skmultiflow.trees.LabelCombinationHoeffdingTreeClassifier

class skmultiflow.trees.LabelCombinationHoeffdingTreeClassifier(max_byte_size=33554432, memory_estimate_period=1000000, grace_period=200, split_criterion='info_gain', split_confidence=1e-07, tie_threshold=0.05, binary_split=False, stop_mem_management=False, remove_poor_atts=False, no_preprune=False, leaf_prediction='nba', nb_threshold=0, nominal_attributes=None, n_labels=None)

Label Combination Hoeffding Tree for multi-label classification.

Label combination transforms the problem from multi-label to multi-class. For each unique combination of labels it assigns a class and proceeds with training the hoeffding tree normally.

The transformation is done by changing the label set which could be seen as a binary number to an int which will represent the class, and after the prediction the int is converted back to a binary number which is the predicted label-set.

The number of labels need to be provided for the transformation to work.

Parameters

max_byte_size: int (default=33554432)

Maximum memory consumed by the tree.

memory_estimate_period: int (default=1000000)

Number of instances between memory consumption checks.

grace_period: int (default=200)

Number of instances a leaf should observe between split attempts.

split_criterion: string (default=’info_gain’)

Split criterion to use.

‘gini’ - Gini

‘info_gain’ - Information Gain

split_confidence: float (default=0.0000001)

Allowed error in split decision, a value closer to 0 takes longer to decide.

tie_threshold: float (default=0.05)

Threshold below which a split will be forced to break ties.

binary_split: boolean (default=False)

If True, only allow binary splits.

stop_mem_management: boolean (default=False)

If True, stop growing as soon as memory limit is hit.

remove_poor_atts: boolean (default=False)

If True, disable poor attributes.

no_preprune: boolean (default=False)

If True, disable pre-pruning.

leaf_prediction: string (default=’nba’)

Prediction mechanism used at leafs.

‘mc’ - Majority Class

‘nb’ - Naive Bayes

‘nba’ - Naive Bayes Adaptive

nb_threshold: int (default=0)

Number of instances a leaf should observe before allowing Naive Bayes.

nominal_attributes: list, optional

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

n_labels: int (default=None)

the number of labels the problem has.

Examples

>>> # Imports
>>> from skmultiflow.data import MultilabelGenerator
>>> from skmultiflow.trees import LabelCombinationHoeffdingTreeClassifier
>>> from skmultiflow.metrics import hamming_score
>>>
>>> # Setting up a data stream
>>> stream = MultilabelGenerator(random_state=1, n_samples=200,
>>>                              n_targets=5, n_features=10)
>>>
>>> # Setup Label Combination Hoeffding Tree classifier
>>> lc_ht = LabelCombinationHoeffdingTreeClassifier(n_labels=stream.n_targets)
>>>
>>> # Setup variables to control loop and track performance
>>> n_samples = 0
>>> max_samples = 200
>>> true_labels = []
>>> predicts = []
>>>
>>> # Train the estimator with the samples provided by the data stream
>>> while n_samples < max_samples and stream.has_more_samples():
>>>     X, y = stream.next_sample()
>>>     y_pred = lc_ht.predict(X)
>>>     lc_ht.partial_fit(X, y)
>>>     predicts.extend(y_pred)
>>>     true_labels.extend(y)
>>>     n_samples += 1
>>>
>>> # Display results
>>> perf = hamming_score(true_labels, predicts)
>>> print('{} samples analyzed.'.format(n_samples))
>>> print('Label Combination Hoeffding Tree Hamming score: ' + str(perf))

Methods

fit(X, y[, classes, sample_weight])	Fit the model.
get_info()	Collects and returns the information about the configuration of the estimator
get_model_description()	Walk the tree and return its structure in a buffer.
get_model_rules()	Returns list of rules describing the tree.
get_params([deep])	Get parameters for this estimator.
get_rules_description()	Prints the description of tree using rules.
measure_byte_size()	Calculate the size of the tree.
partial_fit(X, y[, classes, sample_weight])	Incrementally trains the model.
predict(X)	Predicts the label of the X instance(s)
predict_proba(X)	Predicts probabilities of all label of the X instance(s)
reset()	Reset the Hoeffding Tree to default values.
score(X, y[, sample_weight])	Returns the mean accuracy on the given test data and labels.
set_params(**params)	Set the parameters of this estimator.

Attributes

leaf_prediction
model_measurements	Collect metrics corresponding to the current status of the tree.
n_labels
split_criterion

fit(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()

Collects and returns the information about the configuration of the estimator

Returns

string

Configuration of the estimator.

get_model_description()

Walk the tree and return its structure in a buffer.

Returns

string

The description of the model.

get_model_rules()

Returns list of rules describing the tree.

Returns

list (Rule)

list of the rules describing the tree

get_params(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.

get_rules_description()

Prints the description of tree using rules.

measure_byte_size()

Calculate the size of the tree.

Returns

int

Size of the tree in bytes.

property model_measurements

Collect metrics corresponding to the current status of the tree.

Returns

string

A string buffer containing the measurements of the tree.

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

Incrementally trains the model. Train samples (instances) are composed of X attributes and their corresponding targets y.

Parameters

X: numpy.ndarray of shape (n_samples, n_features)

Instance attributes.

y: array_like

Classes (targets) for all samples in X.

classes: Not used (default=None)

sample_weight: float or array-like, optional (default=None)

Samples weight. If not provided, uniform weights are assumed.

Returns

self

predict(X)

Predicts the label of the X instance(s)

Parameters

X: numpy.ndarray of shape (n_samples, n_features)

Samples for which we want to predict the labels.

Returns

numpy.array

Predicted labels for all instances in X.

predict_proba(X)

Predicts probabilities of all label of the X instance(s)

Parameters

X: numpy.ndarray of shape (n_samples, n_features)

Samples for which we want to predict the labels.

Returns

numpy.array

Predicted the probabilities of all the labels for all instances in X.

reset()

Reset the Hoeffding Tree to default values.

score(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(**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