File: C:/Users/fred/anaconda3/Lib/site-packages/imblearn/metrics/_classification.py
# coding: utf-8
"""Metrics to assess performance on a classification task given class
predictions. The available metrics are complementary from the metrics available
in scikit-learn.
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: Guillaume Lemaitre <[email protected]>
# Dariusz Brzezinski
# License: MIT
import functools
import numbers
import warnings
from inspect import signature
import numpy as np
import scipy as sp
from sklearn.metrics import mean_absolute_error, precision_recall_fscore_support
from sklearn.metrics._classification import _check_targets, _prf_divide
from sklearn.preprocessing import LabelEncoder
from sklearn.utils.multiclass import unique_labels
from sklearn.utils.validation import check_consistent_length, column_or_1d
from ..utils._param_validation import Interval, StrOptions, validate_params
@validate_params(
{
"y_true": ["array-like"],
"y_pred": ["array-like"],
"labels": ["array-like", None],
"pos_label": [str, numbers.Integral, None],
"average": [
None,
StrOptions({"binary", "micro", "macro", "weighted", "samples"}),
],
"warn_for": ["array-like"],
"sample_weight": ["array-like", None],
},
prefer_skip_nested_validation=True,
)
def sensitivity_specificity_support(
y_true,
y_pred,
*,
labels=None,
pos_label=1,
average=None,
warn_for=("sensitivity", "specificity"),
sample_weight=None,
):
"""Compute sensitivity, specificity, and support for each class.
The sensitivity is the ratio ``tp / (tp + fn)`` where ``tp`` is the number
of true positives and ``fn`` the number of false negatives. The sensitivity
quantifies the ability to avoid false negatives_[1].
The specificity is the ratio ``tn / (tn + fp)`` where ``tn`` is the number
of true negatives and ``fn`` the number of false negatives. The specificity
quantifies the ability to avoid false positives_[1].
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 sensitivity and specificity if ``average``
is one of ``'weighted'``.
Read more in the :ref:`User Guide <sensitivity_specificity>`.
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, 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, int or None, default=1
The class to report if ``average='binary'`` and the data is binary.
If ``pos_label is None`` and in binary classification, this function
returns the average sensitivity and specificity if ``average``
is one of ``'weighted'``.
If the data are multiclass, this will be ignored;
setting ``labels=[pos_label]`` and ``average != 'binary'`` will report
scores for that label only.
average : str, 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 of {{"sensitivity", "specificity"}}, 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.
Returns
-------
sensitivity : float (if `average is None`) or ndarray of \
shape (n_unique_labels,)
The sensitivity metric.
specificity : float (if `average is None`) or ndarray of \
shape (n_unique_labels,)
The specificity metric.
support : int (if `average is None`) or ndarray of \
shape (n_unique_labels,)
The number of occurrences of each label in ``y_true``.
References
----------
.. [1] `Wikipedia entry for the Sensitivity and specificity
<https://en.wikipedia.org/wiki/Sensitivity_and_specificity>`_
Examples
--------
>>> import numpy as np
>>> from imblearn.metrics import sensitivity_specificity_support
>>> y_true = np.array(['cat', 'dog', 'pig', 'cat', 'dog', 'pig'])
>>> y_pred = np.array(['cat', 'pig', 'dog', 'cat', 'cat', 'dog'])
>>> sensitivity_specificity_support(y_true, y_pred, average='macro')
(0.33..., 0.66..., None)
>>> sensitivity_specificity_support(y_true, y_pred, average='micro')
(0.33..., 0.66..., None)
>>> sensitivity_specificity_support(y_true, y_pred, average='weighted')
(0.33..., 0.66..., None)
"""
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)
present_labels = unique_labels(y_true, y_pred)
if average == "binary":
if y_type == "binary":
if pos_label not in present_labels:
if len(present_labels) < 2:
# Only negative labels
return (0.0, 0.0, 0)
else:
raise ValueError(
"pos_label=%r is not a valid label: %r"
% (pos_label, present_labels)
)
labels = [pos_label]
else:
raise ValueError(
"Target is %s but average='binary'. Please "
"choose another average setting." % y_type
)
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,
)
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)]
)
# Calculate tp_sum, pred_sum, true_sum ###
if y_type.startswith("multilabel"):
raise ValueError("imblearn does not support multilabel")
elif average == "samples":
raise ValueError(
"Sample-based precision, recall, fscore is "
"not meaningful outside multilabel "
"classification. See the accuracy_score instead."
)
else:
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))
# Compute the true negative
tn_sum = y_true.size - (pred_sum + true_sum - tp_sum)
# 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]
tn_sum = tn_sum[indices]
if average == "micro":
tp_sum = np.array([tp_sum.sum()])
pred_sum = np.array([pred_sum.sum()])
true_sum = np.array([true_sum.sum()])
tn_sum = np.array([tn_sum.sum()])
# Finally, we have all our sufficient statistics. Divide! #
with np.errstate(divide="ignore", invalid="ignore"):
# Divide, and on zero-division, set scores to 0 and warn:
# Oddly, we may get an "invalid" rather than a "divide" error
# here.
specificity = _prf_divide(
tn_sum,
tn_sum + pred_sum - tp_sum,
"specificity",
"predicted",
average,
warn_for,
)
sensitivity = _prf_divide(
tp_sum, true_sum, "sensitivity", "true", average, warn_for
)
# Average the results
if average == "weighted":
weights = true_sum
if weights.sum() == 0:
return 0, 0, None
elif average == "samples":
weights = sample_weight
else:
weights = None
if average is not None:
assert average != "binary" or len(specificity) == 1
specificity = np.average(specificity, weights=weights)
sensitivity = np.average(sensitivity, weights=weights)
true_sum = None # return no support
return sensitivity, specificity, true_sum
@validate_params(
{
"y_true": ["array-like"],
"y_pred": ["array-like"],
"labels": ["array-like", None],
"pos_label": [str, numbers.Integral, None],
"average": [
None,
StrOptions({"binary", "micro", "macro", "weighted", "samples"}),
],
"sample_weight": ["array-like", None],
},
prefer_skip_nested_validation=True,
)
def sensitivity_score(
y_true,
y_pred,
*,
labels=None,
pos_label=1,
average="binary",
sample_weight=None,
):
"""Compute the sensitivity.
The sensitivity is the ratio ``tp / (tp + fn)`` where ``tp`` is the number
of true positives and ``fn`` the number of false negatives. The sensitivity
quantifies the ability to avoid false negatives.
The best value is 1 and the worst value is 0.
Read more in the :ref:`User Guide <sensitivity_specificity>`.
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, 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.
pos_label : str, int or None, default=1
The class to report if ``average='binary'`` and the data is binary.
If ``pos_label is None`` and in binary classification, this function
returns the average sensitivity if ``average`` is one of ``'weighted'``.
If the data are multiclass, this will be ignored;
setting ``labels=[pos_label]`` and ``average != 'binary'`` will report
scores for that label only.
average : str, 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`).
sample_weight : array-like of shape (n_samples,), default=None
Sample weights.
Returns
-------
specificity : float (if `average is None`) or ndarray of \
shape (n_unique_labels,)
The specifcity metric.
Examples
--------
>>> import numpy as np
>>> from imblearn.metrics import sensitivity_score
>>> y_true = [0, 1, 2, 0, 1, 2]
>>> y_pred = [0, 2, 1, 0, 0, 1]
>>> sensitivity_score(y_true, y_pred, average='macro')
0.33...
>>> sensitivity_score(y_true, y_pred, average='micro')
0.33...
>>> sensitivity_score(y_true, y_pred, average='weighted')
0.33...
>>> sensitivity_score(y_true, y_pred, average=None)
array([1., 0., 0.])
"""
s, _, _ = sensitivity_specificity_support(
y_true,
y_pred,
labels=labels,
pos_label=pos_label,
average=average,
warn_for=("sensitivity",),
sample_weight=sample_weight,
)
return s
@validate_params(
{
"y_true": ["array-like"],
"y_pred": ["array-like"],
"labels": ["array-like", None],
"pos_label": [str, numbers.Integral, None],
"average": [
None,
StrOptions({"binary", "micro", "macro", "weighted", "samples"}),
],
"sample_weight": ["array-like", None],
},
prefer_skip_nested_validation=True,
)
def specificity_score(
y_true,
y_pred,
*,
labels=None,
pos_label=1,
average="binary",
sample_weight=None,
):
"""Compute the specificity.
The specificity is the ratio ``tn / (tn + fp)`` where ``tn`` is the number
of true negatives and ``fp`` the number of false positives. The specificity
quantifies the ability to avoid false positives.
The best value is 1 and the worst value is 0.
Read more in the :ref:`User Guide <sensitivity_specificity>`.
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, 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.
pos_label : str, int or None, default=1
The class to report if ``average='binary'`` and the data is binary.
If ``pos_label is None`` and in binary classification, this function
returns the average specificity if ``average`` is one of ``'weighted'``.
If the data are multiclass, this will be ignored;
setting ``labels=[pos_label]`` and ``average != 'binary'`` will report
scores for that label only.
average : str, 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`).
sample_weight : array-like of shape (n_samples,), default=None
Sample weights.
Returns
-------
specificity : float (if `average is None`) or ndarray of \
shape (n_unique_labels,)
The specificity metric.
Examples
--------
>>> import numpy as np
>>> from imblearn.metrics import specificity_score
>>> y_true = [0, 1, 2, 0, 1, 2]
>>> y_pred = [0, 2, 1, 0, 0, 1]
>>> specificity_score(y_true, y_pred, average='macro')
0.66...
>>> specificity_score(y_true, y_pred, average='micro')
0.66...
>>> specificity_score(y_true, y_pred, average='weighted')
0.66...
>>> specificity_score(y_true, y_pred, average=None)
array([0.75, 0.5 , 0.75])
"""
_, s, _ = sensitivity_specificity_support(
y_true,
y_pred,
labels=labels,
pos_label=pos_label,
average=average,
warn_for=("specificity",),
sample_weight=sample_weight,
)
return s
@validate_params(
{
"y_true": ["array-like"],
"y_pred": ["array-like"],
"labels": ["array-like", None],
"pos_label": [str, numbers.Integral, None],
"average": [
None,
StrOptions(
{"binary", "micro", "macro", "weighted", "samples", "multiclass"}
),
],
"sample_weight": ["array-like", None],
"correction": [Interval(numbers.Real, 0, None, closed="left")],
},
prefer_skip_nested_validation=True,
)
def geometric_mean_score(
y_true,
y_pred,
*,
labels=None,
pos_label=1,
average="multiclass",
sample_weight=None,
correction=0.0,
):
"""Compute the geometric mean.
The geometric mean (G-mean) is the root of the product of class-wise
sensitivity. This measure tries to maximize the accuracy on each of the
classes while keeping these accuracies balanced. For binary classification
G-mean is the squared root of the product of the sensitivity
and specificity. For multi-class problems it is a higher root of the
product of sensitivity for each class.
For compatibility with other imbalance performance measures, G-mean can be
calculated for each class separately on a one-vs-rest basis when
``average != 'multiclass'``.
The best value is 1 and the worst value is 0. Traditionally if at least one
class is unrecognized by the classifier, G-mean resolves to zero. To
alleviate this property, for highly multi-class the sensitivity of
unrecognized classes can be "corrected" to be a user specified value
(instead of zero). This option works only if ``average == 'multiclass'``.
Read more in the :ref:`User Guide <imbalanced_metrics>`.
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, 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.
pos_label : str, int or None, default=1
The class to report if ``average='binary'`` and the data is binary.
If ``pos_label is None`` and in binary classification, this function
returns the average geometric mean if ``average`` is one of
``'weighted'``.
If the data are multiclass, this will be ignored;
setting ``labels=[pos_label]`` and ``average != 'binary'`` will report
scores for that label only.
average : str or None, default='multiclass'
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.
``'multiclass'``:
No average is taken.
``'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.
correction : float, default=0.0
Substitutes sensitivity of unrecognized classes from zero to a given
value.
Returns
-------
geometric_mean : float
Returns the geometric mean.
Notes
-----
See :ref:`sphx_glr_auto_examples_evaluation_plot_metrics.py`.
References
----------
.. [1] Kubat, M. and Matwin, S. "Addressing the curse of
imbalanced training sets: one-sided selection" ICML (1997)
.. [2] Barandela, R., Sánchez, J. S., Garcıa, V., & Rangel, E. "Strategies
for learning in class imbalance problems", Pattern Recognition,
36(3), (2003), pp 849-851.
Examples
--------
>>> from imblearn.metrics import geometric_mean_score
>>> y_true = [0, 1, 2, 0, 1, 2]
>>> y_pred = [0, 2, 1, 0, 0, 1]
>>> geometric_mean_score(y_true, y_pred)
0.0
>>> geometric_mean_score(y_true, y_pred, correction=0.001)
0.010...
>>> geometric_mean_score(y_true, y_pred, average='macro')
0.471...
>>> geometric_mean_score(y_true, y_pred, average='micro')
0.471...
>>> geometric_mean_score(y_true, y_pred, average='weighted')
0.471...
>>> geometric_mean_score(y_true, y_pred, average=None)
array([0.866..., 0. , 0. ])
"""
if average is None or average != "multiclass":
sen, spe, _ = sensitivity_specificity_support(
y_true,
y_pred,
labels=labels,
pos_label=pos_label,
average=average,
warn_for=("specificity", "specificity"),
sample_weight=sample_weight,
)
return np.sqrt(sen * spe)
else:
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)]
)
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 = tp_sum = np.zeros(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]
with np.errstate(divide="ignore", invalid="ignore"):
recall = _prf_divide(tp_sum, true_sum, "recall", "true", None, "recall")
recall[recall == 0] = correction
with np.errstate(divide="ignore", invalid="ignore"):
gmean = sp.stats.gmean(recall)
# old version of scipy return MaskedConstant instead of 0.0
if isinstance(gmean, np.ma.core.MaskedConstant):
return 0.0
return gmean
@validate_params(
{"alpha": [numbers.Real], "squared": ["boolean"]},
prefer_skip_nested_validation=True,
)
def make_index_balanced_accuracy(*, alpha=0.1, squared=True):
"""Balance any scoring function using the index balanced accuracy.
This factory function wraps scoring function to express it as the
index balanced accuracy (IBA). You need to use this function to
decorate any scoring function.
Only metrics requiring ``y_pred`` can be corrected with the index
balanced accuracy. ``y_score`` cannot be used since the dominance
cannot be computed.
Read more in the :ref:`User Guide <imbalanced_metrics>`.
Parameters
----------
alpha : float, default=0.1
Weighting factor.
squared : bool, default=True
If ``squared`` is True, then the metric computed will be squared
before to be weighted.
Returns
-------
iba_scoring_func : callable,
Returns the scoring metric decorated which will automatically compute
the index balanced accuracy.
Notes
-----
See :ref:`sphx_glr_auto_examples_evaluation_plot_metrics.py`.
References
----------
.. [1] García, Vicente, Javier Salvador Sánchez, and Ramón Alberto
Mollineda. "On the effectiveness of preprocessing methods when dealing
with different levels of class imbalance." Knowledge-Based Systems 25.1
(2012): 13-21.
Examples
--------
>>> from imblearn.metrics import geometric_mean_score as gmean
>>> from imblearn.metrics import make_index_balanced_accuracy as iba
>>> gmean = iba(alpha=0.1, squared=True)(gmean)
>>> y_true = [1, 0, 0, 1, 0, 1]
>>> y_pred = [0, 0, 1, 1, 0, 1]
>>> print(gmean(y_true, y_pred, average=None))
[0.44... 0.44...]
"""
def decorate(scoring_func):
@functools.wraps(scoring_func)
def compute_score(*args, **kwargs):
signature_scoring_func = signature(scoring_func)
params_scoring_func = set(signature_scoring_func.parameters.keys())
# check that the scoring function does not need a score
# and only a prediction
prohibitied_y_pred = set(["y_score", "y_prob", "y2"])
if prohibitied_y_pred.intersection(params_scoring_func):
raise AttributeError(
f"The function {scoring_func.__name__} has an unsupported"
f" attribute. Metric with`y_pred` are the"
f" only supported metrics is the only"
f" supported."
)
args_scoring_func = signature_scoring_func.bind(*args, **kwargs)
args_scoring_func.apply_defaults()
_score = scoring_func(*args_scoring_func.args, **args_scoring_func.kwargs)
if squared:
_score = np.power(_score, 2)
signature_sens_spec = signature(sensitivity_specificity_support)
params_sens_spec = set(signature_sens_spec.parameters.keys())
common_params = params_sens_spec.intersection(
set(args_scoring_func.arguments.keys())
)
args_sens_spec = {k: args_scoring_func.arguments[k] for k in common_params}
if scoring_func.__name__ == "geometric_mean_score":
if "average" in args_sens_spec:
if args_sens_spec["average"] == "multiclass":
args_sens_spec["average"] = "macro"
elif (
scoring_func.__name__ == "accuracy_score"
or scoring_func.__name__ == "jaccard_score"
):
# We do not support multilabel so the only average supported
# is binary
args_sens_spec["average"] = "binary"
sensitivity, specificity, _ = sensitivity_specificity_support(
**args_sens_spec
)
dominance = sensitivity - specificity
return (1.0 + alpha * dominance) * _score
return compute_score
return decorate
@validate_params(
{
"y_true": ["array-like"],
"y_pred": ["array-like"],
"labels": ["array-like", None],
"target_names": ["array-like", None],
"sample_weight": ["array-like", None],
"digits": [Interval(numbers.Integral, 0, None, closed="left")],
"alpha": [numbers.Real],
"output_dict": ["boolean"],
"zero_division": [
StrOptions({"warn"}),
Interval(numbers.Integral, 0, 1, closed="both"),
],
},
prefer_skip_nested_validation=True,
)
def classification_report_imbalanced(
y_true,
y_pred,
*,
labels=None,
target_names=None,
sample_weight=None,
digits=2,
alpha=0.1,
output_dict=False,
zero_division="warn",
):
"""Build a classification report based on metrics used with imbalanced dataset.
Specific metrics have been proposed to evaluate the classification
performed on imbalanced dataset. This report compiles the
state-of-the-art metrics: precision/recall/specificity, geometric
mean, and index balanced accuracy of the
geometric mean.
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.
alpha : float, default=0.1
Weighting factor.
output_dict : bool, default=False
If True, return output as dict.
.. versionadded:: 0.8
zero_division : "warn" or {0, 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.
.. versionadded:: 0.8
Returns
-------
report : string / dict
Text summary of the precision, recall, specificity, geometric mean,
and index balanced accuracy.
Dictionary returned if output_dict is True. Dictionary has the
following structure::
{'label 1': {'pre':0.5,
'rec':1.0,
...
},
'label 2': { ... },
...
}
Examples
--------
>>> import numpy as np
>>> from imblearn.metrics import classification_report_imbalanced
>>> 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_imbalanced(y_true, y_pred, \
target_names=target_names))
pre rec spe f1 geo iba\
sup
<BLANKLINE>
class 0 0.50 1.00 0.75 0.67 0.87 0.77\
1
class 1 0.00 0.00 0.75 0.00 0.00 0.00\
1
class 2 1.00 0.67 1.00 0.80 0.82 0.64\
3
<BLANKLINE>
avg / total 0.70 0.60 0.90 0.61 0.66 0.54\
5
<BLANKLINE>
"""
if labels is None:
labels = unique_labels(y_true, y_pred)
else:
labels = np.asarray(labels)
last_line_heading = "avg / total"
if target_names is None:
target_names = [f"{label}" for label in labels]
name_width = max(len(cn) for cn in target_names)
width = max(name_width, len(last_line_heading), digits)
headers = ["pre", "rec", "spe", "f1", "geo", "iba", "sup"]
fmt = "%% %ds" % width # first column: class name
fmt += " "
fmt += " ".join(["% 9s" for _ in headers])
fmt += "\n"
headers = [""] + headers
report = fmt % tuple(headers)
report += "\n"
# Compute the different metrics
# Precision/recall/f1
precision, recall, f1, support = precision_recall_fscore_support(
y_true,
y_pred,
labels=labels,
average=None,
sample_weight=sample_weight,
zero_division=zero_division,
)
# Specificity
specificity = specificity_score(
y_true,
y_pred,
labels=labels,
average=None,
sample_weight=sample_weight,
)
# Geometric mean
geo_mean = geometric_mean_score(
y_true,
y_pred,
labels=labels,
average=None,
sample_weight=sample_weight,
)
# Index balanced accuracy
iba_gmean = make_index_balanced_accuracy(alpha=alpha, squared=True)(
geometric_mean_score
)
iba = iba_gmean(
y_true,
y_pred,
labels=labels,
average=None,
sample_weight=sample_weight,
)
report_dict = {}
for i, label in enumerate(labels):
report_dict_label = {}
values = [target_names[i]]
for score_name, score_value in zip(
headers[1:-1],
[
precision[i],
recall[i],
specificity[i],
f1[i],
geo_mean[i],
iba[i],
],
):
values += ["{0:0.{1}f}".format(score_value, digits)]
report_dict_label[score_name] = score_value
values += [f"{support[i]}"]
report_dict_label[headers[-1]] = support[i]
report += fmt % tuple(values)
report_dict[target_names[i]] = report_dict_label
report += "\n"
# compute averages
values = [last_line_heading]
for score_name, score_value in zip(
headers[1:-1],
[
np.average(precision, weights=support),
np.average(recall, weights=support),
np.average(specificity, weights=support),
np.average(f1, weights=support),
np.average(geo_mean, weights=support),
np.average(iba, weights=support),
],
):
values += ["{0:0.{1}f}".format(score_value, digits)]
report_dict[f"avg_{score_name}"] = score_value
values += [f"{np.sum(support)}"]
report += fmt % tuple(values)
report_dict["total_support"] = np.sum(support)
if output_dict:
return report_dict
return report
@validate_params(
{
"y_true": ["array-like"],
"y_pred": ["array-like"],
"sample_weight": ["array-like", None],
},
prefer_skip_nested_validation=True,
)
def macro_averaged_mean_absolute_error(y_true, y_pred, *, sample_weight=None):
"""Compute Macro-Averaged MAE for imbalanced ordinal classification.
This function computes each MAE for each class and average them,
giving an equal weight to each class.
Read more in the :ref:`User Guide <macro_averaged_mean_absolute_error>`.
.. versionadded:: 0.8
Parameters
----------
y_true : array-like of shape (n_samples,) or (n_samples, n_outputs)
Ground truth (correct) target values.
y_pred : array-like of shape (n_samples,) or (n_samples, n_outputs)
Estimated targets as returned by a classifier.
sample_weight : array-like of shape (n_samples,), default=None
Sample weights.
Returns
-------
loss : float or ndarray of floats
Macro-Averaged MAE output is non-negative floating point.
The best value is 0.0.
Examples
--------
>>> import numpy as np
>>> from sklearn.metrics import mean_absolute_error
>>> from imblearn.metrics import macro_averaged_mean_absolute_error
>>> y_true_balanced = [1, 1, 2, 2]
>>> y_true_imbalanced = [1, 2, 2, 2]
>>> y_pred = [1, 2, 1, 2]
>>> mean_absolute_error(y_true_balanced, y_pred)
0.5
>>> mean_absolute_error(y_true_imbalanced, y_pred)
0.25
>>> macro_averaged_mean_absolute_error(y_true_balanced, y_pred)
0.5
>>> macro_averaged_mean_absolute_error(y_true_imbalanced, y_pred)
0.16...
"""
_, y_true, y_pred = _check_targets(y_true, y_pred)
if sample_weight is not None:
sample_weight = column_or_1d(sample_weight)
else:
sample_weight = np.ones(y_true.shape)
check_consistent_length(y_true, y_pred, sample_weight)
labels = unique_labels(y_true, y_pred)
mae = []
for possible_class in labels:
indices = np.flatnonzero(y_true == possible_class)
mae.append(
mean_absolute_error(
y_true[indices],
y_pred[indices],
sample_weight=sample_weight[indices],
)
)
return np.sum(mae) / len(mae)