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from neuralforecast.losses.pytorch import MAE
from neuralforecast.auto import AutoNHITS, AutoTSMixer, AutoiTransformer, AutoTSMixerx, NBEATSx
from neuralforecast import NeuralForecast
from modules.transform import transformData, prepareData, calendarFeatures, createLag
from pytorch_lightning.loggers import CSVLogger
import uuid
from pytorch_lightning import Trainer
import os
import pandas as pd
import pickle
from pathlib import Path
def trainModel(dataset,
artifacts_path,
variate='uni',
y_var='brent_futures_Close',
horizon_len=5,
val_size=0.1,
test_size=0.1,
lag_amt=0): # Maybe change y_var to an input? (via User Interaction)
# Code for univariate time-series forecasting
if variate == 'univariate':
from neuralforecast.auto import AutoNHITS, AutoTSMixer, AutoTSMixerx, AutoNBEATSx
Y_df = dataset.rename({'Date' : 'ds', y_var : 'y'}, axis=1)
Y_df['unique_id'] = 0
# TestValSplit
len_data = len(Y_df.ds.unique())
val_size = int(.1 * len_data)
test_size = int(.1 * len_data)
Y_df['ds'] = pd.to_datetime(Y_df['ds'])
Y_df = Y_df[['ds', 'y', 'unique_id']]
Y_df = calendarFeatures(Y_df)
Y_df.to_csv(os.path.join(artifacts_path, 'train_data.csv'))
print(f'Total length is {len_data}, with validation and test size of {val_size} for each')
import optuna
optuna.logging.set_verbosity(optuna.logging.WARNING) # Use this to disable training prints from optuna
def config_nhits(horizon_len, trial):
return {
"max_steps": 1000, # Number of SGD steps
"input_size" : trial.suggest_categorical("input_size", [horizon_len, horizon_len*2]), # Size of input window
"learning_rate": trial.suggest_loguniform("learning_rate", 1e-5, 1e-1), # Initial Learning rate
"n_pool_kernel_size": trial.suggest_categorical("n_pool_kernel_size", [[2, 2, 2], [16, 8, 1]]), # MaxPool's Kernel size
"n_freq_downsample": trial.suggest_categorical("n_freq_downsample", [[168, 24, 1], [24, 12, 1]]), # Interpolation expressivity ratios
"val_check_steps": 50, # Compute validation every 50 steps
"early_stop_patience_steps": 5, # Stops at 5 steps max if loss doesn't get beter
"random_seed": trial.suggest_int("random_seed", 1, 10), # Random seed
}
def config_tsmixer(horizon_len, trial):
return {
"max_steps": 1000,
"n_series" : 1,
"input_size" : trial.suggest_categorical("input_size", [horizon_len, horizon_len*2]),
"learning_rate": trial.suggest_loguniform("learning_rate", 1e-5, 1e-1),
"ff_dim": trial.suggest_categorical("ff_dim", [64,128]),
"n_block": trial.suggest_categorical("n_block", [4,8]),
"val_check_steps": 50,
"early_stop_patience_steps": 5,
"scaler_type": 'identity',
}
def config_nbeatsx(horizon_len, trial):
return {
"max_steps": 1000, # Number of SGD steps
"futr_exog_list": ['day_of_week', 'is_weekend', 'month', 'day_of_month', 'quarter', 'year', 'is_holiday'],
"input_size" : trial.suggest_categorical("input_size", [horizon_len, horizon_len*2]), # Size of input window
"learning_rate": trial.suggest_loguniform("learning_rate", 1e-5, 1e-1), # Initial Learning rate # Interpolation expressivity ratios
"val_check_steps": 50, # Compute validation every 50 steps
"early_stop_patience_steps": 5, # Stops at 5 steps max if loss doesn't get beter
"random_seed": trial.suggest_int("random_seed", 1, 10), # Random seed
}
def config_tsmixerx(horizon_len, trial):
return {
"max_steps": 1000,
"futr_exog_list": ['day_of_week', 'is_weekend', 'month', 'day_of_month', 'quarter', 'year', 'is_holiday'],
"n_series" : 1,
"input_size" : trial.suggest_categorical("input_size", [horizon_len, horizon_len*2]),
"learning_rate": trial.suggest_loguniform("learning_rate", 1e-5, 1e-1),
"ff_dim": trial.suggest_categorical("ff_dim", [64,128]),
"n_block": trial.suggest_categorical("n_block", [4,8]),
"val_check_steps": 50,
"early_stop_patience_steps": 5,
"scaler_type": 'identity',
}
model = [AutoNHITS(h=horizon_len,
loss=MAE(),
valid_loss=MAE(),
config=lambda trial: config_nhits(horizon_len, trial),
search_alg=optuna.samplers.TPESampler(),
backend='optuna',
num_samples=10),
AutoTSMixer(h=horizon_len,
n_series=1,
loss=MAE(),
valid_loss=MAE(),
config=lambda trial: config_tsmixer(horizon_len, trial),
search_alg=optuna.samplers.TPESampler(),
backend='optuna',
num_samples=10),
AutoNBEATSx(h=horizon_len,
loss=MAE(),
valid_loss=MAE(),
config=lambda trial: config_nbeatsx(horizon_len, trial),
search_alg=optuna.samplers.TPESampler(),
backend='optuna',
num_samples=10),
AutoTSMixerx(h=horizon_len,
n_series=1,
loss=MAE(),
valid_loss=MAE(),
config=lambda trial: config_tsmixerx(horizon_len, trial),
search_alg=optuna.samplers.TPESampler(),
backend='optuna',
num_samples=10)]
# Set up custom logger to change logging directory
log_dir = os.path.join(artifacts_path, 'training_logs')
# Setting logger environment if applicable - for illustrative purposes
Trainer.default_root_dir = log_dir
logger = CSVLogger(save_dir=log_dir, name='forecast_logs')
nf = NeuralForecast(models=model, freq='B')
nf.fit(df=Y_df, val_size=val_size)
results = nf.models[1].results.trials_dataframe()
results.drop(columns='user_attrs_ALL_PARAMS')
return nf, results
# Code for multivariate time-series forecasting
if variate == 'multivariate':
from neuralforecast.auto import AutoTSMixer, AutoiTransformer
Y_df = dataset.melt(id_vars=['Date'], var_name='unique_id', value_name='y')
Y_df = Y_df.rename({'Date' : 'ds'}, axis=1)
# TestValSplit
len_data = len(Y_df.ds.unique())
val_size = int(.1 * len_data)
test_size = int(.1 * len_data)
Y_df['ds'] = pd.to_datetime(Y_df['ds'])
Y_df.to_csv(os.path.join(artifacts_path, 'train_data.csv'))
print(f'Total length is {len_data}, with validation and test size of {val_size} for each')
import optuna
optuna.logging.set_verbosity(optuna.logging.WARNING) # Use this to disable training prints from optuna
def config_autoitransformer(horizon_len, trial):
return {
"max_steps": 1000,
"n_series" : Y_df['unique_id'].nunique(), # Number of SGD steps
"input_size" : trial.suggest_categorical("input_size", [horizon_len, horizon_len*2]), # Size of input window
"learning_rate": trial.suggest_loguniform("learning_rate", 1e-5, 1e-1), # Initial Learning rate
"hidden_size": trial.suggest_categorical("hidden_size", [128, 256]), # MaxPool's Kernel size
"n_heads": trial.suggest_categorical("n_heads", [2,4]), # Interpolation expressivity ratios
"e_layers": trial.suggest_categorical("e_layers", [2,4]),
"val_check_steps": 50, # Compute validation every 50 steps
"early_stop_patience_steps": 5, # Stops at 5 steps max if loss doesn't get beter
"random_seed": trial.suggest_int("random_seed", 1, 10), # Random seed
}
def config_tsmixer(horizon_len, trial):
return {
"max_steps": 1000,
"n_series" : Y_df['unique_id'].nunique(),
"input_size" : trial.suggest_categorical("input_size", [horizon_len, horizon_len*2]),
"learning_rate": trial.suggest_loguniform("learning_rate", 1e-5, 1e-1),
"ff_dim": trial.suggest_categorical("ff_dim", [64,128]),
"n_block": trial.suggest_categorical("n_block", [4,8]),
"val_check_steps": 50,
"early_stop_patience_steps": 5,
"scaler_type": 'identity',
}
model = [AutoiTransformer(h=horizon_len,
n_series=Y_df['unique_id'].nunique(),
loss=MAE(),
valid_loss=MAE(),
config=lambda trial: config_autoitransformer(horizon_len, trial),
search_alg=optuna.samplers.TPESampler(),
backend='optuna',
num_samples=10),
AutoTSMixer(h=horizon_len,
n_series=Y_df['unique_id'].nunique(),
loss=MAE(),
valid_loss=MAE(),
config=lambda trial: config_tsmixer(horizon_len, trial),
search_alg=optuna.samplers.TPESampler(),
backend='optuna',
num_samples=10)]
# Set up custom logger to change logging directory
log_dir = os.path.join(artifacts_path, 'training_logs')
# Setting logger environment if applicable - for illustrative purposes
Trainer.default_root_dir = log_dir
logger = CSVLogger(save_dir=log_dir, name='forecast_logs')
nf = NeuralForecast(models=model, freq='B')
nf.fit(df=Y_df, val_size=val_size)
results = nf.models[1].results.trials_dataframe()
results.drop(columns='user_attrs_ALL_PARAMS')
return nf, results
def main():
import logging
directory = Path(__file__).parent.absolute()
logging.basicConfig(level=logging.INFO)
data_dir = 'crude_oil' # Should be choosable later on?
run_id = str(f'{data_dir}_{str(uuid.uuid4())}')
artifacts_path = os.path.join(directory, 'artifacts', run_id)
logging.info(f'Created forecasting pipeline with id {run_id}')
os.mkdir(artifacts_path)
prepared_data = prepareData(parent_dir=directory, data_dir=data_dir, run_id=run_id)
train_data, transformations = transformData(prepared_data, dir=directory, id=run_id)
train_data.to_csv(os.path.join(artifacts_path, 'transformed_dataset.csv'))
# Save transformations including StandardScaler objects
with open(os.path.join(artifacts_path, 'transformations.pkl'), 'wb') as fp:
pickle.dump(transformations, fp)
nf, results = trainModel(dataset=train_data, variate='univariate', artifacts_path=artifacts_path)
results.to_csv(os.path.join(artifacts_path, 'training_results.csv'))
nf.save(path=os.path.join(artifacts_path, 'model'),
model_index=None,
overwrite=True,
save_dataset=True)
if __name__ == "__main__":
main() |