Automated autopep8 fixes

aimclub · Jun 13, 2024 · 9544f4e · 9544f4e
1 parent 15f96c4
commit 9544f4e
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Showing 2 changed files with 37 additions and 17 deletions.
diff --git a/fedot_ind/tools/uml/uml.py b/fedot_ind/tools/uml/uml.py
@@ -4,9 +4,12 @@
 from fedot_ind.api.utils.path_lib import DEFAULT_PATH_MODELS
 
 with open(DEFAULT_PATH_MODELS, 'r') as file:
-    file_content = [string for string in file.read().split("\n") if not string.startswith("from")]
-file_content = file_content[:next((i for i, line in enumerate(file_content) if line.startswith("def")), None)]
-file_content = file_content[next((i for i, line in enumerate(file_content) if line == '}'), None) + 1:]
+    file_content = [string for string in file.read().split(
+        "\n") if not string.startswith("from")]
+file_content = file_content[:next(
+    (i for i, line in enumerate(file_content) if line.startswith("def")), None)]
+file_content = file_content[next(
+    (i for i, line in enumerate(file_content) if line == '}'), None) + 1:]
 
 pattern = re.compile(r'\b[A-Z_]+\b')
 
@@ -16,7 +19,8 @@
     substrings.extend(matches)
 substrings = list(set(substrings))
 
-res = ["@startuml", "'https://plantuml.com/sequence-diagram", "", "class AtomizedModel {", "    Enum", "}", ""]
+res = ["@startuml", "'https://plantuml.com/sequence-diagram",
+       "", "class AtomizedModel {", "    Enum", "}", ""]
 for ind in substrings:
     res += ["abstract " + ind]
 res += ["", ""]
@@ -31,13 +35,14 @@
         "NEURAL_MODEL -> AtomizedModel", ""]
 
 for ind in substrings:
-    start_index = next((i for i, line in enumerate(file_content) if line.endswith(ind + " = {")), None)
-    end_index = next((i for i, line in enumerate(file_content[start_index:]) if line.endswith("}")),
-                     None) + 1 + start_index
+    start_index = next((i for i, line in enumerate(
+        file_content) if line.endswith(ind + " = {")), None)
+    end_index = next((i for i, line in enumerate(
+        file_content[start_index:]) if line.endswith("}")), None) + 1 + start_index
     list_of_strings = file_content[start_index:end_index]
     pattern = re.compile(r"': ([^']*)")
-    formatted_strings = [pattern.search(string).group(1) if pattern.search(string) else ""
-                         for string in list_of_strings]
+    formatted_strings = [pattern.search(string).group(
+        1) if pattern.search(string) else "" for string in list_of_strings]
     res = res + ["abstract " + ind + " {"] + [string for string in formatted_strings if
                                               string.strip() and string.strip() != "{"] + ["}", ""]
 

diff --git a/tests/unit/core/models/test_ssa_forecaster.py b/tests/unit/core/models/test_ssa_forecaster.py
@@ -3,14 +3,19 @@
 from fedot.core.data.data import InputData
 from fedot_ind.core.models.ts_forecasting.ssa_forecaster import SSAForecasterImplementation
 
+
 class TestSSAForecasterImplementation(unittest.TestCase):
     def test_default_initialization(self):
         forecaster = SSAForecasterImplementation()
         self.assertIsInstance(forecaster, SSAForecasterImplementation)
         self.assertEqual(forecaster.window_size_method, None)
         self.assertEqual(forecaster.history_lookback, 100)
         self.assertEqual(forecaster.low_rank_approximation, False)
-        self.assertEqual(forecaster.tuning_params, {'tuning_iterations': 100, 'tuning_timeout': 20, 'tuning_early_stop': 20, 'tuner': 'SimultaneousTuner'})
+        self.assertEqual(forecaster.tuning_params,
+                         {'tuning_iterations': 100,
+                          'tuning_timeout': 20,
+                          'tuning_early_stop': 20,
+                          'tuner': 'SimultaneousTuner'})
         self.assertEqual(forecaster.component_model, 'topological')
         self.assertEqual(forecaster.mode, 'channel_independent')
 
@@ -19,16 +24,23 @@ def test_custom_initialization(self):
             'window_size_method': 'hac',
             'history_lookback': 50,
             'low_rank_approximation': True,
-            'tuning_params': {'tuning_iterations': 50, 'tuning_timeout': 10, 'tuning_early_stop': 10, 'tuner': 'OptunaTuner'},
+            'tuning_params': {
+                'tuning_iterations': 50,
+                'tuning_timeout': 10,
+                'tuning_early_stop': 10,
+                'tuner': 'OptunaTuner'},
             'component_model': 'ar',
-            'mode': 'one_dimensional'
-        }
+            'mode': 'one_dimensional'}
         forecaster = SSAForecasterImplementation(params)
         self.assertIsInstance(forecaster, SSAForecasterImplementation)
         self.assertEqual(forecaster.window_size_method, 'hac')
         self.assertEqual(forecaster.history_lookback, 50)
         self.assertEqual(forecaster.low_rank_approximation, True)
-        self.assertEqual(forecaster.tuning_params, {'tuning_iterations': 50, 'tuning_timeout': 10, 'tuning_early_stop': 10, 'tuner': 'OptunaTuner'})
+        self.assertEqual(forecaster.tuning_params,
+                         {'tuning_iterations': 50,
+                          'tuning_timeout': 10,
+                          'tuning_early_stop': 10,
+                          'tuner': 'OptunaTuner'})
         self.assertEqual(forecaster.component_model, 'ar')
         self.assertEqual(forecaster.mode, 'one_dimensional')
 
@@ -41,7 +53,8 @@ def test_predict_one_dimensional(self):
         self.assertEqual(forecast.predict.shape, (forecaster.horizon,))
 
     def test_predict_channel_independent(self):
-        forecaster = SSAForecasterImplementation({'mode': 'channel_independent'})
+        forecaster = SSAForecasterImplementation(
+            {'mode': 'channel_independent'})
         time_series = np.array([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]])
         input_data = InputData(features=time_series, target=time_series)
         forecast = forecaster.predict(input_data)
@@ -66,9 +79,11 @@ def test_predict_invalid_input(self):
         with self.assertRaises(ValueError):
             forecaster.predict(input_data)
 
-        input_data = InputData(features=np.array([1, 2, 3]), target=np.array([1, 2]))
+        input_data = InputData(features=np.array(
+            [1, 2, 3]), target=np.array([1, 2]))
         with self.assertRaises(ValueError):
             forecaster.predict(input_data)
 
+
 if __name__ == '__main__':
-    unittest.main()
+    unittest.main()