174 lines
5.9 KiB
Python
174 lines
5.9 KiB
Python
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import pytest
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import numpy as np
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from numpy.testing import assert_allclose
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from sklearn.metrics import plot_roc_curve
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from sklearn.metrics import RocCurveDisplay
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from sklearn.metrics import roc_curve
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from sklearn.metrics import auc
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from sklearn.datasets import load_iris
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from sklearn.datasets import load_breast_cancer
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from sklearn.linear_model import LogisticRegression
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from sklearn.model_selection import train_test_split
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from sklearn.exceptions import NotFittedError
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from sklearn.pipeline import make_pipeline
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from sklearn.preprocessing import StandardScaler
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from sklearn.utils import shuffle
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from sklearn.compose import make_column_transformer
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# TODO: Remove when https://github.com/numpy/numpy/issues/14397 is resolved
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pytestmark = pytest.mark.filterwarnings(
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"ignore:In future, it will be an error for 'np.bool_':DeprecationWarning:"
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"matplotlib.*")
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@pytest.fixture(scope="module")
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def data():
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return load_iris(return_X_y=True)
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@pytest.fixture(scope="module")
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def data_binary(data):
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X, y = data
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return X[y < 2], y[y < 2]
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@pytest.mark.parametrize("response_method",
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["predict_proba", "decision_function"])
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@pytest.mark.parametrize("with_sample_weight", [True, False])
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@pytest.mark.parametrize("drop_intermediate", [True, False])
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@pytest.mark.parametrize("with_strings", [True, False])
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def test_plot_roc_curve(pyplot, response_method, data_binary,
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with_sample_weight, drop_intermediate,
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with_strings):
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X, y = data_binary
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pos_label = None
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if with_strings:
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y = np.array(["c", "b"])[y]
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pos_label = "c"
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if with_sample_weight:
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rng = np.random.RandomState(42)
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sample_weight = rng.randint(1, 4, size=(X.shape[0]))
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else:
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sample_weight = None
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lr = LogisticRegression()
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lr.fit(X, y)
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viz = plot_roc_curve(lr, X, y, alpha=0.8, sample_weight=sample_weight,
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drop_intermediate=drop_intermediate)
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y_pred = getattr(lr, response_method)(X)
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if y_pred.ndim == 2:
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y_pred = y_pred[:, 1]
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fpr, tpr, _ = roc_curve(y, y_pred, sample_weight=sample_weight,
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drop_intermediate=drop_intermediate,
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pos_label=pos_label)
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assert_allclose(viz.roc_auc, auc(fpr, tpr))
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assert_allclose(viz.fpr, fpr)
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assert_allclose(viz.tpr, tpr)
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assert viz.estimator_name == "LogisticRegression"
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# cannot fail thanks to pyplot fixture
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import matplotlib as mpl # noqal
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assert isinstance(viz.line_, mpl.lines.Line2D)
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assert viz.line_.get_alpha() == 0.8
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assert isinstance(viz.ax_, mpl.axes.Axes)
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assert isinstance(viz.figure_, mpl.figure.Figure)
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expected_label = "LogisticRegression (AUC = {:0.2f})".format(viz.roc_auc)
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assert viz.line_.get_label() == expected_label
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expected_pos_label = 1 if pos_label is None else pos_label
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expected_ylabel = f"True Positive Rate (Positive label: " \
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f"{expected_pos_label})"
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expected_xlabel = f"False Positive Rate (Positive label: " \
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f"{expected_pos_label})"
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assert viz.ax_.get_ylabel() == expected_ylabel
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assert viz.ax_.get_xlabel() == expected_xlabel
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@pytest.mark.parametrize(
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"clf", [LogisticRegression(),
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make_pipeline(StandardScaler(), LogisticRegression()),
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make_pipeline(make_column_transformer((StandardScaler(), [0, 1])),
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LogisticRegression())])
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def test_roc_curve_not_fitted_errors(pyplot, data_binary, clf):
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X, y = data_binary
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with pytest.raises(NotFittedError):
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plot_roc_curve(clf, X, y)
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clf.fit(X, y)
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disp = plot_roc_curve(clf, X, y)
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assert clf.__class__.__name__ in disp.line_.get_label()
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assert disp.estimator_name == clf.__class__.__name__
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@pytest.mark.parametrize(
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"roc_auc, estimator_name, expected_label",
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[
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(0.9, None, "AUC = 0.90"),
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(None, "my_est", "my_est"),
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(0.8, "my_est2", "my_est2 (AUC = 0.80)")
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]
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)
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def test_default_labels(pyplot, roc_auc, estimator_name,
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expected_label):
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fpr = np.array([0, 0.5, 1])
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tpr = np.array([0, 0.5, 1])
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disp = RocCurveDisplay(fpr=fpr, tpr=tpr, roc_auc=roc_auc,
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estimator_name=estimator_name).plot()
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assert disp.line_.get_label() == expected_label
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@pytest.mark.parametrize(
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"response_method", ["predict_proba", "decision_function"]
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)
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def test_plot_roc_curve_pos_label(pyplot, response_method):
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# check that we can provide the positive label and display the proper
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# statistics
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X, y = load_breast_cancer(return_X_y=True)
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# create an highly imbalanced
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idx_positive = np.flatnonzero(y == 1)
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idx_negative = np.flatnonzero(y == 0)
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idx_selected = np.hstack([idx_negative, idx_positive[:25]])
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X, y = X[idx_selected], y[idx_selected]
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X, y = shuffle(X, y, random_state=42)
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# only use 2 features to make the problem even harder
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X = X[:, :2]
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y = np.array(
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["cancer" if c == 1 else "not cancer" for c in y], dtype=object
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)
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X_train, X_test, y_train, y_test = train_test_split(
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X, y, stratify=y, random_state=0,
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)
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classifier = LogisticRegression()
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classifier.fit(X_train, y_train)
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# sanity check to be sure the positive class is classes_[0] and that we
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# are betrayed by the class imbalance
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assert classifier.classes_.tolist() == ["cancer", "not cancer"]
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disp = plot_roc_curve(
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classifier, X_test, y_test, pos_label="cancer",
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response_method=response_method
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)
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roc_auc_limit = 0.95679
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assert disp.roc_auc == pytest.approx(roc_auc_limit)
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assert np.trapz(disp.tpr, disp.fpr) == pytest.approx(roc_auc_limit)
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disp = plot_roc_curve(
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classifier, X_test, y_test,
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response_method=response_method,
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)
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assert disp.roc_auc == pytest.approx(roc_auc_limit)
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assert np.trapz(disp.tpr, disp.fpr) == pytest.approx(roc_auc_limit)
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