import torch
import pandas as pd
import numpy as np
from sklearn import preprocessing
from sklearn.model_selection import train_test_split
from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler
from transformers import BertTokenizer, CamembertTokenizer
from transformers import BertForSequenceClassification, AdamW, BertConfig, CamembertForSequenceClassification
from transformers import get_linear_schedule_with_warmup
import time
import datetime
import random
import os
import argparse
import configparser
import csv
def create_dict(df, classColumnName):
return dict(df[classColumnName].value_counts())
def remove_weak_classes(df, classColumnName, threshold):
dictOfClassInstances = create_dict(df,classColumnName)
dictionary = {k: v for k, v in dictOfClassInstances.items() if v >= threshold }
keys = [*dictionary]
df_tmp = df[~ df[classColumnName].isin(keys)]
df = pd.concat([df,df_tmp]).drop_duplicates(keep=False)
return df
def resample_classes(df, classColumnName, numberOfInstances):
#random numberOfInstances elements
replace = False # with replacement
fn = lambda obj: obj.loc[np.random.choice(obj.index, numberOfInstances if len(obj) > numberOfInstances else len(obj), replace),:]
return df.groupby(classColumnName, as_index=False).apply(fn)
def flat_accuracy(preds, labels):
pred_flat = np.argmax(preds, axis=1).flatten()
labels_flat = labels.flatten()
return np.sum(pred_flat == labels_flat) / len(labels_flat)
def format_time(elapsed):
'''
Takes a time in seconds and returns a string hh:mm:ss
'''
# Round to the nearest second.
elapsed_rounded = int(round((elapsed)))
# Format as hh:mm:ss
return str(datetime.timedelta(seconds=elapsed_rounded))
def training_bertFineTuning(chosen_model, model_path, sentences, labels, max_len, batch_size, epochs = 4):
# If there's a GPU available...
if torch.cuda.is_available():
# Tell PyTorch to use the GPU.
device = torch.device("cuda")
print('There are %d GPU(s) available.' % torch.cuda.device_count())
print('We will use the GPU:', torch.cuda.get_device_name(0))
# If not...
else:
print('No GPU available, using the CPU instead.')
device = torch.device("cpu")
############################################################################################################
########################## Model: Tokenization & Input Formatting ###################################################################
###########################################################################################################
if chosen_model == 'bert' :
print('Loading Bert Tokenizer...')
tokenizer = BertTokenizer.from_pretrained(model_path, do_lower_case=True)
elif chosen_model == 'camembert':
print('Loading Camembert Tokenizer...')
tokenizer = CamembertTokenizer.from_pretrained(model_path , do_lower_case=True)
# Tokenize all of the sentences and map the tokens to thier word IDs.
input_ids = []
# For every sentence...
for sent in sentences:
# `encode` will:
# (1) Tokenize the sentence.
# (2) Prepend the `[CLS]` token to the start.
# (3) Append the `[SEP]` token to the end.
# (4) Map tokens to their IDs.
encoded_sent = tokenizer.encode(
str(sent), # Sentence to encode.
add_special_tokens = True, # Add '[CLS]' and '[SEP]'
# This function also supports truncation and conversion
# to pytorch tensors, but I need to do padding, so I
# can't use these features.
#max_length = 128, # Truncate all sentences.
#return_tensors = 'pt', # Return pytorch tensors.
)
# Add the encoded sentence to the list.
input_ids.append(encoded_sent)
padded = []
for i in input_ids:
if len(i) > max_len:
padded.extend([i[:max_len]])
else:
padded.extend([i + [0] * (max_len - len(i))])
padded = np.array(padded)
# Create attention masks
attention_masks = []
# For each sentence...
for sent in padded:
# Create the attention mask.
# - If a token ID is 0, then it's padding, set the mask to 0.
# - If a token ID is > 0, then it's a real token, set the mask to 1.
att_mask = [int(token_id > 0) for token_id in sent]
# Store the attention mask for this sentence.
attention_masks.append(att_mask)
# Use 90% for training and 10% for validation.
train_inputs, validation_inputs, train_labels, validation_labels = train_test_split(padded, labels, random_state=2018, test_size=0.3, stratify = labels )
# Do the same for the masks.
train_masks, validation_masks, _, _ = train_test_split(attention_masks, labels, random_state=2018, test_size=0.3, stratify = labels)
# Convert all inputs and labels into torch tensors, the required datatype
# for my model.
train_inputs = torch.tensor(train_inputs)
validation_inputs = torch.tensor(validation_inputs)
train_labels = torch.tensor(train_labels)
validation_labels = torch.tensor(validation_labels)
train_masks = torch.tensor(train_masks)
validation_masks = torch.tensor(validation_masks)
# The DataLoader needs to know the batch size for training, so I specify it here.
# For fine-tuning BERT on a specific task, the authors recommend a batch size of
# 16 or 32.
# Create the DataLoader for training set.
train_data = TensorDataset(train_inputs, train_masks, train_labels)
train_sampler = RandomSampler(train_data)
train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=batch_size)
# Create the DataLoader for validation set.
validation_data = TensorDataset(validation_inputs, validation_masks, validation_labels)
validation_sampler = SequentialSampler(validation_data)
validation_dataloader = DataLoader(validation_data, sampler=validation_sampler, batch_size=batch_size)
print(' Selecting a model .....')
numberOfClasses = len(set(labels))
# Load BertForSequenceClassification, the pretrained BERT model with a single
# linear classification layer on top.
if chosen_model == 'bert':
model = BertForSequenceClassification.from_pretrained(
model_path, # Use the 12-layer BERT model, with an uncased vocab.
num_labels = numberOfClasses, # The number of output labels--2 for binary classification.
# You can increase this for multi-class tasks.
output_attentions = False, # Whether the model returns attentions weights.
output_hidden_states = False, # Whether the model returns all hidden-states.
)
elif chosen_model == 'camembert':
model = CamembertForSequenceClassification.from_pretrained(
model_path, # Use the 12-layer BERT model, with an uncased vocab.
num_labels = numberOfClasses, # The number of output labels--2 for binary classification.
# You can increase this for multi-class tasks.
output_attentions = False, # Whether the model returns attentions weights.
output_hidden_states = False, # Whether the model returns all hidden-states.
)
# Tell pytorch to run this model on the GPU.
model.cuda()
#Note: AdamW is a class from the huggingface library (as opposed to pytorch)
# I believe the 'W' stands for 'Weight Decay fix"
optimizer = AdamW(model.parameters(),
lr = 2e-5, # args.learning_rate - default is 5e-5, our notebook had 2e-5
eps = 1e-8 # args.adam_epsilon - default is 1e-8.
)
# Total number of training steps is number of batches * number of epochs.
total_steps = len(train_dataloader) * epochs
# Create the learning rate scheduler.
scheduler = get_linear_schedule_with_warmup(optimizer,
num_warmup_steps = 0, # Default value in run_glue.py
num_training_steps = total_steps)
# This training code is based on the `run_glue.py` script here:
# https://github.com/huggingface/transformers/blob/5bfcd0485ece086ebcbed2d008813037968a9e58/examples/run_glue.py#L128
# Set the seed value all over the place to make this reproducible.
seed_val = 42
random.seed(seed_val)
np.random.seed(seed_val)
torch.manual_seed(seed_val)
torch.cuda.manual_seed_all(seed_val)
# Store the average loss after each epoch so I can plot them.
loss_values = []
# For each epoch...
for epoch_i in range(0, epochs):
# ========================================
# Training
# ========================================
# Perform one full pass over the training set.
print("")
print('======== Epoch {:} / {:} ========'.format(epoch_i + 1, epochs))
print('Training...')
# Measure how long the training epoch takes.
t0 = time.time()
# Reset the total loss for this epoch.
total_loss = 0
# Put the model into training mode.
model.train()
# For each batch of training data...
for step, batch in enumerate(train_dataloader):
# Progress update every 40 batches.
if step % 40 == 0 and not step == 0:
# Calculate elapsed time in minutes.
elapsed = format_time(time.time() - t0)
# Report progress.
print(' Batch {:>5,} of {:>5,}. Elapsed: {:}.'.format(step, len(train_dataloader), elapsed))
# Unpack this training batch from the dataloader.
#
# As I unpack the batch, I'll also copy each tensor to the GPU using the
# `to` method.
#
# `batch` contains three pytorch tensors:
# [0]: input ids
# [1]: attention masks
# [2]: labels
b_input_ids = batch[0].to(device)
b_input_mask = batch[1].to(device)
b_labels = batch[2].to(device)
# Always clear any previously calculated gradients before performing a
# backward pass. PyTorch doesn't do this automatically because
# accumulating the gradients is "convenient while training RNNs".
# (source: https://stackoverflow.com/questions/48001598/why-do-we-need-to-call-zero-grad-in-pytorch)
model.zero_grad()
# Perform a forward pass (evaluate the model on this training batch).
# This will return the loss (rather than the model output) because I
# have provided the `labels`.
# The documentation for this `model` function is here:
# https://huggingface.co/transformers/v2.2.0/model_doc/bert.html#transformers.BertForSequenceClassification
outputs = model(b_input_ids,
token_type_ids=None,
attention_mask=b_input_mask,
labels=b_labels)
# The call to `model` always returns a tuple, so I need to pull the
# loss value out of the tuple.
loss = outputs[0]
# Accumulate the training loss over all of the batches so that I can
# calculate the average loss at the end. `loss` is a Tensor containing a
# single value; the `.item()` function just returns the Python value
# from the tensor.
total_loss += loss.item()
# Perform a backward pass to calculate the gradients.
loss.backward()
# Clip the norm of the gradients to 1.0.
# This is to help prevent the "exploding gradients" problem.
torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
# Update parameters and take a step using the computed gradient.
# The optimizer dictates the "update rule"--how the parameters are
# modified based on their gradients, the learning rate, etc.
optimizer.step()
# Update the learning rate.
scheduler.step()
# Calculate the average loss over the training data.
avg_train_loss = total_loss / len(train_dataloader)
# Store the loss value for plotting the learning curve.
loss_values.append(avg_train_loss)
print("")
print(" Average training loss: {0:.2f}".format(avg_train_loss))
print(" Training epoch took: {:}".format(format_time(time.time() - t0)))
# ========================================
# Validation
# ========================================
# After the completion of each training epoch, measure the performance on
# the validation set.
print("")
print("Running Validation...")
t0 = time.time()
# Put the model in evaluation mode--the dropout layers behave differently
# during evaluation.
model.eval()
# Tracking variables
eval_loss, eval_accuracy = 0, 0
nb_eval_steps, nb_eval_examples = 0, 0
# Evaluate data for one epoch
for batch in validation_dataloader:
# Add batch to GPU
batch = tuple(t.to(device) for t in batch)
# Unpack the inputs from dataloader
b_input_ids, b_input_mask, b_labels = batch
# Telling the model not to compute or store gradients, saving memory and
# speeding up validation
with torch.no_grad():
# Forward pass, calculate logit predictions.
# This will return the logits rather than the loss because we have
# not provided labels.
# token_type_ids is the same as the "segment ids", which
# differentiates sentence 1 and 2 in 2-sentence tasks.
# The documentation for this `model` function is here:
# https://huggingface.co/transformers/v2.2.0/model_doc/bert.html#transformers.BertForSequenceClassification
outputs = model(b_input_ids,
token_type_ids=None,
attention_mask=b_input_mask)
# Get the "logits" output by the model. The "logits" are the output
# values prior to applying an activation function like the softmax.
logits = outputs[0]
# Move logits and labels to CPU
logits = logits.detach().cpu().numpy()
label_ids = b_labels.to('cpu').numpy()
# Calculate the accuracy for this batch of test sentences.
tmp_eval_accuracy = flat_accuracy(logits, label_ids)
# Accumulate the total accuracy.
eval_accuracy += tmp_eval_accuracy
# Track the number of batches
nb_eval_steps += 1
# Report the final accuracy for this validation run.
print(" Accuracy: {0:.2f}".format(eval_accuracy/nb_eval_steps))
print(" Validation took: {:}".format(format_time(time.time() - t0)))
print("")
print("Training complete!")
return model
'''print('Saving Model....')
model_save_name = config.get('model','modelName')
path = config.get('model','path')
#torch.save(model.state_dict(), os.path.join(path,model_save_name))
torch.save(model, os.path.join(path,model_save_name))'''
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("input_dataset")
parser.add_argument("conf_file")
parser.add_argument("output_path")
args = parser.parse_args()
INPUT_DATASET = args.input_dataset
CONF_FILE = args.conf_file
OUTPUT_PATH = args.output_path
config = configparser.ConfigParser()
config.read(CONF_FILE)
#dataPath = config.get('general','dataPath')
columnText = config.get('general','columnText')
columnClass = config.get('general','columnClass')
minOfInstancePerClass = int(config.get('general','minOfInstancePerClass'))
maxOfInstancePerClass = int(config.get('general','maxOfInstancePerClass'))
model_path = config.get('model','path')
chosen_model = config.get('model','model')
max_len = int(config.get('model','max_len_sequences'))
batch_size = int(config.get('model','batch_size'))
epochs = int(config.get('model','epochs'))
df = pd.read_csv(INPUT_DATASET, sep="\t")
df = remove_weak_classes(df, columnClass, minOfInstancePerClass)
df = resample_classes(df, columnClass, maxOfInstancePerClass)
#df = df[df[columnClass] != 'unclassified']
y = df[columnClass]
numberOfClasses = y.nunique()
encoder = preprocessing.LabelEncoder()
y = encoder.fit_transform(y)
#train_x, test_x, train_y, test_y = train_test_split(df, y, test_size=0.33, random_state=42, stratify = y )
#sentences = train_x[columnText].values
sentences = df[columnText].values
#labels = train_y.tolist()
labels = y.tolist()
#call train method
model = training_bertFineTuning(chosen_model,model_path, sentences, labels, max_len, batch_size, epochs)
#save the model
model_save_name = chosen_model+"_b"+batch_size+"_e"+epochs
torch.save(model, os.path.join(OUTPUT_PATH,model_save_name))
#print the model parameters
params = list(model.named_parameters())
print('The BERT model has {:} different named parameters.\n'.format(len(params)))
print('==== Embedding Layer ====\n')
for p in params[0:5]:
print("{:<55} {:>12}".format(p[0], str(tuple(p[1].size()))))
print('\n==== First Transformer ====\n')
for p in params[5:21]:
print("{:<55} {:>12}".format(p[0], str(tuple(p[1].size()))))
print('\n==== Output Layer ====\n')
for p in params[-4:]:
print("{:<55} {:>12}".format(p[0], str(tuple(p[1].size()))))