Label Network Embeddings for Multi-label Classification (LNEMLC)¶

class skmultilearn.embedding.OpenNetworkEmbedder(graph_builder, embedding, dimension, aggregation_function, normalize_weights, param_dict=None)[source] ¶

Bases: object

Embed the label space using a label network embedder from OpenNE

Implements an OpenNE based LNEMLC: label network embeddings for multi-label classification.

Method name string Description

GraphFactorization Graph factorization embeddings

GraRep Graph representations with global structural information

HOPE High-order Proximity Preserved Embedding

LaplacianEigenmaps Detecting communities from multiple async label propagation on the graph

LINE Large-scale information network embedding

LLE Locally Linear Embedding

Parameters:	graph_builder (a GraphBuilderBase inherited transformer) – the graph builder to provide the adjacency matrix and weight map for the underlying graph embedding (string, one of {'GraphFactorization', 'GraRep', 'HOPE', 'LaplacianEigenmaps', 'LINE', 'LLE'}) – the selected OpenNE embedding

dimension: int

the dimension of the label embedding vectors

aggregation_function: ‘add’, ‘multiply’, ‘average’ or Callable

the function used to aggregate label vectors for all labels assigned to each of the samples

normalize_weights: boolean

whether to normalize weights in the label graph by the number of samples or not

param_dict

parameters passed to the embedder, don’t use the dimension and graph parameters, this class will set them at fit

If you use this classifier please cite the relevant embedding method paper and the label network embedding for multi-label classification paper:

@article{zhang2007ml,
 title={ML-KNN: A lazy learning approach to multi-label learning},
 author={Zhang, Min-Ling and Zhou, Zhi-Hua},
 journal={Pattern recognition},
 volume={40},
 number={7},
 pages={2038--2048},
 year={2007},
 publisher={Elsevier}
}

Example code for using this embedder looks like this:

from skmultilearn.embedding import OpenNetworkEmbedder, EmbeddingClassifier
from sklearn.ensemble import RandomForestRegressor
from skmultilearn.adapt import MLkNN
from skmultilearn.cluster import LabelCooccurrenceGraphBuilder
graph_builder = LabelCooccurrenceGraphBuilder(weighted=True, include_self_edges=False)
openne_line_params = dict(batch_size=1000, negative_ratio=5)
clf = EmbeddingClassifier(
 OpenNetworkEmbedder(graph_builder, 'LINE', 4, 'add', True, openne_line_params),
 RandomForestRegressor(n_estimators=10),
 MLkNN(k=5)
)
clf.fit(X_train, y_train)
predictions = clf.predict(X_test)