# -*- coding: utf-8 -*-
from ..problem_transform.br 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 = {Szymański, 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 = {http://www.mdpi.com/1099-4300/18/8/282},
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())
)
classifier.fit(X_train,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