def test_roc_curve_display_complex_pipeline(pyplot, data_binary, clf,
constructor_name):
"""Check the behaviour with complex pipeline."""
X, y = data_binary
if constructor_name == "from_estimator":
with pytest.raises(NotFittedError):
RocCurveDisplay.from_estimator(clf, X, y)
clf.fit(X, y)
if constructor_name == "from_estimator":
display = RocCurveDisplay.from_estimator(clf, X, y)
name = clf.__class__.__name__
else:
display = RocCurveDisplay.from_predictions(y, y)
name = "Classifier"
assert name in display.line_.get_label()
assert display.estimator_name == name
def test_roc_curve_display_plotting(
pyplot,
response_method,
data_binary,
with_sample_weight,
drop_intermediate,
with_strings,
constructor_name,
default_name,
):
"""Check the overall plotting behaviour."""
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)
y_pred = getattr(lr, response_method)(X)
y_pred = y_pred if y_pred.ndim == 1 else y_pred[:, 1]
if constructor_name == "from_estimator":
display = RocCurveDisplay.from_estimator(
lr,
X,
y,
sample_weight=sample_weight,
drop_intermediate=drop_intermediate,
pos_label=pos_label,
alpha=0.8,
)
else:
display = RocCurveDisplay.from_predictions(
y,
y_pred,
sample_weight=sample_weight,
drop_intermediate=drop_intermediate,
pos_label=pos_label,
alpha=0.8,
)
fpr, tpr, _ = roc_curve(
y,
y_pred,
sample_weight=sample_weight,
drop_intermediate=drop_intermediate,
pos_label=pos_label,
)
assert_allclose(display.roc_auc, auc(fpr, tpr))
assert_allclose(display.fpr, fpr)
assert_allclose(display.tpr, tpr)
assert display.estimator_name == default_name
import matplotlib as mpl # noqal
assert isinstance(display.line_, mpl.lines.Line2D)
assert display.line_.get_alpha() == 0.8
assert isinstance(display.ax_, mpl.axes.Axes)
assert isinstance(display.figure_, mpl.figure.Figure)
expected_label = f"{default_name} (AUC = {display.roc_auc:.2f})"
assert display.line_.get_label() == expected_label
expected_pos_label = 1 if pos_label is None else pos_label
expected_ylabel = f"True Positive Rate (Positive label: {expected_pos_label})"
expected_xlabel = f"False Positive Rate (Positive label: {expected_pos_label})"
assert display.ax_.get_ylabel() == expected_ylabel
assert display.ax_.get_xlabel() == expected_xlabel
def test_plot_roc_curve_pos_label(pyplot, response_method, constructor_name):
# check that we can provide the positive label and display the proper
# statistics
X, y = load_breast_cancer(return_X_y=True)
# create an highly imbalanced
idx_positive = np.flatnonzero(y == 1)
idx_negative = np.flatnonzero(y == 0)
idx_selected = np.hstack([idx_negative, idx_positive[:25]])
X, y = X[idx_selected], y[idx_selected]
X, y = shuffle(X, y, random_state=42)
# only use 2 features to make the problem even harder
X = X[:, :2]
y = np.array(["cancer" if c == 1 else "not cancer" for c in y],
dtype=object)
X_train, X_test, y_train, y_test = train_test_split(
X,
y,
stratify=y,
random_state=0,
)
classifier = LogisticRegression()
classifier.fit(X_train, y_train)
# sanity check to be sure the positive class is classes_[0] and that we
# are betrayed by the class imbalance
assert classifier.classes_.tolist() == ["cancer", "not cancer"]
y_pred = getattr(classifier, response_method)(X_test)
# we select the correcponding probability columns or reverse the decision
# function otherwise
y_pred_cancer = -1 * y_pred if y_pred.ndim == 1 else y_pred[:, 0]
y_pred_not_cancer = y_pred if y_pred.ndim == 1 else y_pred[:, 1]
if constructor_name == "from_estimator":
display = RocCurveDisplay.from_estimator(
classifier,
X_test,
y_test,
pos_label="cancer",
response_method=response_method,
)
else:
display = RocCurveDisplay.from_predictions(
y_test,
y_pred_cancer,
pos_label="cancer",
)
roc_auc_limit = 0.95679
assert display.roc_auc == pytest.approx(roc_auc_limit)
assert np.trapz(display.tpr, display.fpr) == pytest.approx(roc_auc_limit)
if constructor_name == "from_estimator":
display = RocCurveDisplay.from_estimator(
classifier,
X_test,
y_test,
response_method=response_method,
pos_label="not cancer",
)
else:
display = RocCurveDisplay.from_predictions(
y_test,
y_pred_not_cancer,
pos_label="not cancer",
)
assert display.roc_auc == pytest.approx(roc_auc_limit)
assert np.trapz(display.tpr, display.fpr) == pytest.approx(roc_auc_limit)
# plotting parameters
cols = 2
linewidth = 1
pos_label = 0 # mean 0 belongs to positive class
rows = math.ceil(len(datasets_name) / cols)
fig, axs = plt.subplots(rows, cols, figsize=(10, rows * 3))
for i, dataset_name in enumerate(datasets_name):
(X, y) = preprocess_dataset(dataset_name=dataset_name)
for model_name in models_name:
y_pred = compute_prediction(X, model_name=model_name)
display = RocCurveDisplay.from_predictions(
y,
y_pred,
pos_label=pos_label,
name=model_name,
linewidth=linewidth,
ax=axs[i // cols, i % cols],
)
axs[i // cols, i % cols].plot([0, 1], [0, 1],
linewidth=linewidth,
linestyle=":")
axs[i // cols, i % cols].set_title(dataset_name)
axs[i // cols, i % cols].set_xlabel("False Positive Rate")
axs[i // cols, i % cols].set_ylabel("True Positive Rate")
plt.tight_layout(pad=2.0) # spacing between subplots
plt.show()