treeple.tree.MultiViewDecisionTreeClassifier

class treeple.tree.MultiViewDecisionTreeClassifier(*, criterion='gini', splitter='best', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=None, random_state=None, max_leaf_nodes=None, min_impurity_decrease=0.0, class_weight=None, feature_combinations=None, ccp_alpha=0.0, store_leaf_values=False, monotonic_cst=None, feature_set_ends=None, apply_max_features_per_feature_set=False)

A multi-view axis-aligned decision tree classifier.

This is an experimental feature that applies an oblique decision tree to multiple feature-sets concatenated across columns in X.

Parameters:

criterion{“gini”, “entropy”}, default=”gini”

The function to measure the quality of a split. Supported criteria are “gini” for the Gini impurity and “entropy” for the information gain.

splitter{“best”}, default=”best”

The strategy used to choose the split at each node.

max_depthint, default=None

The maximum depth of the tree. If None, then nodes are expanded until all leaves are pure or until all leaves contain less than min_samples_split samples.

min_samples_splitint or float, default=2

The minimum number of samples required to split an internal node:

• If int, then consider min_samples_split as the minimum number.

• If float, then min_samples_split is a fraction and ceil(min_samples_split * n_samples) are the minimum number of samples for each split.

min_samples_leafint or float, default=1

The minimum number of samples required to be at a leaf node. A split point at any depth will only be considered if it leaves at least min_samples_leaf training samples in each of the left and right branches. This may have the effect of smoothing the model, especially in regression.

• If int, then consider min_samples_leaf as the minimum number.

• If float, then min_samples_leaf is a fraction and ceil(min_samples_leaf * n_samples) are the minimum number of samples for each node.

min_weight_fraction_leaffloat, default=0.0

The minimum weighted fraction of the sum total of weights (of all the input samples) required to be at a leaf node. Samples have equal weight when sample_weight is not provided.

max_featuresarray_like, int, float or {“auto”, “sqrt”, “log2”}, default=None

The number of features to consider when looking for the best split:

• If int, then consider max_features features at each split.

• If float, then max_features is a fraction and int(max_features * n_features) features are considered at each split.

• If “auto”, then max_features=sqrt(n_features).

• If “sqrt”, then max_features=sqrt(n_features).

• If “log2”, then max_features=log2(n_features).

• If None, then max_features=n_features.

If array-like, then max_features is the number of features to consider for each feature set following the same logic as above, where n_features is the number of features in the respective feature set.

Note: the search for a split does not stop until at least one valid partition of the node samples is found, even if it requires to effectively inspect more than max_features features.

Note: Compared to axis-aligned Random Forests, one can set max_features to a number greater then n_features.

random_stateint, RandomState instance or None, default=None

Controls the randomness of the estimator. The features are always randomly permuted at each split, even if splitter is set to "best". When max_features < n_features, the algorithm will select max_features at random at each split before finding the best split among them. But the best found split may vary across different runs, even if max_features=n_features. That is the case, if the improvement of the criterion is identical for several splits and one split has to be selected at random. To obtain a deterministic behaviour during fitting, random_state has to be fixed to an integer. See Glossary for details.

max_leaf_nodesint, default=None

Grow a tree with max_leaf_nodes in best-first fashion. Best nodes are defined as relative reduction in impurity. If None then unlimited number of leaf nodes.

min_impurity_decreasefloat, default=0.0

A node will be split if this split induces a decrease of the impurity greater than or equal to this value.

The weighted impurity decrease equation is the following:

N_t / N * (impurity - N_t_R / N_t * right_impurity
                    - N_t_L / N_t * left_impurity)

where N is the total number of samples, N_t is the number of samples at the current node, N_t_L is the number of samples in the left child, and N_t_R is the number of samples in the right child.

N, N_t, N_t_R and N_t_L all refer to the weighted sum, if sample_weight is passed.

class_weightdict, list of dict or “balanced”, default=None

Weights associated with classes in the form {class_label: weight}. If None, all classes are supposed to have weight one. For multi-output problems, a list of dicts can be provided in the same order as the columns of y.

Note that for multioutput (including multilabel) weights should be defined for each class of every column in its own dict. For example, for four-class multilabel classification weights should be [{0: 1, 1: 1}, {0: 1, 1: 5}, {0: 1, 1: 1}, {0: 1, 1: 1}] instead of [{1:1}, {2:5}, {3:1}, {4:1}].

The “balanced” mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as n_samples / (n_classes * np.bincount(y))

For multi-output, the weights of each column of y will be multiplied.

Note that these weights will be multiplied with sample_weight (passed through the fit method) if sample_weight is specified.

feature_combinationsfloat, default=None

Not used.

ccp_alphanon-negative float, default=0.0

Not used.

store_leaf_valuesbool, default=False

Whether to store the leaf values.

monotonic_cstarray_like of int of shape (n_features), default=None

Indicates the monotonicity constraint to enforce on each feature.

• 1: monotonic increase

• 0: no constraint

• -1: monotonic decrease

Not used.

feature_set_endsarray_like of int of shape (n_feature_sets,), default=None

The indices of the end of each feature set. For example, if the first feature set is the first 10 features, and the second feature set is the next 20 features, then feature_set_ends = [10, 30]. If None, then this will assume that there is only one feature set.

apply_max_features_per_feature_setbool, default=False

Whether to apply sampling per feature set, where max_features is applied to each feature-set. If False, then sampling is applied over the entire feature space.

Attributes:

classes_ndarray of shape (n_classes,) or list of ndarray

The classes labels (single output problem), or a list of arrays of class labels (multi-output problem).

feature_importances_ndarray of shape (n_features,)

Return the feature importances.

max_features_int

The inferred value of max_features.

n_classes_int or list of int

The number of classes (for single output problems), or a list containing the number of classes for each output (for multi-output problems).

n_features_in_int

Number of features seen during fit.

feature_names_in_ndarray of shape (n_features_in_,)

Names of features seen during fit. Defined only when X has feature names that are all strings.

n_outputs_int

The number of outputs when fit is performed.

tree_Tree instance

The underlying Tree object. Please refer to help(sklearn.tree._tree.Tree) for attributes of Tree object.

feature_combinations_float

The number of feature combinations on average taken to fit the tree.

feature_set_ends_array_like of int of shape (n_feature_sets,)

The indices of the end of each feature set.

n_feature_sets_int

The number of feature sets.

max_features_per_set_array_like of int of shape (n_feature_sets,)

The number of features to sample per feature set. If None, then max_features is applied to the entire feature space.

Methods

apply(X[, check_input])	Return the index of the leaf that each sample is predicted as.
compute_similarity_matrix(X)	Compute the similarity matrix of samples in X.
cost_complexity_pruning_path(X, y[, ...])	Compute the pruning path during Minimal Cost-Complexity Pruning.
decision_path(X[, check_input])	Return the decision path in the tree.
fit(X, y[, sample_weight, check_input, classes])	Build a decision tree classifier from the training set (X, y).
get_depth()	Return the depth of the decision tree.
get_leaf_node_samples(X[, check_input])	For each datapoint x in X, get the training samples in the leaf node.
get_metadata_routing()	Get metadata routing of this object.
get_n_leaves()	Return the number of leaves of the decision tree.
get_params([deep])	Get parameters for this estimator.
partial_fit(X, y[, sample_weight, ...])	Update a decision tree classifier from the training set (X, y).
predict(X[, check_input])	Predict class or regression value for X.
predict_log_proba(X)	Predict class log-probabilities of the input samples X.
predict_proba(X[, check_input])	Predict class probabilities of the input samples X.
predict_quantiles(X[, quantiles, method, ...])	Predict class or regression value for X at given quantiles.
score(X, y[, sample_weight])	Return the mean accuracy on the given test data and labels.
set_fit_request(*[, check_input, classes, ...])	Request metadata passed to the fit method.
set_params(**params)	Set the parameters of this estimator.
set_partial_fit_request(*[, check_input, ...])	Request metadata passed to the partial_fit method.
set_predict_proba_request(*[, check_input])	Request metadata passed to the predict_proba method.
set_predict_request(*[, check_input])	Request metadata passed to the predict method.
set_score_request(*[, sample_weight])	Request metadata passed to the score method.

See also

sklearn.tree.DecisionTreeClassifier

An axis-aligned decision tree classifier.

apply(X, check_input=True)

Return the index of the leaf that each sample is predicted as.

New in version 0.17.

Parameters:

X{array_like, 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_inputbool, default=True

Allow to bypass several input checking. Don’t use this parameter unless you know what you’re doing.

Returns:

X_leavesarray_like of shape (n_samples,)

For each datapoint x in X, return the index of the leaf x ends up in. Leaves are numbered within [0; self.tree_.node_count), possibly with gaps in the numbering.

compute_similarity_matrix(X)

Compute the similarity matrix of samples in X.

Parameters:

Xarray_like of shape (n_samples, n_features)

The input data.

Returns:

sim_matrixarray_like of shape (n_samples, n_samples)

The similarity matrix among the samples.

cost_complexity_pruning_path(X, y, sample_weight=None)

Compute the pruning path during Minimal Cost-Complexity Pruning.

See Minimal Cost-Complexity Pruning for details on the pruning process.

Parameters:

X{array_like, 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.

yarray_like of shape (n_samples,) or (n_samples, n_outputs)

The target values (class labels) as integers or strings.

sample_weightarray_like 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.

Returns:

ccp_pathBunch

Dictionary-like object, with the following attributes.

ccp_alphasndarray

Effective alphas of subtree during pruning.

impuritiesndarray

Sum of the impurities of the subtree leaves for the corresponding alpha value in ccp_alphas.

decision_path(X, check_input=True)

Return the decision path in the tree.

New in version 0.18.

Parameters:

X{array_like, 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_inputbool, default=True

Allow to bypass several input checking. Don’t use this parameter unless you know what you’re doing.

Returns:

indicatorsparse matrix of shape (n_samples, n_nodes)

Return a node indicator CSR matrix where non zero elements indicates that the samples goes through the nodes.

fit(X, y, sample_weight=None, check_input=True, classes=None)

Build a decision tree classifier from the training set (X, y).

Parameters:

X{array_like, 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.

yarray_like of shape (n_samples,) or (n_samples, n_outputs)

The target values (class labels) as integers or strings.

sample_weightarray_like 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_inputbool, default=True

Allow to bypass several input checking. Don’t use this parameter unless you know what you’re doing.

classesarray_like of shape (n_classes,), default=None

List of all the classes that can possibly appear in the y vector.

Returns:

selfMultiViewDecisionTreeClassifier

Fitted estimator.

get_depth()

Return the depth of the decision tree.

The depth of a tree is the maximum distance between the root and any leaf.

Returns:

self.tree_.max_depthint

The maximum depth of the tree.

get_leaf_node_samples(X, check_input=True)

For each datapoint x in X, get the training samples in the leaf node.

Parameters:

Xarray_like of shape (n_samples, n_features)

Dataset to apply the forest to.

check_inputbool, default=True

Allow to bypass several input checking.

Returns:

leaf_nodes_samplesa list of array_like of length (n_samples,)

Each sample is represented by the indices of the training samples that reached the leaf node. The n_leaf_node_samples may vary between samples, since the number of samples that fall in a leaf node is variable. Each array has shape (n_leaf_node_samples, n_outputs).

get_metadata_routing()

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routingMetadataRequest

A MetadataRequest encapsulating routing information.

get_n_leaves()

Return the number of leaves of the decision tree.

Returns:

self.tree_.n_leavesint

Number of leaves.

get_params(deep=True)

Get parameters for this estimator.

Parameters:

deepbool, default=True

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

paramsdict

Parameter names mapped to their values.

partial_fit(X, y, sample_weight=None, check_input=True, classes=None)

Update a decision tree classifier from the training set (X, y).

Parameters:

X{array_like, 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.

yarray_like of shape (n_samples,) or (n_samples, n_outputs)

The target values (class labels) as integers or strings.

sample_weightarray_like 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_inputbool, default=True

Allow to bypass several input checking. Don’t use this parameter unless you know what you do.

classesarray_like of shape (n_classes,), default=None

List of all the classes that can possibly appear in the y vector. Must be provided at the first call to partial_fit, can be omitted in subsequent calls.

Returns:

selfDecisionTreeClassifier

Fitted estimator.

predict(X, check_input=True)

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{array_like, 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_inputbool, default=True

Allow to bypass several input checking. Don’t use this parameter unless you know what you’re doing.

Returns:

yarray_like of shape (n_samples,) or (n_samples, n_outputs)

The predicted classes, or the predict values.

predict_log_proba(X)

Predict class log-probabilities of the input samples X.

Parameters:

X{array_like, 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.

Returns:

probandarray of shape (n_samples, n_classes) or list of n_outputs such arrays if n_outputs > 1

The class log-probabilities of the input samples. The order of the classes corresponds to that in the attribute classes_.

predict_proba(X, check_input=True)

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{array_like, 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_inputbool, default=True

Allow to bypass several input checking. Don’t use this parameter unless you know what you’re doing.

Returns:

probandarray 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_.

predict_quantiles(X, quantiles=0.5, method='nearest', check_input=True)

Predict class or regression value for X at given quantiles.

Parameters:

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

Input data.

quantilesfloat, optional

The quantiles at which to evaluate, by default 0.5 (median).

methodstr, optional

The method to interpolate, by default ‘linear’. Can be any keyword argument accepted by numpy.quantile().

check_inputbool, optional

Whether or not to check input, by default True.

Returns:

predictionsarray_like of shape (n_samples, n_outputs, len(quantiles))

The predicted quantiles.

score(X, y, sample_weight=None)

Return the mean accuracy on the given test data and labels.

In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.

Parameters:

Xarray_like of shape (n_samples, n_features)

Test samples.

yarray_like of shape (n_samples,) or (n_samples, n_outputs)

True labels for X.

sample_weightarray_like of shape (n_samples,), default=None

Sample weights.

Returns:

scorefloat

Mean accuracy of self.predict(X) w.r.t. y.

set_fit_request(*, check_input='$UNCHANGED$', classes='$UNCHANGED$', sample_weight='$UNCHANGED$')

Request metadata passed to the fit method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

• True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.

• False: metadata is not requested and the meta-estimator will not pass it to fit.

• None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

• str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

New in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

check_inputstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for check_input parameter in fit.

classesstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for classes parameter in fit.

sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for sample_weight parameter in fit.

Returns:

selfobject

The updated object.

set_params(**params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:

**paramsdict

Estimator parameters.

Returns:

selfestimator instance

Estimator instance.

set_partial_fit_request(*, check_input='$UNCHANGED$', classes='$UNCHANGED$', sample_weight='$UNCHANGED$')

Request metadata passed to the partial_fit method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

• True: metadata is requested, and passed to partial_fit if provided. The request is ignored if metadata is not provided.

• False: metadata is not requested and the meta-estimator will not pass it to partial_fit.

• None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

• str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

New in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

check_inputstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for check_input parameter in partial_fit.

classesstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for classes parameter in partial_fit.

sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for sample_weight parameter in partial_fit.

Returns:

selfobject

The updated object.

set_predict_proba_request(*, check_input='$UNCHANGED$')

Request metadata passed to the predict_proba method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

• True: metadata is requested, and passed to predict_proba if provided. The request is ignored if metadata is not provided.

• False: metadata is not requested and the meta-estimator will not pass it to predict_proba.

• None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

• str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

New in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

check_inputstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for check_input parameter in predict_proba.

Returns:

selfobject

The updated object.

set_predict_request(*, check_input='$UNCHANGED$')

Request metadata passed to the predict method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

• True: metadata is requested, and passed to predict if provided. The request is ignored if metadata is not provided.

• False: metadata is not requested and the meta-estimator will not pass it to predict.

• None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

• str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

New in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

check_inputstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for check_input parameter in predict.

Returns:

selfobject

The updated object.

set_score_request(*, sample_weight='$UNCHANGED$')

Request metadata passed to the score method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

• True: metadata is requested, and passed to score if provided. The request is ignored if metadata is not provided.

• False: metadata is not requested and the meta-estimator will not pass it to score.

• None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

• str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

New in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for sample_weight parameter in score.

Returns:

selfobject

The updated object.

property feature_importances_

Return the feature importances.

The importance of a feature is computed as the (normalized) total reduction of the criterion brought by that feature. It is also known as the Gini importance.

Warning: impurity-based feature importances can be misleading for high cardinality features (many unique values). See sklearn.inspection.permutation_importance() as an alternative.

Returns:

feature_importances_ndarray of shape (n_features,)

Normalized total reduction of criteria by feature (Gini importance).