434 lines
14 KiB
Python
434 lines
14 KiB
Python
# %%
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import torch
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import json
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import random
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import numpy as np
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from transformers import AutoTokenizer
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from transformers import AutoModel
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from loss import (
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batch_all_triplet_loss,
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batch_hard_triplet_loss,
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batch_all_soft_margin_triplet_loss,
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batch_hard_soft_margin_triplet_loss)
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from sklearn.neighbors import KNeighborsClassifier
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from tqdm import tqdm
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import pandas as pd
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import re
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from torch.utils.data import Dataset, DataLoader
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import torch.optim as optim
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import torch.nn as nn
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import torch.nn.functional as F
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torch.set_float32_matmul_precision('high')
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def set_seed(seed):
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"""
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Set the random seed for reproducibility.
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"""
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random.seed(seed) # Python random module
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np.random.seed(seed) # NumPy random
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torch.manual_seed(seed) # PyTorch CPU
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torch.cuda.manual_seed(seed) # PyTorch GPU
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torch.cuda.manual_seed_all(seed) # If using multiple GPUs
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torch.backends.cudnn.deterministic = True # Ensure deterministic behavior
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torch.backends.cudnn.benchmark = False # Disable optimization for reproducibility
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set_seed(42)
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# %%
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SHUFFLES=1
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AMPLIFY_FACTOR=1
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LEARNING_RATE=1e-4
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DEVICE = torch.device('cuda:2') if torch.cuda.is_available() else torch.device('cpu')
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# %%
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EVAL_FILE="top1_curves/hybrid_output.txt"
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with open(EVAL_FILE, "w") as f:
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pass
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# %%
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def generate_train_entity_sets(entity_id_mentions, entity_id_name, group_size, anchor=True):
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# split entity mentions into groups
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# anchor = False, don't add entity name to each group, simply treat it as a normal mention
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entity_sets = []
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if anchor:
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for id, mentions in entity_id_mentions.items():
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random.shuffle(mentions)
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positives = [mentions[i:i + group_size] for i in range(0, len(mentions), group_size)]
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anchor_positive = [([entity_id_name[id]]+p, id) for p in positives]
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entity_sets.extend(anchor_positive)
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else:
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for id, mentions in entity_id_mentions.items():
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group = list(set([entity_id_name[id]] + mentions))
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random.shuffle(group)
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positives = [(mentions[i:i + group_size], id) for i in range(0, len(mentions), group_size)]
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entity_sets.extend(positives)
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return entity_sets
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def batchGenerator(data, batch_size):
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for i in range(0, len(data), batch_size):
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batch = data[i:i+batch_size]
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x, y = [], []
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for t in batch:
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x.extend(t[0])
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y.extend([t[1]]*len(t[0]))
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yield x, y
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with open('../esAppMod/tca_entities.json', 'r') as file:
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entities = json.load(file)
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all_entity_id_name = {entity['entity_id']: entity['entity_name'] for _, entity in entities['data'].items()}
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with open('../esAppMod/train.json', 'r') as file:
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train = json.load(file)
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train_entity_id_mentions = {data['entity_id']: data['mentions'] for _, data in train['data'].items()}
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train_entity_id_name = {data['entity_id']: all_entity_id_name[data['entity_id']] for _, data in train['data'].items()}
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# %%
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###############
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# alternate data import strategy
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###################################################
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# import code
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# import training file
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data_path = '../esAppMod_data_import/train.csv'
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df = pd.read_csv(data_path, skipinitialspace=True)
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# rather than use pattern, we use the real thing and property
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entity_ids = df['entity_id'].to_list()
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target_id_list = sorted(list(set(entity_ids)))
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id2label = {}
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label2id = {}
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for idx, val in enumerate(target_id_list):
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id2label[idx] = val
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label2id[val] = idx
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df["training_id"] = df["entity_id"].map(label2id)
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# %%
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##############################################################
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# augmentation code
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# basic preprocessing
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def preprocess_text(text):
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# 1. Make all uppercase
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text = text.lower()
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# standardize spacing
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text = re.sub(r'\s+', '_', text).strip()
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return text
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def generate_random_shuffles(text, n):
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words = text.split() # Split the input into words
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shuffled_variations = []
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for _ in range(n):
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shuffled = words[:] # Copy the word list to avoid in-place modification
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random.shuffle(shuffled) # Randomly shuffle the words
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shuffled_variations.append(" ".join(shuffled)) # Join the words back into a string
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return shuffled_variations
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def shuffle_text(text, n_shuffles=SHUFFLES):
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all_processed = []
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# add the original text
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all_processed.append(text)
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# Generate random shuffles
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shuffled_variations = generate_random_shuffles(text, n_shuffles)
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all_processed.extend(shuffled_variations)
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return all_processed
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def corrupt_word(word):
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"""Corrupt a single word using random corruption techniques."""
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if len(word) <= 1: # Skip corruption for single-character words
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return word
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corruption_type = random.choice(["delete", "swap"])
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if corruption_type == "delete":
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# Randomly delete a character
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idx = random.randint(0, len(word) - 1)
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word = word[:idx] + word[idx + 1:]
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elif corruption_type == "swap":
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# Swap two adjacent characters
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if len(word) > 1:
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idx = random.randint(0, len(word) - 2)
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word = (word[:idx] + word[idx + 1] + word[idx] + word[idx + 2:])
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return word
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def corrupt_string(sentence, corruption_probability=0.01):
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"""Corrupt each word in the string with a given probability."""
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words = sentence.split()
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corrupted_words = [
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corrupt_word(word) if random.random() < corruption_probability else word
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for word in words
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]
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return " ".join(corrupted_words)
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def create_example(index, mention, entity_name):
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return {'entity_id': index, 'mention': mention, 'entity_name': entity_name}
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# augment whole dataset
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def augment_data(df):
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output_list = []
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for idx,row in df.iterrows():
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index = row['entity_id']
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entity_name = row['entity_name']
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parent_desc = row['mention']
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parent_desc = preprocess_text(parent_desc)
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# add basic example
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output_list.append(create_example(index, parent_desc, entity_name))
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# # add shuffled strings
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# processed_descs = shuffle_text(parent_desc, n_shuffles=SHUFFLES)
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# for desc in processed_descs:
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# if (desc != parent_desc):
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# output_list.append(create_example(index, desc, entity_name))
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# add corrupted strings
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desc = corrupt_string(parent_desc, corruption_probability=0.01)
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if (desc != parent_desc):
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output_list.append(create_example(index, desc, entity_name))
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# add example with stripped non-alphanumerics
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desc = re.sub(r'[^\w\s]', ' ', parent_desc) # Retains only alphanumeric and spaces
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if (desc != parent_desc):
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output_list.append(create_example(index, desc, entity_name))
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# short sequence amplifier
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# short sequences are rare, and we must compensate by including more examples
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# also, short sequence don't usually get affected by shuffle
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words = parent_desc.split()
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word_count = len(words)
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if word_count <= 2:
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for _ in range(AMPLIFY_FACTOR):
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output_list.append(create_example(index, desc, entity_name))
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new_df = pd.DataFrame(output_list)
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return new_df
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# %%
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def make_entity_id_mentions(df):
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entity_id_mentions = {}
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entity_id_list = list(set(df['entity_id']))
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for entity_id in entity_id_list:
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entity_id_mentions[entity_id] = df[df['entity_id']==entity_id]['mention'].to_list()
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return entity_id_mentions
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def make_entity_id_name(df):
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entity_id_name = {}
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entity_id_list = list(set(df['entity_id']))
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for entity_id in entity_id_list:
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# entity_id always matches entity_name, so first value would work
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entity_id_name[entity_id] = df[df['entity_id']==entity_id]['entity_name'].to_list()[0]
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return entity_id_name
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# evaluation
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def run_evaluation(model, tokenizer):
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def preprocess_text(text):
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# 1. Make all uppercase
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text = text.lower()
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# standardize spacing
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text = re.sub(r'\s+', ' ', text).strip()
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return text
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with open('../esAppMod/tca_entities.json', 'r') as file:
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entities = json.load(file)
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all_entity_id_name = {entity['entity_id']: entity['entity_name'] for _, entity in entities['data'].items()}
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with open('../esAppMod/train.json', 'r') as file:
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train = json.load(file)
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train_entity_id_mentions = {data['entity_id']: data['mentions'] for _, data in train['data'].items()}
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train_entity_id_name = {data['entity_id']: all_entity_id_name[data['entity_id']] for _, data in train['data'].items()}
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with open('../esAppMod/infer.json', 'r') as file:
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test = json.load(file)
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x_test = [preprocess_text(d['mention']) for _, d in test['data'].items()]
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y_test = [d['entity_id'] for _, d in test['data'].items()]
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train_entities, labels = list(train_entity_id_name.values()), list(train_entity_id_name.keys())
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train_entities = [preprocess_text(element) for element in train_entities]
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def batch_list(data, batch_size):
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"""Yield successive n-sized chunks from data."""
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for i in range(0, len(data), batch_size):
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yield data[i:i + batch_size]
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batches = batch_list(train_entities, 64)
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embedding_list = []
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for batch in batches:
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inputs = tokenizer(batch, padding=True, return_tensors='pt')
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outputs = model(
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input_ids=inputs['input_ids'].to(DEVICE),
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attention_mask=inputs['attention_mask'].to(DEVICE)
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)
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output = outputs.last_hidden_state[:,0,:]
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output = output.detach().cpu().numpy()
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embedding_list.append(output)
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cls = np.concatenate(embedding_list)
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batches = batch_list(x_test, 64)
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embedding_list = []
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for batch in batches:
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inputs = tokenizer(batch, padding=True, return_tensors='pt')
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outputs = model(
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input_ids=inputs['input_ids'].to(DEVICE),
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attention_mask=inputs['attention_mask'].to(DEVICE)
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)
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output = outputs.last_hidden_state[:,0,:]
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output = output.detach().cpu().numpy()
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embedding_list.append(output)
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cls_test = np.concatenate(embedding_list)
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knn = KNeighborsClassifier(n_neighbors=1, metric='euclidean').fit(cls, labels)
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with open(EVAL_FILE, "a") as f:
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# only compute top-1
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distances, indices = knn.kneighbors(cls_test, n_neighbors=1)
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num = 0
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for a,b in zip(y_test, indices):
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b = [labels[i] for i in b]
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if a in b:
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num += 1
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print(f'{num / len(y_test)}', file=f)
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# %%
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num_sample_per_class = 10 # samples in each group
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batch_size = 64 # number of groups, effective batch_size for computing triplet loss = batch_size * num_sample_per_class
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margin = 2
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epochs = 200
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tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
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bert_model = AutoModel.from_pretrained(MODEL_NAME)
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class BertForClassificationAndTriplet(nn.Module):
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def __init__(self, bert_model, num_classes):
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super().__init__()
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self.bert = bert_model
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self.classifier = nn.Linear(bert_model.config.hidden_size, num_classes)
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def forward(self, input_ids, attention_mask=None):
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outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask)
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cls_embeddings = outputs.last_hidden_state[:, 0, :] # CLS token
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logits = self.classifier(cls_embeddings)
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return cls_embeddings, logits
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model = BertForClassificationAndTriplet(bert_model, num_classes=len(label2id))
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optimizer = torch.optim.AdamW(model.parameters(), lr=LEARNING_RATE)
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# scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=30, gamma=0.1)
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model.to(DEVICE)
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model.train()
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losses = []
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def linear_decay(epoch, max_epochs, initial_lr, final_lr):
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""" Calculate the linearly decayed learning rate. """
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return initial_lr - (epoch / max_epochs) * (initial_lr - final_lr)
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for epoch in tqdm(range(epochs)):
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total_loss = 0.0
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total_cross = 0.0
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total_triplet = 0.0
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batch_number = 0
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# lr = linear_decay(epoch, epochs, initial_lr=1e-5, final_lr=5e-6)
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# # Update optimizer's learning rate
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# for param_group in optimizer.param_groups:
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# param_group['lr'] = lr
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if epoch % 10 == 0:
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augmented_df = augment_data(df)
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train_entity_id_mentions = make_entity_id_mentions(augmented_df)
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train_entity_id_name = make_entity_id_name(augmented_df)
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data = generate_train_entity_sets(train_entity_id_mentions, train_entity_id_name, num_sample_per_class-1, anchor=True)
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random.shuffle(data)
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for x,y in batchGenerator(data, batch_size):
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# print(len(x), len(y), end='-->')
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optimizer.zero_grad()
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inputs = tokenizer(x, padding=True, return_tensors='pt')
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inputs.to(DEVICE)
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cls, logits = model(
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input_ids=inputs['input_ids'],
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attention_mask=inputs['attention_mask']
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)
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# for training less than half the time, train on easy
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labels = y
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labels = [label2id[element] for element in labels]
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labels = torch.tensor(labels).to(DEVICE)
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y = torch.tensor(y).to(DEVICE)
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class_loss = F.cross_entropy(logits, labels)
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if epoch < epochs / 2:
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# triplet_loss, _ = batch_all_soft_margin_triplet_loss(y, cls, squared=False)
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# loss = class_loss + triplet_loss
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# loss,_ = batch_all_soft_margin_triplet_loss(y, cls, squared=False)
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loss = class_loss
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# for training after half the time, train on hard
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# else:
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# triplet_loss = batch_hard_soft_margin_triplet_loss(y, cls, squared=False)
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# loss = triplet_loss
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else:
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loss = batch_hard_soft_margin_triplet_loss(y, cls, squared=False)
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loss.backward()
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optimizer.step()
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total_loss += loss.detach().item()
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# total_cross += class_loss.detach().item()
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# total_triplet += triplet_loss.detach().item()
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batch_number += 1
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# run evaluation on test data
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model.eval()
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with torch.no_grad():
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run_evaluation(model=model.bert, tokenizer=tokenizer)
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model.train()
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# scheduler.step() # Update the learning rate
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# print(f'epoch loss: {total_loss/batch_number}, cross loss: {total_cross/batch_number}, triplet loss: {total_triplet/batch_number}')
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print(f'epoch loss: {total_loss/batch_number}')
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# print(f"Epoch {epoch+1}: lr={lr}")
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# if epoch % 5 == 0:
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# # torch.save(model.bert.state_dict(), './checkpoint/classification.pt')
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# torch.save(model.state_dict(), './checkpoint/hybrid.pt')
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# torch.save(model.bert.state_dict(), './checkpoint/classification.pt')
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# torch.save(model.state_dict(), './checkpoint/hybrid.pt')
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# %%
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