412 lines
17 KiB
Python
412 lines
17 KiB
Python
import json
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import logging
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import os
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import joblib
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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, Model
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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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from utils import exists_or_make_path, get_custom_class_weights
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from arguments import get_model_args
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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_size': 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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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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"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": [8, 16, 32, 64, 128, 256],
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"filter_embedding": [8, 16, 32, 64, 128, 256],
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"kernel_embedding": [1, 3, 5, 7, 9],
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"hidden_embedding": [8, 16, 32, 64, 128, 256],
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"dropout": [0.5],
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"domain_features": [8, 16, 32, 64, 128, 256],
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"filter_main": [8, 16, 32, 64, 128, 256],
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"kernels_main": [1, 3, 5, 7, 9],
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"dense_main": [8, 16, 32, 64, 128, 256],
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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_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 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 = load_or_generate_h5data(args.train_h5data,
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args.train_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='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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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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logger.info(f"select model: {args.model_type}")
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if args.model_type == "staggered":
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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(new_model, args.model_output)
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server_tr = np.expand_dims(server_windows_tr, 2)
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logger.info("compile and train model")
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embedding.summary()
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model.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={"client": 0.0, "server": 1.0},
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metrics=['accuracy'] + custom_metrics)
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model.fit({"ipt_domains": domain_tr, "ipt_flows": flow_tr},
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{"client": client_tr, "server": 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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class_weight=custom_class_weights)
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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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model.compile(optimizer='adam',
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loss='binary_crossentropy',
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loss_weights={"client": 1.0, "server": 0.0},
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metrics=['accuracy'] + custom_metrics)
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model.summary()
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model.fit({"ipt_domains": domain_tr, "ipt_flows": flow_tr},
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{"client": client_tr, "server": 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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else:
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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 == "inter":
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server_tr = np.expand_dims(server_windows_tr, 2)
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model = new_model
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logger.info("compile and train model")
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embedding.summary()
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model.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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metrics=['accuracy'] + custom_metrics)
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if args.model_output == "both":
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labels = [client_tr, server_tr]
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elif args.model_output == "client":
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labels = [client_tr]
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elif args.model_output == "server":
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labels = [server_tr]
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else:
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raise ValueError("unknown model output")
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model.fit([domain_tr, flow_tr],
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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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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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logger.info("start test: load data")
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domain_val, flow_val, name_val, client_val, server_val = load_or_generate_h5data(args.test_h5data,
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args.test_data,
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args.domain_length,
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args.window)
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domain_encs, labels = dataset.load_or_generate_domains(args.test_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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clf_model = load_model(model_args["clf_model"], custom_objects=models.get_metrics())
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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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# dataset.save_predictions(model_args["future_prediction"], c_pred, s_pred)
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embd_model = load_model(model_args["embedding_model"])
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domain_embeddings = embd_model.predict(domain_encs, batch_size=args.batch_size, verbose=1)
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# np.save(model_args["model_path"] + "/domain_embds.npy", domain_embeddings)
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results["domain_embds"] = domain_embeddings
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joblib.dump(results, model_args["model_path"] + "/results.joblib", compress=3)
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def main_visualization():
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domain_val, flow_val, name_val, client_val, server_val = load_or_generate_h5data(args.test_h5data,
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args.test_data,
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args.domain_length,
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args.window)
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# client_val, server_val = client_val.value, server_val.value
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client_val = client_val.value
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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_as(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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if args.model_output == "client":
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visualize.plot_training_curve(logs, "", "{}/client_train.png".format(args.model_path))
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else:
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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 = dataset.load_predictions(args.future_prediction)
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client_pred, server_pred = client_pred.value.flatten(), server_pred.value.flatten()
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logger.info("plot pr curve")
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visualize.plot_clf()
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visualize.plot_precision_recall(client_val, client_pred, args.model_path)
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visualize.plot_legend()
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visualize.plot_save("{}/window_client_prc.png".format(args.model_path))
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# visualize.plot_precision_recall(server_val, server_pred, "{}/server_prc.png".format(args.model_path))
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# visualize.plot_precision_recall_curves(client_val, client_pred, "{}/client_prc2.png".format(args.model_path))
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# visualize.plot_precision_recall_curves(server_val, server_pred, "{}/server_prc2.png".format(args.model_path))
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logger.info("plot roc curve")
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visualize.plot_clf()
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visualize.plot_roc_curve(client_val, client_pred, args.model_path)
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visualize.plot_legend()
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visualize.plot_save("{}/window_client_roc.png".format(args.model_path))
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# visualize.plot_roc_curve(server_val, server_pred, "{}/server_roc.png".format(args.model_path))
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print(f"names {name_val.shape} vals {client_val.shape} preds {client_pred.shape}")
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df_val = pd.DataFrame(data={"names": name_val, "client_val": client_val})
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user_vals = df_val.groupby(df_val.names).max().client_val.as_matrix().astype(float)
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df_pred = pd.DataFrame(data={"names": name_val, "client_val": client_pred})
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user_preds = df_pred.groupby(df_pred.names).max().client_val.as_matrix().astype(float)
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visualize.plot_clf()
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visualize.plot_precision_recall(user_vals, user_preds, args.model_path)
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visualize.plot_legend()
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visualize.plot_save("{}/user_client_prc.png".format(args.model_path))
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visualize.plot_clf()
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visualize.plot_roc_curve(user_vals, user_preds, args.model_path)
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visualize.plot_legend()
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visualize.plot_save("{}/user_client_roc.png".format(args.model_path))
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visualize.plot_confusion_matrix(client_val, client_pred.flatten().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(user_vals, user_preds.flatten().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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logger.info("visualize embedding")
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domain_encs, labels = dataset.load_or_generate_domains(args.test_data, args.domain_length)
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domain_embedding = np.load(args.model_path + "/domain_embds.npy")
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visualize.plot_embedding(domain_embedding, labels, path="{}/embd.png".format(args.model_path))
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def main_visualize_all():
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domain_val, flow_val, name_val, client_val, server_val = load_or_generate_h5data(args.test_h5data,
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args.test_data,
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args.domain_length,
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args.window)
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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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client_pred, server_pred = dataset.load_predictions(model_args["future_prediction"])
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visualize.plot_precision_recall(client_val.value, client_pred.value, model_args["model_path"])
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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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client_pred, server_pred = dataset.load_predictions(model_args["future_prediction"])
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visualize.plot_roc_curve(client_val.value, client_pred.value, model_args["model_path"])
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visualize.plot_legend()
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visualize.plot_save(f"{args.output_prefix}_window_client_roc.png")
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df_val = pd.DataFrame(data={"names": name_val, "client_val": client_val})
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user_vals = df_val.groupby(df_val.names).max().client_val.as_matrix().astype(float)
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logger.info("plot user 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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client_pred, server_pred = dataset.load_predictions(model_args["future_prediction"])
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df_pred = pd.DataFrame(data={"names": name_val, "client_val": client_pred.value.flatten()})
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user_preds = df_pred.groupby(df_pred.names).max().client_val.as_matrix().astype(float)
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visualize.plot_precision_recall(user_vals, user_preds, model_args["model_path"])
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visualize.plot_legend()
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visualize.plot_save(f"{args.output_prefix}_user_client_prc.png")
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logger.info("plot user 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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client_pred, server_pred = dataset.load_predictions(model_args["future_prediction"])
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df_pred = pd.DataFrame(data={"names": name_val, "client_val": client_pred.value.flatten()})
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user_preds = df_pred.groupby(df_pred.names).max().client_val.as_matrix().astype(float)
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visualize.plot_roc_curve(user_vals, user_preds, model_args["model_path"])
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visualize.plot_legend()
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visualize.plot_save(f"{args.output_prefix}_user_client_roc.png")
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def main():
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if "train" == args.mode:
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main_train()
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if "hyperband" == args.mode:
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main_hyperband()
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if "test" == args.mode:
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main_test()
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if "fancy" == args.mode:
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main_visualization()
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if "all_fancy" == args.mode:
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main_visualize_all()
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if "paul" == args.mode:
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main_paul_best()
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if __name__ == "__main__":
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main()
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