domain_mapping/biomedical_train/ncbi/train.py

# %%

# from datasets import load_from_disk
import os

os.environ['NCCL_P2P_DISABLE'] = '1'
os.environ['NCCL_IB_DISABLE'] = '1'
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = "0,1,2,3"

import re
import random

import torch
from transformers import (
    AutoTokenizer,
    AutoModelForSequenceClassification,
    DataCollatorWithPadding,
    Trainer,
    EarlyStoppingCallback,
    TrainingArguments
)
import evaluate
import numpy as np
import pandas as pd
# import matplotlib.pyplot as plt
from datasets import Dataset, DatasetDict



torch.set_float32_matmul_precision('high')

# %%
def set_seed(seed):
    """
    Set the random seed for reproducibility.
    """
    random.seed(seed)  # Python random module
    np.random.seed(seed)  # NumPy random
    torch.manual_seed(seed)  # PyTorch CPU
    torch.cuda.manual_seed(seed)  # PyTorch GPU
    torch.cuda.manual_seed_all(seed)  # If using multiple GPUs
    torch.backends.cudnn.deterministic = True  # Ensure deterministic behavior
    torch.backends.cudnn.benchmark = False  # Disable optimization for reproducibility

set_seed(42)

SHUFFLES=0  # 0 shuffles means it does not re-sample

# %%

# We want to map the entity_id to a consecutive set of id's
# import training file
data_path = '../../biomedical_data_import/bc2gm_train.csv'
train_df = pd.read_csv(data_path, skipinitialspace=True)
# rather than use pattern, we use the real thing and property
entity_ids = train_df['entity_id'].to_list()
target_id_list = sorted(list(set(entity_ids)))


# %%
id2label = {}
label2id = {}
for idx, val in enumerate(target_id_list):
    id2label[idx] = val
    label2id[val] = idx

# %%
# introduce pre-processing functions
def preprocess_text(text):

    # 1. Make all uppercase
    text = text.lower()

    # Substitute digits with 'x'
    # text = re.sub(r'\d+', '#', text)

    # standardize spacing
    text = re.sub(r'\s+', ' ', text).strip()

    return text


def generate_random_shuffles(text, n):
    """
    Generate n strings with randomly shuffled words from the input text.

    Args:
        text (str): The input text.
        n (int): The number of random variations to generate.

    Returns:
        list: A list of strings with shuffled words.
    """
    words = text.split()  # Split the input into words
    shuffled_variations = []
    
    for _ in range(n):
        shuffled = words[:]  # Copy the word list to avoid in-place modification
        random.shuffle(shuffled)  # Randomly shuffle the words
        shuffled_variations.append(" ".join(shuffled))  # Join the words back into a string
    
    return shuffled_variations


# generate n more shuffled examples
def shuffle_text(text, n_shuffles=SHUFFLES):
    """
    Preprocess a list of texts and add n random shuffles for each string.

    Args:
        texts (list): An input strings.
        n_shuffles (int): Number of random shuffles to generate for each string.

    Returns:
        list: A list of preprocessed and shuffled strings.
    """
    all_processed = []
    # add the original text
    all_processed.append(text)
        
    # Generate random shuffles
    shuffled_variations = generate_random_shuffles(text, n_shuffles)
    all_processed.extend(shuffled_variations)
    
    return all_processed


######################################

# augmentation by text corruption

def corrupt_word(word):
    """Corrupt a single word using random corruption techniques."""
    if len(word) <= 1:  # Skip corruption for single-character words
        return word
    
    corruption_type = random.choice(["delete", "swap"])
    
    if corruption_type == "delete":
        # Randomly delete a character
        idx = random.randint(0, len(word) - 1)
        word = word[:idx] + word[idx + 1:]
    
    elif corruption_type == "swap":
        # Swap two adjacent characters
        if len(word) > 1:
            idx = random.randint(0, len(word) - 2)
            word = (word[:idx] + word[idx + 1] + word[idx] + word[idx + 2:])
    
    
    return word

def corrupt_string(sentence, corruption_probability=0.01):
    """Corrupt each word in the string with a given probability."""
    words = sentence.split()
    corrupted_words = [
        corrupt_word(word) if random.random() < corruption_probability else word
        for word in words
    ]
    return " ".join(corrupted_words)


#############################################################
# Data Run code here


# outputs a list of dictionaries
# processes dataframe into lists of dictionaries
# each element maps input to output
# input: tag_description
# output: class label

def process_df_to_dict(df):
    output_list = []
    for _, row in df.iterrows():
        # produce shuffling
        index = row['entity_id']
        parent_desc = row['mention']
        if isinstance(parent_desc, float):
            print(parent_desc)
            parent_desc = f'{parent_desc}'
        parent_desc = preprocess_text(parent_desc)

        # unaugmented data
        element = {
            'text' : parent_desc,
            'label': label2id[index], # ensure labels starts from 0
        }
        output_list.append(element)


        # # short sequences are rare, and we must compensate by including more examples
        # # mutation of other longer sequences might drown out rare short sequences
        # words = parent_desc.split()
        # word_count = len(words)
        # if word_count < 3:
        #     for _ in range(10):
        #         element = {
        #             'text': parent_desc,
        #             'label': label2id[index],
        #         }
        #         output_list.append(element)


        # add shuffled strings
        processed_descs = shuffle_text(parent_desc, n_shuffles=SHUFFLES)
        for desc in processed_descs:
            if (desc != parent_desc):
                element = {
                    'text' : desc,
                    'label': label2id[index], # ensure labels starts from 0
                }
                output_list.append(element)

        # # corrupt string
        # desc = corrupt_string(parent_desc, corruption_probability=0.1)
        # if (desc != parent_desc):
        #     element = {
        #         'text' : desc,
        #         'label': label2id[index], # ensure labels starts from 0
        #     }
        #     output_list.append(element)

        
        # # augmentation
        # # remove all non-alphanumerics
        # desc = re.sub(r'[^\w\s]', ' ', parent_desc)  # Retains only alphanumeric and spaces
        # if (desc != parent_desc):
        #     element = {
        #         'text' : desc,
        #         'label': label2id[index], # ensure labels starts from 0
        #     }
        #     output_list.append(element)


    return output_list


def create_dataset():
    # train 

    data_path = '../../biomedical_data_import/bc2gm_train.csv'
    train_df = pd.read_csv(data_path, skipinitialspace=True)


    combined_data = DatasetDict({
        'train': Dataset.from_list(process_df_to_dict(train_df)),
    })
    return combined_data


# %%
#########################################
# training function

def train():

    save_path = f'checkpoint'
    split_datasets = create_dataset()

    # prepare tokenizer

    model_checkpoint = "distilbert/distilbert-base-uncased"
    # model_checkpoint = 'google-bert/bert-base-cased'
    # model_checkpoint = 'prajjwal1/bert-small'
    tokenizer = AutoTokenizer.from_pretrained(model_checkpoint, return_tensors="pt", clean_up_tokenization_spaces=True)

    # max_length = 120

    # given a dataset entry, run it through the tokenizer
    def preprocess_function(example):
        input = example['text']
        # text_target sets the corresponding label to inputs
        # there is no need to create a separate 'labels'
        model_inputs = tokenizer(
            input,
            truncation=True, # enable truncation for efficiency
        )
        return model_inputs

    # map maps function to each "row" in the dataset
    # aka the data in the immediate nesting
    tokenized_datasets = split_datasets.map(
        preprocess_function,
        batched=True,
        num_proc=8,
        remove_columns="text", # we only need the tokenization, not the original strings
    )

    # %%
    # create data collator

    data_collator = DataCollatorWithPadding(tokenizer=tokenizer)

    # %%
    # compute metrics
    metric = evaluate.load("accuracy")


    def compute_metrics(eval_preds):
        preds, labels = eval_preds
        preds = np.argmax(preds, axis=1)
        return metric.compute(predictions=preds, references=labels)

    # %%
    # create id2label and label2id


    # %%
    model = AutoModelForSequenceClassification.from_pretrained(
        model_checkpoint,
        num_labels=len(target_id_list),
        id2label=id2label,
        label2id=label2id)
    # important! after extending tokens vocab
    model.resize_token_embeddings(len(tokenizer))

    # model = torch.compile(model, backend="inductor", dynamic=True)


    # %%
    # Trainer

    training_args = TrainingArguments(
        output_dir=f"{save_path}",
        # eval_strategy="epoch",
        eval_strategy="no",
        logging_dir="tensorboard-log",
        logging_strategy="epoch",
        # save_strategy="epoch",
        load_best_model_at_end=False,
        learning_rate=1e-3,
        per_device_train_batch_size=512,
        # per_device_eval_batch_size=64,
        auto_find_batch_size=False,
        ddp_find_unused_parameters=False,
        weight_decay=0.01,
        save_total_limit=1,
        num_train_epochs=40,
        warmup_steps=400,
        bf16=True,
        push_to_hub=False,
        remove_unused_columns=False,
    )


    trainer = Trainer(
        model,
        training_args,
        train_dataset=tokenized_datasets["train"],
        tokenizer=tokenizer,
        data_collator=data_collator, # data_collator performs dynamic padding
        compute_metrics=compute_metrics,
        # callbacks=[EarlyStoppingCallback(early_stopping_patience=3)],
    )

    # uncomment to load training from checkpoint
    # checkpoint_path = 'default_40_1/checkpoint-5600'
    # trainer.train(resume_from_checkpoint=checkpoint_path)

    trainer.train()

# execute training
train()


# %%