# Reconstruction of test data
print("Reconstruction of test data... ")
X_test_new = ica.inverse_transform(X_test_ica)
print("Reconstructed matrix shape:", X_test_new.shape)
mse = mean_squared_error(normalized_test_data, X_test_new)
cvscores_mse_test.append(mse)
print('Reconstruction MSE : ', mse)
rms = sqrt(mse)
print('Reconstruction RMSE : ', rms)
cvscores_rmse_test.append(rms)
# Apply dimensionality reduction
directory = '../../../regression/ica/{}/{}/fold_{}/'.format(
v, dim, fold)
if not os.path.exists(directory):
os.makedirs(directory)
for sub in index_subjects:
subject = load_data(sub, view)
normalized_subject = scaler.fit_transform(subject)
transformed_subject = ica.transform(normalized_subject)
file = directory + "X_{}.npy".format(sub)
np.save(file, transformed_subject)
print('Shape of Latent representation:', transformed_subject.shape)
print('Transpose of latent representation',
transformed_subject.T.shape)
print("shape of vector mse train", np.array([cvscores_mse_train]).shape)
print(cvscores_mse_train)
np.save('cvscores_mse_train_ica_dim_{}.npy'.format(dim),
np.array([cvscores_mse_train]))
print("shape of vector mse test", np.array([cvscores_mse_test]).shape)
def __init__(self):
run_name = 'run_test'
self.outdir = './results/' + run_name + '/'
self.outdir_train = self.outdir + 'train/'
self.outdir_test = self.outdir + 'test/'
self.training_counter = 0
try:
sys.makedirs(self.outdir_train)
sys.makedirs(self.outdir_test)
except Exception:
pass
self.writer_train = SummaryWriter(self.outdir_train)
self.writer_test = SummaryWriter(self.outdir_test)
self.monitor_freq = 100
self.testing_samples = 100
self.validation_scores = []
self.training_scores = []
self.settings = {
'past_horzion': 100,
'max_steps': 365,
'inital_account_balance': 1e4,
'stop_below_balance': 1e3,
'transation_fee': .1,
'years_training': 5,
'years_testing': 1,
}
testing_end = date.today()
testing_beginning = testing_end - relativedelta(
years=self.settings['years_testing']) - relativedelta(
days=self.settings['past_horzion'])
training_end = testing_beginning - relativedelta(days=1)
training_beginning = training_end - relativedelta(
years=self.settings['years_training']) - relativedelta(
days=self.settings['past_horzion'])
self.data = {
'train_gold':
self.get_prices(gold_shanghai, 1, training_beginning,
training_end),
'train_copper':
self.get_prices(copper_shanghai, 1, training_beginning,
training_end),
'train_aluminum':
self.get_prices(aluminum_shanghai, 1, training_beginning,
training_end),
'test_gold':
self.get_prices(gold_shanghai, 1, testing_beginning, testing_end),
'test_copper':
self.get_prices(copper_shanghai, 1, testing_beginning,
testing_end),
'test_aluminum':
self.get_prices(aluminum_shanghai, 1, testing_beginning,
testing_end),
'test_soybean_oil':
self.get_prices(soybean_oil, 1, testing_beginning, testing_end),
'test_dax_futures':
self.get_prices(dax_futures, 1, testing_beginning, testing_end),
'test_corn':
self.get_prices(corn, 1, testing_beginning, testing_end),
'test_canadian_dollar':
self.get_prices(canadian_dollar, 1, testing_beginning,
testing_end),
}
# print('\n\n*************\n', self.data['test_corn'], '\n\n')
self.env_test_gold = StockTradingEnv(self.get_prices(
gold_shanghai, 1, testing_beginning, testing_end),
self.settings,
test=True)
self.env_test_copper = StockTradingEnv(self.get_prices(
copper_shanghai, 1, testing_beginning, testing_end),
self.settings,
test=True)
self.env_test_aluminum = StockTradingEnv(self.get_prices(
aluminum_shanghai, 1, testing_beginning, testing_end),
self.settings,
test=True)
self.env_test_soy_bean = StockTradingEnv(self.get_prices(
soybean_oil, 1, testing_beginning, testing_end),
self.settings,
test=True)
self.env_test_dax = StockTradingEnv(self.get_prices(
dax_futures, 1, testing_beginning, testing_end),
self.settings,
test=True)
self.env_test_corn = StockTradingEnv(self.get_prices(
corn, 1, testing_beginning, testing_end),
self.settings,
test=True)
self.env_test_canadian_dollar = StockTradingEnv(self.get_prices(
canadian_dollar, 1, testing_beginning, testing_end),
self.settings,
test=True)
self.env_train = StockTradingEnv(self.data['train_gold'],
self.settings,
test=False)
# self.env_test = StockTradingEnv(self.data['test_gold'], self.settings, test=True)
self.test_envs = {
'gold':
StockTradingEnv(self.data['test_gold'], self.settings, test=True),
'copper':
StockTradingEnv(self.data['test_copper'], self.settings,
test=True),
'aluminum':
StockTradingEnv(self.data['test_aluminum'],
self.settings,
test=True),
}
self.agent = self.rl_agent(self.env_train)
