from collections import Counter
import numpy as np
import pytest
from sklearn.compose import make_column_transformer
from sklearn.datasets import load_breast_cancer, make_classification
from sklearn.exceptions import NotFittedError
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import (
PrecisionRecallDisplay,
average_precision_score,
precision_recall_curve,
)
from sklearn.model_selection import train_test_split
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.utils import shuffle
from sklearn.utils.fixes import trapezoid
# 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.mark.parametrize("constructor_name", ["from_estimator", "from_predictions"])
@pytest.mark.parametrize("response_method", ["predict_proba", "decision_function"])
@pytest.mark.parametrize("drop_intermediate", [True, False])
def test_precision_recall_display_plotting(
pyplot, constructor_name, response_method, drop_intermediate
):
"""Check the overall plotting rendering."""
X, y = make_classification(n_classes=2, n_samples=50, random_state=0)
pos_label = 1
classifier = LogisticRegression().fit(X, y)
classifier.fit(X, y)
y_pred = getattr(classifier, response_method)(X)
y_pred = y_pred if y_pred.ndim == 1 else y_pred[:, pos_label]
# safe guard for the binary if/else construction
assert constructor_name in ("from_estimator", "from_predictions")
if constructor_name == "from_estimator":
display = PrecisionRecallDisplay.from_estimator(
classifier,
X,
y,
response_method=response_method,
drop_intermediate=drop_intermediate,
)
else:
display = PrecisionRecallDisplay.from_predictions(
y, y_pred, pos_label=pos_label, drop_intermediate=drop_intermediate
)
precision, recall, _ = precision_recall_curve(
y, y_pred, pos_label=pos_label, drop_intermediate=drop_intermediate
)
average_precision = average_precision_score(y, y_pred, pos_label=pos_label)
np.testing.assert_allclose(display.precision, precision)
np.testing.assert_allclose(display.recall, recall)
assert display.average_precision == pytest.approx(average_precision)
import matplotlib as mpl
assert isinstance(display.line_, mpl.lines.Line2D)
assert isinstance(display.ax_, mpl.axes.Axes)
assert isinstance(display.figure_, mpl.figure.Figure)
assert display.ax_.get_xlabel() == "Recall (Positive label: 1)"
assert display.ax_.get_ylabel() == "Precision (Positive label: 1)"
assert display.ax_.get_adjustable() == "box"
assert display.ax_.get_aspect() in ("equal", 1.0)
assert display.ax_.get_xlim() == display.ax_.get_ylim() == (-0.01, 1.01)
# plotting passing some new parameters
display.plot(alpha=0.8, name="MySpecialEstimator")
expected_label = f"MySpecialEstimator (AP = {average_precision:0.2f})"
assert display.line_.get_label() == expected_label
assert display.line_.get_alpha() == pytest.approx(0.8)
# Check that the chance level line is not plotted by default
assert display.chance_level_ is None
@pytest.mark.parametrize("chance_level_kw", [None, {"color": "r"}])
@pytest.mark.parametrize("constructor_name", ["from_estimator", "from_predictions"])
def test_precision_recall_chance_level_line(
pyplot,
chance_level_kw,
constructor_name,
):
"""Check the chance level line plotting behavior."""
X, y = make_classification(n_classes=2, n_samples=50, random_state=0)
pos_prevalence = Counter(y)[1] / len(y)
lr = LogisticRegression()
y_pred = lr.fit(X, y).predict_proba(X)[:, 1]
if constructor_name == "from_estimator":
display = PrecisionRecallDisplay.from_estimator(
lr,
X,
y,
plot_chance_level=True,
chance_level_kw=chance_level_kw,
)
else:
display = PrecisionRecallDisplay.from_predictions(
y,
y_pred,
plot_chance_level=True,
chance_level_kw=chance_level_kw,
)
import matplotlib as mpl # noqa
assert isinstance(display.chance_level_, mpl.lines.Line2D)
assert tuple(display.chance_level_.get_xdata()) == (0, 1)
assert tuple(display.chance_level_.get_ydata()) == (pos_prevalence, pos_prevalence)
# Checking for chance level line styles
if chance_level_kw is None:
assert display.chance_level_.get_color() == "k"
else:
assert display.chance_level_.get_color() == "r"
@pytest.mark.parametrize(
"constructor_name, default_label",
[
("from_estimator", "LogisticRegression (AP = {:.2f})"),
("from_predictions", "Classifier (AP = {:.2f})"),
],
)
def test_precision_recall_display_name(pyplot, constructor_name, default_label):
"""Check the behaviour of the name parameters"""
X, y = make_classification(n_classes=2, n_samples=100, random_state=0)
pos_label = 1
classifier = LogisticRegression().fit(X, y)
classifier.fit(X, y)
y_pred = classifier.predict_proba(X)[:, pos_label]
# safe guard for the binary if/else construction
assert constructor_name in ("from_estimator", "from_predictions")
if constructor_name == "from_estimator":
display = PrecisionRecallDisplay.from_estimator(classifier, X, y)
else:
display = PrecisionRecallDisplay.from_predictions(
y, y_pred, pos_label=pos_label
)
average_precision = average_precision_score(y, y_pred, pos_label=pos_label)
# check that the default name is used
assert display.line_.get_label() == default_label.format(average_precision)
# check that the name can be set
display.plot(name="MySpecialEstimator")
assert (
display.line_.get_label()
== f"MySpecialEstimator (AP = {average_precision:.2f})"
)
@pytest.mark.parametrize(
"clf",
[
make_pipeline(StandardScaler(), LogisticRegression()),
make_pipeline(
make_column_transformer((StandardScaler(), [0, 1])), LogisticRegression()
),
],
)
def test_precision_recall_display_pipeline(pyplot, clf):
X, y = make_classification(n_classes=2, n_samples=50, random_state=0)
with pytest.raises(NotFittedError):
PrecisionRecallDisplay.from_estimator(clf, X, y)
clf.fit(X, y)
display = PrecisionRecallDisplay.from_estimator(clf, X, y)
assert display.estimator_name == clf.__class__.__name__
def test_precision_recall_display_string_labels(pyplot):
# regression test #15738
cancer = load_breast_cancer()
X, y = cancer.data, cancer.target_names[cancer.target]
lr = make_pipeline(StandardScaler(), LogisticRegression())
lr.fit(X, y)
for klass in cancer.target_names:
assert klass in lr.classes_
display = PrecisionRecallDisplay.from_estimator(lr, X, y)
y_pred = lr.predict_proba(X)[:, 1]
avg_prec = average_precision_score(y, y_pred, pos_label=lr.classes_[1])
assert display.average_precision == pytest.approx(avg_prec)
assert display.estimator_name == lr.__class__.__name__
err_msg = r"y_true takes value in {'benign', 'malignant'}"
with pytest.raises(ValueError, match=err_msg):
PrecisionRecallDisplay.from_predictions(y, y_pred)
display = PrecisionRecallDisplay.from_predictions(
y, y_pred, pos_label=lr.classes_[1]
)
assert display.average_precision == pytest.approx(avg_prec)
@pytest.mark.parametrize(
"average_precision, estimator_name, expected_label",
[
(0.9, None, "AP = 0.90"),
(None, "my_est", "my_est"),
(0.8, "my_est2", "my_est2 (AP = 0.80)"),
],
)
def test_default_labels(pyplot, average_precision, estimator_name, expected_label):
"""Check the default labels used in the display."""
precision = np.array([1, 0.5, 0])
recall = np.array([0, 0.5, 1])
display = PrecisionRecallDisplay(
precision,
recall,
average_precision=average_precision,
estimator_name=estimator_name,
)
display.plot()
assert display.line_.get_label() == expected_label
@pytest.mark.parametrize("constructor_name", ["from_estimator", "from_predictions"])
@pytest.mark.parametrize("response_method", ["predict_proba", "decision_function"])
def test_plot_precision_recall_pos_label(pyplot, constructor_name, response_method):
# 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 version of the breast cancer dataset
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 corresponding 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 = PrecisionRecallDisplay.from_estimator(
classifier,
X_test,
y_test,
pos_label="cancer",
response_method=response_method,
)
else:
display = PrecisionRecallDisplay.from_predictions(
y_test,
y_pred_cancer,
pos_label="cancer",
)
# we should obtain the statistics of the "cancer" class
avg_prec_limit = 0.65
assert display.average_precision < avg_prec_limit
assert -trapezoid(display.precision, display.recall) < avg_prec_limit
# otherwise we should obtain the statistics of the "not cancer" class
if constructor_name == "from_estimator":
display = PrecisionRecallDisplay.from_estimator(
classifier,
X_test,
y_test,
response_method=response_method,
pos_label="not cancer",
)
else:
display = PrecisionRecallDisplay.from_predictions(
y_test,
y_pred_not_cancer,
pos_label="not cancer",
)
avg_prec_limit = 0.95
assert display.average_precision > avg_prec_limit
assert -trapezoid(display.precision, display.recall) > avg_prec_limit
@pytest.mark.parametrize("constructor_name", ["from_estimator", "from_predictions"])
def test_precision_recall_prevalence_pos_label_reusable(pyplot, constructor_name):
# Check that even if one passes plot_chance_level=False the first time
# one can still call disp.plot with plot_chance_level=True and get the
# chance level line
X, y = make_classification(n_classes=2, n_samples=50, random_state=0)
lr = LogisticRegression()
y_pred = lr.fit(X, y).predict_proba(X)[:, 1]
if constructor_name == "from_estimator":
display = PrecisionRecallDisplay.from_estimator(
lr, X, y, plot_chance_level=False
)
else:
display = PrecisionRecallDisplay.from_predictions(
y, y_pred, plot_chance_level=False
)
assert display.chance_level_ is None
import matplotlib as mpl # noqa
# When calling from_estimator or from_predictions,
# prevalence_pos_label should have been set, so that directly
# calling plot_chance_level=True should plot the chance level line
display.plot(plot_chance_level=True)
assert isinstance(display.chance_level_, mpl.lines.Line2D)
def test_precision_recall_raise_no_prevalence(pyplot):
# Check that raises correctly when plotting chance level with
# no prvelance_pos_label is provided
precision = np.array([1, 0.5, 0])
recall = np.array([0, 0.5, 1])
display = PrecisionRecallDisplay(precision, recall)
msg = (
"You must provide prevalence_pos_label when constructing the "
"PrecisionRecallDisplay object in order to plot the chance "
"level line. Alternatively, you may use "
"PrecisionRecallDisplay.from_estimator or "
"PrecisionRecallDisplay.from_predictions "
"to automatically set prevalence_pos_label"
)
with pytest.raises(ValueError, match=msg):
display.plot(plot_chance_level=True)