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import pytest
from sklearn.datasets import load_iris
from sklearn.tree import DecisionTreeClassifier
from sklearn.utils import shuffle
from sklearn.utils._testing import assert_allclose, assert_array_equal
from sklearn.model_selection import learning_curve
from sklearn.model_selection import LearningCurveDisplay
@pytest.fixture
def data():
return shuffle(*load_iris(return_X_y=True), random_state=0)
@pytest.mark.parametrize(
"params, err_type, err_msg",
[
({"std_display_style": "invalid"}, ValueError, "Unknown std_display_style:"),
({"score_type": "invalid"}, ValueError, "Unknown score_type:"),
],
)
def test_learning_curve_display_parameters_validation(
pyplot, data, params, err_type, err_msg
):
"""Check that we raise a proper error when passing invalid parameters."""
X, y = data
estimator = DecisionTreeClassifier(random_state=0)
train_sizes = [0.3, 0.6, 0.9]
with pytest.raises(err_type, match=err_msg):
LearningCurveDisplay.from_estimator(
estimator, X, y, train_sizes=train_sizes, **params
)
def test_learning_curve_display_default_usage(pyplot, data):
"""Check the default usage of the LearningCurveDisplay class."""
X, y = data
estimator = DecisionTreeClassifier(random_state=0)
train_sizes = [0.3, 0.6, 0.9]
display = LearningCurveDisplay.from_estimator(
estimator, X, y, train_sizes=train_sizes
)
import matplotlib as mpl
assert display.errorbar_ is None
assert isinstance(display.lines_, list)
for line in display.lines_:
assert isinstance(line, mpl.lines.Line2D)
assert isinstance(display.fill_between_, list)
for fill in display.fill_between_:
assert isinstance(fill, mpl.collections.PolyCollection)
assert fill.get_alpha() == 0.5
assert display.score_name == "Score"
assert display.ax_.get_xlabel() == "Number of samples in the training set"
assert display.ax_.get_ylabel() == "Score"
_, legend_labels = display.ax_.get_legend_handles_labels()
assert legend_labels == ["Testing metric"]
train_sizes_abs, train_scores, test_scores = learning_curve(
estimator, X, y, train_sizes=train_sizes
)
assert_array_equal(display.train_sizes, train_sizes_abs)
assert_allclose(display.train_scores, train_scores)
assert_allclose(display.test_scores, test_scores)
def test_learning_curve_display_negate_score(pyplot, data):
"""Check the behaviour of the `negate_score` parameter calling `from_estimator` and
`plot`.
"""
X, y = data
estimator = DecisionTreeClassifier(max_depth=1, random_state=0)
train_sizes = [0.3, 0.6, 0.9]
negate_score = False
display = LearningCurveDisplay.from_estimator(
estimator,
X,
y,
train_sizes=train_sizes,
negate_score=negate_score,
)
positive_scores = display.lines_[0].get_data()[1]
assert (positive_scores >= 0).all()
assert display.ax_.get_ylabel() == "Score"
negate_score = True
display = LearningCurveDisplay.from_estimator(
estimator, X, y, train_sizes=train_sizes, negate_score=negate_score
)
negative_scores = display.lines_[0].get_data()[1]
assert (negative_scores <= 0).all()
assert_allclose(negative_scores, -positive_scores)
assert display.ax_.get_ylabel() == "Score"
negate_score = False
display = LearningCurveDisplay.from_estimator(
estimator,
X,
y,
train_sizes=train_sizes,
negate_score=negate_score,
)
assert display.ax_.get_ylabel() == "Score"
display.plot(negate_score=not negate_score)
assert display.ax_.get_ylabel() == "Score"
assert (display.lines_[0].get_data()[1] < 0).all()
@pytest.mark.parametrize(
"score_name, ylabel", [(None, "Score"), ("Accuracy", "Accuracy")]
)
def test_learning_curve_display_score_name(pyplot, data, score_name, ylabel):
"""Check that we can overwrite the default score name shown on the y-axis."""
X, y = data
estimator = DecisionTreeClassifier(random_state=0)
train_sizes = [0.3, 0.6, 0.9]
display = LearningCurveDisplay.from_estimator(
estimator, X, y, train_sizes=train_sizes, score_name=score_name
)
assert display.ax_.get_ylabel() == ylabel
X, y = data
estimator = DecisionTreeClassifier(max_depth=1, random_state=0)
train_sizes = [0.3, 0.6, 0.9]
display = LearningCurveDisplay.from_estimator(
estimator, X, y, train_sizes=train_sizes, score_name=score_name
)
assert display.score_name == ylabel
@pytest.mark.parametrize("std_display_style", (None, "errorbar"))
def test_learning_curve_display_score_type(pyplot, data, std_display_style):
"""Check the behaviour of setting the `score_type` parameter."""
X, y = data
estimator = DecisionTreeClassifier(random_state=0)
train_sizes = [0.3, 0.6, 0.9]
train_sizes_abs, train_scores, test_scores = learning_curve(
estimator, X, y, train_sizes=train_sizes
)
score_type = "train"
display = LearningCurveDisplay.from_estimator(
estimator,
X,
y,
train_sizes=train_sizes,
score_type=score_type,
std_display_style=std_display_style,
)
_, legend_label = display.ax_.get_legend_handles_labels()
assert legend_label == ["Training metric"]
if std_display_style is None:
assert len(display.lines_) == 1
assert display.errorbar_ is None
x_data, y_data = display.lines_[0].get_data()
else:
assert display.lines_ is None
assert len(display.errorbar_) == 1
x_data, y_data = display.errorbar_[0].lines[0].get_data()
assert_array_equal(x_data, train_sizes_abs)
assert_allclose(y_data, train_scores.mean(axis=1))
score_type = "test"
display = LearningCurveDisplay.from_estimator(
estimator,
X,
y,
train_sizes=train_sizes,
score_type=score_type,
std_display_style=std_display_style,
)
_, legend_label = display.ax_.get_legend_handles_labels()
assert legend_label == ["Testing metric"]
if std_display_style is None:
assert len(display.lines_) == 1
assert display.errorbar_ is None
x_data, y_data = display.lines_[0].get_data()
else:
assert display.lines_ is None
assert len(display.errorbar_) == 1
x_data, y_data = display.errorbar_[0].lines[0].get_data()
assert_array_equal(x_data, train_sizes_abs)
assert_allclose(y_data, test_scores.mean(axis=1))
score_type = "both"
display = LearningCurveDisplay.from_estimator(
estimator,
X,
y,
train_sizes=train_sizes,
score_type=score_type,
std_display_style=std_display_style,
)
_, legend_label = display.ax_.get_legend_handles_labels()
assert legend_label == ["Training metric", "Testing metric"]
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, train_sizes_abs)
assert_allclose(y_data_train, train_scores.mean(axis=1))
assert_array_equal(x_data_test, train_sizes_abs)
assert_allclose(y_data_test, test_scores.mean(axis=1))
def test_learning_curve_display_log_scale(pyplot, data):
"""Check the behaviour of the parameter `log_scale`."""
X, y = data
estimator = DecisionTreeClassifier(random_state=0)
train_sizes = [0.3, 0.6, 0.9]
display = LearningCurveDisplay.from_estimator(
estimator, X, y, train_sizes=train_sizes, log_scale=True
)
assert display.ax_.get_xscale() == "log"
assert display.ax_.get_yscale() == "linear"
display = LearningCurveDisplay.from_estimator(
estimator, X, y, train_sizes=train_sizes, log_scale=False
)
assert display.ax_.get_xscale() == "linear"
assert display.ax_.get_yscale() == "linear"
def test_learning_curve_display_std_display_style(pyplot, data):
"""Check the behaviour of the parameter `std_display_style`."""
X, y = data
estimator = DecisionTreeClassifier(random_state=0)
import matplotlib as mpl
train_sizes = [0.3, 0.6, 0.9]
std_display_style = None
display = LearningCurveDisplay.from_estimator(
estimator,
X,
y,
train_sizes=train_sizes,
std_display_style=std_display_style,
)
assert len(display.lines_) == 1
assert isinstance(display.lines_[0], 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) == 1
std_display_style = "fill_between"
display = LearningCurveDisplay.from_estimator(
estimator,
X,
y,
train_sizes=train_sizes,
std_display_style=std_display_style,
)
assert len(display.lines_) == 1
assert isinstance(display.lines_[0], mpl.lines.Line2D)
assert display.errorbar_ is None
assert len(display.fill_between_) == 1
assert isinstance(display.fill_between_[0], mpl.collections.PolyCollection)
_, legend_label = display.ax_.get_legend_handles_labels()
assert len(legend_label) == 1
std_display_style = "errorbar"
display = LearningCurveDisplay.from_estimator(
estimator,
X,
y,
train_sizes=train_sizes,
std_display_style=std_display_style,
)
assert display.lines_ is None
assert len(display.errorbar_) == 1
assert isinstance(display.errorbar_[0], mpl.container.ErrorbarContainer)
assert display.fill_between_ is None
_, legend_label = display.ax_.get_legend_handles_labels()
assert len(legend_label) == 1
def test_learning_curve_display_plot_kwargs(pyplot, data):
"""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)
train_sizes = [0.3, 0.6, 0.9]
std_display_style = "fill_between"
line_kw = {"color": "red"}
fill_between_kw = {"color": "red", "alpha": 1.0}
display = LearningCurveDisplay.from_estimator(
estimator,
X,
y,
train_sizes=train_sizes,
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 = LearningCurveDisplay.from_estimator(
estimator,
X,
y,
train_sizes=train_sizes,
std_display_style=std_display_style,
errorbar_kw=errorbar_kw,
)
assert display.errorbar_[0].lines[0].get_color() == "red"
|
