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train_vanilla.py
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train_vanilla.py
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import argparse
import copy
import os
import matplotlib
matplotlib.use('TkAgg')
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torch.utils.data
from scipy import linalg as linalg
from scipy.interpolate import interp1d
from scipy import stats
#import myflows as fnn
import flows as fnn
import load
# GLOW_save_folder, GLOW_data_folder, GLOW_data_fname
def train_flow(save_folder, data_folder, data_fname):
print('--------------------')
print('Train GLOW model ...')
# Training settings
parser = argparse.ArgumentParser(description='PyTorch GLOW')
parser.add_argument(
'--batch-size',
type=int,
default=100,
help='input batch size for training (default: 100)')
parser.add_argument(
'--test-batch-size',
type=int,
default=1,
help='input batch size for testing (default: 1000)')
parser.add_argument(
'--epochs',
type=int,
default=2000,
help='number of epochs to train (default: 1000)')
parser.add_argument(
'--lr', type=float, default=1e-5, help='learning rate (default: 0.0001)')
parser.add_argument(
'--no-cuda',
action='store_true',
default=False,
help='disables CUDA training')
parser.add_argument(
'--num-blocks',
type=int,
default=9,
help='number of invertible blocks (default: 5)')
parser.add_argument(
'--num-hidden',
type=int,
default=256,
help='number of hidden layer neurons')
parser.add_argument(
'--num-inputs',
type=int,
default=24,
help='look-ahead horizon of forecasting')
parser.add_argument(
'--num-cond-inputs',
type=int,
default=24,
help='length of historical data')
parser.add_argument(
'--seed', type=int, default=1, help='random seed (default: 1)')
args = parser.parse_args()
args.cuda = not args.no_cuda and torch.cuda.is_available()
device = torch.device("cuda:0" if args.cuda else "cpu")
torch.manual_seed(args.seed)
if args.cuda:
torch.cuda.manual_seed(args.seed)
kwargs = {'num_workers': 4, 'pin_memory': True} if args.cuda else {}
try:
os.makedirs(save_folder)
except OSError:
pass
# Load training_subset, valid_set and test_set
# Just one split: 1-fold
training_subset, valid_set, test_set = load.load_dataset(data_folder, data_fname)
print('Training subset size:', training_subset.N)
print('Validation set size:', valid_set.N)
print('Test set size:', test_set.N)
# Load point estimate
#pred_on_train, pred_on_valid, pred_on_test = load.load_point_estimates(data_folder)
# Transform to torch.Tensor
# train_tensor = torch.from_numpy(training_subset.X)
new_training_subset = np.concatenate((training_subset.y, training_subset.X),-1)
#new_training_subset = np.concatenate((training_subset.y, pred_on_train),-1)
mu = new_training_subset.mean()
std = new_training_subset.std()
print('Mean of new train set:',mu)
print('Std of new train set:',std)
train_tensor = torch.from_numpy((training_subset.X-mu)/std)
#train_tensor= torch.from_numpy((pred_on_train-mu)/std)
train_labels = torch.from_numpy((training_subset.y-mu)/std)
train_dataset = torch.utils.data.TensorDataset(train_tensor, train_labels)
valid_tensor = torch.from_numpy((valid_set.X-mu)/std)
#valid_tensor = torch.from_numpy((pred_on_valid-mu)/std)
valid_labels = torch.from_numpy((valid_set.y-mu)/std)
valid_dataset = torch.utils.data.TensorDataset(valid_tensor, valid_labels)
test_tensor = torch.from_numpy((test_set.X-mu)/std)
#test_tensor = torch.from_numpy((pred_on_test-mu)/std)
test_labels = torch.from_numpy((test_set.y-mu)/std)
test_dataset = torch.utils.data.TensorDataset(test_tensor, test_labels)
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=args.batch_size,
shuffle=False,
**kwargs)
valid_loader = torch.utils.data.DataLoader(
valid_dataset,
batch_size=args.test_batch_size,
shuffle=False,
drop_last=False,
**kwargs)
test_loader = torch.utils.data.DataLoader(
test_dataset,
batch_size=args.test_batch_size,
shuffle=False,
drop_last=False,
**kwargs)
num_inputs = args.num_inputs
num_cond_inputs = args.num_cond_inputs
num_hidden = args.num_hidden
def build_model():
modules = []
mask = torch.arange(0, num_inputs) % 2
#mask = torch.ones(num_inputs)
#mask[round(num_inputs/2):] = 0
mask = mask.to(device).float()
# build each modules
for _ in range(args.num_blocks):
modules += [
fnn.ActNorm(num_inputs),
fnn.LUInvertibleMM(num_inputs),
fnn.CouplingLayer(
num_inputs, num_hidden, mask, num_cond_inputs,
s_act='tanh', t_act='relu')
]
mask = 1 - mask
# build model
model = fnn.FlowSequential(*modules)
# initialize
for module in model.modules():
if isinstance(module, nn.Linear):
nn.init.orthogonal_(module.weight)
if hasattr(module, 'bias') and module.bias is not None:
module.bias.data.fill_(0)
model.to(device)
return model
model = build_model()
optimizer = optim.Adam(model.parameters(), lr=args.lr, weight_decay=1e-6)
train_loss = []
def train(epoch):
model.train()
for batch_idx, data in enumerate(train_loader):
if isinstance(data, list):
if len(data) > 1:
cond_data = data[1].float()
cond_data = cond_data.to(device)
else:
cond_data = None
data = data[0]
data = data.to(device)
optimizer.zero_grad()
loss = -model.log_probs(data, cond_data).mean()
train_loss.append(loss.item())
loss.backward()
optimizer.step()
def validate(epoch, model, loader, prefix='Validation'):
model.eval()
val_loss = 0
for batch_idx, data in enumerate(loader):
if isinstance(data, list):
if len(data) > 1:
cond_data = data[1].float()
cond_data = cond_data.to(device)
else:
cond_data = None
data = data[0]
data = data.to(device)
with torch.no_grad():
val_loss += -model.log_probs(data, cond_data).sum().item()
return val_loss / len(loader.dataset)
best_validation_loss = float('inf')
best_validation_epoch = 0
best_model = model
valid_loss = []
for epoch in range(args.epochs):
print('\nEpoch: {}'.format(epoch))
train(epoch)
validation_loss = validate(epoch, model, valid_loader)
valid_loss.append(validation_loss)
if epoch - best_validation_epoch >= 30 and epoch > 100:
#if epoch - best_validation_epoch >= 30:
break
if validation_loss < best_validation_loss:
best_validation_epoch = epoch
best_validation_loss = validation_loss
best_model = copy.deepcopy(model)
print(
'Best validation at epoch {}: Average Log Likelihood in nats: {:.4f}'.
format(best_validation_epoch, -best_validation_loss))
plt.figure(figsize=(10,10))
plt.plot(range(len(valid_loss)), valid_loss)
plt.title('validation loss over epochs')
plt.savefig(save_folder+'valid_loss.png')
# Save trained model
torch.save(best_model, save_folder+'best_model.pt')
def calculate_dist(true, generated):
distance_of_one_sample = []
for t in range(generated.shape[1]):
y = true[t]
y_hat = generated[:,t]
dist = []
for p in range(50, 101, 1):
if p == 50:
median = stats.scoreatpercentile(y_hat, p)
dist.append(np.abs(y - median))
else:
pl = 100 - p
pu = p
l = stats.scoreatpercentile(y_hat, pl)
u = stats.scoreatpercentile(y_hat, pu)
if y <= u and y >= l:
dist.append(0.0)
elif y < l:
dist.append(np.abs(y - l))
else:
dist.append(np.abs(y - u))
dist = np.array(dist)
if t == 0:
distance_of_one_sample = dist
else:
distance_of_one_sample += dist
return distance_of_one_sample/24
def test(model, test_loader):
model.eval()
median_pred = []
ground_truth = []
point_pred = []
pi_1 = []
pi_99 = []
pi_5 = []
pi_95 = []
pi_15 = []
pi_85 = []
pi_25 = []
pi_75 = []
distance = {}
for index, data in enumerate(test_loader):
#if index == 2: break
inputs = data[0]
cond_inputs = data[1]
with torch.no_grad():
cond_inputs_ = cond_inputs.view(-1,num_cond_inputs) * torch.ones([5000,num_cond_inputs])
yt_hat = model.sample(5000, cond_inputs = cond_inputs_).detach().cpu().numpy()
#test_data = test_set.X[index,:].flatten()
input_data = inputs.detach().numpy().flatten()
cond_data = cond_inputs.detach().numpy().flatten()
input_data = input_data*std + mu
cond_data = cond_data*std + mu
synth = yt_hat*std + mu
median = stats.scoreatpercentile(synth, 50, axis = 0)
percentile1 = stats.scoreatpercentile(synth, 1, axis = 0)
percentile99 = stats.scoreatpercentile(synth, 99, axis = 0)
percentile5 = stats.scoreatpercentile(synth, 5, axis = 0)
percentile95 = stats.scoreatpercentile(synth, 95, axis = 0)
percentile15 = stats.scoreatpercentile(synth, 15, axis = 0)
percentile85 = stats.scoreatpercentile(synth, 85, axis = 0)
percentile25 = stats.scoreatpercentile(synth, 25, axis = 0)
percentile75 = stats.scoreatpercentile(synth, 75, axis = 0)
if index == 0:
median_pred = median
ground_truth = input_data
pi_1 = percentile1
pi_99 = percentile99
pi_5 = percentile5
pi_95 = percentile95
pi_15 = percentile15
pi_85 = percentile85
pi_25 = percentile25
pi_75 = percentile75
else:
median_pred = np.concatenate((median_pred, median))
ground_truth = np.concatenate((ground_truth, input_data))
pi_1 = np.concatenate((pi_1, percentile1))
pi_99 = np.concatenate((pi_99, percentile99))
pi_5 = np.concatenate((pi_5, percentile5))
pi_95 = np.concatenate((pi_95, percentile95))
pi_15 = np.concatenate((pi_15, percentile15))
pi_85 = np.concatenate((pi_85, percentile85))
pi_25 = np.concatenate((pi_25, percentile25))
pi_75 = np.concatenate((pi_75, percentile75))
# distance of test data {index} averaged over 24 hours
distance[index] = calculate_dist(input_data, synth)
GLOW_pred_dict = {}
GLOW_pred_dict['median_pred'] = median_pred
GLOW_pred_dict['ground_truth'] = ground_truth
GLOW_pred_dict['pi1'] = pi_1
GLOW_pred_dict['pi99'] = pi_99
GLOW_pred_dict['pi5'] = pi_5
GLOW_pred_dict['pi95'] = pi_95
GLOW_pred_dict['pi15'] = pi_15
GLOW_pred_dict['pi85'] = pi_85
GLOW_pred_dict['pi25'] = pi_25
GLOW_pred_dict['pi75'] = pi_75
# Save GLOW_pred_dict as .csv file
GLOW_pred = pd.DataFrame.from_dict(GLOW_pred_dict)
GLOW_pred.to_csv(save_folder+'GLOW_pred.csv')
GLOW_distance = pd.DataFrame.from_dict(distance)
GLOW_distance.to_csv(save_folder+'GLOW_distance.csv')
#series = series.mean(axis = 1)
#GLOW_distance = series.values
# Save GLOW_distance as an array
#np.save(save_folder+'GLOW_distance.npy', GLOW_distance)
return None
test(model, test_loader)