Merge branch 'lukas_issue406_refac_improve-snapshot' into 'develop'

Resolve "REFAC: improve snapshot"

See merge request !454

mlair/helpers/testing.py

@@ -170,8 +170,6 @@ def check_nested_equality(obj1, obj2, precision=None, skip_args=None):
                 message = f"{obj1}!={obj2}\n{obj1} and {obj2} do not match"
                 assert obj1 == obj2
     except AssertionError:
-        message = message.split("\n")
-        logging.info(message[0])
-        logging.debug(message[1])
+        logging.info(message)
         return False
     return True

mlair/run_modules/pre_processing.py

@@ -412,19 +412,23 @@ class PreProcessing(RunEnvironment):
         logging.info(f"load snapshot for preprocessing from {file}")
         with open(file, "rb") as f:
             snapshot = dill.load(f)
+        excluded_params = ["activation", "activation_output", "add_dense_layer", "batch_normalization", "batch_path",
+                           "batch_size", "block_length", "bootstrap_method", "bootstrap_path", "bootstrap_type",
+                           "competitor_path", "competitors", "create_new_bootstraps", "create_new_model",
+                           "create_snapshot", "data_collection", "debug_mode", "dense_layer_configuration",
+                           "do_uncertainty_estimate", "dropout", "dropout_rnn", "early_stopping_epochs", "epochs",
+                           "evaluate_competitors", "evaluate_feature_importance", "experiment_name", "experiment_path",
+                           "exponent_last_layer", "forecast_path", "fraction_of_training", "hostname", "hpc_hosts",
+                           "kernel_regularizer", "kernel_size", "layer_configuration", "log_level_stream",
+                           "logging_path", "login_nodes", "loss_type", "loss_weights", "max_number_multiprocessing",
+                           "model_class", "model_display_name", "model_path", "n_boots", "n_hidden", "n_layer",
+                           "neighbors", "plot_list", "plot_path", "regularizer", "restore_best_model_weights",
+                           "snapshot_load_path", "snapshot_path", "stations", "tmp_path", "train_model",
+                           "transformation", "use_multiprocessing", ]
 
-        excluded_params = ["batch_path", "batch_size", "block_length", "bootstrap_method", "bootstrap_path",
-                           "bootstrap_type", "competitor_path", "competitors", "create_new_bootstraps",
-                           "create_new_model", "create_snapshot", "data_collection", "debug_mode",
-                           "do_uncertainty_estimate", "early_stopping_epochs", "epochs", "evaluate_competitors",
-                           "evaluate_feature_importance", "experiment_name", "experiment_path", "forecast_path",
-                           "fraction_of_training", "hostname", "hpc_hosts", "log_level_stream", "logging_path",
-                           "login_nodes", "max_number_multiprocessing", "model_class", "model_path", "n_boots",
-                           "neighbors", "plot_list", "plot_path", "restore_best_model_weights", "snapshot_load_path",
-                           "snapshot_path", "stations", "tmp_path", "train_model", "transformation",
-                           "use_multiprocessing", ]
         data_handler = self.data_store.get("data_handler")
-        excluded_params = list(set(excluded_params + data_handler.store_attributes()))
+        model_class = self.data_store.get("model_class")
+        excluded_params = list(set(excluded_params + data_handler.store_attributes() + model_class.requirements()))
 
         if check_nested_equality(self.data_store._store, snapshot._store, skip_args=excluded_params) is True:
             self.update_datastore(snapshot, excluded_params=remove_items(excluded_params, ["transformation",