interpret_community.mimic.models.tree_model module

Defines an explainable tree model.

class interpret_community.mimic.models.tree_model.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.