def main():
# modeling photos
modeling_photos()
# modeling users
# modeling_users()
# print('Finished.')
logger.write('Finished.' + '\n')
def train_model(data: TData, epochs: int, batch_size: int,
lr: float) -> TModel:
l.write('# Setting Up Data')
l.write(f'Training example count: {len(data)}')
encoding = BOWEncoding(data, min_word_freq=5)
encoding.prepare()
dataset = WordTokenDataset(data, encoding)
dataset.prepare()
l.write('# Training')
data_loader = DataLoader(dataset=dataset,
batch_size=batch_size,
shuffle=False,
collate_fn=data_utils.collate_samples)
model = Model(vocab_size=encoding.vocab_size,
n_classes=encoding.n_classes())
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=lr)
for epoch in range(epochs):
epoch_total_loss = 0
epoch_progress = l.progressbar(key=f'epoch-{epoch}',
name=f'Training Epoch {epoch + 1}')
epoch_progress.show()
batch_count = len(data_loader)
for i, samples in enumerate(data_loader):
optimizer.zero_grad()
output = model(samples)
loss = criterion(output, samples.label)
loss.backward()
optimizer.step()
epoch_progress.set_progress((i + 1) / float(batch_count))
epoch_total_loss += loss.item()
# Log the accuracy on predicting the first x examples.
samples = dataset[:10000]
predictions = model.predict(samples)
labels = samples.label
total = len(labels)
correct = torch.sum(labels == predictions)
l.write(f'Accuracy: {float(correct)/total*100:.02f}%.')
l.write(f'Training Loss: {epoch_total_loss}')
return model
def main():
if not os.path.exists(CLEAN_DATA_PATH):
os.makedirs(CLEAN_DATA_PATH)
build_photo_examples(os.path.join(RAW_DATA_PATH, DATASET_TRAIN_FACE),
os.path.join(RAW_DATA_PATH, DATASET_TRAIN_TEXT),
os.path.join(CLEAN_DATA_PATH, 'train_photo_examples'))
build_photo_examples(os.path.join(RAW_DATA_PATH, DATASET_TEST_FACE),
os.path.join(RAW_DATA_PATH, DATASET_TEST_TEXT),
os.path.join(CLEAN_DATA_PATH, 'test_photo_examples'))
# print('Finished.')
logger.write('Finished.' + '\n')
def train_multiple(hyperparams_list, train_dataset, valid_dataset, encoding,
epochs):
models = []
train_losses_list = []
valid_losses = []
for i, hyperparams in enumerate(hyperparams_list):
l.write(f'## Model {i+1} / {len(hyperparams_list)}...')
start_time = time.time()
batch_size = hyperparams['batch_size']
lr = hyperparams['lr']
# 1. Setup Data Loader
data_loader = DataLoader(dataset=train_dataset,
batch_size=batch_size,
shuffle=False,
collate_fn=data_utils.collate_samples)
# 2. Create the Model
model = Model(vocab_size=encoding.vocab_size,
n_classes=encoding.n_classes())
# 3. Setup Criterion and Optimizer
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=lr)
# 4. Train the Model
train_losses = train(model, i, criterion, optimizer, train_dataset,
data_loader, epochs)
# 5. Calculate Validation Loss
with torch.no_grad():
valid_samples = valid_dataset[:]
outputs = model(valid_samples)
valid_loss = criterion(outputs, valid_samples.label)
valid_losses.append(valid_loss)
end_time = time.time()
models.append(model)
train_losses_list.append(train_losses)
l.write(f'Model completed in {(end_time - start_time)/60:.02f}m.\n')
return models, train_losses_list, valid_losses
def train(model,
model_idx,
criterion,
optimizer,
dataset,
data_loader,
epochs,
should_log=True):
train_losses = []
log_every = 1
train_loss_estimator_size = 10000
for epoch in range(epochs):
losses = []
epoch_progress = l.progressbar(key=f'model_{model_idx}_epoch{epoch}',
name=f'Training Epoch {epoch + 1}')
epoch_progress.show()
batch_count = len(data_loader)
for i, samples in enumerate(data_loader):
optimizer.zero_grad()
output = model(samples)
loss = criterion(output, samples.label)
loss.backward()
optimizer.step()
losses.append(loss.item())
epoch_progress.set_progress((i + 1) / float(batch_count))
train_loss = np.mean(losses)
train_losses.append(train_loss)
if should_log and (epoch + 1) % log_every == 0:
train_loss_estimator_start = max(
1,
len(dataset) - train_loss_estimator_size)
random_start = torch.randint(high=train_loss_estimator_start,
size=(1, )).item()
samples = dataset[random_start:(random_start +
train_loss_estimator_size)]
predictions = model.predict(samples)
labels = samples.label
total = len(labels)
correct = torch.sum(labels == predictions)
l.write(f'Accuracy: {float(correct)/total*100:.02f}%.')
l.write(f'Training Loss: {train_loss.item()}\n')
return train_losses
def pick_best_model(data: TData, models: List[TModel]) -> TModel:
l.write('# Loading Data')
encoding = BOWEncoding(data, min_word_freq=5)
encoding.prepare()
dataset = WordTokenDataset(data, encoding)
dataset.prepare()
valid_accuracies = []
l.write('# Calculating Accuracies')
samples = dataset[:]
labels = samples.label
for i, model in enumerate(models):
l.write(f'Calculating accuracy for Model {i+1}')
predictions = model.predict(samples)
total = len(samples)
correct = torch.sum(predictions == labels).item()
accuracy = float(correct) / total
valid_accuracies.append(accuracy)
highest_accuracy = max(valid_accuracies)
highest_accuracy_idx = valid_accuracies.index(highest_accuracy)
best_model = models[highest_accuracy_idx]
l.write(f'Best accuracy: {highest_accuracy*100:.02f}%')
return best_model
def main():
K1s = [10, 30, 100, 300, 1000]
num_process_stats = list()
for K1 in K1s:
num_process_stats.append(
build_pop_examples(
os.path.join(preprocessing_photos.RAW_DATA_PATH,
preprocessing_photos.DATASET_TRAIN_INTERACTION),
os.path.join(preprocessing_photos.DATA_HOUSE_PATH,
'photo-{}.pkl'.format(K1)),
os.path.join(preprocessing_photos.CLEAN_DATA_PATH,
'pop_examples-{}.txt'.format(K1))))
# print('Examples building finished.')
logger.write('Examples building finished.' + '\n')
for tup in num_process_stats:
# print('interacts #total: {}, #filtered for missing: {}, #filtered for pic: {}, #users: {}'.format(tup[0], tup[1], tup[2], tup[3]))
logger.write(
'interacts #total: {}, #filtered for missing in trained photos: {}, #filtered for duration time is 0: {}; #users for preferences: {}'
.format(tup[0], tup[1], tup[2], tup[3]) + '\n')
def store(example_filename, NUM_TEXT_FEATURE, photos_id, face_info, text_info_photos):
cnt = 0
num_unfound_photo = 0
examples = np.zeros(shape=(len(photos_id), 1 + NUM_FACE_FEATURE + NUM_TEXT_FEATURE), dtype=np.float32)
examples[:, 0] = list(photos_id)
for exam_idx, photo_id in enumerate(photos_id):
if cnt % 10000 == 0:
print('Generating {}: {}'.format(example_filename, cnt))
if photo_id in face_info.keys():
examples[exam_idx, 1: NUM_FACE_FEATURE + 1] = face_info[photo_id]
if photo_id in text_info_photos:
topic = photo_topic_map[photo_id]
if NUM_TEXT_FEATURE == 1:
examples[exam_idx, NUM_FACE_FEATURE + 1:] = [topic]
else:
idx = common_word_idx_map[topic] if topic in common_word_idx_map.keys() else 0
examples[exam_idx, NUM_FACE_FEATURE + 1:] = embeddings[idx]
else:
num_unfound_photo += 1
cnt += 1
np.save(example_filename, examples)
logger.write('#Unfound photo: {}'.format(num_unfound_photo) + '\n')
def build_photo_examples(face_filename, text_filename, example_filename_prefix):
print()
photos_id = set() # integers
face_info = dict() # {photo_id: integer, features: []}
cnt = 0
with open(face_filename, 'r') as face_file:
for line in face_file:
cnt += 1
if cnt % 10000 == 0:
print('Processing {}: {}'.format(face_filename, cnt))
line = line.strip()
segs = line.split(maxsplit=1)
if len(segs) == 2:
photo_id = int(segs[0])
faces_list = json.loads(segs[1])
if type(faces_list) is list:
faces = np.array(faces_list, dtype=np.float32)
num_face = faces.shape[0]
face_occu = np.sum(faces[:, 0])
gender_pref = np.mean(faces[:, 1])
age = np.mean(faces[:, 2])
looking = np.mean(faces[:, 3])
face_info[photo_id] = [num_face, face_occu, gender_pref, age, looking]
photos_id.add(photo_id)
# print('#photos with face info = {}'.format(len(face_info)))
logger.write('#photos with face info = {}'.format(len(face_info)) + '\n')
text_info_photos = set() # integers
if text_filename is not None:
cnt = 0
with open(text_filename, 'r') as text_file:
for line in text_file:
cnt += 1
if cnt % 10000 == 0:
print('Processing {}: {}'.format(text_filename, cnt))
line = line.strip()
segs = line.split(maxsplit=1)
if len(segs) == 2:
photo_id = int(segs[0])
text_info_photos.add(photo_id)
photos_id.add(photo_id)
# print('#photos with text info = {}'.format(len(text_info_photos)))
logger.write('#photos with text info = {}'.format(len(text_info_photos)) + '\n')
# print('#photos in total = {}'.format(len(photos_id)))
logger.write('#photos in total = {}'.format(len(photos_id)) + '\n')
store(example_filename_prefix + '-topic.npy', 1, photos_id, face_info, text_info_photos)
store(example_filename_prefix + '.npy', embeddings.shape[1], photos_id, face_info, text_info_photos)
def recommend(sub_prefix):
print('Loading moldes...')
photo_model_prefix = 'photo-'
pop_examples_prefix = 'pop_examples-'
magicians = list()
for file in os.listdir(preprocessing_photos.DATA_HOUSE_PATH):
if file.startswith(photo_model_prefix):
photo_kmeans = joblib.load(os.path.join(preprocessing_photos.DATA_HOUSE_PATH, file))
photo_kmeans.verbose = 0
first_sep = file.index('-')
second_sep = file.rindex('.')
K1 = int(file[first_sep + 1: second_sep])
pop_examples = np.loadtxt(os.path.join(preprocessing_photos.CLEAN_DATA_PATH, pop_examples_prefix + str(K1) + '.txt'),
delimiter=',')
if len(pop_examples.shape) == 1:
pop_examples = pop_examples.reshape(-1, pop_examples.shape[0])
magicians.append(Magician(photo_kmeans, K1, pop_examples))
print('{} models loaded.'.format(len(magicians)))
# sorting models by multiplication of inertia
magicians.sort(key=attrgetter('total_inertia'))
for magician in magicians:
print(str(magician))
print('#photo_cate_map={}\n'.format(len(magician.photo_cate_map)))
# normalization
print('Normalizing dataset...')
photo_examples = np.load(os.path.join(preprocessing_photos.CLEAN_DATA_PATH, 'test_photo_examples.npy'))
train_photo_examples = np.load(os.path.join(preprocessing_photos.CLEAN_DATA_PATH, 'train_photo_examples.npy'))
scaler = MinMaxScaler()
scaler.fit(train_photo_examples[:, 1:])
data = scaler.transform(photo_examples[:, 1:])
photo_idx_map = dict(zip(np.array(photo_examples[:, 0], dtype=int), range(photo_examples.shape[0])))
del train_photo_examples
# inference
print('Inferring..')
magician_predicts_map = dict()
predict_data = pd.read_csv(os.path.join(preprocessing_photos.RAW_DATA_PATH, preprocessing_photos.DATASET_TEST_INTERACTION), delim_whitespace=True,
header=None, names=['user_id', 'photo_id', 'time', 'duration_time'])
logger.write('Predict data size: {}'.format(predict_data.shape[0]) + '\n')
# os.path.join(preprocessing_photos.DATA_HOUSE_PATH, sub_prefix + '-' + str(rank) + '_' + magician.name)
for magician in magicians:
magician_predicts_map[magician.name] = np.ndarray(shape=(predict_data.shape[0]), dtype=np.float32)
tot_cnt = 0
cnt_unk_photo = 0
cnt_existed_photo = 0
cnt_predict_photo = 0
cnt_new_user = 0
for i in range(predict_data.shape[0]):
user_id = predict_data.loc[i, 'user_id']
photo_id = predict_data.loc[i, 'photo_id']
for magician in magicians:
tot_cnt += 1
if user_id not in magician.user_matrix_map:
click_probability = max(magician.fashion)
cnt_new_user += 1
else:
if photo_id in magician.photo_cate_map.keys():
cate_id = magician.photo_cate_map[photo_id]
cnt_existed_photo += 1
elif photo_id in photo_idx_map.keys(): # Almost examples should hit here.
features = data[photo_idx_map[photo_id]]
cate_id = magician.photo_kmeans.predict(np.array([features]))[0]
cnt_predict_photo += 1
else: # No example should hit here.
cate_id = None
cnt_unk_photo += 1
if cate_id is None:
click_probability = 0.0
else:
matrix_idx = magician.user_matrix_map[user_id]
click_probability = magician.matrix[matrix_idx, cate_id]
magician_predicts_map[magician.name][i] = click_probability
if i % 10000 == 0:
print('Predicted examples: {}'.format(i))
# print('#new users={}, #existed={}, #predict={}, #unknown={}, #total={}\n'
# .format(cnt_new_user, cnt_existed_photo, cnt_predict_photo, cnt_unk_photo, tot_cnt)
# )
logger.write('#new users={}, #existed={}, #predict={}, #new photos beyond train and test dataset={}, #total={}\n'
.format(cnt_new_user, cnt_existed_photo, cnt_predict_photo, cnt_unk_photo, tot_cnt))
print('Saving prediction...')
for rank, magician in enumerate(magicians):
predict_data['click_prob'] = magician_predicts_map[magician.name]
predict_data.to_csv(os.path.join(preprocessing_photos.DATA_HOUSE_PATH, sub_prefix + '-' + str(rank) + '_' + magician.name),
columns=['user_id', 'photo_id', 'click_prob'],
sep='\t', header=False, index=False, float_format='%.6f')
def main(sub_prefix):
recommend(sub_prefix)
print('Finished.')
logger.write('Finished.' + '\n')
def main():
l.write('# Loading Data')
with s3_read('ml/data/news_classifier/train_data.json') as file:
data = pd.read_json(file, orient='records')
data = data.sample(frac=1) # Shuffle the data.
l.write(f'Training example count: {len(data)}')
train_test_split = 0.95
split_idx = math.floor(len(data) * train_test_split)
train_data = data.iloc[0:split_idx]
valid_data = data.iloc[split_idx:]
encoding = BOWEncoding(data, min_word_freq=5)
encoding.prepare()
train_dataset = WordTokenDataset(train_data, encoding)
train_dataset.prepare()
valid_dataset = WordTokenDataset(valid_data, encoding)
valid_dataset.prepare()
l.write('# Training')
hyperparams_list = [
{
'batch_size': 100,
'lr': 1e-3
},
{
'batch_size': 10,
'lr': 1e-3
},
{
'batch_size': 100,
'lr': 1e-2
},
{
'batch_size': 10,
'lr': 1e-2
},
]
models, train_loss_list, valid_losses = train_multiple(hyperparams_list,
train_dataset,
valid_dataset,
encoding,
epochs=EPOCHS)
l.write('# Viewing Results')
best_model_idx = torch.argmin(torch.FloatTensor(valid_losses)).item()
best_model = models[best_model_idx]
l.write(f'Best Model: {best_model_idx+1}')
valid_samples = valid_dataset[:]
predictions = best_model.predict(valid_samples)
total = len(valid_samples.label)
correct = torch.sum(predictions == valid_samples.label)
accuracy = float(correct) / total
l.write(f'Accuracy of Best Model: {accuracy*100:.02f}%.')
confusion_matrix, category_encoder = create_confusion_matrix(
valid_samples.label, predictions)
category_decoder = {i: c for c, i in category_encoder.items()}
labeling_errors = top_k_labeling_errors(confusion_matrix,
category_decoder,
k=5)
label_decoder = {i: l for l, i in encoding._label_encoder.items()}
# Looking at the most frequent labeling errors.
for i, error in enumerate(labeling_errors):
error_0 = label_decoder[error[0]]
error_1 = label_decoder[error[1]]
l.write(f'{i+1}. "{error_0}" confused for "{error_1}"')
l.write('# Persisting Model')
with s3_write('ml/models/news_classifier/bow_model.torch', 'b') as file:
torch.save(best_model.state_dict(), file)
def main():
l.write('# Loading and Setting Up Data')
l.write('Loading Training Data')
with s3_read('ml/data/news_classifier/train_data.json') as file:
data = pd.read_json(file, orient="records")
data = data[:1000]
l.write('Loading embeddings')
with s3_read('ml/glove_embeddings/glove.6B.100d.txt') as file:
embeddings = data_utils.load_embeddings(file, embedding_dim=100)
l.write('Preparing data')
train_test_split = 0.95
split_idx = math.floor(len(data) * train_test_split)
train_data = data.iloc[0:split_idx]
valid_data = data.iloc[split_idx:]
encoding = WordEmbeddingEncoding(data, embeddings)
encoding.prepare()
train_dataset = WordTokenDataset(train_data, encoding)
train_dataset.prepare()
valid_dataset = WordTokenDataset(valid_data, encoding)
valid_dataset.prepare()
print('# Training the Model')
hyperparams_list = [
{
'weighting': 'uniform',
'lr': 0.001,
'batch_size': 100
},
{
'weighting': 'uniform',
'lr': 0.01,
'batch_size': 100
},
{
'weighting': 'uniform',
'lr': 0.001,
'batch_size': 50
},
{
'weighting': 'uniform',
'lr': 0.01,
'batch_size': 50
},
]
models = []
train_losses_list = []
valid_losses = []
accepted_tokens = {t for t in embeddings.index}
for i, hyperparams in enumerate(hyperparams_list):
l.write(f'Model {i+1} / {len(hyperparams_list)}')
start_time = time()
batch_size = hyperparams['batch_size']
lr = hyperparams['lr']
weighting = hyperparams['weighting']
# 1. Setup Data Loader
data_loader = DataLoader(dataset=train_dataset,
batch_size=batch_size,
shuffle=False,
collate_fn=data_utils.collate_samples)
# 2. Create the Model
model = Model(embeddings=embeddings,
n_classes=encoding.n_classes(),
weighting=weighting)
# 3. Setup Criterion and Optimizer
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=lr)
# 4. Train the Model
train_losses = train(model,
criterion,
optimizer,
train_dataset,
data_loader,
epochs=EPOCHS)
# 5. Calculate Validation Loss
with torch.no_grad():
valid_samples = valid_dataset[:]
outputs = model(valid_samples)
valid_loss = criterion(outputs, valid_samples.label)
valid_losses.append(valid_loss)
end_time = time()
models.append(model)
train_losses_list.append(train_losses)
l.write(f'Model completed in {(end_time - start_time)/60:.02f}m.\n')
l.write('# Results')
uniform_mask = [hp['weighting'] == 'uniform' for hp in hyperparams_list]
models = [m for i, m in enumerate(models) if uniform_mask[i]]
train_losses_list = [
losses for i, losses in enumerate(train_losses_list) if uniform_mask[i]
]
valid_losses = [
loss.item() for i, loss in enumerate(valid_losses) if uniform_mask[i]
]
best_model_idx = valid_losses.index(min(valid_losses))
best_model = models[best_model_idx]
l.write(f'Best Model: {best_model_idx+1}')
l.write('Computing Model Accuracy...')
samples = valid_dataset[:]
predictions = best_model.predict(samples)
total = len(samples.label)
correct = torch.sum(predictions == samples.label)
l.write(f'Accuracy of Model: {(float(correct) / total)*100:.02f}%.')
l.write('Persisting Models...')
with s3_write('ml/models/news_classifier/glove_model.torch', 'b') as file:
torch.save(best_model.state_dict(), file)
l.write('Done!')
import preprocessing_photos
import modeling_k_means
import preprocessing_user_preferences
import recommend_for_each_user
import datetime
from utils import logger
#logger.write('{}: {}\n'.format(datetime.datetime.now(), 'Started preprocessing_photos'))
#logger.flush()
#preprocessing_photos.main()
#logger.write('{}: {}\n'.format(datetime.datetime.now(), 'Started modeling_k_means'))
#logger.flush()
#modeling_k_means.main()
#logger.write('{}: {}\n'.format(datetime.datetime.now(), 'Started preprocessing_user_preferences'))
#logger.flush()
#preprocessing_user_preferences.main()
#logger.write('{}: {}\n'.format(datetime.datetime.now(), 'Started recommend_for_each_user'))
#logger.flush()
recommend_for_each_user.main('v0.9.0')
logger.write('{}: {}\n'.format(datetime.datetime.now(), 'Finished'))
logger.flush()
logger.close()
buildoption.append(v[4])
my_upload[key] = tuple(buildoption)
utils.buildall(None, my_upload)
if logger.errors:
sys.exit(1)
utils.upload_binaries()
if logger.errors:
sys.exit(1)
# here it applies specific patch and shares libraries
if csv_feature == True:
cmd = ''.join(["hg import ", my_patchlocation])
p = Popen(cmd, cwd=my_x265_source, stdout=PIPE, stderr=PIPE)
my_patchrevision = utils.hgversion(my_x265_source)
if p.returncode:
logger.write('\nfailed to apply patch\n')
p = Popen("hg revert --all",
cwd=my_x265_source,
stdout=PIPE,
stderr=PIPE)
p = Popen("hg clean",
cwd=my_x265_source,
stdout=PIPE,
stderr=PIPE)
cmd = ''.join(["hg strip ", my_patchrevision])
p = Popen(cmd, cwd=my_x265_source, stdout=PIPE, stderr=PIPE)
else:
utils.buildall(None, my_upload)
extras = [
'--psnr', '--ssim', '--csv-log-level=3', '--csv=test.csv',
'--frames=10'
def logJson(self):
self.log['final']['SimulationTime'] = self.getTime()
logger.write(self.log)
