diff --git a/dataset.py b/dataset.py
index 46c89f5..b1af606 100644
--- a/dataset.py
+++ b/dataset.py
@@ -96,7 +96,7 @@ def create_dataset_from_flows(user_flow_df, char_dict, max_len, window_size=10,
     domains = []
     features = []
     print("get chunks from user data frames")
-    for i, user_flow in tqdm(list(enumerate(get_flow_per_user(user_flow_df)))):
+    for i, user_flow in tqdm(list(enumerate(get_flow_per_user(user_flow_df)))[:50]):
         (domain_windows, feature_windows) = get_user_chunks(user_flow,
                                                             windowSize=window_size,
                                                             overlapping=False,
diff --git a/main.py b/main.py
index 6ee5a7c..d92eb06 100644
--- a/main.py
+++ b/main.py
@@ -2,7 +2,6 @@ import argparse
 import os
 
 from keras.models import load_model
-from keras.utils import np_utils
 
 import dataset
 import hyperband
@@ -94,19 +93,24 @@ def exists_or_make_path(p):
 
 def main_paul_best():
     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")
+        raise FileNotFoundError()
+    except FileNotFoundError:
+        print("h5 data not found - load csv file")
+        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)
 
     param = models.pauls_networks.best_config
     param["vocab_size"] = len(char_dict) + 1
-    print(param)
-
-    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)
-    client_tr = np_utils.to_categorical(client_tr, 2)
-    server_tr = np_utils.to_categorical(server_tr, 2)
 
     embedding, model = models.get_models_by_params(param)
 
@@ -163,12 +167,14 @@ def main_hyperband():
 
 
 def main_train():
-    exists_or_make_path(args.clf_model)
+    # exists_or_make_path(args.clf_model)
     char_dict = dataset.get_character_dict()
     print("check for h5data")
     try:
         open(args.h5data, "r")
+        raise FileNotFoundError()
     except FileNotFoundError:
+        print("h5 data not found - load csv file")
         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(
diff --git a/scripts/make_csv_dataset.py b/scripts/make_csv_dataset.py
index 479582b..5473880 100644
--- a/scripts/make_csv_dataset.py
+++ b/scripts/make_csv_dataset.py
@@ -10,9 +10,8 @@ df.reset_index(inplace=True)
 df.dropna(axis=0, how="any", inplace=True)
 df[["duration", "bytes_down", "bytes_up"]] = df[["duration", "bytes_down", "bytes_up"]].astype(np.int)
 df[["domain", "server_ip"]] = df[["domain", "server_ip"]].astype(str)
-df[["server_label"]] = df[["server_label"]].astype(np.bool)
 df.serverLabel = df.serverLabel.astype(np.bool)
-df.virusTotalHits = df.virusTotalHits.astype(np.int)
-df.trustedHits = df.trustedHits.astype(np.int)
+df.virusTotalHits = df.virusTotalHits.astype(np.int8)
+df.trustedHits = df.trustedHits.astype(np.int8)
 
 df.to_csv("/tmp/rk/full_future_dataset.csv.gz", compression="gzip")