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Intelegentny_Pszczelarz/.venv/Lib/site-packages/sklearn/metrics/_classification.py
Ulad 36e4e4f8a5 feature: "ANN commit 2"
2023-06-19 00:49:18 +02:00

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"""Metrics to assess performance on classification task given class prediction.
Functions named as ``*_score`` return a scalar value to maximize: the higher
the better.
Function named as ``*_error`` or ``*_loss`` return a scalar value to minimize:
the lower the better.
"""
# Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Mathieu Blondel <mathieu@mblondel.org>
# Olivier Grisel <olivier.grisel@ensta.org>
# Arnaud Joly <a.joly@ulg.ac.be>
# Jochen Wersdorfer <jochen@wersdoerfer.de>
# Lars Buitinck
# Joel Nothman <joel.nothman@gmail.com>
# Noel Dawe <noel@dawe.me>
# Jatin Shah <jatindshah@gmail.com>
# Saurabh Jha <saurabh.jhaa@gmail.com>
# Bernardo Stein <bernardovstein@gmail.com>
# Shangwu Yao <shangwuyao@gmail.com>
# Michal Karbownik <michakarbownik@gmail.com>
# License: BSD 3 clause
import warnings
import numpy as np
from scipy.sparse import coo_matrix
from scipy.sparse import csr_matrix
from scipy.special import xlogy
from ..preprocessing import LabelBinarizer
from ..preprocessing import LabelEncoder
from ..utils import assert_all_finite
from ..utils import check_array
from ..utils import check_consistent_length
from ..utils import column_or_1d
from ..utils.multiclass import unique_labels
from ..utils.multiclass import type_of_target
from ..utils.validation import _num_samples
from ..utils.sparsefuncs import count_nonzero
from ..utils._param_validation import validate_params
from ..exceptions import UndefinedMetricWarning
from ._base import _check_pos_label_consistency
def _check_zero_division(zero_division):
if isinstance(zero_division, str) and zero_division == "warn":
return
elif isinstance(zero_division, (int, float)) and zero_division in [0, 1]:
return
raise ValueError(
'Got zero_division={0}. Must be one of ["warn", 0, 1]'.format(zero_division)
)
def _check_targets(y_true, y_pred):
"""Check that y_true and y_pred belong to the same classification task.
This converts multiclass or binary types to a common shape, and raises a
ValueError for a mix of multilabel and multiclass targets, a mix of
multilabel formats, for the presence of continuous-valued or multioutput
targets, or for targets of different lengths.
Column vectors are squeezed to 1d, while multilabel formats are returned
as CSR sparse label indicators.
Parameters
----------
y_true : array-like
y_pred : array-like
Returns
-------
type_true : one of {'multilabel-indicator', 'multiclass', 'binary'}
The type of the true target data, as output by
``utils.multiclass.type_of_target``.
y_true : array or indicator matrix
y_pred : array or indicator matrix
"""
check_consistent_length(y_true, y_pred)
type_true = type_of_target(y_true, input_name="y_true")
type_pred = type_of_target(y_pred, input_name="y_pred")
y_type = {type_true, type_pred}
if y_type == {"binary", "multiclass"}:
y_type = {"multiclass"}
if len(y_type) > 1:
raise ValueError(
"Classification metrics can't handle a mix of {0} and {1} targets".format(
type_true, type_pred
)
)
# We can't have more than one value on y_type => The set is no more needed
y_type = y_type.pop()
# No metrics support "multiclass-multioutput" format
if y_type not in ["binary", "multiclass", "multilabel-indicator"]:
raise ValueError("{0} is not supported".format(y_type))
if y_type in ["binary", "multiclass"]:
y_true = column_or_1d(y_true)
y_pred = column_or_1d(y_pred)
if y_type == "binary":
try:
unique_values = np.union1d(y_true, y_pred)
except TypeError as e:
# We expect y_true and y_pred to be of the same data type.
# If `y_true` was provided to the classifier as strings,
# `y_pred` given by the classifier will also be encoded with
# strings. So we raise a meaningful error
raise TypeError(
"Labels in y_true and y_pred should be of the same type. "
f"Got y_true={np.unique(y_true)} and "
f"y_pred={np.unique(y_pred)}. Make sure that the "
"predictions provided by the classifier coincides with "
"the true labels."
) from e
if len(unique_values) > 2:
y_type = "multiclass"
if y_type.startswith("multilabel"):
y_true = csr_matrix(y_true)
y_pred = csr_matrix(y_pred)
y_type = "multilabel-indicator"
return y_type, y_true, y_pred
def _weighted_sum(sample_score, sample_weight, normalize=False):
if normalize:
return np.average(sample_score, weights=sample_weight)
elif sample_weight is not None:
return np.dot(sample_score, sample_weight)
else:
return sample_score.sum()
@validate_params(
{
"y_true": ["array-like", "sparse matrix"],
"y_pred": ["array-like", "sparse matrix"],
"normalize": ["boolean"],
"sample_weight": ["array-like", None],
}
)
def accuracy_score(y_true, y_pred, *, normalize=True, sample_weight=None):
"""Accuracy classification score.
In multilabel classification, this function computes subset accuracy:
the set of labels predicted for a sample must *exactly* match the
corresponding set of labels in y_true.
Read more in the :ref:`User Guide <accuracy_score>`.
Parameters
----------
y_true : 1d array-like, or label indicator array / sparse matrix
Ground truth (correct) labels.
y_pred : 1d array-like, or label indicator array / sparse matrix
Predicted labels, as returned by a classifier.
normalize : bool, default=True
If ``False``, return the number of correctly classified samples.
Otherwise, return the fraction of correctly classified samples.
sample_weight : array-like of shape (n_samples,), default=None
Sample weights.
Returns
-------
score : float
If ``normalize == True``, return the fraction of correctly
classified samples (float), else returns the number of correctly
classified samples (int).
The best performance is 1 with ``normalize == True`` and the number
of samples with ``normalize == False``.
See Also
--------
balanced_accuracy_score : Compute the balanced accuracy to deal with
imbalanced datasets.
jaccard_score : Compute the Jaccard similarity coefficient score.
hamming_loss : Compute the average Hamming loss or Hamming distance between
two sets of samples.
zero_one_loss : Compute the Zero-one classification loss. By default, the
function will return the percentage of imperfectly predicted subsets.
Notes
-----
In binary classification, this function is equal to the `jaccard_score`
function.
Examples
--------
>>> from sklearn.metrics import accuracy_score
>>> y_pred = [0, 2, 1, 3]
>>> y_true = [0, 1, 2, 3]
>>> accuracy_score(y_true, y_pred)
0.5
>>> accuracy_score(y_true, y_pred, normalize=False)
2
In the multilabel case with binary label indicators:
>>> import numpy as np
>>> accuracy_score(np.array([[0, 1], [1, 1]]), np.ones((2, 2)))
0.5
"""
# Compute accuracy for each possible representation
y_type, y_true, y_pred = _check_targets(y_true, y_pred)
check_consistent_length(y_true, y_pred, sample_weight)
if y_type.startswith("multilabel"):
differing_labels = count_nonzero(y_true - y_pred, axis=1)
score = differing_labels == 0
else:
score = y_true == y_pred
return _weighted_sum(score, sample_weight, normalize)
def confusion_matrix(
y_true, y_pred, *, labels=None, sample_weight=None, normalize=None
):
"""Compute confusion matrix to evaluate the accuracy of a classification.
By definition a confusion matrix :math:`C` is such that :math:`C_{i, j}`
is equal to the number of observations known to be in group :math:`i` and
predicted to be in group :math:`j`.
Thus in binary classification, the count of true negatives is
:math:`C_{0,0}`, false negatives is :math:`C_{1,0}`, true positives is
:math:`C_{1,1}` and false positives is :math:`C_{0,1}`.
Read more in the :ref:`User Guide <confusion_matrix>`.
Parameters
----------
y_true : array-like of shape (n_samples,)
Ground truth (correct) target values.
y_pred : array-like of shape (n_samples,)
Estimated targets as returned by a classifier.
labels : array-like of shape (n_classes), default=None
List of labels to index the matrix. This may be used to reorder
or select a subset of labels.
If ``None`` is given, those that appear at least once
in ``y_true`` or ``y_pred`` are used in sorted order.
sample_weight : array-like of shape (n_samples,), default=None
Sample weights.
.. versionadded:: 0.18
normalize : {'true', 'pred', 'all'}, default=None
Normalizes confusion matrix over the true (rows), predicted (columns)
conditions or all the population. If None, confusion matrix will not be
normalized.
Returns
-------
C : ndarray of shape (n_classes, n_classes)
Confusion matrix whose i-th row and j-th
column entry indicates the number of
samples with true label being i-th class
and predicted label being j-th class.
See Also
--------
ConfusionMatrixDisplay.from_estimator : Plot the confusion matrix
given an estimator, the data, and the label.
ConfusionMatrixDisplay.from_predictions : Plot the confusion matrix
given the true and predicted labels.
ConfusionMatrixDisplay : Confusion Matrix visualization.
References
----------
.. [1] `Wikipedia entry for the Confusion matrix
<https://en.wikipedia.org/wiki/Confusion_matrix>`_
(Wikipedia and other references may use a different
convention for axes).
Examples
--------
>>> from sklearn.metrics import confusion_matrix
>>> y_true = [2, 0, 2, 2, 0, 1]
>>> y_pred = [0, 0, 2, 2, 0, 2]
>>> confusion_matrix(y_true, y_pred)
array([[2, 0, 0],
[0, 0, 1],
[1, 0, 2]])
>>> y_true = ["cat", "ant", "cat", "cat", "ant", "bird"]
>>> y_pred = ["ant", "ant", "cat", "cat", "ant", "cat"]
>>> confusion_matrix(y_true, y_pred, labels=["ant", "bird", "cat"])
array([[2, 0, 0],
[0, 0, 1],
[1, 0, 2]])
In the binary case, we can extract true positives, etc as follows:
>>> tn, fp, fn, tp = confusion_matrix([0, 1, 0, 1], [1, 1, 1, 0]).ravel()
>>> (tn, fp, fn, tp)
(0, 2, 1, 1)
"""
y_type, y_true, y_pred = _check_targets(y_true, y_pred)
if y_type not in ("binary", "multiclass"):
raise ValueError("%s is not supported" % y_type)
if labels is None:
labels = unique_labels(y_true, y_pred)
else:
labels = np.asarray(labels)
n_labels = labels.size
if n_labels == 0:
raise ValueError("'labels' should contains at least one label.")
elif y_true.size == 0:
return np.zeros((n_labels, n_labels), dtype=int)
elif len(np.intersect1d(y_true, labels)) == 0:
raise ValueError("At least one label specified must be in y_true")
if sample_weight is None:
sample_weight = np.ones(y_true.shape[0], dtype=np.int64)
else:
sample_weight = np.asarray(sample_weight)
check_consistent_length(y_true, y_pred, sample_weight)
if normalize not in ["true", "pred", "all", None]:
raise ValueError("normalize must be one of {'true', 'pred', 'all', None}")
n_labels = labels.size
# If labels are not consecutive integers starting from zero, then
# y_true and y_pred must be converted into index form
need_index_conversion = not (
labels.dtype.kind in {"i", "u", "b"}
and np.all(labels == np.arange(n_labels))
and y_true.min() >= 0
and y_pred.min() >= 0
)
if need_index_conversion:
label_to_ind = {y: x for x, y in enumerate(labels)}
y_pred = np.array([label_to_ind.get(x, n_labels + 1) for x in y_pred])
y_true = np.array([label_to_ind.get(x, n_labels + 1) for x in y_true])
# intersect y_pred, y_true with labels, eliminate items not in labels
ind = np.logical_and(y_pred < n_labels, y_true < n_labels)
if not np.all(ind):
y_pred = y_pred[ind]
y_true = y_true[ind]
# also eliminate weights of eliminated items
sample_weight = sample_weight[ind]
# Choose the accumulator dtype to always have high precision
if sample_weight.dtype.kind in {"i", "u", "b"}:
dtype = np.int64
else:
dtype = np.float64
cm = coo_matrix(
(sample_weight, (y_true, y_pred)),
shape=(n_labels, n_labels),
dtype=dtype,
).toarray()
with np.errstate(all="ignore"):
if normalize == "true":
cm = cm / cm.sum(axis=1, keepdims=True)
elif normalize == "pred":
cm = cm / cm.sum(axis=0, keepdims=True)
elif normalize == "all":
cm = cm / cm.sum()
cm = np.nan_to_num(cm)
return cm
def multilabel_confusion_matrix(
y_true, y_pred, *, sample_weight=None, labels=None, samplewise=False
):
"""Compute a confusion matrix for each class or sample.
.. versionadded:: 0.21
Compute class-wise (default) or sample-wise (samplewise=True) multilabel
confusion matrix to evaluate the accuracy of a classification, and output
confusion matrices for each class or sample.
In multilabel confusion matrix :math:`MCM`, the count of true negatives
is :math:`MCM_{:,0,0}`, false negatives is :math:`MCM_{:,1,0}`,
true positives is :math:`MCM_{:,1,1}` and false positives is
:math:`MCM_{:,0,1}`.
Multiclass data will be treated as if binarized under a one-vs-rest
transformation. Returned confusion matrices will be in the order of
sorted unique labels in the union of (y_true, y_pred).
Read more in the :ref:`User Guide <multilabel_confusion_matrix>`.
Parameters
----------
y_true : {array-like, sparse matrix} of shape (n_samples, n_outputs) or \
(n_samples,)
Ground truth (correct) target values.
y_pred : {array-like, sparse matrix} of shape (n_samples, n_outputs) or \
(n_samples,)
Estimated targets as returned by a classifier.
sample_weight : array-like of shape (n_samples,), default=None
Sample weights.
labels : array-like of shape (n_classes,), default=None
A list of classes or column indices to select some (or to force
inclusion of classes absent from the data).
samplewise : bool, default=False
In the multilabel case, this calculates a confusion matrix per sample.
Returns
-------
multi_confusion : ndarray of shape (n_outputs, 2, 2)
A 2x2 confusion matrix corresponding to each output in the input.
When calculating class-wise multi_confusion (default), then
n_outputs = n_labels; when calculating sample-wise multi_confusion
(samplewise=True), n_outputs = n_samples. If ``labels`` is defined,
the results will be returned in the order specified in ``labels``,
otherwise the results will be returned in sorted order by default.
See Also
--------
confusion_matrix : Compute confusion matrix to evaluate the accuracy of a
classifier.
Notes
-----
The `multilabel_confusion_matrix` calculates class-wise or sample-wise
multilabel confusion matrices, and in multiclass tasks, labels are
binarized under a one-vs-rest way; while
:func:`~sklearn.metrics.confusion_matrix` calculates one confusion matrix
for confusion between every two classes.
Examples
--------
Multilabel-indicator case:
>>> import numpy as np
>>> from sklearn.metrics import multilabel_confusion_matrix
>>> y_true = np.array([[1, 0, 1],
... [0, 1, 0]])
>>> y_pred = np.array([[1, 0, 0],
... [0, 1, 1]])
>>> multilabel_confusion_matrix(y_true, y_pred)
array([[[1, 0],
[0, 1]],
<BLANKLINE>
[[1, 0],
[0, 1]],
<BLANKLINE>
[[0, 1],
[1, 0]]])
Multiclass case:
>>> y_true = ["cat", "ant", "cat", "cat", "ant", "bird"]
>>> y_pred = ["ant", "ant", "cat", "cat", "ant", "cat"]
>>> multilabel_confusion_matrix(y_true, y_pred,
... labels=["ant", "bird", "cat"])
array([[[3, 1],
[0, 2]],
<BLANKLINE>
[[5, 0],
[1, 0]],
<BLANKLINE>
[[2, 1],
[1, 2]]])
"""
y_type, y_true, y_pred = _check_targets(y_true, y_pred)
if sample_weight is not None:
sample_weight = column_or_1d(sample_weight)
check_consistent_length(y_true, y_pred, sample_weight)
if y_type not in ("binary", "multiclass", "multilabel-indicator"):
raise ValueError("%s is not supported" % y_type)
present_labels = unique_labels(y_true, y_pred)
if labels is None:
labels = present_labels
n_labels = None
else:
n_labels = len(labels)
labels = np.hstack(
[labels, np.setdiff1d(present_labels, labels, assume_unique=True)]
)
if y_true.ndim == 1:
if samplewise:
raise ValueError(
"Samplewise metrics are not available outside of "
"multilabel classification."
)
le = LabelEncoder()
le.fit(labels)
y_true = le.transform(y_true)
y_pred = le.transform(y_pred)
sorted_labels = le.classes_
# labels are now from 0 to len(labels) - 1 -> use bincount
tp = y_true == y_pred
tp_bins = y_true[tp]
if sample_weight is not None:
tp_bins_weights = np.asarray(sample_weight)[tp]
else:
tp_bins_weights = None
if len(tp_bins):
tp_sum = np.bincount(
tp_bins, weights=tp_bins_weights, minlength=len(labels)
)
else:
# Pathological case
true_sum = pred_sum = tp_sum = np.zeros(len(labels))
if len(y_pred):
pred_sum = np.bincount(y_pred, weights=sample_weight, minlength=len(labels))
if len(y_true):
true_sum = np.bincount(y_true, weights=sample_weight, minlength=len(labels))
# Retain only selected labels
indices = np.searchsorted(sorted_labels, labels[:n_labels])
tp_sum = tp_sum[indices]
true_sum = true_sum[indices]
pred_sum = pred_sum[indices]
else:
sum_axis = 1 if samplewise else 0
# All labels are index integers for multilabel.
# Select labels:
if not np.array_equal(labels, present_labels):
if np.max(labels) > np.max(present_labels):
raise ValueError(
"All labels must be in [0, n labels) for "
"multilabel targets. "
"Got %d > %d" % (np.max(labels), np.max(present_labels))
)
if np.min(labels) < 0:
raise ValueError(
"All labels must be in [0, n labels) for "
"multilabel targets. "
"Got %d < 0"
% np.min(labels)
)
if n_labels is not None:
y_true = y_true[:, labels[:n_labels]]
y_pred = y_pred[:, labels[:n_labels]]
# calculate weighted counts
true_and_pred = y_true.multiply(y_pred)
tp_sum = count_nonzero(
true_and_pred, axis=sum_axis, sample_weight=sample_weight
)
pred_sum = count_nonzero(y_pred, axis=sum_axis, sample_weight=sample_weight)
true_sum = count_nonzero(y_true, axis=sum_axis, sample_weight=sample_weight)
fp = pred_sum - tp_sum
fn = true_sum - tp_sum
tp = tp_sum
if sample_weight is not None and samplewise:
sample_weight = np.array(sample_weight)
tp = np.array(tp)
fp = np.array(fp)
fn = np.array(fn)
tn = sample_weight * y_true.shape[1] - tp - fp - fn
elif sample_weight is not None:
tn = sum(sample_weight) - tp - fp - fn
elif samplewise:
tn = y_true.shape[1] - tp - fp - fn
else:
tn = y_true.shape[0] - tp - fp - fn
return np.array([tn, fp, fn, tp]).T.reshape(-1, 2, 2)
def cohen_kappa_score(y1, y2, *, labels=None, weights=None, sample_weight=None):
r"""Compute Cohen's kappa: a statistic that measures inter-annotator agreement.
This function computes Cohen's kappa [1]_, a score that expresses the level
of agreement between two annotators on a classification problem. It is
defined as
.. math::
\kappa = (p_o - p_e) / (1 - p_e)
where :math:`p_o` is the empirical probability of agreement on the label
assigned to any sample (the observed agreement ratio), and :math:`p_e` is
the expected agreement when both annotators assign labels randomly.
:math:`p_e` is estimated using a per-annotator empirical prior over the
class labels [2]_.
Read more in the :ref:`User Guide <cohen_kappa>`.
Parameters
----------
y1 : array of shape (n_samples,)
Labels assigned by the first annotator.
y2 : array of shape (n_samples,)
Labels assigned by the second annotator. The kappa statistic is
symmetric, so swapping ``y1`` and ``y2`` doesn't change the value.
labels : array-like of shape (n_classes,), default=None
List of labels to index the matrix. This may be used to select a
subset of labels. If `None`, all labels that appear at least once in
``y1`` or ``y2`` are used.
weights : {'linear', 'quadratic'}, default=None
Weighting type to calculate the score. `None` means no weighted;
"linear" means linear weighted; "quadratic" means quadratic weighted.
sample_weight : array-like of shape (n_samples,), default=None
Sample weights.
Returns
-------
kappa : float
The kappa statistic, which is a number between -1 and 1. The maximum
value means complete agreement; zero or lower means chance agreement.
References
----------
.. [1] :doi:`J. Cohen (1960). "A coefficient of agreement for nominal scales".
Educational and Psychological Measurement 20(1):37-46.
<10.1177/001316446002000104>`
.. [2] `R. Artstein and M. Poesio (2008). "Inter-coder agreement for
computational linguistics". Computational Linguistics 34(4):555-596
<https://www.mitpressjournals.org/doi/pdf/10.1162/coli.07-034-R2>`_.
.. [3] `Wikipedia entry for the Cohen's kappa
<https://en.wikipedia.org/wiki/Cohen%27s_kappa>`_.
"""
confusion = confusion_matrix(y1, y2, labels=labels, sample_weight=sample_weight)
n_classes = confusion.shape[0]
sum0 = np.sum(confusion, axis=0)
sum1 = np.sum(confusion, axis=1)
expected = np.outer(sum0, sum1) / np.sum(sum0)
if weights is None:
w_mat = np.ones([n_classes, n_classes], dtype=int)
w_mat.flat[:: n_classes + 1] = 0
elif weights == "linear" or weights == "quadratic":
w_mat = np.zeros([n_classes, n_classes], dtype=int)
w_mat += np.arange(n_classes)
if weights == "linear":
w_mat = np.abs(w_mat - w_mat.T)
else:
w_mat = (w_mat - w_mat.T) ** 2
else:
raise ValueError("Unknown kappa weighting type.")
k = np.sum(w_mat * confusion) / np.sum(w_mat * expected)
return 1 - k
def jaccard_score(
y_true,
y_pred,
*,
labels=None,
pos_label=1,
average="binary",
sample_weight=None,
zero_division="warn",
):
"""Jaccard similarity coefficient score.
The Jaccard index [1], or Jaccard similarity coefficient, defined as
the size of the intersection divided by the size of the union of two label
sets, is used to compare set of predicted labels for a sample to the
corresponding set of labels in ``y_true``.
Read more in the :ref:`User Guide <jaccard_similarity_score>`.
Parameters
----------
y_true : 1d array-like, or label indicator array / sparse matrix
Ground truth (correct) labels.
y_pred : 1d array-like, or label indicator array / sparse matrix
Predicted labels, as returned by a classifier.
labels : array-like of shape (n_classes,), default=None
The set of labels to include when ``average != 'binary'``, and their
order if ``average is None``. Labels present in the data can be
excluded, for example to calculate a multiclass average ignoring a
majority negative class, while labels not present in the data will
result in 0 components in a macro average. For multilabel targets,
labels are column indices. By default, all labels in ``y_true`` and
``y_pred`` are used in sorted order.
pos_label : str or int, default=1
The class to report if ``average='binary'`` and the data is binary.
If the data are multiclass or multilabel, this will be ignored;
setting ``labels=[pos_label]`` and ``average != 'binary'`` will report
scores for that label only.
average : {'micro', 'macro', 'samples', 'weighted', \
'binary'} or None, default='binary'
If ``None``, the scores for each class are returned. Otherwise, this
determines the type of averaging performed on the data:
``'binary'``:
Only report results for the class specified by ``pos_label``.
This is applicable only if targets (``y_{true,pred}``) are binary.
``'micro'``:
Calculate metrics globally by counting the total true positives,
false negatives and false positives.
``'macro'``:
Calculate metrics for each label, and find their unweighted
mean. This does not take label imbalance into account.
``'weighted'``:
Calculate metrics for each label, and find their average, weighted
by support (the number of true instances for each label). This
alters 'macro' to account for label imbalance.
``'samples'``:
Calculate metrics for each instance, and find their average (only
meaningful for multilabel classification).
sample_weight : array-like of shape (n_samples,), default=None
Sample weights.
zero_division : "warn", {0.0, 1.0}, default="warn"
Sets the value to return when there is a zero division, i.e. when there
there are no negative values in predictions and labels. If set to
"warn", this acts like 0, but a warning is also raised.
Returns
-------
score : float or ndarray of shape (n_unique_labels,), dtype=np.float64
The Jaccard score. When `average` is not `None`, a single scalar is
returned.
See Also
--------
accuracy_score : Function for calculating the accuracy score.
f1_score : Function for calculating the F1 score.
multilabel_confusion_matrix : Function for computing a confusion matrix\
for each class or sample.
Notes
-----
:func:`jaccard_score` may be a poor metric if there are no
positives for some samples or classes. Jaccard is undefined if there are
no true or predicted labels, and our implementation will return a score
of 0 with a warning.
References
----------
.. [1] `Wikipedia entry for the Jaccard index
<https://en.wikipedia.org/wiki/Jaccard_index>`_.
Examples
--------
>>> import numpy as np
>>> from sklearn.metrics import jaccard_score
>>> y_true = np.array([[0, 1, 1],
... [1, 1, 0]])
>>> y_pred = np.array([[1, 1, 1],
... [1, 0, 0]])
In the binary case:
>>> jaccard_score(y_true[0], y_pred[0])
0.6666...
In the 2D comparison case (e.g. image similarity):
>>> jaccard_score(y_true, y_pred, average="micro")
0.6
In the multilabel case:
>>> jaccard_score(y_true, y_pred, average='samples')
0.5833...
>>> jaccard_score(y_true, y_pred, average='macro')
0.6666...
>>> jaccard_score(y_true, y_pred, average=None)
array([0.5, 0.5, 1. ])
In the multiclass case:
>>> y_pred = [0, 2, 1, 2]
>>> y_true = [0, 1, 2, 2]
>>> jaccard_score(y_true, y_pred, average=None)
array([1. , 0. , 0.33...])
"""
labels = _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)
samplewise = average == "samples"
MCM = multilabel_confusion_matrix(
y_true,
y_pred,
sample_weight=sample_weight,
labels=labels,
samplewise=samplewise,
)
numerator = MCM[:, 1, 1]
denominator = MCM[:, 1, 1] + MCM[:, 0, 1] + MCM[:, 1, 0]
if average == "micro":
numerator = np.array([numerator.sum()])
denominator = np.array([denominator.sum()])
jaccard = _prf_divide(
numerator,
denominator,
"jaccard",
"true or predicted",
average,
("jaccard",),
zero_division=zero_division,
)
if average is None:
return jaccard
if average == "weighted":
weights = MCM[:, 1, 0] + MCM[:, 1, 1]
if not np.any(weights):
# numerator is 0, and warning should have already been issued
weights = None
elif average == "samples" and sample_weight is not None:
weights = sample_weight
else:
weights = None
return np.average(jaccard, weights=weights)
def matthews_corrcoef(y_true, y_pred, *, sample_weight=None):
"""Compute the Matthews correlation coefficient (MCC).
The Matthews correlation coefficient is used in machine learning as a
measure of the quality of binary and multiclass classifications. It takes
into account true and false positives and negatives and is generally
regarded as a balanced measure which can be used even if the classes are of
very different sizes. The MCC is in essence a correlation coefficient value
between -1 and +1. A coefficient of +1 represents a perfect prediction, 0
an average random prediction and -1 an inverse prediction. The statistic
is also known as the phi coefficient. [source: Wikipedia]
Binary and multiclass labels are supported. Only in the binary case does
this relate to information about true and false positives and negatives.
See references below.
Read more in the :ref:`User Guide <matthews_corrcoef>`.
Parameters
----------
y_true : array, shape = [n_samples]
Ground truth (correct) target values.
y_pred : array, shape = [n_samples]
Estimated targets as returned by a classifier.
sample_weight : array-like of shape (n_samples,), default=None
Sample weights.
.. versionadded:: 0.18
Returns
-------
mcc : float
The Matthews correlation coefficient (+1 represents a perfect
prediction, 0 an average random prediction and -1 and inverse
prediction).
References
----------
.. [1] :doi:`Baldi, Brunak, Chauvin, Andersen and Nielsen, (2000). Assessing the
accuracy of prediction algorithms for classification: an overview.
<10.1093/bioinformatics/16.5.412>`
.. [2] `Wikipedia entry for the Matthews Correlation Coefficient
<https://en.wikipedia.org/wiki/Matthews_correlation_coefficient>`_.
.. [3] `Gorodkin, (2004). Comparing two K-category assignments by a
K-category correlation coefficient
<https://www.sciencedirect.com/science/article/pii/S1476927104000799>`_.
.. [4] `Jurman, Riccadonna, Furlanello, (2012). A Comparison of MCC and CEN
Error Measures in MultiClass Prediction
<https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0041882>`_.
Examples
--------
>>> from sklearn.metrics import matthews_corrcoef
>>> y_true = [+1, +1, +1, -1]
>>> y_pred = [+1, -1, +1, +1]
>>> matthews_corrcoef(y_true, y_pred)
-0.33...
"""
y_type, y_true, y_pred = _check_targets(y_true, y_pred)
check_consistent_length(y_true, y_pred, sample_weight)
if y_type not in {"binary", "multiclass"}:
raise ValueError("%s is not supported" % y_type)
lb = LabelEncoder()
lb.fit(np.hstack([y_true, y_pred]))
y_true = lb.transform(y_true)
y_pred = lb.transform(y_pred)
C = confusion_matrix(y_true, y_pred, sample_weight=sample_weight)
t_sum = C.sum(axis=1, dtype=np.float64)
p_sum = C.sum(axis=0, dtype=np.float64)
n_correct = np.trace(C, dtype=np.float64)
n_samples = p_sum.sum()
cov_ytyp = n_correct * n_samples - np.dot(t_sum, p_sum)
cov_ypyp = n_samples**2 - np.dot(p_sum, p_sum)
cov_ytyt = n_samples**2 - np.dot(t_sum, t_sum)
if cov_ypyp * cov_ytyt == 0:
return 0.0
else:
return cov_ytyp / np.sqrt(cov_ytyt * cov_ypyp)
def zero_one_loss(y_true, y_pred, *, normalize=True, sample_weight=None):
"""Zero-one classification loss.
If normalize is ``True``, return the fraction of misclassifications
(float), else it returns the number of misclassifications (int). The best
performance is 0.
Read more in the :ref:`User Guide <zero_one_loss>`.
Parameters
----------
y_true : 1d array-like, or label indicator array / sparse matrix
Ground truth (correct) labels.
y_pred : 1d array-like, or label indicator array / sparse matrix
Predicted labels, as returned by a classifier.
normalize : bool, default=True
If ``False``, return the number of misclassifications.
Otherwise, return the fraction of misclassifications.
sample_weight : array-like of shape (n_samples,), default=None
Sample weights.
Returns
-------
loss : float or int,
If ``normalize == True``, return the fraction of misclassifications
(float), else it returns the number of misclassifications (int).
See Also
--------
accuracy_score : Compute the accuracy score. By default, the function will
return the fraction of correct predictions divided by the total number
of predictions.
hamming_loss : Compute the average Hamming loss or Hamming distance between
two sets of samples.
jaccard_score : Compute the Jaccard similarity coefficient score.
Notes
-----
In multilabel classification, the zero_one_loss function corresponds to
the subset zero-one loss: for each sample, the entire set of labels must be
correctly predicted, otherwise the loss for that sample is equal to one.
Examples
--------
>>> from sklearn.metrics import zero_one_loss
>>> y_pred = [1, 2, 3, 4]
>>> y_true = [2, 2, 3, 4]
>>> zero_one_loss(y_true, y_pred)
0.25
>>> zero_one_loss(y_true, y_pred, normalize=False)
1
In the multilabel case with binary label indicators:
>>> import numpy as np
>>> zero_one_loss(np.array([[0, 1], [1, 1]]), np.ones((2, 2)))
0.5
"""
score = accuracy_score(
y_true, y_pred, normalize=normalize, sample_weight=sample_weight
)
if normalize:
return 1 - score
else:
if sample_weight is not None:
n_samples = np.sum(sample_weight)
else:
n_samples = _num_samples(y_true)
return n_samples - score
def f1_score(
y_true,
y_pred,
*,
labels=None,
pos_label=1,
average="binary",
sample_weight=None,
zero_division="warn",
):
"""Compute the F1 score, also known as balanced F-score or F-measure.
The F1 score can be interpreted as a harmonic mean of the precision and
recall, where an F1 score reaches its best value at 1 and worst score at 0.
The relative contribution of precision and recall to the F1 score are
equal. The formula for the F1 score is::
F1 = 2 * (precision * recall) / (precision + recall)
In the multi-class and multi-label case, this is the average of
the F1 score of each class with weighting depending on the ``average``
parameter.
Read more in the :ref:`User Guide <precision_recall_f_measure_metrics>`.
Parameters
----------
y_true : 1d array-like, or label indicator array / sparse matrix
Ground truth (correct) target values.
y_pred : 1d array-like, or label indicator array / sparse matrix
Estimated targets as returned by a classifier.
labels : array-like, default=None
The set of labels to include when ``average != 'binary'``, and their
order if ``average is None``. Labels present in the data can be
excluded, for example to calculate a multiclass average ignoring a
majority negative class, while labels not present in the data will
result in 0 components in a macro average. For multilabel targets,
labels are column indices. By default, all labels in ``y_true`` and
``y_pred`` are used in sorted order.
.. versionchanged:: 0.17
Parameter `labels` improved for multiclass problem.
pos_label : str or int, default=1
The class to report if ``average='binary'`` and the data is binary.
If the data are multiclass or multilabel, this will be ignored;
setting ``labels=[pos_label]`` and ``average != 'binary'`` will report
scores for that label only.
average : {'micro', 'macro', 'samples', 'weighted', 'binary'} or None, \
default='binary'
This parameter is required for multiclass/multilabel targets.
If ``None``, the scores for each class are returned. Otherwise, this
determines the type of averaging performed on the data:
``'binary'``:
Only report results for the class specified by ``pos_label``.
This is applicable only if targets (``y_{true,pred}``) are binary.
``'micro'``:
Calculate metrics globally by counting the total true positives,
false negatives and false positives.
``'macro'``:
Calculate metrics for each label, and find their unweighted
mean. This does not take label imbalance into account.
``'weighted'``:
Calculate metrics for each label, and find their average weighted
by support (the number of true instances for each label). This
alters 'macro' to account for label imbalance; it can result in an
F-score that is not between precision and recall.
``'samples'``:
Calculate metrics for each instance, and find their average (only
meaningful for multilabel classification where this differs from
:func:`accuracy_score`).
sample_weight : array-like of shape (n_samples,), default=None
Sample weights.
zero_division : "warn", 0 or 1, default="warn"
Sets the value to return when there is a zero division, i.e. when all
predictions and labels are negative. If set to "warn", this acts as 0,
but warnings are also raised.
Returns
-------
f1_score : float or array of float, shape = [n_unique_labels]
F1 score of the positive class in binary classification or weighted
average of the F1 scores of each class for the multiclass task.
See Also
--------
fbeta_score : Compute the F-beta score.
precision_recall_fscore_support : Compute the precision, recall, F-score,
and support.
jaccard_score : Compute the Jaccard similarity coefficient score.
multilabel_confusion_matrix : Compute a confusion matrix for each class or
sample.
Notes
-----
When ``true positive + false positive == 0``, precision is undefined.
When ``true positive + false negative == 0``, recall is undefined.
In such cases, by default the metric will be set to 0, as will f-score,
and ``UndefinedMetricWarning`` will be raised. This behavior can be
modified with ``zero_division``.
References
----------
.. [1] `Wikipedia entry for the F1-score
<https://en.wikipedia.org/wiki/F1_score>`_.
Examples
--------
>>> from sklearn.metrics import f1_score
>>> y_true = [0, 1, 2, 0, 1, 2]
>>> y_pred = [0, 2, 1, 0, 0, 1]
>>> f1_score(y_true, y_pred, average='macro')
0.26...
>>> f1_score(y_true, y_pred, average='micro')
0.33...
>>> f1_score(y_true, y_pred, average='weighted')
0.26...
>>> f1_score(y_true, y_pred, average=None)
array([0.8, 0. , 0. ])
>>> y_true = [0, 0, 0, 0, 0, 0]
>>> y_pred = [0, 0, 0, 0, 0, 0]
>>> f1_score(y_true, y_pred, zero_division=1)
1.0...
>>> # multilabel classification
>>> y_true = [[0, 0, 0], [1, 1, 1], [0, 1, 1]]
>>> y_pred = [[0, 0, 0], [1, 1, 1], [1, 1, 0]]
>>> f1_score(y_true, y_pred, average=None)
array([0.66666667, 1. , 0.66666667])
"""
return fbeta_score(
y_true,
y_pred,
beta=1,
labels=labels,
pos_label=pos_label,
average=average,
sample_weight=sample_weight,
zero_division=zero_division,
)
def fbeta_score(
y_true,
y_pred,
*,
beta,
labels=None,
pos_label=1,
average="binary",
sample_weight=None,
zero_division="warn",
):
"""Compute the F-beta score.
The F-beta score is the weighted harmonic mean of precision and recall,
reaching its optimal value at 1 and its worst value at 0.
The `beta` parameter determines the weight of recall in the combined
score. ``beta < 1`` lends more weight to precision, while ``beta > 1``
favors recall (``beta -> 0`` considers only precision, ``beta -> +inf``
only recall).
Read more in the :ref:`User Guide <precision_recall_f_measure_metrics>`.
Parameters
----------
y_true : 1d array-like, or label indicator array / sparse matrix
Ground truth (correct) target values.
y_pred : 1d array-like, or label indicator array / sparse matrix
Estimated targets as returned by a classifier.
beta : float
Determines the weight of recall in the combined score.
labels : array-like, default=None
The set of labels to include when ``average != 'binary'``, and their
order if ``average is None``. Labels present in the data can be
excluded, for example to calculate a multiclass average ignoring a
majority negative class, while labels not present in the data will
result in 0 components in a macro average. For multilabel targets,
labels are column indices. By default, all labels in ``y_true`` and
``y_pred`` are used in sorted order.
.. versionchanged:: 0.17
Parameter `labels` improved for multiclass problem.
pos_label : str or int, default=1
The class to report if ``average='binary'`` and the data is binary.
If the data are multiclass or multilabel, this will be ignored;
setting ``labels=[pos_label]`` and ``average != 'binary'`` will report
scores for that label only.
average : {'micro', 'macro', 'samples', 'weighted', 'binary'} or None, \
default='binary'
This parameter is required for multiclass/multilabel targets.
If ``None``, the scores for each class are returned. Otherwise, this
determines the type of averaging performed on the data:
``'binary'``:
Only report results for the class specified by ``pos_label``.
This is applicable only if targets (``y_{true,pred}``) are binary.
``'micro'``:
Calculate metrics globally by counting the total true positives,
false negatives and false positives.
``'macro'``:
Calculate metrics for each label, and find their unweighted
mean. This does not take label imbalance into account.
``'weighted'``:
Calculate metrics for each label, and find their average weighted
by support (the number of true instances for each label). This
alters 'macro' to account for label imbalance; it can result in an
F-score that is not between precision and recall.
``'samples'``:
Calculate metrics for each instance, and find their average (only
meaningful for multilabel classification where this differs from
:func:`accuracy_score`).
sample_weight : array-like of shape (n_samples,), default=None
Sample weights.
zero_division : "warn", 0 or 1, default="warn"
Sets the value to return when there is a zero division, i.e. when all
predictions and labels are negative. If set to "warn", this acts as 0,
but warnings are also raised.
Returns
-------
fbeta_score : float (if average is not None) or array of float, shape =\
[n_unique_labels]
F-beta score of the positive class in binary classification or weighted
average of the F-beta score of each class for the multiclass task.
See Also
--------
precision_recall_fscore_support : Compute the precision, recall, F-score,
and support.
multilabel_confusion_matrix : Compute a confusion matrix for each class or
sample.
Notes
-----
When ``true positive + false positive == 0`` or
``true positive + false negative == 0``, f-score returns 0 and raises
``UndefinedMetricWarning``. This behavior can be
modified with ``zero_division``.
References
----------
.. [1] R. Baeza-Yates and B. Ribeiro-Neto (2011).
Modern Information Retrieval. Addison Wesley, pp. 327-328.
.. [2] `Wikipedia entry for the F1-score
<https://en.wikipedia.org/wiki/F1_score>`_.
Examples
--------
>>> from sklearn.metrics import fbeta_score
>>> y_true = [0, 1, 2, 0, 1, 2]
>>> y_pred = [0, 2, 1, 0, 0, 1]
>>> fbeta_score(y_true, y_pred, average='macro', beta=0.5)
0.23...
>>> fbeta_score(y_true, y_pred, average='micro', beta=0.5)
0.33...
>>> fbeta_score(y_true, y_pred, average='weighted', beta=0.5)
0.23...
>>> fbeta_score(y_true, y_pred, average=None, beta=0.5)
array([0.71..., 0. , 0. ])
"""
_, _, f, _ = precision_recall_fscore_support(
y_true,
y_pred,
beta=beta,
labels=labels,
pos_label=pos_label,
average=average,
warn_for=("f-score",),
sample_weight=sample_weight,
zero_division=zero_division,
)
return f
def _prf_divide(
numerator, denominator, metric, modifier, average, warn_for, zero_division="warn"
):
"""Performs division and handles divide-by-zero.
On zero-division, sets the corresponding result elements equal to
0 or 1 (according to ``zero_division``). Plus, if
``zero_division != "warn"`` raises a warning.
The metric, modifier and average arguments are used only for determining
an appropriate warning.
"""
mask = denominator == 0.0
denominator = denominator.copy()
denominator[mask] = 1 # avoid infs/nans
result = numerator / denominator
if not np.any(mask):
return result
# if ``zero_division=1``, set those with denominator == 0 equal to 1
result[mask] = 0.0 if zero_division in ["warn", 0] else 1.0
# the user will be removing warnings if zero_division is set to something
# different than its default value. If we are computing only f-score
# the warning will be raised only if precision and recall are ill-defined
if zero_division != "warn" or metric not in warn_for:
return result
# build appropriate warning
# E.g. "Precision and F-score are ill-defined and being set to 0.0 in
# labels with no predicted samples. Use ``zero_division`` parameter to
# control this behavior."
if metric in warn_for and "f-score" in warn_for:
msg_start = "{0} and F-score are".format(metric.title())
elif metric in warn_for:
msg_start = "{0} is".format(metric.title())
elif "f-score" in warn_for:
msg_start = "F-score is"
else:
return result
_warn_prf(average, modifier, msg_start, len(result))
return result
def _warn_prf(average, modifier, msg_start, result_size):
axis0, axis1 = "sample", "label"
if average == "samples":
axis0, axis1 = axis1, axis0
msg = (
"{0} ill-defined and being set to 0.0 {{0}} "
"no {1} {2}s. Use `zero_division` parameter to control"
" this behavior.".format(msg_start, modifier, axis0)
)
if result_size == 1:
msg = msg.format("due to")
else:
msg = msg.format("in {0}s with".format(axis1))
warnings.warn(msg, UndefinedMetricWarning, stacklevel=2)
def _check_set_wise_labels(y_true, y_pred, average, labels, pos_label):
"""Validation associated with set-wise metrics.
Returns identified labels.
"""
average_options = (None, "micro", "macro", "weighted", "samples")
if average not in average_options and average != "binary":
raise ValueError("average has to be one of " + str(average_options))
y_type, y_true, y_pred = _check_targets(y_true, y_pred)
# Convert to Python primitive type to avoid NumPy type / Python str
# comparison. See https://github.com/numpy/numpy/issues/6784
present_labels = unique_labels(y_true, y_pred).tolist()
if average == "binary":
if y_type == "binary":
if pos_label not in present_labels:
if len(present_labels) >= 2:
raise ValueError(
f"pos_label={pos_label} is not a valid label. It "
f"should be one of {present_labels}"
)
labels = [pos_label]
else:
average_options = list(average_options)
if y_type == "multiclass":
average_options.remove("samples")
raise ValueError(
"Target is %s but average='binary'. Please "
"choose another average setting, one of %r." % (y_type, average_options)
)
elif pos_label not in (None, 1):
warnings.warn(
"Note that pos_label (set to %r) is ignored when "
"average != 'binary' (got %r). You may use "
"labels=[pos_label] to specify a single positive class."
% (pos_label, average),
UserWarning,
)
return labels
def precision_recall_fscore_support(
y_true,
y_pred,
*,
beta=1.0,
labels=None,
pos_label=1,
average=None,
warn_for=("precision", "recall", "f-score"),
sample_weight=None,
zero_division="warn",
):
"""Compute precision, recall, F-measure and support for each class.
The precision is the ratio ``tp / (tp + fp)`` where ``tp`` is the number of
true positives and ``fp`` the number of false positives. The precision is
intuitively the ability of the classifier not to label a negative sample as
positive.
The recall is the ratio ``tp / (tp + fn)`` where ``tp`` is the number of
true positives and ``fn`` the number of false negatives. The recall is
intuitively the ability of the classifier to find all the positive samples.
The F-beta score can be interpreted as a weighted harmonic mean of
the precision and recall, where an F-beta score reaches its best
value at 1 and worst score at 0.
The F-beta score weights recall more than precision by a factor of
``beta``. ``beta == 1.0`` means recall and precision are equally important.
The support is the number of occurrences of each class in ``y_true``.
If ``pos_label is None`` and in binary classification, this function
returns the average precision, recall and F-measure if ``average``
is one of ``'micro'``, ``'macro'``, ``'weighted'`` or ``'samples'``.
Read more in the :ref:`User Guide <precision_recall_f_measure_metrics>`.
Parameters
----------
y_true : 1d array-like, or label indicator array / sparse matrix
Ground truth (correct) target values.
y_pred : 1d array-like, or label indicator array / sparse matrix
Estimated targets as returned by a classifier.
beta : float, default=1.0
The strength of recall versus precision in the F-score.
labels : array-like, default=None
The set of labels to include when ``average != 'binary'``, and their
order if ``average is None``. Labels present in the data can be
excluded, for example to calculate a multiclass average ignoring a
majority negative class, while labels not present in the data will
result in 0 components in a macro average. For multilabel targets,
labels are column indices. By default, all labels in ``y_true`` and
``y_pred`` are used in sorted order.
pos_label : str or int, default=1
The class to report if ``average='binary'`` and the data is binary.
If the data are multiclass or multilabel, this will be ignored;
setting ``labels=[pos_label]`` and ``average != 'binary'`` will report
scores for that label only.
average : {'binary', 'micro', 'macro', 'samples', 'weighted'}, \
default=None
If ``None``, the scores for each class are returned. Otherwise, this
determines the type of averaging performed on the data:
``'binary'``:
Only report results for the class specified by ``pos_label``.
This is applicable only if targets (``y_{true,pred}``) are binary.
``'micro'``:
Calculate metrics globally by counting the total true positives,
false negatives and false positives.
``'macro'``:
Calculate metrics for each label, and find their unweighted
mean. This does not take label imbalance into account.
``'weighted'``:
Calculate metrics for each label, and find their average weighted
by support (the number of true instances for each label). This
alters 'macro' to account for label imbalance; it can result in an
F-score that is not between precision and recall.
``'samples'``:
Calculate metrics for each instance, and find their average (only
meaningful for multilabel classification where this differs from
:func:`accuracy_score`).
warn_for : tuple or set, for internal use
This determines which warnings will be made in the case that this
function is being used to return only one of its metrics.
sample_weight : array-like of shape (n_samples,), default=None
Sample weights.
zero_division : "warn", 0 or 1, default="warn"
Sets the value to return when there is a zero division:
- recall: when there are no positive labels
- precision: when there are no positive predictions
- f-score: both
If set to "warn", this acts as 0, but warnings are also raised.
Returns
-------
precision : float (if average is not None) or array of float, shape =\
[n_unique_labels]
Precision score.
recall : float (if average is not None) or array of float, shape =\
[n_unique_labels]
Recall score.
fbeta_score : float (if average is not None) or array of float, shape =\
[n_unique_labels]
F-beta score.
support : None (if average is not None) or array of int, shape =\
[n_unique_labels]
The number of occurrences of each label in ``y_true``.
Notes
-----
When ``true positive + false positive == 0``, precision is undefined.
When ``true positive + false negative == 0``, recall is undefined.
In such cases, by default the metric will be set to 0, as will f-score,
and ``UndefinedMetricWarning`` will be raised. This behavior can be
modified with ``zero_division``.
References
----------
.. [1] `Wikipedia entry for the Precision and recall
<https://en.wikipedia.org/wiki/Precision_and_recall>`_.
.. [2] `Wikipedia entry for the F1-score
<https://en.wikipedia.org/wiki/F1_score>`_.
.. [3] `Discriminative Methods for Multi-labeled Classification Advances
in Knowledge Discovery and Data Mining (2004), pp. 22-30 by Shantanu
Godbole, Sunita Sarawagi
<http://www.godbole.net/shantanu/pubs/multilabelsvm-pakdd04.pdf>`_.
Examples
--------
>>> import numpy as np
>>> from sklearn.metrics import precision_recall_fscore_support
>>> y_true = np.array(['cat', 'dog', 'pig', 'cat', 'dog', 'pig'])
>>> y_pred = np.array(['cat', 'pig', 'dog', 'cat', 'cat', 'dog'])
>>> precision_recall_fscore_support(y_true, y_pred, average='macro')
(0.22..., 0.33..., 0.26..., None)
>>> precision_recall_fscore_support(y_true, y_pred, average='micro')
(0.33..., 0.33..., 0.33..., None)
>>> precision_recall_fscore_support(y_true, y_pred, average='weighted')
(0.22..., 0.33..., 0.26..., None)
It is possible to compute per-label precisions, recalls, F1-scores and
supports instead of averaging:
>>> precision_recall_fscore_support(y_true, y_pred, average=None,
... labels=['pig', 'dog', 'cat'])
(array([0. , 0. , 0.66...]),
array([0., 0., 1.]), array([0. , 0. , 0.8]),
array([2, 2, 2]))
"""
_check_zero_division(zero_division)
if beta < 0:
raise ValueError("beta should be >=0 in the F-beta score")
labels = _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)
# Calculate tp_sum, pred_sum, true_sum ###
samplewise = average == "samples"
MCM = multilabel_confusion_matrix(
y_true,
y_pred,
sample_weight=sample_weight,
labels=labels,
samplewise=samplewise,
)
tp_sum = MCM[:, 1, 1]
pred_sum = tp_sum + MCM[:, 0, 1]
true_sum = tp_sum + MCM[:, 1, 0]
if average == "micro":
tp_sum = np.array([tp_sum.sum()])
pred_sum = np.array([pred_sum.sum()])
true_sum = np.array([true_sum.sum()])
# Finally, we have all our sufficient statistics. Divide! #
beta2 = beta**2
# Divide, and on zero-division, set scores and/or warn according to
# zero_division:
precision = _prf_divide(
tp_sum, pred_sum, "precision", "predicted", average, warn_for, zero_division
)
recall = _prf_divide(
tp_sum, true_sum, "recall", "true", average, warn_for, zero_division
)
# warn for f-score only if zero_division is warn, it is in warn_for
# and BOTH prec and rec are ill-defined
if zero_division == "warn" and ("f-score",) == warn_for:
if (pred_sum[true_sum == 0] == 0).any():
_warn_prf(average, "true nor predicted", "F-score is", len(true_sum))
# if tp == 0 F will be 1 only if all predictions are zero, all labels are
# zero, and zero_division=1. In all other case, 0
if np.isposinf(beta):
f_score = recall
else:
denom = beta2 * precision + recall
denom[denom == 0.0] = 1 # avoid division by 0
f_score = (1 + beta2) * precision * recall / denom
# Average the results
if average == "weighted":
weights = true_sum
if weights.sum() == 0:
zero_division_value = np.float64(1.0)
if zero_division in ["warn", 0]:
zero_division_value = np.float64(0.0)
# precision is zero_division if there are no positive predictions
# recall is zero_division if there are no positive labels
# fscore is zero_division if all labels AND predictions are
# negative
if pred_sum.sum() == 0:
return (
zero_division_value,
zero_division_value,
zero_division_value,
None,
)
else:
return (np.float64(0.0), zero_division_value, np.float64(0.0), None)
elif average == "samples":
weights = sample_weight
else:
weights = None
if average is not None:
assert average != "binary" or len(precision) == 1
precision = np.average(precision, weights=weights)
recall = np.average(recall, weights=weights)
f_score = np.average(f_score, weights=weights)
true_sum = None # return no support
return precision, recall, f_score, true_sum
def class_likelihood_ratios(
y_true,
y_pred,
*,
labels=None,
sample_weight=None,
raise_warning=True,
):
"""Compute binary classification positive and negative likelihood ratios.
The positive likelihood ratio is `LR+ = sensitivity / (1 - specificity)`
where the sensitivity or recall is the ratio `tp / (tp + fn)` and the
specificity is `tn / (tn + fp)`. The negative likelihood ratio is `LR- = (1
- sensitivity) / specificity`. Here `tp` is the number of true positives,
`fp` the number of false positives, `tn` is the number of true negatives and
`fn` the number of false negatives. Both class likelihood ratios can be used
to obtain post-test probabilities given a pre-test probability.
`LR+` ranges from 1 to infinity. A `LR+` of 1 indicates that the probability
of predicting the positive class is the same for samples belonging to either
class; therefore, the test is useless. The greater `LR+` is, the more a
positive prediction is likely to be a true positive when compared with the
pre-test probability. A value of `LR+` lower than 1 is invalid as it would
indicate that the odds of a sample being a true positive decrease with
respect to the pre-test odds.
`LR-` ranges from 0 to 1. The closer it is to 0, the lower the probability
of a given sample to be a false negative. A `LR-` of 1 means the test is
useless because the odds of having the condition did not change after the
test. A value of `LR-` greater than 1 invalidates the classifier as it
indicates an increase in the odds of a sample belonging to the positive
class after being classified as negative. This is the case when the
classifier systematically predicts the opposite of the true label.
A typical application in medicine is to identify the positive/negative class
to the presence/absence of a disease, respectively; the classifier being a
diagnostic test; the pre-test probability of an individual having the
disease can be the prevalence of such disease (proportion of a particular
population found to be affected by a medical condition); and the post-test
probabilities would be the probability that the condition is truly present
given a positive test result.
Read more in the :ref:`User Guide <class_likelihood_ratios>`.
Parameters
----------
y_true : 1d array-like, or label indicator array / sparse matrix
Ground truth (correct) target values.
y_pred : 1d array-like, or label indicator array / sparse matrix
Estimated targets as returned by a classifier.
labels : array-like, default=None
List of labels to index the matrix. This may be used to select the
positive and negative classes with the ordering `labels=[negative_class,
positive_class]`. If `None` is given, those that appear at least once in
`y_true` or `y_pred` are used in sorted order.
sample_weight : array-like of shape (n_samples,), default=None
Sample weights.
raise_warning : bool, default=True
Whether or not a case-specific warning message is raised when there is a
zero division. Even if the error is not raised, the function will return
nan in such cases.
Returns
-------
(positive_likelihood_ratio, negative_likelihood_ratio) : tuple
A tuple of two float, the first containing the Positive likelihood ratio
and the second the Negative likelihood ratio.
Warns
-----
When `false positive == 0`, the positive likelihood ratio is undefined.
When `true negative == 0`, the negative likelihood ratio is undefined.
When `true positive + false negative == 0` both ratios are undefined.
In such cases, `UserWarning` will be raised if raise_warning=True.
References
----------
.. [1] `Wikipedia entry for the Likelihood ratios in diagnostic testing
<https://en.wikipedia.org/wiki/Likelihood_ratios_in_diagnostic_testing>`_.
Examples
--------
>>> import numpy as np
>>> from sklearn.metrics import class_likelihood_ratios
>>> class_likelihood_ratios([0, 1, 0, 1, 0], [1, 1, 0, 0, 0])
(1.5, 0.75)
>>> y_true = np.array(["non-cat", "cat", "non-cat", "cat", "non-cat"])
>>> y_pred = np.array(["cat", "cat", "non-cat", "non-cat", "non-cat"])
>>> class_likelihood_ratios(y_true, y_pred)
(1.33..., 0.66...)
>>> y_true = np.array(["non-zebra", "zebra", "non-zebra", "zebra", "non-zebra"])
>>> y_pred = np.array(["zebra", "zebra", "non-zebra", "non-zebra", "non-zebra"])
>>> class_likelihood_ratios(y_true, y_pred)
(1.5, 0.75)
To avoid ambiguities, use the notation `labels=[negative_class,
positive_class]`
>>> y_true = np.array(["non-cat", "cat", "non-cat", "cat", "non-cat"])
>>> y_pred = np.array(["cat", "cat", "non-cat", "non-cat", "non-cat"])
>>> class_likelihood_ratios(y_true, y_pred, labels=["non-cat", "cat"])
(1.5, 0.75)
"""
y_type, y_true, y_pred = _check_targets(y_true, y_pred)
if y_type != "binary":
raise ValueError(
"class_likelihood_ratios only supports binary classification "
f"problems, got targets of type: {y_type}"
)
cm = confusion_matrix(
y_true,
y_pred,
sample_weight=sample_weight,
labels=labels,
)
# Case when `y_test` contains a single class and `y_test == y_pred`.
# This may happen when cross-validating imbalanced data and should
# not be interpreted as a perfect score.
if cm.shape == (1, 1):
msg = "samples of only one class were seen during testing "
if raise_warning:
warnings.warn(msg, UserWarning, stacklevel=2)
positive_likelihood_ratio = np.nan
negative_likelihood_ratio = np.nan
else:
tn, fp, fn, tp = cm.ravel()
support_pos = tp + fn
support_neg = tn + fp
pos_num = tp * support_neg
pos_denom = fp * support_pos
neg_num = fn * support_neg
neg_denom = tn * support_pos
# If zero division warn and set scores to nan, else divide
if support_pos == 0:
msg = "no samples of the positive class were present in the testing set "
if raise_warning:
warnings.warn(msg, UserWarning, stacklevel=2)
positive_likelihood_ratio = np.nan
negative_likelihood_ratio = np.nan
if fp == 0:
if tp == 0:
msg = "no samples predicted for the positive class"
else:
msg = "positive_likelihood_ratio ill-defined and being set to nan "
if raise_warning:
warnings.warn(msg, UserWarning, stacklevel=2)
positive_likelihood_ratio = np.nan
else:
positive_likelihood_ratio = pos_num / pos_denom
if tn == 0:
msg = "negative_likelihood_ratio ill-defined and being set to nan "
if raise_warning:
warnings.warn(msg, UserWarning, stacklevel=2)
negative_likelihood_ratio = np.nan
else:
negative_likelihood_ratio = neg_num / neg_denom
return positive_likelihood_ratio, negative_likelihood_ratio
def precision_score(
y_true,
y_pred,
*,
labels=None,
pos_label=1,
average="binary",
sample_weight=None,
zero_division="warn",
):
"""Compute the precision.
The precision is the ratio ``tp / (tp + fp)`` where ``tp`` is the number of
true positives and ``fp`` the number of false positives. The precision is
intuitively the ability of the classifier not to label as positive a sample
that is negative.
The best value is 1 and the worst value is 0.
Read more in the :ref:`User Guide <precision_recall_f_measure_metrics>`.
Parameters
----------
y_true : 1d array-like, or label indicator array / sparse matrix
Ground truth (correct) target values.
y_pred : 1d array-like, or label indicator array / sparse matrix
Estimated targets as returned by a classifier.
labels : array-like, default=None
The set of labels to include when ``average != 'binary'``, and their
order if ``average is None``. Labels present in the data can be
excluded, for example to calculate a multiclass average ignoring a
majority negative class, while labels not present in the data will
result in 0 components in a macro average. For multilabel targets,
labels are column indices. By default, all labels in ``y_true`` and
``y_pred`` are used in sorted order.
.. versionchanged:: 0.17
Parameter `labels` improved for multiclass problem.
pos_label : str or int, default=1
The class to report if ``average='binary'`` and the data is binary.
If the data are multiclass or multilabel, this will be ignored;
setting ``labels=[pos_label]`` and ``average != 'binary'`` will report
scores for that label only.
average : {'micro', 'macro', 'samples', 'weighted', 'binary'} or None, \
default='binary'
This parameter is required for multiclass/multilabel targets.
If ``None``, the scores for each class are returned. Otherwise, this
determines the type of averaging performed on the data:
``'binary'``:
Only report results for the class specified by ``pos_label``.
This is applicable only if targets (``y_{true,pred}``) are binary.
``'micro'``:
Calculate metrics globally by counting the total true positives,
false negatives and false positives.
``'macro'``:
Calculate metrics for each label, and find their unweighted
mean. This does not take label imbalance into account.
``'weighted'``:
Calculate metrics for each label, and find their average weighted
by support (the number of true instances for each label). This
alters 'macro' to account for label imbalance; it can result in an
F-score that is not between precision and recall.
``'samples'``:
Calculate metrics for each instance, and find their average (only
meaningful for multilabel classification where this differs from
:func:`accuracy_score`).
sample_weight : array-like of shape (n_samples,), default=None
Sample weights.
zero_division : "warn", 0 or 1, default="warn"
Sets the value to return when there is a zero division. If set to
"warn", this acts as 0, but warnings are also raised.
Returns
-------
precision : float (if average is not None) or array of float of shape \
(n_unique_labels,)
Precision of the positive class in binary classification or weighted
average of the precision of each class for the multiclass task.
See Also
--------
precision_recall_fscore_support : Compute precision, recall, F-measure and
support for each class.
recall_score : Compute the ratio ``tp / (tp + fn)`` where ``tp`` is the
number of true positives and ``fn`` the number of false negatives.
PrecisionRecallDisplay.from_estimator : Plot precision-recall curve given
an estimator and some data.
PrecisionRecallDisplay.from_predictions : Plot precision-recall curve given
binary class predictions.
multilabel_confusion_matrix : Compute a confusion matrix for each class or
sample.
Notes
-----
When ``true positive + false positive == 0``, precision returns 0 and
raises ``UndefinedMetricWarning``. This behavior can be
modified with ``zero_division``.
Examples
--------
>>> from sklearn.metrics import precision_score
>>> y_true = [0, 1, 2, 0, 1, 2]
>>> y_pred = [0, 2, 1, 0, 0, 1]
>>> precision_score(y_true, y_pred, average='macro')
0.22...
>>> precision_score(y_true, y_pred, average='micro')
0.33...
>>> precision_score(y_true, y_pred, average='weighted')
0.22...
>>> precision_score(y_true, y_pred, average=None)
array([0.66..., 0. , 0. ])
>>> y_pred = [0, 0, 0, 0, 0, 0]
>>> precision_score(y_true, y_pred, average=None)
array([0.33..., 0. , 0. ])
>>> precision_score(y_true, y_pred, average=None, zero_division=1)
array([0.33..., 1. , 1. ])
>>> # multilabel classification
>>> y_true = [[0, 0, 0], [1, 1, 1], [0, 1, 1]]
>>> y_pred = [[0, 0, 0], [1, 1, 1], [1, 1, 0]]
>>> precision_score(y_true, y_pred, average=None)
array([0.5, 1. , 1. ])
"""
p, _, _, _ = precision_recall_fscore_support(
y_true,
y_pred,
labels=labels,
pos_label=pos_label,
average=average,
warn_for=("precision",),
sample_weight=sample_weight,
zero_division=zero_division,
)
return p
def recall_score(
y_true,
y_pred,
*,
labels=None,
pos_label=1,
average="binary",
sample_weight=None,
zero_division="warn",
):
"""Compute the recall.
The recall is the ratio ``tp / (tp + fn)`` where ``tp`` is the number of
true positives and ``fn`` the number of false negatives. The recall is
intuitively the ability of the classifier to find all the positive samples.
The best value is 1 and the worst value is 0.
Read more in the :ref:`User Guide <precision_recall_f_measure_metrics>`.
Parameters
----------
y_true : 1d array-like, or label indicator array / sparse matrix
Ground truth (correct) target values.
y_pred : 1d array-like, or label indicator array / sparse matrix
Estimated targets as returned by a classifier.
labels : array-like, default=None
The set of labels to include when ``average != 'binary'``, and their
order if ``average is None``. Labels present in the data can be
excluded, for example to calculate a multiclass average ignoring a
majority negative class, while labels not present in the data will
result in 0 components in a macro average. For multilabel targets,
labels are column indices. By default, all labels in ``y_true`` and
``y_pred`` are used in sorted order.
.. versionchanged:: 0.17
Parameter `labels` improved for multiclass problem.
pos_label : str or int, default=1
The class to report if ``average='binary'`` and the data is binary.
If the data are multiclass or multilabel, this will be ignored;
setting ``labels=[pos_label]`` and ``average != 'binary'`` will report
scores for that label only.
average : {'micro', 'macro', 'samples', 'weighted', 'binary'} or None, \
default='binary'
This parameter is required for multiclass/multilabel targets.
If ``None``, the scores for each class are returned. Otherwise, this
determines the type of averaging performed on the data:
``'binary'``:
Only report results for the class specified by ``pos_label``.
This is applicable only if targets (``y_{true,pred}``) are binary.
``'micro'``:
Calculate metrics globally by counting the total true positives,
false negatives and false positives.
``'macro'``:
Calculate metrics for each label, and find their unweighted
mean. This does not take label imbalance into account.
``'weighted'``:
Calculate metrics for each label, and find their average weighted
by support (the number of true instances for each label). This
alters 'macro' to account for label imbalance; it can result in an
F-score that is not between precision and recall. Weighted recall
is equal to accuracy.
``'samples'``:
Calculate metrics for each instance, and find their average (only
meaningful for multilabel classification where this differs from
:func:`accuracy_score`).
sample_weight : array-like of shape (n_samples,), default=None
Sample weights.
zero_division : "warn", 0 or 1, default="warn"
Sets the value to return when there is a zero division. If set to
"warn", this acts as 0, but warnings are also raised.
Returns
-------
recall : float (if average is not None) or array of float of shape \
(n_unique_labels,)
Recall of the positive class in binary classification or weighted
average of the recall of each class for the multiclass task.
See Also
--------
precision_recall_fscore_support : Compute precision, recall, F-measure and
support for each class.
precision_score : Compute the ratio ``tp / (tp + fp)`` where ``tp`` is the
number of true positives and ``fp`` the number of false positives.
balanced_accuracy_score : Compute balanced accuracy to deal with imbalanced
datasets.
multilabel_confusion_matrix : Compute a confusion matrix for each class or
sample.
PrecisionRecallDisplay.from_estimator : Plot precision-recall curve given
an estimator and some data.
PrecisionRecallDisplay.from_predictions : Plot precision-recall curve given
binary class predictions.
Notes
-----
When ``true positive + false negative == 0``, recall returns 0 and raises
``UndefinedMetricWarning``. This behavior can be modified with
``zero_division``.
Examples
--------
>>> from sklearn.metrics import recall_score
>>> y_true = [0, 1, 2, 0, 1, 2]
>>> y_pred = [0, 2, 1, 0, 0, 1]
>>> recall_score(y_true, y_pred, average='macro')
0.33...
>>> recall_score(y_true, y_pred, average='micro')
0.33...
>>> recall_score(y_true, y_pred, average='weighted')
0.33...
>>> recall_score(y_true, y_pred, average=None)
array([1., 0., 0.])
>>> y_true = [0, 0, 0, 0, 0, 0]
>>> recall_score(y_true, y_pred, average=None)
array([0.5, 0. , 0. ])
>>> recall_score(y_true, y_pred, average=None, zero_division=1)
array([0.5, 1. , 1. ])
>>> # multilabel classification
>>> y_true = [[0, 0, 0], [1, 1, 1], [0, 1, 1]]
>>> y_pred = [[0, 0, 0], [1, 1, 1], [1, 1, 0]]
>>> recall_score(y_true, y_pred, average=None)
array([1. , 1. , 0.5])
"""
_, r, _, _ = precision_recall_fscore_support(
y_true,
y_pred,
labels=labels,
pos_label=pos_label,
average=average,
warn_for=("recall",),
sample_weight=sample_weight,
zero_division=zero_division,
)
return r
def balanced_accuracy_score(y_true, y_pred, *, sample_weight=None, adjusted=False):
"""Compute the balanced accuracy.
The balanced accuracy in binary and multiclass classification problems to
deal with imbalanced datasets. It is defined as the average of recall
obtained on each class.
The best value is 1 and the worst value is 0 when ``adjusted=False``.
Read more in the :ref:`User Guide <balanced_accuracy_score>`.
.. versionadded:: 0.20
Parameters
----------
y_true : 1d array-like
Ground truth (correct) target values.
y_pred : 1d array-like
Estimated targets as returned by a classifier.
sample_weight : array-like of shape (n_samples,), default=None
Sample weights.
adjusted : bool, default=False
When true, the result is adjusted for chance, so that random
performance would score 0, while keeping perfect performance at a score
of 1.
Returns
-------
balanced_accuracy : float
Balanced accuracy score.
See Also
--------
average_precision_score : Compute average precision (AP) from prediction
scores.
precision_score : Compute the precision score.
recall_score : Compute the recall score.
roc_auc_score : Compute Area Under the Receiver Operating Characteristic
Curve (ROC AUC) from prediction scores.
Notes
-----
Some literature promotes alternative definitions of balanced accuracy. Our
definition is equivalent to :func:`accuracy_score` with class-balanced
sample weights, and shares desirable properties with the binary case.
See the :ref:`User Guide <balanced_accuracy_score>`.
References
----------
.. [1] Brodersen, K.H.; Ong, C.S.; Stephan, K.E.; Buhmann, J.M. (2010).
The balanced accuracy and its posterior distribution.
Proceedings of the 20th International Conference on Pattern
Recognition, 3121-24.
.. [2] John. D. Kelleher, Brian Mac Namee, Aoife D'Arcy, (2015).
`Fundamentals of Machine Learning for Predictive Data Analytics:
Algorithms, Worked Examples, and Case Studies
<https://mitpress.mit.edu/books/fundamentals-machine-learning-predictive-data-analytics>`_.
Examples
--------
>>> from sklearn.metrics import balanced_accuracy_score
>>> y_true = [0, 1, 0, 0, 1, 0]
>>> y_pred = [0, 1, 0, 0, 0, 1]
>>> balanced_accuracy_score(y_true, y_pred)
0.625
"""
C = confusion_matrix(y_true, y_pred, sample_weight=sample_weight)
with np.errstate(divide="ignore", invalid="ignore"):
per_class = np.diag(C) / C.sum(axis=1)
if np.any(np.isnan(per_class)):
warnings.warn("y_pred contains classes not in y_true")
per_class = per_class[~np.isnan(per_class)]
score = np.mean(per_class)
if adjusted:
n_classes = len(per_class)
chance = 1 / n_classes
score -= chance
score /= 1 - chance
return score
def classification_report(
y_true,
y_pred,
*,
labels=None,
target_names=None,
sample_weight=None,
digits=2,
output_dict=False,
zero_division="warn",
):
"""Build a text report showing the main classification metrics.
Read more in the :ref:`User Guide <classification_report>`.
Parameters
----------
y_true : 1d array-like, or label indicator array / sparse matrix
Ground truth (correct) target values.
y_pred : 1d array-like, or label indicator array / sparse matrix
Estimated targets as returned by a classifier.
labels : array-like of shape (n_labels,), default=None
Optional list of label indices to include in the report.
target_names : list of str of shape (n_labels,), default=None
Optional display names matching the labels (same order).
sample_weight : array-like of shape (n_samples,), default=None
Sample weights.
digits : int, default=2
Number of digits for formatting output floating point values.
When ``output_dict`` is ``True``, this will be ignored and the
returned values will not be rounded.
output_dict : bool, default=False
If True, return output as dict.
.. versionadded:: 0.20
zero_division : "warn", 0 or 1, default="warn"
Sets the value to return when there is a zero division. If set to
"warn", this acts as 0, but warnings are also raised.
Returns
-------
report : str or dict
Text summary of the precision, recall, F1 score for each class.
Dictionary returned if output_dict is True. Dictionary has the
following structure::
{'label 1': {'precision':0.5,
'recall':1.0,
'f1-score':0.67,
'support':1},
'label 2': { ... },
...
}
The reported averages include macro average (averaging the unweighted
mean per label), weighted average (averaging the support-weighted mean
per label), and sample average (only for multilabel classification).
Micro average (averaging the total true positives, false negatives and
false positives) is only shown for multi-label or multi-class
with a subset of classes, because it corresponds to accuracy
otherwise and would be the same for all metrics.
See also :func:`precision_recall_fscore_support` for more details
on averages.
Note that in binary classification, recall of the positive class
is also known as "sensitivity"; recall of the negative class is
"specificity".
See Also
--------
precision_recall_fscore_support: Compute precision, recall, F-measure and
support for each class.
confusion_matrix: Compute confusion matrix to evaluate the accuracy of a
classification.
multilabel_confusion_matrix: Compute a confusion matrix for each class or sample.
Examples
--------
>>> from sklearn.metrics import classification_report
>>> y_true = [0, 1, 2, 2, 2]
>>> y_pred = [0, 0, 2, 2, 1]
>>> target_names = ['class 0', 'class 1', 'class 2']
>>> print(classification_report(y_true, y_pred, target_names=target_names))
precision recall f1-score support
<BLANKLINE>
class 0 0.50 1.00 0.67 1
class 1 0.00 0.00 0.00 1
class 2 1.00 0.67 0.80 3
<BLANKLINE>
accuracy 0.60 5
macro avg 0.50 0.56 0.49 5
weighted avg 0.70 0.60 0.61 5
<BLANKLINE>
>>> y_pred = [1, 1, 0]
>>> y_true = [1, 1, 1]
>>> print(classification_report(y_true, y_pred, labels=[1, 2, 3]))
precision recall f1-score support
<BLANKLINE>
1 1.00 0.67 0.80 3
2 0.00 0.00 0.00 0
3 0.00 0.00 0.00 0
<BLANKLINE>
micro avg 1.00 0.67 0.80 3
macro avg 0.33 0.22 0.27 3
weighted avg 1.00 0.67 0.80 3
<BLANKLINE>
"""
y_type, y_true, y_pred = _check_targets(y_true, y_pred)
if labels is None:
labels = unique_labels(y_true, y_pred)
labels_given = False
else:
labels = np.asarray(labels)
labels_given = True
# labelled micro average
micro_is_accuracy = (y_type == "multiclass" or y_type == "binary") and (
not labels_given or (set(labels) == set(unique_labels(y_true, y_pred)))
)
if target_names is not None and len(labels) != len(target_names):
if labels_given:
warnings.warn(
"labels size, {0}, does not match size of target_names, {1}".format(
len(labels), len(target_names)
)
)
else:
raise ValueError(
"Number of classes, {0}, does not match size of "
"target_names, {1}. Try specifying the labels "
"parameter".format(len(labels), len(target_names))
)
if target_names is None:
target_names = ["%s" % l for l in labels]
headers = ["precision", "recall", "f1-score", "support"]
# compute per-class results without averaging
p, r, f1, s = precision_recall_fscore_support(
y_true,
y_pred,
labels=labels,
average=None,
sample_weight=sample_weight,
zero_division=zero_division,
)
rows = zip(target_names, p, r, f1, s)
if y_type.startswith("multilabel"):
average_options = ("micro", "macro", "weighted", "samples")
else:
average_options = ("micro", "macro", "weighted")
if output_dict:
report_dict = {label[0]: label[1:] for label in rows}
for label, scores in report_dict.items():
report_dict[label] = dict(zip(headers, [i.item() for i in scores]))
else:
longest_last_line_heading = "weighted avg"
name_width = max(len(cn) for cn in target_names)
width = max(name_width, len(longest_last_line_heading), digits)
head_fmt = "{:>{width}s} " + " {:>9}" * len(headers)
report = head_fmt.format("", *headers, width=width)
report += "\n\n"
row_fmt = "{:>{width}s} " + " {:>9.{digits}f}" * 3 + " {:>9}\n"
for row in rows:
report += row_fmt.format(*row, width=width, digits=digits)
report += "\n"
# compute all applicable averages
for average in average_options:
if average.startswith("micro") and micro_is_accuracy:
line_heading = "accuracy"
else:
line_heading = average + " avg"
# compute averages with specified averaging method
avg_p, avg_r, avg_f1, _ = precision_recall_fscore_support(
y_true,
y_pred,
labels=labels,
average=average,
sample_weight=sample_weight,
zero_division=zero_division,
)
avg = [avg_p, avg_r, avg_f1, np.sum(s)]
if output_dict:
report_dict[line_heading] = dict(zip(headers, [i.item() for i in avg]))
else:
if line_heading == "accuracy":
row_fmt_accuracy = (
"{:>{width}s} "
+ " {:>9.{digits}}" * 2
+ " {:>9.{digits}f}"
+ " {:>9}\n"
)
report += row_fmt_accuracy.format(
line_heading, "", "", *avg[2:], width=width, digits=digits
)
else:
report += row_fmt.format(line_heading, *avg, width=width, digits=digits)
if output_dict:
if "accuracy" in report_dict.keys():
report_dict["accuracy"] = report_dict["accuracy"]["precision"]
return report_dict
else:
return report
def hamming_loss(y_true, y_pred, *, sample_weight=None):
"""Compute the average Hamming loss.
The Hamming loss is the fraction of labels that are incorrectly predicted.
Read more in the :ref:`User Guide <hamming_loss>`.
Parameters
----------
y_true : 1d array-like, or label indicator array / sparse matrix
Ground truth (correct) labels.
y_pred : 1d array-like, or label indicator array / sparse matrix
Predicted labels, as returned by a classifier.
sample_weight : array-like of shape (n_samples,), default=None
Sample weights.
.. versionadded:: 0.18
Returns
-------
loss : float or int
Return the average Hamming loss between element of ``y_true`` and
``y_pred``.
See Also
--------
accuracy_score : Compute the accuracy score. By default, the function will
return the fraction of correct predictions divided by the total number
of predictions.
jaccard_score : Compute the Jaccard similarity coefficient score.
zero_one_loss : Compute the Zero-one classification loss. By default, the
function will return the percentage of imperfectly predicted subsets.
Notes
-----
In multiclass classification, the Hamming loss corresponds to the Hamming
distance between ``y_true`` and ``y_pred`` which is equivalent to the
subset ``zero_one_loss`` function, when `normalize` parameter is set to
True.
In multilabel classification, the Hamming loss is different from the
subset zero-one loss. The zero-one loss considers the entire set of labels
for a given sample incorrect if it does not entirely match the true set of
labels. Hamming loss is more forgiving in that it penalizes only the
individual labels.
The Hamming loss is upperbounded by the subset zero-one loss, when
`normalize` parameter is set to True. It is always between 0 and 1,
lower being better.
References
----------
.. [1] Grigorios Tsoumakas, Ioannis Katakis. Multi-Label Classification:
An Overview. International Journal of Data Warehousing & Mining,
3(3), 1-13, July-September 2007.
.. [2] `Wikipedia entry on the Hamming distance
<https://en.wikipedia.org/wiki/Hamming_distance>`_.
Examples
--------
>>> from sklearn.metrics import hamming_loss
>>> y_pred = [1, 2, 3, 4]
>>> y_true = [2, 2, 3, 4]
>>> hamming_loss(y_true, y_pred)
0.25
In the multilabel case with binary label indicators:
>>> import numpy as np
>>> hamming_loss(np.array([[0, 1], [1, 1]]), np.zeros((2, 2)))
0.75
"""
y_type, y_true, y_pred = _check_targets(y_true, y_pred)
check_consistent_length(y_true, y_pred, sample_weight)
if sample_weight is None:
weight_average = 1.0
else:
weight_average = np.mean(sample_weight)
if y_type.startswith("multilabel"):
n_differences = count_nonzero(y_true - y_pred, sample_weight=sample_weight)
return n_differences / (y_true.shape[0] * y_true.shape[1] * weight_average)
elif y_type in ["binary", "multiclass"]:
return _weighted_sum(y_true != y_pred, sample_weight, normalize=True)
else:
raise ValueError("{0} is not supported".format(y_type))
def log_loss(
y_true, y_pred, *, eps="auto", normalize=True, sample_weight=None, labels=None
):
r"""Log loss, aka logistic loss or cross-entropy loss.
This is the loss function used in (multinomial) logistic regression
and extensions of it such as neural networks, defined as the negative
log-likelihood of a logistic model that returns ``y_pred`` probabilities
for its training data ``y_true``.
The log loss is only defined for two or more labels.
For a single sample with true label :math:`y \in \{0,1\}` and
a probability estimate :math:`p = \operatorname{Pr}(y = 1)`, the log
loss is:
.. math::
L_{\log}(y, p) = -(y \log (p) + (1 - y) \log (1 - p))
Read more in the :ref:`User Guide <log_loss>`.
Parameters
----------
y_true : array-like or label indicator matrix
Ground truth (correct) labels for n_samples samples.
y_pred : array-like of float, shape = (n_samples, n_classes) or (n_samples,)
Predicted probabilities, as returned by a classifier's
predict_proba method. If ``y_pred.shape = (n_samples,)``
the probabilities provided are assumed to be that of the
positive class. The labels in ``y_pred`` are assumed to be
ordered alphabetically, as done by
:class:`preprocessing.LabelBinarizer`.
eps : float or "auto", default="auto"
Log loss is undefined for p=0 or p=1, so probabilities are
clipped to `max(eps, min(1 - eps, p))`. The default will depend on the
data type of `y_pred` and is set to `np.finfo(y_pred.dtype).eps`.
.. versionadded:: 1.2
.. versionchanged:: 1.2
The default value changed from `1e-15` to `"auto"` that is
equivalent to `np.finfo(y_pred.dtype).eps`.
normalize : bool, default=True
If true, return the mean loss per sample.
Otherwise, return the sum of the per-sample losses.
sample_weight : array-like of shape (n_samples,), default=None
Sample weights.
labels : array-like, default=None
If not provided, labels will be inferred from y_true. If ``labels``
is ``None`` and ``y_pred`` has shape (n_samples,) the labels are
assumed to be binary and are inferred from ``y_true``.
.. versionadded:: 0.18
Returns
-------
loss : float
Log loss, aka logistic loss or cross-entropy loss.
Notes
-----
The logarithm used is the natural logarithm (base-e).
References
----------
C.M. Bishop (2006). Pattern Recognition and Machine Learning. Springer,
p. 209.
Examples
--------
>>> from sklearn.metrics import log_loss
>>> log_loss(["spam", "ham", "ham", "spam"],
... [[.1, .9], [.9, .1], [.8, .2], [.35, .65]])
0.21616...
"""
y_pred = check_array(
y_pred, ensure_2d=False, dtype=[np.float64, np.float32, np.float16]
)
eps = np.finfo(y_pred.dtype).eps if eps == "auto" else eps
check_consistent_length(y_pred, y_true, sample_weight)
lb = LabelBinarizer()
if labels is not None:
lb.fit(labels)
else:
lb.fit(y_true)
if len(lb.classes_) == 1:
if labels is None:
raise ValueError(
"y_true contains only one label ({0}). Please "
"provide the true labels explicitly through the "
"labels argument.".format(lb.classes_[0])
)
else:
raise ValueError(
"The labels array needs to contain at least two "
"labels for log_loss, "
"got {0}.".format(lb.classes_)
)
transformed_labels = lb.transform(y_true)
if transformed_labels.shape[1] == 1:
transformed_labels = np.append(
1 - transformed_labels, transformed_labels, axis=1
)
# Clipping
y_pred = np.clip(y_pred, eps, 1 - eps)
# If y_pred is of single dimension, assume y_true to be binary
# and then check.
if y_pred.ndim == 1:
y_pred = y_pred[:, np.newaxis]
if y_pred.shape[1] == 1:
y_pred = np.append(1 - y_pred, y_pred, axis=1)
# Check if dimensions are consistent.
transformed_labels = check_array(transformed_labels)
if len(lb.classes_) != y_pred.shape[1]:
if labels is None:
raise ValueError(
"y_true and y_pred contain different number of "
"classes {0}, {1}. Please provide the true "
"labels explicitly through the labels argument. "
"Classes found in "
"y_true: {2}".format(
transformed_labels.shape[1], y_pred.shape[1], lb.classes_
)
)
else:
raise ValueError(
"The number of classes in labels is different "
"from that in y_pred. Classes found in "
"labels: {0}".format(lb.classes_)
)
# Renormalize
y_pred_sum = y_pred.sum(axis=1)
y_pred = y_pred / y_pred_sum[:, np.newaxis]
loss = -xlogy(transformed_labels, y_pred).sum(axis=1)
return _weighted_sum(loss, sample_weight, normalize)
def hinge_loss(y_true, pred_decision, *, labels=None, sample_weight=None):
"""Average hinge loss (non-regularized).
In binary class case, assuming labels in y_true are encoded with +1 and -1,
when a prediction mistake is made, ``margin = y_true * pred_decision`` is
always negative (since the signs disagree), implying ``1 - margin`` is
always greater than 1. The cumulated hinge loss is therefore an upper
bound of the number of mistakes made by the classifier.
In multiclass case, the function expects that either all the labels are
included in y_true or an optional labels argument is provided which
contains all the labels. The multilabel margin is calculated according
to Crammer-Singer's method. As in the binary case, the cumulated hinge loss
is an upper bound of the number of mistakes made by the classifier.
Read more in the :ref:`User Guide <hinge_loss>`.
Parameters
----------
y_true : array of shape (n_samples,)
True target, consisting of integers of two values. The positive label
must be greater than the negative label.
pred_decision : array of shape (n_samples,) or (n_samples, n_classes)
Predicted decisions, as output by decision_function (floats).
labels : array-like, default=None
Contains all the labels for the problem. Used in multiclass hinge loss.
sample_weight : array-like of shape (n_samples,), default=None
Sample weights.
Returns
-------
loss : float
Average hinge loss.
References
----------
.. [1] `Wikipedia entry on the Hinge loss
<https://en.wikipedia.org/wiki/Hinge_loss>`_.
.. [2] Koby Crammer, Yoram Singer. On the Algorithmic
Implementation of Multiclass Kernel-based Vector
Machines. Journal of Machine Learning Research 2,
(2001), 265-292.
.. [3] `L1 AND L2 Regularization for Multiclass Hinge Loss Models
by Robert C. Moore, John DeNero
<https://storage.googleapis.com/pub-tools-public-publication-data/pdf/37362.pdf>`_.
Examples
--------
>>> from sklearn import svm
>>> from sklearn.metrics import hinge_loss
>>> X = [[0], [1]]
>>> y = [-1, 1]
>>> est = svm.LinearSVC(random_state=0)
>>> est.fit(X, y)
LinearSVC(random_state=0)
>>> pred_decision = est.decision_function([[-2], [3], [0.5]])
>>> pred_decision
array([-2.18..., 2.36..., 0.09...])
>>> hinge_loss([-1, 1, 1], pred_decision)
0.30...
In the multiclass case:
>>> import numpy as np
>>> X = np.array([[0], [1], [2], [3]])
>>> Y = np.array([0, 1, 2, 3])
>>> labels = np.array([0, 1, 2, 3])
>>> est = svm.LinearSVC()
>>> est.fit(X, Y)
LinearSVC()
>>> pred_decision = est.decision_function([[-1], [2], [3]])
>>> y_true = [0, 2, 3]
>>> hinge_loss(y_true, pred_decision, labels=labels)
0.56...
"""
check_consistent_length(y_true, pred_decision, sample_weight)
pred_decision = check_array(pred_decision, ensure_2d=False)
y_true = column_or_1d(y_true)
y_true_unique = np.unique(labels if labels is not None else y_true)
if y_true_unique.size > 2:
if pred_decision.ndim <= 1:
raise ValueError(
"The shape of pred_decision cannot be 1d array"
"with a multiclass target. pred_decision shape "
"must be (n_samples, n_classes), that is "
f"({y_true.shape[0]}, {y_true_unique.size})."
f" Got: {pred_decision.shape}"
)
# pred_decision.ndim > 1 is true
if y_true_unique.size != pred_decision.shape[1]:
if labels is None:
raise ValueError(
"Please include all labels in y_true "
"or pass labels as third argument"
)
else:
raise ValueError(
"The shape of pred_decision is not "
"consistent with the number of classes. "
"With a multiclass target, pred_decision "
"shape must be "
"(n_samples, n_classes), that is "
f"({y_true.shape[0]}, {y_true_unique.size}). "
f"Got: {pred_decision.shape}"
)
if labels is None:
labels = y_true_unique
le = LabelEncoder()
le.fit(labels)
y_true = le.transform(y_true)
mask = np.ones_like(pred_decision, dtype=bool)
mask[np.arange(y_true.shape[0]), y_true] = False
margin = pred_decision[~mask]
margin -= np.max(pred_decision[mask].reshape(y_true.shape[0], -1), axis=1)
else:
# Handles binary class case
# this code assumes that positive and negative labels
# are encoded as +1 and -1 respectively
pred_decision = column_or_1d(pred_decision)
pred_decision = np.ravel(pred_decision)
lbin = LabelBinarizer(neg_label=-1)
y_true = lbin.fit_transform(y_true)[:, 0]
try:
margin = y_true * pred_decision
except TypeError:
raise TypeError("pred_decision should be an array of floats.")
losses = 1 - margin
# The hinge_loss doesn't penalize good enough predictions.
np.clip(losses, 0, None, out=losses)
return np.average(losses, weights=sample_weight)
def brier_score_loss(y_true, y_prob, *, sample_weight=None, pos_label=None):
"""Compute the Brier score loss.
The smaller the Brier score loss, the better, hence the naming with "loss".
The Brier score measures the mean squared difference between the predicted
probability and the actual outcome. The Brier score always
takes on a value between zero and one, since this is the largest
possible difference between a predicted probability (which must be
between zero and one) and the actual outcome (which can take on values
of only 0 and 1). It can be decomposed as the sum of refinement loss and
calibration loss.
The Brier score is appropriate for binary and categorical outcomes that
can be structured as true or false, but is inappropriate for ordinal
variables which can take on three or more values (this is because the
Brier score assumes that all possible outcomes are equivalently
"distant" from one another). Which label is considered to be the positive
label is controlled via the parameter `pos_label`, which defaults to
the greater label unless `y_true` is all 0 or all -1, in which case
`pos_label` defaults to 1.
Read more in the :ref:`User Guide <brier_score_loss>`.
Parameters
----------
y_true : array of shape (n_samples,)
True targets.
y_prob : array of shape (n_samples,)
Probabilities of the positive class.
sample_weight : array-like of shape (n_samples,), default=None
Sample weights.
pos_label : int or str, default=None
Label of the positive class. `pos_label` will be inferred in the
following manner:
* if `y_true` in {-1, 1} or {0, 1}, `pos_label` defaults to 1;
* else if `y_true` contains string, an error will be raised and
`pos_label` should be explicitly specified;
* otherwise, `pos_label` defaults to the greater label,
i.e. `np.unique(y_true)[-1]`.
Returns
-------
score : float
Brier score loss.
References
----------
.. [1] `Wikipedia entry for the Brier score
<https://en.wikipedia.org/wiki/Brier_score>`_.
Examples
--------
>>> import numpy as np
>>> from sklearn.metrics import brier_score_loss
>>> y_true = np.array([0, 1, 1, 0])
>>> y_true_categorical = np.array(["spam", "ham", "ham", "spam"])
>>> y_prob = np.array([0.1, 0.9, 0.8, 0.3])
>>> brier_score_loss(y_true, y_prob)
0.037...
>>> brier_score_loss(y_true, 1-y_prob, pos_label=0)
0.037...
>>> brier_score_loss(y_true_categorical, y_prob, pos_label="ham")
0.037...
>>> brier_score_loss(y_true, np.array(y_prob) > 0.5)
0.0
"""
y_true = column_or_1d(y_true)
y_prob = column_or_1d(y_prob)
assert_all_finite(y_true)
assert_all_finite(y_prob)
check_consistent_length(y_true, y_prob, sample_weight)
y_type = type_of_target(y_true, input_name="y_true")
if y_type != "binary":
raise ValueError(
"Only binary classification is supported. The type of the target "
f"is {y_type}."
)
if y_prob.max() > 1:
raise ValueError("y_prob contains values greater than 1.")
if y_prob.min() < 0:
raise ValueError("y_prob contains values less than 0.")
try:
pos_label = _check_pos_label_consistency(pos_label, y_true)
except ValueError:
classes = np.unique(y_true)
if classes.dtype.kind not in ("O", "U", "S"):
# for backward compatibility, if classes are not string then
# `pos_label` will correspond to the greater label
pos_label = classes[-1]
else:
raise
y_true = np.array(y_true == pos_label, int)
return np.average((y_true - y_prob) ** 2, weights=sample_weight)
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