refactor server training into separate file; add additional info to hyperband log
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d1da3d6ca3
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13
hyperband.py
13
hyperband.py
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@ -14,7 +14,7 @@ from keras.callbacks import EarlyStopping
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import models
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from main import create_model
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logger = logging.getLogger('logger')
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logger = logging.getLogger('cisco_logger')
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def sample_params(param_distribution: dict):
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@ -72,7 +72,8 @@ class Hyperband:
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validation_split=0.4)
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return {"loss": np.min(history.history['val_loss']),
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"early_stop": len(history.history["loss"]) < n_iterations}
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"early_stop": len(history.history["loss"]) < n_iterations,
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"stop_after": len(history.history["val_loss"])}
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# can be called multiple times
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def run(self, skip_last=0, dry_run=False):
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@ -95,8 +96,8 @@ class Hyperband:
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n_configs = n * self.eta ** (-i)
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n_iterations = r * self.eta ** (i)
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logger.info("\n*** {} configurations x {:.1f} iterations each".format(
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logger.info("*** {} configurations x {:.1f} iterations each".format(
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n_configs, n_iterations))
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val_losses = []
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@ -105,7 +106,7 @@ class Hyperband:
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for t in random_configs:
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self.counter += 1
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logger.info("\n{} | {} | lowest loss so far: {:.4f} (run {})\n".format(
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logger.info("Config {} | {} | lowest loss so far: {:.4f} (run {})".format(
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self.counter, ctime(), self.best_loss, self.best_counter))
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start_time = time()
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@ -119,7 +120,7 @@ class Hyperband:
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assert ('loss' in result)
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seconds = int(round(time() - start_time))
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logger.info("\n{} seconds.".format(seconds))
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logger.info("{} seconds.".format(seconds))
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loss = result['loss']
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val_losses.append(loss)
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@ -10,11 +10,11 @@ Model = namedtuple("Model", ["in_domains", "in_flows", "out_client", "out_server
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def get_embedding(embedding_size, input_length, filter_size, kernel_size, hidden_dims, drop_out=0.5):
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x = y = Input(shape=(input_length,))
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y = Embedding(input_dim=dataset.get_vocab_size(), output_dim=embedding_size)(y)
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y = Conv1D(filter_size, kernel_size=kernel_size, activation="relu")(y)
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y = Conv1D(filter_size, kernel_size=3, activation="relu")(y)
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y = Conv1D(filter_size, kernel_size=3, activation="relu")(y)
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x = Input(shape=(input_length,))
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y = Embedding(input_dim=dataset.get_vocab_size(), output_dim=embedding_size)(x)
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y = Conv1D(filter_size, kernel_size=kernel_size, activation="relu", padding="same")(y)
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y = Conv1D(filter_size, kernel_size=3, activation="relu", padding="same")(y)
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y = Conv1D(filter_size, kernel_size=3, activation="relu", padding="same")(y)
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y = GlobalAveragePooling1D()(y)
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y = Dense(hidden_dims, activation="relu")(y)
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return KerasModel(x, y)
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@ -2,7 +2,7 @@ from collections import namedtuple
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import keras
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from keras.engine import Input, Model as KerasModel
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from keras.layers import Activation, Conv1D, Dense, Dropout, Embedding, GlobalMaxPooling1D, TimeDistributed
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from keras.layers import Conv1D, Dense, Dropout, Embedding, GlobalMaxPooling1D, TimeDistributed
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import dataset
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@ -38,8 +38,7 @@ def get_embedding(embedding_size, input_length, filter_size, kernel_size, hidden
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activation='relu')(y)
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y = GlobalMaxPooling1D()(y)
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y = Dropout(drop_out)(y)
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y = Dense(hidden_dims)(y)
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y = Activation('relu')(y)
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y = Dense(hidden_dims, activation="relu")(y)
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return KerasModel(x, y)
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@ -0,0 +1,123 @@
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import logging
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from keras.callbacks import CSVLogger, EarlyStopping, ModelCheckpoint
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import arguments
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import dataset
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import models
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# create logger
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import visualize
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from arguments import get_model_args
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from utils import exists_or_make_path, load_model
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logger = logging.getLogger('cisco_logger')
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args = arguments.parse()
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def train_server_only(params):
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logger.info(f"Create model path {args.model_path}")
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exists_or_make_path(args.model_path)
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logger.info(f"Use command line arguments: {args}")
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domain_tr, flow_tr, name_tr, client_tr, server_windows_tr = dataset.load_or_generate_h5data(args.data,
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args.data,
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args.domain_length,
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args.window)
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domain_tr = domain_tr.value.reshape(-1, 40)
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flow_tr = flow_tr.value.reshape(-1, 3)
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server_tr = server_windows_tr.value.reshape(-1)
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logger.info("define callbacks")
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callbacks = []
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callbacks.append(ModelCheckpoint(filepath=args.clf_model,
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monitor='loss',
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verbose=False,
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save_best_only=True))
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callbacks.append(CSVLogger(args.train_log))
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logger.info(f"Use early stopping: {args.stop_early}")
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if args.stop_early:
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callbacks.append(EarlyStopping(monitor='val_loss',
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patience=5,
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verbose=False))
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custom_metrics = models.get_metric_functions()
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model = models.get_server_model_by_params(params=params)
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features = {"ipt_domains": domain_tr, "ipt_flows": flow_tr}
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if args.model_output == "both":
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labels = {"client": client_tr, "server": server_tr}
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elif args.model_output == "client":
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labels = {"client": client_tr}
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elif args.model_output == "server":
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labels = {"server": server_tr}
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else:
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raise ValueError("unknown model output")
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logger.info("compile and train model")
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logger.info(model.get_config())
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model.compile(optimizer='adam',
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loss='binary_crossentropy',
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metrics=['accuracy'] + custom_metrics)
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model.summary()
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model.fit(features, labels,
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batch_size=args.batch_size,
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epochs=args.epochs,
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callbacks=callbacks)
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def test_server_only():
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logger.info("start test: load data")
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domain_val, flow_val, _, _, _, _ = dataset.load_or_generate_raw_h5data(args.data,
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args.data,
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args.domain_length,
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args.window)
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domain_val = domain_val.value.reshape(-1, 40)
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flow_val = flow_val.value.reshape(-1, 3)
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domain_encs, _ = dataset.load_or_generate_domains(args.data, args.domain_length)
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for model_args in get_model_args(args):
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results = {}
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logger.info(f"process model {model_args['model_path']}")
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embd_model, clf_model = load_model(model_args["clf_model"], custom_objects=models.get_custom_objects())
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pred = clf_model.predict([domain_val, flow_val],
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batch_size=args.batch_size,
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verbose=1)
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results["server_pred"] = pred
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domain_embeddings = embd_model.predict(domain_encs, batch_size=args.batch_size, verbose=1)
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results["domain_embds"] = domain_embeddings
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dataset.save_predictions(model_args["model_path"], results)
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def vis_server():
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def load_model(m, c):
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from keras.models import load_model
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clf = load_model(m, custom_objects=c)
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emdb = clf.layers[1]
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return emdb, clf
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domain_raw, flow_raw, name_raw, hits_vt_raw, hits_trusted_raw, server_raw = dataset.load_or_generate_raw_h5data(
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args.data,
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args.data,
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args.domain_length,
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args.window)
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results = dataset.load_predictions(args.clf_model)
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visualize.plot_clf()
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visualize.plot_precision_recall(server_raw.flatten(), results["server_pred"].flatten(), "server")
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visualize.plot_legend()
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visualize.plot_save("results/server_model/windows_prc.pdf")
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visualize.plot_clf()
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visualize.plot_precision_recall(server_raw.flatten(), results["server_pred"].flatten(), "server")
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visualize.plot_legend()
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visualize.plot_save("results/server_model/windows_prc.pdf")
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visualize.plot_clf()
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visualize.plot_roc_curve(server_raw.flatten(), results["server_pred"].flatten(), "server")
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visualize.plot_legend()
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visualize.plot_save("results/server_model/windows_roc.pdf")
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