features=None, classes=None, transformations=None, allow_all_transformations=False, **kwargs)

Bases: interpret_community.common.structured_model_explainer.StructuredInitModelExplainer

available_explanations = ['global', 'local']

explain_global(evaluation_examples, sampling_policy=None, include_local=True, batch_size=100)

Explain the model globally by aggregating local explanations to global.

Parameters:

• evaluation_examples (numpy.array or pandas.DataFrame or scipy.sparse.csr_matrix) – A matrix of feature vector examples (# examples x # features) on which to explain the model’s output.
• sampling_policy (interpret_community.common.policy.SamplingPolicy) – Optional policy for sampling the evaluation examples. See documentation on SamplingPolicy for more information.
• include_local (bool) – Include the local explanations in the returned global explanation. If include_local is False, will stream the local explanations to aggregate to global.
• batch_size (int) – If include_local is False, specifies the batch size for aggregating local explanations to global.

Returns:

A model explanation object. It is guaranteed to be a GlobalExplanation which also has the properties of LocalExplanation and ExpectedValuesMixin. If the model is a classifier, it will have the properties of PerClassMixin.

Return type:

DynamicGlobalExplanation

explain_local(evaluation_examples)

Explain the model by using SHAP’s linear explainer.

Parameters:evaluation_examples (DatasetWrapper) – A matrix of feature vector examples (# examples x # features) on which to explain the model’s output. Returns:A model explanation object. It is guaranteed to be a LocalExplanation which also has the properties of ExpectedValuesMixin. If the model is a classifier, it will have the properties of the ClassesMixin. Return type:DynamicLocalExplanation

explainer_type = 'specific'

Defines the LinearExplainer for returning explanations for linear models.

Parameters:

• model ((coef, intercept) or sklearn.linear_model.*) – The linear model to explain as the coefficient and intercept or scikit learn model.
• initialization_examples (numpy.array or pandas.DataFrame or scipy.sparse.csr_matrix) – A matrix of feature vector examples (# examples x # features) for initializing the explainer.
• explain_subset (list[int]) – List of feature indices. If specified, only selects a subset of the features in the evaluation dataset for explanation. The subset can be the top-k features from the model summary.
• features (list[str]) – A list of feature names.
• classes (list[str]) – Class names as a list of strings. The order of the class names should match that of the model output. Only required if explaining classifier.
• transformations (sklearn.compose.ColumnTransformer or list[tuple]) –

  sklearn.compose.ColumnTransformer or a list of tuples describing the column name and transformer. When transformations are provided, explanations are of the features before the transformation. The format for a list of transformations is same as the one here: https://github.com/scikit-learn-contrib/sklearn-pandas.

  If you are using a transformation that is not in the list of sklearn.preprocessing transformations that are supported by the interpret-community package, then this parameter cannot take a list of more than one column as input for the transformation. You can use the following sklearn.preprocessing transformations with a list of columns since these are already one to many or one to one: Binarizer, KBinsDiscretizer, KernelCenterer, LabelEncoder, MaxAbsScaler, MinMaxScaler, Normalizer, OneHotEncoder, OrdinalEncoder, PowerTransformer, QuantileTransformer, RobustScaler, StandardScaler.

  Examples for transformations that work:

  [
      (["col1", "col2"], sklearn_one_hot_encoder),
      (["col3"], None) #col3 passes as is
  ]
  [
      (["col1"], my_own_transformer),
      (["col2"], my_own_transformer),
  ]
  

  An example of a transformation that would raise an error since it cannot be interpreted as one to many:

  [
      (["col1", "col2"], my_own_transformer)
  ]
  

  The last example would not work since the interpret-community package can’t determine whether my_own_transformer gives a many to many or one to many mapping when taking a sequence of columns.

• allow_all_transformations (bool) – Allow many to many and many to one transformations