add feature: generate and use h5 data

dataset.py

@@ -130,8 +130,8 @@ def create_dataset_from_flows(user_flow_df, char_dict, max_len, window_size=10,
     return domain_tr, flow_tr, client_tr, server_tr
 
 
-def store_h5dataset(domain_tr, flow_tr, client_tr, server_tr):
+def store_h5dataset(path, domain_tr, flow_tr, client_tr, server_tr):
-    f = h5py.File("data/full_dataset.h5", "w")
+    f = h5py.File(path, "w")
     domain_tr = domain_tr.astype(np.int8)
     f.create_dataset("domain", data=domain_tr)
     f.create_dataset("flow", data=flow_tr)
@@ -142,6 +142,11 @@ def store_h5dataset(domain_tr, flow_tr, client_tr, server_tr):
     f.close()
 
 
+def load_h5dataset(path):
+    data = h5py.File(path, "r")
+    return data["domain"], data["flow"], data["client"], data["server"]
+
+
 def create_dataset_from_lists(domains, flows, vocab, max_len, window_size=10):
     """
     combines domain and feature windows to sequential training data

main.py

@@ -1,6 +1,6 @@
 import argparse
+import os
 
-import h5py
 from keras.models import load_model
 from keras.utils import np_utils
 
@@ -78,6 +78,7 @@ args = parser.parse_args()
 
 args.embedding_model = args.models + "_embd.h5"
 args.clf_model = args.models + "_clf.h5"
+args.h5data = args.train_data + ".h5"
 
 
 # config = tf.ConfigProto(log_device_placement=True)
@@ -86,6 +87,11 @@ args.clf_model = args.models + "_clf.h5"
 # session = tf.Session(config=config)
 
 
+def exists_or_make_path(p):
+    if not os.path.exists(p):
+        os.makedirs(p)
+
+
 def main_paul_best():
     char_dict = dataset.get_character_dict()
     user_flow_df = dataset.get_user_flow_data(args.train_data)
@@ -157,28 +163,46 @@ def main_hyperband():
 
 
 def main_train():
-    # parameter
+    exists_or_make_path(args.clf_model)
-    dropout_main = 0.5
-    dense_main = 512
-    kernel_main = 3
-    filter_main = 128
-    network = models.pauls_networks if args.model_type == "paul" else models.renes_networks
-
     char_dict = dataset.get_character_dict()
-    user_flow_df = dataset.get_user_flow_data(args.train_data)
+    print("check for h5data")
+    try:
+        open(args.h5data, "r")
+    except FileNotFoundError:
+        user_flow_df = dataset.get_user_flow_data(args.train_data)
+        print("create training dataset")
+        domain_tr, flow_tr, client_tr, server_tr = dataset.create_dataset_from_flows(
+            user_flow_df, char_dict,
+            max_len=args.domain_length, window_size=args.window)
+        print("store training dataset as h5 file")
+        dataset.store_h5dataset(args.h5data, domain_tr, flow_tr, client_tr, server_tr)
+    print("load h5 dataset")
+    domain_tr, flow_tr, client_tr, server_tr = dataset.load_h5dataset(args.h5data)
 
-    print("create training dataset")
+    # parameter
-    domain_tr, flow_tr, client_tr, server_tr = dataset.create_dataset_from_flows(
+    param = {
-        user_flow_df, char_dict,
+        "type": "paul",
-        max_len=args.domain_length, window_size=args.window)
+        "batch_size": 64,
+        "window_size": args.window,
+        "domain_length": args.domain_length,
+        "flow_features": 3,
+        "vocab_size": len(char_dict) + 1,
+        #
+        'dropout': 0.5,
+        'domain_features': args.domain_embedding,
+        'embedding_size': args.embedding,
+        'filter_main': 128,
+        'flow_features': 3,
+        'dense_main': 512,
+        'filter_embedding': args.hidden_char_dims,
+        'hidden_embedding': args.domain_embedding,
+        'kernel_embedding': 3,
+        'kernels_main': 3,
+        'input_length': 40
+    }
 
-    embedding = network.get_embedding(len(char_dict) + 1, args.embedding, args.domain_length,
+    embedding, model = models.get_models_by_params(param)
-                                      args.hidden_char_dims, kernel_main, args.domain_embedding, 0.5)
     embedding.summary()
-
-    model = network.get_model(dropout_main, flow_tr.shape[-1], args.domain_embedding,
-                              args.window, args.domain_length, filter_main, kernel_main,
-                              dense_main, embedding)
     model.summary()
 
     model.compile(optimizer='adam',
@@ -196,41 +220,6 @@ def main_train():
     model.save(args.clf_model)
 
 
-def main_train_h5():
-    # parameter
-    dropout_main = 0.5
-    dense_main = 512
-    kernel_main = 3
-    filter_main = 128
-    network = models.pauls_networks if args.model_type == "paul" else models.renes_networks
-
-    char_dict = dataset.get_character_dict()
-    data = h5py.File("data/full_dataset.h5", "r")
-
-    embedding = network.get_embedding(len(char_dict) + 1, args.embedding, args.domain_length,
-                                      args.hidden_char_dims, kernel_main, args.domain_embedding, 0.5)
-    embedding.summary()
-
-    model = network.get_model(dropout_main, data["flow"].shape[-1], args.domain_embedding,
-                              args.window, args.domain_length, filter_main, kernel_main,
-                              dense_main, embedding)
-    model.summary()
-
-    model.compile(optimizer='adam',
-                  loss='categorical_crossentropy',
-                  metrics=['accuracy'])
-
-    model.fit([data["domain"], data["flow"]],
-              [data["client"], data["server"]],
-              batch_size=args.batch_size,
-              epochs=args.epochs,
-              shuffle=True,
-              validation_split=0.2)
-
-    embedding.save(args.embedding_model)
-    model.save(args.clf_model)
-
-
 def main_test():
     char_dict = dataset.get_character_dict()
     user_flow_df = dataset.get_user_flow_data(args.test_data)