401 lines
13 KiB
Python
401 lines
13 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 batch_all_triplet_loss, batch_hard_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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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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CORRUPT=0.1
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LEARNING_RATE=1e-5
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DEVICE = torch.device('cuda:0') if torch.cuda.is_available() else torch.device('cpu')
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# MODEL_NAME = 'distilbert-base-cased' #'prajjwal1/bert-small' #'bert-base-cased'
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MODEL_NAME = 'prajjwal1/bert-small' # 'prajjwal1/bert-small' 'bert-base-cased' 'distilbert-base-cased'
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# %%
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with open("top1_curves/baseline_output.txt", "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=CORRUPT)
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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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# def sample_from_df(df, sample_size_per_class=5):
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# sampled_df = (df.groupby("entity_id")[['entity_id', 'mention', 'entity_name']] # explicit give column names
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# .apply(lambda x: x.sample(n=min(sample_size_per_class, len(x))))
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# .reset_index(drop=True))
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#
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# return sampled_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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# %%
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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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eval_entities = json.load(file)
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eval_all_entity_id_name = {entity['entity_id']: entity['entity_name'] for _, entity in eval_entities['data'].items()}
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with open('../esAppMod/train.json', 'r') as file:
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eval_train = json.load(file)
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eval_train_entity_id_mentions = {data['entity_id']: data['mentions'] for _, data in eval_train['data'].items()}
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eval_train_entity_id_name = {data['entity_id']: all_entity_id_name[data['entity_id']] for _, data in eval_train['data'].items()}
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with open('../esAppMod/infer.json', 'r') as file:
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eval_test = json.load(file)
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x_test = [preprocess_text(d['mention']) for _, d in eval_test['data'].items()]
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y_test = [d['entity_id'] for _, d in eval_test['data'].items()]
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eval_train_entities, eval_labels = list(eval_train_entity_id_name.values()), list(eval_train_entity_id_name.keys())
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eval_train_entities = [preprocess_text(element) for element in eval_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(eval_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, eval_labels)
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with open("top1_curves/baseline_output.txt", "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 = [eval_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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model = AutoModel.from_pretrained(MODEL_NAME)
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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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# scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, 'min', patience=10, factor=0.9, cooldown=5, verbose=True)
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model.to(DEVICE)
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model.train()
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losses = []
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for epoch in tqdm(range(epochs)):
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total_loss = 0.0
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batch_number = 0
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if epoch % 1 == 0:
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augmented_df = augment_data(df)
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# sampled_df = sample_from_df(augmented_df, sample_size_per_class=num_sample_per_class)
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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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outputs = model(**inputs)
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cls = outputs.last_hidden_state[:,0,:]
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# for training less than half the time, train on easy
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y = torch.tensor(y).to(DEVICE)
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if epoch < epochs / 2:
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loss, _ = batch_all_triplet_loss(y, cls, margin, squared=False)
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# for training after half the time, train on hard
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else:
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loss = batch_hard_triplet_loss(y, cls, margin, 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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batch_number += 1
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# del x, y, outputs, cls, loss
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# torch.cuda.empty_cache()
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epoch_loss = total_loss/batch_number
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# scheduler.step(epoch_loss)
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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, 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: {epoch_loss}')
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# print(f"Epoch {epoch+1}: lr={scheduler.get_last_lr()[0]}")
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if epoch == 125:
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torch.save(model.state_dict(), './checkpoint/baseline.pt')
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# torch.save(model.state_dict(), './checkpoint/baseline.pt')
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# %%
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