MNT refactor _get_response_values #21538

glemaitre · 2021-11-03T10:48:25Z

Partially address #18212
This is a simplification towards merging #20999

glemaitre · 2021-11-03T13:30:13Z

sklearn/metrics/_plot/tests/test_plot_precision_recall.py

@@ -26,72 +24,6 @@
 )


-def test_errors(pyplot):


Those tests are moved in the common test in test_plot_curve_common.py

glemaitre · 2021-11-03T13:30:30Z

sklearn/tests/test_calibration.py

@@ -634,42 +634,6 @@ def iris_data_binary(iris_data):
    return X[y < 2], y[y < 2]


-def test_calibration_display_validation(pyplot, iris_data, iris_data_binary):


Those tests are moved in the common test in test_plot_curve_common.py

glemaitre · 2021-11-03T13:30:52Z

sklearn/tests/test_calibration.py

@@ -189,7 +189,7 @@ def test_parallel_execution(data, method, ensemble):
    X, y = data
    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)

-    base_estimator = LinearSVC(random_state=42)
+    base_estimator = make_pipeline(StandardScaler(), LinearSVC(random_state=42))


Remove a bunch of ConvergenceWarning for free

glemaitre · 2021-11-03T13:31:41Z

sklearn/metrics/_plot/tests/test_precision_recall_display.py

@@ -21,48 +20,6 @@
 )


-def test_precision_recall_display_validation(pyplot):


Those tests are moved in the common test in plot_common_curve_display.py

glemaitre · 2021-11-03T14:14:13Z

Maybe this would be easier to review @ogrisel @thomasjpfan

thomasjpfan

Overall, I like the refactor.

sklearn/metrics/_plot/base.py

sklearn/metrics/_plot/det_curve.py

sklearn/metrics/_plot/precision_recall_curve.py

sklearn/utils/validation.py

sklearn/utils/_mocking.py

sklearn/utils/__init__.py

sklearn/utils/validation.py

Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com>

thomasjpfan

Thanks for the follow up!

sklearn/calibration.py

thomasjpfan · 2021-12-15T18:13:38Z

sklearn/metrics/_plot/det_curve.py

+        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."
+            )


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."

)

sklearn/metrics/_plot/precision_recall_curve.py

…nto is/18212_again_again

lesteve

First pass of comments

lesteve · 2022-04-06T10:15:40Z

sklearn/utils/tests/test_utils.py

+        response_method="predict",
+        target_type=target_type,
+    )
+    assert_allclose(y_pred, regressor.predict(X))


This one could be checking strict equality right?

lesteve · 2022-04-06T13:45:13Z

sklearn/utils/validation.py

+
+    Raises
+    ------
+    ValueError


Suggested change

ValueError

AttributeError

lesteve · 2022-04-06T14:38:17Z

sklearn/utils/__init__.py

+                    y_pred = y_pred[:, col_idx]
+                else:
+                    err_msg = (
+                        f"Got predict_proba of shape {y_pred.shape}, but need "


I find the error message not super explicit, I would find it easier to understand if it was saying the classifier was fitted with only one class (rather that complaining about the shape of the predict_proba). Maybe there are some edge cases I haven't thought of though.

lesteve · 2022-04-06T14:46:20Z

sklearn/utils/__init__.py

+                f"one of {classes}"
+            )
+        elif pos_label is None and target_type == "binary":
+            pos_label = pos_label if pos_label is not None else classes[-1]


Although -1 and 1 are equivalent I would use 1 since classes[1] is used elsewhere e.g. in _get_response_values docstring:

Suggested change

pos_label = pos_label if pos_label is not None else classes[-1]

pos_label = pos_label if pos_label is not None else classes[1]

lesteve · 2022-04-06T14:54:39Z

sklearn/ensemble/_stacking.py

+        method = (
+            ["predict_proba", "decision_function", "predict"]
+            if method == "auto"
+            else method
+        )


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"]

lesteve · 2022-04-06T14:57:13Z

sklearn/metrics/_plot/base.py

+            "'fit' with appropriate arguments before intending to use it to plotting "
+            "functionalities."


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."

lesteve · 2022-04-06T15:35:34Z

The CI is red at the moment, maybe I got something wrong in my merge ...

thomasjpfan

Comment that still needs to be addressed, with the same concern:

~~The metric checks if the input is binary. If the *Display object checks too, then there is double validation and np.unqiue is called twice.~~

I guess this is okay.

thomasjpfan · 2022-04-06T18:49:29Z

sklearn/metrics/_plot/roc_curve.py

@@ -330,6 +342,12 @@ def from_predictions(
        """
        check_matplotlib_support(f"{cls.__name__}.from_predictions")

+        target_type = type_of_target(y_true)
+        if target_type != "binary":


I do not think we need this check. roc_curve calls _binary_clf_curve which ends up doing the binary check itself:

scikit-learn/sklearn/metrics/_ranking.py

Lines 736 to 738 in 22ca942

y_type = type_of_target(y_true, input_name="y_true")

if not (y_type == "binary" or (y_type == "multiclass" and pos_label is not None)):

raise ValueError("{0} format is not supported".format(y_type))

jeremiedbb · 2022-04-19T13:03:53Z

Let's move that to 1.2

glemaitre · 2023-03-24T18:03:39Z

Closing this one since we merged #23073

MNT refactor _get_response_values [ci skip]

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glemaitre commented Nov 3, 2021

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glemaitre added 2 commits November 29, 2021 16:17

Merge remote-tracking branch 'origin/main' into is/18212_again_again

5a4f33f

reuse type_of_target

a7e46da

glemaitre force-pushed the is/18212_again_again branch from abea58f to a7e46da Compare November 29, 2021 15:18

glemaitre and others added 3 commits November 29, 2021 16:33

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make response_method to not be None

8214f8b

Update sklearn/utils/_mocking.py

d7e4912

Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com>

glemaitre removed the cython label Nov 29, 2021

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glemaitre added this to the 1.1 milestone Jan 26, 2022

Merge branch 'main' of https://github.com/scikit-learn/scikit-learn i…

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…nto is/18212_again_again

lesteve reviewed Apr 6, 2022

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thomasjpfan reviewed Apr 6, 2022

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glemaitre mentioned this pull request Apr 7, 2022

MAINT introduce _get_response_values and _check_response_methods #23073

Merged

jeremiedbb modified the milestones: 1.1, 1.2 Apr 19, 2022

glemaitre modified the milestones: 1.2, 1.3 Nov 16, 2022

glemaitre closed this Mar 24, 2023

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MNT refactor _get_response_values #21538

MNT refactor _get_response_values #21538

glemaitre commented Nov 3, 2021 •

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jeremiedbb commented Apr 19, 2022

glemaitre commented Mar 24, 2023

		@@ -634,42 +634,6 @@ def iris_data_binary(iris_data):
		return X[y < 2], y[y < 2]


		def test_calibration_display_validation(pyplot, iris_data, iris_data_binary):

		@@ -21,48 +20,6 @@
		)


		def test_precision_recall_display_validation(pyplot):

	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."
	)

	pos_label = pos_label if pos_label is not None else classes[-1]
	pos_label = pos_label if pos_label is not None else classes[1]

		"'fit' with appropriate arguments before intending to use it to plotting "
		"functionalities."

	y_type = type_of_target(y_true, input_name="y_true")
	if not (y_type == "binary" or (y_type == "multiclass" and pos_label is not None)):
	raise ValueError("{0} format is not supported".format(y_type))

MNT refactor _get_response_values #21538

MNT refactor _get_response_values #21538

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