346 lines
11 KiB
Python
346 lines
11 KiB
Python
# Author: Arnaud Joly, Joel Nothman
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#
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# License: BSD 3 clause
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"""
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Multi-class / multi-label utility function
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==========================================
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"""
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from collections import Sequence
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from itertools import chain
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import numpy as np
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from ..externals.six import string_types
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def _unique_multiclass(y):
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if isinstance(y, np.ndarray):
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return np.unique(y)
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else:
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return set(y)
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def _unique_sequence_of_sequence(y):
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return set(chain.from_iterable(y))
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def _unique_indicator(y):
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return np.arange(y.shape[1])
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_FN_UNIQUE_LABELS = {
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'binary': _unique_multiclass,
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'multiclass': _unique_multiclass,
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'multilabel-sequences': _unique_sequence_of_sequence,
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'multilabel-indicator': _unique_indicator,
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}
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def unique_labels(*ys):
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"""Extract an ordered array of unique labels
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We don't allow:
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- mix of multilabel and multiclass (single label) targets
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- mix of label indicator matrix and anything else,
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because there are no explicit labels)
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- mix of label indicator matrices of different sizes
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- mix of string and integer labels
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At the moment, we also don't allow "mutliclass-multioutput" input type.
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Parameters
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----------
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ys : array-likes,
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Returns
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-------
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out : numpy array of shape [n_unique_labels]
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An ordered array of unique labels.
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Examples
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--------
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>>> from sklearn.utils.multiclass import unique_labels
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>>> unique_labels([3, 5, 5, 5, 7, 7])
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array([3, 5, 7])
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>>> unique_labels([1, 2, 3, 4], [2, 2, 3, 4])
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array([1, 2, 3, 4])
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>>> unique_labels([1, 2, 10], [5, 11])
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array([ 1, 2, 5, 10, 11])
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>>> unique_labels(np.array([[0.0, 1.0], [1.0, 1.0]]), np.zeros((2, 2)))
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array([0, 1])
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>>> unique_labels([(1, 2), (3,)], [(1, 2), tuple()])
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array([1, 2, 3])
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"""
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if not ys:
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raise ValueError('No argument has been passed.')
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# Check that we don't mix label format
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ys_types = set(type_of_target(x) for x in ys)
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if ys_types == set(["binary", "multiclass"]):
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ys_types = set(["multiclass"])
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if len(ys_types) > 1:
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raise ValueError("Mix type of y not allowed, got types %s" % ys_types)
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label_type = ys_types.pop()
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# Check consistency for the indicator format
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if (label_type == "multilabel-indicator" and
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len(set(y.shape[1] for y in ys)) > 1):
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raise ValueError("Multi-label binary indicator input with "
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"different numbers of labels")
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# Get the unique set of labels
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_unique_labels = _FN_UNIQUE_LABELS.get(label_type, None)
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if not _unique_labels:
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raise ValueError("Unknown label type")
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ys_labels = set(chain.from_iterable(_unique_labels(y) for y in ys))
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# Check that we don't mix string type with number type
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if (len(set(isinstance(label, string_types) for label in ys_labels)) > 1):
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raise ValueError("Mix of label input types (string and number)")
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return np.array(sorted(ys_labels))
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def _is_integral_float(y):
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return y.dtype.kind == 'f' and np.all(y.astype(int) == y)
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def is_label_indicator_matrix(y):
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""" Check if ``y`` is in the label indicator matrix format (multilabel).
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Parameters
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----------
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y : numpy array of shape [n_samples] or sequence of sequences
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Target values. In the multilabel case the nested sequences can
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have variable lengths.
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Returns
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-------
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out : bool,
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Return ``True``, if ``y`` is in a label indicator matrix format,
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else ``False``.
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Examples
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--------
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>>> import numpy as np
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>>> from sklearn.utils.multiclass import is_label_indicator_matrix
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>>> is_label_indicator_matrix([0, 1, 0, 1])
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False
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>>> is_label_indicator_matrix([[1], [0, 2], []])
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False
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>>> is_label_indicator_matrix(np.array([[1, 0], [0, 0]]))
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True
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>>> is_label_indicator_matrix(np.array([[1], [0], [0]]))
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False
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>>> is_label_indicator_matrix(np.array([[1, 0, 0]]))
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True
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"""
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if not (hasattr(y, "shape") and y.ndim == 2 and y.shape[1] > 1):
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return False
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labels = np.unique(y)
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return len(labels) <= 2 and (y.dtype.kind in 'biu' # bool, int, uint
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or _is_integral_float(labels))
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def is_sequence_of_sequences(y):
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""" Check if ``y`` is in the sequence of sequences format (multilabel).
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Parameters
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----------
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y : sequence or array.
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Returns
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-------
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out : bool,
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Return ``True``, if ``y`` is a sequence of sequences else ``False``.
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>>> import numpy as np
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>>> from sklearn.utils.multiclass import is_multilabel
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>>> is_sequence_of_sequences([0, 1, 0, 1])
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False
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>>> is_sequence_of_sequences([[1], [0, 2], []])
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True
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>>> is_sequence_of_sequences(np.array([[1], [0, 2], []], dtype=object))
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True
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>>> is_sequence_of_sequences([(1,), (0, 2), ()])
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True
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>>> is_sequence_of_sequences(np.array([[1, 0], [0, 0]]))
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False
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>>> is_sequence_of_sequences(np.array([[1], [0], [0]]))
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False
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>>> is_sequence_of_sequences(np.array([[1, 0, 0]]))
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False
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"""
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# the explicit check for ndarray is for forward compatibility; future
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# versions of Numpy might want to register ndarray as a Sequence
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try:
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return (not isinstance(y[0], np.ndarray) and isinstance(y[0], Sequence)
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and not isinstance(y[0], string_types))
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except IndexError:
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return False
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def is_multilabel(y):
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""" Check if ``y`` is in a multilabel format.
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Parameters
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----------
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y : numpy array of shape [n_samples] or sequence of sequences
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Target values. In the multilabel case the nested sequences can
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have variable lengths.
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Returns
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-------
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out : bool,
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Return ``True``, if ``y`` is in a multilabel format, else ```False``.
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Examples
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--------
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>>> import numpy as np
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>>> from sklearn.utils.multiclass import is_multilabel
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>>> is_multilabel([0, 1, 0, 1])
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False
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>>> is_multilabel([[1], [0, 2], []])
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True
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>>> is_multilabel(np.array([[1, 0], [0, 0]]))
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True
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>>> is_multilabel(np.array([[1], [0], [0]]))
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False
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>>> is_multilabel(np.array([[1, 0, 0]]))
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True
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"""
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return is_label_indicator_matrix(y) or is_sequence_of_sequences(y)
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def type_of_target(y):
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"""Determine the type of data indicated by target `y`
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Parameters
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----------
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y : array-like
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Returns
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-------
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target_type : string
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One of:
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* 'continuous': `y` is an array-like of floats that are not all
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integers, and is 1d or a column vector.
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* 'continuous-multioutput': `y` is a 2d array of floats that are
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not all integers, and both dimensions are of size > 1.
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* 'binary': `y` contains <= 2 discrete values and is 1d or a column
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vector.
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* 'multiclass': `y` contains more than two discrete values, is not a
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sequence of sequences, and is 1d or a column vector.
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* 'mutliclass-multioutput': `y` is a 2d array that contains more
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than two discrete values, is not a sequence of sequences, and both
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dimensions are of size > 1.
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* 'multilabel-sequences': `y` is a sequence of sequences, a 1d
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array-like of objects that are sequences of labels.
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* 'multilabel-indicator': `y` is a label indicator matrix, an array
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of two dimensions with at least two columns, and at most 2 unique
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values.
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* 'unknown': `y` is array-like but none of the above, such as a 3d
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array, or an array of non-sequence objects.
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Examples
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--------
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>>> import numpy as np
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>>> type_of_target([0.1, 0.6])
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'continuous'
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>>> type_of_target([1, -1, -1, 1])
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'binary'
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>>> type_of_target(['a', 'b', 'a'])
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'binary'
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>>> type_of_target([1, 0, 2])
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'multiclass'
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>>> type_of_target(['a', 'b', 'c'])
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'multiclass'
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>>> type_of_target(np.array([[1, 2], [3, 1]]))
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'multiclass-multioutput'
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>>> type_of_target(np.array([[1.5, 2.0], [3.0, 1.6]]))
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'continuous-multioutput'
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>>> type_of_target([['a', 'b'], ['c'], []])
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'multilabel-sequences'
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>>> type_of_target([[]])
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'multilabel-sequences'
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>>> type_of_target(np.array([[0, 1], [1, 1]]))
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'multilabel-indicator'
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"""
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# XXX: is there a way to duck-type this condition?
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valid = (isinstance(y, (np.ndarray, Sequence))
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and not isinstance(y, string_types))
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if not valid:
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raise ValueError('Expected array-like (array or non-string sequence), '
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'got %r' % y)
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if is_sequence_of_sequences(y):
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return 'multilabel-sequences'
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elif is_label_indicator_matrix(y):
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return 'multilabel-indicator'
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try:
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y = np.asarray(y)
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except ValueError:
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# known to fail in numpy 1.3 for array of arrays
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return 'unknown'
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if y.ndim > 2 or y.dtype == object:
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return 'unknown'
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if y.ndim == 2 and y.shape[1] == 0:
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return 'unknown'
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elif y.ndim == 2 and y.shape[1] > 1:
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suffix = '-multioutput'
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else:
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# column vector or 1d
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suffix = ''
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# check float and contains non-integer float values:
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if y.dtype.kind == 'f' and np.any(y != y.astype(int)):
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return 'continuous' + suffix
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if len(np.unique(y)) <= 2:
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assert not suffix, "2d binary array-like should be multilabel"
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return 'binary'
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else:
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return 'multiclass' + suffix
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def _check_partial_fit_first_call(clf, classes=None):
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"""Private helper function for factorizing common classes param logic
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Estimator that implement the ``partial_fit`` API need to be provided with
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the list of possible classes at the first call to partial fit.and
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Subsequent calls to partial_fit should check that ``classes`` is still
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consistent with a previous value of ``clf.classes_`` when provided.
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This function returns True if it detects that this was the first call to
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``partial_fit`` on ``clf``. In that case the ``classes_`` attribute is also
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set on ``clf``.
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"""
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if getattr(clf, 'classes_', None) is None and classes is None:
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raise ValueError("classes must be passed on the first call "
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"to partial_fit.")
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elif classes is not None:
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if getattr(clf, 'classes_', None) is not None:
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if not np.all(clf.classes_ == unique_labels(classes)):
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raise ValueError(
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"`classes=%r` is not the same as on last call "
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"to partial_fit, was: %r" % (classes, clf.classes_))
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else:
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# This is the first call to partial_fit
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clf.classes_ = unique_labels(classes)
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return True
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# classes is None and clf.classes_ has already previously been set:
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# nothing to do
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return False
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