164 lines
5.7 KiB
Python
164 lines
5.7 KiB
Python
import pytest
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from numpy.testing import assert_allclose
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from sklearn.datasets import load_diabetes
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from sklearn.exceptions import NotFittedError
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from sklearn.linear_model import Ridge
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from sklearn.metrics import PredictionErrorDisplay
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X, y = load_diabetes(return_X_y=True)
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@pytest.fixture
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def regressor_fitted():
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return Ridge().fit(X, y)
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@pytest.mark.parametrize(
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"regressor, params, err_type, err_msg",
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[
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(
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Ridge().fit(X, y),
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{"subsample": -1},
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ValueError,
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"When an integer, subsample=-1 should be",
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),
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(
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Ridge().fit(X, y),
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{"subsample": 20.0},
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ValueError,
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"When a floating-point, subsample=20.0 should be",
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),
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(
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Ridge().fit(X, y),
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{"subsample": -20.0},
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ValueError,
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"When a floating-point, subsample=-20.0 should be",
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),
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(
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Ridge().fit(X, y),
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{"kind": "xxx"},
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ValueError,
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"`kind` must be one of",
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),
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],
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)
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@pytest.mark.parametrize("class_method", ["from_estimator", "from_predictions"])
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def test_prediction_error_display_raise_error(
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pyplot, class_method, regressor, params, err_type, err_msg
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):
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"""Check that we raise the proper error when making the parameters
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# validation."""
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with pytest.raises(err_type, match=err_msg):
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if class_method == "from_estimator":
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PredictionErrorDisplay.from_estimator(regressor, X, y, **params)
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else:
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y_pred = regressor.predict(X)
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PredictionErrorDisplay.from_predictions(y_true=y, y_pred=y_pred, **params)
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def test_from_estimator_not_fitted(pyplot):
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"""Check that we raise a `NotFittedError` when the passed regressor is not
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fit."""
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regressor = Ridge()
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with pytest.raises(NotFittedError, match="is not fitted yet."):
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PredictionErrorDisplay.from_estimator(regressor, X, y)
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@pytest.mark.parametrize("class_method", ["from_estimator", "from_predictions"])
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@pytest.mark.parametrize("kind", ["actual_vs_predicted", "residual_vs_predicted"])
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def test_prediction_error_display(pyplot, regressor_fitted, class_method, kind):
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"""Check the default behaviour of the display."""
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if class_method == "from_estimator":
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display = PredictionErrorDisplay.from_estimator(
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regressor_fitted, X, y, kind=kind
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)
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else:
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y_pred = regressor_fitted.predict(X)
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display = PredictionErrorDisplay.from_predictions(
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y_true=y, y_pred=y_pred, kind=kind
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)
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if kind == "actual_vs_predicted":
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assert_allclose(display.line_.get_xdata(), display.line_.get_ydata())
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assert display.ax_.get_xlabel() == "Predicted values"
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assert display.ax_.get_ylabel() == "Actual values"
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assert display.line_ is not None
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else:
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assert display.ax_.get_xlabel() == "Predicted values"
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assert display.ax_.get_ylabel() == "Residuals (actual - predicted)"
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assert display.line_ is not None
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assert display.ax_.get_legend() is None
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@pytest.mark.parametrize("class_method", ["from_estimator", "from_predictions"])
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@pytest.mark.parametrize(
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"subsample, expected_size",
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[(5, 5), (0.1, int(X.shape[0] * 0.1)), (None, X.shape[0])],
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)
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def test_plot_prediction_error_subsample(
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pyplot, regressor_fitted, class_method, subsample, expected_size
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):
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"""Check the behaviour of `subsample`."""
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if class_method == "from_estimator":
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display = PredictionErrorDisplay.from_estimator(
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regressor_fitted, X, y, subsample=subsample
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)
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else:
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y_pred = regressor_fitted.predict(X)
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display = PredictionErrorDisplay.from_predictions(
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y_true=y, y_pred=y_pred, subsample=subsample
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)
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assert len(display.scatter_.get_offsets()) == expected_size
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@pytest.mark.parametrize("class_method", ["from_estimator", "from_predictions"])
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def test_plot_prediction_error_ax(pyplot, regressor_fitted, class_method):
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"""Check that we can pass an axis to the display."""
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_, ax = pyplot.subplots()
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if class_method == "from_estimator":
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display = PredictionErrorDisplay.from_estimator(regressor_fitted, X, y, ax=ax)
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else:
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y_pred = regressor_fitted.predict(X)
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display = PredictionErrorDisplay.from_predictions(
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y_true=y, y_pred=y_pred, ax=ax
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)
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assert display.ax_ is ax
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@pytest.mark.parametrize("class_method", ["from_estimator", "from_predictions"])
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def test_prediction_error_custom_artist(pyplot, regressor_fitted, class_method):
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"""Check that we can tune the style of the lines."""
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extra_params = {
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"kind": "actual_vs_predicted",
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"scatter_kwargs": {"color": "red"},
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"line_kwargs": {"color": "black"},
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}
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if class_method == "from_estimator":
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display = PredictionErrorDisplay.from_estimator(
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regressor_fitted, X, y, **extra_params
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)
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else:
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y_pred = regressor_fitted.predict(X)
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display = PredictionErrorDisplay.from_predictions(
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y_true=y, y_pred=y_pred, **extra_params
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)
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assert display.line_.get_color() == "black"
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assert_allclose(display.scatter_.get_edgecolor(), [[1.0, 0.0, 0.0, 0.8]])
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# create a display with the default values
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if class_method == "from_estimator":
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display = PredictionErrorDisplay.from_estimator(regressor_fitted, X, y)
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else:
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y_pred = regressor_fitted.predict(X)
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display = PredictionErrorDisplay.from_predictions(y_true=y, y_pred=y_pred)
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pyplot.close("all")
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display.plot(**extra_params)
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assert display.line_.get_color() == "black"
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assert_allclose(display.scatter_.get_edgecolor(), [[1.0, 0.0, 0.0, 0.8]])
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