379 lines
13 KiB
Python
379 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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import 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.functional as F
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# %%
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SHUFFLES=0
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AMPLIFY_FACTOR=0
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LEARNING_RATE=1e-5
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DEVICE = torch.device('cuda:1') 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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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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# %%
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num_sample_per_class = 10 # samples in each group
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batch_size = 16 # 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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model.to(DEVICE)
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model.train()
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losses = []
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# %%
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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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# %%
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x, y = next(iter(batchGenerator(data, batch_size)))
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# %%
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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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# %%
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def _pairwise_distances(embeddings, squared=False):
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"""Compute the 2D matrix of distances between all the embeddings.
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Args:
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embeddings: tensor of shape (batch_size, embed_dim)
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squared: Boolean. If true, output is the pairwise squared euclidean distance matrix.
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If false, output is the pairwise euclidean distance matrix.
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Returns:
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pairwise_distances: tensor of shape (batch_size, batch_size)
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"""
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dot_product = torch.matmul(embeddings, embeddings.t())
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# Get squared L2 norm for each embedding. We can just take the diagonal of `dot_product`.
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# This also provides more numerical stability (the diagonal of the result will be exactly 0).
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# shape (batch_size,)
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square_norm = torch.diag(dot_product)
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# Compute the pairwise distance matrix as we have:
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# ||a - b||^2 = ||a||^2 - 2 <a, b> + ||b||^2
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# shape (batch_size, batch_size)
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distances = square_norm.unsqueeze(0) - 2.0 * dot_product + square_norm.unsqueeze(1)
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# Apply a lower bound to distances to ensure they are non-negative and avoid tiny negative numbers due to computation errors
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distances = torch.clamp(distances, min=0.0)
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if not squared:
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# Because the gradient of sqrt is infinite when distances == 0.0 (ex: on the diagonal)
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# we need to add a small epsilon where distances == 0.0
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epsilon = 1e-16
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mask = (distances < epsilon).float()
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distances = distances + mask * epsilon
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distances = (1.0 - mask) * torch.sqrt(distances)
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return distances
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# %%
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embeddings = cls
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squared = False
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# %%
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# Get the pairwise distance matrix
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pairwise_dist = loss._pairwise_distances(embeddings, squared=squared) # 96x96
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anchor_positive_dist = pairwise_dist.unsqueeze(2) # 96x96x1
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anchor_negative_dist = pairwise_dist.unsqueeze(1) # 96x1x96
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# Compute a 3D tensor of size (batch_size, batch_size, batch_size)
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# triplet_loss[i, j, k] will contain the triplet loss of anchor=i, positive=j, negative=k
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# every (i,j) pairwise distance - every (i,k) pairwise distance
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# fixing for i, we get (i,j) - (i,k), for every j and k, which is 96x96
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# Uses broadcasting where the 1st argument has shape (batch_size, batch_size, 1)
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# and the 2nd (batch_size, 1, batch_size)
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# remember that broadcasting is repeating the other axis n-times
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# this broadcasting trick is to get every possible triple combination
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triplet_loss = anchor_positive_dist - anchor_negative_dist + margin
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# triplet_loss 96x96x96
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# %%
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labels = y
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# %%
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# Put to zero the invalid triplets
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# (where label(a) != label(p) or label(n) == label(a) or a == p)
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mask = loss._get_triplet_mask(labels)
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triplet_loss = mask.float() * triplet_loss
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# Remove negative losses (i.e. the easy triplets)
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triplet_loss = F.relu(triplet_loss)
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# Count number of positive triplets (where triplet_loss > 0)
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valid_triplets = triplet_loss[triplet_loss > 1e-16]
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num_positive_triplets = valid_triplets.size(0)
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num_valid_triplets = mask.sum()
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fraction_positive_triplets = num_positive_triplets / (num_valid_triplets.float() + 1e-16)
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# Get final mean triplet loss over the positive valid triplets
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triplet_loss = triplet_loss.sum() / (num_positive_triplets + 1e-16)
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# %%
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# %%
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loss, _ = batch_all_triplet_loss(y, cls, margin, squared=False)
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# %%
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loss = batch_hard_triplet_loss(y, cls, margin, squared=False)
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# %%
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# Check that i, j and k are distinct
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# create an identity matrix of size 96
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indices_equal = torch.eye(labels.size(0), device=labels.device).bool()
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# %%
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indices_not_equal = ~indices_equal
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i_not_equal_j = indices_not_equal.unsqueeze(2) # [96,96,1]
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i_not_equal_k = indices_not_equal.unsqueeze(1) # [96,1,96]
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j_not_equal_k = indices_not_equal.unsqueeze(0) # [1,96,96]
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# %%
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# eliminate any combination that uses the diagonal values (aka sharing same values)
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distinct_indices = (i_not_equal_j & i_not_equal_k) & j_not_equal_k
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# %%
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label_equal = labels.unsqueeze(0) == labels.unsqueeze(1)
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# label_equal is a 96x96 matrix showing where 2 labels equate
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# perform the same unsqueeze to 1 and 2 axis and broadcast to get all possible combinations
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# note that we have 96 elements, but we want all (i,j,k) combinations from these 96 elements
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i_equal_j = label_equal.unsqueeze(2)
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i_equal_k = label_equal.unsqueeze(1)
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# ~i_equal_k means that it checks for non-equality between i and k
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# i_equal_j checks for equality between i and j
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# we want (i,j) to be the same label, (i,k) to be different labels
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valid_labels = ~i_equal_k & i_equal_j
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# %%
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final_mask = distinct_indices & valid_labels
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