interpret_community package

Module for interpreting, including feature and class importance for blackbox, greybox and glassbox models.

You can use model interpretability to explain why a model model makes the predictions it does and help build confidence in the model.

class interpret_community.TabularExplainer(model, initialization_examples, explain_subset=None, features=None, classes=None, transformations=None, allow_all_transformations=False, model_task=ModelTask.Unknown, use_gpu=False, **kwargs)

Bases: interpret_community.common.base_explainer.BaseExplainer

available_explanations = ['global', 'local']
explain_global(evaluation_examples, sampling_policy=None, include_local=True, batch_size=100)

Globally explains the black box model or function.

Parameters

  • evaluation_examples (numpy.ndarray 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. If SHAP is used for the explanation, it will also have the properties of a LocalExplanation and the ExpectedValuesMixin. If the model does classification, it will have the properties of the PerClassMixin.

Return type

DynamicGlobalExplanation

explain_local(evaluation_examples)

Locally explains the black box model or function.

Parameters

evaluation_examples (numpy.ndarray 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.

Returns

A model explanation object. It is guaranteed to be a LocalExplanation. If SHAP is used for the explanation, it will also have the properties of the ExpectedValuesMixin. If the model does classification, it will have the properties of the ClassesMixin.

Return type

DynamicLocalExplanation

explainer_type = 'blackbox'

The tabular explainer meta-api for returning the best explanation result based on the given model.

Parameters

  • model (object) – The model or pipeline to explain. A model that implements sklearn.predict() or sklearn.predict_proba() or pipeline function that accepts a 2d ndarray

  • initialization_examples (numpy.ndarray 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, which will speed up the explanation process when number of features is large and the user already knows the set of interested features. The subset can be the top-k features from the model summary. This argument is not supported when transformations are set.

  • 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 the user is 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. A user 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

Subpackages

Submodules