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 or scipy array) – 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(self)

Retrieve the model type.

Returns:Tree explainable model type. Return type: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 or scipy array param labels:The labels to train the model on. type labels:numpy or scipy 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 : arraylike of shape (n_samples, n_features), default=None

The indexes of the sorted training input samples. If many tree are grown on the same dataset, this allows the ordering to be cached between trees. If None, the data will be sorted here. Don’t use this parameter unless you know what to do.

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 : arraylike of shape (n_samples, n_features), default=None

The indexes of the sorted training input samples. If many tree are grown on the same dataset, this allows the ordering to be cached between trees. If None, the data will be sorted here. Don’t use this parameter unless you know what to do.

Returns

self : DecisionTreeRegressor

Fitted estimator.

model

Retrieve the underlying model.

Returns:The decision tree model, either classifier or regressor. Return type:Union[DecisionTreeClassifier, DecisionTreeRegressor]

predict(dataset, **kwargs)

Call tree predict to predict labels using the explainable model.

param dataset:The dataset to predict on. type dataset:numpy or scipy array 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 or scipy array 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.