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helper.py
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helper.py
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import argparse, os, logging, random, time
import data_helpers as dh
import numpy as np
import math
import time
import sklearn.metrics
import scipy.sparse
import lightgbm as lgb
import torch
import torch.nn as nn
import torchvision
import torchvision.transforms as transforms
# import tensorboardX as tbx
# import tensorflow as tf
from sklearn.utils.extmath import softmax
from torch.autograd import Variable
from torch.nn.parameter import Parameter
from torch.optim import Optimizer
import pdb
# summaryPath = "/share_data/deepgbm/0201_sum/"
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
if torch.cuda.is_available():
torch.set_default_tensor_type(torch.cuda.FloatTensor)
type_prefix = torch.cuda
else:
type_prefix = torch
class AdamW(torch.optim.Adam):
def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8,
weight_decay=0, amsgrad=False, model_decay_opt=None,
weight_decay_opt=None, key_opt=''):
super(AdamW, self).__init__(params, lr=lr, betas=betas, eps=eps, weight_decay=0, amsgrad=amsgrad)
self.weight_decay = weight_decay
self.weight_decay_opt = weight_decay_opt
self.model_decay_opt = model_decay_opt
self.key_opt = key_opt
def step(self, closure=None):
loss = super(AdamW, self).step(closure)
for group in self.param_groups:
if self.weight_decay != 0:
for p in group['params']:
if p.grad is None:
continue
p.data.add_(-self.weight_decay, p.data)
if self.model_decay_opt is not None:
for name, parameter in self.model_decay_opt.named_parameters():
if self.key_opt in name:
if parameter.grad is not None:
parameter.data.add_(-self.weight_decay_opt, parameter.data)
return loss
def eval_metrics(task, true, pred):
if task == 'binary':
logloss = sklearn.metrics.log_loss(true.astype(np.float64), pred.astype(np.float64))
auc = sklearn.metrics.roc_auc_score(true, pred)
# error = 1-sklearn.metrics.accuracy_score(true,(pred+0.5).astype(np.int32))
return (logloss, auc)#, error)
else:
mseloss = sklearn.metrics.mean_squared_error(true, pred)
return mseloss
def EvalTestset(test_x, test_y, model, test_batch_size, test_x_opt=None):
test_len = test_x.shape[0]
test_num_batch = math.ceil(test_len / test_batch_size)
sum_loss = 0.0
y_preds = []
model.eval()
with torch.no_grad():
for jdx in range(test_num_batch):
tst_st = jdx * test_batch_size
tst_ed = min(test_len, tst_st + test_batch_size)
inputs = torch.from_numpy(test_x[tst_st:tst_ed].astype(np.float32)).to(device)
if test_x_opt is not None:
inputs_opt = torch.from_numpy(test_x_opt[tst_st:tst_ed].astype(np.float32)).to(device)
outputs = model(inputs, inputs_opt)
else:
outputs = model(inputs)
targets = torch.from_numpy(test_y[tst_st:tst_ed]).to(device)
if isinstance(outputs, tuple):
outputs = outputs[0]
y_preds.append(outputs)
loss_tst = model.true_loss(outputs, targets).item()
sum_loss += (tst_ed - tst_st) * loss_tst
return sum_loss / test_len, np.concatenate(y_preds, 0)
def TrainWithLog(args, plot_title, train_x, train_y,
train_y_opt, test_x, test_y, model, opt,
epoch, batch_size, n_output, key="",
train_x_opt=None, test_x_opt=None):
# trn_writer = tf.summary.FileWriter(summaryPath+plot_title+key+"_output/train")
# tst_writer = tf.summary.FileWriter(summaryPath+plot_title+key+"_output/test")
if isinstance(test_x, scipy.sparse.csr_matrix):
test_x = test_x.todense()
train_len = train_x.shape[0]
global_iter = 0
trn_batch_size = batch_size
train_num_batch = math.ceil(train_len / trn_batch_size)
total_iterations = epoch * train_num_batch
start_time = time.time()
total_time = 0.0
min_loss = float("Inf")
# min_error = float("Inf")
max_auc = 0.0
for epoch in range(epoch):
shuffled_indices = np.random.permutation(np.arange(train_x.shape[0]))
Loss_trn_epoch = 0.0
Loss_trn_log = 0.0
log_st = 0
for local_iter in range(train_num_batch):
trn_st = local_iter * trn_batch_size
trn_ed = min(train_len, trn_st + trn_batch_size)
batch_trn_x = train_x[shuffled_indices[trn_st:trn_ed]]
if isinstance(batch_trn_x, scipy.sparse.csr_matrix):
batch_trn_x = batch_trn_x.todense()
inputs = torch.from_numpy(batch_trn_x.astype(np.float32)).to(device)
targets = torch.from_numpy(train_y[shuffled_indices[trn_st:trn_ed],:]).to(device)
model.train()
if train_x_opt is not None:
inputs_opt = torch.from_numpy(train_x_opt[shuffled_indices[trn_st:trn_ed]].astype(np.float32)).to(device)
outputs = model(inputs, inputs_opt)
else:
outputs = model(inputs)
opt.zero_grad()
if isinstance(outputs, tuple) and train_y_opt is not None:
# targets_inner = torch.from_numpy(s_train_y_opt[trn_st:trn_ed,:]).to(device)
targets_inner = torch.from_numpy(train_y_opt[shuffled_indices[trn_st:trn_ed],:]).to(device)
loss_ratio = args.loss_init * max(0.3,args.loss_dr ** (epoch // args.loss_de))#max(0.5, args.loss_dr ** (epoch // args.loss_de))
if len(outputs) == 3:
loss_val = model.joint_loss(outputs[0], targets, outputs[1], targets_inner, loss_ratio, outputs[2])
else:
loss_val = model.joint_loss(outputs[0], targets, outputs[1], targets_inner, loss_ratio)
loss_val.backward()
loss_val = model.true_loss(outputs[0], targets)
elif isinstance(outputs, tuple):
loss_val = model.true_loss(outputs[0], targets)
loss_val.backward()
else:
loss_val = model.true_loss(outputs, targets)
loss_val.backward()
opt.step()
loss_val = loss_val.item()
global_iter += 1
Loss_trn_epoch += (trn_ed - trn_st) * loss_val
Loss_trn_log += (trn_ed - trn_st) * loss_val
if global_iter % args.log_freq == 0:
print(key+"Epoch-{:0>3d} {:>5d} Batches, Step {:>6d}, Training Loss: {:>9.6f} (AllAvg {:>9.6f})"
.format(epoch, local_iter + 1, global_iter, Loss_trn_log/(trn_ed-log_st), Loss_trn_epoch/trn_ed))
# trn_summ = tf.Summary()
# trn_summ.value.add(tag=args.data+ "/Train/Loss", simple_value = Loss_trn_log/(trn_ed-log_st))
# trn_writer.add_summary(trn_summ, global_iter)
log_st = trn_ed
Loss_trn_log = 0.0
if global_iter % args.test_freq == 0 or local_iter == train_num_batch - 1:
if args.model == 'deepgbm' or args.model == 'd1':
try:
print('Alpha: '+str(model.alpha))
print('Beta: '+str(model.beta))
except:
pass
# tst_summ = tf.Summary()
torch.cuda.empty_cache()
test_loss, pred_y = EvalTestset(test_x, test_y, model, args.test_batch_size, test_x_opt)
current_used_time = time.time() - start_time
start_time = time.time()
total_time += current_used_time
remaining_time = (total_iterations - (global_iter) ) * (total_time / (global_iter))
if args.task == 'binary':
metrics = eval_metrics(args.task, test_y, pred_y)
_, test_auc = metrics
# min_error = min(min_error, test_error)
max_auc = max(max_auc, test_auc)
# tst_summ.value.add(tag=args.data+"/Test/Eval/Error", simple_value = test_error)
# tst_summ.value.add(tag=args.data+"/Test/Eval/AUC", simple_value = test_auc)
# tst_summ.value.add(tag=args.data+"/Test/Eval/Min_Error", simple_value = min_error)
# tst_summ.value.add(tag=args.data+"/Test/Eval/Max_AUC", simple_value = max_auc)
print(key+"Evaluate Result:\nEpoch-{:0>3d} {:>5d} Batches, Step {:>6d}, Testing Loss: {:>9.6f}, Testing AUC: {:8.6f}, Used Time: {:>5.1f}m, Remaining Time: {:5.1f}m"
.format(epoch, local_iter + 1, global_iter, test_loss, test_auc, total_time/60.0, remaining_time/60.0))
else:
print(key+"Evaluate Result:\nEpoch-{:0>3d} {:>5d} Batches, Step {:>6d}, Testing Loss: {:>9.6f}, Used Time: {:>5.1f}m, Remaining Time: {:5.1f}m"
.format(epoch, local_iter + 1, global_iter, test_loss, total_time/60.0, remaining_time/60.0))
min_loss = min(min_loss, test_loss)
# tst_summ.value.add(tag=args.data+"/Test/Loss", simple_value = test_loss)
# tst_summ.value.add(tag=args.data+"/Test/Min_Loss", simple_value = min_loss)
print("-------------------------------------------------------------------------------")
# tst_writer.add_summary(tst_summ, global_iter)
# tst_writer.flush()
print("Best Metric: %s"%(str(max_auc) if args.task=='binary' else str(min_loss)))
print("####################################################################################")
print("Final Best Metric: %s"%(str(max_auc) if args.task=='binary' else str(min_loss)))
return min_loss
def GetEmbPred(model, fun, X, test_batch_size):
model.eval()
tst_len = X.shape[0]
test_num_batch = math.ceil(tst_len / test_batch_size)
y_preds = []
with torch.no_grad():
for jdx in range(test_num_batch):
tst_st = jdx * test_batch_size
tst_ed = min(tst_len, tst_st + test_batch_size)
inputs = torch.from_numpy(X[tst_st:tst_ed]).to(device)
t_preds = fun(inputs).data.cpu().numpy()
y_preds.append(t_preds)
y_preds = np.concatenate(y_preds, 0)
return y_preds