experiment_name = 'ptf_dcc_MLP_1010'
dataset_name = 'PTF'
baseline_model = 'dcc'
hidden_layer_dim = 32
batch_size = 16
total_epochs = 30000
plot_points = 30
# Data from .csv
dsf = DatasetFactory.get(name=dataset_name)
# Train test split
train_time = dsf.split_train_test(sequential=True, test_size=0.3)
# Features computation
dsf.withColumn('y', *T.standardize(*dsf['y']))
dsf.withColumn('crosses', *T.elements_cross(*dsf['y']))
dsf.withColumn('x', *T.lagify(*dsf['crosses'], lag=6, collapse=True))
# The baseline model
baseline_estimator = {
'dcc': T.dcc,
'lw': T.LedoitWolf,
}[baseline_model]
baseline_model_line = f'{baseline_model}_line'
dsf.withColumn(baseline_model, *baseline_estimator(*dsf['y']))
states_line = [
M.mu_vcv_linearize(**states) for states in dsf[baseline_model]
]
dsf.withColumn(baseline_model_line, *states_line)
# Drop rows with NA (empty features)
dsf.dropna()
models = {
if __name__ == '__main__':
from experiment_econometrics import DatasetFactory
import timeseries_transformations as T
x_col = 'x'
y_col = 'y'
states0_col = 'ledoitWolf_static'
dsf = DatasetFactory.get(name='FRED_raw')
# Train test split
train_time = dsf.split_train_test(sequential=True, test_size=0.3)
# Features computation
dsf.withColumn('y', *T.standardize(*dsf['y']))
dsf.withColumn('x', *T.lagify(*dsf['y'], lag=12, collapse=True))
dsf.withColumn('ledoitWolf_static', *T.staticLedoitWolf(*dsf['y']))
dsf.dropna()
x, y, states0 = dsf.select([x_col, y_col, states0_col],
train=True,
test=False)
x_test, y_test, states0_test = dsf.select([x_col, y_col, states0_col],
train=False,
test=True)
model = HDE_mu_vcv(dsf, 'x', 'y', 'ledoitWolf_static', learning_rate=1e-1)
llkls = []
for i in range(10):
print(i)
pdf = tfp.distributions.MultivariateNormalFullCovariance(
states0['mu'][:, tf.newaxis, :], states0['vcv'][:,
tf.newaxis, :, :])
if __name__ == '__main__':
data_config = {
'name': 'FRED'
}
train_test_config = {
'sequential': True,
'test_size': 0.3
}
dsf = DatasetFactory.get(**data_config)
train_time = dsf.split_train_test(**train_test_config)
# Test all functions
# dsf.withColumn('dcc', *T.dcc(*dsf['y']))
dsf.withColumn('crosses', *T.elements_cross(*dsf['y']))
dsf.withColumn('x_timesteps', *T.lagify(*dsf['y'], collapse=False))
dsf.withColumn('x', *T.lagify(*dsf['y'], collapse=True))
dsf.withColumn('ledoitWolf_static', *T.staticLedoitWolf(*dsf['y']))
dsf.dropna()
# concrete wrappers testing
##Bayesian mlp
m = CholeskyMLPBayesian(dsf, 'x', 'y', 'ledoitWolf_static')
m.fit(dsf, 10, 8)
states, theta, llkl, p0, log_p = m.density_forecast(dsf)
m.density_forecast_multi_particles(dsf)
# DCC
m = DCC_HVI('x_timesteps', 'y', n=dsf.df.shape[1])
dataset_name = 'FRED'
baseline_model = 'dcc'
hidden_layer_dim = 32
batch_size = 16
total_epochs = 30000
plot_points = 30
# Data from .csv
dsf = DatasetFactory.get(name=dataset_name)
# Train test split
train_time = dsf.split_train_test(sequential=True, test_size=0.3)
# Features computation
dsf.withColumn('y', *T.standardize(*dsf['y']))
dsf.withColumn('crosses', *T.elements_cross(*dsf['y']))
dsf.withColumn('x_timesteps',
*T.lagify(*dsf['crosses'], lag=6, collapse=False))
# The baseline model
baseline_estimator = {
'dcc': T.dcc,
'lw': T.LedoitWolf,
}[baseline_model]
baseline_model_line = f'{baseline_model}_line'
dsf.withColumn(baseline_model, *baseline_estimator(*dsf['y']))
states_line = [
M.mu_vcv_linearize(**states) for states in dsf[baseline_model]
]
dsf.withColumn(baseline_model_line, *states_line)
# Drop rows with NA (empty features)
dsf.dropna()
models = {