def main(scenarios_file, device, epochs, patience, verbose, hyper_params_file):
hps = get_hyperparameter_options(hyper_params_file)[0]
batch_size = hps['batch_size']
limit_vectors = hps['limit_vectors']
dlf = MVSDataLoaderFactory(batch_size=batch_size, limit_vectors=limit_vectors)
scenarios = load_scenarios(scenarios_file)
print('training in', device)
# dumb model only to save specs
dmodel = HS_Model(vector_size=vector_size, device=device, patience=patience, **hps)
save_config(dmodel, hps, results_config_file)
for scenario in scenarios:
reset_all_seeds(RANDOM_SEED)
print('\nTraining scenario:',scenario)
print('Hyperparameters:',hps)
train_loader, dev_loader, test_loader = dlf.data_loaders_from_scenario(scenario)
model = HS_Model(vector_size=vector_size, device=device, patience=patience,
save_best=True, scenario=scenario, model_path=model_path, **hps)
model.train(train_loader, dev_loader, epochs=epochs, verbose=verbose)
best_model = load_model(model_path, scenario)
save_summary(results_file, scenario, model, best_model, train_loader, dev_loader, test_loader, verbose=1)
save_history(history_path, scenario, model)
print('Finish training scenario:',scenario)
def start_summary():
path = utils.initialize_summary()
latlong = utils.read_latlong()
place_list = list(latlong.keys())
for place in place_list:
df = utils.load_year(place)
summary = utils.make_summary(df)
utils.save_summary(summary, place)
def main():
# path = "./asset/Donald-Trump-vs-Barack-Obama-on-Nuclear-Weapons-in-East-Asia.txt"
path = "./asset/People-Arent-Upgrading-Smartphones-as-Quickly-and-That-Is-Bad-for-Apple.txt"
# path = "./asset/The-Last-Man-on-the-Moon--Eugene-Cernan-gives-a-compelling-account.txt"
path_synsets = "./asset/synsets.txt"
path_nasari = "./asset/dd-nasari.txt"
# Lettura del file Synset ottenuto con lo script titleSynset.py
synsets = utils.read_file_synset(path_synsets)
# Dizionario di synsets con parola come key e babel synset id come valore
word_to_synset = utils.word_to_synset_dict(synsets)
# Lettura del file nasari
nasari = utils.read_file_nasari(path_nasari)
# Lettura file da testare
text = utils.read_file(path)
# Individuazione di 10 keyword nel file
keywords = utils.get_key_words(text)
# print(keywords)
# Divisione del testo in titolo e paragrafi
dictionary = utils.paragraph(text)
# Pulizia del titolo con unione dei nomi propri in unico token ed eliminazione delle stop words
dictionary = utils.clean_title(dictionary)
# print(dictionary)
# Determinazione del contesto
context = get_context(dictionary["Titolo"], word_to_synset, nasari)
# print(context)
# context = []
# Determinazione dell'importanza/rank dei paragrafi
rank_p = rank_paragraphs(dictionary, context, keywords)
rank_p2 = copy.deepcopy(rank_p)
print("\n\n\nORIGINAL\n\n\n" + utils.generate_summary(rank_p))
# Creazione riassunti con metodo trivial
summary = summarize_trivial(
rank_p2, ratio=0.3
) # Il ratio si può cambiare in base alla percentuale di riassunto
print("\n\n\nSUMMARY TRIVIAL\n\n\n" + utils.generate_summary(summary))
# Creazione riassunti con metodo efficiente
summary = summarize(
rank_p, ratio=0.3
) # Il ratio si può cambiare in base alla percentuale di riassunto
print("\n\n\nSUMMARY\n\n\n" + utils.generate_summary(summary))
# Salvataggio riassunti
utils.save_summary(summary)
def _save_progress(self, dir, summaries=None, is_best=False):
last_chkpt_path = join_path(dir, self.CHECKPOINT_LAST)
state = {
'last_step': self.global_step,
'network_state_dict': self.network.state_dict(),
'optimizer_state_dict': self.optimizer.state_dict()
}
th.save(state, last_chkpt_path)
logging.info('The state of the agent is saved at step #%d' %
self.global_step)
if (summaries is not None) and len(summaries) > 0:
summaries_path = join_path(dir, self.SUMMARY_FILE)
utils.save_summary(summaries, summaries_path)
if is_best:
best_chkpt_path = join_path(dir, self.CHECKPOINT_BEST)
shutil.copyfile(last_chkpt_path, best_chkpt_path)
# metric in the 'metrics' MetricList
lr_scheduler = ReduceLROnPlateau(
optimizer, mode="max", patience=config["lr_patience"], verbose=True
)
model_checkpoint = engine.ModelCheckpoint(model_path, mode="max")
early_stopping = engine.EarlyStopping(mode="max", patience=config["stop_patience"])
# Train the model
print()
train = engine.Trainer(
net,
optimizer,
criterion,
metrics,
config["epochs"],
start_epoch=start_epoch,
lr_scheduler=lr_scheduler,
early_stop=early_stopping,
model_checkpoint=model_checkpoint,
device=config["device"],
)
net, checkpoint = train.fit(train_loader, val_loader, output_fn=sigmoid_threshold)
# Save a summary file containing the args, losses, and metrics
config_path = os.path.join(checkpoint_dir, os.path.basename(args.config))
utils.save_config(config_path, config)
summary_path = os.path.join(checkpoint_dir, "summary.json")
utils.save_summary(
summary_path, vars(args), config, checkpoint["losses"], checkpoint["metrics"]
)
os.makedirs(os.path.join(model_path, 'history'), exist_ok=True)
# os.makedirs(os.path.join(model_path, model_name + '-Checkpoint'), exist_ok = True) # Create folder if not present
# checkpoint = ModelCheckpoint(os.path.join(model_path, model_name + '-Checkpoint', model_name) + '-Checkpoint-{epoch:03d}.h5')
# early_stop = EarlyStopping(monitor = 'val_loss', patience = 3, verbose = 1, min_delta = 1e-4)
reduce_lr = ReduceLROnPlateau(monitor='loss',
factor=0.5,
patience=3,
verbose=1,
min_delta=1e-4)
kappa_metrics = Metrics()
callbacks_list = [logger, reduce_lr, kappa_metrics]
# callbacks_list = [logger, reduce_lr]
for i in range(EPOCHS):
print("Epoch:", i)
for folder in train_folders:
print("Folder:", folder)
load_and_train(folder)
if i == EPOCHS // 4:
"Setting full model to trainable"
model.trainable = True
# model.save(os.path.join(model_path, model_name) + '.h5')
# model = load_model(os.path.join(model_path, model_name) + '_best.h5', custom_objects = {'kappa_loss': kappa_loss, 'ordinal_loss': ordinal_loss, 'cauchy_loss': cauchy_loss, 'correntropy_loss': correntropy_loss})
#####
save_summary(model_name,
best_kappa=best_kappa,
epoch=best_kappa_epoch,
filename='models/performance.csv')