update tests for consistency and clarity

fedot_ind/core/operation/IndustrialCachableOperation.py

@@ -101,7 +101,7 @@ def transform(
                                                 operation_info=class_params.__repr__())
             try:
                 transformed_features = self.try_load_from_cache(hashed_info)
-            except FileNotFoundError:
+            except (FileNotFoundError, ValueError):
                 transformed_features = self._transform(input_data)
                 self.cacher.cache_data(hashed_info, transformed_features)
 

fedot_ind/core/operation/caching.py

@@ -77,7 +77,7 @@ def load_data_from_cache(self, hashed_info: str):
             raise FileNotFoundError(f'File {file_path} was not found')
         elapsed_time = round(timeit.default_timer() - start, 5)
         print(
-            f'{self.data_type} of {type(data)} type is loaded from cache in {elapsed_time} sec')
+            f'{self.data_type} of {type(data)} type are loaded from cache in {elapsed_time} sec')
         return data
 
     def cache_data(self, hashed_info: str, data: pd.DataFrame):

fedot_ind/core/operation/transformation/basis/fourier.py

@@ -78,6 +78,7 @@ def _build_spectrum(self, input_data):
         return estimator
 
     def _decompose_signal(self, input_data):
+
         estimator = self._build_spectrum(input_data)
         # self._visualise_spectrum(estimator)
         psd = estimator.psd

fedot_ind/core/operation/transformation/data/point_cloud.py

@@ -136,8 +136,11 @@ def point_cloud_to_persistent_cohomology_ripser(
         # normalize epsilon distance in diagrams so max is 1
         diagrams = [np.array([dg for dg in diag if np.isfinite(dg).all()])
                     for diag in diagrams]
-        diagrams = diagrams / max([np.array([dg for dg in diag if np.isfinite(
-            dg).all()]).max() for diag in diagrams if diag.shape[0] > 0])
+        # diagrams = diagrams / max([np.array([dg for dg in diag if np.isfinite(
+            # dg).all()]).max() for diag in diagrams if diag.shape[0] > 0])
+
+        diagrams = [d / max([np.array([dg for dg in diag if np.isfinite(
+            dg).all()]).max() for diag in diagrams if diag.shape[0] > 0]) for d in diagrams]
 
         ep_ran_len = len(epsilon_range)
 

fedot_ind/core/operation/transformation/representation/statistical/quantile_extractor.py

@@ -58,7 +58,7 @@ def _concatenate_global_and_local_feature(
         window_stat_features = np.nan_to_num(window_stat_features)
         return window_stat_features
 
-    def extract_stats_features(self, ts: np.array, axis=None) -> InputData:
+    def extract_stats_features(self, ts: np.array, axis: int) -> InputData:
         global_features = self.get_statistical_features(ts, add_global_features=self.add_global_features, axis=axis)
         window_stat_features = self.get_statistical_features(ts, axis=axis) if self.window_size == 0 else \
             self.apply_window_for_stat_feature(ts_data=ts, feature_generator=self.get_statistical_features,
@@ -71,7 +71,7 @@ def generate_features_from_ts(self,
                                   ts: np.array,
                                   window_length: int = None) -> InputData:
         ts = ts[None, :] if len(ts.shape) == 1 else ts  # sanity check for map method
-        statistical_representation = np.array(list(map(lambda channel: self.extract_stats_features(channel), ts)))
+        statistical_representation = np.array(list(map(lambda channel: self.extract_stats_features(channel, axis=0), ts)))
         return statistical_representation
 
     def generate_features_from_array(self, array: np.array) -> InputData:

fedot_ind/core/operation/transformation/representation/topological/topological_extractor.py

@@ -114,13 +114,13 @@ def _generate_features_from_ts(self, ts_data: np.array, persistence_params: dict
 
         point_cloud = self.data_transformer.time_series_to_point_cloud(input_data=ts_data, use_gtda=True)
         topological_features = self.feature_extractor.transform(point_cloud)
-        topological_features = InputData(idx=np.arange(len(topological_features.values)),
-                                         features=topological_features.values,
-                                         target='no_target',
-                                         task='no_task',
-                                         data_type=DataTypesEnum.table,
-                                         supplementary_data={'feature_name': topological_features.columns})
-        return topological_features
+        # topological_features = InputData(idx=np.arange(len(topological_features.values)),
+        #                                  features=topological_features.values,
+        #                                  target='no_target',
+        #                                  task='no_task',
+        #                                  data_type=DataTypesEnum.table,
+        #                                  supplementary_data={'feature_name': topological_features.columns})
+        return topological_features.values
 
     def generate_topological_features(self, ts: np.array, persistence_params: dict = None) -> InputData:
         if persistence_params is not None:

tests/unit/core/operation/decomposition/test_column_sampling_decomposition.py

@@ -16,21 +16,21 @@ def sample_ts():
 
 def test_fit_transform(sample_matrix):
     rank = 1
-    cur = CURDecomposition(rank)
+    cur = CURDecomposition({'rank': rank})
     result = cur.fit_transform(sample_matrix)
     approximated = result[0]
     assert isinstance(result, tuple)
     assert approximated.shape[0] == rank
 
 
 def test_ts_to_matrix(sample_ts):
-    cur = CURDecomposition(rank=2)
+    cur = CURDecomposition({'rank': 2})
     matrix = cur.ts_to_matrix(time_series=sample_ts, window=10)
     assert isinstance(matrix, np.ndarray)
 
 
 def test_matrix_to_ts(sample_matrix):
-    cur = CURDecomposition(rank=2)
+    cur = CURDecomposition({'rank': 2})
     ts = cur.matrix_to_ts(sample_matrix)
     assert isinstance(ts, np.ndarray)
     assert len(ts.shape) == 1

tests/unit/core/operation/transformation/basis/test_eigen_basis.py

@@ -36,6 +36,7 @@ def test_transform_one_sample():
     basis.SV_threshold = 3
     sample = input_train_data.features[0]
     transformed_sample = basis._transform_one_sample(sample)
+    transformed_sample = transformed_sample.compute()
     assert isinstance(transformed_sample, np.ndarray)
     assert transformed_sample.shape[0] == basis.SV_threshold
     assert transformed_sample.shape[1] == len(sample)