sktree.ObliqueRandomForestClassifier

class sktree.ObliqueRandomForestClassifier(n_estimators=100, *, criterion='gini', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features='sqrt', max_leaf_nodes=None, min_impurity_decrease=0.0, bootstrap=True, oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False, class_weight=None, max_samples=None, feature_combinations=None)

An oblique random forest classifier.

A oblique random forest is a meta estimator similar to a random forest that fits a number of oblique decision tree classifiers on various sub-samples of the dataset and uses averaging to improve the predictive accuracy and control over-fitting.

The sub-sample size is controlled with the max_samples parameter if bootstrap=True (default), otherwise the whole dataset is used to build each tree.

Read more in the User Guide.

Parameters:

n_estimatorsint, default=100

The number of trees in the forest.

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. Note: this parameter is tree-specific.

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

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 round(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.

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.

max_leaf_nodesint, default=None

Grow trees 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.

bootstrapbool, default=True

Whether bootstrap samples are used when building trees. If False, the whole dataset is used to build each tree.

oob_scorebool, default=False

Whether to use out-of-bag samples to estimate the generalization score. Only available if bootstrap=True.

n_jobsint, default=None

The number of jobs to run in parallel. fit(), predict(), decision_path() and apply() are all parallelized over the trees. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See Glossary for more details.

random_stateint, RandomState instance or None, default=None

Controls both the randomness of the bootstrapping of the samples used when building trees (if bootstrap=True) and the sampling of the features to consider when looking for the best split at each node (if max_features < n_features). See Glossary for details.

verboseint, default=0

Controls the verbosity when fitting and predicting.

warm_startbool, default=False

When set to True, reuse the solution of the previous call to fit and add more estimators to the ensemble, otherwise, just fit a whole new forest. See the Glossary.

class_weight{“balanced”, “balanced_subsample”}, dict or list of dicts, default=None

Weights associated with classes in the form {class_label: weight}. If not given, 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))

The “balanced_subsample” mode is the same as “balanced” except that weights are computed based on the bootstrap sample for every tree grown.

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.

max_samplesint or float, default=None

If bootstrap is True, the number of samples to draw from X to train each base estimator.

• If None (default), then draw X.shape[0] samples.

• If int, then draw max_samples samples.

• If float, then draw max_samples * X.shape[0] samples. Thus, max_samples should be in the interval (0.0, 1.0].

feature_combinationsfloat, default=None

The number of features to combine on average at each split of the decision trees. If None, then will default to the minimum of (1.5, n_features). This controls the number of non-zeros is the projection matrix. Setting the value to 1.0 is equivalent to a traditional decision-tree. feature_combinations * max_features gives the number of expected non-zeros in the projection matrix of shape (max_features, n_features). Thus this value must always be less than n_features in order to be valid.

See also

sktree.tree.ObliqueDecisionTreeClassifier

An oblique decision tree classifier.

sklearn.ensemble.RandomForestClassifier

An axis-aligned decision forest classifier.

Notes

The default values for the parameters controlling the size of the trees (e.g. max_depth, min_samples_leaf, etc.) lead to fully grown and unpruned trees which can potentially be very large on some data sets. To reduce memory consumption, the complexity and size of the trees should be controlled by setting those parameter values.

The features are always randomly permuted at each split. Therefore, the best found split may vary, even with the same training data, max_features=n_features and bootstrap=False, if the improvement of the criterion is identical for several splits enumerated during the search of the best split. To obtain a deterministic behaviour during fitting, random_state has to be fixed.

References

[1]

12. Breiman, “Random Forests”, Machine Learning, 45(1), 5-32, 2001.

Examples

>>> from sktree.ensemble import ObliqueRandomForestClassifier
>>> from sklearn.datasets import make_classification
>>> X, y = make_classification(n_samples=1000, n_features=4,
...                            n_informative=2, n_redundant=0,
...                            random_state=0, shuffle=False)
>>> clf = ObliqueRandomForestClassifier(max_depth=2, random_state=0)
>>> clf.fit(X, y)
ObliqueRandomForestClassifier(...)
>>> print(clf.predict([[0, 0, 0, 0]]))
[1]

Attributes:

estimators_list of sktree.tree.ObliqueDecisionTreeClassifier

The collection of fitted sub-estimators.

classes_ndarray of shape (n_classes,) or a list of such arrays

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

n_classes_int or list

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

n_features_int

The number of features when fit is performed.

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.

feature_importances_ndarray of shape (n_features,)

The impurity-based feature importances.

oob_score_float

Score of the training dataset obtained using an out-of-bag estimate. This attribute exists only when oob_score is True.

oob_decision_function_ndarray of shape (n_samples, n_classes) or (n_samples, n_classes, n_outputs)

Decision function computed with out-of-bag estimate on the training set. If n_estimators is small it might be possible that a data point was never left out during the bootstrap. In this case, oob_decision_function_ might contain NaN. This attribute exists only when oob_score is True.

Methods

apply(X)	Apply trees in the forest to X, return leaf indices.
compute_similarity_matrix(X)	Compute the similarity matrix of samples in X.
decision_path(X)	Return the decision path in the forest.
fit(X, y[, sample_weight, classes])	Build a forest of trees from the training set (X, y).
get_leaf_node_samples(X)	For each datapoint x in X, get the training samples in the leaf node.
get_metadata_routing()	Get metadata routing of this object.
get_params([deep])	Get parameters for this estimator.
partial_fit(X, y[, sample_weight, classes])	Update a decision tree classifier from the training set (X, y).
predict(X)	Predict class for X.
predict_log_proba(X)	Predict class log-probabilities for X.
predict_proba(X)	Predict class probabilities for X.
predict_proba_per_tree(X[, indices])	Compute the probability estimates for each tree in the forest.
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(*[, classes, sample_weight])	Request metadata passed to the fit method.
set_params(**params)	Set the parameters of this estimator.
set_partial_fit_request(*[, classes, ...])	Request metadata passed to the partial_fit method.
set_score_request(*[, sample_weight])	Request metadata passed to the score method.

apply(X)

Apply trees in the forest to X, return leaf indices.

Parameters:

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

The input samples. Internally, its dtype will be converted to dtype=np.float32. If a sparse matrix is provided, it will be converted into a sparse csr_matrix.

Returns:

X_leavesndarray of shape (n_samples, n_estimators)

For each datapoint x in X and for each tree in the forest, return the index of the leaf x ends up in.

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.

decision_path(X)

Return the decision path in the forest.

New in version 0.18.

Parameters:

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

The input samples. Internally, its dtype will be converted to dtype=np.float32. If a sparse matrix is provided, it will be converted into a sparse csr_matrix.

Returns:

indicatorsparse matrix of shape (n_samples, n_nodes)

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

n_nodes_ptrndarray of shape (n_estimators + 1,)

The columns from indicator[n_nodes_ptr[i]:n_nodes_ptr[i+1]] gives the indicator value for the i-th estimator.

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

Build a forest of trees from the training set (X, y).

Parameters:

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

The training input samples. Internally, its dtype will be converted to dtype=np.float32. If a sparse matrix is provided, it will be converted into a sparse csc_matrix.

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

The target values (class labels in classification, real numbers in regression).

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. In the case of classification, splits are also ignored if they would result in any single class carrying a negative weight in either child node.

classesarray_like of shape (n_classes,), default=None

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

Returns:

selfobject

Fitted estimator.

get_leaf_node_samples(X)

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.

Returns:

leaf_node_samplesa list of array_like

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-like 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_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, 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.

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:

selfobject

Returns the instance itself.

predict(X)

Predict class for X.

The predicted class of an input sample is a vote by the trees in the forest, weighted by their probability estimates. That is, the predicted class is the one with highest mean probability estimate across the trees.

Parameters:

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

The input samples. Internally, its dtype will be converted to dtype=np.float32. If a sparse matrix is provided, it will be converted into a sparse csr_matrix.

Returns:

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

The predicted classes.

predict_log_proba(X)

Predict class log-probabilities for X.

The predicted class log-probabilities of an input sample is computed as the log of the mean predicted class probabilities of the trees in the forest.

Parameters:

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

The input samples. Internally, its dtype will be converted to dtype=np.float32. If a sparse matrix is provided, it will be converted into a sparse csr_matrix.

Returns:

pndarray of shape (n_samples, n_classes), or a list of such arrays

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

predict_proba(X)

Predict class probabilities for X.

The predicted class probabilities of an input sample are computed as the mean predicted class probabilities of the trees in the forest. The class probability of a single tree 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, its dtype will be converted to dtype=np.float32. If a sparse matrix is provided, it will be converted into a sparse csr_matrix.

Returns:

pndarray of shape (n_samples, n_classes), or a list of such arrays

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

predict_proba_per_tree(X, indices=None)

Compute the probability estimates for each tree in the forest.

Parameters:

Xarray_like of shape (n_samples, n_features)

The input data.

indiceslist of n_estimators length of array_like of shape (n_samples,), optional

The indices of the samples used to compute the probability estimates for each tree in the forest. If None, the indices are every sample in the input data.

Returns:

proba_per_treearray_like of shape (n_estimators, n_samples, n_classes)

The probability estimates for each tree in the forest.

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

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().

Returns:

yndarray of shape (n_samples, n_quantiles, [n_outputs])

The predicted values. The n_outputs dimension is present only for multi-output regressors.

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(*, 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:

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(*, 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:

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_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 estimators_samples_

The subset of drawn samples for each base estimator.

Returns a dynamically generated list of indices identifying the samples used for fitting each member of the ensemble, i.e., the in-bag samples.

Note: the list is re-created at each call to the property in order to reduce the object memory footprint by not storing the sampling data. Thus fetching the property may be slower than expected.

property feature_importances_

The impurity-based feature importances.

The higher, the more important the feature. 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,)

The values of this array sum to 1, unless all trees are single node trees consisting of only the root node, in which case it will be an array of zeros.

property oob_samples_

The sample indices that are out-of-bag.

Only utilized if bootstrap=True, otherwise, all samples are “in-bag”.