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informerStudy.py
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import pandas as pd
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
import optuna
import utils
import models
from model_trainer import _process_one_batch, vali, train, _evaluate
class InformerStudy:
def __init__(self, seq_len, label_len, pred_len, embed, freq, device, path, train_epochs, lradj, padding, inverse, features, train_loader, val_data, val_loader, test_data, test_loader):
self.seq_len = seq_len
self.label_len = label_len
self.pred_len = pred_len
self.embed = embed
self.freq = freq
self.device = device
self.path = path
self.train_epochs = train_epochs
self.lradj = lradj
self.padding = padding
self.inverse = inverse
self.features = features
self.train_loader = train_loader
self.val_data = val_data
self.val_loader = val_loader
self.test_data = test_data
self.test_loader = test_loader
self.actions_mae = []
def objective(self, trial):
e_layers = trial.suggest_int('e_layers', 1, 6)
d_layers = trial.suggest_int('d_layers', 1, 6)
dropout = trial.suggest_categorical('dropout', [0.0, 0.05, 0.10, 0.15])
factor = trial.suggest_int('factor', 3, 8)
n_heads = trial.suggest_int('n_heads', 4, 8)
d_ff = trial.suggest_categorical('d_ff', [512, 1024, 2048])
d_model = trial.suggest_categorical('d_model', [512, 1024, 2048])
parameters = np.array([e_layers, d_layers, dropout, factor, n_heads, d_ff, d_model])
#build model candidate
model = models.Informer(
enc_in=7,
dec_in=7,
c_out=7,
seq_len=self.seq_len,
label_len=self.label_len,
out_len=self.pred_len,
e_layers=e_layers,
d_layers=d_layers,
factor=factor,
d_model=d_model,
n_heads=n_heads,
d_ff=d_ff,
dropout=dropout,
attn='prob',
embed=self.embed,
freq=self.freq,
activation='gelu',
output_attention=False,
distil=True,
mix=True,
).float()
model = model.to(self.device)
#initiate parameters
learning_rate = 0.0001
patience=3
train_steps = len(self.train_loader)
early_stopping = utils.EarlyStopping(patience=patience, verbose=True)
model_optim = optim.Adam(model.parameters(), lr=learning_rate)
criterion = nn.MSELoss()
#train model
train(model, self.train_loader, self.val_data, self.val_loader, criterion, model_optim, self.path, self.train_epochs, learning_rate, self.lradj, early_stopping, self.device, self.padding, self.pred_len, self.label_len , self.inverse, self.features)
#evaluate on validation data
preds, trues, val_mae, val_mse, val_rmse, val_mape, val_mspe = _evaluate(model, self.val_loader, self.val_data, _process_one_batch, self.device, self.padding, self.pred_len, self.label_len, self.inverse, self.features)
torch.cuda.empty_cache()
self.actions_mae.append([parameters, val_mae])
return val_mae
def run_study(self, n_trials=5):
study = optuna.create_study(direction='minimize')
study.optimize(self.objective, n_trials=n_trials)
best_params = study.best_params
e_layers = best_params['e_layers']
d_layers = best_params['d_layers']
dropout = best_params['dropout']
factor = best_params['factor']
n_heads = best_params['n_heads']
d_ff = best_params['d_ff']
d_model = best_params['d_model']
#build best model
model = models.Informer(
enc_in=7,
dec_in=7,
c_out=7,
seq_len=self.seq_len,
label_len=self.label_len,
out_len=self.pred_len,
e_layers=e_layers,
d_layers=d_layers,
factor=factor,
d_model=d_model,
n_heads=n_heads,
d_ff=d_ff,
dropout=dropout,
attn='prob',
embed=self.embed,
freq=self.freq,
activation='gelu',
output_attention=False,
distil=True,
mix=True,
).float()
model = model.to(self.device)
#initiate parameters
learning_rate = 0.0001
patience=3
train_steps = len(self.train_loader)
early_stopping = utils.EarlyStopping(patience=patience, verbose=True)
model_optim = optim.Adam(model.parameters(), lr=learning_rate)
criterion = nn.MSELoss()
#train best model
train(model, self.train_loader, self.val_data, self.val_loader, criterion, model_optim, self.path, self.train_epochs, learning_rate, self.lradj, early_stopping, self.device, self.padding, self.pred_len, self.label_len , self.inverse, self.features)
#evaluate on test data
preds, trues, test_mae, test_mse, test_rmse, test_mape, test_mspe = _evaluate(model, self.test_loader, self.test_data, _process_one_batch, self.device, self.padding, self.pred_len, self.label_len, self.inverse, self.features)
torch.save(model.state_dict(), self.path+'/'+'optuna_informer.pth')
torch.cuda.empty_cache()
return study, preds, trues, test_mae, test_mse, test_rmse, test_mape, test_mspe, self.actions_mae