def nn_inference_pipeline(model_name: Text,
model_path: Text,
data_params: Dict,
dataloader_params: Dict,
save_dir=None):
model = init_model(model_name)
model = model.load(model_path)
x_test, y_test, dataset = preprocessing_pipeline(data_params)
dataloader = generate_dataloader(x_test, y_test, dataloader_params)
pred = [model.predict(x)[0] for x in dataloader['valid']]
post_pred = dataset.postprocessing(pred, model_name)
y_true = [model.predict(x)[1] for x in dataloader['valid']]
report_df = report(y_true, post_pred)
logger.info(f' > Model: {model.name}')
logger.info(f' > Dataset: {dataset.__class__.__name__}')
logger.info(f' > Test result: \n {report_df}')
if save_dir:
save_test(model_name,
data_params,
report_df,
save_dir)
def inference_without_trained_model(model_name: Text,
data_params: Dict,
model_params: Dict,
save_dir=None):
x, y, dataset, _ = preprocessing_pipeline(data_params)
model_func = init_model(model_name)
model = model_func(model_name, model_params)
y_pred = model.predict(x)
y_pred = model.postprocessing(y_pred)
report_df = report(y, y_pred)
logger.info(f' > Model: {model.name}')
logger.info(f' > Dataset: {dataset.__class__.__name__}')
logger.info(f' > Test result: \n {report_df}')
if save_dir:
save_model_data(None,
data_params,
model_params,
report_df,
save_dir=save_dir)
return model
def model_training(model_name: Text,
data_params: Dict,
model_params: Dict,
seed: int,
save_dir=None):
x, y, dataset, vectorizer = preprocessing_pipeline(data_params)
x_train, x_test, y_train, y_test = train_test_split(
x,
y,
test_size=data_params['test_size'],
shuffle=True,
random_state=seed)
model_func = init_model(model_name)
model = model_func(model_name, model_params)
model.fit(x_train, y_train)
y_pred = model.predict(x_test)
y_pred = dataset.postprocessing(y_pred, model_name)
report_df = report(y_test, y_pred)
logger.info(f' > Model: {model.name}')
logger.info(f' > Test result: \n {report_df}')
if save_dir:
save_model_data(model,
data_params,
model_params,
report_df,
save_dir=save_dir)
return model
def model_training_nn(model_name: Text,
data_params: Dict,
dataloader_params: Dict,
model_params: Dict,
save_dir=None):
x, y, dataset, vectorizer = preprocessing_pipeline(data_params)
dataloader = generate_dataloader(x, y, dataloader_params)
model_params['dataloader'] = dataloader
model_params['network']['n_words'] = vectorizer.get_n_words() if vectorizer is not None else None
model_func = init_model(model_name)
model = model_func(model_name, model_params)
model.fit()
y_pred = np.ravel([model.predict(x) for x in dataloader['valid']])
y_pred_post = dataset.postprocessing(y_pred, model_name)
y_true = np.ravel([x[1].numpy() for x in dataloader['valid']])
report_df = report(y_true, y_pred_post)
logger.info(f' > Model: {model.name}')
logger.info(f' > Test result: \n {report_df}')
# if save_dir:
# save_model_data(model,
# data_params,
# model_params,
# report_df,
# save_dir=save_dir)
return model
def test_network():
seed = 2021
emb_dim = 100
dataset_params = {
'data_path': 'resources/preprocessed_data/cleaned_data_v1.csv',
'dataset_type': MOVIE_DATASET,
'preprocessed': True,
# 'target_scaling': (0, 1),
'vectorization': TOKENIZER,
# 'vector_params': {'ngram_range': (1, 3),
# 'max_features': emb_dim},
'imbalance': None,
# 'imb_params': {'random_state': seed,
# 'k_neighbors': 3},
'train': True
}
dataloader_params = {
'split_size': 0.7,
'shuffle': True,
'batch_size': 32,
'random_seed': seed
}
network_params = {
'emb_dim': emb_dim,
'dataset_type': TOKENIZER,
'kernel_size': [3, 5, 7],
'out_channels': 30,
'batch_size': 32,
'stride': 1,
'padding': [0, 1, 2],
'pooling_kernel': 2,
'dropout': 0.4
}
training_params = {'epochs': 10, 'lr': 0.001}
x, y, dataset, vectorizer = preprocessing_pipeline(dataset_params)
network_params['n_words'] = vectorizer.get_n_words()
dataloader = generate_dataloader(x, y, dataloader_params)
network = Conv1D_Network(network_params)
loss = nn.BCELoss()
optimizer = torch.optim.Adam
model = NetworkModel(network, dataloader, loss, optimizer)
model._init_optimizer(training_params['lr'])
model.train(training_params['epochs'])
return
def test_bert_model(self):
logger = logging.getLogger()
logger.setLevel(logging.INFO)
seed = 2021
dataset_params = {
'data_path': None,
'dataset_type': SST_DATASET,
'preprocessed': False,
'vectorization': None,
'imbalance': None,
'train': True
}
dataloader_params = {
'split_size': 0.7,
'shuffle': True,
'batch_size': 64,
'random_seed': seed
}
network_params = {'dropout': 0.2, 'device': torch.device('cpu:0')}
training_params = {
'epochs': 10,
'lr': 0.001,
'save_dir': 'resources/models/',
'patience': 5
}
x, y, dataset, vectorizer = preprocessing_pipeline(dataset_params)
dataloader = generate_dataloader(x, y, dataloader_params,
Bert_NN_Dataset)
network = Pretrained_Bert_Network(network_params).to(
network_params['device'])
loss = nn.BCELoss()
optimizer = AdamW
model = Pretrained_Bert_Model(network, dataloader, loss, optimizer,
training_params['save_dir'],
network_params['device'])
model._init_optimizer(training_params['lr'])
model.train(training_params['epochs'],
patience=training_params['patience'])
def test_tokenizing(self):
seed = 2021
emb_dim = 100
dataset_params = {'data_path': 'resources/preprocessed_data/cleaned_data_v1.csv',
'dataset_type': MOVIE_DATASET,
'preprocessed': True,
# 'target_scaling': (0, 1),
'vectorization': TOKENIZER,
'imbalance': None,
'imb_params': {'random_state': seed,
'k_neighbors': 3},
'train': True}
x, y, dataset = preprocessing_pipeline(dataset_params)
self.assertEqual(len(x), len(y))
def inference_pipeline(model_name: Text,
model_path: Text,
data_params: Dict,
save_dir=None):
model = init_model(model_name)
model = model.load(model_path)
x_test, y_test, dataset = preprocessing_pipeline(data_params)
pred = model.predict(x_test)
post_pred = dataset.postprocessing(pred, model_name)
report_df = report(y_test, post_pred)
logger.info(f' > Model: {model.name}')
logger.info(f' > Dataset: {dataset.__class__.__name__}')
logger.info(f' > Test result: \n {report_df}')
if save_dir:
save_test(model_name, data_params, report_df, save_dir)
def test_train_model(self):
logger = logging.getLogger()
logger.setLevel(logging.INFO)
seed = 2021
emb_dim = 100
dataset_params = {
'data_path': 'resources/preprocessed_data/cleaned_data_v1.csv',
'dataset_type': MOVIE_DATASET,
'preprocessed': True,
# 'target_scaling': (0, 1),
'vectorization': TOKENIZER,
# 'vector_params': {'ngram_range': (1, 3),
# 'max_features': emb_dim},
'imbalance': None,
# 'imb_params': {'random_state': seed,
# 'k_neighbors': 3},
'train': True
}
dataloader_params = {
'split_size': 0.7,
'shuffle': True,
'batch_size': 64,
'random_seed': seed,
'dataset_class': NN_Dataset
}
network_params = {
'emb_dim': MAX_WORD_SENTENCE,
'dataset_type': TOKENIZER,
'kernel_size': [5, 7, 9],
'out_channels': 10,
'stride': 1,
'padding': [0, 1, 2],
'pooling_kernel': 2,
'dropout': 0.2,
'device': torch.device('cpu:0')
}
training_params = {
'epochs': 10,
'lr': 0.001,
'save_dir': 'resources/models/',
'patience': 5
}
x, y, dataset, vectorizer = preprocessing_pipeline(dataset_params)
network_params['n_words'] = vectorizer.get_n_words()
dataloader = generate_dataloader(x, y, dataloader_params)
network = Conv1D_Network(network_params).to(network_params['device'])
loss = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam
model = ConvModel(network, dataloader, loss, optimizer,
training_params['save_dir'])
model._init_optimizer(training_params['lr'])
model.train(training_params['epochs'],
patience=training_params['patience'])