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MNT refactor _get_response_values #21538

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51a3bcb
MNT refactor _get_response_values [ci skip]
glemaitre Nov 3, 2021
8675def
iter
glemaitre Nov 3, 2021
64dfc86
iter
glemaitre Nov 3, 2021
5a4f33f
Merge remote-tracking branch 'origin/main' into is/18212_again_again
glemaitre Nov 29, 2021
a7e46da
reuse type_of_target
glemaitre Nov 29, 2021
a69176d
iter
glemaitre Nov 29, 2021
8214f8b
make response_method to not be None
glemaitre Nov 29, 2021
d7e4912
Update sklearn/utils/_mocking.py
glemaitre Nov 29, 2021
75bd0e1
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glemaitre Nov 29, 2021
04b54c9
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glemaitre Nov 29, 2021
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glemaitre Nov 29, 2021
3487701
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glemaitre Nov 29, 2021
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glemaitre Nov 29, 2021
8863fad
Merge branch 'main' of https://github.com/scikit-learn/scikit-learn i…
lesteve Apr 6, 2022
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32 changes: 22 additions & 10 deletions sklearn/calibration.py
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Original file line number Diff line number Diff line change
Expand Up @@ -25,7 +25,6 @@
RegressorMixin,
clone,
MetaEstimatorMixin,
is_classifier,
)
from .preprocessing import label_binarize, LabelEncoder
from .utils import (
Expand All @@ -34,7 +33,10 @@
check_matplotlib_support,
)

from .utils.multiclass import check_classification_targets
from .utils.multiclass import (
check_classification_targets,
type_of_target,
)
from .utils.fixes import delayed
from .utils.validation import (
_check_fit_params,
Expand All @@ -43,12 +45,12 @@
check_consistent_length,
check_is_fitted,
)
from .utils import _safe_indexing
from .utils import _get_response_values, _safe_indexing
from .isotonic import IsotonicRegression
from .svm import LinearSVC
from .model_selection import check_cv, cross_val_predict
from .metrics._base import _check_pos_label_consistency
from .metrics._plot.base import _get_response
from .metrics._plot.base import _check_estimator_and_target_is_binary


class CalibratedClassifierCV(ClassifierMixin, MetaEstimatorMixin, BaseEstimator):
Expand Down Expand Up @@ -1235,11 +1237,15 @@ def from_estimator(
method_name = f"{cls.__name__}.from_estimator"
check_matplotlib_support(method_name)

if not is_classifier(estimator):
raise ValueError("'estimator' should be a fitted classifier.")

y_prob, pos_label = _get_response(
X, estimator, response_method="predict_proba", pos_label=pos_label
target_type = type_of_target(y)
_check_estimator_and_target_is_binary(estimator, y, target_type=target_type)
y_prob, pos_label = _get_response_values(
estimator,
X,
y,
response_method="predict_proba",
pos_label=pos_label,
target_type=target_type,
)

name = name if name is not None else estimator.__class__.__name__
Expand Down Expand Up @@ -1352,9 +1358,15 @@ def from_predictions(
>>> disp = CalibrationDisplay.from_predictions(y_test, y_prob)
>>> plt.show()
"""
method_name = f"{cls.__name__}.from_estimator"
method_name = f"{cls.__name__}.from_predictions"
check_matplotlib_support(method_name)

target_type = type_of_target(y_true)
if target_type != "binary":
raise ValueError(
f"The target y is not binary. Got {target_type} type of target."
)

prob_true, prob_pred = calibration_curve(
y_true, y_prob, n_bins=n_bins, strategy=strategy, pos_label=pos_label
)
Expand Down
34 changes: 17 additions & 17 deletions sklearn/ensemble/_stacking.py
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Original file line number Diff line number Diff line change
Expand Up @@ -30,8 +30,11 @@
from ..utils import Bunch
from ..utils.metaestimators import available_if
from ..utils.multiclass import check_classification_targets
from ..utils.validation import check_is_fitted
from ..utils.validation import column_or_1d
from ..utils.validation import (
_check_response_method,
check_is_fitted,
column_or_1d,
)
from ..utils.fixes import delayed
from ..utils.validation import _check_feature_names_in

Expand Down Expand Up @@ -120,21 +123,18 @@ def _concatenate_predictions(self, X, predictions):
def _method_name(name, estimator, method):
if estimator == "drop":
return None
if method == "auto":
if getattr(estimator, "predict_proba", None):
return "predict_proba"
elif getattr(estimator, "decision_function", None):
return "decision_function"
else:
return "predict"
else:
if not hasattr(estimator, method):
raise ValueError(
"Underlying estimator {} does not implement the method {}.".format(
name, method
)
)
return method
method = (
["predict_proba", "decision_function", "predict"]
if method == "auto"
else method
)
Comment on lines +126 to +130
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A bit lighter to parse IMO (and also what is used in other places in this PR):

Suggested change
method = (
["predict_proba", "decision_function", "predict"]
if method == "auto"
else method
)
if method == "auto":
method = ["predict_proba", "decision_function", "predict"]

try:
method_name = _check_response_method(estimator, method).__name__
except AttributeError as e:
raise ValueError(
f"Underlying estimator {name} does not implement the method {method}."
) from e
return method_name

def fit(self, X, y, sample_weight=None):
"""Fit the estimators.
Expand Down
150 changes: 45 additions & 105 deletions sklearn/metrics/_plot/base.py
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Original file line number Diff line number Diff line change
@@ -1,116 +1,56 @@
from ...base import is_classifier
from ...exceptions import NotFittedError
from ...utils.multiclass import type_of_target
from ...utils.validation import check_is_fitted


def _check_classifier_response_method(estimator, response_method):
"""Return prediction method from the response_method
def _check_estimator_and_target_is_binary(estimator, y, target_type=None):
"""Helper to check that estimator is a binary classifier and y is binary.

Parameters
----------
estimator: object
Classifier to check

response_method: {'auto', 'predict_proba', 'decision_function'}
Specifies whether to use :term:`predict_proba` or
:term:`decision_function` as the target response. If set to 'auto',
:term:`predict_proba` is tried first and if it does not exist
:term:`decision_function` is tried next.

Returns
-------
prediction_method: callable
prediction method of estimator
"""

if response_method not in ("predict_proba", "decision_function", "auto"):
raise ValueError(
"response_method must be 'predict_proba', 'decision_function' or 'auto'"
)

error_msg = "response method {} is not defined in {}"
if response_method != "auto":
prediction_method = getattr(estimator, response_method, None)
if prediction_method is None:
raise ValueError(
error_msg.format(response_method, estimator.__class__.__name__)
)
else:
predict_proba = getattr(estimator, "predict_proba", None)
decision_function = getattr(estimator, "decision_function", None)
prediction_method = predict_proba or decision_function
if prediction_method is None:
raise ValueError(
error_msg.format(
"decision_function or predict_proba", estimator.__class__.__name__
)
)

return prediction_method


def _get_response(X, estimator, response_method, pos_label=None):
"""Return response and positive label.

Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
Input values.

estimator : estimator instance
Fitted classifier or a fitted :class:`~sklearn.pipeline.Pipeline`
in which the last estimator is a classifier.

response_method: {'auto', 'predict_proba', 'decision_function'}
Specifies whether to use :term:`predict_proba` or
:term:`decision_function` as the target response. If set to 'auto',
:term:`predict_proba` is tried first and if it does not exist
:term:`decision_function` is tried next.

pos_label : str or int, default=None
The class considered as the positive class when computing
the metrics. By default, `estimators.classes_[1]` is
considered as the positive class.

Returns
-------
y_pred: ndarray of shape (n_samples,)
Target scores calculated from the provided response_method
and pos_label.

pos_label: str or int
The class considered as the positive class when computing
the metrics.
An estimator that should be used to predict the target.

y : ndarray
The associated target.

target_type : str, default=None
The type of the target `y` as returned by
:func:`~sklearn.utils.multiclass.type_of_target`. If `None`, the type
will be inferred by calling :func:`~sklearn.utils.multiclass.type_of_target`.
Providing the type of the target could save time by avoid calling the
:func:`~sklearn.utils.multiclass.type_of_target` function.

Raises
------
ValueError
If the estimator or the target are not binary.
"""
classification_error = (
"Expected 'estimator' to be a binary classifier, but got"
f" {estimator.__class__.__name__}"
)
try:
check_is_fitted(estimator)
except NotFittedError as e:
raise NotFittedError(
f"This {estimator.__class__.__name__} instance is not fitted yet. Call "
"'fit' with appropriate arguments before intending to use it to plotting "
"functionalities."
Comment on lines +35 to +36
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Maybe a bit lighter to read (better suggestion welcome):

Suggested change
"'fit' with appropriate arguments before intending to use it to plotting "
"functionalities."
"'fit' with appropriate arguments before using it for plotting "
"functionalities."

) from e

if not is_classifier(estimator):
raise ValueError(classification_error)

prediction_method = _check_classifier_response_method(estimator, response_method)
y_pred = prediction_method(X)
if pos_label is not None:
try:
class_idx = estimator.classes_.tolist().index(pos_label)
except ValueError as e:
raise ValueError(
"The class provided by 'pos_label' is unknown. Got "
f"{pos_label} instead of one of {set(estimator.classes_)}"
) from e
else:
class_idx = 1
pos_label = estimator.classes_[class_idx]

if y_pred.ndim != 1: # `predict_proba`
y_pred_shape = y_pred.shape[1]
if y_pred_shape != 2:
raise ValueError(
f"{classification_error} fit on multiclass ({y_pred_shape} classes)"
" data"
)
y_pred = y_pred[:, class_idx]
elif pos_label == estimator.classes_[0]: # `decision_function`
y_pred *= -1
raise ValueError(
"This plotting functionalities only support a binary classifier. "
f"Got a {estimator.__class__.__name__} instead."
)
elif len(estimator.classes_) != 2:
raise ValueError(
f"This {estimator.__class__.__name__} instance is not a binary "
"classifier. It was fitted on multiclass problem with "
f"{len(estimator.classes_)} classes."
)

return y_pred, pos_label
if target_type is None:
target_type = type_of_target(y)
if target_type != "binary":
raise ValueError(
f"The target y is not binary. Got {target_type} type of target."
)
37 changes: 30 additions & 7 deletions sklearn/metrics/_plot/det_curve.py
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Original file line number Diff line number Diff line change
@@ -1,12 +1,16 @@
import scipy as sp

from .base import _get_response
from .base import _check_estimator_and_target_is_binary

from .. import det_curve
from .._base import _check_pos_label_consistency

from ...utils import check_matplotlib_support
from ...utils import deprecated
from ...utils import (
check_matplotlib_support,
deprecated,
_get_response_values,
)
from ...utils.multiclass import type_of_target


class DetCurveDisplay:
Expand Down Expand Up @@ -168,13 +172,20 @@ def from_estimator(
"""
check_matplotlib_support(f"{cls.__name__}.from_estimator")

target_type = type_of_target(y)
_check_estimator_and_target_is_binary(estimator, y, target_type=target_type)
if response_method == "auto":
response_method = ["predict_proba", "decision_function"]

name = estimator.__class__.__name__ if name is None else name

y_pred, pos_label = _get_response(
X,
y_pred, pos_label = _get_response_values(
estimator,
X,
y,
response_method,
pos_label=pos_label,
target_type=target_type,
)

return cls.from_predictions(
Expand Down Expand Up @@ -265,6 +276,13 @@ def from_predictions(
>>> plt.show()
"""
check_matplotlib_support(f"{cls.__name__}.from_predictions")

target_type = type_of_target(y_true)
if target_type != "binary":
raise ValueError(
f"The target y is not binary. Got {target_type} type of target."
)
Comment on lines +280 to +284
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Do we need this check here, since we do the check in det_curve?

scikit-learn/sklearn/metrics/_ranking.py

Lines 310 to 314 in 845b1fa

if len(np.unique(y_true)) != 2:
raise ValueError(
"Only one class present in y_true. Detection error "
"tradeoff curve is not defined in that case."
)


fpr, fnr, _ = det_curve(
y_true,
y_pred,
Expand Down Expand Up @@ -454,8 +472,13 @@ def plot_det_curve(
"""
check_matplotlib_support("plot_det_curve")

y_pred, pos_label = _get_response(
X, estimator, response_method, pos_label=pos_label
target_type = type_of_target(y)
_check_estimator_and_target_is_binary(estimator, y, target_type=target_type)
if response_method == "auto":
response_method = ["predict_proba", "decision_function"]

y_pred, pos_label = _get_response_values(
estimator, X, y, response_method, pos_label=pos_label, target_type=target_type
)

fpr, fnr, _ = det_curve(
Expand Down
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