596 lines
19 KiB
Python
596 lines
19 KiB
Python
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import numpy as np
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import pytest
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from sklearn.datasets import load_iris
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from sklearn.model_selection import (
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LearningCurveDisplay,
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ValidationCurveDisplay,
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learning_curve,
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validation_curve,
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)
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from sklearn.tree import DecisionTreeClassifier
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from sklearn.utils import shuffle
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from sklearn.utils._testing import assert_allclose, assert_array_equal
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@pytest.fixture
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def data():
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return shuffle(*load_iris(return_X_y=True), random_state=0)
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@pytest.mark.parametrize(
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"params, err_type, err_msg",
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[
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({"std_display_style": "invalid"}, ValueError, "Unknown std_display_style:"),
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({"score_type": "invalid"}, ValueError, "Unknown score_type:"),
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],
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)
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@pytest.mark.parametrize(
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"CurveDisplay, specific_params",
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[
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(ValidationCurveDisplay, {"param_name": "max_depth", "param_range": [1, 3, 5]}),
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(LearningCurveDisplay, {"train_sizes": [0.3, 0.6, 0.9]}),
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],
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)
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def test_curve_display_parameters_validation(
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pyplot, data, params, err_type, err_msg, CurveDisplay, specific_params
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):
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"""Check that we raise a proper error when passing invalid parameters."""
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X, y = data
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estimator = DecisionTreeClassifier(random_state=0)
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with pytest.raises(err_type, match=err_msg):
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CurveDisplay.from_estimator(estimator, X, y, **specific_params, **params)
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def test_learning_curve_display_default_usage(pyplot, data):
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"""Check the default usage of the LearningCurveDisplay class."""
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X, y = data
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estimator = DecisionTreeClassifier(random_state=0)
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train_sizes = [0.3, 0.6, 0.9]
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display = LearningCurveDisplay.from_estimator(
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estimator, X, y, train_sizes=train_sizes
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)
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import matplotlib as mpl
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assert display.errorbar_ is None
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assert isinstance(display.lines_, list)
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for line in display.lines_:
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assert isinstance(line, mpl.lines.Line2D)
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assert isinstance(display.fill_between_, list)
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for fill in display.fill_between_:
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assert isinstance(fill, mpl.collections.PolyCollection)
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assert fill.get_alpha() == 0.5
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assert display.score_name == "Score"
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assert display.ax_.get_xlabel() == "Number of samples in the training set"
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assert display.ax_.get_ylabel() == "Score"
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_, legend_labels = display.ax_.get_legend_handles_labels()
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assert legend_labels == ["Train", "Test"]
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train_sizes_abs, train_scores, test_scores = learning_curve(
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estimator, X, y, train_sizes=train_sizes
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)
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assert_array_equal(display.train_sizes, train_sizes_abs)
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assert_allclose(display.train_scores, train_scores)
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assert_allclose(display.test_scores, test_scores)
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def test_validation_curve_display_default_usage(pyplot, data):
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"""Check the default usage of the ValidationCurveDisplay class."""
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X, y = data
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estimator = DecisionTreeClassifier(random_state=0)
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param_name, param_range = "max_depth", [1, 3, 5]
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display = ValidationCurveDisplay.from_estimator(
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estimator, X, y, param_name=param_name, param_range=param_range
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)
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import matplotlib as mpl
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assert display.errorbar_ is None
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assert isinstance(display.lines_, list)
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for line in display.lines_:
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assert isinstance(line, mpl.lines.Line2D)
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assert isinstance(display.fill_between_, list)
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for fill in display.fill_between_:
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assert isinstance(fill, mpl.collections.PolyCollection)
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assert fill.get_alpha() == 0.5
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assert display.score_name == "Score"
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assert display.ax_.get_xlabel() == f"{param_name}"
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assert display.ax_.get_ylabel() == "Score"
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_, legend_labels = display.ax_.get_legend_handles_labels()
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assert legend_labels == ["Train", "Test"]
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train_scores, test_scores = validation_curve(
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estimator, X, y, param_name=param_name, param_range=param_range
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)
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assert_array_equal(display.param_range, param_range)
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assert_allclose(display.train_scores, train_scores)
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assert_allclose(display.test_scores, test_scores)
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@pytest.mark.parametrize(
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"CurveDisplay, specific_params",
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[
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(ValidationCurveDisplay, {"param_name": "max_depth", "param_range": [1, 3, 5]}),
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(LearningCurveDisplay, {"train_sizes": [0.3, 0.6, 0.9]}),
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],
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)
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def test_curve_display_negate_score(pyplot, data, CurveDisplay, specific_params):
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"""Check the behaviour of the `negate_score` parameter calling `from_estimator` and
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`plot`.
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"""
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X, y = data
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estimator = DecisionTreeClassifier(max_depth=1, random_state=0)
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negate_score = False
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display = CurveDisplay.from_estimator(
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estimator, X, y, **specific_params, negate_score=negate_score
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)
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positive_scores = display.lines_[0].get_data()[1]
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assert (positive_scores >= 0).all()
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assert display.ax_.get_ylabel() == "Score"
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negate_score = True
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display = CurveDisplay.from_estimator(
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estimator, X, y, **specific_params, negate_score=negate_score
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)
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negative_scores = display.lines_[0].get_data()[1]
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assert (negative_scores <= 0).all()
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assert_allclose(negative_scores, -positive_scores)
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assert display.ax_.get_ylabel() == "Negative score"
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negate_score = False
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display = CurveDisplay.from_estimator(
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estimator, X, y, **specific_params, negate_score=negate_score
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)
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assert display.ax_.get_ylabel() == "Score"
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display.plot(negate_score=not negate_score)
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assert display.ax_.get_ylabel() == "Score"
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assert (display.lines_[0].get_data()[1] < 0).all()
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@pytest.mark.parametrize(
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"score_name, ylabel", [(None, "Score"), ("Accuracy", "Accuracy")]
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)
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@pytest.mark.parametrize(
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"CurveDisplay, specific_params",
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[
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(ValidationCurveDisplay, {"param_name": "max_depth", "param_range": [1, 3, 5]}),
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(LearningCurveDisplay, {"train_sizes": [0.3, 0.6, 0.9]}),
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],
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)
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def test_curve_display_score_name(
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pyplot, data, score_name, ylabel, CurveDisplay, specific_params
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):
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"""Check that we can overwrite the default score name shown on the y-axis."""
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X, y = data
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estimator = DecisionTreeClassifier(random_state=0)
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display = CurveDisplay.from_estimator(
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estimator, X, y, **specific_params, score_name=score_name
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)
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assert display.ax_.get_ylabel() == ylabel
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X, y = data
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estimator = DecisionTreeClassifier(max_depth=1, random_state=0)
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display = CurveDisplay.from_estimator(
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estimator, X, y, **specific_params, score_name=score_name
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)
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assert display.score_name == ylabel
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@pytest.mark.parametrize("std_display_style", (None, "errorbar"))
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def test_learning_curve_display_score_type(pyplot, data, std_display_style):
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"""Check the behaviour of setting the `score_type` parameter."""
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X, y = data
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estimator = DecisionTreeClassifier(random_state=0)
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train_sizes = [0.3, 0.6, 0.9]
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train_sizes_abs, train_scores, test_scores = learning_curve(
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estimator, X, y, train_sizes=train_sizes
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)
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score_type = "train"
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display = LearningCurveDisplay.from_estimator(
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estimator,
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X,
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y,
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train_sizes=train_sizes,
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score_type=score_type,
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std_display_style=std_display_style,
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)
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_, legend_label = display.ax_.get_legend_handles_labels()
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assert legend_label == ["Train"]
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if std_display_style is None:
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assert len(display.lines_) == 1
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assert display.errorbar_ is None
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x_data, y_data = display.lines_[0].get_data()
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else:
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assert display.lines_ is None
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assert len(display.errorbar_) == 1
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x_data, y_data = display.errorbar_[0].lines[0].get_data()
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assert_array_equal(x_data, train_sizes_abs)
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assert_allclose(y_data, train_scores.mean(axis=1))
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score_type = "test"
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display = LearningCurveDisplay.from_estimator(
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estimator,
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X,
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y,
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train_sizes=train_sizes,
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score_type=score_type,
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std_display_style=std_display_style,
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)
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_, legend_label = display.ax_.get_legend_handles_labels()
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assert legend_label == ["Test"]
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if std_display_style is None:
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assert len(display.lines_) == 1
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assert display.errorbar_ is None
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x_data, y_data = display.lines_[0].get_data()
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else:
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assert display.lines_ is None
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assert len(display.errorbar_) == 1
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x_data, y_data = display.errorbar_[0].lines[0].get_data()
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assert_array_equal(x_data, train_sizes_abs)
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assert_allclose(y_data, test_scores.mean(axis=1))
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score_type = "both"
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display = LearningCurveDisplay.from_estimator(
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estimator,
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X,
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y,
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train_sizes=train_sizes,
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score_type=score_type,
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std_display_style=std_display_style,
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)
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_, legend_label = display.ax_.get_legend_handles_labels()
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assert legend_label == ["Train", "Test"]
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if std_display_style is None:
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assert len(display.lines_) == 2
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assert display.errorbar_ is None
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x_data_train, y_data_train = display.lines_[0].get_data()
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x_data_test, y_data_test = display.lines_[1].get_data()
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else:
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assert display.lines_ is None
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assert len(display.errorbar_) == 2
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x_data_train, y_data_train = display.errorbar_[0].lines[0].get_data()
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x_data_test, y_data_test = display.errorbar_[1].lines[0].get_data()
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assert_array_equal(x_data_train, train_sizes_abs)
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assert_allclose(y_data_train, train_scores.mean(axis=1))
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assert_array_equal(x_data_test, train_sizes_abs)
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assert_allclose(y_data_test, test_scores.mean(axis=1))
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@pytest.mark.parametrize("std_display_style", (None, "errorbar"))
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def test_validation_curve_display_score_type(pyplot, data, std_display_style):
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"""Check the behaviour of setting the `score_type` parameter."""
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X, y = data
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estimator = DecisionTreeClassifier(random_state=0)
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param_name, param_range = "max_depth", [1, 3, 5]
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train_scores, test_scores = validation_curve(
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estimator, X, y, param_name=param_name, param_range=param_range
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)
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score_type = "train"
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display = ValidationCurveDisplay.from_estimator(
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estimator,
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X,
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y,
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param_name=param_name,
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param_range=param_range,
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score_type=score_type,
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std_display_style=std_display_style,
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)
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_, legend_label = display.ax_.get_legend_handles_labels()
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assert legend_label == ["Train"]
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if std_display_style is None:
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assert len(display.lines_) == 1
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assert display.errorbar_ is None
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x_data, y_data = display.lines_[0].get_data()
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else:
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assert display.lines_ is None
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assert len(display.errorbar_) == 1
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x_data, y_data = display.errorbar_[0].lines[0].get_data()
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assert_array_equal(x_data, param_range)
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assert_allclose(y_data, train_scores.mean(axis=1))
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score_type = "test"
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display = ValidationCurveDisplay.from_estimator(
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estimator,
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X,
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y,
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param_name=param_name,
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param_range=param_range,
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score_type=score_type,
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std_display_style=std_display_style,
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)
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_, legend_label = display.ax_.get_legend_handles_labels()
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assert legend_label == ["Test"]
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if std_display_style is None:
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assert len(display.lines_) == 1
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assert display.errorbar_ is None
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x_data, y_data = display.lines_[0].get_data()
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else:
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assert display.lines_ is None
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assert len(display.errorbar_) == 1
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x_data, y_data = display.errorbar_[0].lines[0].get_data()
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assert_array_equal(x_data, param_range)
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assert_allclose(y_data, test_scores.mean(axis=1))
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score_type = "both"
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display = ValidationCurveDisplay.from_estimator(
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estimator,
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X,
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y,
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param_name=param_name,
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param_range=param_range,
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score_type=score_type,
|
||
|
|
std_display_style=std_display_style,
|
||
|
|
)
|
||
|
|
|
||
|
|
_, legend_label = display.ax_.get_legend_handles_labels()
|
||
|
|
assert legend_label == ["Train", "Test"]
|
||
|
|
|
||
|
|
if std_display_style is None:
|
||
|
|
assert len(display.lines_) == 2
|
||
|
|
assert display.errorbar_ is None
|
||
|
|
x_data_train, y_data_train = display.lines_[0].get_data()
|
||
|
|
x_data_test, y_data_test = display.lines_[1].get_data()
|
||
|
|
else:
|
||
|
|
assert display.lines_ is None
|
||
|
|
assert len(display.errorbar_) == 2
|
||
|
|
x_data_train, y_data_train = display.errorbar_[0].lines[0].get_data()
|
||
|
|
x_data_test, y_data_test = display.errorbar_[1].lines[0].get_data()
|
||
|
|
|
||
|
|
assert_array_equal(x_data_train, param_range)
|
||
|
|
assert_allclose(y_data_train, train_scores.mean(axis=1))
|
||
|
|
assert_array_equal(x_data_test, param_range)
|
||
|
|
assert_allclose(y_data_test, test_scores.mean(axis=1))
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"CurveDisplay, specific_params, expected_xscale",
|
||
|
|
[
|
||
|
|
(
|
||
|
|
ValidationCurveDisplay,
|
||
|
|
{"param_name": "max_depth", "param_range": np.arange(1, 5)},
|
||
|
|
"linear",
|
||
|
|
),
|
||
|
|
(LearningCurveDisplay, {"train_sizes": np.linspace(0.1, 0.9, num=5)}, "linear"),
|
||
|
|
(
|
||
|
|
ValidationCurveDisplay,
|
||
|
|
{
|
||
|
|
"param_name": "max_depth",
|
||
|
|
"param_range": np.round(np.logspace(0, 2, num=5)).astype(np.int64),
|
||
|
|
},
|
||
|
|
"log",
|
||
|
|
),
|
||
|
|
(LearningCurveDisplay, {"train_sizes": np.logspace(-1, 0, num=5)}, "log"),
|
||
|
|
],
|
||
|
|
)
|
||
|
|
def test_curve_display_xscale_auto(
|
||
|
|
pyplot, data, CurveDisplay, specific_params, expected_xscale
|
||
|
|
):
|
||
|
|
"""Check the behaviour of the x-axis scaling depending on the data provided."""
|
||
|
|
X, y = data
|
||
|
|
estimator = DecisionTreeClassifier(random_state=0)
|
||
|
|
|
||
|
|
display = CurveDisplay.from_estimator(estimator, X, y, **specific_params)
|
||
|
|
assert display.ax_.get_xscale() == expected_xscale
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"CurveDisplay, specific_params",
|
||
|
|
[
|
||
|
|
(ValidationCurveDisplay, {"param_name": "max_depth", "param_range": [1, 3, 5]}),
|
||
|
|
(LearningCurveDisplay, {"train_sizes": [0.3, 0.6, 0.9]}),
|
||
|
|
],
|
||
|
|
)
|
||
|
|
def test_curve_display_std_display_style(pyplot, data, CurveDisplay, specific_params):
|
||
|
|
"""Check the behaviour of the parameter `std_display_style`."""
|
||
|
|
X, y = data
|
||
|
|
estimator = DecisionTreeClassifier(random_state=0)
|
||
|
|
|
||
|
|
import matplotlib as mpl
|
||
|
|
|
||
|
|
std_display_style = None
|
||
|
|
display = CurveDisplay.from_estimator(
|
||
|
|
estimator,
|
||
|
|
X,
|
||
|
|
y,
|
||
|
|
**specific_params,
|
||
|
|
std_display_style=std_display_style,
|
||
|
|
)
|
||
|
|
|
||
|
|
assert len(display.lines_) == 2
|
||
|
|
for line in display.lines_:
|
||
|
|
assert isinstance(line, mpl.lines.Line2D)
|
||
|
|
assert display.errorbar_ is None
|
||
|
|
assert display.fill_between_ is None
|
||
|
|
_, legend_label = display.ax_.get_legend_handles_labels()
|
||
|
|
assert len(legend_label) == 2
|
||
|
|
|
||
|
|
std_display_style = "fill_between"
|
||
|
|
display = CurveDisplay.from_estimator(
|
||
|
|
estimator,
|
||
|
|
X,
|
||
|
|
y,
|
||
|
|
**specific_params,
|
||
|
|
std_display_style=std_display_style,
|
||
|
|
)
|
||
|
|
|
||
|
|
assert len(display.lines_) == 2
|
||
|
|
for line in display.lines_:
|
||
|
|
assert isinstance(line, mpl.lines.Line2D)
|
||
|
|
assert display.errorbar_ is None
|
||
|
|
assert len(display.fill_between_) == 2
|
||
|
|
for fill_between in display.fill_between_:
|
||
|
|
assert isinstance(fill_between, mpl.collections.PolyCollection)
|
||
|
|
_, legend_label = display.ax_.get_legend_handles_labels()
|
||
|
|
assert len(legend_label) == 2
|
||
|
|
|
||
|
|
std_display_style = "errorbar"
|
||
|
|
display = CurveDisplay.from_estimator(
|
||
|
|
estimator,
|
||
|
|
X,
|
||
|
|
y,
|
||
|
|
**specific_params,
|
||
|
|
std_display_style=std_display_style,
|
||
|
|
)
|
||
|
|
|
||
|
|
assert display.lines_ is None
|
||
|
|
assert len(display.errorbar_) == 2
|
||
|
|
for errorbar in display.errorbar_:
|
||
|
|
assert isinstance(errorbar, mpl.container.ErrorbarContainer)
|
||
|
|
assert display.fill_between_ is None
|
||
|
|
_, legend_label = display.ax_.get_legend_handles_labels()
|
||
|
|
assert len(legend_label) == 2
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"CurveDisplay, specific_params",
|
||
|
|
[
|
||
|
|
(ValidationCurveDisplay, {"param_name": "max_depth", "param_range": [1, 3, 5]}),
|
||
|
|
(LearningCurveDisplay, {"train_sizes": [0.3, 0.6, 0.9]}),
|
||
|
|
],
|
||
|
|
)
|
||
|
|
def test_curve_display_plot_kwargs(pyplot, data, CurveDisplay, specific_params):
|
||
|
|
"""Check the behaviour of the different plotting keyword arguments: `line_kw`,
|
||
|
|
`fill_between_kw`, and `errorbar_kw`."""
|
||
|
|
X, y = data
|
||
|
|
estimator = DecisionTreeClassifier(random_state=0)
|
||
|
|
|
||
|
|
std_display_style = "fill_between"
|
||
|
|
line_kw = {"color": "red"}
|
||
|
|
fill_between_kw = {"color": "red", "alpha": 1.0}
|
||
|
|
display = CurveDisplay.from_estimator(
|
||
|
|
estimator,
|
||
|
|
X,
|
||
|
|
y,
|
||
|
|
**specific_params,
|
||
|
|
std_display_style=std_display_style,
|
||
|
|
line_kw=line_kw,
|
||
|
|
fill_between_kw=fill_between_kw,
|
||
|
|
)
|
||
|
|
|
||
|
|
assert display.lines_[0].get_color() == "red"
|
||
|
|
assert_allclose(
|
||
|
|
display.fill_between_[0].get_facecolor(),
|
||
|
|
[[1.0, 0.0, 0.0, 1.0]], # trust me, it's red
|
||
|
|
)
|
||
|
|
|
||
|
|
std_display_style = "errorbar"
|
||
|
|
errorbar_kw = {"color": "red"}
|
||
|
|
display = CurveDisplay.from_estimator(
|
||
|
|
estimator,
|
||
|
|
X,
|
||
|
|
y,
|
||
|
|
**specific_params,
|
||
|
|
std_display_style=std_display_style,
|
||
|
|
errorbar_kw=errorbar_kw,
|
||
|
|
)
|
||
|
|
|
||
|
|
assert display.errorbar_[0].lines[0].get_color() == "red"
|
||
|
|
|
||
|
|
|
||
|
|
# TODO(1.5): to be removed
|
||
|
|
def test_learning_curve_display_deprecate_log_scale(data, pyplot):
|
||
|
|
"""Check that we warn for the deprecated parameter `log_scale`."""
|
||
|
|
X, y = data
|
||
|
|
estimator = DecisionTreeClassifier(random_state=0)
|
||
|
|
|
||
|
|
with pytest.warns(FutureWarning, match="`log_scale` parameter is deprecated"):
|
||
|
|
display = LearningCurveDisplay.from_estimator(
|
||
|
|
estimator, X, y, train_sizes=[0.3, 0.6, 0.9], log_scale=True
|
||
|
|
)
|
||
|
|
|
||
|
|
assert display.ax_.get_xscale() == "log"
|
||
|
|
assert display.ax_.get_yscale() == "linear"
|
||
|
|
|
||
|
|
with pytest.warns(FutureWarning, match="`log_scale` parameter is deprecated"):
|
||
|
|
display = LearningCurveDisplay.from_estimator(
|
||
|
|
estimator, X, y, train_sizes=[0.3, 0.6, 0.9], log_scale=False
|
||
|
|
)
|
||
|
|
|
||
|
|
assert display.ax_.get_xscale() == "linear"
|
||
|
|
assert display.ax_.get_yscale() == "linear"
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"param_range, xscale",
|
||
|
|
[([5, 10, 15], "linear"), ([-50, 5, 50, 500], "symlog"), ([5, 50, 500], "log")],
|
||
|
|
)
|
||
|
|
def test_validation_curve_xscale_from_param_range_provided_as_a_list(
|
||
|
|
pyplot, data, param_range, xscale
|
||
|
|
):
|
||
|
|
"""Check the induced xscale from the provided param_range values."""
|
||
|
|
X, y = data
|
||
|
|
estimator = DecisionTreeClassifier(random_state=0)
|
||
|
|
|
||
|
|
param_name = "max_depth"
|
||
|
|
display = ValidationCurveDisplay.from_estimator(
|
||
|
|
estimator,
|
||
|
|
X,
|
||
|
|
y,
|
||
|
|
param_name=param_name,
|
||
|
|
param_range=param_range,
|
||
|
|
)
|
||
|
|
|
||
|
|
assert display.ax_.get_xscale() == xscale
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"Display, params",
|
||
|
|
[
|
||
|
|
(LearningCurveDisplay, {}),
|
||
|
|
(ValidationCurveDisplay, {"param_name": "max_depth", "param_range": [1, 3, 5]}),
|
||
|
|
],
|
||
|
|
)
|
||
|
|
def test_subclassing_displays(pyplot, data, Display, params):
|
||
|
|
"""Check that named constructors return the correct type when subclassed.
|
||
|
|
|
||
|
|
Non-regression test for:
|
||
|
|
https://github.com/scikit-learn/scikit-learn/pull/27675
|
||
|
|
"""
|
||
|
|
X, y = data
|
||
|
|
estimator = DecisionTreeClassifier(random_state=0)
|
||
|
|
|
||
|
|
class SubclassOfDisplay(Display):
|
||
|
|
pass
|
||
|
|
|
||
|
|
display = SubclassOfDisplay.from_estimator(estimator, X, y, **params)
|
||
|
|
assert isinstance(display, SubclassOfDisplay)
|