skmultilearn.neurofuzzy package¶

The skmultilearn.neurofuzzy module provides implementations of neural network and fuzzy approaches to multi-label classification.

Currently the available classes include:

class skmultilearn.neurofuzzy.MLARAM(vigilance=0.9, threshold=0.02, neurons=[])[source]

HARAM: A Hierarchical ARAM Neural Network for Large-Scale Text Classification

Parameters: vigilance : vigilance parameter for adaptiv resonance theory networks, controls how large a hyperbox can be, 1 it is small (no compression), 0 should assume all range. Normally set between 0.8 and 0.999, it is dataset dependent. It is responsible for the creation of the prototypes, therefore training of the network. threshold : controls how many prototypes participate by the prediction, can be changed at the testing phase. tneurons : if the network should inherited neurons (prototypes) from another network tdebug : set debug modus
BRIEFNAME = 'ML-ARAM'
fit(X, y)[source]

Fit classifier with training data

Parameters: X (matrix (n_samples, n_features)) – input features y (dense or sparse matrix of {0, 1} (n_samples, n_labels)) – binary indicator matrix with label assignments Fitted instance of self
predict(X)[source]

Predict labels for X

Parameters: X (dense or sparse matrix (n_samples, n_features)) – input features binary indicator matrix with label assignments array of arrays of int (n_samples, n_labels)
predict_proba(X)[source]

Predict probabilities of label assignments for X

Parameters: X (dense or sparse matrix (n_samples, n_labels)) – input features matrix with label assignment probabilities array of arrays of float (n_samples, n_labels)
reset()[source]

skmultilearn.neurofuzzy.MLARAMfast module¶

class skmultilearn.neurofuzzy.MLARAMfast.MLARAM(vigilance=0.9, threshold=0.02, neurons=[])[source]

HARAM: A Hierarchical ARAM Neural Network for Large-Scale Text Classification

Parameters: vigilance : vigilance parameter for adaptiv resonance theory networks, controls how large a hyperbox can be, 1 it is small (no compression), 0 should assume all range. Normally set between 0.8 and 0.999, it is dataset dependent. It is responsible for the creation of the prototypes, therefore training of the network. threshold : controls how many prototypes participate by the prediction, can be changed at the testing phase. tneurons : if the network should inherited neurons (prototypes) from another network tdebug : set debug modus
BRIEFNAME = 'ML-ARAM'
fit(X, y)[source]

Fit classifier with training data

Parameters: X (matrix (n_samples, n_features)) – input features y (dense or sparse matrix of {0, 1} (n_samples, n_labels)) – binary indicator matrix with label assignments Fitted instance of self
predict(X)[source]

Predict labels for X

Parameters: X (dense or sparse matrix (n_samples, n_features)) – input features binary indicator matrix with label assignments array of arrays of int (n_samples, n_labels)
predict_proba(X)[source]

Predict probabilities of label assignments for X

Parameters: X (dense or sparse matrix (n_samples, n_labels)) – input features matrix with label assignment probabilities array of arrays of float (n_samples, n_labels)
reset()[source]
class skmultilearn.neurofuzzy.MLARAMfast.Neuron(startpoint, label)[source]

Bases: future.types.newobject.newobject