forked from 170010011/fr
903 lines
29 KiB
Python
903 lines
29 KiB
Python
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# Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
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# Mathieu Blondel <mathieu@mblondel.org>
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# Olivier Grisel <olivier.grisel@ensta.org>
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# Andreas Mueller <amueller@ais.uni-bonn.de>
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# Joel Nothman <joel.nothman@gmail.com>
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# Hamzeh Alsalhi <ha258@cornell.edu>
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# License: BSD 3 clause
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from collections import defaultdict
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import itertools
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import array
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import warnings
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import numpy as np
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import scipy.sparse as sp
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from ..base import BaseEstimator, TransformerMixin
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from ..utils.sparsefuncs import min_max_axis
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from ..utils import column_or_1d
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from ..utils.validation import check_array
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from ..utils.validation import check_is_fitted
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from ..utils.validation import _num_samples
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from ..utils.validation import _deprecate_positional_args
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from ..utils.multiclass import unique_labels
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from ..utils.multiclass import type_of_target
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from ..utils._encode import _encode, _unique
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__all__ = [
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'label_binarize',
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'LabelBinarizer',
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'LabelEncoder',
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'MultiLabelBinarizer',
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]
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class LabelEncoder(TransformerMixin, BaseEstimator):
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"""Encode target labels with value between 0 and n_classes-1.
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This transformer should be used to encode target values, *i.e.* `y`, and
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not the input `X`.
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Read more in the :ref:`User Guide <preprocessing_targets>`.
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.. versionadded:: 0.12
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Attributes
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----------
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classes_ : ndarray of shape (n_classes,)
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Holds the label for each class.
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Examples
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--------
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`LabelEncoder` can be used to normalize labels.
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>>> from sklearn import preprocessing
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>>> le = preprocessing.LabelEncoder()
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>>> le.fit([1, 2, 2, 6])
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LabelEncoder()
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>>> le.classes_
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array([1, 2, 6])
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>>> le.transform([1, 1, 2, 6])
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array([0, 0, 1, 2]...)
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>>> le.inverse_transform([0, 0, 1, 2])
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array([1, 1, 2, 6])
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It can also be used to transform non-numerical labels (as long as they are
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hashable and comparable) to numerical labels.
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>>> le = preprocessing.LabelEncoder()
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>>> le.fit(["paris", "paris", "tokyo", "amsterdam"])
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LabelEncoder()
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>>> list(le.classes_)
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['amsterdam', 'paris', 'tokyo']
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>>> le.transform(["tokyo", "tokyo", "paris"])
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array([2, 2, 1]...)
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>>> list(le.inverse_transform([2, 2, 1]))
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['tokyo', 'tokyo', 'paris']
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See Also
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--------
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OrdinalEncoder : Encode categorical features using an ordinal encoding
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scheme.
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OneHotEncoder : Encode categorical features as a one-hot numeric array.
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"""
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def fit(self, y):
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"""Fit label encoder.
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Parameters
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----------
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y : array-like of shape (n_samples,)
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Target values.
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Returns
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-------
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self : returns an instance of self.
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"""
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y = column_or_1d(y, warn=True)
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self.classes_ = _unique(y)
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return self
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def fit_transform(self, y):
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"""Fit label encoder and return encoded labels.
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Parameters
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----------
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y : array-like of shape (n_samples,)
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Target values.
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Returns
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-------
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y : array-like of shape (n_samples,)
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"""
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y = column_or_1d(y, warn=True)
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self.classes_, y = _unique(y, return_inverse=True)
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return y
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def transform(self, y):
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"""Transform labels to normalized encoding.
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Parameters
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----------
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y : array-like of shape (n_samples,)
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Target values.
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Returns
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-------
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y : array-like of shape (n_samples,)
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"""
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check_is_fitted(self)
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y = column_or_1d(y, warn=True)
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# transform of empty array is empty array
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if _num_samples(y) == 0:
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return np.array([])
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return _encode(y, uniques=self.classes_)
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def inverse_transform(self, y):
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"""Transform labels back to original encoding.
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Parameters
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----------
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y : ndarray of shape (n_samples,)
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Target values.
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Returns
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-------
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y : ndarray of shape (n_samples,)
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"""
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check_is_fitted(self)
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y = column_or_1d(y, warn=True)
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# inverse transform of empty array is empty array
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if _num_samples(y) == 0:
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return np.array([])
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diff = np.setdiff1d(y, np.arange(len(self.classes_)))
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if len(diff):
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raise ValueError(
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"y contains previously unseen labels: %s" % str(diff))
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y = np.asarray(y)
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return self.classes_[y]
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def _more_tags(self):
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return {'X_types': ['1dlabels']}
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class LabelBinarizer(TransformerMixin, BaseEstimator):
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"""Binarize labels in a one-vs-all fashion.
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Several regression and binary classification algorithms are
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available in scikit-learn. A simple way to extend these algorithms
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to the multi-class classification case is to use the so-called
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one-vs-all scheme.
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At learning time, this simply consists in learning one regressor
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or binary classifier per class. In doing so, one needs to convert
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multi-class labels to binary labels (belong or does not belong
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to the class). LabelBinarizer makes this process easy with the
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transform method.
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At prediction time, one assigns the class for which the corresponding
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model gave the greatest confidence. LabelBinarizer makes this easy
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with the inverse_transform method.
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Read more in the :ref:`User Guide <preprocessing_targets>`.
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Parameters
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----------
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neg_label : int, default=0
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Value with which negative labels must be encoded.
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pos_label : int, default=1
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Value with which positive labels must be encoded.
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sparse_output : bool, default=False
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True if the returned array from transform is desired to be in sparse
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CSR format.
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Attributes
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----------
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classes_ : ndarray of shape (n_classes,)
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Holds the label for each class.
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y_type_ : str
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Represents the type of the target data as evaluated by
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utils.multiclass.type_of_target. Possible type are 'continuous',
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'continuous-multioutput', 'binary', 'multiclass',
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'multiclass-multioutput', 'multilabel-indicator', and 'unknown'.
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sparse_input_ : bool
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True if the input data to transform is given as a sparse matrix, False
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otherwise.
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Examples
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--------
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>>> from sklearn import preprocessing
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>>> lb = preprocessing.LabelBinarizer()
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>>> lb.fit([1, 2, 6, 4, 2])
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LabelBinarizer()
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>>> lb.classes_
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array([1, 2, 4, 6])
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>>> lb.transform([1, 6])
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array([[1, 0, 0, 0],
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[0, 0, 0, 1]])
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Binary targets transform to a column vector
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>>> lb = preprocessing.LabelBinarizer()
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>>> lb.fit_transform(['yes', 'no', 'no', 'yes'])
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array([[1],
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[0],
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[0],
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[1]])
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Passing a 2D matrix for multilabel classification
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>>> import numpy as np
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>>> lb.fit(np.array([[0, 1, 1], [1, 0, 0]]))
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LabelBinarizer()
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>>> lb.classes_
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array([0, 1, 2])
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>>> lb.transform([0, 1, 2, 1])
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array([[1, 0, 0],
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[0, 1, 0],
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[0, 0, 1],
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[0, 1, 0]])
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See Also
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--------
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label_binarize : Function to perform the transform operation of
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LabelBinarizer with fixed classes.
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OneHotEncoder : Encode categorical features using a one-hot aka one-of-K
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scheme.
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"""
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@_deprecate_positional_args
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def __init__(self, *, neg_label=0, pos_label=1, sparse_output=False):
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if neg_label >= pos_label:
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raise ValueError("neg_label={0} must be strictly less than "
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"pos_label={1}.".format(neg_label, pos_label))
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if sparse_output and (pos_label == 0 or neg_label != 0):
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raise ValueError("Sparse binarization is only supported with non "
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"zero pos_label and zero neg_label, got "
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"pos_label={0} and neg_label={1}"
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"".format(pos_label, neg_label))
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self.neg_label = neg_label
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self.pos_label = pos_label
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self.sparse_output = sparse_output
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def fit(self, y):
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"""Fit label binarizer.
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Parameters
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----------
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y : ndarray of shape (n_samples,) or (n_samples, n_classes)
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Target values. The 2-d matrix should only contain 0 and 1,
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represents multilabel classification.
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Returns
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-------
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self : returns an instance of self.
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"""
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self.y_type_ = type_of_target(y)
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if 'multioutput' in self.y_type_:
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raise ValueError("Multioutput target data is not supported with "
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"label binarization")
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if _num_samples(y) == 0:
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raise ValueError('y has 0 samples: %r' % y)
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self.sparse_input_ = sp.issparse(y)
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self.classes_ = unique_labels(y)
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return self
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def fit_transform(self, y):
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"""Fit label binarizer and transform multi-class labels to binary
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labels.
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The output of transform is sometimes referred to as
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the 1-of-K coding scheme.
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Parameters
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----------
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y : {ndarray, sparse matrix} of shape (n_samples,) or \
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(n_samples, n_classes)
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Target values. The 2-d matrix should only contain 0 and 1,
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represents multilabel classification. Sparse matrix can be
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CSR, CSC, COO, DOK, or LIL.
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Returns
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-------
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Y : {ndarray, sparse matrix} of shape (n_samples, n_classes)
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Shape will be (n_samples, 1) for binary problems. Sparse matrix
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will be of CSR format.
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"""
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return self.fit(y).transform(y)
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def transform(self, y):
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"""Transform multi-class labels to binary labels.
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The output of transform is sometimes referred to by some authors as
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the 1-of-K coding scheme.
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Parameters
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----------
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y : {array, sparse matrix} of shape (n_samples,) or \
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(n_samples, n_classes)
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Target values. The 2-d matrix should only contain 0 and 1,
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represents multilabel classification. Sparse matrix can be
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CSR, CSC, COO, DOK, or LIL.
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Returns
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-------
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Y : {ndarray, sparse matrix} of shape (n_samples, n_classes)
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Shape will be (n_samples, 1) for binary problems. Sparse matrix
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will be of CSR format.
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"""
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check_is_fitted(self)
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y_is_multilabel = type_of_target(y).startswith('multilabel')
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if y_is_multilabel and not self.y_type_.startswith('multilabel'):
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raise ValueError("The object was not fitted with multilabel"
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" input.")
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return label_binarize(y, classes=self.classes_,
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pos_label=self.pos_label,
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neg_label=self.neg_label,
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sparse_output=self.sparse_output)
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def inverse_transform(self, Y, threshold=None):
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"""Transform binary labels back to multi-class labels.
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Parameters
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----------
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Y : {ndarray, sparse matrix} of shape (n_samples, n_classes)
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Target values. All sparse matrices are converted to CSR before
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inverse transformation.
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threshold : float, default=None
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Threshold used in the binary and multi-label cases.
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Use 0 when ``Y`` contains the output of decision_function
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(classifier).
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Use 0.5 when ``Y`` contains the output of predict_proba.
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If None, the threshold is assumed to be half way between
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neg_label and pos_label.
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Returns
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-------
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y : {ndarray, sparse matrix} of shape (n_samples,)
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Target values. Sparse matrix will be of CSR format.
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Notes
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-----
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In the case when the binary labels are fractional
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(probabilistic), inverse_transform chooses the class with the
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greatest value. Typically, this allows to use the output of a
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linear model's decision_function method directly as the input
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of inverse_transform.
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"""
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check_is_fitted(self)
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if threshold is None:
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threshold = (self.pos_label + self.neg_label) / 2.
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if self.y_type_ == "multiclass":
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y_inv = _inverse_binarize_multiclass(Y, self.classes_)
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||
|
|
else:
|
||
|
|
y_inv = _inverse_binarize_thresholding(Y, self.y_type_,
|
||
|
|
self.classes_, threshold)
|
||
|
|
|
||
|
|
if self.sparse_input_:
|
||
|
|
y_inv = sp.csr_matrix(y_inv)
|
||
|
|
elif sp.issparse(y_inv):
|
||
|
|
y_inv = y_inv.toarray()
|
||
|
|
|
||
|
|
return y_inv
|
||
|
|
|
||
|
|
def _more_tags(self):
|
||
|
|
return {'X_types': ['1dlabels']}
|
||
|
|
|
||
|
|
|
||
|
|
@_deprecate_positional_args
|
||
|
|
def label_binarize(y, *, classes, neg_label=0, pos_label=1,
|
||
|
|
sparse_output=False):
|
||
|
|
"""Binarize labels in a one-vs-all fashion.
|
||
|
|
|
||
|
|
Several regression and binary classification algorithms are
|
||
|
|
available in scikit-learn. A simple way to extend these algorithms
|
||
|
|
to the multi-class classification case is to use the so-called
|
||
|
|
one-vs-all scheme.
|
||
|
|
|
||
|
|
This function makes it possible to compute this transformation for a
|
||
|
|
fixed set of class labels known ahead of time.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
y : array-like
|
||
|
|
Sequence of integer labels or multilabel data to encode.
|
||
|
|
|
||
|
|
classes : array-like of shape (n_classes,)
|
||
|
|
Uniquely holds the label for each class.
|
||
|
|
|
||
|
|
neg_label : int, default=0
|
||
|
|
Value with which negative labels must be encoded.
|
||
|
|
|
||
|
|
pos_label : int, default=1
|
||
|
|
Value with which positive labels must be encoded.
|
||
|
|
|
||
|
|
sparse_output : bool, default=False,
|
||
|
|
Set to true if output binary array is desired in CSR sparse format.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
Y : {ndarray, sparse matrix} of shape (n_samples, n_classes)
|
||
|
|
Shape will be (n_samples, 1) for binary problems. Sparse matrix will
|
||
|
|
be of CSR format.
|
||
|
|
|
||
|
|
Examples
|
||
|
|
--------
|
||
|
|
>>> from sklearn.preprocessing import label_binarize
|
||
|
|
>>> label_binarize([1, 6], classes=[1, 2, 4, 6])
|
||
|
|
array([[1, 0, 0, 0],
|
||
|
|
[0, 0, 0, 1]])
|
||
|
|
|
||
|
|
The class ordering is preserved:
|
||
|
|
|
||
|
|
>>> label_binarize([1, 6], classes=[1, 6, 4, 2])
|
||
|
|
array([[1, 0, 0, 0],
|
||
|
|
[0, 1, 0, 0]])
|
||
|
|
|
||
|
|
Binary targets transform to a column vector
|
||
|
|
|
||
|
|
>>> label_binarize(['yes', 'no', 'no', 'yes'], classes=['no', 'yes'])
|
||
|
|
array([[1],
|
||
|
|
[0],
|
||
|
|
[0],
|
||
|
|
[1]])
|
||
|
|
|
||
|
|
See Also
|
||
|
|
--------
|
||
|
|
LabelBinarizer : Class used to wrap the functionality of label_binarize and
|
||
|
|
allow for fitting to classes independently of the transform operation.
|
||
|
|
"""
|
||
|
|
if not isinstance(y, list):
|
||
|
|
# XXX Workaround that will be removed when list of list format is
|
||
|
|
# dropped
|
||
|
|
y = check_array(y, accept_sparse='csr', ensure_2d=False, dtype=None)
|
||
|
|
else:
|
||
|
|
if _num_samples(y) == 0:
|
||
|
|
raise ValueError('y has 0 samples: %r' % y)
|
||
|
|
if neg_label >= pos_label:
|
||
|
|
raise ValueError("neg_label={0} must be strictly less than "
|
||
|
|
"pos_label={1}.".format(neg_label, pos_label))
|
||
|
|
|
||
|
|
if (sparse_output and (pos_label == 0 or neg_label != 0)):
|
||
|
|
raise ValueError("Sparse binarization is only supported with non "
|
||
|
|
"zero pos_label and zero neg_label, got "
|
||
|
|
"pos_label={0} and neg_label={1}"
|
||
|
|
"".format(pos_label, neg_label))
|
||
|
|
|
||
|
|
# To account for pos_label == 0 in the dense case
|
||
|
|
pos_switch = pos_label == 0
|
||
|
|
if pos_switch:
|
||
|
|
pos_label = -neg_label
|
||
|
|
|
||
|
|
y_type = type_of_target(y)
|
||
|
|
if 'multioutput' in y_type:
|
||
|
|
raise ValueError("Multioutput target data is not supported with label "
|
||
|
|
"binarization")
|
||
|
|
if y_type == 'unknown':
|
||
|
|
raise ValueError("The type of target data is not known")
|
||
|
|
|
||
|
|
n_samples = y.shape[0] if sp.issparse(y) else len(y)
|
||
|
|
n_classes = len(classes)
|
||
|
|
classes = np.asarray(classes)
|
||
|
|
|
||
|
|
if y_type == "binary":
|
||
|
|
if n_classes == 1:
|
||
|
|
if sparse_output:
|
||
|
|
return sp.csr_matrix((n_samples, 1), dtype=int)
|
||
|
|
else:
|
||
|
|
Y = np.zeros((len(y), 1), dtype=int)
|
||
|
|
Y += neg_label
|
||
|
|
return Y
|
||
|
|
elif len(classes) >= 3:
|
||
|
|
y_type = "multiclass"
|
||
|
|
|
||
|
|
sorted_class = np.sort(classes)
|
||
|
|
if y_type == "multilabel-indicator":
|
||
|
|
y_n_classes = y.shape[1] if hasattr(y, 'shape') else len(y[0])
|
||
|
|
if classes.size != y_n_classes:
|
||
|
|
raise ValueError("classes {0} mismatch with the labels {1}"
|
||
|
|
" found in the data"
|
||
|
|
.format(classes, unique_labels(y)))
|
||
|
|
|
||
|
|
if y_type in ("binary", "multiclass"):
|
||
|
|
y = column_or_1d(y)
|
||
|
|
|
||
|
|
# pick out the known labels from y
|
||
|
|
y_in_classes = np.in1d(y, classes)
|
||
|
|
y_seen = y[y_in_classes]
|
||
|
|
indices = np.searchsorted(sorted_class, y_seen)
|
||
|
|
indptr = np.hstack((0, np.cumsum(y_in_classes)))
|
||
|
|
|
||
|
|
data = np.empty_like(indices)
|
||
|
|
data.fill(pos_label)
|
||
|
|
Y = sp.csr_matrix((data, indices, indptr),
|
||
|
|
shape=(n_samples, n_classes))
|
||
|
|
elif y_type == "multilabel-indicator":
|
||
|
|
Y = sp.csr_matrix(y)
|
||
|
|
if pos_label != 1:
|
||
|
|
data = np.empty_like(Y.data)
|
||
|
|
data.fill(pos_label)
|
||
|
|
Y.data = data
|
||
|
|
else:
|
||
|
|
raise ValueError("%s target data is not supported with label "
|
||
|
|
"binarization" % y_type)
|
||
|
|
|
||
|
|
if not sparse_output:
|
||
|
|
Y = Y.toarray()
|
||
|
|
Y = Y.astype(int, copy=False)
|
||
|
|
|
||
|
|
if neg_label != 0:
|
||
|
|
Y[Y == 0] = neg_label
|
||
|
|
|
||
|
|
if pos_switch:
|
||
|
|
Y[Y == pos_label] = 0
|
||
|
|
else:
|
||
|
|
Y.data = Y.data.astype(int, copy=False)
|
||
|
|
|
||
|
|
# preserve label ordering
|
||
|
|
if np.any(classes != sorted_class):
|
||
|
|
indices = np.searchsorted(sorted_class, classes)
|
||
|
|
Y = Y[:, indices]
|
||
|
|
|
||
|
|
if y_type == "binary":
|
||
|
|
if sparse_output:
|
||
|
|
Y = Y.getcol(-1)
|
||
|
|
else:
|
||
|
|
Y = Y[:, -1].reshape((-1, 1))
|
||
|
|
|
||
|
|
return Y
|
||
|
|
|
||
|
|
|
||
|
|
def _inverse_binarize_multiclass(y, classes):
|
||
|
|
"""Inverse label binarization transformation for multiclass.
|
||
|
|
|
||
|
|
Multiclass uses the maximal score instead of a threshold.
|
||
|
|
"""
|
||
|
|
classes = np.asarray(classes)
|
||
|
|
|
||
|
|
if sp.issparse(y):
|
||
|
|
# Find the argmax for each row in y where y is a CSR matrix
|
||
|
|
|
||
|
|
y = y.tocsr()
|
||
|
|
n_samples, n_outputs = y.shape
|
||
|
|
outputs = np.arange(n_outputs)
|
||
|
|
row_max = min_max_axis(y, 1)[1]
|
||
|
|
row_nnz = np.diff(y.indptr)
|
||
|
|
|
||
|
|
y_data_repeated_max = np.repeat(row_max, row_nnz)
|
||
|
|
# picks out all indices obtaining the maximum per row
|
||
|
|
y_i_all_argmax = np.flatnonzero(y_data_repeated_max == y.data)
|
||
|
|
|
||
|
|
# For corner case where last row has a max of 0
|
||
|
|
if row_max[-1] == 0:
|
||
|
|
y_i_all_argmax = np.append(y_i_all_argmax, [len(y.data)])
|
||
|
|
|
||
|
|
# Gets the index of the first argmax in each row from y_i_all_argmax
|
||
|
|
index_first_argmax = np.searchsorted(y_i_all_argmax, y.indptr[:-1])
|
||
|
|
# first argmax of each row
|
||
|
|
y_ind_ext = np.append(y.indices, [0])
|
||
|
|
y_i_argmax = y_ind_ext[y_i_all_argmax[index_first_argmax]]
|
||
|
|
# Handle rows of all 0
|
||
|
|
y_i_argmax[np.where(row_nnz == 0)[0]] = 0
|
||
|
|
|
||
|
|
# Handles rows with max of 0 that contain negative numbers
|
||
|
|
samples = np.arange(n_samples)[(row_nnz > 0) &
|
||
|
|
(row_max.ravel() == 0)]
|
||
|
|
for i in samples:
|
||
|
|
ind = y.indices[y.indptr[i]:y.indptr[i + 1]]
|
||
|
|
y_i_argmax[i] = classes[np.setdiff1d(outputs, ind)][0]
|
||
|
|
|
||
|
|
return classes[y_i_argmax]
|
||
|
|
else:
|
||
|
|
return classes.take(y.argmax(axis=1), mode="clip")
|
||
|
|
|
||
|
|
|
||
|
|
def _inverse_binarize_thresholding(y, output_type, classes, threshold):
|
||
|
|
"""Inverse label binarization transformation using thresholding."""
|
||
|
|
|
||
|
|
if output_type == "binary" and y.ndim == 2 and y.shape[1] > 2:
|
||
|
|
raise ValueError("output_type='binary', but y.shape = {0}".
|
||
|
|
format(y.shape))
|
||
|
|
|
||
|
|
if output_type != "binary" and y.shape[1] != len(classes):
|
||
|
|
raise ValueError("The number of class is not equal to the number of "
|
||
|
|
"dimension of y.")
|
||
|
|
|
||
|
|
classes = np.asarray(classes)
|
||
|
|
|
||
|
|
# Perform thresholding
|
||
|
|
if sp.issparse(y):
|
||
|
|
if threshold > 0:
|
||
|
|
if y.format not in ('csr', 'csc'):
|
||
|
|
y = y.tocsr()
|
||
|
|
y.data = np.array(y.data > threshold, dtype=int)
|
||
|
|
y.eliminate_zeros()
|
||
|
|
else:
|
||
|
|
y = np.array(y.toarray() > threshold, dtype=int)
|
||
|
|
else:
|
||
|
|
y = np.array(y > threshold, dtype=int)
|
||
|
|
|
||
|
|
# Inverse transform data
|
||
|
|
if output_type == "binary":
|
||
|
|
if sp.issparse(y):
|
||
|
|
y = y.toarray()
|
||
|
|
if y.ndim == 2 and y.shape[1] == 2:
|
||
|
|
return classes[y[:, 1]]
|
||
|
|
else:
|
||
|
|
if len(classes) == 1:
|
||
|
|
return np.repeat(classes[0], len(y))
|
||
|
|
else:
|
||
|
|
return classes[y.ravel()]
|
||
|
|
|
||
|
|
elif output_type == "multilabel-indicator":
|
||
|
|
return y
|
||
|
|
|
||
|
|
else:
|
||
|
|
raise ValueError("{0} format is not supported".format(output_type))
|
||
|
|
|
||
|
|
|
||
|
|
class MultiLabelBinarizer(TransformerMixin, BaseEstimator):
|
||
|
|
"""Transform between iterable of iterables and a multilabel format.
|
||
|
|
|
||
|
|
Although a list of sets or tuples is a very intuitive format for multilabel
|
||
|
|
data, it is unwieldy to process. This transformer converts between this
|
||
|
|
intuitive format and the supported multilabel format: a (samples x classes)
|
||
|
|
binary matrix indicating the presence of a class label.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
classes : array-like of shape (n_classes,), default=None
|
||
|
|
Indicates an ordering for the class labels.
|
||
|
|
All entries should be unique (cannot contain duplicate classes).
|
||
|
|
|
||
|
|
sparse_output : bool, default=False
|
||
|
|
Set to True if output binary array is desired in CSR sparse format.
|
||
|
|
|
||
|
|
Attributes
|
||
|
|
----------
|
||
|
|
classes_ : ndarray of shape (n_classes,)
|
||
|
|
A copy of the `classes` parameter when provided.
|
||
|
|
Otherwise it corresponds to the sorted set of classes found
|
||
|
|
when fitting.
|
||
|
|
|
||
|
|
Examples
|
||
|
|
--------
|
||
|
|
>>> from sklearn.preprocessing import MultiLabelBinarizer
|
||
|
|
>>> mlb = MultiLabelBinarizer()
|
||
|
|
>>> mlb.fit_transform([(1, 2), (3,)])
|
||
|
|
array([[1, 1, 0],
|
||
|
|
[0, 0, 1]])
|
||
|
|
>>> mlb.classes_
|
||
|
|
array([1, 2, 3])
|
||
|
|
|
||
|
|
>>> mlb.fit_transform([{'sci-fi', 'thriller'}, {'comedy'}])
|
||
|
|
array([[0, 1, 1],
|
||
|
|
[1, 0, 0]])
|
||
|
|
>>> list(mlb.classes_)
|
||
|
|
['comedy', 'sci-fi', 'thriller']
|
||
|
|
|
||
|
|
A common mistake is to pass in a list, which leads to the following issue:
|
||
|
|
|
||
|
|
>>> mlb = MultiLabelBinarizer()
|
||
|
|
>>> mlb.fit(['sci-fi', 'thriller', 'comedy'])
|
||
|
|
MultiLabelBinarizer()
|
||
|
|
>>> mlb.classes_
|
||
|
|
array(['-', 'c', 'd', 'e', 'f', 'h', 'i', 'l', 'm', 'o', 'r', 's', 't',
|
||
|
|
'y'], dtype=object)
|
||
|
|
|
||
|
|
To correct this, the list of labels should be passed in as:
|
||
|
|
|
||
|
|
>>> mlb = MultiLabelBinarizer()
|
||
|
|
>>> mlb.fit([['sci-fi', 'thriller', 'comedy']])
|
||
|
|
MultiLabelBinarizer()
|
||
|
|
>>> mlb.classes_
|
||
|
|
array(['comedy', 'sci-fi', 'thriller'], dtype=object)
|
||
|
|
|
||
|
|
See Also
|
||
|
|
--------
|
||
|
|
OneHotEncoder : Encode categorical features using a one-hot aka one-of-K
|
||
|
|
scheme.
|
||
|
|
"""
|
||
|
|
|
||
|
|
@_deprecate_positional_args
|
||
|
|
def __init__(self, *, classes=None, sparse_output=False):
|
||
|
|
self.classes = classes
|
||
|
|
self.sparse_output = sparse_output
|
||
|
|
|
||
|
|
def fit(self, y):
|
||
|
|
"""Fit the label sets binarizer, storing :term:`classes_`.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
y : iterable of iterables
|
||
|
|
A set of labels (any orderable and hashable object) for each
|
||
|
|
sample. If the `classes` parameter is set, `y` will not be
|
||
|
|
iterated.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
self : returns this MultiLabelBinarizer instance
|
||
|
|
"""
|
||
|
|
self._cached_dict = None
|
||
|
|
if self.classes is None:
|
||
|
|
classes = sorted(set(itertools.chain.from_iterable(y)))
|
||
|
|
elif len(set(self.classes)) < len(self.classes):
|
||
|
|
raise ValueError("The classes argument contains duplicate "
|
||
|
|
"classes. Remove these duplicates before passing "
|
||
|
|
"them to MultiLabelBinarizer.")
|
||
|
|
else:
|
||
|
|
classes = self.classes
|
||
|
|
dtype = int if all(isinstance(c, int) for c in classes) else object
|
||
|
|
self.classes_ = np.empty(len(classes), dtype=dtype)
|
||
|
|
self.classes_[:] = classes
|
||
|
|
return self
|
||
|
|
|
||
|
|
def fit_transform(self, y):
|
||
|
|
"""Fit the label sets binarizer and transform the given label sets.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
y : iterable of iterables
|
||
|
|
A set of labels (any orderable and hashable object) for each
|
||
|
|
sample. If the `classes` parameter is set, `y` will not be
|
||
|
|
iterated.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
y_indicator : {ndarray, sparse matrix} of shape (n_samples, n_classes)
|
||
|
|
A matrix such that `y_indicator[i, j] = 1` i.f.f. `classes_[j]`
|
||
|
|
is in `y[i]`, and 0 otherwise. Sparse matrix will be of CSR
|
||
|
|
format.
|
||
|
|
"""
|
||
|
|
self._cached_dict = None
|
||
|
|
|
||
|
|
if self.classes is not None:
|
||
|
|
return self.fit(y).transform(y)
|
||
|
|
|
||
|
|
# Automatically increment on new class
|
||
|
|
class_mapping = defaultdict(int)
|
||
|
|
class_mapping.default_factory = class_mapping.__len__
|
||
|
|
yt = self._transform(y, class_mapping)
|
||
|
|
|
||
|
|
# sort classes and reorder columns
|
||
|
|
tmp = sorted(class_mapping, key=class_mapping.get)
|
||
|
|
|
||
|
|
# (make safe for tuples)
|
||
|
|
dtype = int if all(isinstance(c, int) for c in tmp) else object
|
||
|
|
class_mapping = np.empty(len(tmp), dtype=dtype)
|
||
|
|
class_mapping[:] = tmp
|
||
|
|
self.classes_, inverse = np.unique(class_mapping, return_inverse=True)
|
||
|
|
# ensure yt.indices keeps its current dtype
|
||
|
|
yt.indices = np.array(inverse[yt.indices], dtype=yt.indices.dtype,
|
||
|
|
copy=False)
|
||
|
|
|
||
|
|
if not self.sparse_output:
|
||
|
|
yt = yt.toarray()
|
||
|
|
|
||
|
|
return yt
|
||
|
|
|
||
|
|
def transform(self, y):
|
||
|
|
"""Transform the given label sets.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
y : iterable of iterables
|
||
|
|
A set of labels (any orderable and hashable object) for each
|
||
|
|
sample. If the `classes` parameter is set, `y` will not be
|
||
|
|
iterated.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
y_indicator : array or CSR matrix, shape (n_samples, n_classes)
|
||
|
|
A matrix such that `y_indicator[i, j] = 1` iff `classes_[j]` is in
|
||
|
|
`y[i]`, and 0 otherwise.
|
||
|
|
"""
|
||
|
|
check_is_fitted(self)
|
||
|
|
|
||
|
|
class_to_index = self._build_cache()
|
||
|
|
yt = self._transform(y, class_to_index)
|
||
|
|
|
||
|
|
if not self.sparse_output:
|
||
|
|
yt = yt.toarray()
|
||
|
|
|
||
|
|
return yt
|
||
|
|
|
||
|
|
def _build_cache(self):
|
||
|
|
if self._cached_dict is None:
|
||
|
|
self._cached_dict = dict(zip(self.classes_,
|
||
|
|
range(len(self.classes_))))
|
||
|
|
|
||
|
|
return self._cached_dict
|
||
|
|
|
||
|
|
def _transform(self, y, class_mapping):
|
||
|
|
"""Transforms the label sets with a given mapping
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
y : iterable of iterables
|
||
|
|
class_mapping : Mapping
|
||
|
|
Maps from label to column index in label indicator matrix.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
y_indicator : sparse matrix of shape (n_samples, n_classes)
|
||
|
|
Label indicator matrix. Will be of CSR format.
|
||
|
|
"""
|
||
|
|
indices = array.array('i')
|
||
|
|
indptr = array.array('i', [0])
|
||
|
|
unknown = set()
|
||
|
|
for labels in y:
|
||
|
|
index = set()
|
||
|
|
for label in labels:
|
||
|
|
try:
|
||
|
|
index.add(class_mapping[label])
|
||
|
|
except KeyError:
|
||
|
|
unknown.add(label)
|
||
|
|
indices.extend(index)
|
||
|
|
indptr.append(len(indices))
|
||
|
|
if unknown:
|
||
|
|
warnings.warn('unknown class(es) {0} will be ignored'
|
||
|
|
.format(sorted(unknown, key=str)))
|
||
|
|
data = np.ones(len(indices), dtype=int)
|
||
|
|
|
||
|
|
return sp.csr_matrix((data, indices, indptr),
|
||
|
|
shape=(len(indptr) - 1, len(class_mapping)))
|
||
|
|
|
||
|
|
def inverse_transform(self, yt):
|
||
|
|
"""Transform the given indicator matrix into label sets.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
yt : {ndarray, sparse matrix} of shape (n_samples, n_classes)
|
||
|
|
A matrix containing only 1s ands 0s.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
y : list of tuples
|
||
|
|
The set of labels for each sample such that `y[i]` consists of
|
||
|
|
`classes_[j]` for each `yt[i, j] == 1`.
|
||
|
|
"""
|
||
|
|
check_is_fitted(self)
|
||
|
|
|
||
|
|
if yt.shape[1] != len(self.classes_):
|
||
|
|
raise ValueError('Expected indicator for {0} classes, but got {1}'
|
||
|
|
.format(len(self.classes_), yt.shape[1]))
|
||
|
|
|
||
|
|
if sp.issparse(yt):
|
||
|
|
yt = yt.tocsr()
|
||
|
|
if len(yt.data) != 0 and len(np.setdiff1d(yt.data, [0, 1])) > 0:
|
||
|
|
raise ValueError('Expected only 0s and 1s in label indicator.')
|
||
|
|
return [tuple(self.classes_.take(yt.indices[start:end]))
|
||
|
|
for start, end in zip(yt.indptr[:-1], yt.indptr[1:])]
|
||
|
|
else:
|
||
|
|
unexpected = np.setdiff1d(yt, [0, 1])
|
||
|
|
if len(unexpected) > 0:
|
||
|
|
raise ValueError('Expected only 0s and 1s in label indicator. '
|
||
|
|
'Also got {0}'.format(unexpected))
|
||
|
|
return [tuple(self.classes_.compress(indicators)) for indicators
|
||
|
|
in yt]
|
||
|
|
|
||
|
|
def _more_tags(self):
|
||
|
|
return {'X_types': ['2dlabels']}
|