706 lines
30 KiB
Python
706 lines
30 KiB
Python
import logging
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import os
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import numpy as np
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import pandas as pd
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import tensorflow as tf
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from keras.callbacks import CSVLogger, EarlyStopping, ModelCheckpoint
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from keras.models import Model
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from sklearn.metrics import confusion_matrix
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import arguments
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import dataset
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import hyperband
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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, get_custom_class_weights, load_model
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logger = logging.getLogger('logger')
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logger.setLevel(logging.DEBUG)
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# create console handler and set level to debug
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ch = logging.StreamHandler()
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ch.setLevel(logging.DEBUG)
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# create formatter
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formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
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# add formatter to ch
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ch.setFormatter(formatter)
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# add ch to logger
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logger.addHandler(ch)
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ch = logging.FileHandler("info.log")
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ch.setLevel(logging.DEBUG)
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# create formatter
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formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
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# add formatter to ch
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ch.setFormatter(formatter)
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# add ch to logger
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logger.addHandler(ch)
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args = arguments.parse()
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if args.gpu:
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config = tf.ConfigProto(log_device_placement=True)
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config.gpu_options.per_process_gpu_memory_fraction = 0.5
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config.gpu_options.allow_growth = True
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session = tf.Session(config=config)
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# default parameter
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PARAMS = {
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"type": args.model_type,
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"depth": args.model_depth,
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# "batch_size": 64,
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"window_size": args.window,
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"domain_length": args.domain_length,
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"flow_features": 3,
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#
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'dropout': 0.5, # currently fix
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'domain_features': args.domain_embedding,
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'embedding': args.embedding,
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'flow_features': 3,
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'filter_embedding': args.filter_embedding,
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'dense_embedding': args.dense_embedding,
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'kernel_embedding': args.kernel_embedding,
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'filter_main': args.filter_main,
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'dense_main': args.dense_main,
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'kernel_main': args.kernel_main,
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'input_length': 40,
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'model_output': args.model_output
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}
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def create_model(model, output_type):
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if output_type == "both":
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return Model(inputs=[model.in_domains, model.in_flows], outputs=(model.out_client, model.out_server))
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elif output_type == "client":
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return Model(inputs=[model.in_domains, model.in_flows], outputs=(model.out_client,))
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else:
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raise Exception("unknown model output")
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def main_paul_best():
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pauls_best_params = models.pauls_networks.best_config
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main_train(pauls_best_params)
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def main_hyperband():
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param_dist = {
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# static params
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"type": [args.model_type],
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"depth": [args.model_depth],
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"model_output": [args.model_output],
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"batch_size": [args.batch_size],
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"window_size": [args.window],
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"flow_features": [3],
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"domain_length": [args.domain_length],
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'input_length': [40],
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# model params
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"embedding_size": [2 ** x for x in range(3, 7)],
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"filter_embedding": [2 ** x for x in range(1, 10)],
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"kernel_embedding": [1, 3, 5, 7, 9],
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"dense_embedding": [2 ** x for x in range(4, 10)],
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"dropout": [0.5],
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"filter_main": [2 ** x for x in range(1, 10)],
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"kernel_main": [1, 3, 5, 7, 9],
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"dense_main": [2 ** x for x in range(1, 12)],
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}
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logger.info("create training dataset")
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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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server_tr = np.max(server_windows_tr, axis=1)
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if args.model_type in ("inter", "staggered"):
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server_tr = np.expand_dims(server_windows_tr, 2)
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hp = hyperband.Hyperband(param_dist,
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[domain_tr, flow_tr],
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[client_tr, server_tr],
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max_iter=81,
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savefile=args.hyperband_results)
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results = hp.run()
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return results
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def main_train(param=None):
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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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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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server_tr = np.max(server_windows_tr, axis=1)
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if args.class_weights:
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logger.info("class weights: compute custom weights")
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custom_class_weights = get_custom_class_weights(client_tr.value, server_tr)
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logger.info(custom_class_weights)
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else:
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logger.info("class weights: set default")
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custom_class_weights = None
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if not param:
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param = PARAMS
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logger.info(f"Generator model with params: {param}")
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embedding, model, new_model = models.get_models_by_params(param)
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model = create_model(model, args.model_output)
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new_model = create_model(new_model, args.model_output)
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if args.model_type in ("inter", "staggered"):
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server_tr = np.expand_dims(server_windows_tr, 2)
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model = new_model
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features = {"ipt_domains": domain_tr.value, "ipt_flows": flow_tr.value}
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if args.model_output == "both":
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labels = {"client": client_tr.value, "server": server_tr}
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loss_weights = {"client": 1.0, "server": 1.0}
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elif args.model_output == "client":
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labels = {"client": client_tr.value}
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loss_weights = {"client": 1.0}
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elif args.model_output == "server":
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labels = {"server": server_tr}
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loss_weights = {"server": 1.0}
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else:
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raise ValueError("unknown model output")
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logger.info(f"select model: {args.model_type}")
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if args.model_type == "staggered":
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logger.info("compile and pre-train server 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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loss_weights={"client": 0.0, "server": 1.0},
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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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class_weight=custom_class_weights)
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logger.info("fix server model")
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model.get_layer("domain_cnn").trainable = False
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model.get_layer("domain_cnn").layer.trainable = False
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model.get_layer("dense_server").trainable = False
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model.get_layer("server").trainable = False
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loss_weights = {"client": 1.0, "server": 0.0}
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logger.info("compile and train model")
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embedding.summary()
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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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loss_weights=loss_weights,
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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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class_weight=custom_class_weights)
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def main_retrain():
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source = os.path.join(args.model_source, "clf.h5")
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destination = os.path.join(args.model_destination, "clf.h5")
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logger.info(f"Use command line arguments: {args}")
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exists_or_make_path(args.model_destination)
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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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logger.info("define callbacks")
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callbacks = []
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callbacks.append(ModelCheckpoint(filepath=destination,
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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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server_tr = np.max(server_windows_tr, axis=1)
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if args.class_weights:
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logger.info("class weights: compute custom weights")
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custom_class_weights = get_custom_class_weights(client_tr.value, server_tr)
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logger.info(custom_class_weights)
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else:
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logger.info("class weights: set default")
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custom_class_weights = None
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logger.info(f"Load pretrained model")
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embedding, model = load_model(source, custom_objects=models.get_custom_objects())
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if args.model_type in ("inter", "staggered"):
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server_tr = np.expand_dims(server_windows_tr, 2)
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features = {"ipt_domains": domain_tr.value, "ipt_flows": flow_tr.value}
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if args.model_output == "both":
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labels = {"client": client_tr.value, "server": server_tr}
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elif args.model_output == "client":
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labels = {"client": client_tr.value}
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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("re-train model")
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embedding.summary()
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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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class_weight=custom_class_weights,
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initial_epoch=args.initial_epoch)
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def main_test():
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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_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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if args.model_output == "both":
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c_pred, s_pred = pred
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results["client_pred"] = c_pred
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results["server_pred"] = s_pred
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elif args.model_output == "client":
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results["client_pred"] = pred
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else:
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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 main_visualization():
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def plot_model(clf_model, path):
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embd, model = load_model(clf_model, custom_objects=models.get_custom_objects())
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visualize.plot_model_as(embd, os.path.join(path, "model_embd.pdf"))
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visualize.plot_model_as(model, os.path.join(path, "model_clf.pdf"))
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def vis(model_name, model_path, df, df_paul, aggregation, curve):
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visualize.plot_clf()
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if aggregation == "user":
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df = df.groupby(df.names).max()
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df_paul = df_paul.groupby(df_paul.names).max()
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if curve == "prc":
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visualize.plot_precision_recall(df.client_val.as_matrix(), df.client_pred.as_matrix(), model_name)
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visualize.plot_precision_recall(df_paul.client_val.as_matrix(), df_paul.client_pred.as_matrix(), "paul")
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elif curve == "roc":
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visualize.plot_roc_curve(df.client_val.as_matrix(), df.client_pred.as_matrix(), model_name)
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visualize.plot_roc_curve(df_paul.client_val.as_matrix(), df_paul.client_pred.as_matrix(), "paul")
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visualize.plot_legend()
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visualize.plot_save("{}/{}_{}.png".format(model_path, aggregation, curve))
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_, _, name_val, hits_vt, hits_trusted, server_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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results = dataset.load_predictions(args.model_path)
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df = pd.DataFrame(data={
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"names": name_val, "client_pred": results["client_pred"].flatten(),
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"hits_vt": hits_vt, "hits_trusted": hits_trusted
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})
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df["client_val"] = np.logical_or(df.hits_vt == 1.0, df.hits_trusted >= 3)
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df_user = df.groupby(df.names).max()
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paul = dataset.load_predictions("results/paul/")
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df_paul = pd.DataFrame(data={
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"names": paul["testNames"].flatten(), "client_pred": paul["testScores"].flatten(),
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"hits_vt": paul["testLabel"].flatten(), "hits_trusted": paul["testHits"].flatten()
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})
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df_paul["client_val"] = np.logical_or(df_paul.hits_vt == 1.0, df_paul.hits_trusted >= 3)
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logger.info("plot model")
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plot_model(args.clf_model, args.model_path)
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# logger.info("plot training curve")
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# logs = pd.read_csv(args.train_log)
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# if "acc" in logs.keys():
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# visualize.plot_training_curve(logs, "", "{}/client_train.png".format(args.model_path))
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# elif "client_acc" in logs.keys() and "server_acc" in logs.keys():
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# visualize.plot_training_curve(logs, "client_", "{}/client_train.png".format(args.model_path))
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# visualize.plot_training_curve(logs, "server_", "{}/server_train.png".format(args.model_path))
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# else:
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# logger.warning("Error while plotting training curves")
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logger.info("plot window prc")
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vis(args.model_name, args.model_path, df, df_paul, "window", "prc")
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logger.info("plot window roc")
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vis(args.model_name, args.model_path, df, df_paul, "window", "roc")
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logger.info("plot user prc")
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vis(args.model_name, args.model_path, df, df_paul, "user", "prc")
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logger.info("plot user roc")
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vis(args.model_name, args.model_path, df, df_paul, "user", "roc")
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# absolute values
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visualize.plot_confusion_matrix(df.client_val.as_matrix(), df.client_pred.as_matrix().round(),
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"{}/client_cov.png".format(args.model_path),
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normalize=False, title="Client Confusion Matrix")
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visualize.plot_confusion_matrix(df_user.client_val.as_matrix(), df_user.client_pred.as_matrix().round(),
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"{}/user_cov.png".format(args.model_path),
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normalize=False, title="User Confusion Matrix")
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# normalized
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visualize.plot_confusion_matrix(df.client_val.as_matrix(), df.client_pred.as_matrix().round(),
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"{}/client_cov_norm.png".format(args.model_path),
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normalize=True, title="Client Confusion Matrix")
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visualize.plot_confusion_matrix(df_user.client_val.as_matrix(), df_user.client_pred.as_matrix().round(),
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"{}/user_cov_norm.png".format(args.model_path),
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normalize=True, title="User Confusion Matrix")
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plot_embedding(args.model_path, results["domain_embds"], args.data, args.domain_length)
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def plot_embedding(model_path, domain_embedding, data, domain_length):
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logger.info("visualize embedding")
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domain_encs, labels = dataset.load_or_generate_domains(data, domain_length)
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visualize.plot_embedding(domain_embedding, labels, path="{}/embd_svd.png".format(model_path), method="svd")
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def main_visualize_all():
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_, _, name_val, hits_vt, hits_trusted, server_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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def load_df(path):
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res = dataset.load_predictions(path)
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res = pd.DataFrame(data={
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"names": name_val, "client_pred": res["client_pred"].flatten(),
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"hits_vt": hits_vt, "hits_trusted": hits_trusted
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})
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res["client_val"] = np.logical_or(res.hits_vt == 1.0, res.hits_trusted >= 3)
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return res
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paul = dataset.load_predictions("results/paul/")
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df_paul = pd.DataFrame(data={
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"names": paul["testNames"].flatten(), "client_pred": paul["testScores"].flatten(),
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"hits_vt": paul["testLabel"].flatten(), "hits_trusted": paul["testHits"].flatten()
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})
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df_paul["client_val"] = np.logical_or(df_paul.hits_vt == 1.0, df_paul.hits_trusted >= 3)
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df_paul_user = df_paul.groupby(df_paul.names).max()
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logger.info("plot pr curves")
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visualize.plot_clf()
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for model_args in get_model_args(args):
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df = load_df(model_args["model_path"])
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visualize.plot_precision_recall(df.client_val.as_matrix(), df.client_pred.as_matrix(), model_args["model_name"])
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visualize.plot_precision_recall(df_paul.client_val.as_matrix(), df_paul.client_pred.as_matrix(), "paul")
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visualize.plot_legend()
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visualize.plot_save(f"{args.output_prefix}_window_client_prc.png")
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logger.info("plot roc curves")
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visualize.plot_clf()
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for model_args in get_model_args(args):
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df = load_df(model_args["model_path"])
|
|
visualize.plot_roc_curve(df.client_val.as_matrix(), df.client_pred.as_matrix(), model_args["model_name"])
|
|
visualize.plot_roc_curve(df_paul.client_val.as_matrix(), df_paul.client_pred.as_matrix(), "paul")
|
|
visualize.plot_legend()
|
|
visualize.plot_save(f"{args.output_prefix}_window_client_roc.png")
|
|
|
|
logger.info("plot user pr curves")
|
|
visualize.plot_clf()
|
|
for model_args in get_model_args(args):
|
|
df = load_df(model_args["model_path"])
|
|
df = df.groupby(df.names).max()
|
|
visualize.plot_precision_recall(df.client_val.as_matrix(), df.client_pred.as_matrix(), model_args["model_name"])
|
|
visualize.plot_precision_recall(df_paul_user.client_val.as_matrix(), df_paul_user.client_pred.as_matrix(), "paul")
|
|
visualize.plot_legend()
|
|
visualize.plot_save(f"{args.output_prefix}_user_client_prc.png")
|
|
|
|
logger.info("plot user roc curves")
|
|
visualize.plot_clf()
|
|
for model_args in get_model_args(args):
|
|
df = load_df(model_args["model_path"])
|
|
df = df.groupby(df.names).max()
|
|
visualize.plot_roc_curve(df.client_val.as_matrix(), df.client_pred.as_matrix(), model_args["model_name"])
|
|
visualize.plot_roc_curve(df_paul_user.client_val.as_matrix(), df_paul_user.client_pred.as_matrix(), "paul")
|
|
visualize.plot_legend()
|
|
visualize.plot_save(f"{args.output_prefix}_user_client_roc.png")
|
|
|
|
|
|
import joblib
|
|
|
|
|
|
def main_beta():
|
|
_, _, name_val, hits_vt, hits_trusted, server_val = dataset.load_or_generate_raw_h5data(args.data,
|
|
args.data,
|
|
args.domain_length,
|
|
args.window)
|
|
path, model_prefix = os.path.split(os.path.normpath(args.output_prefix))
|
|
try:
|
|
results = joblib.load(f"{path}/curves.joblib")
|
|
except Exception:
|
|
results = {}
|
|
results[model_prefix] = {"all": {}}
|
|
|
|
def load_df(path):
|
|
res = dataset.load_predictions(path)
|
|
res = pd.DataFrame(data={
|
|
"names": name_val, "client_pred": res["client_pred"].flatten(),
|
|
"hits_vt": hits_vt, "hits_trusted": hits_trusted
|
|
})
|
|
res["client_val"] = np.logical_or(res.hits_vt == 1.0, res.hits_trusted >= 3)
|
|
return res
|
|
|
|
paul = dataset.load_predictions("results/paul/")
|
|
df_paul = pd.DataFrame(data={
|
|
"names": paul["testNames"].flatten(), "client_pred": paul["testScores"].flatten(),
|
|
"hits_vt": paul["testLabel"].flatten(), "hits_trusted": paul["testHits"].flatten()
|
|
})
|
|
df_paul["client_val"] = np.logical_or(df_paul.hits_vt == 1.0, df_paul.hits_trusted >= 3)
|
|
df_paul_user = df_paul.groupby(df_paul.names).max()
|
|
|
|
logger.info("plot pr curves")
|
|
visualize.plot_clf()
|
|
predictions = []
|
|
for model_args in get_model_args(args):
|
|
df = load_df(model_args["model_path"])
|
|
predictions.append(df.client_pred.as_matrix())
|
|
results[model_prefix][model_args["model_name"]] = confusion_matrix(df.client_val.as_matrix(),
|
|
df.client_pred.as_matrix().round())
|
|
results[model_prefix]["all"]["window_prc"] = visualize.calc_pr_mean(df.client_val.as_matrix(), predictions)
|
|
visualize.plot_pr_mean(df.client_val.as_matrix(), predictions, "mean")
|
|
visualize.plot_pr_mean(df_paul.client_val.as_matrix(), [df_paul.client_pred.as_matrix()], "paul")
|
|
visualize.plot_legend()
|
|
visualize.plot_save(f"{args.output_prefix}_window_client_prc_all.png")
|
|
|
|
logger.info("plot roc curves")
|
|
visualize.plot_clf()
|
|
predictions = []
|
|
for model_args in get_model_args(args):
|
|
df = load_df(model_args["model_path"])
|
|
predictions.append(df.client_pred.as_matrix())
|
|
results[model_prefix][model_args["model_name"]] = confusion_matrix(df.client_val.as_matrix(),
|
|
df.client_pred.as_matrix().round())
|
|
results[model_prefix]["all"]["window_roc"] = visualize.calc_roc_mean(df.client_val.as_matrix(), predictions)
|
|
visualize.plot_roc_mean(df.client_val.as_matrix(), predictions, "mean")
|
|
visualize.plot_roc_mean(df_paul.client_val.as_matrix(), [df_paul.client_pred.as_matrix()], "paul")
|
|
visualize.plot_legend()
|
|
visualize.plot_save(f"{args.output_prefix}_window_client_roc_all.png")
|
|
|
|
logger.info("plot user pr curves")
|
|
visualize.plot_clf()
|
|
predictions = []
|
|
for model_args in get_model_args(args):
|
|
df = load_df(model_args["model_path"])
|
|
df = df.groupby(df.names).max()
|
|
predictions.append(df.client_pred.as_matrix())
|
|
results[model_prefix][model_args["model_name"]] = confusion_matrix(df.client_val.as_matrix(),
|
|
df.client_pred.as_matrix().round())
|
|
results[model_prefix]["all"]["user_prc"] = visualize.calc_pr_mean(df.client_val.as_matrix(), predictions)
|
|
visualize.plot_pr_mean(df.client_val.as_matrix(), predictions, "mean")
|
|
visualize.plot_pr_mean(df_paul_user.client_val.as_matrix(), [df_paul_user.client_pred.as_matrix()], "paul")
|
|
visualize.plot_legend()
|
|
visualize.plot_save(f"{args.output_prefix}_user_client_prc_all.png")
|
|
|
|
logger.info("plot user roc curves")
|
|
visualize.plot_clf()
|
|
predictions = []
|
|
for model_args in get_model_args(args):
|
|
df = load_df(model_args["model_path"])
|
|
df = df.groupby(df.names).max()
|
|
predictions.append(df.client_pred.as_matrix())
|
|
results[model_prefix]["all"]["user_roc"] = visualize.calc_roc_mean(df.client_val.as_matrix(), predictions)
|
|
visualize.plot_roc_mean(df.client_val.as_matrix(), predictions, "mean")
|
|
visualize.plot_roc_mean(df_paul_user.client_val.as_matrix(), [df_paul_user.client_pred.as_matrix()], "paul")
|
|
visualize.plot_legend()
|
|
visualize.plot_save(f"{args.output_prefix}_user_client_roc_all.png")
|
|
|
|
joblib.dump(results, f"{path}/curves.joblib")
|
|
|
|
plot_overall_result()
|
|
|
|
|
|
def plot_overall_result():
|
|
path, model_prefix = os.path.split(os.path.normpath(args.output_prefix))
|
|
try:
|
|
results = joblib.load(f"{path}/curves.joblib")
|
|
except Exception:
|
|
results = {}
|
|
|
|
import matplotlib.pyplot as plt
|
|
x = np.linspace(0, 1, 10000)
|
|
for vis in ["window_prc", "window_roc", "user_prc", "user_roc"]:
|
|
logger.info(f"plot {vis}")
|
|
visualize.plot_clf()
|
|
for model_key in results.keys():
|
|
ys_mean, ys_std, score = results[model_key]["all"][vis]
|
|
plt.plot(x, ys_mean, label=f"{model_key} - {score:5.4}")
|
|
plt.fill_between(x, ys_mean - ys_std, ys_mean + ys_std, alpha=0.2)
|
|
if vis.endswith("prc"):
|
|
plt.xlabel('Recall')
|
|
plt.ylabel('Precision')
|
|
else:
|
|
plt.xlabel('False Positive Rate')
|
|
plt.ylabel('True Positive Rate')
|
|
plt.ylim([0.0, 1.0])
|
|
plt.xlim([0.0, 1.0])
|
|
visualize.plot_legend()
|
|
visualize.plot_save(f"{path}/{vis}_all.png")
|
|
|
|
for cat, models in results.items():
|
|
visualize.plot_clf()
|
|
visualize.plot_error_bars(models)
|
|
visualize.plot_legend()
|
|
visualize.plot_save(f"{path}/error_bars_{cat}.png")
|
|
|
|
|
|
def train_server_only():
|
|
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,
|
|
validation_split=0.2,
|
|
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 main():
|
|
if "train" == args.mode:
|
|
main_train()
|
|
if "retrain" == args.mode:
|
|
main_retrain()
|
|
if "hyperband" == args.mode:
|
|
main_hyperband()
|
|
if "test" == args.mode:
|
|
main_test()
|
|
if "fancy" == args.mode:
|
|
main_visualization()
|
|
if "all_fancy" == args.mode:
|
|
main_visualize_all()
|
|
if "beta" == args.mode:
|
|
main_beta()
|
|
if "all_beta" == args.mode:
|
|
plot_overall_result()
|
|
if "server" == args.mode:
|
|
train_server_only()
|
|
if "server_test" == args.mode:
|
|
test_server_only()
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|