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glemaitreglemaitre
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TST set random state in scoring tests
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‎imblearn/metrics/tests/test_score_objects.py

Lines changed: 32 additions & 16 deletions
Original file line numberDiff line numberDiff line change
@@ -32,86 +32,102 @@ def test_imblearn_classification_scorers():
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# sensitivity scorer
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scorer = make_scorer(sensitivity_score, pos_label=None, average='macro')
35-
grid = GridSearchCV(LinearSVC(), param_grid={'C': [1, 10]}, scoring=scorer)
35+
grid = GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [1, 10]},
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scoring=scorer)
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grid.fit(X_train, y_train).predict(X_test)
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assert_allclose(grid.best_score_, 0.92, rtol=R_TOL)
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scorer = make_scorer(sensitivity_score, pos_label=None, average='weighted')
40-
grid = GridSearchCV(LinearSVC(), param_grid={'C': [1, 10]}, scoring=scorer)
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grid = GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [1, 10]},
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scoring=scorer)
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grid.fit(X_train, y_train).predict(X_test)
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assert_allclose(grid.best_score_, 0.92, rtol=R_TOL)
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scorer = make_scorer(sensitivity_score, pos_label=None, average='micro')
45-
grid = GridSearchCV(LinearSVC(), param_grid={'C': [1, 10]}, scoring=scorer)
47+
grid = GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [1, 10]},
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scoring=scorer)
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grid.fit(X_train, y_train).predict(X_test)
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assert_allclose(grid.best_score_, 0.92, rtol=R_TOL)
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scorer = make_scorer(sensitivity_score, pos_label=1)
50-
grid = GridSearchCV(LinearSVC(), param_grid={'C': [1, 10]}, scoring=scorer)
53+
grid = GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [1, 10]},
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scoring=scorer)
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grid.fit(X_train, y_train).predict(X_test)
5256
assert_allclose(grid.best_score_, 0.92, rtol=R_TOL)
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# specificity scorer
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scorer = make_scorer(specificity_score, pos_label=None, average='macro')
56-
grid = GridSearchCV(LinearSVC(), param_grid={'C': [1, 10]}, scoring=scorer)
60+
grid = GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [1, 10]},
61+
scoring=scorer)
5762
grid.fit(X_train, y_train).predict(X_test)
5863
assert_allclose(grid.best_score_, 0.92, rtol=R_TOL)
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scorer = make_scorer(specificity_score, pos_label=None, average='weighted')
61-
grid = GridSearchCV(LinearSVC(), param_grid={'C': [1, 10]}, scoring=scorer)
66+
grid = GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [1, 10]},
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scoring=scorer)
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grid.fit(X_train, y_train).predict(X_test)
6369
assert_allclose(grid.best_score_, 0.92, rtol=R_TOL)
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6571
scorer = make_scorer(specificity_score, pos_label=None, average='micro')
66-
grid = GridSearchCV(LinearSVC(), param_grid={'C': [1, 10]}, scoring=scorer)
72+
grid = GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [1, 10]},
73+
scoring=scorer)
6774
grid.fit(X_train, y_train).predict(X_test)
6875
assert_allclose(grid.best_score_, 0.92, rtol=R_TOL)
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7077
scorer = make_scorer(specificity_score, pos_label=1)
71-
grid = GridSearchCV(LinearSVC(), param_grid={'C': [1, 10]}, scoring=scorer)
78+
grid = GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [1, 10]},
79+
scoring=scorer)
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grid.fit(X_train, y_train).predict(X_test)
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assert_allclose(grid.best_score_, 0.95, rtol=R_TOL)
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# geometric_mean scorer
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scorer = make_scorer(geometric_mean_score, pos_label=None, average='macro')
77-
grid = GridSearchCV(LinearSVC(), param_grid={'C': [1, 10]}, scoring=scorer)
85+
grid = GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [1, 10]},
86+
scoring=scorer)
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grid.fit(X_train, y_train).predict(X_test)
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assert_allclose(grid.best_score_, 0.92, rtol=R_TOL)
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8190
scorer = make_scorer(
8291
geometric_mean_score, pos_label=None, average='weighted')
83-
grid = GridSearchCV(LinearSVC(), param_grid={'C': [1, 10]}, scoring=scorer)
92+
grid = GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [1, 10]},
93+
scoring=scorer)
8494
grid.fit(X_train, y_train).predict(X_test)
8595
assert_allclose(grid.best_score_, 0.92, rtol=R_TOL)
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8797
scorer = make_scorer(geometric_mean_score, pos_label=None, average='micro')
88-
grid = GridSearchCV(LinearSVC(), param_grid={'C': [1, 10]}, scoring=scorer)
98+
grid = GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [1, 10]},
99+
scoring=scorer)
89100
grid.fit(X_train, y_train).predict(X_test)
90101
assert_allclose(grid.best_score_, 0.92, rtol=R_TOL)
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92103
scorer = make_scorer(geometric_mean_score, pos_label=1)
93-
grid = GridSearchCV(LinearSVC(), param_grid={'C': [1, 10]}, scoring=scorer)
104+
grid = GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [1, 10]},
105+
scoring=scorer)
94106
grid.fit(X_train, y_train).predict(X_test)
95107
assert_allclose(grid.best_score_, 0.92, rtol=R_TOL)
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# make a iba metric before a scorer
98110
geo_mean_iba = make_index_balanced_accuracy()(geometric_mean_score)
99111
scorer = make_scorer(geo_mean_iba, pos_label=None, average='macro')
100-
grid = GridSearchCV(LinearSVC(), param_grid={'C': [1, 10]}, scoring=scorer)
112+
grid = GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [1, 10]},
113+
scoring=scorer)
101114
grid.fit(X_train, y_train).predict(X_test)
102115
assert_allclose(grid.best_score_, 0.85, rtol=R_TOL)
103116

104117
scorer = make_scorer(geo_mean_iba, pos_label=None, average='weighted')
105-
grid = GridSearchCV(LinearSVC(), param_grid={'C': [1, 10]}, scoring=scorer)
118+
grid = GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [1, 10]},
119+
scoring=scorer)
106120
grid.fit(X_train, y_train).predict(X_test)
107121
assert_allclose(grid.best_score_, 0.85, rtol=R_TOL)
108122

109123
scorer = make_scorer(geo_mean_iba, pos_label=None, average='micro')
110-
grid = GridSearchCV(LinearSVC(), param_grid={'C': [1, 10]}, scoring=scorer)
124+
grid = GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [1, 10]},
125+
scoring=scorer)
111126
grid.fit(X_train, y_train).predict(X_test)
112127
assert_allclose(grid.best_score_, 0.85, rtol=R_TOL)
113128

114129
scorer = make_scorer(geo_mean_iba, pos_label=1)
115-
grid = GridSearchCV(LinearSVC(), param_grid={'C': [1, 10]}, scoring=scorer)
130+
grid = GridSearchCV(LinearSVC(random_state=0), param_grid={'C': [1, 10]},
131+
scoring=scorer)
116132
grid.fit(X_train, y_train).predict(X_test)
117133
assert_allclose(grid.best_score_, 0.84, rtol=R_TOL)

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