Update tests

ML-KULeuven · Dec 5, 2024 · d88849c · d88849c
1 parent f0f2326
commit d88849c
Showing 1 changed file with 18 additions and 6 deletions.
diff --git a/tests/semantic_segmentation/test_LogisticRegressionSegmentor.py b/tests/semantic_segmentation/test_LogisticRegressionSegmentor.py
@@ -50,28 +50,40 @@ def test_initialization_additional_args(self):
         with pytest.raises(TypeError):
             LogisticRegressionSegmentor(something_invalid=0)
 
-    def test_fit(self, pattern_based_embedding):
+    def test_fit(self):
+        univariate_time_series = np.sin(np.arange(0, 50, 0.05)) + np.random.normal(0, 0.25, 1000)
+        pattern_based_embedding = PatternBasedEmbedder().fit_transform(univariate_time_series)
         clf = LogisticRegressionSegmentor()
         assert clf.fit(pattern_based_embedding) == clf
 
-    def test_predict_proba(self, pattern_based_embedding):
+    def test_predict_proba(self):
+        univariate_time_series = np.sin(np.arange(0, 50, 0.05)) + np.random.normal(0, 0.25, 1000)
+        pattern_based_embedding = PatternBasedEmbedder().fit_transform(univariate_time_series)
         clf = LogisticRegressionSegmentor()
         clf.fit(pattern_based_embedding)
         pred = clf.predict_proba(pattern_based_embedding)
         assert pred.shape[0] == pattern_based_embedding.shape[1]
 
-    def test_fit_predict_proba(self, pattern_based_embedding):
+    def test_fit_predict_proba(self):
+        univariate_time_series = np.sin(np.arange(0, 50, 0.05)) + np.random.normal(0, 0.25, 1000)
+        pattern_based_embedding = PatternBasedEmbedder().fit_transform(univariate_time_series)
         pred = LogisticRegressionSegmentor().fit_predict_proba(pattern_based_embedding)
         assert pred.shape[0] == pattern_based_embedding.shape[1]
 
-    def test_fit_predict_proba_one_n_segment(self, pattern_based_embedding):
+    def test_fit_predict_proba_one_n_segment(self):
+        univariate_time_series = np.sin(np.arange(0, 50, 0.05)) + np.random.normal(0, 0.25, 1000)
+        pattern_based_embedding = PatternBasedEmbedder().fit_transform(univariate_time_series)
         pred = LogisticRegressionSegmentor(n_segments=3).fit_predict_proba(pattern_based_embedding)
         assert pred.shape == (pattern_based_embedding.shape[1], 3)
 
-    def test_fit_predict_proba_multiple_jobs(self, pattern_based_embedding):
+    def test_fit_predict_proba_multiple_jobs(self):
+        univariate_time_series = np.sin(np.arange(0, 50, 0.05)) + np.random.normal(0, 0.25, 1000)
+        pattern_based_embedding = PatternBasedEmbedder().fit_transform(univariate_time_series)
         pred = LogisticRegressionSegmentor(n_jobs=4).fit_predict_proba(pattern_based_embedding)
         assert pred.shape[0] == pattern_based_embedding.shape[1]
 
-    def test_predict_proba_not_fitted(self, pattern_based_embedding):
+    def test_predict_proba_not_fitted(self):
+        univariate_time_series = np.sin(np.arange(0, 50, 0.05)) + np.random.normal(0, 0.25, 1000)
+        pattern_based_embedding = PatternBasedEmbedder().fit_transform(univariate_time_series)
         with pytest.raises(NotFittedError):
             LogisticRegressionSegmentor().predict_proba(pattern_based_embedding)