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import pytest
from numpy.testing import assert_allclose
import numpy as np
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import plot_roc_curve
from sklearn.metrics import RocCurveDisplay
from sklearn.datasets import load_iris
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import roc_curve, auc
from sklearn.base import ClassifierMixin
from sklearn.exceptions import NotFittedError
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.compose import make_column_transformer
# TODO: Remove when https://github.com/numpy/numpy/issues/14397 is resolved
pytestmark = pytest.mark.filterwarnings(
"ignore:In future, it will be an error for 'np.bool_':DeprecationWarning:"
"matplotlib.*")
@pytest.fixture(scope="module")
def data():
return load_iris(return_X_y=True)
@pytest.fixture(scope="module")
def data_binary(data):
X, y = data
return X[y < 2], y[y < 2]
def test_plot_roc_curve_error_non_binary(pyplot, data):
X, y = data
clf = DecisionTreeClassifier()
clf.fit(X, y)
msg = "DecisionTreeClassifier should be a binary classifier"
with pytest.raises(ValueError, match=msg):
plot_roc_curve(clf, X, y)
@pytest.mark.parametrize(
"response_method, msg",
[("predict_proba", "response method predict_proba is not defined in "
"MyClassifier"),
("decision_function", "response method decision_function is not defined "
"in MyClassifier"),
("auto", "response method decision_function or predict_proba is not "
"defined in MyClassifier"),
("bad_method", "response_method must be 'predict_proba', "
"'decision_function' or 'auto'")])
def test_plot_roc_curve_error_no_response(pyplot, data_binary, response_method,
msg):
X, y = data_binary
class MyClassifier(ClassifierMixin):
def fit(self, X, y):
self.classes_ = [0, 1]
return self
clf = MyClassifier().fit(X, y)
with pytest.raises(ValueError, match=msg):
plot_roc_curve(clf, X, y, response_method=response_method)
@pytest.mark.parametrize("response_method",
["predict_proba", "decision_function"])
@pytest.mark.parametrize("with_sample_weight", [True, False])
@pytest.mark.parametrize("drop_intermediate", [True, False])
@pytest.mark.parametrize("with_strings", [True, False])
def test_plot_roc_curve(pyplot, response_method, data_binary,
with_sample_weight, drop_intermediate,
with_strings):
X, y = data_binary
pos_label = None
if with_strings:
y = np.array(["c", "b"])[y]
pos_label = "c"
if with_sample_weight:
rng = np.random.RandomState(42)
sample_weight = rng.randint(1, 4, size=(X.shape[0]))
else:
sample_weight = None
lr = LogisticRegression()
lr.fit(X, y)
viz = plot_roc_curve(lr, X, y, alpha=0.8, sample_weight=sample_weight,
drop_intermediate=drop_intermediate)
y_pred = getattr(lr, response_method)(X)
if y_pred.ndim == 2:
y_pred = y_pred[:, 1]
fpr, tpr, _ = roc_curve(y, y_pred, sample_weight=sample_weight,
drop_intermediate=drop_intermediate,
pos_label=pos_label)
assert_allclose(viz.roc_auc, auc(fpr, tpr))
assert_allclose(viz.fpr, fpr)
assert_allclose(viz.tpr, tpr)
assert viz.estimator_name == "LogisticRegression"
# cannot fail thanks to pyplot fixture
import matplotlib as mpl # noqal
assert isinstance(viz.line_, mpl.lines.Line2D)
assert viz.line_.get_alpha() == 0.8
assert isinstance(viz.ax_, mpl.axes.Axes)
assert isinstance(viz.figure_, mpl.figure.Figure)
expected_label = "LogisticRegression (AUC = {:0.2f})".format(viz.roc_auc)
assert viz.line_.get_label() == expected_label
assert viz.ax_.get_ylabel() == "True Positive Rate"
assert viz.ax_.get_xlabel() == "False Positive Rate"
@pytest.mark.parametrize(
"clf", [LogisticRegression(),
make_pipeline(StandardScaler(), LogisticRegression()),
make_pipeline(make_column_transformer((StandardScaler(), [0, 1])),
LogisticRegression())])
def test_roc_curve_not_fitted_errors(pyplot, data_binary, clf):
X, y = data_binary
with pytest.raises(NotFittedError):
plot_roc_curve(clf, X, y)
clf.fit(X, y)
disp = plot_roc_curve(clf, X, y)
assert clf.__class__.__name__ in disp.line_.get_label()
assert disp.estimator_name == clf.__class__.__name__
def test_plot_roc_curve_estimator_name_multiple_calls(pyplot, data_binary):
# non-regression test checking that the `name` used when calling
# `plot_roc_curve` is used as well when calling `disp.plot()`
X, y = data_binary
clf_name = "my hand-crafted name"
clf = LogisticRegression().fit(X, y)
disp = plot_roc_curve(clf, X, y, name=clf_name)
assert disp.estimator_name == clf_name
pyplot.close("all")
disp.plot()
assert clf_name in disp.line_.get_label()
pyplot.close("all")
clf_name = "another_name"
disp.plot(name=clf_name)
assert clf_name in disp.line_.get_label()
@pytest.mark.parametrize(
"roc_auc, estimator_name, expected_label",
[
(0.9, None, "AUC = 0.90"),
(None, "my_est", "my_est"),
(0.8, "my_est2", "my_est2 (AUC = 0.80)")
]
)
def test_default_labels(pyplot, roc_auc, estimator_name,
expected_label):
fpr = np.array([0, 0.5, 1])
tpr = np.array([0, 0.5, 1])
disp = RocCurveDisplay(fpr=fpr, tpr=tpr, roc_auc=roc_auc,
estimator_name=estimator_name).plot()
assert disp.line_.get_label() == expected_label
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