skmultilearn.neurofuzzy package¶
The skmultilearn.neurofuzzy
module provides implementations of neural network
and fuzzy approaches to multi-label classification.
Currently the available classes include:
MLARAM
- A Multi-Label Hierarchical ARAM Neural Network
-
class
skmultilearn.neurofuzzy.
MLARAM
(vigilance=0.9, threshold=0.02, neurons=[])[source]¶ Bases:
skmultilearn.base.base.MLClassifierBase
HARAM: A Hierarchical ARAM Neural Network for Large-Scale Text Classification
See http://dx.doi.org/10.1109/ICDMW.2015.14
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
Returns: Fitted instance of self
-
predict
(X)[source]¶ Predict labels for X
Parameters: X (dense or sparse matrix (n_samples, n_features)) – input features Returns: binary indicator matrix with label assignments Return type: array of arrays of int (n_samples, n_labels)
-
Submodules¶
skmultilearn.neurofuzzy.MLARAMfast module¶
-
class
skmultilearn.neurofuzzy.MLARAMfast.
MLARAM
(vigilance=0.9, threshold=0.02, neurons=[])[source]¶ Bases:
skmultilearn.base.base.MLClassifierBase
HARAM: A Hierarchical ARAM Neural Network for Large-Scale Text Classification
See http://dx.doi.org/10.1109/ICDMW.2015.14
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
Returns: Fitted instance of self
-
predict
(X)[source]¶ Predict labels for X
Parameters: X (dense or sparse matrix (n_samples, n_features)) – input features Returns: binary indicator matrix with label assignments Return type: array of arrays of int (n_samples, n_labels)
-