312 lines
11 KiB
Python
312 lines
11 KiB
Python
import json
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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 ModelCheckpoint, CSVLogger, EarlyStopping
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from keras.models import load_model
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from sklearn.decomposition import PCA
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from sklearn.utils import class_weight
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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 dataset import load_or_generate_h5data
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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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print = logger.info
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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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def exists_or_make_path(p):
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if not os.path.exists(p):
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os.makedirs(p)
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def main_paul_best():
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char_dict = dataset.get_character_dict()
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pauls_best_params = models.pauls_networks.best_config
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pauls_best_params["vocab_size"] = len(char_dict) + 1
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main_train(pauls_best_params)
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def main_hyperband():
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char_dict = dataset.get_character_dict()
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params = {
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# static params
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"type": ["paul"],
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"batch_size": [args.batch_size],
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"vocab_size": [len(char_dict) + 1],
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"window_size": [10],
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"domain_length": [40],
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"flow_features": [3],
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"input_length": [40],
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# model params
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"embedding_size": [16, 32, 64, 128, 256, 512],
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"filter_embedding": [16, 32, 64, 128, 256, 512],
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"kernel_embedding": [1, 3, 5, 7, 9],
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"hidden_embedding": [16, 32, 64, 128, 256, 512],
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"dropout": [0.5],
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"domain_features": [16, 32, 64, 128, 256, 512],
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"filter_main": [16, 32, 64, 128, 256, 512],
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"kernels_main": [1, 3, 5, 7, 9],
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"dense_main": [16, 32, 64, 128, 256, 512],
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}
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logger.info("create training dataset")
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domain_tr, flow_tr, client_tr, server_tr = load_or_generate_h5data(args.train_h5data, args.train_data,
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args.domain_length, args.window)
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hp = hyperband.Hyperband(params,
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[domain_tr, flow_tr],
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[client_tr, server_tr])
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results = hp.run()
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json.dump(results, open("hyperband.json"))
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def get_custom_class_weights(client_tr, server_tr):
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client = client_tr.value.argmax(1)
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server = server_tr.value.argmax(1)
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client_class_weight = class_weight.compute_class_weight('balanced', np.unique(client), client)
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server_class_weight = class_weight.compute_class_weight('balanced', np.unique(server), server)
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return {
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"client": client_class_weight,
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"server": server_class_weight
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}
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def main_train(param=None):
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exists_or_make_path(args.model_path)
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char_dict = dataset.get_character_dict()
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domain_tr, flow_tr, client_tr, server_tr = load_or_generate_h5data(args.train_h5data, args.train_data,
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args.domain_length, args.window)
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# parameter
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p = {
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"type": args.model_type,
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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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"vocab_size": len(char_dict) + 1,
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#
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'dropout': 0.5,
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'domain_features': args.domain_embedding,
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'embedding_size': args.embedding,
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'filter_main': 128,
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'flow_features': 3,
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# 'dense_main': 512,
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'dense_main': 128,
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'filter_embedding': args.hidden_char_dims,
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'hidden_embedding': args.domain_embedding,
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'kernel_embedding': 3,
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'kernels_main': 3,
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'input_length': 40
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}
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if not param:
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param = p
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embedding, model = models.get_models_by_params(param)
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embedding.summary()
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model.summary()
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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='val_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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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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logger.info("compile model")
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custom_metrics = models.get_metric_functions()
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model.compile(optimizer='adam',
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loss='categorical_crossentropy',
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metrics=['accuracy'] + custom_metrics)
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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, 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("start training")
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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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callbacks=callbacks,
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shuffle=True,
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validation_split=0.2,
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class_weight=custom_class_weights)
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logger.info("save embedding")
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embedding.save(args.embedding_model)
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def main_test():
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domain_val, flow_val, client_val, server_val = load_or_generate_h5data(args.test_h5data, args.test_data,
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args.domain_length, args.window)
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clf = load_model(args.clf_model, custom_objects=models.get_metrics())
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# stats = clf.evaluate([domain_val, flow_val],
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# [client_val, server_val],
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# batch_size=args.batch_size)
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y_pred = clf.predict([domain_val, flow_val],
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batch_size=args.batch_size,
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verbose=1)
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np.save(args.future_prediction, y_pred)
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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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domains = user_flow_df.domain.unique()
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def get_domain_features_reduced(d):
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return dataset.get_domain_features(d[0], char_dict, args.domain_length)
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domain_features = []
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for ds in domains:
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domain_features.append(np.apply_along_axis(get_domain_features_reduced, 2, np.atleast_3d(ds)))
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model = load_model(args.embedding_model)
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domain_features = np.stack(domain_features).reshape((-1, 40))
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pred = model.predict(domains, batch_size=args.batch_size, verbose=1)
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np.save("/tmp/rk/domains.npy", domains)
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np.save("/tmp/rk/domain_features.npy", domain_features)
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np.save("/tmp/rk/domain_embd.npy", pred)
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def main_visualization():
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domain_val, flow_val, client_val, server_val = load_or_generate_h5data(args.test_h5data, args.test_data,
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args.domain_length, args.window)
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logger.info("plot model")
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model = load_model(args.clf_model, custom_objects=models.get_metrics())
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visualize.plot_model(model, os.path.join(args.model_path, "model.png"))
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try:
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logger.info("plot training curve")
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logs = pd.read_csv(args.train_log)
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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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except Exception as e:
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logger.warning(f"could not generate training curves: {e}")
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client_pred, server_pred = np.load(args.future_prediction)
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logger.info("plot pr curve")
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visualize.plot_precision_recall(client_val.value, client_pred, "{}/client_prc.png".format(args.model_path))
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visualize.plot_precision_recall(server_val.value, server_pred, "{}/server_prc.png".format(args.model_path))
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visualize.plot_precision_recall_curves(client_val.value, client_pred, "{}/client_prc2.png".format(args.model_path))
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visualize.plot_precision_recall_curves(server_val.value, server_pred, "{}/server_prc2.png".format(args.model_path))
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logger.info("plot roc curve")
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visualize.plot_roc_curve(client_val.value, client_pred, "{}/client_roc.png".format(args.model_path))
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visualize.plot_roc_curve(server_val.value, server_pred, "{}/server_roc.png".format(args.model_path))
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visualize.plot_confusion_matrix(client_val.value.argmax(1), client_pred.argmax(1),
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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(server_val.value.argmax(1), server_pred.argmax(1),
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"{}/server_cov.png".format(args.model_path),
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normalize=False, title="Server Confusion Matrix")
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# embedding visi
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import matplotlib.pyplot as plt
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model = load_model(args.embedding_model)
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domains = np.reshape(domain_val, (domain_val.shape[0] * domain_val.shape[1], 40))
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domain_embedding = model.predict(domains, batch_size=args.batch_size, verbose=1)
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pca = PCA(n_components=2)
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domain_reduced = pca.fit_transform(domain_embedding)
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print(pca.explained_variance_ratio_)
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clients = np.repeat(client_val, 10, axis=0)
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clients = clients.argmax(1)
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servers = np.repeat(server_val, 10, axis=0)
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servers = servers.argmax(1)
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plt.scatter(domain_reduced[:, 0], domain_reduced[:, 1], c=clients, cmap=plt.cm.bwr, s=2)
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plt.show()
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plt.scatter(domain_reduced[:, 0], domain_reduced[:, 1], c=servers, cmap=plt.cm.bwr, s=2)
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plt.show()
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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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pass
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def main_data():
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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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logger.info("create training dataset")
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domain_tr, flow_tr, client_tr, server_tr = dataset.create_dataset_from_flows(user_flow_df, char_dict,
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max_len=args.domain_length,
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window_size=args.window)
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print(f"domain shape {domain_tr.shape}")
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print(f"flow shape {flow_tr.shape}")
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print(f"client shape {client_tr.shape}")
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print(f"server shape {server_tr.shape}")
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def main():
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if "train" in args.modes:
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main_train()
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if "hyperband" in args.modes:
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main_hyperband()
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if "test" in args.modes:
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main_test()
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if "fancy" in args.modes:
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main_visualization()
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if "score" in args.modes:
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main_score()
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if "paul" in args.modes:
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main_paul_best()
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if "data" in args.modes:
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main_data()
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if __name__ == "__main__":
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main()
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