162 lines
5.5 KiB
Python
162 lines
5.5 KiB
Python
import argparse
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from keras.utils import np_utils
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import dataset
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import models
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parser = argparse.ArgumentParser()
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# parser.add_argument("--modes", action="store", dest="modes", nargs="+")
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parser.add_argument("--train", action="store", dest="train_data",
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default="data/full_dataset.csv.tar.bz2")
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parser.add_argument("--test", action="store", dest="test_data",
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default="data/full_future_dataset.csv.tar.bz2")
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# parser.add_argument("--h5data", action="store", dest="h5data",
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# default="")
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#
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parser.add_argument("--model", action="store", dest="model",
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default="model_x")
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# parser.add_argument("--pred", action="store", dest="pred",
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# default="")
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#
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# parser.add_argument("--type", action="store", dest="model_type",
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# default="simple_conv")
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#
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parser.add_argument("--batch", action="store", dest="batch_size",
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default=64, type=int)
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parser.add_argument("--epochs", action="store", dest="epochs",
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default=10, type=int)
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# parser.add_argument("--samples", action="store", dest="samples",
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# default=100000, type=int)
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#
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# parser.add_argument("--samples_val", action="store", dest="samples_val",
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# default=10000, type=int)
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#
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parser.add_argument("--embd", action="store", dest="embedding",
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default=128, type=int)
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parser.add_argument("--hidden_char_dims", action="store", dest="hidden_char_dims",
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default=256, type=int)
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parser.add_argument("--window", action="store", dest="window",
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default=10, type=int)
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parser.add_argument("--domain_length", action="store", dest="domain_length",
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default=40, type=int)
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parser.add_argument("--domain_embd", action="store", dest="domain_embedding",
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default=512, type=int)
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# parser.add_argument("--queue", action="store", dest="queue_size",
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# default=50, type=int)
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#
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# parser.add_argument("--p", action="store", dest="p_train",
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# default=0.5, type=float)
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#
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# parser.add_argument("--p_val", action="store", dest="p_val",
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# default=0.01, type=float)
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#
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# parser.add_argument("--gpu", action="store", dest="gpu",
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# default=0, type=int)
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#
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# parser.add_argument("--tmp", action="store_true", dest="tmp")
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#
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# parser.add_argument("--test", action="store_true", dest="test")
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args = parser.parse_args()
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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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def main_train():
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# parameter
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cnnDropout = 0.5
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cnnHiddenDims = 512
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kernel_size = 3
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filters = 128
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network = models.pauls_networks
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char_dict = dataset.get_character_dict()
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user_flow_df = dataset.get_user_flow_data(args.train_data)
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print("create training dataset")
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domain_tr, flow_tr, client_tr, server_tr = dataset.create_dataset_from_flows(
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user_flow_df, char_dict,
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max_len=args.domain_length, window_size=args.window)
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shared_cnn = network.get_embedding(len(char_dict) + 1, args.embedding, args.domain_length,
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args.hidden_char_dims, kernel_size, args.domain_embedding, 0.5)
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shared_cnn.summary()
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model = network.get_model(cnnDropout, flow_tr.shape[-1], args.domain_embedding,
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args.window, args.domain_length, filters, kernel_size,
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cnnHiddenDims, shared_cnn)
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model.summary()
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model.compile(optimizer='adam',
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loss='binary_crossentropy',
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metrics=['accuracy'])
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client_tr = np_utils.to_categorical(client_tr, 2)
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server_tr = np_utils.to_categorical(server_tr, 2)
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model.fit([domain_tr, flow_tr],
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[client_tr, server_tr],
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batch_size=args.batch_size,
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epochs=args.epochs,
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shuffle=True,
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validation_split=0.2)
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def main_test():
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char_dict = dataset.get_character_dict()
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user_flow_df = dataset.get_user_flow_data(args.test_data)
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domain_val, flow_val, client_val, server_val = dataset.create_dataset_from_flows(
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user_flow_df, char_dict,
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max_len=args.domain_length, window_size=args.window)
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# TODO: get model and exec model.evaluate(...)
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def main_visualization():
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mask = dataset.load_mask_eval(args.data, args.test_image)
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y_pred_path = args.model_path + "pred.npy"
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print("plot model")
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model = load_model(args.model_path + "model.h5",
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custom_objects=evaluation.get_metrics())
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visualize.plot_model(model, args.model_path + "model.png")
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print("plot training curve")
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logs = pd.read_csv(args.model_path + "train.log")
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visualize.plot_training_curve(logs, "{}/train.png".format(args.model_path))
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pred = np.load(y_pred_path)
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print("plot pr curve")
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visualize.plot_precision_recall(mask, pred, "{}/prc.png".format(args.model_path))
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visualize.plot_precision_recall_curves(mask, pred, "{}/prc2.png".format(args.model_path))
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print("plot roc curve")
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visualize.plot_roc_curve(mask, pred, "{}/roc.png".format(args.model_path))
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print("store prediction image")
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visualize.save_image_as(pred, "{}/pred.png".format(args.model_path))
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def main_score():
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mask = dataset.load_mask_eval(args.data, args.test_image)
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pred = np.load(args.pred)
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visualize.score_model(mask, pred)
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def main():
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main_train()
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if __name__ == "__main__":
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main()
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