Fix

aimclub · Sep 5, 2023 · 5976907 · 5976907
1 parent e0dc147
commit 5976907
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diff --git a/fedot/api/api_utils/assumptions/task_assumptions.py b/fedot/api/api_utils/assumptions/task_assumptions.py
@@ -58,30 +58,17 @@ def builders(self):
                 PipelineBuilder()
                 .add_branch('polyfit', 'lagged')
                 .grow_branches(None, 'ridge')
-<<<<<<< Updated upstream
-<<<<<<< Updated upstream
                 .join_branches('ridge'),
             'smoothing_ar':
                 PipelineBuilder()
                 .add_sequence('smoothing', 'ar'),
             'gru':
                 PipelineBuilder()
                 .add_branch('gru', 'lagged')
-=======
-<<<<<<< Updated upstream
-=======
->>>>>>> Stashed changes
-                .join_branches('ridge')
-=======
                 .join_branches('ridge'),
             'gru':
                 PipelineBuilder()
                 .add_sequence('lagged', 'gru')
->>>>>>> Stashed changes
-<<<<<<< Updated upstream
->>>>>>> Stashed changes
-=======
->>>>>>> Stashed changes
         }
 
     def ensemble_operation(self) -> str:

diff --git a/fedot/core/composer/metrics.py b/fedot/core/composer/metrics.py
@@ -74,27 +74,6 @@ 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)):
@@ -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