skmultiflow.meta.ProbabilisticClassifierChain

class skmultiflow.meta.ProbabilisticClassifierChain(base_estimator=LogisticRegression(), order=None, random_state=None)

Probabilistic Classifier Chains for multi-label learning.

Published as ‘PCC’

Parameters

base_estimator: skmultiflow or sklearn model (default=LogisticRegression)

This is the ensemble classifier type, each ensemble classifier is going to be a copy of the base_estimator.

orderstr (default=None)

None to use default order, ‘random’ for random order.

random_state: int, RandomState instance or None, optional (default=None)

If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.

Examples

>>> from skmultiflow.data import make_logical
>>>
>>> X, Y = make_logical(random_state=1)
>>>
>>> print("TRUE: ")
>>> print(Y)
>>> print("vs")
>>> print("PCC")
>>> pcc = ProbabilisticClassifierChain(SGDClassifier(max_iter=100, loss='log', random_state=1))
>>> pcc.fit(X, Y)
>>> print(pcc.predict(X))
TRUE:
[[1. 0. 1.]
 [1. 1. 0.]
 [0. 0. 0.]
 [1. 1. 0.]]
vs
PCC
[[1. 0. 1.]
 [1. 1. 0.]
 [0. 0. 0.]
 [1. 1. 0.]]

Methods

fit(self, X, y[, classes, sample_weight])	Fit the model.
get_info(self)	Collects and returns the information about the configuration of the estimator
get_params(self[, deep])	Get parameters for this estimator.
partial_fit(self, X, y[, classes, sample_weight])	Partially (incrementally) fit the model.
predict(self, X)	Predict classes for the passed data.
predict_proba(self, X)	Estimates the probability of each sample in X belonging to each of the class-labels.
reset(self)	Resets the estimator to its initial state.
score(self, X, y[, sample_weight])	Returns the mean accuracy on the given test data and labels.
set_params(self, **params)	Set the parameters of this estimator.

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

Fit the model.

Parameters

Xnumpy.ndarray of shape (n_samples, n_features)

The features to train the model.

y: numpy.ndarray of shape (n_samples, n_targets)

An array-like with the labels of all samples in X.

classes: Not used (default=None)

sample_weight: Not used (default=None)

Returns

self

get_info(self)

Collects and returns the information about the configuration of the estimator

Returns

string

Configuration of the estimator.

get_params(self, deep=True)

Get parameters for this estimator.

Parameters

deepboolean, optional

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

Returns

paramsmapping of string to any

Parameter names mapped to their values.

partial_fit(self, X, y, classes=None, sample_weight=None)

Partially (incrementally) fit the model.

Parameters

Xnumpy.ndarray of shape (n_samples, n_features)

The features to train the model.

y: numpy.ndarray of shape (n_samples)

An array-like with the labels of all samples in X.

classes: Not used (default=None)

sample_weight: NOT used (default=None)

Returns

self

predict(self, X)

Predict classes for the passed data.

Parameters

Xnumpy.ndarray of shape (n_samples, n_features)

The set of data samples to predict the labels for.

Returns

A numpy.ndarray with all the predictions for the samples in X.

Notes

Explores all possible branches of the probability tree (i.e., all possible 2^L label combinations).

predict_proba(self, X)

Estimates the probability of each sample in X belonging to each of the class-labels.

Parameters

Xnumpy.ndarray of shape (n_samples, n_features)

The matrix of samples one wants to predict the class probabilities for.

Returns

A numpy.ndarray of shape (n_samples, n_labels), in which each outer entry is associated with the X entry of the

same index. And where the list in index [i] contains len(self.target_values) elements, each of which represents

the probability that the i-th sample of X belongs to a certain class-label.

Notes

Returns marginals [P(y_1=1|x),…,P(y_L=1|x,y_1,…,y_{L-1})] i.e., confidence predictions given inputs, for each instance.

This function suitable for multi-label (binary) data only at the moment (may give index-out-of-bounds error if uni- or multi-target (of > 2 values) data is used in training).

reset(self)

Resets the estimator to its initial state.

Returns

self

score(self, X, y, sample_weight=None)

Returns 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, shape = (n_samples, n_features)

Test samples.

yarray-like, shape = (n_samples) or (n_samples, n_outputs)

True labels for X.

sample_weightarray-like, shape = [n_samples], optional

Sample weights.

Returns

scorefloat

Mean accuracy of self.predict(X) wrt. y.

set_params(self, **params)

Set the parameters of this estimator.

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

Returns

self