interpret_community.mimic.models.linear_model module

Defines an explainable linear model.

class interpret_community.mimic.models.linear_model.LinearExplainableModel(multiclass=False, random_state=123, classification=True, sparse_data=False, **kwargs)

Bases: interpret_community.mimic.models.explainable_model.BaseExplainableModel

available_explanations = ['global', 'local']

property expected_values

Use LinearExplainer to get the expected values.

Returns

The expected values of the linear model.

Return type

list

explain_global(**kwargs)

Call coef to get the global feature importances from the linear surrogate model.

Returns

The global explanation of feature importances.

Return type

list

explain_local(evaluation_examples, **kwargs)

Use LinearExplainer 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.

Returns

The local explanation of feature importances.

Return type

Union[list, numpy.ndarray]

static explainable_model_type()

Retrieve the model type.

Returns

Linear explainable model type.

Return type

ExplainableModelType

explainer_type = 'model'

Linear explainable model.

Parameters

• multiclass (bool) – Set to true to generate a multiclass model.

• random_state (int) – Int to seed the model.

• classification (bool) – Indicates whether the model is used for classification or regression scenario.

• sparse_data (bool) – Indicates whether the training data will be sparse.

fit(dataset, labels, **kwargs)

Call linear fit to fit the explainable model.

Store the mean and covariance of the background data for local explanation.

param dataset

The dataset to train the model on.

type dataset

numpy.ndarray or pandas.DataFrame or scipy.sparse.csr_matrix

param labels

The labels to train the model on.

type labels

numpy.ndarray

If multiclass=True, uses the parameters for LogisticRegression:

Fit the model according to the given training data.

Parameters

X{arraylike, sparse matrix} of shape (n_samples, n_features)

Training vector, where n_samples is the number of samples and n_features is the number of features.

yarraylike of shape (n_samples,)

Target vector relative to X.

sample_weightarraylike of shape (n_samples,) default=None

Array of weights that are assigned to individual samples. If not provided, then each sample is given unit weight.

New in version 0.17: sample_weight support to LogisticRegression.

Returns

self

Fitted estimator.

Notes

The SAGA solver supports both float64 and float32 bit arrays.

Otherwise, if multiclass=False, uses the parameters for LinearRegression:

Fit linear model.

Parameters

X{arraylike, sparse matrix} of shape (n_samples, n_features)

Training data.

yarraylike of shape (n_samples,) or (n_samples, n_targets)

Target values. Will be cast to X’s dtype if necessary.

sample_weightarraylike of shape (n_samples,), default=None

Individual weights for each sample.

New in version 0.17: parameter sample_weight support to LinearRegression.

Returns

selfobject

Fitted Estimator.

property model

Retrieve the underlying model.

Returns

The linear model, either classifier or regressor.

Return type

Union[LogisticRegression, LinearRegression]

predict(dataset, **kwargs)

Call linear predict to predict labels using the explainable model.

param dataset

The dataset to predict on.

type dataset

numpy.ndarray or pandas.DataFrame or scipy.sparse.csr_matrix

return

The predictions of the model.

rtype

list

If multiclass=True, uses the parameters for LogisticRegression:

Predict class labels for samples in X.

Parameters

X{arraylike, sparse matrix} of shape (n_samples, n_features)

The data matrix for which we want to get the predictions.

Returns

y_predndarray of shape (n_samples,)

Vector containing the class labels for each sample.

Otherwise, if multiclass=False, uses the parameters for LinearRegression:

Predict using the linear model.

Parameters

Xarraylike or sparse matrix, shape (n_samples, n_features)

Samples.

Returns

Carray, shape (n_samples,)

Returns predicted values.

predict_proba(dataset, **kwargs)

Call linear predict_proba to predict probabilities using the explainable model.

param dataset

The dataset to predict probabilities on.

type dataset

numpy.ndarray or pandas.DataFrame or scipy.sparse.csr_matrix

return

The predictions of the model.

rtype

list

If multiclass=True, uses the parameters for LogisticRegression:

Probability estimates.

The returned estimates for all classes are ordered by the label of classes.

For a multi_class problem, if multi_class is set to be “multinomial” the softmax function is used to find the predicted probability of each class. Else use a onevsrest approach, i.e calculate the probability of each class assuming it to be positive using the logistic function. and normalize these values across all the classes.

Parameters

Xarraylike of shape (n_samples, n_features)

Vector to be scored, where n_samples is the number of samples and n_features is the number of features.

Returns

Tarraylike of shape (n_samples, n_classes)

Returns the probability of the sample for each class in the model, where classes are ordered as they are in self.classes_.

Otherwise predict_proba is not supported for regression or binary classification.

class interpret_community.mimic.models.linear_model.LinearExplainer(model, masker, **kwargs)

Bases: shap.explainers._linear.Linear

Linear explainer with support for sparse data and sparse output.

shap_values(evaluation_examples)

Estimate the SHAP values for a set of samples.

Parameters

evaluation_examples (numpy.ndarray or pandas.DataFrame or scipy.sparse.csr_matrix) – The evaluation examples.

Returns

For models with a single output this returns a matrix of SHAP values (# samples x # features). Each row sums to the difference between the model output for that sample and the expected value of the model output (which is stored as expected_value attribute of the explainer).

Return type

Union[list, numpy.ndarray]

class interpret_community.mimic.models.linear_model.SGDExplainableModel(multiclass=False, random_state=123, classification=True, **kwargs)

Bases: interpret_community.mimic.models.explainable_model.BaseExplainableModel

available_explanations = ['global', 'local']

property expected_values

Use LinearExplainer to get the expected values.

Returns

The expected values of the linear model.

Return type

list

explain_global(**kwargs)

Call coef to get the global feature importances from the SGD surrogate model.

Returns

The global explanation of feature importances.

Return type

list

explain_local(evaluation_examples, **kwargs)

Use LinearExplainer 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.

Returns

The local explanation of feature importances.

Return type

Union[list, numpy.ndarray]

explainer_type = 'model'

Stochastic Gradient Descent explainable model.

Parameters

• multiclass (bool) – Set to true to generate a multiclass model.

• random_state (int) – Int to seed the model.

fit(dataset, labels, **kwargs)

Call linear fit to fit the explainable model.

Store the mean and covariance of the background data for local explanation.

param dataset

The dataset to train the model on.

type dataset

numpy.ndarray or pandas.DataFrame or scipy.sparse.csr_matrix

param labels

The labels to train the model on.

type labels

numpy.ndarray

If multiclass=True, uses the parameters for SGDClassifier: Fit linear model with Stochastic Gradient Descent.

Parameters

X{arraylike, sparse matrix}, shape (n_samples, n_features)

Training data.

yndarray of shape (n_samples,)

Target values.

coef_initndarray of shape (n_classes, n_features), default=None

The initial coefficients to warmstart the optimization.

intercept_initndarray of shape (n_classes,), default=None

The initial intercept to warmstart the optimization.

sample_weightarraylike, shape (n_samples,), default=None

Weights applied to individual samples. If not provided, uniform weights are assumed. These weights will be multiplied with class_weight (passed through the constructor) if class_weight is specified.

Returns

selfobject

Returns an instance of self.

Otherwise, if multiclass=False, uses the parameters for SGDRegressor: Fit linear model with Stochastic Gradient Descent.

Parameters

X{arraylike, sparse matrix}, shape (n_samples, n_features)

Training data.

yndarray of shape (n_samples,)

Target values.

coef_initndarray of shape (n_features,), default=None

The initial coefficients to warmstart the optimization.

intercept_initndarray of shape (1,), default=None

The initial intercept to warmstart the optimization.

sample_weightarraylike, shape (n_samples,), default=None

Weights applied to individual samples (1. for unweighted).

Returns

selfobject

Fitted SGDRegressor estimator.

property model

Retrieve the underlying model.

Returns

The SGD model, either classifier or regressor.

Return type

Union[SGDClassifier, SGDRegressor]

predict(dataset, **kwargs)

Call SGD predict to predict labels using the explainable model.

param dataset

The dataset to predict on.

type dataset

numpy.ndarray or pandas.DataFrame or scipy.sparse.csr_matrix

return

The predictions of the model.

rtype

list

If multiclass=True, uses the parameters for SGDClassifier:

Predict class labels for samples in X.

Parameters

X{arraylike, sparse matrix} of shape (n_samples, n_features)

The data matrix for which we want to get the predictions.

Returns

y_predndarray of shape (n_samples,)

Vector containing the class labels for each sample.

Otherwise, if multiclass=False, uses the parameters for SGDRegressor: Predict using the linear model.

Parameters

X{arraylike, sparse matrix}, shape (n_samples, n_features)

Input data.

Returns

ndarray of shape (n_samples,)

Predicted target values per element in X.

predict_proba(dataset, **kwargs)

Call SGD predict_proba to predict probabilities using the explainable model.

param dataset

The dataset to predict probabilities on.

type dataset

numpy.ndarray or pandas.DataFrame or scipy.sparse.csr_matrix

return

The predictions of the model.

rtype

list

If multiclass=True, uses the parameters for SGDClassifier: Probability estimates.

This method is only available for log loss and modified Huber loss.

Multiclass probability estimates are derived from binary (onevs.rest) estimates by simple normalization, as recommended by Zadrozny and Elkan.

Binary probability estimates for loss=”modified_huber” are given by (clip(decision_function(X), 1, 1) + 1) / 2. For other loss functions it is necessary to perform proper probability calibration by wrapping the classifier with CalibratedClassifierCV instead.

Parameters

X{arraylike, sparse matrix}, shape (n_samples, n_features)

Input data for prediction.

Returns

ndarray of shape (n_samples, n_classes)

Returns the probability of the sample for each class in the model, where classes are ordered as they are in self.classes_.

References

Zadrozny and Elkan, “Transforming classifier scores into multiclass probability estimates”, SIGKDD’02, https://dl.acm.org/doi/pdf/10.1145/775047.775151

The justification for the formula in the loss=”modified_huber” case is in the appendix B in: http://jmlr.csail.mit.edu/papers/volume2/zhang02c/zhang02c.pdf

Otherwise predict_proba is not supported for regression or binary classification.