Merge remote-tracking branch 'origin/Fedot_ver_0.3' into Fedot_ver_0.3

# Conflicts: # fedot_ind/core/architecture/experiment/TimeSeriesClassifierPreset.py # fedot_ind/core/operation/transformation/basis/data_driven.py
aimclub · Aug 23, 2023 · bc08cee · bc08cee
2 parents 359f2f1 + e7548b0
commit bc08cee
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Showing 3 changed files with 7 additions and 19 deletions.
diff --git a/fedot_ind/core/architecture/experiment/TimeSeriesClassifierPreset.py b/fedot_ind/core/architecture/experiment/TimeSeriesClassifierPreset.py
@@ -231,9 +231,9 @@ def predict(self, features: pd.DataFrame, target: np.array) -> dict:
 
         test_data = self._init_input_data(features, target)
         test_data_preprocessed = self.preprocessing_pipeline.root_node.predict(test_data)
-        data_cacher = DataCacher()
+        # data_cacher = DataCacher()
         # get unique hash of input data
-        test_predict_hash = data_cacher.hash_info(data=features)
+        # test_predict_hash = data_cacher.hash_info(data=features)
         # compare it to existed hash
         # if self.test_predict_hash != test_predict_hash:
         #     test_data = self._init_input_data(features, target)

diff --git a/fedot_ind/core/models/recurrence/RecurrenceExtractor.py b/fedot_ind/core/models/recurrence/RecurrenceExtractor.py
@@ -66,7 +66,7 @@ def generate_vector_from_ts(self, ts_frame: pd.DataFrame) -> pd.DataFrame:
         """
 
         parallel = Parallel(n_jobs=self.n_processes, verbose=0, pre_dispatch="2*n_jobs")
-        components_and_vectors = parallel(delayed(self.generate_features_from_ts)(sample) for sample in ts_frame.value)
+        components_and_vectors = parallel(delayed(self._ts_chunk_function)(component) for component in ts_frame)
         if self.image_mode:
             components_and_vectors = np.asarray(components_and_vectors)
             components_and_vectors = components_and_vectors[:, np.newaxis, :, :]

diff --git a/fedot_ind/core/operation/transformation/basis/data_driven.py b/fedot_ind/core/operation/transformation/basis/data_driven.py
@@ -94,33 +94,21 @@ def _transform(self, input_data: InputData) -> np.array:
         #     if predict[0].shape[0] == new_shape or predict[0].shape[0] == 1:
         #         reduce_dimension = False
         #     new_shape = predict[0].shape[0]
-        #predict = self._clean_predict(np.array(v))
+        # predict = self._clean_predict(np.array(v))
         return predict
 
     def get_threshold(self, data, selector: str):
 
-        selectors = {'median': np.median,
-                     'mode': self.mode}
-                     # 'mode': stats.mode}
+        selectors = {'median': stats.mode,
+                     'mode': stats.mode}
 
         svd_numbers = []
         with tqdm(total=len(data), desc='SVD estimation') as pbar:
             for signal in data:
                 svd_numbers.append(self._transform_one_sample(signal, svd_flag=True))
                 pbar.update(1)
-        return int(selectors[selector](svd_numbers))
-
-    def mode(self, arr):
-        frequency_dict = {}
-        for num in arr:
-            if num in frequency_dict:
-                frequency_dict[num] += 1
-            else:
-                frequency_dict[num] = 1
-        max_frequency = max(frequency_dict.values())
-        modes = [num for num, frequency in frequency_dict.items() if frequency == max_frequency]
-        return modes[0]
 
+        return selectors[selector](svd_numbers).mode[0]
 
     def _transform_one_sample(self, series: np.array, svd_flag: bool = False):
         trajectory_transformer = HankelMatrix(time_series=series, window_size=self.window_size)