85 lines
2.2 KiB
Python
85 lines
2.2 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 plot_det_curve
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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(
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"response_method", ["predict_proba", "decision_function"]
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)
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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_plot_det_curve(
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pyplot,
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response_method,
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data_binary,
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with_sample_weight,
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with_strings
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):
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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_det_curve(
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lr, X, y, alpha=0.8, sample_weight=sample_weight,
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)
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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, fnr, _ = det_curve(
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y, y_pred, sample_weight=sample_weight, pos_label=pos_label,
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)
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assert_allclose(viz.fpr, fpr)
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assert_allclose(viz.fnr, fnr)
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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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assert viz.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 = (
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f"False Negative Rate (Positive label: {expected_pos_label})"
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)
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expected_xlabel = (
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f"False Positive Rate (Positive label: {expected_pos_label})"
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)
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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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