1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
|
import pytest
from numpy.testing import assert_allclose
from sklearn.datasets import load_diabetes
from sklearn.exceptions import NotFittedError
from sklearn.linear_model import Ridge
from sklearn.metrics import PredictionErrorDisplay
X, y = load_diabetes(return_X_y=True)
@pytest.fixture
def regressor_fitted():
return Ridge().fit(X, y)
@pytest.mark.parametrize(
"regressor, params, err_type, err_msg",
[
(
Ridge().fit(X, y),
{"subsample": -1},
ValueError,
"When an integer, subsample=-1 should be",
),
(
Ridge().fit(X, y),
{"subsample": 20.0},
ValueError,
"When a floating-point, subsample=20.0 should be",
),
(
Ridge().fit(X, y),
{"subsample": -20.0},
ValueError,
"When a floating-point, subsample=-20.0 should be",
),
(
Ridge().fit(X, y),
{"kind": "xxx"},
ValueError,
"`kind` must be one of",
),
],
)
@pytest.mark.parametrize("class_method", ["from_estimator", "from_predictions"])
def test_prediction_error_display_raise_error(
pyplot, class_method, regressor, params, err_type, err_msg
):
"""Check that we raise the proper error when making the parameters
# validation."""
with pytest.raises(err_type, match=err_msg):
if class_method == "from_estimator":
PredictionErrorDisplay.from_estimator(regressor, X, y, **params)
else:
y_pred = regressor.predict(X)
PredictionErrorDisplay.from_predictions(y_true=y, y_pred=y_pred, **params)
def test_from_estimator_not_fitted(pyplot):
"""Check that we raise a `NotFittedError` when the passed regressor is not
fit."""
regressor = Ridge()
with pytest.raises(NotFittedError, match="is not fitted yet."):
PredictionErrorDisplay.from_estimator(regressor, X, y)
@pytest.mark.parametrize("class_method", ["from_estimator", "from_predictions"])
@pytest.mark.parametrize("kind", ["actual_vs_predicted", "residual_vs_predicted"])
def test_prediction_error_display(pyplot, regressor_fitted, class_method, kind):
"""Check the default behaviour of the display."""
if class_method == "from_estimator":
display = PredictionErrorDisplay.from_estimator(
regressor_fitted, X, y, kind=kind
)
else:
y_pred = regressor_fitted.predict(X)
display = PredictionErrorDisplay.from_predictions(
y_true=y, y_pred=y_pred, kind=kind
)
if kind == "actual_vs_predicted":
assert_allclose(display.line_.get_xdata(), display.line_.get_ydata())
assert display.ax_.get_xlabel() == "Predicted values"
assert display.ax_.get_ylabel() == "Actual values"
assert display.line_ is not None
else:
assert display.ax_.get_xlabel() == "Predicted values"
assert display.ax_.get_ylabel() == "Residuals (actual - predicted)"
assert display.line_ is not None
assert display.ax_.get_legend() is None
@pytest.mark.parametrize("class_method", ["from_estimator", "from_predictions"])
@pytest.mark.parametrize(
"subsample, expected_size",
[(5, 5), (0.1, int(X.shape[0] * 0.1)), (None, X.shape[0])],
)
def test_plot_prediction_error_subsample(
pyplot, regressor_fitted, class_method, subsample, expected_size
):
"""Check the behaviour of `subsample`."""
if class_method == "from_estimator":
display = PredictionErrorDisplay.from_estimator(
regressor_fitted, X, y, subsample=subsample
)
else:
y_pred = regressor_fitted.predict(X)
display = PredictionErrorDisplay.from_predictions(
y_true=y, y_pred=y_pred, subsample=subsample
)
assert len(display.scatter_.get_offsets()) == expected_size
@pytest.mark.parametrize("class_method", ["from_estimator", "from_predictions"])
def test_plot_prediction_error_ax(pyplot, regressor_fitted, class_method):
"""Check that we can pass an axis to the display."""
_, ax = pyplot.subplots()
if class_method == "from_estimator":
display = PredictionErrorDisplay.from_estimator(regressor_fitted, X, y, ax=ax)
else:
y_pred = regressor_fitted.predict(X)
display = PredictionErrorDisplay.from_predictions(
y_true=y, y_pred=y_pred, ax=ax
)
assert display.ax_ is ax
@pytest.mark.parametrize("class_method", ["from_estimator", "from_predictions"])
def test_prediction_error_custom_artist(pyplot, regressor_fitted, class_method):
"""Check that we can tune the style of the lines."""
extra_params = {
"kind": "actual_vs_predicted",
"scatter_kwargs": {"color": "red"},
"line_kwargs": {"color": "black"},
}
if class_method == "from_estimator":
display = PredictionErrorDisplay.from_estimator(
regressor_fitted, X, y, **extra_params
)
else:
y_pred = regressor_fitted.predict(X)
display = PredictionErrorDisplay.from_predictions(
y_true=y, y_pred=y_pred, **extra_params
)
assert display.line_.get_color() == "black"
assert_allclose(display.scatter_.get_edgecolor(), [[1.0, 0.0, 0.0, 0.8]])
# create a display with the default values
if class_method == "from_estimator":
display = PredictionErrorDisplay.from_estimator(regressor_fitted, X, y)
else:
y_pred = regressor_fitted.predict(X)
display = PredictionErrorDisplay.from_predictions(y_true=y, y_pred=y_pred)
pyplot.close("all")
display.plot(**extra_params)
assert display.line_.get_color() == "black"
assert_allclose(display.scatter_.get_edgecolor(), [[1.0, 0.0, 0.0, 0.8]])
|
