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TransformerModel.py
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TransformerModel.py
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# Copyright Software Engineering Analytics Lab (SEAL), Wayne State University, 2022
# Authors: Jaydeb Sarker <[email protected]> and Amiangshu Bosu <[email protected]>
# This program is free software; you can redistribute it and/or
#modify it under the terms of the GNU General Public License
# version 3 as published by the Free Software Foundation.
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
from random import random
from tqdm import tqdm, trange
import tensorflow as tf
import tensorflow_hub as hub
import tensorflow_text as text
from official.nlp.optimization import WarmUp
import BertLocator
import random
import numpy as np
import math
from official.nlp import optimization # to create AdamW optmizer
tf.get_logger().setLevel('ERROR')
from sklearn.model_selection import KFold, train_test_split
def getPTM(model_name):
if model_name == "BERT":
return 'bert_en_uncased_L-12_H-768_A-12'
elif model_name == "ALBERT":
return 'albert_en_base'
elif model_name == 'SBERT':
return 'wiki-books-sst'
else:
return None
def df_to_dataset(dataframe, batch_size=16):
dataframe = dataframe.copy()
labels = dataframe.pop('target')
ds = tf.data.Dataset.from_tensor_slices((dict(dataframe), labels))
ds = ds.batch(batch_size)
ds = ds.prefetch(batch_size)
return ds
class TransformerModel:
def __init__(self, X_train=None, Y_train=None,
model_name="BERT", load_from_file=None):
encoder = getPTM(model_name)
if encoder is None:
print("Unknown transformer: " + model_name)
exit(1)
self.tfhub_handle_encoder = BertLocator.getBERTEncoderURL(encoder)
self.tfhub_handle_preprocess = BertLocator.getPreprocessURL(encoder)
print(f'BERT model selected : {self.tfhub_handle_encoder}')
print(f'Preprocess model auto-selected: {self.tfhub_handle_preprocess}')
self.bert_preprocess_model = hub.KerasLayer(self.tfhub_handle_preprocess)
self.bert_model = hub.KerasLayer(self.tfhub_handle_encoder)
self.epochs = 20
if load_from_file is not None:
self.steps_per_epoch = 19571 # size of our dataset
custom_model = self.build_classifier_model()
config =custom_model.get_config()
optimizer = self.get_optimizer()
self._model = tf.keras.models.load_model(load_from_file, custom_objects={'KerasLayer': hub.KerasLayer,
'AdamWeightDecay': optimizer,
'WarmUp': WarmUp})
else:
self.steps_per_epoch = X_train.shape[0]
self._train(X_train, Y_train)
def append_numerical_feature(self, row):
message = row['message']
if row['profane_count'] > 0:
message = message + " includes profanity. "
if row['anger_count'] > 0:
message = message + " includes anger word. "
if row['emoticon_count'] > 0:
message = message + " includes emoticon. "
return message
def build_classifier_model(self):
text_input = tf.keras.layers.Input(shape=(), dtype=tf.string, name='text')
preprocessing_layer = hub.KerasLayer(self.tfhub_handle_preprocess, name='preprocessing')
encoder_inputs = preprocessing_layer(text_input)
encoder = hub.KerasLayer(self.tfhub_handle_encoder, trainable=True, name='BERT_encoder')
outputs = encoder(encoder_inputs)
net = outputs['pooled_output']
net = tf.keras.layers.Dropout(0.1)(net)
net = tf.keras.layers.Dense(2)(net)
net = tf.keras.layers.Dense(1, activation=None, name='classifier')(net)
classifier_model = tf.keras.Model(text_input, net)
loss = tf.keras.losses.BinaryCrossentropy(from_logits=True)
metrics = tf.metrics.BinaryAccuracy()
optimizer = self.get_optimizer()
classifier_model.compile(optimizer=optimizer,
loss=loss,
metrics=metrics)
# if self.plot is not None:
# tf.keras.utils.plot_model(classifier_model, to_file=self.plot, show_shapes=True)
return classifier_model
def get_optimizer(self):
num_train_steps = self.steps_per_epoch * self.epochs
num_warmup_steps = int(0.1 * num_train_steps)
init_lr = 3e-5
optimizer = optimization.create_optimizer(init_lr=init_lr,
num_train_steps=num_train_steps,
num_warmup_steps=num_warmup_steps,
optimizer_type='adamw')
return optimizer
def _train(self, X_train, Y_train):
self._model = self.build_classifier_model()
if self._model is not None:
X_train["text"] = X_train.apply(self.append_numerical_feature, axis=1)
X_train = X_train[["text"]]
x_new_train, x_validation, y_new_train, y_validation = train_test_split(X_train, Y_train, test_size=0.11115,
random_state=random.randint(1,
10000))
x_new_train = x_new_train.reset_index(drop=True) # resetting index is necessary due to the random split
x_new_train.fillna(value='', inplace=True)
y_new_train = y_new_train.reset_index(drop=True)
x_new_train['target'] = y_new_train.to_numpy() # merging the two df
train_ds = df_to_dataset(x_new_train) # converting to dataset format
x_validation = x_validation.reset_index(drop=True)
x_validation.fillna(value='', inplace=True)
y_validation = y_validation.reset_index(drop=True)
x_validation['target'] = y_validation.to_numpy()
validation_ds = df_to_dataset(x_validation)
es_callback = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=4, restore_best_weights=True)
self._model.fit(x=train_ds, validation_data=validation_ds, epochs=20, callbacks=[es_callback])
def predict(self, X_values, batch_size=256):
test_values =X_values["message"].values
value_count = len(test_values)
predictions = np.array([])
num_partitions = math.ceil(value_count / batch_size)
progress_bar=tqdm(num_partitions)
for part in trange(0, num_partitions):
start = part * batch_size
end = start + batch_size
if end > value_count:
end = value_count
partition = test_values[start:end]
y_pred = tf.sigmoid(self._model(tf.constant(partition)))
#y_pred = [1 if pred >= 0.5 else 0 for pred in y_pred] # Threshold: 0.5
predictions = np.append(predictions, y_pred)
return predictions
def save_to_file(self, filename):
self._model.save(filename)