extract weighting function

dataset.py

@@ -263,8 +263,11 @@ def load_or_generate_domains(train_data, domain_length):
     fn = f"{train_data}_domains.gz"
 
     try:
+        logger.info(f"Load file {fn}.")
         user_flow_df = pd.read_csv(fn)
+        logger.info(f"File successfully loaded.")
     except FileNotFoundError:
+        logger.info(f"File {fn} not found, recreate.")
         user_flow_df = get_user_flow_data(train_data)
         # user_flow_df.reset_index(inplace=True)
         user_flow_df = user_flow_df[["domain", "serverLabel", "trustedHits", "virusTotalHits"]].dropna(axis=0,
@@ -279,6 +282,7 @@ def load_or_generate_domains(train_data, domain_length):
 
         user_flow_df.to_csv(fn, compression="gzip")
 
+    logger.info(f"Extract features from domains")
     domain_encs = user_flow_df.domain.apply(lambda d: get_domain_features(d, domain_length))
     domain_encs = np.stack(domain_encs)
 

main.py

@@ -197,6 +197,26 @@ def load_data(data, domain_length, window_size, model_type):
     return domain_tr, flow_tr, client_tr, server_tr
 
 
+def get_weighting(class_weights, sample_weights, client, server):
+    if class_weights:
+        logger.info("class weights: compute custom weights")
+        custom_class_weights = get_custom_class_weights(client, server)
+        logger.info(custom_class_weights)
+    else:
+        logger.info("class weights: set default")
+        custom_class_weights = None
+    
+    if sample_weights:
+        logger.info("class weights: compute custom weights")
+        custom_sample_weights = get_custom_sample_weights(client, server)
+        logger.info(custom_sample_weights)
+    else:
+        logger.info("class weights: set default")
+        custom_sample_weights = None
+    
+    return custom_class_weights, custom_sample_weights
+
+
 def main_train(param=None):
     logger.info(f"Create model path {args.model_path}")
     exists_or_make_path(args.model_path)
@@ -220,6 +240,10 @@ def main_train(param=None):
     if not param:
         param = PARAMS
 
+    # custom class or sample weights
+    custom_class_weights, custom_sample_weights = get_weighting(args.class_weights, args.sample_weights,
+                                                                client_tr.value, server_tr)
+
     for i in range(args.runs):
         model_path = os.path.join(args.model_path, f"clf_{i}.h5")
         train_log_path = os.path.join(args.model_path, f"train_{i}.log.csv")
@@ -238,23 +262,6 @@ def main_train(param=None):
                                            verbose=False))
         custom_metrics = models.get_metric_functions()
         
-        # custom class or sample weights
-        if args.class_weights:
-            logger.info("class weights: compute custom weights")
-            custom_class_weights = get_custom_class_weights(client_tr.value, server_tr)
-            logger.info(custom_class_weights)
-        else:
-            logger.info("class weights: set default")
-            custom_class_weights = None
-        
-        if args.sample_weights:
-            logger.info("class weights: compute custom weights")
-            custom_sample_weights = get_custom_sample_weights(client_tr.value, server_tr)
-            logger.info(custom_class_weights)
-        else:
-            logger.info("class weights: set default")
-            custom_sample_weights = None
-        
         logger.info(f"Generator model with params: {param}")
         model = models.get_models_by_params(param)
         
@@ -372,9 +379,13 @@ def main_retrain():
 
 
 def main_test():
-    logger.info("start test: load data")
+    logger.info("load test data")
     domain_val, flow_val, _, _, _, _ = dataset.load_or_generate_raw_h5data(args.data, args.domain_length, args.window)
+    logger.info("load test domains")
     domain_encs, _, _ = dataset.load_or_generate_domains(args.data, args.domain_length)
+    
+    def get_dir(path):
+        return os.path.split(os.path.normpath(path))
 
     results = {}
     for model_path in args.model_paths:
@@ -398,8 +409,8 @@ def main_test():
 
         domain_embeddings = embd_model.predict(domain_encs, batch_size=args.batch_size, verbose=1)
         results["domain_embds"] = domain_embeddings
-
-    dataset.save_predictions(get_dir(model_path)[0], results)
+        # store results every round - safety first!
+        dataset.save_predictions(get_dir(model_path)[0], results)
 
 
 def main_visualization():

models/__init__.py

@@ -1,8 +1,17 @@
+from collections import namedtuple
+
 from keras.models import Model
 
 from . import networks
 from .metrics import *
 
+NetworkParameters = namedtuple("NetworkParameters", [
+    "type", "flow_features", "window_size", "domain_length", "output",
+    "embedding_size",
+    "domain_filter", "domain_kernel", "domain_dense", "domain_dropout",
+    "main_filter", "main_kernel", "main_dense", "main_dropout",
+])
+
 
 def create_model(model, output_type):
     if output_type == "both":
@@ -14,6 +23,7 @@ def create_model(model, output_type):
 
 
 def get_models_by_params(params: dict):
+    K.clear_session()
     # decomposing param section
     # mainly embedding model
     network_type = params.get("type")