fix after rebase

experiments/fedot_warm_start/run.py

@@ -33,10 +33,19 @@
 from meta_automl.data_preparation.datasets_train_test_split import openml_datasets_train_test_split
 from meta_automl.data_preparation.file_system import get_cache_dir
 
-CONFIG_PATH = Path(__file__).parent.joinpath('config_light.yaml')
+CONFIGS_DIR = Path(__file__).parent
 
-with open(CONFIG_PATH, 'r') as config_file:
-    config = yaml.load(config_file, yaml.Loader)
+with open(CONFIGS_DIR / 'configs_list.yaml', 'r') as config_file:
+    configs_list = yaml.load(config_file, yaml.Loader)
+
+config = {}
+for conf_name in configs_list:
+    with open(CONFIGS_DIR / conf_name, 'r') as config_file:
+        conf = yaml.load(config_file, yaml.Loader)
+    intersection = set(config).intersection(set(conf))
+    if intersection:
+        raise ValueError(f'Parameter values given twice: {conf_name}, {intersection}.')
+    config.update(conf)
 
 # Load constants
 SEED = config['seed']
@@ -94,8 +103,8 @@ def get_current_formatted_date() -> Tuple[datetime, str, str]:
 def get_save_dir(time_now_for_path) -> Path:
     save_dir = get_cache_dir(). \
         joinpath('experiments').joinpath('fedot_warm_start').joinpath(f'run_{time_now_for_path}')
-    if 'debug' in CONFIG_PATH.name:
-        save_dir = save_dir.with_name('debug_' + save_dir.name)
+    if SAVE_DIR_PREFIX:
+        save_dir = save_dir.with_name(SAVE_DIR_PREFIX + save_dir.name)
     if save_dir.exists():
         shutil.rmtree(save_dir)
     save_dir.mkdir(parents=True)
@@ -320,6 +329,7 @@ def main():
 
     dataset_ids = get_dataset_ids()
     dataset_ids_train, dataset_ids_test = split_datasets(dataset_ids, N_DATASETS, UPDATE_TRAIN_TEST_DATASETS_SPLIT)
+    dataset_ids = dataset_ids_train + dataset_ids_test
 
     algorithm = KNNSimilarityModelAdvice(
         N_BEST_DATASET_MODELS_TO_MEMORIZE,
@@ -350,6 +360,7 @@ def main():
                                                random_state=DATA_SPLIT_SEED)
         train_data, test_data = dataset_data[idx_train], dataset_data[idx_test]
         dataset_splits[dataset_id] = dict(train=train_data, test=test_data)
+
     knowledge_base = {dataset_id: [] for dataset_id in dataset_ids_train}
     fedot_evaluations_cache = CacheDict(get_cache_dir() / 'fedot_runs.pkl')
     description = 'FEDOT, all datasets'