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run.py
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run.py
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#!/usr/bin/env python3
import time
import argparse
import pandas as pd
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.utils.rnn
from torch.utils.data import DataLoader
import utils
from model import RNN, LSTM
def evaluate(data_set, model, params, criterion, validation=False):
data_loader = DataLoader(data_set, batch_size=128, shuffle=False, num_workers=4)
if not validation:
model.load_state_dict(torch.load(params["PATH"]))
model.to(params["DEVICE"])
with torch.set_grad_enabled(False):
running_loss = 0.0
model.eval()
for batch in data_loader:
x = batch['sequence'].to(params["DEVICE"])
y = batch['target'].to(params["DEVICE"])
seq_len = batch['size'].to(params["DEVICE"])
y_hat, hidden = model(x, seq_len)
loss = criterion(y_hat, y)
if not validation:
df = pd.DataFrame({'y': y, 'y_hat':y_hat}, columns=['y', 'y_hat'])
pred_path = "test_preds/" + params["PATH"][7:-2] + "csv"
df.to_csv(pred_path)
running_loss += loss
test_loss = running_loss/len(data_set)
return test_loss
def main():
parser = argparse.ArgumentParser(description="==========[RNN]==========")
parser.add_argument("--mode", default="train", help="available modes: train, test, eval")
parser.add_argument("--model", default="rnn", help="available models: rnn, lstm")
parser.add_argument("--dataset", default="all", help="available datasets: all, MA, MI, TN")
parser.add_argument("--rnn_layers", default=3, type=int, help="number of stacked rnn layers")
parser.add_argument("--hidden_dim", default=16, type=int, help="number of hidden dimensions")
parser.add_argument("--lin_layers", default=1, type=int, help="number of linear layers before output")
parser.add_argument("--epochs", default=100, type=int, help="number of max training epochs")
parser.add_argument("--dropout", default=0.0, type=float, help="dropout probability")
parser.add_argument("--learning_rate", default=0.01, type=float, help="learning rate")
parser.add_argument("--verbose", default=2, type=int, help="how much training output?")
options = parser.parse_args()
verbose = options.verbose
if torch.cuda.is_available():
device = torch.device("cuda")
if verbose > 0:
print("GPU available, using cuda...")
print()
else:
device = torch.device("cpu")
if verbose > 0:
print("No available GPU, using CPU...")
print()
params = {
"MODE": options.mode,
"MODEL": options.model,
"DATASET": options.dataset,
"RNN_LAYERS": options.rnn_layers,
"HIDDEN_DIM": options.hidden_dim,
"LIN_LAYERS": options.lin_layers,
"EPOCHS": options.epochs,
"DROPOUT_PROB": options.dropout,
"LEARNING_RATE": options.learning_rate,
"DEVICE": device,
"OUTPUT_SIZE": 1
}
params["PATH"] = "models/" + params["MODEL"] + "_" + params["DATASET"] + "_" + str(params["RNN_LAYERS"]) + "_" + str(params["HIDDEN_DIM"]) + "_" + str(params["LIN_LAYERS"]) + "_" + str(params["LEARNING_RATE"]) + "_" + str(params["DROPOUT_PROB"]) + "_" + str(params["EPOCHS"]) + "_model.pt"
#if options.mode == "train":
# print("training placeholder...")
train_data = utils.DistrictData(params["DATASET"], "train")
val_data = utils.DistrictData(params["DATASET"], "val")
params["INPUT_SIZE"] = train_data[0]['sequence'].size()[1]
if params["MODEL"] == "rnn":
model = RNN(params)
elif params["MODEL"] == "lstm":
model = LSTM(params)
model.to(params["DEVICE"])
criterion = nn.MSELoss(reduction='sum')
optimizer = torch.optim.Adam(model.parameters(), lr=params["LEARNING_RATE"])
if verbose == 0:
print(params["PATH"])
else:
utils.print_params(params)
print("Beginning training...")
print()
since = time.time()
best_val_loss = 10.0
for e in range(params["EPOCHS"]):
running_loss = 0.0
#model.zero_grad()
model.train()
train_loader = DataLoader(train_data, batch_size=32, shuffle=True, num_workers=4)
for batch in train_loader:
x = batch['sequence'].to(device)
y = batch['target'].to(device)
seq_len = batch['size'].to(device)
optimizer.zero_grad()
y_hat, hidden = model(x, seq_len)
loss = criterion(y_hat, y)
running_loss += loss
loss.backward()
optimizer.step()
mean_loss = running_loss/len(train_data)
val_loss = evaluate(val_data, model, params, criterion, validation=True)
if verbose == 2 or (verbose == 1 and (e+1) % 100 == 0):
print('=' * 25 + ' EPOCH {}/{} '.format(e+1, params["EPOCHS"]) + '=' * 25)
print('Training Loss: {}'.format(mean_loss))
print('Validation Loss: {}'.format(val_loss))
print()
if e > params["EPOCHS"]/3:
if val_loss < best_val_loss:
best_val_loss = val_loss
best_model = model.state_dict()
torch.save(best_model, params["PATH"])
time_elapsed = time.time() - since
print('Training complete in {:.0f}m {:.0f}s'.format(time_elapsed//60, time_elapsed % 60))
print('Final Training Loss: {:4f}'.format(mean_loss))
print('Best Validation Loss: {:4f}'.format(best_val_loss))
test_data = utils.DistrictData(params["DATASET"], "test")
test_loss = evaluate(test_data, model, params, criterion)
print('Test Loss: {}'.format(test_loss))
print()
if __name__ == "__main__":
main()