From e7f6f159fa4b99afbcec38a6c14a8745765f8775 Mon Sep 17 00:00:00 2001
From: Khalleud <ledk14@gmail.com>
Date: Sun, 12 Sep 2021 00:45:06 +0200
Subject: [PATCH] [ADD] Training mode Bert Fine Tuning
---
training_bertFineTuning.py | 465 +++++++++++++++++++++++++++++++++++++
1 file changed, 465 insertions(+)
create mode 100644 training_bertFineTuning.py
diff --git a/training_bertFineTuning.py b/training_bertFineTuning.py
new file mode 100644
index 0000000..72a5929
--- /dev/null
+++ b/training_bertFineTuning.py
@@ -0,0 +1,465 @@
+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!")
--
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