8000 TST check the legend instead of label names in CalibrationDisplay by jjerphan · Pull Request #21697 · scikit-learn/scikit-learn · GitHub
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TST check the legend instead of label names in CalibrationDisplay #21697

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Merged
merged 3 commits into from
Nov 18, 2021
Merged

TST check the legend instead of label names in CalibrationDisplay #21697

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b2f75a5
Unify AxesSubplot string representation for labels across matplotlib
jjerphan Nov 17, 2021
de3b742
Check for the integrity of the legend
glemaitre Nov 17, 2021
b9f694a
Merge remote-tracking branch 'origin/main' into pr/jjerphan/21697
glemaitre Nov 17, 2021
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4 changes: 2 additions & 2 deletions sklearn/calibration.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1099,8 +1099,8 @@ def plot(self, *, ax=None, name=None, ref_line=True, **kwargs):
ax.plot([0, 1], [0, 1], "k:", label=ref_line_label)
self.line_ = ax.plot(self.prob_pred, self.prob_true, "s-", **line_kwargs)[0]

if "label" in line_kwargs:
ax.legend(loc="lower right")
# We always have to show the legend for at least the reference line
ax.legend(loc="lower right")

xlabel = f"Mean predicted probability {info_pos_label}"
ylabel = f"Fraction of positives {info_pos_label}"
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49 changes: 41 additions & 8 deletions sklearn/tests/test_calibration.py
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Original file line number Diff line number Diff line change
Expand Up @@ -689,7 +689,12 @@ def test_calibration_display_compute(pyplot, iris_data_binary, n_bins, strategy)

assert viz.ax_.get_xlabel() == "Mean predicted probability (Positive class: 1)"
assert viz.ax_.get_ylabel() == "Fraction of positives (Positive class: 1)"
assert viz.line_.get_label() == "LogisticRegression"

expected_legend_labels = ["LogisticRegression", "Perfectly calibrated"]
legend_labels = viz.ax_.get_legend().get_texts()
assert len(legend_labels) == len(expected_legend_labels)
for labels in legend_labels:
assert labels.get_text() in expected_legend_labels


def test_plot_calibration_curve_pipeline(pyplot, iris_data_binary):
Expand All @@ -698,8 +703,12 @@ def test_plot_calibration_curve_pipeline(pyplot, iris_data_binary):
clf = make_pipeline(StandardScaler(), LogisticRegression())
clf.fit(X, y)
viz = CalibrationDisplay.from_estimator(clf, X, y)
assert clf.__class__.__name__ in viz.line_.get_label()
assert viz.estimator_name == clf.__class__.__name__

expected_legend_labels = [viz.estimator_name, "Perfectly calibrated"]
legend_labels = viz.ax_.get_legend().get_texts()
assert len(legend_labels) == len(expected_legend_labels)
for labels in legend_labels:
assert labels.get_text() in expected_legend_labels


@pytest.mark.parametrize(
Expand All @@ -712,7 +721,13 @@ def test_calibration_display_default_labels(pyplot, name, expected_label):

viz = CalibrationDisplay(prob_true, prob_pred, y_prob, estimator_name=name)
viz.plot()
assert viz.line_.get_label() == expected_label

expected_legend_labels = [] if name is None else [name]
expected_legend_labels.append("Perfectly calibrated")
legend_labels = viz.ax_.get_legend().get_texts()
assert len(legend_labels) == len(expected_legend_labels)
for labels in legend_labels:
assert labels.get_text() in expected_legend_labels


def test_calibration_display_label_class_plot(pyplot):
Expand All @@ -727,7 +742,12 @@ def test_calibration_display_label_class_plot(pyplot):
assert viz.estimator_name == name
name = "name two"
viz.plot(name=name)
assert viz.line_.get_label() == name

expected_legend_labels = [name, "Perfectly calibrated"]
legend_labels = viz.ax_.get_legend().get_texts()
assert len(legend_labels) == len(expected_legend_labels)
for labels in legend_labels:
assert labels.get_text() in expected_legend_labels


@pytest.mark.parametrize("constructor_name", ["from_estimator", "from_predictions"])
Expand All @@ -750,11 +770,19 @@ def test_calibration_display_name_multiple_calls(
assert viz.estimator_name == clf_name
pyplot.close("all")
viz.plot()
assert clf_name == viz.line_.get_label()

expected_legend_labels = [clf_name, "Perfectly calibrated"]
legend_labels = viz.ax_.get_legend().get_texts()
assert len(legend_labels) == len(expected_legend_labels)
for labels in legend_labels:
assert labels.get_text() in expected_legend_labels

pyplot.close("all")
clf_name = "another_name"
viz.plot(name=clf_name)
assert clf_name == viz.line_.get_label()
assert len(legend_labels) == len(expected_legend_labels)
for labels in legend_labels:
assert labels.get_text() in expected_legend_labels


def test_calibration_display_ref_line(pyplot, iris_data_binary):
Expand Down Expand Up @@ -832,7 +860,12 @@ def test_calibration_display_pos_label(
viz.ax_.get_ylabel()
== f"Fraction of positives (Positive class: {expected_pos_label})"
)
assert viz.line_.get_label() == "LogisticRegression"

expected_legend_labels = [lr.__class__.__name__, "Perfectly calibrated"]
legend_labels = viz.ax_.get_legend().get_texts()
assert len(legend_labels) == len(expected_legend_labels)
for labels in legend_labels:
assert labels.get_text() in expected_legend_labels


@pytest.mark.parametrize("method", ["sigmoid", "isotonic"])
Expand Down
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