examples refactoring

aimclub · Apr 12, 2024 · f962d6f · f962d6f
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Showing 19 changed files with 8 additions and 145 deletions.
diff --git a/examples/automl_example/api_example/advanced_example/__init__.py b/examples/automl_example/api_example/advanced_example/__init__.py
diff --git a/examples/automl_example/api_example/advanced_example/explainability/__init__.py b/examples/automl_example/api_example/advanced_example/explainability/__init__.py
diff --git a/...eries/neural_networks/advanced_example.py → ...xample/explainability/advanced_example.py b/...eries/neural_networks/advanced_example.py → ...xample/explainability/advanced_example.py
diff --git a/examples/automl_example/explainability.ipynb → ...ample/explainability/explainability.ipynb b/examples/automl_example/explainability.ipynb → ...ample/explainability/explainability.ipynb
diff --git a/examples/automl_example/api_example/advanced_example/multimodal/__init__.py b/examples/automl_example/api_example/advanced_example/multimodal/__init__.py
diff --git a/...time_series/neural_networks/multimodal.py → ...advanced_example/multimodal/multimodal.py b/...time_series/neural_networks/multimodal.py → ...advanced_example/multimodal/multimodal.py
diff --git a/...eries/neural_networks/recurrent_resnet.py → ...ed_example/multimodal/recurrent_resnet.py b/...eries/neural_networks/recurrent_resnet.py → ...ed_example/multimodal/recurrent_resnet.py
diff --git a/examples/automl_example/api_example/advanced_example/specific_strategy/__init__.py b/examples/automl_example/api_example/advanced_example/specific_strategy/__init__.py
diff --git a/examples/automl_example/callibration.py → ...example/specific_strategy/callibration.py b/examples/automl_example/callibration.py → ...example/specific_strategy/callibration.py
diff --git a/...semble_example/kernel_ensemble_example.py → ...cific_strategy/kernel_ensemble_example.py b/...semble_example/kernel_ensemble_example.py → ...cific_strategy/kernel_ensemble_example.py
diff --git a/examples/pipeline_example/time_series/neural_networks/basic_example.py b/examples/pipeline_example/time_series/neural_networks/basic_example.py
diff --git a/examples/pipeline_example/time_series/ts_forecasting/ssa_forecasting.py b/examples/pipeline_example/time_series/ts_forecasting/ssa_forecasting.py
diff --git a/.../real_world_examples/industrial_examples/enviroment_monitoring/ts_forecasting/__init__.py b/.../real_world_examples/industrial_examples/enviroment_monitoring/ts_forecasting/__init__.py
diff --git a/.../time_series/ts_forecasting/arctic_sea.py → ...t_monitoring/ts_forecasting/arctic_sea.py b/.../time_series/ts_forecasting/arctic_sea.py → ...t_monitoring/ts_forecasting/arctic_sea.py
diff --git a/...s/real_world_examples/industrial_examples/enviroment_monitoring/ts_regression/__init__.py b/...s/real_world_examples/industrial_examples/enviroment_monitoring/ts_regression/__init__.py
diff --git a/...oment_monitoring/air_quality_madrid.ipynb → ...ng/ts_regression/air_quality_madrid.ipynb b/...oment_monitoring/air_quality_madrid.ipynb → ...ng/ts_regression/air_quality_madrid.ipynb
diff --git a/fedot_ind/api/main.py b/fedot_ind/api/main.py
@@ -169,7 +169,8 @@ def fit(self,
         self.__init_solver()
         if self.industrial_strategy is not None:
             self.solver = self.industrial_strategy_class.fit(self.train_data)
-        self.solver.fit(self.train_data)
+        else:
+            self.solver.fit(self.train_data)
 
     def predict(self,
                 predict_data: tuple,

diff --git a/fedot_ind/api/utils/checkers_collections.py b/fedot_ind/api/utils/checkers_collections.py
@@ -92,8 +92,11 @@ def _init_input_data(self) -> None:
                 features_array = np.array(self.input_data.values)
             task = Task(TaskTypesEnum.ts_forecasting,
                         TsForecastingParams(forecast_length=self.task_params['forecast_length']))
+            features_array = features_array[:-self.task_params['forecast_length']]
+            target = features_array[-self.task_params['forecast_length']:]
             self.input_data = InputData.from_numpy_time_series(
                 features_array=features_array,
+                target_array=target,
                 task=task)
         else:
             self.input_data = InputData(idx=np.arange(len(X)),

diff --git a/fedot_ind/api/utils/industrial_strategy.py b/fedot_ind/api/utils/industrial_strategy.py
@@ -33,8 +33,8 @@ def fit(self, input_data):
         self.industrial_strategy_fit[self.industrial_strategy](input_data)
         return self.solver
 
-    def predict(self, input_data):
-        return self.industrial_strategy_predict[self.industrial_strategy](input_data)
+    def predict(self, input_data, predict_mode):
+        return self.industrial_strategy_predict[self.industrial_strategy](input_data, predict_mode)
 
     def _federated_strategy(self, input_data):
         if input_data.features.shape[0] > BATCH_SIZE_FOR_FEDOT_WORKER:
@@ -53,10 +53,9 @@ def _finetune_loop(self,
                        kernel_data: dict,
                        tuning_params: dict = {}):
         tuned_kernels = {}
-        tuned_metric = 0
         tuning_params['metric'] = FEDOT_TUNING_METRICS[self.config_dict['problem']]
-
         for generator, kernel_model in kernel_ensemble.items():
+            tuned_metric = 0
             for tuner_name, tuner_type in FEDOT_TUNER_STRATEGY.items():
                 tuning_params['tuner'] = tuner_type
                 model_to_tune = deepcopy(kernel_model)