import argparse import h5py from keras.models import load_model from keras.utils import np_utils import dataset import hyperband import models parser = argparse.ArgumentParser() parser.add_argument("--modes", action="store", dest="modes", nargs="+", default=[]) parser.add_argument("--train", action="store", dest="train_data", default="data/full_dataset.csv.tar.bz2") parser.add_argument("--test", action="store", dest="test_data", default="data/full_future_dataset.csv.tar.bz2") # parser.add_argument("--h5data", action="store", dest="h5data", # default="") # parser.add_argument("--models", action="store", dest="models", default="models/model_x") # parser.add_argument("--pred", action="store", dest="pred", # default="") # parser.add_argument("--type", action="store", dest="model_type", default="paul") parser.add_argument("--batch", action="store", dest="batch_size", default=64, type=int) parser.add_argument("--epochs", action="store", dest="epochs", default=10, type=int) # parser.add_argument("--samples", action="store", dest="samples", # default=100000, type=int) # # parser.add_argument("--samples_val", action="store", dest="samples_val", # default=10000, type=int) # parser.add_argument("--embd", action="store", dest="embedding", default=128, type=int) parser.add_argument("--hidden_char_dims", action="store", dest="hidden_char_dims", default=256, type=int) parser.add_argument("--window", action="store", dest="window", default=10, type=int) parser.add_argument("--domain_length", action="store", dest="domain_length", default=40, type=int) parser.add_argument("--domain_embd", action="store", dest="domain_embedding", default=512, type=int) # parser.add_argument("--queue", action="store", dest="queue_size", # default=50, type=int) # # parser.add_argument("--p", action="store", dest="p_train", # default=0.5, type=float) # # parser.add_argument("--p_val", action="store", dest="p_val", # default=0.01, type=float) # # parser.add_argument("--gpu", action="store", dest="gpu", # default=0, type=int) # # parser.add_argument("--tmp", action="store_true", dest="tmp") # # parser.add_argument("--test", action="store_true", dest="test") args = parser.parse_args() args.embedding_model = args.models + "_embd.h5" args.clf_model = args.models + "_clf.h5" # 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 main_paul_best(): char_dict = dataset.get_character_dict() user_flow_df = dataset.get_user_flow_data(args.train_data) 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) 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": [64], "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], } param = hyperband.sample_params(params) 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) hp = hyperband.Hyperband(params, [domain_tr, flow_tr], [client_tr, server_tr]) hp.run() def main_train(): # parameter dropout_main = 0.5 dense_main = 512 kernel_main = 3 filter_main = 128 network = models.pauls_networks if args.model_type == "paul" else models.renes_networks char_dict = dataset.get_character_dict() 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) embedding = network.get_embedding(len(char_dict) + 1, args.embedding, args.domain_length, args.hidden_char_dims, kernel_main, args.domain_embedding, 0.5) embedding.summary() model = network.get_model(dropout_main, flow_tr.shape[-1], args.domain_embedding, args.window, args.domain_length, filter_main, kernel_main, dense_main, embedding) model.summary() 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_train_h5(): # parameter dropout_main = 0.5 dense_main = 512 kernel_main = 3 filter_main = 128 network = models.pauls_networks if args.model_type == "paul" else models.renes_networks char_dict = dataset.get_character_dict() data = h5py.File("data/full_dataset.h5", "r") embedding = network.get_embedding(len(char_dict) + 1, args.embedding, args.domain_length, args.hidden_char_dims, kernel_main, args.domain_embedding, 0.5) embedding.summary() model = network.get_model(dropout_main, data["flow"].shape[-1], args.domain_embedding, args.window, args.domain_length, filter_main, kernel_main, dense_main, embedding) model.summary() model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy']) model.fit([data["domain"], data["flow"]], [data["client"], data["server"]], 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_test(): char_dict = dataset.get_character_dict() user_flow_df = dataset.get_user_flow_data(args.test_data) domain_val, flow_val, client_val, server_val = dataset.create_dataset_from_flows( user_flow_df, char_dict, max_len=args.domain_length, window_size=args.window) # embedding = load_model(args.embedding_model) clf = load_model(args.clf_model) print(clf.evaluate([domain_val, flow_val], [client_val, server_val], batch_size=args.batch_size)) def main_visualization(): mask = dataset.load_mask_eval(args.data, args.test_image) y_pred_path = args.model_path + "pred.npy" print("plot model") model = load_model(args.model_path + "model.h5", custom_objects=evaluation.get_metrics()) visualize.plot_model(model, args.model_path + "model.png") print("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) print("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)) print("plot roc curve") visualize.plot_roc_curve(mask, pred, "{}/roc.png".format(args.model_path)) print("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()