def main(address):
directories = glob.glob(address)
for directory in directories:
if(files_exits(directory)):
print(directory)
args = utils.load_model_config(directory)
lc_path, log_path, experiment = parse_args(directory)
x = np.load(lc_path)[()]
experiment = generate_experiment_string(args)
epoch_times = extract_epoch_time(log_path)
train_ppls, val_ppls, train_losses, val_losses = [x['train_ppls'], x['val_ppls'], x['train_losses'], x['val_losses'] ]
plots(train_losses, val_losses, train_ppls, val_ppls, epoch_times, experiment, directory)
def main():
"""
Main predict function for the wikikg90m
"""
args = ArgParser().parse_args()
config = load_model_config(
os.path.join(args.model_path, 'model_config.json'))
args = use_config_replace_args(args, config)
dataset = get_dataset(args, args.data_path, args.dataset, args.format,
args.delimiter, args.data_files,
args.has_edge_importance)
print("Load the dataset done.")
eval_dataset = EvalDataset(dataset, args)
model = BaseKEModel(
args=args,
n_entities=dataset.n_entities,
n_relations=dataset.n_relations,
model_name=args.model_name,
hidden_size=args.hidden_dim,
entity_feat_dim=dataset.entity_feat.shape[1],
relation_feat_dim=dataset.relation_feat.shape[1],
gamma=args.gamma,
double_entity_emb=args.double_ent,
cpu_emb=args.cpu_emb,
relation_times=args.ote_size,
scale_type=args.scale_type)
print("Create the model done.")
model.entity_feat = dataset.entity_feat
model.relation_feat = dataset.relation_feat
load_model_from_checkpoint(model, args.model_path)
print("The model load the checkpoint done.")
if args.infer_valid:
valid_sampler_tail = eval_dataset.create_sampler(
'valid',
args.batch_size_eval,
mode='tail',
num_workers=args.num_workers,
rank=0,
ranks=1)
infer(args, model, config, 0, [valid_sampler_tail], "valid")
if args.infer_test:
test_sampler_tail = eval_dataset.create_sampler(
'test',
args.batch_size_eval,
mode='tail',
num_workers=args.num_workers,
rank=i,
ranks=args.num_proc)
infer(args, model, config, 0, [test_sampler_tail], "test")
def summarize_models(address):
directories = glob.glob(address)
train_ppls_list, val_ppls_list, train_losses_list, val_losses_list, experiment_list, directory_list, epoch_times_list = [], [], [], [], [], [], []
for directory in directories:
if(files_exits(directory)):
print(directory)
args = utils.load_model_config(directory)
lc_path, log_path, experiment = parse_args(directory)
x = np.load(lc_path)[()]
experiment = generate_experiment_string(args)
epoch_times = extract_epoch_time(log_path)
train_ppls, val_ppls, train_losses, val_losses = [x['train_ppls'], x['val_ppls'], x['train_losses'], x['val_losses'] ]
train_ppls_list.append(train_ppls)
val_ppls_list.append(val_ppls)
train_losses_list.append(train_losses)
val_losses_list.append(val_losses)
experiment_list.append(experiment)
directory_list.append(directory)
epoch_times_list.append(epoch_times)
summarize_plots(train_losses_list, val_losses_list, train_ppls_list, val_ppls_list, epoch_times_list, experiment_list, directory_list)
parser = argparse.ArgumentParser(
description='PyTorch Penn Treebank Language Modeling')
parser.add_argument('--saved_models_dir',
type=str,
help='Directory with saved models \
(best_params.pt and exp_config.txt must be present there). \
All its\' individual subdirectories will be iterated')
saved_model_dir = parser.parse_args().saved_models_dir
plt.figure() #(figsize=(12, 12))
dirs = [
x[0] for x in os.walk(saved_model_dir)
if x[0] != saved_model_dir and 'TRANSFORMER' not in x[0]
]
for dir_name in dirs:
args = utils.load_model_config(dir_name)
x_raw = np.load(os.path.join(dir_name, 'timestep_grads.npy'))
x = minmax_scale(x_raw)
plt.plot(x, marker='o', label=args['model'])
plt.title('{}'.format('Final time-step loss gradient wrt hidden states'))
plt.xlabel("Hidden state (concatenated)")
plt.ylabel("Rescaled gradient norm")
plt.grid()
plt.legend()
for dir in dirs:
plt.savefig('{}/timestamp_grads_corrected.png'.format(dir))
plt.show()
training_data.iloc[::downsample_full_train].loc[:,
feature_cols],
training_data.iloc[::downsample_full_train][target])
save_model(model, model_name)
gc.collect()
model_config["feature_cols"] = feature_cols
model_config["targets"] = targets
model_config["best_pred_col"] = best_pred_col
model_config["riskiest_features"] = riskiest_features
print(f"saving model config for {model_config_name}")
save_model_config(model_config, model_config_name)
else:
# load model config from previous model selection loop
print(f"loading model config for {model_config_name}")
model_config = load_model_config(model_config_name)
feature_cols = model_config["feature_cols"]
targets = model_config["targets"]
best_pred_col = model_config["best_pred_col"]
riskiest_features = model_config["riskiest_features"]
""" Things that we always do even if we've already trained """
gc.collect()
print("reading tournament_data")
live_data = pd.read_parquet('v4/live.parquet')
print("reading validation_data")
validation_data = pd.read_parquet('v4/validation.parquet')
print("reading example_predictions")
example_preds = pd.read_parquet('v4/live_example_preds.parquet')
print("reading example_validaton_predictions")
validation_example_preds = pd.read_parquet(
type=str,
help='Directory with saved models \
(best_params.pt and exp_config.txt must be present there). \
All its\' individual subdirectories will be iterated')
parser.add_argument('--generated_seq_len',
type=int,
default=35,
help='length of generated sequences')
args = parser.parse_args()
output_dir = parser.parse_args().saved_models_dir
seq_len = args.generated_seq_len
# load model configuration
args = load_model_config(output_dir)
# Set the random seed manually for reproducibility.
torch.manual_seed(args.seed)
# LOAD DATA
print('Loading data from ' + args.data)
raw_data = ptb_raw_data(data_path=args.data)
train_data, valid_data, test_data, word_to_id, id_2_word = raw_data
vocab_size = len(word_to_id)
print(' vocabulary size: {}'.format(vocab_size))
if args.model == 'RNN':
model = RNN(emb_size=args.emb_size,
hidden_size=args.hidden_size,
seq_len=args.seq_len,
