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train.py
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import argparse
import time
from datetime import datetime
import os,sys
from os.path import exists
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.optim import Adam
from data_loader import PolyphonicDataset
import models, configs
from helper import get_logger, gVar
from tensorboardX import SummaryWriter # install tensorboardX (pip install tensorboardX) before importing this package
from imputation import impute_with_mean
from torch.autograd import Variable
sys.path.append(os.path.dirname(os.path.abspath(__file__)) + '/lib')
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
sys.path.append(os.path.dirname(os.path.abspath(__file__))+'/imputation')
sys.path.append(os.path.dirname(os.path.abspath(__file__)) + '/lib')
sys.path.append(os.path.dirname(os.path.abspath(__file__)) + '/initialize')
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
sys.path.append(os.path.abspath(__file__))
# from initialize.initialize_kmeans import *
# print(os.path.dirname(os.path.abspath(__file__)) + '/lib')
from data.generate_time_series import *
from lib.utils import *
# cluster_models = ['DHMM_cluster', 'DHMM_cluster2', 'DHMM_cluster3', 'DHMM_cluster_tlstm']
def save_model(model, epoch):
ckpt_path='./output/{}/{}/{}/models/model_epo{}.pkl'.format(args.model, args.expname, args.dataset, epoch)
print("saving model to %s..." % ckpt_path)
torch.save(model.state_dict(), ckpt_path)
def load_model(model, epoch):
ckpt_path='./output/{}/{}/{}/models/model_epo{}.pkl'.format(args.model, args.expname, args.dataset, epoch)
assert exists(ckpt_path), "epoch misspecified"
print("loading model from %s..." % ckpt_path)
model.load_state_dict(torch.load(ckpt_path))
# setup, training, and evaluation
def evaluate_imputation_errors(data_obj, model, is_GPU, device):
with torch.no_grad():
training_imputed_mse_loss = 0
training_imputed_mse_loss2 = 0
training_imputed_mae_loss = 0
training_imputed_mae_loss2 = 0
testing_imputed_mse_loss = 0
testing_imputed_mse_loss2 = 0
testing_imputed_mae_loss = 0
testing_imputed_mae_loss2 = 0
training_count = 0
testing_count = 0
model.evaluate = True
for id, batch_dict in enumerate(data_obj["train_dataloader"]):
imputed_data,(imputed_mse_loss, imputed_mse_loss2, imputed_loss, imputed_loss2) = model.infer(batch_dict["observed_data"], batch_dict["origin_observed_data"], batch_dict['observed_mask'], batch_dict["observed_origin_mask"], batch_dict["observed_new_mask"], batch_dict["observed_lens"], batch_dict['data_to_predict'], batch_dict["origin_data_to_predict"], batch_dict['mask_predicted_data'], batch_dict['origin_mask_predicted_data'], batch_dict['new_mask_predicted_data'], batch_dict["lens_to_predict"], is_GPU, device)
new_x_mask_count = torch.sum(1-batch_dict["observed_new_mask"])
training_count += new_x_mask_count
training_imputed_mse_loss += imputed_mse_loss**2*new_x_mask_count
training_imputed_mse_loss2 += imputed_mse_loss2**2*new_x_mask_count
training_imputed_mae_loss += imputed_loss*new_x_mask_count
training_imputed_mae_loss2 += imputed_loss2*new_x_mask_count
for id, batch_dict in enumerate(data_obj["test_dataloader"]):
imputed_data,(imputed_mse_loss, imputed_mse_loss2, imputed_loss, imputed_loss2) = model.infer(batch_dict["observed_data"], batch_dict["origin_observed_data"], batch_dict['observed_mask'], batch_dict["observed_origin_mask"], batch_dict["observed_new_mask"], batch_dict["observed_lens"], batch_dict['data_to_predict'], batch_dict["origin_data_to_predict"], batch_dict['mask_predicted_data'], batch_dict['origin_mask_predicted_data'], batch_dict['new_mask_predicted_data'], batch_dict["lens_to_predict"], is_GPU, device)
new_x_mask_count = torch.sum(1-batch_dict["observed_new_mask"])
testing_count += new_x_mask_count
testing_imputed_mse_loss += imputed_mse_loss**2*new_x_mask_count
testing_imputed_mse_loss2 += imputed_mse_loss2**2*new_x_mask_count
testing_imputed_mae_loss += imputed_loss*new_x_mask_count
testing_imputed_mae_loss2 += imputed_loss2*new_x_mask_count
final_training_imputed_mse_loss = torch.sqrt(training_imputed_mse_loss/training_count)
final_training_imputed_mse_loss2 = torch.sqrt(training_imputed_mse_loss2/training_count)
final_training_imputed_mae_loss = training_imputed_mae_loss/training_count
final_training_imputed_mae_loss2 = training_imputed_mae_loss2/training_count
final_testing_imputed_mse_loss = torch.sqrt(testing_imputed_mse_loss/testing_count)
final_testing_imputed_mse_loss2 = torch.sqrt(testing_imputed_mse_loss2/testing_count)
final_testing_imputed_mae_loss = testing_imputed_mae_loss/testing_count
final_testing_imputed_mae_loss2 = testing_imputed_mae_loss2/testing_count
print('training imputation rmse loss::', final_training_imputed_mse_loss)
print('training imputation rmse loss 2::', final_training_imputed_mse_loss2)
print('training imputation mae loss::', final_training_imputed_mae_loss)
print('training imputation mae loss 2::', final_training_imputed_mae_loss2)
print('testing imputation rmse loss::', final_testing_imputed_mse_loss)
print('testing imputation rmse loss 2::', final_testing_imputed_mse_loss2)
print('testing imputation mae loss::', final_testing_imputed_mae_loss)
print('testing imputation mae loss 2::', final_testing_imputed_mae_loss2)
def test(data_obj, model, is_GPU, device):
observed_test_data = []
observed_test_mask = []
observed_test_lens = []
pred_test_data = []
pred_test_mask = []
pred_test_lens = []
final_rmse_loss = 0
final_rmse_loss2 = 0
final_mae_losses = 0
final_mae_losses2 = 0
final_nll_loss = 0
final_nll_loss2 = 0
all_count1 = 0
all_count2 = 0
final_imputed_rmse_loss = 0
final_imputed_mae_loss = 0
final_imputed_rmse_loss2 = 0
final_imputed_mae_loss2 = 0
all_count3 = 0
all_count4 = 0
all_count5 = 0
forecasting_rmse_list = 0
forecasting_mae_list = 0
forecasting_rmse_list2 = 0
forecasting_mae_list2 = 0
forecasting_count = 0
with torch.no_grad():
for id, data_dict in enumerate(data_obj["test_dataloader"]):
batch_dict = data_dict
# curr_seq_len = len(batch_dict['observed_tp'])
rmse_loss, rmse_loss_count, mae_losses, mae_loss_count, nll_loss, nll_loss_count, list_res, imputed_res = model.test_samples(Variable(batch_dict["observed_data"]), Variable(batch_dict["origin_observed_data"]), Variable(batch_dict['observed_mask']), Variable(batch_dict["observed_origin_mask"]), Variable(batch_dict["observed_new_mask"]), Variable(batch_dict['observed_lens']), Variable(batch_dict['data_to_predict']), Variable(batch_dict["origin_data_to_predict"]), Variable(batch_dict['mask_predicted_data']), Variable(batch_dict['origin_mask_predicted_data']), Variable(batch_dict['new_mask_predicted_data']), Variable(batch_dict['lens_to_predict']), is_GPU, device, batch_dict["delta_time_stamps"], batch_dict["delta_time_stamps_to_predict"], batch_dict["time_stamps"], batch_dict["time_stamps_to_predict"])
# imputed_res = None
all_count1 += rmse_loss_count
all_count2 += mae_loss_count
# print(type(rmse_loss))
if type(rmse_loss) is tuple and len(list(rmse_loss)) == 2:
rmse_loss_list = list(rmse_loss)
mae_loss_list = list(mae_losses)
final_rmse_loss += (rmse_loss_list[0]**2)*rmse_loss_count
final_mae_losses += (mae_loss_list[0])*mae_loss_count
final_rmse_loss2 += (rmse_loss_list[1]**2)*rmse_loss_count
final_mae_losses2 += (mae_loss_list[1])*mae_loss_count
else:
final_rmse_loss += (rmse_loss**2)*rmse_loss_count
final_mae_losses += (mae_losses)*mae_loss_count
if nll_loss_count is not None:
if type(nll_loss) is tuple and len(list(nll_loss)) == 2:
nll_loss_list = list(nll_loss)
final_nll_loss += (nll_loss_list[0])*nll_loss_count
final_nll_loss2 += (nll_loss_list[1])*nll_loss_count
else:
final_nll_loss += (nll_loss)*nll_loss_count
all_count3 += nll_loss_count
else:
final_nll_loss = None
if imputed_res is not None:
imputed_mae_res, imputed_mae_count, imputed_rmse_res, imputed_rmse_count = imputed_res
if type(imputed_mae_res) is tuple:
imputed_rmse_loss, imputed_rmse_loss2 = imputed_rmse_res
imputed_mae_loss, imputed_mae_loss2 = imputed_mae_res
final_imputed_rmse_loss += (imputed_rmse_loss ** 2)*imputed_rmse_count
final_imputed_mae_loss += (imputed_mae_loss)*imputed_mae_count
final_imputed_rmse_loss2 += (imputed_rmse_loss2 ** 2)*imputed_rmse_count
final_imputed_mae_loss2 += (imputed_mae_loss2)*imputed_mae_count
else:
final_imputed_rmse_loss += (imputed_rmse_res ** 2)*imputed_rmse_count
final_imputed_mae_loss += (imputed_mae_res)*imputed_mae_count
all_count4 += imputed_rmse_count
all_count5 += imputed_mae_count
if type(list_res[0]) is tuple:
curr_forecasting_rmse_list = list(list_res[0])[0]
curr_forecasting_mae_list = list(list_res[1])[0]
curr_forecasting_rmse_list2 = list(list_res[0])[1]
curr_forecasting_mae_list2 = list(list_res[1])[1]
curr_forecasting_count = list_res[2]
forecasting_rmse_list += (curr_forecasting_rmse_list**2)*curr_forecasting_count
forecasting_mae_list += curr_forecasting_mae_list*curr_forecasting_count
forecasting_rmse_list2 += (curr_forecasting_rmse_list2**2)*curr_forecasting_count
forecasting_mae_list2 += curr_forecasting_mae_list2*curr_forecasting_count
forecasting_count += curr_forecasting_count
else:
curr_forecasting_rmse_list = list_res[0]
curr_forecasting_mae_list = list_res[1]
curr_forecasting_count = list_res[2]
forecasting_rmse_list += (curr_forecasting_rmse_list**2)*curr_forecasting_count
forecasting_mae_list += curr_forecasting_mae_list*curr_forecasting_count
forecasting_count += curr_forecasting_count
final_rmse_loss = torch.sqrt(final_rmse_loss/all_count1)
final_mae_losses = final_mae_losses/all_count2
final_rmse_loss2 = torch.sqrt(final_rmse_loss2/all_count1)
final_mae_losses2 = final_mae_losses2/all_count2
print('test results::')
print('test forecasting rmse loss::', final_rmse_loss)
print('test forecasting mae loss::', final_mae_losses)
print('test forecasting rmse loss 2::', final_rmse_loss2)
print('test forecasting mae loss 2::', final_mae_losses2)
if final_nll_loss is not None:
final_nll_loss = final_nll_loss/all_count3
final_nll_loss2 = final_nll_loss2/all_count3
print('test forecasting neg likelihood::', final_nll_loss)
print('test forecasting neg likelihood 2::', final_nll_loss2)
forecasting_rmse_list = torch.sqrt(forecasting_rmse_list/forecasting_count)
forecasting_mae_list = forecasting_mae_list/forecasting_count
forecasting_rmse_list2 = torch.sqrt(forecasting_rmse_list2/forecasting_count)
forecasting_mae_list2 = forecasting_mae_list2/forecasting_count
print('test forecasting rmse loss by time steps::')
print(forecasting_rmse_list)
print(forecasting_mae_list)
print('test forecasting rmse loss 2 by time steps::')
print(forecasting_rmse_list2)
print(forecasting_mae_list2)
if imputed_res is not None:
final_imputed_rmse_loss = torch.sqrt(final_imputed_rmse_loss/all_count4)
final_imputed_rmse_loss2 = torch.sqrt(final_imputed_rmse_loss2/all_count4)
final_imputed_mae_loss = (final_imputed_mae_loss/all_count5)
final_imputed_mae_loss2 = (final_imputed_mae_loss2/all_count5)
print('test imputation rmse loss::', final_imputed_rmse_loss)
print('test imputation mae loss::', final_imputed_mae_loss)
print('test imputation rmse loss 2::', final_imputed_rmse_loss2)
print('test imputation mae loss 2::', final_imputed_mae_loss2)
if not os.path.exists(data_dir + output_dir):
os.makedirs(data_dir + output_dir)
torch.save(model, data_dir + output_dir + 'model')
return (final_rmse_loss, final_mae_losses, final_rmse_loss2, final_mae_losses2, final_imputed_rmse_loss, final_imputed_mae_loss, final_imputed_rmse_loss2, final_imputed_mae_loss2)
# observed_test_mask.append(batch_dict['observed_mask'])
#
# pred_test_mask.append(batch_dict['mask_predicted_data'])
#
# observed_test_data.append(batch_dict["observed_data"])
#
# pred_test_data.append(batch_dict['data_to_predict'])
#
# observed_test_lens.append(batch_dict['observed_lens'])
#
# pred_test_lens.append(batch_dict['lens_to_predict'])
# return torch.cat(observed_test_data, 0), torch.cat(pred_test_data, 0), torch.cat(observed_test_mask, 0), torch.cat(pred_test_mask, 0), torch.cat(observed_test_lens, 0), torch.cat(pred_test_lens, 0)
def validate(data_obj, model, is_GPU, device):
observed_test_data = []
observed_test_mask = []
observed_test_lens = []
pred_test_data = []
pred_test_mask = []
pred_test_lens = []
final_rmse_loss = 0
final_rmse_loss2 = 0
final_mae_losses = 0
final_mae_losses2 = 0
final_nll_loss = 0
final_nll_loss2 = 0
all_count1 = 0
all_count2 = 0
final_imputed_rmse_loss = 0
final_imputed_mae_loss = 0
final_imputed_rmse_loss2 = 0
final_imputed_mae_loss2 = 0
all_count3 = 0
all_count4 = 0
all_count5 = 0
forecasting_rmse_list = 0
forecasting_mae_list = 0
forecasting_rmse_list2 = 0
forecasting_mae_list2 = 0
forecasting_count = 0
with torch.no_grad():
for id, data_dict in enumerate(data_obj["valid_dataloader"]):
batch_dict = data_dict
# curr_seq_len = len(batch_dict['observed_tp'])
rmse_loss, rmse_loss_count, mae_losses, mae_loss_count, nll_loss, nll_loss_count, list_res, imputed_res = model.test_samples(Variable(batch_dict["observed_data"]), Variable(batch_dict["origin_observed_data"]), Variable(batch_dict['observed_mask']), Variable(batch_dict["observed_origin_mask"]), Variable(batch_dict["observed_new_mask"]), Variable(batch_dict['observed_lens']), Variable(batch_dict['data_to_predict']), Variable(batch_dict["origin_data_to_predict"]), Variable(batch_dict['mask_predicted_data']), Variable(batch_dict['origin_mask_predicted_data']), Variable(batch_dict['new_mask_predicted_data']), Variable(batch_dict['lens_to_predict']), is_GPU, device, batch_dict["delta_time_stamps"], batch_dict["delta_time_stamps_to_predict"], batch_dict["time_stamps"], batch_dict["time_stamps_to_predict"])
# imputed_res = None
all_count1 += rmse_loss_count
all_count2 += mae_loss_count
# print(type(rmse_loss))
if type(rmse_loss) is tuple and len(list(rmse_loss)) == 2:
rmse_loss_list = list(rmse_loss)
mae_loss_list = list(mae_losses)
final_rmse_loss += (rmse_loss_list[0]**2)*rmse_loss_count
final_mae_losses += (mae_loss_list[0])*mae_loss_count
final_rmse_loss2 += (rmse_loss_list[1]**2)*rmse_loss_count
final_mae_losses2 += (mae_loss_list[1])*mae_loss_count
else:
final_rmse_loss += (rmse_loss**2)*rmse_loss_count
final_mae_losses += (mae_losses)*mae_loss_count
if nll_loss_count is not None:
if type(nll_loss) is tuple and len(list(nll_loss)) == 2:
nll_loss_list = list(nll_loss)
final_nll_loss += (nll_loss_list[0])*nll_loss_count
final_nll_loss2 += (nll_loss_list[1])*nll_loss_count
else:
final_nll_loss += (nll_loss)*nll_loss_count
all_count3 += nll_loss_count
else:
final_nll_loss = None
if imputed_res is not None:
imputed_mae_res, imputed_mae_count, imputed_rmse_res, imputed_rmse_count = imputed_res
if type(imputed_mae_res) is tuple:
imputed_rmse_loss, imputed_rmse_loss2 = imputed_rmse_res
imputed_mae_loss, imputed_mae_loss2 = imputed_mae_res
final_imputed_rmse_loss += (imputed_rmse_loss ** 2)*imputed_rmse_count
final_imputed_mae_loss += (imputed_mae_loss)*imputed_mae_count
final_imputed_rmse_loss2 += (imputed_rmse_loss2 ** 2)*imputed_rmse_count
final_imputed_mae_loss2 += (imputed_mae_loss2)*imputed_mae_count
else:
final_imputed_rmse_loss += (imputed_rmse_res ** 2)*imputed_rmse_count
final_imputed_mae_loss += (imputed_mae_res)*imputed_mae_count
all_count4 += imputed_rmse_count
all_count5 += imputed_mae_count
if type(list_res[0]) is tuple:
curr_forecasting_rmse_list = list(list_res[0])[0]
curr_forecasting_mae_list = list(list_res[1])[0]
curr_forecasting_rmse_list2 = list(list_res[0])[1]
curr_forecasting_mae_list2 = list(list_res[1])[1]
curr_forecasting_count = list_res[2]
forecasting_rmse_list += (curr_forecasting_rmse_list**2)*curr_forecasting_count
forecasting_mae_list += curr_forecasting_mae_list*curr_forecasting_count
forecasting_rmse_list2 += (curr_forecasting_rmse_list2**2)*curr_forecasting_count
forecasting_mae_list2 += curr_forecasting_mae_list2*curr_forecasting_count
forecasting_count += curr_forecasting_count
else:
curr_forecasting_rmse_list = list_res[0]
curr_forecasting_mae_list = list_res[1]
curr_forecasting_count = list_res[2]
forecasting_rmse_list += (curr_forecasting_rmse_list**2)*curr_forecasting_count
forecasting_mae_list += curr_forecasting_mae_list*curr_forecasting_count
forecasting_count += curr_forecasting_count
final_rmse_loss = torch.sqrt(final_rmse_loss/all_count1)
final_mae_losses = final_mae_losses/all_count2
final_rmse_loss2 = torch.sqrt(final_rmse_loss2/all_count1)
final_mae_losses2 = final_mae_losses2/all_count2
print('validation results::')
print('validation forecasting rmse loss::', final_rmse_loss)
print('validation forecasting mae loss::', final_mae_losses)
print('validation forecasting rmse loss 2::', final_rmse_loss2)
print('validation forecasting mae loss 2::', final_mae_losses2)
if final_nll_loss is not None:
final_nll_loss = final_nll_loss/all_count3
final_nll_loss2 = final_nll_loss2/all_count3
print('validation forecasting neg likelihood::', final_nll_loss)
print('validation forecasting neg likelihood 2::', final_nll_loss2)
forecasting_rmse_list = torch.sqrt(forecasting_rmse_list/forecasting_count)
forecasting_mae_list = forecasting_mae_list/forecasting_count
forecasting_rmse_list2 = torch.sqrt(forecasting_rmse_list2/forecasting_count)
forecasting_mae_list2 = forecasting_mae_list2/forecasting_count
print('validation forecasting rmse loss by time steps::')
print(forecasting_rmse_list)
print(forecasting_mae_list)
print('validation forecasting rmse loss 2 by time steps::')
print(forecasting_rmse_list2)
print(forecasting_mae_list2)
if imputed_res is not None:
final_imputed_rmse_loss = torch.sqrt(final_imputed_rmse_loss/all_count4)
final_imputed_rmse_loss2 = torch.sqrt(final_imputed_rmse_loss2/all_count4)
final_imputed_mae_loss = (final_imputed_mae_loss/all_count5)
final_imputed_mae_loss2 = (final_imputed_mae_loss2/all_count5)
print('validation imputation rmse loss::', final_imputed_rmse_loss)
print('validation imputation mae loss::', final_imputed_mae_loss)
print('validation imputation rmse loss 2::', final_imputed_rmse_loss2)
print('validation imputation mae loss 2::', final_imputed_mae_loss2)
if not os.path.exists(data_dir + output_dir):
os.makedirs(data_dir + output_dir)
torch.save(model, data_dir + output_dir + 'model')
return final_rmse_loss
# observed_test_mask.append(batch_dict['observed_mask'])
#
# pred_test_mask.append(batch_dict['mask_predicted_data'])
#
# observed_test_data.append(batch_dict["observed_data"])
#
# pred_test_data.append(batch_dict['data_to_predict'])
#
# observed_test_lens.append(batch_dict['observed_lens'])
#
# pred_test_lens.append(batch_dict['lens_to_predict'])
# return torch.cat(observed_test_data, 0), torch.cat(pred_test_data, 0), torch.cat(observed_test_mask, 0), torch.cat(pred_test_mask, 0), torch.cat(observed_test_lens, 0), torch.cat(pred_test_lens, 0)
def print_test_res(all_valid_rmse_list, all_test_res, args):
all_valid_rmse_array = np.array(all_valid_rmse_list)
# for i in range(all_valid_rmse_array.shape[0]):
selected_id = np.argmin(all_valid_rmse_array)
test_res = all_test_res[selected_id]
final_rmse_loss, final_mae_losses, final_rmse_loss2, final_mae_losses2, final_imputed_rmse_loss, final_imputed_mae_loss, final_imputed_rmse_loss2, final_imputed_mae_loss2 = test_res
print('test results::')
if args.model.startswith(cluster_ODE_method):
rmse_loss = min(final_rmse_loss, final_rmse_loss2)
mae_loss = min(final_mae_losses, final_mae_losses2)
imputed_rmse_loss = min(final_imputed_rmse_loss, final_imputed_rmse_loss2)
imputed_mae_loss = min(final_imputed_mae_loss, final_imputed_mae_loss2)
print('test forecasting rmse loss::', rmse_loss)
print('test forecasting mae loss::', mae_loss)
# print('test forecasting rmse loss 2::', final_rmse_loss2)
#
# print('test forecasting mae loss 2::', final_mae_losses2)
print('test imputation rmse loss::', imputed_rmse_loss)
print('test imputation mae loss::', imputed_mae_loss)
else:
print('test forecasting rmse loss::', final_rmse_loss)
print('test forecasting mae loss::', final_mae_losses)
# print('test forecasting rmse loss 2::', final_rmse_loss2)
#
# print('test forecasting mae loss 2::', final_mae_losses2)
print('test imputation rmse loss::', final_imputed_rmse_loss)
print('test imputation mae loss::', final_imputed_mae_loss)
# print('test imputation rmse loss 2::', final_imputed_rmse_loss2)
#
# print('test imputation mae loss 2::', final_imputed_mae_loss2)
# if final_nll_loss is not None:
# final_nll_loss = final_nll_loss/all_count3
#
# final_nll_loss2 = final_nll_loss2/all_count3
# print('test forecasting neg likelihood::', final_nll_loss)
#
# print('test forecasting neg likelihood 2::', final_nll_loss2)
def main(args):
# setup logging
# device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# log = get_logger(args.log)
# log(args)
args.GRUD = False
if args.model == GRUD_method:
args.GRUD = True
# timestamp = datetime.now().strftime('%Y%m%d%H%M')
# tb_writer = SummaryWriter("./output/{}/{}/{}/logs/".format(args.model, args.expname, args.dataset)\
# +timestamp) if args.visual else None
config=getattr(configs, 'config_'+args.model)()
# train_set=PolyphonicDataset(args.data_path+'train.pkl')
# valid_set=PolyphonicDataset(args.data_path+'valid.pkl')
# test_set=PolyphonicDataset(args.data_path+'test.pkl')
# data_obj, time_steps_extrap, is_missing, train_mean = parse_datasets(args, device)
data_obj, is_missing, train_mean = load_time_series(args)
config['cluster_num'] = args.cluster_num
config['input_dim'] = data_obj['input_dim']
config['phi_std'] = args.std
config['epochs'] = args.epochs
config['is_missing'] = is_missing
# if args.missing_ratio == 0:
# if is_missing:
# config['is_missing'] = True
# else:
# config['is_missing'] = False
# else:
# config['is_missing'] = True
if args.use_gate:
config['use_gate'] = True
else:
config['use_gate'] = False
config['train_mean'] = train_mean
config['gaussian'] = args.gaussian
max_kl =args.max_kl
is_GPU = args.GPU
if not is_GPU:
device = torch.device("cpu")
else:
GPU_ID = int(args.GPUID)
device = torch.device("cuda:"+str(GPU_ID) if torch.cuda.is_available() else "cpu")
config['device'] = device
model = getattr(models, args.model)(config)
model.init_params()
model = model.to(device)
# if args.model in cluster_models:
# model.loss_on_missing = args.loss_missing
# model = model.cuda()
# if args.reload_from>=0:
# load_model(model, args.reload_from)
# model.gaussian = args.gaussian
# if args.init:
#
# print('start initialize')
#
# impute_method = mean_impute(train_mean)
#
# save_dir = data_dir + '/' + output_dir
#
# init_kmeans = initial_kmeans(data_obj["train_dataloader"], impute_method, config['cluster_num'], save_dir)
#
# print('start kmeans')
#
# init_kmeans.start_selecting_samples()
#
#
# if not args.model == 'DHMM_cluster2':
#
# centroids = init_kmeans.run_kmeans()
#
# model.init_phi_table(centroids, False)
#
# torch.save(init_kmeans.selected_sample_ids, data_dir + '/' + output_dir + 'selected_sample_ids')
#
# torch.save(init_kmeans.selected_time_ids, data_dir + '/' + output_dir + 'selected_time_ids')
#
# print('end initialize')
#
# if args.loadinit:
#
#
# save_dir = data_dir + '/' + output_dir
#
#
#
# # init_kmeans = torch.load(data_dir + '/' + output_dir + 'init_kmeans_obj')
#
#
#
# selected_sample_ids = torch.load(data_dir + '/' + output_dir + 'selected_sample_ids')
#
# selected_time_ids = torch.load(data_dir + '/' + output_dir + 'selected_time_ids')
#
# impute_method = mean_impute(train_mean)
#
# init_kmeans = initial_kmeans(data_obj["train_dataloader"], impute_method, config['cluster_num'], save_dir)
#
# init_kmeans.selected_sample_ids = selected_sample_ids
#
# init_kmeans.selected_time_ids = selected_time_ids
#
# if not args.model == 'DHMM_cluster2':
#
# init_phi_table = torch.load(data_dir + '/' + output_dir + 'init_phi_table')
# model.init_phi_table(init_phi_table, True)
# sequence_len = len(time_steps_extrap)
#
# input_dim = data_obj["input_dim"]
# dataset = dataset_obj.sample_traj(time_steps_extrap, n_samples = args.n,
# noise_weight = args.noise_weight)
#################
# TRAINING LOOP #
#################
times = [time.time()]
wait_until_kl_inc = args.wait_epoch
wait_until_gumbel = 0
wait_until_sparsemax = 0
itr = 0
decay_period = 5
test_period = 1
rec_anneal = 0
all_valid_rmse_list = []
all_test_res = []
for epoch in range(config['epochs']):
# for itr in range(1, config['batch_size'] * (args.niters + 1)):
# for itr in range(0, config['batch_size'], time_steps_extrap.shape[0]):
epoch_nll = 0.0 # accumulator for our estimate of the negative log likelihood (or rather -elbo) for this epoch
i_batch=1
n_slices=0
if epoch >= 25:
print('here')
if epoch < wait_until_kl_inc:
kl_anneal = 0.0
else:
# if (epoch - wait_until_kl_inc) % decay_period == 0:
print('max kl coefficient::', max_kl)
kl_anneal = min((20-20*0.9**(((epoch - wait_until_kl_inc)*1.0)), max_kl))
print('epoch::', epoch, kl_anneal)
# print('KL::', kl_anneal)
loss_records={}
for id, data_dict in enumerate(data_obj["train_dataloader"]):
# print('ids::',data_dict['ids'])
# batch_dict = get_next_batch(data_dict)
batch_dict = data_dict
# curr_seq_len = len(batch_dict['observed_tp'])
# print(batch_dict.keys())
# print('id::', id, batch_dict['observed_data'].shape)
# if config['anneal_epochs'] > 0 and epoch < config['anneal_epochs']: # compute the KL annealing factor
# min_af = config['min_anneal']
# kl_anneal = min_af+(1.0-min_af)*(float(i_batch+epoch*n_iters+1)/float(config['anneal_epochs']*n_iters))
# else:
# kl_anneal = 0.01 # by default the KL annealing factor is unity
# print("sample count::", ids.shape[0])
print(id)
if id >= 1:
print('here')
loss_AE = model.train_AE(batch_dict["observed_data"], batch_dict["origin_observed_data"], batch_dict['observed_mask'], batch_dict["observed_origin_mask"], batch_dict["observed_new_mask"], batch_dict["observed_lens"], kl_anneal, batch_dict['data_to_predict'], batch_dict["origin_data_to_predict"], batch_dict['mask_predicted_data'], batch_dict['origin_mask_predicted_data'], batch_dict['new_mask_predicted_data'], batch_dict["lens_to_predict"], is_GPU, device, batch_dict["delta_time_stamps"], batch_dict["delta_time_stamps_to_predict"], batch_dict["time_stamps"], batch_dict["time_stamps_to_predict"])
epoch_nll += loss_AE['train_loss_AE']
i_batch=i_batch+1
itr += 1
if epoch % test_period == 0:
print("test loss::")
valid_rmse = validate(data_obj, model, is_GPU, device)
all_valid_rmse_list.append(valid_rmse)
# model.test_samples(batch_dict["observed_data"], batch_dict['data_to_predict'], batch_dict['tp_to_predict'], curr_seq_len, is_GPU, device)
# final_rmse_loss, final_mae_losses, final_rmse_loss2, final_mae_losses2, final_imputed_rmse_loss, final_imputed_mae_loss, final_imputed_rmse_loss2, final_imputed_mae_loss2
test_res = test(data_obj, model, is_GPU, device)
all_test_res.append(test_res)
# if args.model == 'DHMM_cluster4' or args.model == 'DHMM_cluster2':
# updated_centroids = init_kmeans.update_cluster(model)
#
# model.init_phi_table(updated_centroids, False)