Fix

aimclub · Sep 5, 2023 · c70b39f · c70b39f
1 parent f206221
commit c70b39f
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Showing 3 changed files with 26 additions and 3 deletions.
diff --git a/cases/metocean_forecasting_problem.py b/cases/metocean_forecasting_problem.py
@@ -9,7 +9,7 @@
 from fedot.core.utils import fedot_project_root
 
 
-def prepare_input_data(train_file_path, test_file_path, history_size: int = 1000):
+def prepare_input_data(train_file_path, test_file_path, history_size: int = 10000):
     """ Function for preparing InputData for train and test algorithm
 
     :param train_file_path: path to the csv file for training
@@ -69,5 +69,5 @@ def run_metocean_forecasting_problem(train_file_path, test_file_path,
     file_path_test = 'cases/data/metocean/metocean_data_test.csv'
 
     run_metocean_forecasting_problem(file_path_train, file_path_test,
-                                     forecast_length=6, timeout=5,
+                                     forecast_length=20, timeout=5,
                                      visualization=True)
diff --git a/cases/multi_ts_level_forecasting.py b/cases/multi_ts_level_forecasting.py
@@ -59,7 +59,7 @@ def run_multi_ts_forecast(forecast_length, is_multi_ts):
 
     # use model to obtain forecast
     forecast = model.predict(test_data)
-    target = np.ravel(test_data.target)
+    target = np.ravel(test_data.target[:, 0])
 
     # visualize results
     model.plot_prediction()

diff --git a/fedot/core/composer/metrics.py b/fedot/core/composer/metrics.py
@@ -74,6 +74,7 @@ def get_value(cls, pipeline: 'Pipeline', reference_data: InputData,
                 metric = cls.metric(reference_data, results)
             else:
                 # Perform time series in-sample validation
+<<<<<<< Updated upstream
                 if isinstance(reference_data, MultiModalData):
                     is_multi_ts = reference_data[list(reference_data.keys())[0]].is_multi_ts
                 else:
@@ -89,6 +90,28 @@ def get_value(cls, pipeline: 'Pipeline', reference_data: InputData,
                 else:
                     reference_data_out, results = cls._in_sample_prediction(pipeline, reference_data, validation_blocks)
                     metric = cls.metric(reference_data, results)
+=======
+<<<<<<< Updated upstream
+                reference_data, results = cls._in_sample_prediction(pipeline, reference_data, validation_blocks)
+            metric = cls.metric(reference_data, results)
+=======
+                is_multimodal = isinstance(reference_data, MultiModalData)
+                if ((is_multimodal and reference_data[list(reference_data)[0]].is_multi_ts) or
+                    ((not is_multimodal) and reference_data.is_multi_ts)):
+                    # multi_ts
+                    metrics = []
+                    for i in range(reference_data.features.shape[1]):
+                        data = reference_data.subset_features([i], True, True)
+                        data.data_type = DataTypesEnum.ts
+                        reference_data_out, results = cls._in_sample_prediction(pipeline, data, validation_blocks)
+                        metrics.append(cls.metric(reference_data_out, results))
+                    metric = np.mean(metrics)
+                else:
+                    # ts
+                    reference_data_out, results = cls._in_sample_prediction(pipeline, reference_data, validation_blocks)
+                    metric = cls.metric(reference_data, results)
+>>>>>>> Stashed changes
+>>>>>>> Stashed changes
 
             if is_analytic_mode():
                 from fedot.core.data.visualisation import plot_forecast