Fix

aimclub · Sep 5, 2023 · 457fbfc · 457fbfc
1 parent c70b39f
commit 457fbfc
Showing 1 changed file with 1 addition and 24 deletions.
diff --git a/fedot/core/composer/metrics.py b/fedot/core/composer/metrics.py
@@ -74,30 +74,9 @@ 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:
-                    is_multi_ts = reference_data.is_multi_ts
-                if is_multi_ts:
-                    metrics = []
-                    for i in range(reference_data.features.shape[1]):
-                        reference_data_out, results = cls._in_sample_prediction(pipeline,
-                                                                                reference_data.subset_features([i], True),
-                                                                                validation_blocks)
-                        metrics.append(cls.metric(reference_data_out, results))
-                    metric = np.mean(metrics)
-                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)):
+                        ((not is_multimodal) and reference_data.is_multi_ts)):
                     # multi_ts
                     metrics = []
                     for i in range(reference_data.features.shape[1]):
@@ -110,8 +89,6 @@ def get_value(cls, pipeline: 'Pipeline', reference_data: InputData,
                     # 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