refactor server training into separate file; add additional info to hyperband log

hyperband.py

@@ -14,7 +14,7 @@ from keras.callbacks import EarlyStopping
 import models
 from main import create_model
 
-logger = logging.getLogger('logger')
+logger = logging.getLogger('cisco_logger')
 
 
 def sample_params(param_distribution: dict):
@@ -72,7 +72,8 @@ class Hyperband:
                             validation_split=0.4)
 
         return {"loss": np.min(history.history['val_loss']),
-                "early_stop": len(history.history["loss"]) < n_iterations}
+                "early_stop": len(history.history["loss"]) < n_iterations,
+                "stop_after": len(history.history["val_loss"])}
     
     # can be called multiple times
     def run(self, skip_last=0, dry_run=False):
@@ -95,8 +96,8 @@ class Hyperband:
     
                 n_configs = n * self.eta ** (-i)
                 n_iterations = r * self.eta ** (i)
-    
+
-                logger.info("\n*** {} configurations x {:.1f} iterations each".format(
+                logger.info("*** {} configurations x {:.1f} iterations each".format(
                         n_configs, n_iterations))
                 
                 val_losses = []
@@ -105,7 +106,7 @@ class Hyperband:
                 for t in random_configs:
                     
                     self.counter += 1
-                    logger.info("\n{} | {} | lowest loss so far: {:.4f} (run {})\n".format(
+                    logger.info("Config {} | {} | lowest loss so far: {:.4f} (run {})".format(
                             self.counter, ctime(), self.best_loss, self.best_counter))
                     
                     start_time = time()
@@ -119,7 +120,7 @@ class Hyperband:
                     assert ('loss' in result)
 
                     seconds = int(round(time() - start_time))
-                    logger.info("\n{} seconds.".format(seconds))
+                    logger.info("{} seconds.".format(seconds))
 
                     loss = result['loss']
                     val_losses.append(loss)

models/deep1.py

@@ -10,11 +10,11 @@ Model = namedtuple("Model", ["in_domains", "in_flows", "out_client", "out_server
 
 
 def get_embedding(embedding_size, input_length, filter_size, kernel_size, hidden_dims, drop_out=0.5):
-    x = y = Input(shape=(input_length,))
+    x = Input(shape=(input_length,))
-    y = Embedding(input_dim=dataset.get_vocab_size(), output_dim=embedding_size)(y)
+    y = Embedding(input_dim=dataset.get_vocab_size(), output_dim=embedding_size)(x)
-    y = Conv1D(filter_size, kernel_size=kernel_size, activation="relu")(y)
+    y = Conv1D(filter_size, kernel_size=kernel_size, activation="relu", padding="same")(y)
-    y = Conv1D(filter_size, kernel_size=3, activation="relu")(y)
+    y = Conv1D(filter_size, kernel_size=3, activation="relu", padding="same")(y)
-    y = Conv1D(filter_size, kernel_size=3, activation="relu")(y)
+    y = Conv1D(filter_size, kernel_size=3, activation="relu", padding="same")(y)
     y = GlobalAveragePooling1D()(y)
     y = Dense(hidden_dims, activation="relu")(y)
     return KerasModel(x, y)

models/pauls_networks.py

@@ -2,7 +2,7 @@ from collections import namedtuple
 
 import keras
 from keras.engine import Input, Model as KerasModel
-from keras.layers import Activation, Conv1D, Dense, Dropout, Embedding, GlobalMaxPooling1D, TimeDistributed
+from keras.layers import Conv1D, Dense, Dropout, Embedding, GlobalMaxPooling1D, TimeDistributed
 
 import dataset
 
@@ -38,8 +38,7 @@ def get_embedding(embedding_size, input_length, filter_size, kernel_size, hidden
                activation='relu')(y)
     y = GlobalMaxPooling1D()(y)
     y = Dropout(drop_out)(y)
-    y = Dense(hidden_dims)(y)
+    y = Dense(hidden_dims, activation="relu")(y)
-    y = Activation('relu')(y)
     return KerasModel(x, y)
 
 

server.py

@@ -0,0 +1,123 @@
+import logging
+
+from keras.callbacks import CSVLogger, EarlyStopping, ModelCheckpoint
+
+import arguments
+import dataset
+import models
+# create logger
+import visualize
+from arguments import get_model_args
+from utils import exists_or_make_path, load_model
+
+logger = logging.getLogger('cisco_logger')
+
+args = arguments.parse()
+
+
+def train_server_only(params):
+    logger.info(f"Create model path {args.model_path}")
+    exists_or_make_path(args.model_path)
+    logger.info(f"Use command line arguments: {args}")
+    
+    domain_tr, flow_tr, name_tr, client_tr, server_windows_tr = dataset.load_or_generate_h5data(args.data,
+                                                                                                args.data,
+                                                                                                args.domain_length,
+                                                                                                args.window)
+    domain_tr = domain_tr.value.reshape(-1, 40)
+    flow_tr = flow_tr.value.reshape(-1, 3)
+    server_tr = server_windows_tr.value.reshape(-1)
+    
+    logger.info("define callbacks")
+    callbacks = []
+    callbacks.append(ModelCheckpoint(filepath=args.clf_model,
+                                     monitor='loss',
+                                     verbose=False,
+                                     save_best_only=True))
+    callbacks.append(CSVLogger(args.train_log))
+    logger.info(f"Use early stopping: {args.stop_early}")
+    if args.stop_early:
+        callbacks.append(EarlyStopping(monitor='val_loss',
+                                       patience=5,
+                                       verbose=False))
+    custom_metrics = models.get_metric_functions()
+    
+    model = models.get_server_model_by_params(params=params)
+    
+    features = {"ipt_domains": domain_tr, "ipt_flows": flow_tr}
+    if args.model_output == "both":
+        labels = {"client": client_tr, "server": server_tr}
+    elif args.model_output == "client":
+        labels = {"client": client_tr}
+    elif args.model_output == "server":
+        labels = {"server": server_tr}
+    else:
+        raise ValueError("unknown model output")
+    
+    logger.info("compile and train model")
+    logger.info(model.get_config())
+    model.compile(optimizer='adam',
+                  loss='binary_crossentropy',
+                  metrics=['accuracy'] + custom_metrics)
+    
+    model.summary()
+    model.fit(features, labels,
+              batch_size=args.batch_size,
+              epochs=args.epochs,
+              callbacks=callbacks)
+
+
+def test_server_only():
+    logger.info("start test: load data")
+    domain_val, flow_val, _, _, _, _ = dataset.load_or_generate_raw_h5data(args.data,
+                                                                           args.data,
+                                                                           args.domain_length,
+                                                                           args.window)
+    domain_val = domain_val.value.reshape(-1, 40)
+    flow_val = flow_val.value.reshape(-1, 3)
+    domain_encs, _ = dataset.load_or_generate_domains(args.data, args.domain_length)
+    
+    for model_args in get_model_args(args):
+        results = {}
+        logger.info(f"process model {model_args['model_path']}")
+        embd_model, clf_model = load_model(model_args["clf_model"], custom_objects=models.get_custom_objects())
+        
+        pred = clf_model.predict([domain_val, flow_val],
+                                 batch_size=args.batch_size,
+                                 verbose=1)
+        
+        results["server_pred"] = pred
+        
+        domain_embeddings = embd_model.predict(domain_encs, batch_size=args.batch_size, verbose=1)
+        results["domain_embds"] = domain_embeddings
+        
+        dataset.save_predictions(model_args["model_path"], results)
+
+
+def vis_server():
+    def load_model(m, c):
+        from keras.models import load_model
+        clf = load_model(m, custom_objects=c)
+        emdb = clf.layers[1]
+        return emdb, clf
+    
+    domain_raw, flow_raw, name_raw, hits_vt_raw, hits_trusted_raw, server_raw = dataset.load_or_generate_raw_h5data(
+            args.data,
+            args.data,
+            args.domain_length,
+            args.window)
+    
+    results = dataset.load_predictions(args.clf_model)
+    
+    visualize.plot_clf()
+    visualize.plot_precision_recall(server_raw.flatten(), results["server_pred"].flatten(), "server")
+    visualize.plot_legend()
+    visualize.plot_save("results/server_model/windows_prc.pdf")
+    visualize.plot_clf()
+    visualize.plot_precision_recall(server_raw.flatten(), results["server_pred"].flatten(), "server")
+    visualize.plot_legend()
+    visualize.plot_save("results/server_model/windows_prc.pdf")
+    visualize.plot_clf()
+    visualize.plot_roc_curve(server_raw.flatten(), results["server_pred"].flatten(), "server")
+    visualize.plot_legend()
+    visualize.plot_save("results/server_model/windows_roc.pdf")