import json import logging import os import numpy as np from keras.callbacks import ModelCheckpoint, CSVLogger, EarlyStopping from keras.models import load_model import arguments import dataset import hyperband import models # create logger logger = logging.getLogger('logger') logger.setLevel(logging.DEBUG) # create console handler and set level to debug ch = logging.StreamHandler() ch.setLevel(logging.DEBUG) # create formatter formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s') # add formatter to ch ch.setFormatter(formatter) # add ch to logger logger.addHandler(ch) ch = logging.FileHandler("info.log") ch.setLevel(logging.DEBUG) # create formatter formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s') # add formatter to ch ch.setFormatter(formatter) # add ch to logger logger.addHandler(ch) args = arguments.parse() # config = tf.ConfigProto(log_device_placement=True) # config.gpu_options.per_process_gpu_memory_fraction = 0.5 # config.gpu_options.allow_growth = True # 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() domain_tr, flow_tr, client_tr, server_tr = load_or_generate_h5data(args.h5data, args.train_data, args.domain_length, args.window) param = models.pauls_networks.best_config param["vocab_size"] = len(char_dict) + 1 embedding, model = models.get_models_by_params(param) model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy']) model.fit([domain_tr, flow_tr], [client_tr, server_tr], 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_hyperband(): char_dict = dataset.get_character_dict() user_flow_df = dataset.get_user_flow_data(args.train_data) params = { # static params "type": ["paul"], "batch_size": [args.batch_size], "vocab_size": [len(char_dict) + 1], "window_size": [10], "domain_length": [40], "flow_features": [3], "input_length": [40], # model params "embedding_size": [16, 32, 64, 128, 256, 512], "filter_embedding": [16, 32, 64, 128, 256, 512], "kernel_embedding": [1, 3, 5, 7, 9], "hidden_embedding": [16, 32, 64, 128, 256, 512], "dropout": [0.5], "domain_features": [16, 32, 64, 128, 256, 512], "filter_main": [16, 32, 64, 128, 256, 512], "kernels_main": [1, 3, 5, 7, 9], "dense_main": [16, 32, 64, 128, 256, 512], } logger.info("create training dataset") domain_tr, flow_tr, client_tr, server_tr = load_or_generate_h5data(args.h5data, args.train_data, args.domain_length, args.window) hp = hyperband.Hyperband(params, [domain_tr, flow_tr], [client_tr, server_tr]) results = hp.run() json.dump(results, open("hyperband.json")) def load_or_generate_h5data(h5data, train_data, domain_length, window_size): char_dict = dataset.get_character_dict() logger.info(f"check for h5data {h5data}") try: open(h5data, "r") except FileNotFoundError: logger.info("h5 data not found - load csv file") user_flow_df = dataset.get_user_flow_data(train_data) logger.info("create training dataset") domain_tr, flow_tr, client_tr, server_tr = dataset.create_dataset_from_flows(user_flow_df, char_dict, max_len=domain_length, window_size=window_size) logger.info("store training dataset as h5 file") dataset.store_h5dataset(args.h5data, domain_tr, flow_tr, client_tr, server_tr) logger.info("load h5 dataset") return dataset.load_h5dataset(h5data) def main_train(): exists_or_make_path(args.model_path) char_dict = dataset.get_character_dict() domain_tr, flow_tr, client_tr, server_tr = load_or_generate_h5data(args.h5data, args.train_data, args.domain_length, args.window) # parameter param = { "type": "paul", "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, model = models.get_models_by_params(param) embedding.summary() model.summary() logger.info("define callbacks") cp = ModelCheckpoint(filepath=args.clf_model, monitor='val_loss', verbose=False, save_best_only=True) csv = CSVLogger(args.train_log) early = EarlyStopping(monitor='val_loss', patience=5, verbose=False) logger.info("compile model") model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy']) logger.info("start training") model.fit([domain_tr, flow_tr], [client_tr, server_tr], batch_size=args.batch_size, epochs=args.epochs, callbacks=[cp, csv, early], shuffle=True, validation_split=0.2) logger.info("save embedding") embedding.save(args.embedding_model) def main_test(): domain_val, flow_val, client_val, server_val = load_or_generate_h5data(args.h5data, args.train_data, args.domain_length, args.window) clf = load_model(args.clf_model) loss, _, _, client_acc, server_acc = clf.evaluate([domain_val, flow_val], [client_val, server_val], batch_size=args.batch_size) logger.info(f"loss: {loss}\nclient acc: {client_acc}\nserver acc: {server_acc}") y_pred = clf.predict([domain_val, flow_val], batch_size=args.batch_size) np.save(os.path.join(args.model_path, "future_predict.npy"), y_pred) def main_visualization(): mask = dataset.load_mask_eval(args.data, args.test_image) y_pred_path = args.model_path + "pred.npy" logger.info("plot model") model = load_model(args.model_path + "model.h5", custom_objects=evaluation.get_metrics()) visualize.plot_model(model, args.model_path + "model.png") logger.info("plot training curve") logs = pd.read_csv(args.model_path + "train.log") visualize.plot_training_curve(logs, "{}/train.png".format(args.model_path)) pred = np.load(y_pred_path) logger.info("plot pr curve") visualize.plot_precision_recall(mask, pred, "{}/prc.png".format(args.model_path)) visualize.plot_precision_recall_curves(mask, pred, "{}/prc2.png".format(args.model_path)) logger.info("plot roc curve") visualize.plot_roc_curve(mask, pred, "{}/roc.png".format(args.model_path)) logger.info("store prediction image") visualize.save_image_as(pred, "{}/pred.png".format(args.model_path)) def main_score(): mask = dataset.load_mask_eval(args.data, args.test_image) pred = np.load(args.pred) visualize.score_model(mask, pred) def main(): if "train" in args.modes: main_train() if "hyperband" in args.modes: main_hyperband() if "test" in args.modes: main_test() if "fancy" in args.modes: main_visualization() if "score" in args.modes: main_score() if "paul" in args.modes: main_paul_best() if __name__ == "__main__": main()