def run_iters_experiment(dir_path, file_name):
res = {}
res['x'] = []
res['y'] = []
for num_of_iters in ITERS:
X, y, view1, view2 = du.extract_data(dir_path + '/' + file_name,
file_name)
acc = []
for i in range(10):
print("CV: " + str(i))
X, y = shuffle(X, y)
X_labeled, X_unlabeled, y_labeled, X_test, y_test = du.split_data(
X, y, train_test_split=0.8, labeled_unlabeled_split=0.2)
base_models = [
BASE_MODELS[ITERS_BASE_MODEL], BASE_MODELS[ITERS_BASE_MODEL]
]
model = SP_coTrain.SP_coTrain(base_models,
num_of_iters,
add_rate=0.1,
gamma=0.5)
model.fit(X_labeled, X_unlabeled, y_labeled, view1, view2)
y_pred = model.predict(X_test)
acc.append(accuracy_score(y_test, y_pred))
res['x'].append(num_of_iters)
res['y'].append(sum(acc) / len(acc))
plot_graph(res)
def train(FLAGS):
# Load the data
en_token_ids, en_seq_lens, en_vocab_dict, en_rev_vocab_dict = \
process_data('data/en.p', max_vocab_size=5000, target_lang=False)
sp_token_ids, sp_seq_lens, sp_vocab_dict, sp_rev_vocab_dict = \
process_data('data/sp.p', max_vocab_size=5000, target_lang=True)
# Split into train and validation sets
train_encoder_inputs, train_decoder_inputs, train_targets, \
train_en_seq_lens, train_sp_seq_len, \
valid_encoder_inputs, valid_decoder_inputs, valid_targets, \
valid_en_seq_lens, valid_sp_seq_len = \
split_data(en_token_ids, sp_token_ids, en_seq_lens, sp_seq_lens,
train_ratio=0.8)
# Update parameters
FLAGS.en_vocab_size = len(en_vocab_dict)
FLAGS.sp_vocab_size = len(sp_vocab_dict)
# Start session
with tf.Session() as sess:
# Create new model or load old one
model = create_model(sess, FLAGS)
# Training begins
losses = []
for epoch_num, epoch in enumerate(
generate_epoch(train_encoder_inputs, train_decoder_inputs,
train_targets, train_en_seq_lens,
train_sp_seq_len, FLAGS.num_epochs,
FLAGS.batch_size)):
print "EPOCH: %i" % (epoch_num)
# Decay learning rate
sess.run(tf.assign(model.lr, FLAGS.learning_rate * \
(FLAGS.learning_rate_decay_factor ** epoch_num)))
batch_loss = []
for batch_num, (batch_encoder_inputs, batch_decoder_inputs,
batch_targets, batch_en_seq_lens,
batch_sp_seq_lens) in enumerate(epoch):
loss, _ = model.step(sess, FLAGS, batch_encoder_inputs,
batch_decoder_inputs, batch_targets,
batch_en_seq_lens, batch_sp_seq_lens,
FLAGS.dropout)
batch_loss.append(loss)
losses.append(np.mean(batch_loss))
plt.plot(losses, label='loss')
plt.legend()
plt.show()
def predict_on_stocks(array: numpy.array, model_path: str, interval: str,
stock_path: str):
scaler = StandardScaler()
open_data, close_data = init_data(array)
open_data, close_data = normalize_data(open_data, close_data, scaler)
(x_train, y_train, x_test, y_test) = split_data(open_data, close_data)
(x_train, y_train) = shuffle_data(x_train, y_train)
(model, checkpoint_callback) = create_model(model_path)
model.fit(x_train,
y_train,
validation_data=(x_test, y_test),
batch_size=64,
epochs=EPOCHS,
callbacks=[checkpoint_callback])
#test_model(model, x_test, y_test, scaler, interval) // uncomment this if you want to test the ai efficiency
dump(scaler, f'{model_path}/std_scaler.bin', compress=True)
def cross_validation(dir_path,
file_name,
cv=10,
base_model="RandomForest",
labeled_rate=0.2):
X, y, view1, view2 = du.extract_data(dir_path + '/' + file_name, file_name)
res_spaco = []
res_co = []
res_base = []
res_reg_base = []
for i in range(cv):
print("CV: " + str(i))
X, y = shuffle(X, y)
X_labeled, X_unlabeled, y_labeled, X_test, y_test = du.split_data(
X, y, train_test_split=0.8, labeled_unlabeled_split=labeled_rate)
res_spaco.append(
evaluate_model("spaco", base_model, X_labeled, X_unlabeled,
y_labeled, X_test, y_test, view1, view2,
labeled_rate))
res_co.append(
evaluate_model("co", base_model, X_labeled, X_unlabeled, y_labeled,
X_test, y_test, view1, view2, labeled_rate))
res_base.append(
evaluate_model("base", base_model, X_labeled, X_unlabeled,
y_labeled, X_test, y_test, view1, view2,
labeled_rate))
res_reg_base.append(
evaluate_model("reg_base", base_model, X_labeled, X_unlabeled,
y_labeled, X_test, y_test, view1, view2,
labeled_rate))
return res_spaco, res_co, res_base, res_reg_base
def main(args):
# set random seed
np.random.seed(args.seed)
torch.manual_seed(args.seed)
print(args.dataset_root)
# load data
data, meta = data_utils.load_data(args.dataset_root,
args.dataset,
is_training=True)
train_data, val_data = data_utils.split_data(data,
args.validate_rate,
shuffle=True)
# build train dataloader
train_dataset = data_utils.ImageDataset(*train_data,
is_training=True,
is_flip=args.dataset
not in ['mnist', 'svhn'])
train_dataloader = torch.utils.data.DataLoader(train_dataset,
args.batch_size,
shuffle=True,
num_workers=2,
pin_memory=True)
# build val dataloader
val_dataset = data_utils.ImageDataset(*val_data, is_training=False)
val_dataloader = torch.utils.data.DataLoader(val_dataset,
args.batch_size,
shuffle=False,
num_workers=2,
pin_memory=True)
# remove temp dataset variables to reduce memory usage
del data, train_data, val_data
device = torch.device(args.device)
# build model
if args.model == 'resnet_20':
model = models.Resnet20
else:
model = models.SimpleCNN
net = model(train_dataset.shape, meta['n_class']).to(device=device)
net.apply(init_param)
criterion = torch.nn.CrossEntropyLoss()
# build optim
optim = torch.optim.SGD(make_param_groups(net, args.weight_decay),
0.1,
momentum=0.9)
# make log directory
logdir = Path(args.logdir)
if not logdir.exists():
os.makedirs(str(logdir))
global_step = 0
start_epoch = 0
if args.restore:
# restore checkpoint
state = torch.load(args.restore)
start_epoch = state['epoch'] + 1
global_step = state['global_step']
net.load_state_dict(state['net'])
optim.load_state_dict(state['optim'])
# lr strategy
lr_boundaries = list(map(int, args.boundaries.split(',')))
lr_values = list(map(float, args.values.split(',')))
lr_manager = LRManager(lr_boundaries, lr_values)
for e in range(start_epoch, args.n_epoch):
print('-------epoch: {:d}-------'.format(e))
# training phrase
net.train()
mean_loss, acc = MeanValue(), Accuracy()
lr_manager.set_lr_for_optim(e, optim)
tm = TimeMeter()
tm.start()
train_log = {}
for i, (x, y) in enumerate(train_dataloader):
tm.add_counter()
if device.type == 'cuda':
x = x.cuda(device, non_blocking=True)
y = y.cuda(device, non_blocking=True)
optim.zero_grad()
logits = net(x)
loss = criterion(logits, y)
loss.backward()
optim.step()
global_step += 1
loss = loss.detach().cpu().numpy()
predicts = torch.argmax(logits, dim=1).detach().cpu().numpy()
y = y.detach().cpu().numpy()
mean_loss.add(loss)
acc.add(predicts, y)
if i % args.log_every == 0:
torch.cuda.synchronize()
tm.stop()
print(
'step: {:d}, lr: {:g}, loss: {:.4f}, acc: {:.2%}, speed: {:.2f} i/s.'
.format(i, lr_manager.get(e), mean_loss.get(), acc.get(),
args.batch_size / tm.get()))
train_log[global_step] = {
'loss': mean_loss.get(),
'acc': acc.get()
}
tm.reset()
tm.start()
mean_loss.reset()
acc.reset()
# val phrase
net.eval()
mean_loss, acc = MeanValue(), Accuracy()
for x, y in val_dataloader:
if device.type == 'cuda':
x = x.cuda(device, non_blocking=True)
y = y.cuda(device, non_blocking=True)
logits = net(x)
loss = criterion(logits, y)
loss = loss.detach().cpu().numpy()
predicts = torch.argmax(logits, dim=1).detach().cpu().numpy()
y = y.detach().cpu().numpy()
mean_loss.add(loss)
acc.add(predicts, y)
print('val_loss: {:.4f}, val_acc: {:.2%}'.format(
mean_loss.get(), acc.get()))
val_log = {global_step: {'loss': mean_loss.get(), 'acc': acc.get()}}
# save checkpoint
vars_to_saver = {
'net': net.state_dict(),
'optim': optim.state_dict(),
'epoch': e,
'global_step': global_step
}
cpt_file = logdir / 'checkpoint_{:d}.pk'.format(e)
torch.save(vars_to_saver, str(cpt_file))
log_file = logdir / 'log_{:d}.pk'.format(e)
torch.save({'train': train_log, 'val': val_log}, str(log_file))
def train(FLAGS):
# Load the data
en_token_ids, en_seq_lens, en_vocab_dict, en_rev_vocab_dict = \
process_data('data/en.p', max_vocab_size=5000, target_lang=False)
sp_token_ids, sp_seq_lens, sp_vocab_dict, sp_rev_vocab_dict = \
process_data('data/sp.p', max_vocab_size=5000, target_lang=True)
# Split into train and validation sets
train_encoder_inputs, train_decoder_inputs, train_targets, \
train_en_seq_lens, train_sp_seq_len, \
valid_encoder_inputs, valid_decoder_inputs, valid_targets, \
valid_en_seq_lens, valid_sp_seq_len = \
split_data(en_token_ids, sp_token_ids, en_seq_lens, sp_seq_lens,
train_ratio=0.8)
# Update parameters
FLAGS.en_vocab_size = len(en_vocab_dict)
FLAGS.sp_vocab_size = len(sp_vocab_dict)
FLAGS.sp_max_len = max(sp_seq_lens) + 1 # GO token
# Start session
with tf.Session() as sess:
# Create new model or load old one
model = create_model(sess, FLAGS, forward_only=False)
# Training begins
train_losses = []
valid_losses = []
for epoch_num, epoch in enumerate(generate_epoch(train_encoder_inputs,
train_decoder_inputs, train_targets,
train_en_seq_lens, train_sp_seq_len,
FLAGS.num_epochs, FLAGS.batch_size)):
print "EPOCH: %i" % (epoch_num)
# Decay learning rate
sess.run(tf.assign(model.lr, FLAGS.learning_rate * \
(FLAGS.learning_rate_decay_factor ** epoch_num)))
batch_loss = []
for batch_num, (batch_encoder_inputs, batch_decoder_inputs,
batch_targets, batch_en_seq_lens,
batch_sp_seq_lens) in enumerate(epoch):
y_pred, loss, _ = model.step(sess, FLAGS,
batch_encoder_inputs, batch_decoder_inputs, batch_targets,
batch_en_seq_lens, batch_sp_seq_lens,
FLAGS.dropout, forward_only=False)
batch_loss.append(loss)
train_losses.append(np.mean(batch_loss))
for valid_epoch_num, valid_epoch in enumerate(generate_epoch(valid_encoder_inputs,
valid_decoder_inputs, valid_targets,
valid_en_seq_lens, valid_sp_seq_len,
num_epochs=1, batch_size=FLAGS.batch_size)):
batch_loss = []
for batch_num, (batch_encoder_inputs, batch_decoder_inputs,
batch_targets, batch_en_seq_lens,
batch_sp_seq_lens) in enumerate(valid_epoch):
loss = model.step(sess, FLAGS,
batch_encoder_inputs, batch_decoder_inputs, batch_targets,
batch_en_seq_lens, batch_sp_seq_lens,
dropout=0.0, forward_only=True, sampling=False)
batch_loss.append(loss)
valid_losses.append(np.mean(batch_loss))
# Save checkpoint.
if not os.path.isdir(FLAGS.ckpt_dir):
os.makedirs(FLAGS.ckpt_dir)
checkpoint_path = os.path.join(FLAGS.ckpt_dir, "model.ckpt")
print "Saving the model."
model.saver.save(sess, checkpoint_path,
global_step=model.global_step)
plt.plot(train_losses, label='train_loss')
plt.plot(valid_losses, label='valid_loss')
plt.legend()
plt.show()
import tensorflow as tf
import numpy as np
import data_processing
import config
import data_utils
import seq2seq_wrapper
from os import path
#load data and split into train and test sets
idx_headings, idx_descriptions = data_processing.process_data()
article_metadata = data_processing.unpickle_articles()
(x_train, x_test), (y_train, y_test), (x_valid, y_valid) = data_utils.split_data(idx_descriptions, idx_headings)
#define parameters
xseq_length = x_train.shape[-1]
yseq_length = y_train.shape[-1]
batch_size = config.batch_size
xvocab_size = len(article_metadata['idx2word'])
yvocab_size = xvocab_size
checkpoint_path = path.join(config.path_outputs, 'checkpoint')
print (checkpoint_path)
#define model
model = seq2seq_wrapper.Seq2Seq(xseq_len=xseq_length,
yseq_len=yseq_length,
xvocab_size=xvocab_size,
yvocab_size=yvocab_size,
emb_dim=config.embedding_dim,
num_layers=3,
ckpt_path=checkpoint_path)
from sklearn.pipeline import Pipeline, FeatureUnion
from sklearn.preprocessing import StandardScaler
from sklearn.impute import SimpleImputer
from sklearn.model_selection import RandomizedSearchCV
from sklearn.svm import SVR
from sklearn.metrics import mean_squared_error
from scipy.stats import uniform
from data_utils import get_data, split_data
from utils import DataFrameSelector, CombinedAttributesAdder, CustomLabelBinarizer
# laoding the dataset
housing = get_data()
# split into train_set and test_set
train_set, test_set = split_data(housing)
housing_train = train_set.drop(
"median_house_value",
axis=1) # median_house_value column contains the target values
housing_test = test_set.drop("median_house_value", axis=1)
housing_train_labels = train_set["median_house_value"].copy()
housing_test_labels = test_set["median_house_value"].copy()
# data preparation and prediction going to be done in a pipeline
# pipeline to preprocess numerical features
numerical_attributes = train_set.drop(
['ocean_proximity', 'median_house_value'], axis=1).columns
numerical_pipeline = Pipeline(
#seq2seq train
import tensorflow as tf
import numpy as np
import data_processing
import config
import data_utils
import seq2seq_wrapper
from os import path
#load data and split into train and test sets
idx_headings, idx_descriptions = data_processing.process_data()
article_metadata = data_processing.unpickle_articles()
(x_train, x_test), (y_train, y_test), (x_valid,
y_valid) = data_utils.split_data(
idx_descriptions, idx_headings)
#define parameters
xseq_length = x_train.shape[-1]
yseq_length = y_train.shape[-1]
batch_size = config.batch_size
xvocab_size = len(article_metadata['idx2word'])
yvocab_size = xvocab_size
checkpoint_path = path.join(config.path_outputs, 'checkpoint')
print(checkpoint_path)
#define model
model = seq2seq_wrapper.Seq2Seq(xseq_len=xseq_length,
yseq_len=yseq_length,
xvocab_size=xvocab_size,
yvocab_size=yvocab_size,
if __name__ == "__main__":
if len(sys.argv) != 2:
print "USAGE: " + sys.argv[0] + " input_file output_model_file"
sys.exit(1)
input_file = sys.argv[1]
output_model_file = sys.argv[2]
data = data_utils.read_from_csv(input_file)
filtered_data = [x for x in data if x.diag_tag != "" and x.diag_tag != "u"]
labels = [np.float32(x.diag_tag == "p") for x in filtered_data]
data = [x.processed_sentence for x in filtered_data]
report_ids = [x.report_id for x in filtered_data]
train_data, train_labels, test_data, test_labels = data_utils.split_data(data, labels, report_ids, split=0.7)
# change these parameters for the grid search
# parameters = {'lsi__n_components': [100],
# 'classifier__C': [3, 4, 5, 6, 7, 8, 9, 10],
# 'classifier__kernel': ["rbf"]
# }
parameters = {'lsi__n_components': [100],
'classifier__n_estimators': [1000],
'classifier__max_depth': [5, 10],
'classifier__min_samples_split': [5, 10],
'classifier__min_samples_leaf': [5, 10],
}
# clf = GridSearchCV(pipelines.get_count_lsi_SVM(), parameters)
def train(FLAGS):
# Load the data
en_token_ids, en_seq_lens, en_vocab_dict, en_rev_vocab_dict = \
process_data('data/my_en.txt', max_vocab_size=5000, target_lang=False)
sp_token_ids, sp_seq_lens, sp_vocab_dict, sp_rev_vocab_dict = \
process_data('data/my_sp.txt', max_vocab_size=5000, target_lang=True)
# Split into train and validation sets
train_encoder_inputs, train_decoder_inputs, train_targets, \
train_en_seq_lens, train_sp_seq_len, \
valid_encoder_inputs, valid_decoder_inputs, valid_targets, \
valid_en_seq_lens, valid_sp_seq_len = \
split_data(en_token_ids, sp_token_ids, en_seq_lens, sp_seq_lens,
train_ratio=0.8)
output = open('data/vocab_en.pkl', 'wb')
pickle.dump(en_vocab_dict, output)
output.close()
output = open('data/vocab_sp.pkl', 'wb')
pickle.dump(sp_vocab_dict, output)
output.close()
# Update parameters
FLAGS.en_vocab_size = len(en_vocab_dict)
FLAGS.sp_vocab_size = len(sp_vocab_dict)
print 'len(en_vocab_dict)', len(en_vocab_dict)
print 'len(sp_vocab_dict)', len(sp_vocab_dict)
# Start session
with tf.Session() as sess:
# Create new model or load old one
model = create_model(sess, FLAGS)
# Training begins
losses = []
for epoch_num, epoch in enumerate(
generate_epoch(train_encoder_inputs, train_decoder_inputs,
train_targets, train_en_seq_lens,
train_sp_seq_len, FLAGS.num_epochs,
FLAGS.batch_size)):
print "EPOCH: %i" % (epoch_num)
# Decay learning rate
sess.run(tf.assign(model.lr, FLAGS.learning_rate * \
(FLAGS.learning_rate_decay_factor ** epoch_num)))
batch_loss = []
for batch_num, (batch_encoder_inputs, batch_decoder_inputs,
batch_targets, batch_en_seq_lens,
batch_sp_seq_lens) in enumerate(epoch):
loss, _ = model.step(sess, FLAGS, batch_encoder_inputs,
batch_decoder_inputs, batch_targets,
batch_en_seq_lens, batch_sp_seq_lens,
FLAGS.dropout)
batch_loss.append(loss)
losses.append(np.mean(batch_loss))
checkpoint_path = "/tmp/model.ckpt"
print "Saving the model."
model.saver.save(sess, checkpoint_path)
plt.plot(losses, label='loss')
plt.legend()
plt.savefig('seq_01.png')
acc = accuracy_score(y_test, y_pred)
return fit_time, predict_time, auc, acc
X, y, view1, view2 = du.extract_data(dir_path + '/' + file_name, file_name)
res_spaco = []
res_spaco_ours = []
res_spaco_ours2 = []
res_reg=[]
for i in range(cv):
print("CV: " + str(i))
X, y = shuffle(X, y)
X_labeled, X_unlabeled, y_labeled, X_test, y_test = du.split_data(X, y, train_test_split=0.8,
labeled_unlabeled_split=labeled_split)
res_spaco.append(evaluate_model("spaco",base_model,X_labeled, X_unlabeled, y_labeled, X_test, y_test, view1, view2))
res_spaco_ours.append(evaluate_model("spaco_ours",base_model,X_labeled, X_unlabeled, y_labeled, X_test, y_test, view1, view2))
res_spaco_ours2.append(evaluate_model("spaco_ours2",base_model,X_labeled, X_unlabeled, y_labeled, X_test, y_test, view1, view2))
res_reg.append(evaluate_model("reg",base_model,X_labeled, X_unlabeled, y_labeled, X_test, y_test, view1, view2))
f=open('./test_res.txt',mode='w')
f.write("spaco\n")
f.write(str(res_spaco[0]))
f.write("\n\n")
f.write("spaco_ours\n")
f.write(str(res_spaco_ours[0]))
def train():
X, y = load_data_and_labels()
vocab_list, vocab_dict, rev_vocab_dict = create_vocabulary(
X, FLAGS.en_vocab_size)
X, seq_lens = data_to_token_ids(X, vocab_dict)
train_X, train_y, train_seq_lens, valid_X, valid_y, valid_seq_lens = \
split_data(X, y, seq_lens)
FLAGS.max_sequence_length = len(train_X[0])
with tf.Session() as sess:
# Load old model or create new one
model = create_model(sess, FLAGS)
# Train results
for epoch_num, epoch in enumerate(
generate_epoch(train_X, train_y, train_seq_lens,
FLAGS.num_epochs, FLAGS.batch_size)):
print "EPOCH:", epoch_num
sess.run(tf.assign(model.lr, FLAGS.learning_rate * \
(FLAGS.learning_rate_decay_factor ** epoch_num)))
train_loss = []
train_accuracy = []
for batch_num, (batch_X, batch_y,
batch_seq_lens) in enumerate(epoch):
_, loss, accuracy = model.step(
sess,
batch_X,
batch_seq_lens,
batch_y,
dropout_keep_prob=FLAGS.dropout_keep_prob,
forward_only=False,
sampling=False)
train_loss.append(loss)
train_accuracy.append(accuracy)
print
print "EPOCH %i SUMMARY" % epoch_num
print "Training loss %.3f" % np.mean(train_loss)
print "Training accuracy %.3f" % np.mean(train_accuracy)
print "----------------------"
# Validation results
for valid_epoch_num, valid_epoch in enumerate(
generate_epoch(valid_X,
valid_y,
valid_seq_lens,
num_epochs=1,
batch_size=FLAGS.batch_size)):
valid_loss = []
valid_accuracy = []
for valid_batch_num, \
(valid_batch_X, valid_batch_y, valid_batch_seq_lens) in \
enumerate(valid_epoch):
loss, accuracy = model.step(sess,
valid_batch_X,
valid_batch_seq_lens,
valid_batch_y,
dropout_keep_prob=1.0,
forward_only=True,
sampling=False)
valid_loss.append(loss)
valid_accuracy.append(accuracy)
print "Validation loss %.3f" % np.mean(valid_loss)
print "Validation accuracy %.3f" % np.mean(valid_accuracy)
print "----------------------"
# Save checkpoint every epoch.
if not os.path.isdir(FLAGS.ckpt_dir):
os.makedirs(FLAGS.ckpt_dir)
checkpoint_path = os.path.join(FLAGS.ckpt_dir, "model.ckpt")
print "Saving the model."
model.saver.save(sess,
checkpoint_path,
global_step=model.global_step)
def main():
"""
Wrapper to run the classification task
"""
# Parse command-line arguments
parser = build_parser()
options = parser.parse_args()
if options.mode == "gen_data":
# Split the data into train/dev/test sets
split_data()
# Load the data and reshape for training and evaluation
X, y_media, y_emotion = load_data(update=options.update,
remove_broken=options.remove_broken)
for set_type in ["train", "dev", "test"]:
total_media = np.sum(y_media[set_type], axis=0)
total_emotion = np.sum(y_emotion[set_type], axis=0)
print(f"Total images for each media category in {set_type} set:")
for v, k in enumerate(MEDIA_LABELS):
print(f"\t{k}: {total_media[v]}")
print(f"Total images for each emotion category in {set_type} set:")
for v, k in enumerate(EMOTION_LABELS):
print(f"\t{k}: {total_emotion[v]}")
elif options.mode == "train":
# Create directory to save the results
results_dir = "results"
if not os.path.exists("./" + results_dir):
os.makedirs("./" + results_dir)
# Check if the given log folder already exists
results_subdirs = os.listdir("./" + results_dir)
if not options.log_folder:
raise Exception(
'Please specify log_folder argument to store results.')
elif options.log_folder in results_subdirs:
raise Exception('The given log folder already exists.')
else:
# Create a folder for each training run
log_folder = os.path.join(results_dir, options.log_folder)
os.makedirs(log_folder)
# Load the data and organize into three tuples (train, val/dev, test)
# Each tuple consists of input arrays, media labels, and emotion labels
train_data, val_data, test_data = load_data(DATA_DIR, INPUT_FILE,
MEDIA_LABEL_FILE,
EMOTION_LABEL_FILE)
# Preprocess the data
train_dset, val_dset, test_dset = preprocess(
train_data,
val_data,
test_data,
augment=options.augment,
train_stats_dir=TRAIN_STATS_DIR)
# Specify the device:
if options.device == "cpu":
device = "/cpu:0"
elif options.device == "gpu":
device = "/device:GPU:0"
# Train the model
train(train_dset,
val_dset,
log_folder=log_folder,
device=device,
batch_size=64,
num_epochs=100,
model_type=options.model_type)
elif options.mode == "test":
# Load the data and organize into three tuples (train, val/dev, test)
# Each tuple consists of input arrays, media labels, and emotion labels
train_data, val_data, test_data = load_data(DATA_DIR, INPUT_FILE,
MEDIA_LABEL_FILE,
EMOTION_LABEL_FILE)
# Preprocess the data
if os.path.isfile(os.path.join(TRAIN_STATS_DIR, "train_stats.npz")):
print(
"Preprocess test data using saved statistics from train data..."
)
train_stats_file = os.path.join(TRAIN_STATS_DIR, "train_stats.npz")
test_dset = preprocess_from_file(train_stats_file,
test_data,
augment=options.augment)
else:
print("Preprocess test data using train data...")
train_dset, val_dset, test_dset = preprocess(
train_data,
val_data,
test_data,
augment=options.augment,
train_stats_dir=TRAIN_STATS_DIR)
# Specify the device:
if options.device == "cpu":
device = "/cpu:0"
elif options.device == "gpu":
device = "/device:GPU:0"
# Load the model
model_path = os.path.join("test_models", options.model_name)
evaluate_test(model_path,
options.model_type,
test_dset,
batch_size=64,
confusion_mat=options.confusion_mat)
elif options.mode == "ensemble":
# Load the data and organize into three tuples (train, val/dev, test)
# Each tuple consists of input arrays, media labels, and emotion labels
train_data, val_data, test_data = load_data(DATA_DIR, INPUT_FILE,
MEDIA_LABEL_FILE,
EMOTION_LABEL_FILE)
# Preprocess the data
if os.path.isfile(os.path.join(TRAIN_STATS_DIR, "train_stats.npz")):
print(
"Preprocess test data using saved statistics from train data..."
)
train_stats_file = os.path.join(TRAIN_STATS_DIR, "train_stats.npz")
test_dset = preprocess_from_file(train_stats_file,
test_data,
augment=options.augment)
else:
print("Preprocess test data using train data...")
train_dset, val_dset, test_dset = preprocess(
train_data,
val_data,
test_data,
augment=options.augment,
train_stats_dir=TRAIN_STATS_DIR)
# Specify the device:
if options.device == "cpu":
device = "/cpu:0"
elif options.device == "gpu":
device = "/device:GPU:0"
if not options.ensemble_folder:
raise Exception(
'Please specify ensemble_folder argument to find ensemble folders.'
)
elif len(os.listdir(options.ensemble_folder)) == 0:
raise Exception('Ensemble folder is empty.')
# Evaluate the ensemble
evaluate_ensemble(options.ensemble_folder,
test_dset,
batch_size=64,
confusion_mat=options.confusion_mat)
elif options.mode == "test_single":
x_test = load_image(
os.path.join('stylized_images_configs', options.image))
train_stats_file = os.path.join(TRAIN_STATS_DIR, "train_stats.npz")
x_test = preprocess_image(train_stats_file,
x_test,
augment=options.augment)
model_path = os.path.join("test_models", options.model_name)
predict_image(x_test, model_path)
logging.basicConfig(
level=logging.INFO,
format="%(asctime)s [%(levelname)s] %(message)s",
handlers=[
logging.FileHandler(logfile, mode='w'),
logging.StreamHandler()]
)
logging.info('Loading dataset...')
concepts, relation_types, relations = load_umls(umls_directory, data_folder)
# logging.warning('Testing system with only 1000 examples!!!')
# relations = relations[:1000]
concept_list = list(concepts.values())
train_data, val_data, _ = split_data(relations)
train_relations_set = set(train_data)
train_dataset = UmlsRelationDataset(train_data)
val_dataset = UmlsRelationDataset(val_data)
callbacks = []
logging.info('Loading collator...')
example_creator = NameRelationExampleCreator()
# train_neg_sampler = BatchNegativeSampler(
# negative_sample_size
# )
# val_neg_sampler = train_neg_sampler
train_neg_sampler = UniformNegativeSampler(
concept_list,
train_relations_set,
negative_sample_size,
def train(params):
hindi_token_ids, hindi_seq_lens, hindi_vocab_dict, hindi_rev_vocab_dict = process_data('../data/hindi_dump.p', max_vocab_size=100000, target_lang=False)
bengali_token_ids, bengali_seq_lens, bengali_vocab_dict, bengali_rev_vocab_dict = process_data('../data/bengali_dump.p', max_vocab_size=100000, target_lang=True)
train_encoder_inputs, train_decoder_inputs, train_targets, train_hindi_seq_lens, train_bengali_seq_len, valid_encoder_inputs, valid_decoder_inputs, valid_targets, valid_hindi_seq_lens, valid_bengali_seq_lens = split_data(hindi_token_ids, bengali_token_ids, hindi_seq_lens, bengali_seq_lens,train_ratio=0.8)
params.hindi_vocab_size = len(hindi_vocab_dict)
params.bengali_vocab_size = len(bengali_vocab_dict)
print params.hindi_vocab_size, params.bengali_vocab_size
with tf.Session() as sess:
_model = model(params)
sess.run(tf.global_variables_initializer())
losses = []
accs = []
for epoch_num, epoch in enumerate(generate_epoch(train_encoder_inputs,train_decoder_inputs, train_targets,train_hindi_seq_lens, train_bengali_seq_len,params.num_epochs, params.batch_size)):
print "EPOCH : ", epoch_num
sess.run(tf.assign(_model.lr, 0.01 * (0.99 ** epoch_num)))
batch_loss = []
batch_acc = []
for batch_num, (batch_encoder_inputs, batch_decoder_inputs,batch_targets, batch_hindi_seq_lens,batch_bengali_seq_lens) in enumerate(epoch):
loss, _,acc = _model.step(sess, params,batch_encoder_inputs, batch_decoder_inputs, batch_targets,batch_hindi_seq_lens, batch_bengali_seq_lens,params.dropout)
batch_loss.append(loss)
batch_acc.append(acc)
losses.append(np.mean(batch_loss))
accs.append(np.mean(batch_acc))
print "Training Loss: ",losses[-1]
print "Training Accuracy",accs[-1]
plt.plot(losses, label='loss')
plt.legend()
# plt.show()
plt.title('Plot for Training Error versus Epochs', fontsize='20', style='oblique')
plt.xlabel('Epochs', fontsize='16', color='green')
plt.ylabel('Training Error', fontsize='16', color='green')
plt.savefig('../output/plot.png')
plt.show()
acc = _model.test(sess, params, valid_encoder_inputs, valid_decoder_inputs, valid_targets, valid_hindi_seq_lens, valid_bengali_seq_lens, params.dropout)
print acc
def train(FLAGS):
# Load the data
en_token_ids, en_seq_lens, en_vocab_dict, en_rev_vocab_dict = \
process_data('data/tst2013.en', max_vocab_size=30000, target_lang=False)
sp_token_ids, sp_seq_lens, sp_vocab_dict, sp_rev_vocab_dict = \
process_data('data/tst2013.tr', max_vocab_size=30000, target_lang=True)
# Split into train and validation sets
train_encoder_inputs, train_decoder_inputs, train_targets, \
train_en_seq_lens, train_sp_seq_len, \
valid_encoder_inputs, valid_decoder_inputs, valid_targets, \
valid_en_seq_lens, valid_sp_seq_len = \
split_data(en_token_ids, sp_token_ids, en_seq_lens, sp_seq_lens,
train_ratio=0.8)
output = open('data/vocab_en.pkl', 'wb')
pickle.dump(en_vocab_dict, output)
output.close()
output = open('data/vocab_sp.pkl', 'wb')
pickle.dump(sp_vocab_dict, output)
output.close()
# Update parameters
FLAGS.en_vocab_size = len(en_vocab_dict)
FLAGS.sp_vocab_size = len(sp_vocab_dict)
print 'len(en_vocab_dict)', len(en_vocab_dict)
print 'len(sp_vocab_dict)', len(sp_vocab_dict)
# Start session
with tf.Session() as sess:
model = None
# Create new model or load old one
f = checkpoint_path + ".index"
print f
exit()
if os.path.isfile(f):
model = restore_model(sess)
else:
model = create_model(sess, FLAGS)
# Training begins
losses = []
for epoch_num, epoch in enumerate(generate_epoch(train_encoder_inputs,
train_decoder_inputs, train_targets,
train_en_seq_lens, train_sp_seq_len,
FLAGS.num_epochs, FLAGS.batch_size)):
print "EPOCH: %i" % (epoch_num)
# Decay learning rate
sess.run(tf.assign(model.lr, FLAGS.learning_rate * \
(FLAGS.learning_rate_decay_factor ** epoch_num)))
batch_loss = []
for batch_num, (batch_encoder_inputs, batch_decoder_inputs,
batch_targets, batch_en_seq_lens,
batch_sp_seq_lens) in enumerate(epoch):
loss, _ = model.step(sess, FLAGS,
batch_encoder_inputs, batch_decoder_inputs, batch_targets,
batch_en_seq_lens, batch_sp_seq_lens,
FLAGS.dropout)
print loss
batch_loss.append(loss)
print 'mean: ', np.mean(batch_loss)
print "Saving the model."
model.saver.save(sess, checkpoint_path)
from os import path
import data_processing
import config
import data_utils
import seq2seq_wrapper
#load data and split into train and test sets
idx_headings, idx_descriptions = data_processing.process_data()
article_metadata = data_processing.unpickle_articles()
(x_train, x_test), (y_train, y_test), (x_valid, y_valid) = data_utils.split_data(idx_descriptions, idx_headings)
#define parameters
xseq_length = x_train.shape[-1]
yseq_length = y_train.shape[-1]
batch_size = config.batch_size
xvocab_size = len(article_metadata['idx2word'])
yvocab_size = xvocab_size
checkpoint_path = path.join(config.path_outputs, 'checkpoint')
print (checkpoint_path)
#define model
model = seq2seq_wrapper.Seq2Seq(xseq_len=xseq_length,
yseq_len=yseq_length,
xvocab_size=xvocab_size,
yvocab_size=yvocab_size,
emb_dim=config.embedding_dim,
num_layers=3,
ckpt_path=checkpoint_path)
val_batch_gen = data_utils.generate_random_batch(x_valid, y_valid, config.batch_size)
def main():
args = parser.parse_args()
pprint(args)
# check and create directories
if not os.path.exists(args.checkpoint):
os.makedirs(args.checkpoint)
if not os.path.exists(args.log):
os.makedirs(args.log)
print('==> Preparing data..')
transformations_train = transforms.Compose([
data.RandomTranslateWithReflect(32),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(), normalize
])
transformations_test = transforms.Compose(
[transforms.ToTensor(), normalize])
mode = {'train': True, 'test': True}
image_datasets = Cifar10(root='./data',
train=True,
transform=None,
download=True)
trainData, trainLabel, testData, testLabel = du.split_data(image_datasets,
select_num=9000)
unlabeled_idx, labeled_idx = du.split_idx(trainLabel, select_num=21000)
idx = np.squeeze([0, 1, 2, 3, 4, 7, 8])
labeled_trainData_75, labeled_trainLabel_75, labeled_trainData_25, labeled_trainLabel_25 = du.split_class(
trainData[labeled_idx, :, :, :],
trainLabel[labeled_idx],
selected_class=idx)
testData_75, testLabel_75, testData_25, testLabel_25 = du.split_class(
testData, testLabel, selected_class=idx)
anchor_idx = du.select_anchors_1(labeled_trainLabel_75,
anchor_num=args.anchor_num)
print("labeled_idx is:{}".format(labeled_idx.size))
print("anchor_idx is:{}".format(anchor_idx.size))
print("unique of labeled_trainLabel_25 is:{}".format(
np.unique(labeled_trainLabel_25)))
print("unique of testLabel_25 is:{}".format(np.unique(testLabel_25)))
dict_data = DT(trainData=labeled_trainData_75[anchor_idx, :, :, :],
trainLabel=labeled_trainLabel_75[anchor_idx],
transform=transformations_train)
dict_loader = torch.utils.data.DataLoader(dict_data,
batch_size=args.anchor_num,
shuffle=False,
num_workers=args.workers)
c_trainData = np.concatenate(
(trainData[unlabeled_idx, :, :, :], labeled_trainData_75), axis=0)
c_trainLabel = np.concatenate(
(trainLabel[unlabeled_idx], labeled_trainLabel_75), axis=0)
u_trn = unlabeled_idx.size
l_trn = labeled_trainLabel_75.shape[0]
n = u_trn + l_trn
unlabeled_idx = np.squeeze(np.arange(u_trn).astype(np.int32))
labeled_idx = np.squeeze(np.arange(u_trn, n).astype(np.int32))
mask_labels = np.squeeze(np.zeros((n, 1)))
mask_labels[u_trn:n] = 1
train_data = DT(trainData=c_trainData,
trainLabel=c_trainLabel,
transform=transformations_train)
test_data = DT(trainData=testData_25,
trainLabel=testLabel_25,
transform=transformations_test)
labeled_trainData_25 = np.concatenate((labeled_trainData_25, testData_75),
axis=0)
labeled_trainLabel_25 = np.concatenate(
(labeled_trainLabel_25, testLabel_75), axis=0)
train_data_test = DT(trainData=labeled_trainData_25,
trainLabel=labeled_trainLabel_25,
transform=transformations_test)
train_loader_test = torch.utils.data.DataLoader(train_data_test,
batch_size=100,
shuffle=True,
num_workers=args.workers)
batch_sampler = data.TwoStreamBatchSampler(unlabeled_idx, labeled_idx,
args.batch_size, 50)
train_loader = torch.utils.data.DataLoader(train_data,
batch_sampler=batch_sampler,
num_workers=args.workers,
pin_memory=True)
# train_loader = torch.utils.data.DataLoader(train_data, batch_size=100,
# shuffle=True, num_workers=args.workers)
test_loader = torch.utils.data.DataLoader(test_data,
batch_size=100,
shuffle=False,
num_workers=args.workers)
for iter in range(5):
model = pre_train(train_loader,
test_loader,
dict_loader,
train_loader_test,
mask_labels,
total_epochs=100,
use_gpu=True,
seed=args.seed)
TARGET_POSITIVE = "p"
elif test_type == "class":
tag_attr = "report_class"
TARGET_POSITIVE = TARGET_CLASS
else:
raise ValueError("Unknown tag: " + test_type)
data = data_utils.read_from_csv(data_file)
filtered_data = [x for x in data if getattr(x, tag_attr) != "" and getattr(x, tag_attr) != "u"]
filtered_data = filtered_data[:2500] # put a limit on the size for performance
labels = [np.float32(getattr(x, tag_attr) == TARGET_POSITIVE) for x in filtered_data]
report_ids = [x.report_id for x in filtered_data]
sentences = [x.processed_sentence for x in filtered_data]
train_data, train_labels, test_data, test_labels = data_utils.split_data(sentences, labels, report_ids, split_value)
# Create transformation pipeline
if USE_RF:
pipe = pipelines.get_count_lsi_randomforest()
else:
pipe = pipelines.get_count_lsi_SVM()
# set pipe parameters and train model
pipe.set_params(**model_params)
pipe.fit(train_data, train_labels)
print "Total = " + str(len(filtered_data)) + " [" + str(labels.count(0)) + ", " + str(labels.count(1)) + "]"
print "Train = " + str(len(train_data)) + " [" + str(train_labels.count(0)) + ", " + str(
train_labels.count(1)
) + "]"
def main(FLAGS):
# set seed
np.random.seed(FLAGS.seed)
tf.set_random_seed(FLAGS.seed)
with tf.device('/cpu:0'), tf.name_scope('input'):
# load dataset into main memory
data, meta = load_data(FLAGS.dataset_root,
FLAGS.dataset,
is_training=True)
train_data, val_data = split_data(data, FLAGS.validate_rate)
# build tf_dataset for training
train_dataset = (tf.data.Dataset.from_tensor_slices(train_data).map(
preprocess_for_train(args.dataset not in ['mnist', 'svhn']),
8).shuffle(10000,
seed=FLAGS.seed).batch(FLAGS.batch_size).prefetch(1))
# build tf_dataset for val
val_dataset = (tf.data.Dataset.from_tensor_slices(val_data).map(
preprocess_for_eval, 8).batch(FLAGS.batch_size).prefetch(1))
# clean up and release memory
del data, train_data, val_data
# construct data iterator
data_iterator = tf.data.Iterator.from_structure(
train_dataset.output_types, train_dataset.output_shapes)
# construct iterator initializer for training and validation
train_data_init = data_iterator.make_initializer(train_dataset)
val_data_init = data_iterator.make_initializer(val_dataset)
# define useful scalars
learning_rate = tf.placeholder(tf.float32, shape=(), name='learning_rate')
tf.summary.scalar('lr', learning_rate)
is_training = tf.placeholder(tf.bool, [], name='is_training')
global_step = tf.train.create_global_step()
# define optimizer
optimizer = tf.train.MomentumOptimizer(learning_rate, 0.9)
# build the net
model = importlib.import_module('models.{}'.format(FLAGS.model))
net = model.Net(meta['n_class'], FLAGS.weight_decay)
# get data from data iterator
images, labels = data_iterator.get_next()
tf.summary.image('images', tf.transpose(images, [0, 2, 3, 1]))
# get logits
logits = net(images, is_training)
tf.summary.histogram('logits', logits)
# summary variable defined in net
for w in net.global_variables:
tf.summary.histogram(w.name, w)
with tf.name_scope('losses'):
# compute loss
loss = tf.losses.sparse_softmax_cross_entropy(labels=labels,
logits=logits)
# compute l2 regularization
l2_reg = tf.losses.get_regularization_loss()
with tf.name_scope('metrics') as scope:
mean_loss, mean_loss_update_op = tf.metrics.mean(loss,
name='mean_loss')
prediction = tf.argmax(logits, axis=1)
accuracy, accuracy_update_op = tf.metrics.accuracy(labels,
prediction,
name='accuracy')
reset_metrics = tf.variables_initializer(
tf.get_collection(tf.GraphKeys.LOCAL_VARIABLES, scope))
metrics_update_op = tf.group(mean_loss_update_op, accuracy_update_op)
# collect metric summary alone, because it need to
# summary after metrics update
metric_summary = [
tf.summary.scalar('loss', mean_loss, collections=[]),
tf.summary.scalar('accuracy', accuracy, collections=[])
]
# compute grad
grads_and_vars = optimizer.compute_gradients(loss + l2_reg)
# summary grads
for g, v in grads_and_vars:
tf.summary.histogram(v.name + '/grad', g)
# run train_op and update_op together
train_op = optimizer.apply_gradients(grads_and_vars,
global_step=global_step)
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
train_op = tf.group(train_op, *update_ops)
# build summary
train_summary_str = tf.summary.merge_all()
metric_summary_str = tf.summary.merge(metric_summary)
# init op
init_op = tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer())
# prepare for the logdir
if not tf.gfile.Exists(FLAGS.logdir):
tf.gfile.MakeDirs(FLAGS.logdir)
# saver
saver = tf.train.Saver(max_to_keep=FLAGS.n_epoch)
# summary writer
train_writer = tf.summary.FileWriter(os.path.join(FLAGS.logdir, 'train'),
tf.get_default_graph())
val_writer = tf.summary.FileWriter(os.path.join(FLAGS.logdir, 'val'),
tf.get_default_graph())
# session
config = tf.ConfigProto(allow_soft_placement=True,
log_device_placement=False,
intra_op_parallelism_threads=4,
inter_op_parallelism_threads=4)
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
# do initialization
sess.run(init_op)
# restore
if FLAGS.restore:
saver.restore(sess, FLAGS.restore)
lr_boundaries = list(map(int, FLAGS.boundaries.split(',')))
lr_values = list(map(float, FLAGS.values.split(',')))
lr_manager = LRManager(lr_boundaries, lr_values)
time_meter = TimeMeter()
# start to train
for e in range(FLAGS.n_epoch):
print('-' * 40)
print('Epoch: {:d}'.format(e))
# training loop
try:
i = 0
sess.run([train_data_init, reset_metrics])
while True:
lr = lr_manager.get(e)
fetch = [train_summary_str] if i % FLAGS.log_every == 0 else []
time_meter.start()
result = sess.run([train_op, metrics_update_op] + fetch, {
learning_rate: lr,
is_training: True
})
time_meter.stop()
if i % FLAGS.log_every == 0:
# fetch summary str
t_summary = result[-1]
t_metric_summary = sess.run(metric_summary_str)
t_loss, t_acc = sess.run([mean_loss, accuracy])
sess.run(reset_metrics)
spd = FLAGS.batch_size / time_meter.get()
time_meter.reset()
print(
'Iter: {:d}, LR: {:g}, Loss: {:.4f}, Acc: {:.2f}, Spd: {:.2f} i/s'
.format(i, lr, t_loss, t_acc, spd))
train_writer.add_summary(t_summary,
global_step=sess.run(global_step))
train_writer.add_summary(t_metric_summary,
global_step=sess.run(global_step))
i += 1
except tf.errors.OutOfRangeError:
pass
# save checkpoint
saver.save(sess,
'{}/{}'.format(FLAGS.logdir, FLAGS.model),
global_step=sess.run(global_step),
write_meta_graph=False)
# val loop
try:
sess.run([val_data_init, reset_metrics])
while True:
sess.run([metrics_update_op], {is_training: False})
except tf.errors.OutOfRangeError:
pass
v_loss, v_acc = sess.run([mean_loss, accuracy])
print('[VAL]Loss: {:.4f}, Acc: {:.2f}'.format(v_loss, v_acc))
val_writer.add_summary(sess.run(metric_summary_str),
global_step=sess.run(global_step))
print('-' * 40)
def main(*kargs, **kwargs):
# ============ Parse global parameters ============
get_kwargs(kwargs)
test_fname = kwargs['test']
embeds_type = kwargs['embeds_type']
logger_fname = kwargs['logger']
# warm_start = kwargs['warm_start']
# model_warm_start = [model.lower() for model in kwargs['model_warm_start']]
config = kwargs['config']
train_clean = kwargs['train_clean']
train_labels = kwargs['train_labels']
test_clean = kwargs['test_clean']
embeds_clean = kwargs['embeds_clean']
result_path = './outputs/'
oof_path = './oof_predictions'
if not os.path.exists(result_path):
os.mkdir(result_path)
if not os.path.exists(oof_path):
os.mkdir(oof_path)
# ==== Create logger ====
logger = Logger(logging.getLogger(), logger_fname)
# ==== Load data ====
logger.info('Loading data...')
test_df = load_data(test_fname)
train_x = np.load(train_clean)
test_x = np.load(test_clean)
embedding_matrix = np.load(embeds_clean)
train_y = np.load(train_labels)
target_labels = [
'toxic', 'severe_toxic', 'obscene', 'threat', 'insult', 'identity_hate'
]
# ==== Splitting training data ====
x_train_nn, x_eval_nn, y_train_nn, y_eval_nn, train_idxs, eval_idxs = split_data(
train_x, train_y, eval_size=0.1, shuffle=True, random_state=42)
logger.debug('X shape = {}'.format(np.shape(x_train_nn)))
# ============= Load params of models =============
params = Params(config)
models = params.get('models')
# ============ Train models =============
for model_name in models:
model_func = get_model(model_name, embedding_matrix, params)
if params.get(model_name).get('folding'):
# =========== Training on folds ============
batch_size = params.get(model_name).get('batch_size')
logger.debug(
'Starting {0} training on folds...'.format(model_name))
models, val_predictions = train_folds(
train_x,
train_y,
params.get(model_name).get('num_folds'),
batch_size,
model_func,
params.get(model_name).get('optimizer'),
logger=logger)
val_predictions_array = np.concatenate(
[minmax_scale(fold) for fold in val_predictions], axis=0)
np.save(
os.path.join(oof_path,
"{0}_{1}_oof.npy".format(model_name,
embeds_type)),
val_predictions_array)
logger.debug('Predicting results...')
test_predicts_list = []
for fold_id, model in enumerate(models):
model_path = os.path.join(
result_path,
"{1}_{0}_{2}_weights.npy".format(fold_id, model_name,
embeds_type))
np.save(model_path, model.get_weights())
test_predicts_path = os.path.join(
result_path, "{1}_{2}_test_predicts{0}.npy".format(
fold_id, model_name, embeds_type))
test_predictions = model.predict(test_x, batch_size=batch_size)
test_predicts_list.append(test_predictions)
np.save(test_predicts_path, test_predictions)
test_predictions = np.ones(test_predicts_list[0].shape)
for fold_predict in test_predicts_list:
test_predictions *= minmax_scale(fold_predict)
if params.get(model_name).get('norm_folds'):
test_predictions **= (1. / len(test_predicts_list))
logger.info('Saving prediction...')
test_ids = test_df["id"].values
test_ids = test_ids.reshape((len(test_ids), 1))
test_predictions = pd.DataFrame(data=test_predictions,
columns=target_labels)
test_predictions["id"] = test_ids
test_predictions = test_predictions[["id"] + target_labels]
submit_path = os.path.join(
result_path,
"{0}_{1}_folds.submit".format(model_name, embeds_type))
test_predictions.to_csv(submit_path, index=False)
else:
# ============ Single model training =============
logger.info('Training single {0} training...'.format(model_name))
model = model_func()
model_tr = _train_model(
model,
batch_size=params.get(model_name).get('batch_size'),
train_x=x_train_nn,
train_y=y_train_nn,
val_x=x_eval_nn,
val_y=y_eval_nn,
opt=params.get(model_name).get('optimizer'),
logger=logger)
test_predictions = model_tr.predict(
test_x, batch_size=params.get(model_name).get('batch_size'))
# ============== Saving trained parameters ================
logger.info('Saving model parameters...')
model_path = os.path.join(
result_path,
"{0}_{1}_weights.npy".format(model_name, embeds_type))
np.save(model_path, model.get_weights())
# ============== Postprocessing ===============
# test_predictions **= PROBABILITIES_NORMALIZE_COEFFICIENT
# ============== Saving predictions ==============
logger.info('Saving predictions...')
test_ids = test_df["id"].values
test_ids = test_ids.reshape((len(test_ids), 1))
test_predicts = pd.DataFrame(data=test_predictions,
columns=target_labels)
test_predicts["id"] = test_ids
test_predicts = test_predicts[["id"] + target_labels]
submit_path = os.path.join(
result_path, "{0}_{1}.csv".format(model_name, embeds_type))
test_predicts.to_csv(submit_path, index=False)
def main():
args = parser.parse_args()
pprint(args)
# check and create directories
if not os.path.exists(args.checkpoint):
os.makedirs(args.checkpoint)
if not os.path.exists(args.log):
os.makedirs(args.log)
print('==> Preparing data..')
transformations_train = transforms.Compose([
data.RandomTranslateWithReflect(32),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(), normalize
])
transformations_test = transforms.Compose(
[transforms.ToTensor(), normalize])
mode = {'train': True, 'test': True}
image_datasets = Cifar10(root='./data',
train=True,
transform=None,
download=True)
trainData, trainLabel, testData, testLabel = du.split_data(image_datasets,
select_num=1000)
unlabeled_idx, labeled_idx = du.split_idx(trainLabel, select_num=5000)
anchor_idx = du.select_anchors(trainLabel,
labeled_idx,
anchor_num=args.anchor_num)
print("labeled_idx is:{}".format(labeled_idx.size))
print("anchor_idx is:{}".format(anchor_idx.size))
dict_data = DT(trainData=trainData[anchor_idx, :, :, :],
trainLabel=trainLabel[anchor_idx],
transform=transformations_train)
dict_loader = torch.utils.data.DataLoader(dict_data,
batch_size=args.anchor_num,
shuffle=False,
num_workers=args.workers)
n = trainLabel.shape[0]
mask_labels = np.squeeze(np.zeros((n, 1)))
mask_labels[labeled_idx] = 1
train_data = DT(trainData=trainData,
trainLabel=trainLabel,
transform=transformations_train)
test_data = DT(trainData=testData,
trainLabel=testLabel,
transform=transformations_test)
train_data_test = DT(trainData=trainData,
trainLabel=trainLabel,
transform=transformations_test)
train_loader_test = torch.utils.data.DataLoader(train_data_test,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers)
batch_sampler = data.TwoStreamBatchSampler(unlabeled_idx, labeled_idx,
args.batch_size, 40)
train_loader = torch.utils.data.DataLoader(train_data,
batch_sampler=batch_sampler,
num_workers=args.workers,
pin_memory=True)
test_loader = torch.utils.data.DataLoader(test_data,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers)
model = pre_train(train_loader,
test_loader,
dict_loader,
train_loader_test,
mask_labels,
total_epochs=100,
use_gpu=True,
seed=args.seed)
