ai-content-maker/.venv/Lib/site-packages/sklearn/utils/multiclass.py

554 lines
18 KiB
Python

"""
The :mod:`sklearn.utils.multiclass` module includes utilities to handle
multiclass/multioutput target in classifiers.
"""
# Author: Arnaud Joly, Joel Nothman, Hamzeh Alsalhi
#
# License: BSD 3 clause
import warnings
from collections.abc import Sequence
from itertools import chain
import numpy as np
from scipy.sparse import issparse
from ..utils._array_api import get_namespace
from ..utils.fixes import VisibleDeprecationWarning
from .validation import _assert_all_finite, check_array
def _unique_multiclass(y):
xp, is_array_api_compliant = get_namespace(y)
if hasattr(y, "__array__") or is_array_api_compliant:
return xp.unique_values(xp.asarray(y))
else:
return set(y)
def _unique_indicator(y):
xp, _ = get_namespace(y)
return xp.arange(
check_array(y, input_name="y", accept_sparse=["csr", "csc", "coo"]).shape[1]
)
_FN_UNIQUE_LABELS = {
"binary": _unique_multiclass,
"multiclass": _unique_multiclass,
"multilabel-indicator": _unique_indicator,
}
def unique_labels(*ys):
"""Extract an ordered array of unique labels.
We don't allow:
- mix of multilabel and multiclass (single label) targets
- mix of label indicator matrix and anything else,
because there are no explicit labels)
- mix of label indicator matrices of different sizes
- mix of string and integer labels
At the moment, we also don't allow "multiclass-multioutput" input type.
Parameters
----------
*ys : array-likes
Label values.
Returns
-------
out : ndarray of shape (n_unique_labels,)
An ordered array of unique labels.
Examples
--------
>>> from sklearn.utils.multiclass import unique_labels
>>> unique_labels([3, 5, 5, 5, 7, 7])
array([3, 5, 7])
>>> unique_labels([1, 2, 3, 4], [2, 2, 3, 4])
array([1, 2, 3, 4])
>>> unique_labels([1, 2, 10], [5, 11])
array([ 1, 2, 5, 10, 11])
"""
xp, is_array_api_compliant = get_namespace(*ys)
if not ys:
raise ValueError("No argument has been passed.")
# Check that we don't mix label format
ys_types = set(type_of_target(x) for x in ys)
if ys_types == {"binary", "multiclass"}:
ys_types = {"multiclass"}
if len(ys_types) > 1:
raise ValueError("Mix type of y not allowed, got types %s" % ys_types)
label_type = ys_types.pop()
# Check consistency for the indicator format
if (
label_type == "multilabel-indicator"
and len(
set(
check_array(y, accept_sparse=["csr", "csc", "coo"]).shape[1] for y in ys
)
)
> 1
):
raise ValueError(
"Multi-label binary indicator input with different numbers of labels"
)
# Get the unique set of labels
_unique_labels = _FN_UNIQUE_LABELS.get(label_type, None)
if not _unique_labels:
raise ValueError("Unknown label type: %s" % repr(ys))
if is_array_api_compliant:
# array_api does not allow for mixed dtypes
unique_ys = xp.concat([_unique_labels(y) for y in ys])
return xp.unique_values(unique_ys)
ys_labels = set(chain.from_iterable((i for i in _unique_labels(y)) for y in ys))
# Check that we don't mix string type with number type
if len(set(isinstance(label, str) for label in ys_labels)) > 1:
raise ValueError("Mix of label input types (string and number)")
return xp.asarray(sorted(ys_labels))
def _is_integral_float(y):
xp, is_array_api_compliant = get_namespace(y)
return xp.isdtype(y.dtype, "real floating") and bool(
xp.all(xp.astype((xp.astype(y, xp.int64)), y.dtype) == y)
)
def is_multilabel(y):
"""Check if ``y`` is in a multilabel format.
Parameters
----------
y : ndarray of shape (n_samples,)
Target values.
Returns
-------
out : bool
Return ``True``, if ``y`` is in a multilabel format, else ```False``.
Examples
--------
>>> import numpy as np
>>> from sklearn.utils.multiclass import is_multilabel
>>> is_multilabel([0, 1, 0, 1])
False
>>> is_multilabel([[1], [0, 2], []])
False
>>> is_multilabel(np.array([[1, 0], [0, 0]]))
True
>>> is_multilabel(np.array([[1], [0], [0]]))
False
>>> is_multilabel(np.array([[1, 0, 0]]))
True
"""
xp, is_array_api_compliant = get_namespace(y)
if hasattr(y, "__array__") or isinstance(y, Sequence) or is_array_api_compliant:
# DeprecationWarning will be replaced by ValueError, see NEP 34
# https://numpy.org/neps/nep-0034-infer-dtype-is-object.html
check_y_kwargs = dict(
accept_sparse=True,
allow_nd=True,
force_all_finite=False,
ensure_2d=False,
ensure_min_samples=0,
ensure_min_features=0,
)
with warnings.catch_warnings():
warnings.simplefilter("error", VisibleDeprecationWarning)
try:
y = check_array(y, dtype=None, **check_y_kwargs)
except (VisibleDeprecationWarning, ValueError) as e:
if str(e).startswith("Complex data not supported"):
raise
# dtype=object should be provided explicitly for ragged arrays,
# see NEP 34
y = check_array(y, dtype=object, **check_y_kwargs)
if not (hasattr(y, "shape") and y.ndim == 2 and y.shape[1] > 1):
return False
if issparse(y):
if y.format in ("dok", "lil"):
y = y.tocsr()
labels = xp.unique_values(y.data)
return (
len(y.data) == 0
or (labels.size == 1 or (labels.size == 2) and (0 in labels))
and (y.dtype.kind in "biu" or _is_integral_float(labels)) # bool, int, uint
)
else:
labels = xp.unique_values(y)
return labels.shape[0] < 3 and (
xp.isdtype(y.dtype, ("bool", "signed integer", "unsigned integer"))
or _is_integral_float(labels)
)
def check_classification_targets(y):
"""Ensure that target y is of a non-regression type.
Only the following target types (as defined in type_of_target) are allowed:
'binary', 'multiclass', 'multiclass-multioutput',
'multilabel-indicator', 'multilabel-sequences'
Parameters
----------
y : array-like
Target values.
"""
y_type = type_of_target(y, input_name="y")
if y_type not in [
"binary",
"multiclass",
"multiclass-multioutput",
"multilabel-indicator",
"multilabel-sequences",
]:
raise ValueError(
f"Unknown label type: {y_type}. Maybe you are trying to fit a "
"classifier, which expects discrete classes on a "
"regression target with continuous values."
)
def type_of_target(y, input_name=""):
"""Determine the type of data indicated by the target.
Note that this type is the most specific type that can be inferred.
For example:
* ``binary`` is more specific but compatible with ``multiclass``.
* ``multiclass`` of integers is more specific but compatible with
``continuous``.
* ``multilabel-indicator`` is more specific but compatible with
``multiclass-multioutput``.
Parameters
----------
y : {array-like, sparse matrix}
Target values. If a sparse matrix, `y` is expected to be a
CSR/CSC matrix.
input_name : str, default=""
The data name used to construct the error message.
.. versionadded:: 1.1.0
Returns
-------
target_type : str
One of:
* 'continuous': `y` is an array-like of floats that are not all
integers, and is 1d or a column vector.
* 'continuous-multioutput': `y` is a 2d array of floats that are
not all integers, and both dimensions are of size > 1.
* 'binary': `y` contains <= 2 discrete values and is 1d or a column
vector.
* 'multiclass': `y` contains more than two discrete values, is not a
sequence of sequences, and is 1d or a column vector.
* 'multiclass-multioutput': `y` is a 2d array that contains more
than two discrete values, is not a sequence of sequences, and both
dimensions are of size > 1.
* 'multilabel-indicator': `y` is a label indicator matrix, an array
of two dimensions with at least two columns, and at most 2 unique
values.
* 'unknown': `y` is array-like but none of the above, such as a 3d
array, sequence of sequences, or an array of non-sequence objects.
Examples
--------
>>> from sklearn.utils.multiclass import type_of_target
>>> import numpy as np
>>> type_of_target([0.1, 0.6])
'continuous'
>>> type_of_target([1, -1, -1, 1])
'binary'
>>> type_of_target(['a', 'b', 'a'])
'binary'
>>> type_of_target([1.0, 2.0])
'binary'
>>> type_of_target([1, 0, 2])
'multiclass'
>>> type_of_target([1.0, 0.0, 3.0])
'multiclass'
>>> type_of_target(['a', 'b', 'c'])
'multiclass'
>>> type_of_target(np.array([[1, 2], [3, 1]]))
'multiclass-multioutput'
>>> type_of_target([[1, 2]])
'multilabel-indicator'
>>> type_of_target(np.array([[1.5, 2.0], [3.0, 1.6]]))
'continuous-multioutput'
>>> type_of_target(np.array([[0, 1], [1, 1]]))
'multilabel-indicator'
"""
xp, is_array_api_compliant = get_namespace(y)
valid = (
(isinstance(y, Sequence) or issparse(y) or hasattr(y, "__array__"))
and not isinstance(y, str)
or is_array_api_compliant
)
if not valid:
raise ValueError(
"Expected array-like (array or non-string sequence), got %r" % y
)
sparse_pandas = y.__class__.__name__ in ["SparseSeries", "SparseArray"]
if sparse_pandas:
raise ValueError("y cannot be class 'SparseSeries' or 'SparseArray'")
if is_multilabel(y):
return "multilabel-indicator"
# DeprecationWarning will be replaced by ValueError, see NEP 34
# https://numpy.org/neps/nep-0034-infer-dtype-is-object.html
# We therefore catch both deprecation (NumPy < 1.24) warning and
# value error (NumPy >= 1.24).
check_y_kwargs = dict(
accept_sparse=True,
allow_nd=True,
force_all_finite=False,
ensure_2d=False,
ensure_min_samples=0,
ensure_min_features=0,
)
with warnings.catch_warnings():
warnings.simplefilter("error", VisibleDeprecationWarning)
if not issparse(y):
try:
y = check_array(y, dtype=None, **check_y_kwargs)
except (VisibleDeprecationWarning, ValueError) as e:
if str(e).startswith("Complex data not supported"):
raise
# dtype=object should be provided explicitly for ragged arrays,
# see NEP 34
y = check_array(y, dtype=object, **check_y_kwargs)
# The old sequence of sequences format
try:
first_row = y[[0], :] if issparse(y) else y[0]
if (
not hasattr(first_row, "__array__")
and isinstance(first_row, Sequence)
and not isinstance(first_row, str)
):
raise ValueError(
"You appear to be using a legacy multi-label data"
" representation. Sequence of sequences are no"
" longer supported; use a binary array or sparse"
" matrix instead - the MultiLabelBinarizer"
" transformer can convert to this format."
)
except IndexError:
pass
# Invalid inputs
if y.ndim not in (1, 2):
# Number of dimension greater than 2: [[[1, 2]]]
return "unknown"
if not min(y.shape):
# Empty ndarray: []/[[]]
if y.ndim == 1:
# 1-D empty array: []
return "binary" # []
# 2-D empty array: [[]]
return "unknown"
if not issparse(y) and y.dtype == object and not isinstance(y.flat[0], str):
# [obj_1] and not ["label_1"]
return "unknown"
# Check if multioutput
if y.ndim == 2 and y.shape[1] > 1:
suffix = "-multioutput" # [[1, 2], [1, 2]]
else:
suffix = "" # [1, 2, 3] or [[1], [2], [3]]
# Check float and contains non-integer float values
if xp.isdtype(y.dtype, "real floating"):
# [.1, .2, 3] or [[.1, .2, 3]] or [[1., .2]] and not [1., 2., 3.]
data = y.data if issparse(y) else y
if xp.any(data != xp.astype(data, int)):
_assert_all_finite(data, input_name=input_name)
return "continuous" + suffix
# Check multiclass
if issparse(first_row):
first_row = first_row.data
if xp.unique_values(y).shape[0] > 2 or (y.ndim == 2 and len(first_row) > 1):
# [1, 2, 3] or [[1., 2., 3]] or [[1, 2]]
return "multiclass" + suffix
else:
return "binary" # [1, 2] or [["a"], ["b"]]
def _check_partial_fit_first_call(clf, classes=None):
"""Private helper function for factorizing common classes param logic.
Estimators that implement the ``partial_fit`` API need to be provided with
the list of possible classes at the first call to partial_fit.
Subsequent calls to partial_fit should check that ``classes`` is still
consistent with a previous value of ``clf.classes_`` when provided.
This function returns True if it detects that this was the first call to
``partial_fit`` on ``clf``. In that case the ``classes_`` attribute is also
set on ``clf``.
"""
if getattr(clf, "classes_", None) is None and classes is None:
raise ValueError("classes must be passed on the first call to partial_fit.")
elif classes is not None:
if getattr(clf, "classes_", None) is not None:
if not np.array_equal(clf.classes_, unique_labels(classes)):
raise ValueError(
"`classes=%r` is not the same as on last call "
"to partial_fit, was: %r" % (classes, clf.classes_)
)
else:
# This is the first call to partial_fit
clf.classes_ = unique_labels(classes)
return True
# classes is None and clf.classes_ has already previously been set:
# nothing to do
return False
def class_distribution(y, sample_weight=None):
"""Compute class priors from multioutput-multiclass target data.
Parameters
----------
y : {array-like, sparse matrix} of size (n_samples, n_outputs)
The labels for each example.
sample_weight : array-like of shape (n_samples,), default=None
Sample weights.
Returns
-------
classes : list of size n_outputs of ndarray of size (n_classes,)
List of classes for each column.
n_classes : list of int of size n_outputs
Number of classes in each column.
class_prior : list of size n_outputs of ndarray of size (n_classes,)
Class distribution of each column.
"""
classes = []
n_classes = []
class_prior = []
n_samples, n_outputs = y.shape
if sample_weight is not None:
sample_weight = np.asarray(sample_weight)
if issparse(y):
y = y.tocsc()
y_nnz = np.diff(y.indptr)
for k in range(n_outputs):
col_nonzero = y.indices[y.indptr[k] : y.indptr[k + 1]]
# separate sample weights for zero and non-zero elements
if sample_weight is not None:
nz_samp_weight = sample_weight[col_nonzero]
zeros_samp_weight_sum = np.sum(sample_weight) - np.sum(nz_samp_weight)
else:
nz_samp_weight = None
zeros_samp_weight_sum = y.shape[0] - y_nnz[k]
classes_k, y_k = np.unique(
y.data[y.indptr[k] : y.indptr[k + 1]], return_inverse=True
)
class_prior_k = np.bincount(y_k, weights=nz_samp_weight)
# An explicit zero was found, combine its weight with the weight
# of the implicit zeros
if 0 in classes_k:
class_prior_k[classes_k == 0] += zeros_samp_weight_sum
# If an there is an implicit zero and it is not in classes and
# class_prior, make an entry for it
if 0 not in classes_k and y_nnz[k] < y.shape[0]:
classes_k = np.insert(classes_k, 0, 0)
class_prior_k = np.insert(class_prior_k, 0, zeros_samp_weight_sum)
classes.append(classes_k)
n_classes.append(classes_k.shape[0])
class_prior.append(class_prior_k / class_prior_k.sum())
else:
for k in range(n_outputs):
classes_k, y_k = np.unique(y[:, k], return_inverse=True)
classes.append(classes_k)
n_classes.append(classes_k.shape[0])
class_prior_k = np.bincount(y_k, weights=sample_weight)
class_prior.append(class_prior_k / class_prior_k.sum())
return (classes, n_classes, class_prior)
def _ovr_decision_function(predictions, confidences, n_classes):
"""Compute a continuous, tie-breaking OvR decision function from OvO.
It is important to include a continuous value, not only votes,
to make computing AUC or calibration meaningful.
Parameters
----------
predictions : array-like of shape (n_samples, n_classifiers)
Predicted classes for each binary classifier.
confidences : array-like of shape (n_samples, n_classifiers)
Decision functions or predicted probabilities for positive class
for each binary classifier.
n_classes : int
Number of classes. n_classifiers must be
``n_classes * (n_classes - 1 ) / 2``.
"""
n_samples = predictions.shape[0]
votes = np.zeros((n_samples, n_classes))
sum_of_confidences = np.zeros((n_samples, n_classes))
k = 0
for i in range(n_classes):
for j in range(i + 1, n_classes):
sum_of_confidences[:, i] -= confidences[:, k]
sum_of_confidences[:, j] += confidences[:, k]
votes[predictions[:, k] == 0, i] += 1
votes[predictions[:, k] == 1, j] += 1
k += 1
# Monotonically transform the sum_of_confidences to (-1/3, 1/3)
# and add it with votes. The monotonic transformation is
# f: x -> x / (3 * (|x| + 1)), it uses 1/3 instead of 1/2
# to ensure that we won't reach the limits and change vote order.
# The motivation is to use confidence levels as a way to break ties in
# the votes without switching any decision made based on a difference
# of 1 vote.
transformed_confidences = sum_of_confidences / (
3 * (np.abs(sum_of_confidences) + 1)
)
return votes + transformed_confidences