interpret_community.mimic.models package

Module for explainable surrogate models.

class interpret_community.mimic.models.BaseExplainableModel(**kwargs)

Bases: abc.ABC, interpret_community.common.chained_identity.ChainedIdentity

The base class for models that can be explained.

expected_values

Abstract property to get the expected values.

explain_global(**kwargs)

Abstract method to get the global feature importances from the trained explainable model.

explain_local(evaluation_examples, **kwargs)

Abstract method to get the local feature importances from the trained explainable model.

static explainable_model_type()

Retrieve the model type.

fit(**kwargs)

Abstract method to fit the explainable model.

model

Abstract property to get the underlying model.

predict(dataset, **kwargs)

Abstract method to predict labels using the explainable model.

predict_proba(dataset, **kwargs)

Abstract method to predict probabilities using the explainable model.

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

Bases: interpret_community.mimic.models.explainable_model.BaseExplainableModel

available_explanations = ['global', 'local']
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.array 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.array or pandas.DataFrame or scipy.sparse.csr_matrix
param labels:The labels to train the model on.
type labels:numpy.array

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.
y : ndarray of shape (n_samples,)
Target values.
coef_init : ndarray of shape (n_classes, n_features), default=None
The initial coefficients to warmstart the optimization.
intercept_init : ndarray of shape (n_classes,), default=None
The initial intercept to warmstart the optimization.
sample_weight : arraylike, 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

self :
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
y : ndarray of shape (n_samples,)
Target values
coef_init : ndarray of shape (n_features,), default=None
The initial coefficients to warmstart the optimization.
intercept_init : ndarray of shape (1,), default=None
The initial intercept to warmstart the optimization.
sample_weight : arraylike, shape (n_samples,), default=None
Weights applied to individual samples (1. for unweighted).

Returns

self : returns an instance of self.

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.array 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 or sparse matrix, shape (n_samples, n_features)
Samples.

Returns

C : array, shape [n_samples]
Predicted class label per 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)

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.array 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, http://www.research.ibm.com/people/z/zadrozny/kdd2002Transf.pdf

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.

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

Bases: interpret_community.mimic.models.explainable_model.BaseExplainableModel

available_explanations = ['global', 'local']
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.array 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.array or pandas.DataFrame or scipy.sparse.csr_matrix
param labels:The labels to train the model on.
type labels:numpy.array

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.
y : arraylike of shape (n_samples,)
Target vector relative to X.
sample_weight : arraylike 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
y : arraylike of shape (n_samples,) or (n_samples, n_targets)
Target values. Will be cast to X’s dtype if necessary
sample_weight : arraylike of shape (n_samples,), default=None

Individual weights for each sample

New in version 0.17: parameter sample_weight support to LinearRegression.

Returns

self : returns an instance of self.

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.array 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 or sparse matrix, shape (n_samples, n_features)
Samples.

Returns

C : array, shape [n_samples]
Predicted class label per sample.

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

Predict using the linear model.

Parameters

X : arraylike or sparse matrix, shape (n_samples, n_features)
Samples.

Returns

C : array, 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.array 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

X : arraylike 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

T : arraylike 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.DecisionTreeExplainableModel(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 decision tree tree model.
Return type:list
explain_global(**kwargs)

Call tree model feature importances to get the global feature importances from the tree surrogate model.

Returns:The global explanation of feature importances.
Return type:list
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:interpret_community.common.constants.ExplainableModelType
explainer_type = 'model'

Decision Tree explainable model.

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 tree fit to fit the explainable model.

param dataset:The dataset to train the model on.
type dataset:numpy.array or pandas.DataFrame or scipy.sparse.csr_matrix
param labels:The labels to train the model on.
type labels:numpy.array

If multiclass=True, uses the parameters for DecisionTreeClassifier: Build a decision tree classifier from the training set (X, y).

Parameters

X : {arraylike, sparse matrix} of shape (n_samples, n_features)
The training input samples. Internally, it will be converted to dtype=np.float32 and if a sparse matrix is provided to a sparse csc_matrix.
y : arraylike of shape (n_samples,) or (n_samples, n_outputs)
The target values (class labels) as integers or strings.
sample_weight : arraylike of shape (n_samples,), default=None
Sample weights. If None, then samples are equally weighted. Splits that would create child nodes with net zero or negative weight are ignored while searching for a split in each node. Splits are also ignored if they would result in any single class carrying a negative weight in either child node.
check_input : bool, default=True
Allow to bypass several input checking. Don’t use this parameter unless you know what you do.
X_idx_sorted : deprecated, default=”deprecated”

This parameter is deprecated and has no effect. It will be removed in 1.1 (renaming of 0.26).

Deprecated since version 0.24.

Returns

self : DecisionTreeClassifier
Fitted estimator.

Otherwise, if multiclass=False, uses the parameters for DecisionTreeRegressor: Build a decision tree regressor from the training set (X, y).

Parameters

X : {arraylike, sparse matrix} of shape (n_samples, n_features)
The training input samples. Internally, it will be converted to dtype=np.float32 and if a sparse matrix is provided to a sparse csc_matrix.
y : arraylike of shape (n_samples,) or (n_samples, n_outputs)
The target values (real numbers). Use dtype=np.float64 and order='C' for maximum efficiency.
sample_weight : arraylike of shape (n_samples,), default=None
Sample weights. If None, then samples are equally weighted. Splits that would create child nodes with net zero or negative weight are ignored while searching for a split in each node.
check_input : bool, default=True
Allow to bypass several input checking. Don’t use this parameter unless you know what you do.
X_idx_sorted : deprecated, default=”deprecated”

This parameter is deprecated and has no effect. It will be removed in 1.1 (renaming of 0.26).

Deprecated since version 0.24.

Returns

self : DecisionTreeRegressor
Fitted estimator.
model

Retrieve the underlying model.

Returns:The decision tree model, either classifier or regressor.
Return type:Union[sklearn.tree.DecisionTreeClassifier, sklearn.tree.DecisionTreeRegressor]
predict(dataset, **kwargs)

Call tree predict to predict labels using the explainable model.

param dataset:The dataset to predict on.
type dataset:numpy.array or pandas.DataFrame or scipy.sparse.csr_matrix
return:The predictions of the model.
rtype:list

If multiclass=True, uses the parameters for DecisionTreeClassifier: Predict class or regression value for X.

For a classification model, the predicted class for each sample in X is returned. For a regression model, the predicted value based on X is returned.

Parameters

X : {arraylike, sparse matrix} of shape (n_samples, n_features)
The input samples. Internally, it will be converted to dtype=np.float32 and if a sparse matrix is provided to a sparse csr_matrix.
check_input : bool, default=True
Allow to bypass several input checking. Don’t use this parameter unless you know what you do.

Returns

y : arraylike of shape (n_samples,) or (n_samples, n_outputs)
The predicted classes, or the predict values.

Otherwise, if multiclass=False, uses the parameters for DecisionTreeRegressor: Predict class or regression value for X.

For a classification model, the predicted class for each sample in X is returned. For a regression model, the predicted value based on X is returned.

Parameters

X : {arraylike, sparse matrix} of shape (n_samples, n_features)
The input samples. Internally, it will be converted to dtype=np.float32 and if a sparse matrix is provided to a sparse csr_matrix.
check_input : bool, default=True
Allow to bypass several input checking. Don’t use this parameter unless you know what you do.

Returns

y : arraylike of shape (n_samples,) or (n_samples, n_outputs)
The predicted classes, or the predict values.
predict_proba(dataset, **kwargs)

Call tree predict_proba to predict probabilities using the explainable model.

param dataset:The dataset to predict probabilities on.
type dataset:numpy.array or pandas.DataFrame or scipy.sparse.csr_matrix
return:The predictions of the model.
rtype:list

If multiclass=True, uses the parameters for DecisionTreeClassifier: Predict class probabilities of the input samples X.

The predicted class probability is the fraction of samples of the same class in a leaf.

Parameters

X : {arraylike, sparse matrix} of shape (n_samples, n_features)
The input samples. Internally, it will be converted to dtype=np.float32 and if a sparse matrix is provided to a sparse csr_matrix.
check_input : bool, default=True
Allow to bypass several input checking. Don’t use this parameter unless you know what you do.

Returns

proba : ndarray of shape (n_samples, n_classes) or list of n_outputs such arrays if n_outputs > 1
The class probabilities of the input samples. The order of the classes corresponds to that in the attribute classes_.

Otherwise predict_proba is not supported for regression or binary classification.