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"""Test for score"""
# Authors: Guillaume Lemaitre <g.lemaitre58@gmail.com>
# Christos Aridas
# License: MIT
import pytest
from sklearn.datasets import make_blobs
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import make_scorer
from sklearn.model_selection import GridSearchCV, train_test_split
from imblearn.metrics import (
geometric_mean_score,
make_index_balanced_accuracy,
sensitivity_score,
specificity_score,
)
R_TOL = 1e-2
@pytest.fixture
def data():
X, y = make_blobs(random_state=0, centers=2)
return train_test_split(X, y, random_state=0)
@pytest.mark.parametrize(
"score, expected_score",
[
(sensitivity_score, 0.90),
(specificity_score, 0.90),
(geometric_mean_score, 0.90),
(make_index_balanced_accuracy()(geometric_mean_score), 0.82),
],
)
@pytest.mark.parametrize("average", ["macro", "weighted", "micro"])
def test_scorer_common_average(data, score, expected_score, average):
X_train, X_test, y_train, _ = data
scorer = make_scorer(score, pos_label=None, average=average)
grid = GridSearchCV(
LogisticRegression(),
param_grid={"C": [1, 10]},
scoring=scorer,
cv=3,
)
grid.fit(X_train, y_train).predict(X_test)
assert grid.best_score_ >= expected_score
@pytest.mark.parametrize(
"score, average, expected_score",
[
(sensitivity_score, "binary", 0.94),
(specificity_score, "binary", 0.89),
(geometric_mean_score, "multiclass", 0.90),
(
make_index_balanced_accuracy()(geometric_mean_score),
"multiclass",
0.82,
),
],
)
def test_scorer_default_average(data, score, average, expected_score):
X_train, X_test, y_train, _ = data
scorer = make_scorer(score, pos_label=1, average=average)
grid = GridSearchCV(
LogisticRegression(),
param_grid={"C": [1, 10]},
scoring=scorer,
cv=3,
)
grid.fit(X_train, y_train).predict(X_test)
assert grid.best_score_ >= expected_score
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