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letter_paper.py
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letter_paper.py
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import multiprocessing as mp
import os
import pickle
import warnings
import numpy as np
warnings.filterwarnings(action='ignore', category=UserWarning)
from Synthetic_Dataset_Prep import ARIMA
from Pipelines import Hybrid_Model_Pipeline,Single_LightGBM_Pipeline
if __name__ == '__main__':
warnings.filterwarnings(action='ignore', category=UserWarning)
np.random.seed(12)
seeds = np.arange(start=300,stop=330)
lists_exists = os.path.isfile('mse_list.pkl')
if lists_exists:
with open('mape_list.pkl', 'rb') as f:
mape_list = pickle.load(f)
with open('mae_list.pkl', 'rb') as f:
mae_list = pickle.load(f)
with open('mse_list.pkl', 'rb') as f:
mse_list = pickle.load(f)
else:
mse_list = []
mape_list = []
mae_list = []
with open('mape_list.pkl', 'wb') as f:
pickle.dump(mape_list, f)
with open('mae_list.pkl', 'wb') as f:
pickle.dump(mape_list, f)
with open('mse_list.pkl', 'wb') as f:
pickle.dump(mape_list, f)
check_point_exists = os.path.isfile('seeds.pkl')
if check_point_exists:
with open('seeds.pkl', 'rb') as f:
seeds = pickle.load(f)
else:
with open('seeds.pkl', 'wb') as f:
pickle.dump(seeds, f)
for seed in seeds:
folder_path = 'Hybrid_Model_Results'
specific_word = str(seed)
# Get a list of all files in the folder
all_files = os.listdir(folder_path)
checker = False
for file_name in all_files:
file_seed = file_name.split("_")[3]
if specific_word == file_seed:
print(f"{file_name} contains the seed {specific_word}.")
checker = True
break
else:
pass
if checker:
continue
else:
print(f"Seed = {seed}")
np.random.seed(seed)
test_size = 100
phi = np.array([0.125, 0.125, -0.125, 0.125])
theta = np.array([0.65, 0.35, 0.3, -0.15, -0.3, ])
mu = 0
sigma = 1
t = 0
n = 1250
seeds_pkl = list(seeds)
seeds_pkl.remove(seed)
with open('seeds.pkl', 'wb') as f:
pickle.dump(seeds_pkl, f)
with open('mape_list.pkl', 'rb') as f:
mape_list = pickle.load(f)
with open('mae_list.pkl', 'rb') as f:
mae_list = pickle.load(f)
with open('mse_list.pkl', 'rb') as f:
mse_list = pickle.load(f)
# Use Synthetic Data
y = ARIMA(phi=phi, theta=theta, mu=mu, sigma=sigma, n=n, t=t)
# y_2 = synthetic_dataset(phi=phi,theta=theta,mu=mu,sigma=sigma,dataset_length = 1001)
np.random.seed(seed)
if abs(y.max()) > 7: # or adfuller(y)[1] <= .05:
print("Passed due to extremely divergent y or stationary y")
pass
else:
# Train Test Split
y_train_new = y[:-test_size]
y_test_new = y[-test_size:]
# model = LGBModel(y_train_new, lags=[1, 2, 3], iterations=1000)
# model.train(learning_rate=0.005, trees_initial_model=2, max_depth=3, num_leaves=5, early_stop_tolerance=30)
params_mlp = {
"hidden_layer_sizes": (3,),
"activation": "relu",
"alpha": 0.0001,
"learning_rate_init": 0.1,
# "random_state": 1,
"max_iter": 100
}
params_lgbm = {'boosting': 'goss',
'metric': 'l2',
'learning_rate': 0.1,
"bagging_fraction": 1.0,
"bagging_freq": 0,
'max_depth': 2,
'num_leaves': 3,
'verbose': -1,
# "min_data_in_bin": 1,
"min_data_in_leaf": 1,
"lambda_l1": 0.0,
"lambda_l2": 0.5,
"boost_from_average": False,
"num_boost_round": 2
}
# model_LGBM_MA = HybridModel(data=y_train_new, lags=[1, 2, 3], lr=0.1, learner="lgbm", val_ratio=0.2)
process_lgbm = mp.Process(target=Hybrid_Model_Pipeline,
args=(
seed, phi, theta, y_train_new, 500, params_lgbm, 10, y_test_new,
mape_list,
mae_list, mse_list))
process_lgbm.start()
process_lgbm.join(timeout=60 * 45)
if process_lgbm.is_alive():
print("Timeout! Moving on to the next iteration...")
process_lgbm.kill()
continue
# config = MLP_Config(**{
# 'task_name': 'reg_weights',
# 'learning_rate': 0.1,
# 'regularizer': 'L2',
# 'momentum': 0.9,
# 'epochs': 1000,
# 'early_stopping': False,
# 'hidden_params': [(5, 4), (4, 5)],
# 'lambda':[0.01],
# 'sigma':[0.05]
# })
#
# train_loader, val_loader, test_loader = create_pytorch_data()
# checkpoint_cb = ModelCheckpoint(dirpath="ckpt", monitor='val_loss', mode='min')
# callbacks = [checkpoint_cb]
# if config.early_stopping:
# callbacks.append(EarlyStopping(monitor='val_loss', patience=10))
# model_MLP = MLP(hidden_params=config.hidden_params, configs=config)
#
# trainer = pl.Trainer(max_epochs=config.epochs, callbacks=callbacks)
# trainer.fit(model_MLP, train_dataloader=train_loader, val_dataloaders=val_loader)
# plt.plot(predicted_output)
# plt.plot(y_test_new)
# plt.legend(["Predicted (w/ Ground truth)", "Ground truth"])
# plt.show
print()
# plt.plot(model.mse)
# plt.plot(model.mse_val)
# plt.show()
# Multiple Simulations with Different Seeds
# arima_params = {"phi": np.array([0.8, -0.6]),
# "theta": np.array([0.7, -0.4, 0.3]),
# "mu": 0,
# "sigma": 1,
# "t": 0,
# "n": 700,
# "test_size": 100}
# mape_values, mae_values, mse_values = run_simulation(50, 300, [1, 2, 3], arima_params, 0.1, params_mlp)
#
# cum_mse = np.cumsum(mse_values) / (1 + np.arange(len(mse_values)))
# plt.plot(cum_mse)