import json import logging import os import joblib import numpy as np import pandas as pd import tensorflow as tf from keras.callbacks import ModelCheckpoint, CSVLogger, EarlyStopping from keras.models import load_model, Model import arguments import dataset import hyperband import models # create logger import visualize from dataset import load_or_generate_h5data from utils import exists_or_make_path, get_custom_class_weights from arguments import get_model_args 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() if args.gpu: 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) # default parameter PARAMS = { "type": args.model_type, "depth": args.model_depth, # "batch_size": 64, "window_size": args.window, "domain_length": args.domain_length, "flow_features": 3, # 'dropout': 0.5, # currently fix 'domain_features': args.domain_embedding, 'embedding_size': args.embedding, 'flow_features': 3, 'filter_embedding': args.filter_embedding, 'dense_embedding': args.dense_embedding, 'kernel_embedding': args.kernel_embedding, 'filter_main': args.filter_main, 'dense_main': args.dense_main, 'kernel_main': args.kernel_main, 'input_length': 40, 'model_output': args.model_output } def create_model(model, output_type): if output_type == "both": return Model(inputs=[model.in_domains, model.in_flows], outputs=(model.out_client, model.out_server)) elif output_type == "client": return Model(inputs=[model.in_domains, model.in_flows], outputs=(model.out_client,)) else: raise Exception("unknown model output") def main_paul_best(): pauls_best_params = models.pauls_networks.best_config main_train(pauls_best_params) def main_hyperband(): params = { # static params "type": ["paul"], "batch_size": [args.batch_size], "window_size": [10], "domain_length": [40], "flow_features": [3], "input_length": [40], # model params "embedding_size": [8, 16, 32, 64, 128, 256], "filter_embedding": [8, 16, 32, 64, 128, 256], "kernel_embedding": [1, 3, 5, 7, 9], "hidden_embedding": [8, 16, 32, 64, 128, 256], "dropout": [0.5], "domain_features": [8, 16, 32, 64, 128, 256], "filter_main": [8, 16, 32, 64, 128, 256], "kernels_main": [1, 3, 5, 7, 9], "dense_main": [8, 16, 32, 64, 128, 256], } logger.info("create training dataset") domain_tr, flow_tr, name_tr, client_tr, server_tr = load_or_generate_h5data(args.train_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 main_train(param=None): logger.info(f"Create model path {args.model_path}") exists_or_make_path(args.model_path) logger.info(f"Use command line arguments: {args}") domain_tr, flow_tr, name_tr, client_tr, server_windows_tr = load_or_generate_h5data(args.train_h5data, args.train_data, args.domain_length, args.window) logger.info("define callbacks") callbacks = [] callbacks.append(ModelCheckpoint(filepath=args.clf_model, monitor='val_loss', verbose=False, save_best_only=True)) callbacks.append(CSVLogger(args.train_log)) logger.info(f"Use early stopping: {args.stop_early}") if args.stop_early: callbacks.append(EarlyStopping(monitor='val_loss', patience=5, verbose=False)) custom_metrics = models.get_metric_functions() server_tr = np.max(server_windows_tr, axis=1) if args.class_weights: logger.info("class weights: compute custom weights") custom_class_weights = get_custom_class_weights(client_tr.value, server_tr) logger.info(custom_class_weights) else: logger.info("class weights: set default") custom_class_weights = None logger.info(f"select model: {args.model_type}") if args.model_type == "staggered": if not param: param = PARAMS logger.info(f"Generator model with params: {param}") embedding, model, new_model = models.get_models_by_params(param) model = create_model(new_model, args.model_output) server_tr = np.expand_dims(server_windows_tr, 2) logger.info("compile and train model") embedding.summary() model.summary() logger.info(model.get_config()) model.compile(optimizer='adam', loss='binary_crossentropy', loss_weights={"client": 0.0, "server": 1.0}, metrics=['accuracy'] + custom_metrics) model.fit({"ipt_domains": domain_tr, "ipt_flows": flow_tr}, {"client": client_tr, "server": server_tr}, batch_size=args.batch_size, epochs=args.epochs, shuffle=True, validation_split=0.2, class_weight=custom_class_weights) model.get_layer("dense_server").trainable = False model.get_layer("server").trainable = False model.compile(optimizer='adam', loss='binary_crossentropy', loss_weights={"client": 1.0, "server": 0.0}, metrics=['accuracy'] + custom_metrics) model.summary() model.fit({"ipt_domains": domain_tr, "ipt_flows": flow_tr}, {"client": client_tr, "server": server_tr}, batch_size=args.batch_size, epochs=args.epochs, callbacks=callbacks, shuffle=True, validation_split=0.2, class_weight=custom_class_weights) else: if not param: param = PARAMS logger.info(f"Generator model with params: {param}") embedding, model, new_model = models.get_models_by_params(param) model = create_model(model, args.model_output) new_model = create_model(new_model, args.model_output) if args.model_type == "inter": server_tr = np.expand_dims(server_windows_tr, 2) model = new_model logger.info("compile and train model") embedding.summary() model.summary() logger.info(model.get_config()) model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'] + custom_metrics) if args.model_output == "both": labels = [client_tr, server_tr] elif args.model_output == "client": labels = [client_tr] elif args.model_output == "server": labels = [server_tr] else: raise ValueError("unknown model output") model.fit([domain_tr, flow_tr], labels, batch_size=args.batch_size, epochs=args.epochs, callbacks=callbacks, shuffle=True, validation_split=0.2, class_weight=custom_class_weights) logger.info("save embedding") embedding.save(args.embedding_model) def main_test(): logger.info("start test: load data") domain_val, flow_val, name_val, client_val, server_val = load_or_generate_h5data(args.test_h5data, args.test_data, args.domain_length, args.window) domain_encs, labels = dataset.load_or_generate_domains(args.test_data, args.domain_length) for model_args in get_model_args(args): results = {} logger.info(f"process model {model_args['model_path']}") clf_model = load_model(model_args["clf_model"], custom_objects=models.get_metrics()) pred = clf_model.predict([domain_val, flow_val], batch_size=args.batch_size, verbose=1) if args.model_output == "both": c_pred, s_pred = pred results["client_pred"] = c_pred results["server_pred"] = s_pred elif args.model_output == "client": results["client_pred"] = pred else: results["server_pred"] = pred # dataset.save_predictions(model_args["future_prediction"], c_pred, s_pred) embd_model = load_model(model_args["embedding_model"]) domain_embeddings = embd_model.predict(domain_encs, batch_size=args.batch_size, verbose=1) # np.save(model_args["model_path"] + "/domain_embds.npy", domain_embeddings) results["domain_embds"] = domain_embeddings joblib.dump(results, model_args["model_path"] + "results.joblib", compress=3) def main_visualization(): domain_val, flow_val, name_val, client_val, server_val = load_or_generate_h5data(args.test_h5data, args.test_data, args.domain_length, args.window) # client_val, server_val = client_val.value, server_val.value client_val = client_val.value logger.info("plot model") model = load_model(args.clf_model, custom_objects=models.get_metrics()) visualize.plot_model_as(model, os.path.join(args.model_path, "model.png")) try: logger.info("plot training curve") logs = pd.read_csv(args.train_log) if args.model_output == "client": visualize.plot_training_curve(logs, "", "{}/client_train.png".format(args.model_path)) else: visualize.plot_training_curve(logs, "client_", "{}/client_train.png".format(args.model_path)) visualize.plot_training_curve(logs, "server_", "{}/server_train.png".format(args.model_path)) except Exception as e: logger.warning(f"could not generate training curves: {e}") client_pred, server_pred = dataset.load_predictions(args.future_prediction) client_pred, server_pred = client_pred.value.flatten(), server_pred.value.flatten() logger.info("plot pr curve") visualize.plot_clf() visualize.plot_precision_recall(client_val, client_pred, args.model_path) visualize.plot_legend() visualize.plot_save("{}/window_client_prc.png".format(args.model_path)) # visualize.plot_precision_recall(server_val, server_pred, "{}/server_prc.png".format(args.model_path)) # visualize.plot_precision_recall_curves(client_val, client_pred, "{}/client_prc2.png".format(args.model_path)) # visualize.plot_precision_recall_curves(server_val, server_pred, "{}/server_prc2.png".format(args.model_path)) logger.info("plot roc curve") visualize.plot_clf() visualize.plot_roc_curve(client_val, client_pred, args.model_path) visualize.plot_legend() visualize.plot_save("{}/window_client_roc.png".format(args.model_path)) # visualize.plot_roc_curve(server_val, server_pred, "{}/server_roc.png".format(args.model_path)) print(f"names {name_val.shape} vals {client_val.shape} preds {client_pred.shape}") df_val = pd.DataFrame(data={"names": name_val, "client_val": client_val}) user_vals = df_val.groupby(df_val.names).max().client_val.as_matrix().astype(float) df_pred = pd.DataFrame(data={"names": name_val, "client_val": client_pred}) user_preds = df_pred.groupby(df_pred.names).max().client_val.as_matrix().astype(float) visualize.plot_clf() visualize.plot_precision_recall(user_vals, user_preds, args.model_path) visualize.plot_legend() visualize.plot_save("{}/user_client_prc.png".format(args.model_path)) visualize.plot_clf() visualize.plot_roc_curve(user_vals, user_preds, args.model_path) visualize.plot_legend() visualize.plot_save("{}/user_client_roc.png".format(args.model_path)) visualize.plot_confusion_matrix(client_val, client_pred.flatten().round(), "{}/client_cov.png".format(args.model_path), normalize=False, title="Client Confusion Matrix") visualize.plot_confusion_matrix(user_vals, user_preds.flatten().round(), "{}/user_cov.png".format(args.model_path), normalize=False, title="User Confusion Matrix") logger.info("visualize embedding") domain_encs, labels = dataset.load_or_generate_domains(args.test_data, args.domain_length) domain_embedding = np.load(args.model_path + "/domain_embds.npy") visualize.plot_embedding(domain_embedding, labels, path="{}/embd.png".format(args.model_path)) def main_visualize_all(): domain_val, flow_val, name_val, client_val, server_val = load_or_generate_h5data(args.test_h5data, args.test_data, args.domain_length, args.window) logger.info("plot pr curves") visualize.plot_clf() for model_args in get_model_args(args): client_pred, server_pred = dataset.load_predictions(model_args["future_prediction"]) visualize.plot_precision_recall(client_val.value, client_pred.value, model_args["model_path"]) visualize.plot_legend() visualize.plot_save(f"{args.output_prefix}_window_client_prc.png") logger.info("plot roc curves") visualize.plot_clf() for model_args in get_model_args(args): client_pred, server_pred = dataset.load_predictions(model_args["future_prediction"]) visualize.plot_roc_curve(client_val.value, client_pred.value, model_args["model_path"]) visualize.plot_legend() visualize.plot_save(f"{args.output_prefix}_window_client_roc.png") df_val = pd.DataFrame(data={"names": name_val, "client_val": client_val}) user_vals = df_val.groupby(df_val.names).max().client_val.as_matrix().astype(float) logger.info("plot user pr curves") visualize.plot_clf() for model_args in get_model_args(args): client_pred, server_pred = dataset.load_predictions(model_args["future_prediction"]) df_pred = pd.DataFrame(data={"names": name_val, "client_val": client_pred.value.flatten()}) user_preds = df_pred.groupby(df_pred.names).max().client_val.as_matrix().astype(float) visualize.plot_precision_recall(user_vals, user_preds, model_args["model_path"]) visualize.plot_legend() visualize.plot_save(f"{args.output_prefix}_user_client_prc.png") logger.info("plot user roc curves") visualize.plot_clf() for model_args in get_model_args(args): client_pred, server_pred = dataset.load_predictions(model_args["future_prediction"]) df_pred = pd.DataFrame(data={"names": name_val, "client_val": client_pred.value.flatten()}) user_preds = df_pred.groupby(df_pred.names).max().client_val.as_matrix().astype(float) visualize.plot_roc_curve(user_vals, user_preds, model_args["model_path"]) visualize.plot_legend() visualize.plot_save(f"{args.output_prefix}_user_client_roc.png") def main(): if "train" == args.mode: main_train() if "hyperband" == args.mode: main_hyperband() if "test" == args.mode: main_test() if "fancy" == args.mode: main_visualization() if "all_fancy" == args.mode: main_visualize_all() if "paul" == args.mode: main_paul_best() if __name__ == "__main__": main()