skmultiflow.meta.StreamingRandomPatchesClassifier

class skmultiflow.meta.StreamingRandomPatchesClassifier(base_estimator=HoeffdingTreeClassifier(binary_split=False, grace_period=50, leaf_prediction='nba', max_byte_size=33554432, memory_estimate_period=1000000, nb_threshold=0, no_preprune=False, nominal_attributes=None, remove_poor_atts=False, split_confidence=0.01, split_criterion='info_gain', stop_mem_management=False, tie_threshold=0.05), n_estimators=100, subspace_mode='percentage', subspace_size=60, training_method='randompatches', lam=6.0, drift_detection_method=ADWIN(delta=1e-05), warning_detection_method=ADWIN(delta=0.0001), disable_weighted_vote=False, disable_drift_detection=False, disable_background_learner=False, nominal_attributes=None, random_state=None)

Streaming Random Patches ensemble classifier.

Parameters

base_estimator: BaseSKMObject or sklearn.BaseObject, (default=HoeffdingTreeClassifier)

The base estimator.

n_estimators: int, (default=100)

Number of members in the ensemble.

subspace_mode: str, (default=’percentage’)

Indicates how m, defined by subspace_size, is interpreted. M represents the total number of features.

Only applies when training method is random subspaces or random patches.

‘m’ - Specified value

‘sqrtM1’ - sqrt(M)+1

‘MsqrtM1’ - M-(sqrt(M)+1)

‘percentage’ - Percentage

subspace_size: int, (default=60)

Number of features per subset for each classifier. Negative value means total_features - subspace_size.

training_method: str, (default=’randompatches’)

The training method to use.

‘randomsubspaces’ - Random subspaces

‘resampling’ - Resampling (bagging)

‘randompatches’ - Random patches

lam: float, (default=6.0)

Lambda value for bagging.

drift_detection_method: BaseDriftDetector, (default=ADWIN(delta=1e-5))

Drift detection method.

warning_detection_method: BaseDriftDetector, (default=ADWIN(delta=1e-4))

Warning detection method.

disable_weighted_vote: bool (default=False)

If True, disables weighted voting.

disable_drift_detection: bool (default=False)

If True, disables drift detection and background learner.

disable_background_learner: bool (default=False)

If True, disables background learner and trees are reset immediately if drift is detected.

nominal_attributes: list, optional

List of Nominal attributes. If emtpy, then assume that all attributes are numerical.

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.

Notes

The Streaming Random Patches (SRP) [1] ensemble method simulates bagging or random subspaces. The default algorithm uses both bagging and random subspaces, namely Random Patches. The default base estimator is a Hoeffding Tree, but it can be used with any other base estimator (differently from random forest variations).

References

1

Heitor Murilo Gomes, Jesse Read, Albert Bifet. Streaming Random Patches for Evolving Data Stream Classification. IEEE International Conference on Data Mining (ICDM), 2019.

Examples

>>> from skmultiflow.data import AGRAWALGenerator
>>> from skmultiflow.meta import StreamingRandomPatchesClassifier
>>>
>>> stream = AGRAWALGenerator(random_state=1)
>>> srp = StreamingRandomPatchesClassifier(random_state=1,
>>>                                              n_estimators=3)
>>>
>>> # Variables to control loop and track performance
>>> n_samples = 0
>>> correct_cnt = 0
>>> max_samples = 200
>>>
>>> # Run test-then-train loop for max_samples
>>> # or while there is data in the stream
>>> while n_samples < max_samples and stream.has_more_samples():
>>>     X, y = stream.next_sample()
>>>     y_pred = srp.predict(X)
>>>     if y[0] == y_pred[0]:
>>>         correct_cnt += 1
>>>     srp.partial_fit(X, y)
>>>     n_samples += 1
>>>
>>> print('{} samples analyzed.'.format(n_samples))

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)	Estimate the probability of X belonging to each 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 class labels of all samples in X.

classes: numpy.ndarray, optional (default=None)

Contains all possible/known class labels. Usage varies depending on the learning method.

sample_weight: numpy.ndarray, optional (default=None)

Samples weight. If not provided, uniform weights are assumed. Usage varies depending on the learning method.

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 class labels of all samples in X.

classes: numpy.ndarray, optional (default=None)

No used.

sample_weight: numpy.ndarray of shape (n_samples), optional (default=None)

Samples weight. If not provided, uniform weights are assumed. Usage varies depending on the learning method.

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 class labels for.

Returns

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

predict_proba(self, X)

Estimate the probability of X belonging to each class-labels.

Parameters

Xnumpy.ndarray of shape (n_samples, n_features)

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.

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