109 lines
3.4 KiB
Python
109 lines
3.4 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.datasets import load_iris
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from sklearn.linear_model import LogisticRegression
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from sklearn.metrics import det_curve
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from sklearn.metrics import DetCurveDisplay
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@pytest.mark.parametrize("constructor_name", ["from_estimator", "from_predictions"])
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@pytest.mark.parametrize("response_method", ["predict_proba", "decision_function"])
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@pytest.mark.parametrize("with_sample_weight", [True, False])
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@pytest.mark.parametrize("with_strings", [True, False])
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def test_det_curve_display(
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pyplot, constructor_name, response_method, with_sample_weight, with_strings
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):
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X, y = load_iris(return_X_y=True)
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# Binarize the data with only the two first classes
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X, y = X[y < 2], y[y < 2]
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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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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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# safe guard for the binary if/else construction
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assert constructor_name in ("from_estimator", "from_predictions")
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common_kwargs = {
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"name": lr.__class__.__name__,
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"alpha": 0.8,
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"sample_weight": sample_weight,
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"pos_label": pos_label,
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}
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if constructor_name == "from_estimator":
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disp = DetCurveDisplay.from_estimator(lr, X, y, **common_kwargs)
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else:
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disp = DetCurveDisplay.from_predictions(y, y_pred, **common_kwargs)
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fpr, fnr, _ = det_curve(
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y,
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y_pred,
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sample_weight=sample_weight,
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pos_label=pos_label,
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)
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assert_allclose(disp.fpr, fpr)
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assert_allclose(disp.fnr, fnr)
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assert disp.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(disp.line_, mpl.lines.Line2D)
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assert disp.line_.get_alpha() == 0.8
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assert isinstance(disp.ax_, mpl.axes.Axes)
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assert isinstance(disp.figure_, mpl.figure.Figure)
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assert disp.line_.get_label() == "LogisticRegression"
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expected_pos_label = 1 if pos_label is None else pos_label
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expected_ylabel = f"False Negative Rate (Positive label: {expected_pos_label})"
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expected_xlabel = f"False Positive Rate (Positive label: {expected_pos_label})"
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assert disp.ax_.get_ylabel() == expected_ylabel
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assert disp.ax_.get_xlabel() == expected_xlabel
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@pytest.mark.parametrize(
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"constructor_name, expected_clf_name",
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[
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("from_estimator", "LogisticRegression"),
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("from_predictions", "Classifier"),
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],
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)
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def test_det_curve_display_default_name(
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pyplot,
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constructor_name,
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expected_clf_name,
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):
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# Check the default name display in the figure when `name` is not provided
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X, y = load_iris(return_X_y=True)
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# Binarize the data with only the two first classes
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X, y = X[y < 2], y[y < 2]
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lr = LogisticRegression().fit(X, y)
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y_pred = lr.predict_proba(X)[:, 1]
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if constructor_name == "from_estimator":
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disp = DetCurveDisplay.from_estimator(lr, X, y)
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else:
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disp = DetCurveDisplay.from_predictions(y, y_pred)
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assert disp.estimator_name == expected_clf_name
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assert disp.line_.get_label() == expected_clf_name
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