Source code for skmultilearn.ensemble.partition

# -*- coding: utf-8 -*-
from import BinaryRelevance

from scipy import sparse

[docs]class LabelSpacePartitioningClassifier(BinaryRelevance): """Partition label space and classify each subspace separately This classifier performs classification by: 1. partitioning the label space into separate, smaller multi-label sub problems, using the supplied label space clusterer 2. training an instance of the supplied base mult-label classifier for each label space subset in the partition 3. predicting the result with each of subclassifiers and returning the sum of their results Parameters ---------- classifier : :class:`~sklearn.base.BaseEstimator` the base classifier that will be used in a class, will be automatically put under :code:`self.classifier`. clusterer : :class:`~skmultilearn.cluster.LabelSpaceClustererBase` object that partitions the output space, will be automatically put under :code:`self.clusterer`. require_dense : [bool, bool] whether the base classifier requires [input, output] matrices in dense representation, will be automatically put under :code:`self.require_dense`. Attributes ---------- model_count_ : int number of trained models, in this classifier equal to the number of partitions partition_ : List[List[int]], shape=(`model_count_`,) list of lists of label indexes, used to index the output space matrix, set in :meth:`_generate_partition` via :meth:`fit` classifiers : List[:class:`~sklearn.base.BaseEstimator`], shape=(`model_count_`,) list of classifiers trained per partition, set in :meth:`fit` References ---------- If you use this clusterer please cite the clustering paper: .. code:: latex @Article{datadriven, author = {SzymaƄski, Piotr and Kajdanowicz, Tomasz and Kersting, Kristian}, title = {How Is a Data-Driven Approach Better than Random Choice in Label Space Division for Multi-Label Classification?}, journal = {Entropy}, volume = {18}, year = {2016}, number = {8}, article_number = {282}, url = {}, issn = {1099-4300}, doi = {10.3390/e18080282} } Examples -------- Here's an example of building a partitioned ensemble of Classifier Chains .. code :: python from skmultilearn.ensemble import MajorityVotingClassifier from skmultilearn.cluster import FixedLabelSpaceClusterer from skmultilearn.problem_transform import ClassifierChain from sklearn.naive_bayes import GaussianNB classifier = MajorityVotingClassifier( clusterer = FixedLabelSpaceClusterer(clusters = [[1,3,4], [0, 2, 5]]), classifier = ClassifierChain(classifier=GaussianNB()) ),y_train) predictions = classifier.predict(X_test) More advanced examples can be found in `the label relations exploration guide <../labelrelations.ipynb>`_ """ def __init__(self, classifier=None, clusterer=None, require_dense=None): super(LabelSpacePartitioningClassifier, self).__init__(classifier, require_dense) self.clusterer = clusterer self.copyable_attrs = ['clusterer', 'classifier', 'require_dense']
[docs] def predict(self, X): """Predict labels for X Parameters ---------- X : numpy.ndarray or scipy.sparse.csc_matrix input features of shape :code:`(n_samples, n_features)` Returns ------- scipy.sparse of int binary indicator matrix with label assignments with shape :code:`(n_samples, n_labels)` """ X = self._ensure_input_format( X, sparse_format='csr', enforce_sparse=True) result = sparse.lil_matrix((X.shape[0], self._label_count), dtype=int) for model in range(self.model_count_): predictions = self._ensure_output_format(self.classifiers_[model].predict( X), sparse_format=None, enforce_sparse=True).nonzero() for row, column in zip(predictions[0], predictions[1]): result[row, self.partition_[model][column]] = 1 return result
def _generate_partition(self, X, y): """Cluster the label space Saves the partiton generated by the clusterer to :code:`self.partition_` and sets :code:`self.model_count_` to number of clusers and :code:`self._label_count` to number of labels. Parameters ----------- X : numpy.ndarray or scipy.sparse input features of shape :code:`(n_samples, n_features)`, passed to clusterer y : numpy.ndarray or scipy.sparse binary indicator matrix with label assigments of shape :code:`(n_samples, n_labels)` Returns ------- LabelSpacePartitioningClassifier returns an instance of itself """ self.partition_ = self.clusterer.fit_predict(X, y) self.model_count_ = len(self.partition_) self._label_count = y.shape[1] return self