training_bertFineTuning.py

import torch
import pandas as pd
import numpy as np
from sklearn import preprocessing
from sklearn.model_selection import train_test_split
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





###########################################################################
########################## Utils Functions ################################
###########################################################################

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)

##############################################################################################################
########################## Setup GPU #########################################################################
##############################################################################################################

# 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")




#############################################################################################################
########################## parameters ###################################################################
###########################################################################################################

config = configparser.ConfigParser()
config.read('settings.conf')

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'))

chosen_tokeniser = config.get('model','tokeniser')
chosen_model = config.get('model','model')

max_len = int(config.get('model','max_len_sequences'))


#############################################################################################################
########################## Load Data ###################################################################
###########################################################################################################




df = pd.read_csv(dataPath)
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)



sentences = train_x[columnText].values
labels = train_y.tolist()



############################################################################################################
########################## Model: Tokenization & Input Formatting ###################################################################
###########################################################################################################


# Load the BERT tokenizer.
print('Loading BERT tokenizer...')
tokenizer = BertTokenizer.from_pretrained(tokeniser_bert, 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(
                        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 = input_ids = 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=0.1, stratify = labels )
# Do the same for the masks.
train_masks, validation_masks, _, _ = train_test_split(attention_masks, labels,
                                             random_state=2018, test_size=0.1, 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)



from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler

# 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.

batch_size = int(config.get('model','batch_size'))

# 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)




############################################################################################################
########################## Model: Training ###################################################################
###########################################################################################################


print(' Selecting a model .....')



# Load BertForSequenceClassification, the pretrained BERT model with a single
# linear classification layer on top.

model = BertForSequenceClassification.from_pretrained(
    chosen_model, # 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.
                )



# Number of training epochs (authors recommend between 2 and 4)
epochs = int(config.get('model','epochs'))

# 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)


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))




# 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!")