commit a1d000d9c8733698ad9a420342ac74c9e46e7d8c Author: Richard Wong Date: Mon Jan 13 19:05:13 2025 +0900 First commit - added classification-based mapping for esAppMod data diff --git a/analysis/.gitignore b/analysis/.gitignore new file mode 100644 index 0000000..ed8ebf5 --- /dev/null +++ b/analysis/.gitignore @@ -0,0 +1 @@ +__pycache__ \ No newline at end of file diff --git a/analysis/bert_label_clustering.py b/analysis/bert_label_clustering.py new file mode 100644 index 0000000..49e5909 --- /dev/null +++ b/analysis/bert_label_clustering.py @@ -0,0 +1,80 @@ +# %% +import json +import pandas as pd +from utils import Retriever, cosine_similarity_chunked +from sklearn.metrics.pairwise import cosine_similarity + +########################################## +# %% + +# Load the JSON file +data_path = '../esAppMod/tca_entities.json' +with open(data_path, 'r') as file: + data = json.load(file) + +# Initialize an empty list to store the rows +rows = [] + +# %% +# Loop through all entities in the JSON +for entity in data["data"].items(): + entity_data = entity[1] + entity_id = entity_data['entity_id'] + entity_name = entity_data['entity_name'] + + # Add each mention and its entity_id to the rows list + rows.append({"id": entity_id, "name": entity_name}) + +# Create a DataFrame from the rows +df = pd.DataFrame(rows) + + +# %% +# df.to_csv('entity.csv', index=False) + + +# %% +# we want to automatically identify clusters +class Embedder(): + input_df: pd.DataFrame + fold: int + + def __init__(self, input_df): + self.input_df = input_df + + + def make_embedding(self, checkpoint_path): + + def generate_input_list(df): + input_list = [] + for _, row in df.iterrows(): + desc = row['name'] + input_list.append(desc) + return input_list + + # prepare reference embed + train_data = list(generate_input_list(self.input_df)) + # Define the directory and the pattern + retriever_train = Retriever(train_data, checkpoint_path) + retriever_train.make_embedding(batch_size=64) + return retriever_train.embeddings.to('cpu') + +# model_checkpoint = 'google-bert/bert-base-cased' +model_checkpoint = '../train/class_bert_simple/checkpoint/checkpoint-4500' +embedder = Embedder(input_df=df) +embeddings = embedder.make_embedding(model_checkpoint) + +# %% +similarity_matrix = cosine_similarity(embeddings) + +# %% +similarity_matrix.shape + +# %% +from sklearn.cluster import AgglomerativeClustering + +clustering = AgglomerativeClustering(metric='precomputed', linkage='average') +clustering.fit(1 - similarity_matrix) # Use distance = 1 - similarity + +print(clustering.labels_) # Cluster assignments +# %% diff --git a/analysis/class_imbalance.py b/analysis/class_imbalance.py new file mode 100644 index 0000000..a57b8fe --- /dev/null +++ b/analysis/class_imbalance.py @@ -0,0 +1,17 @@ +# %% +import pandas as pd +import matplotlib.pyplot as plt + +# %% +# import training file +data_path = '../data_import/train.csv' +train_df = pd.read_csv(data_path, skipinitialspace=True) + + +# %% +id_counts = train_df['entity_id'].value_counts() + +# %% + +plt.hist(id_counts, bins=50) +# %% diff --git a/analysis/entity_hierarchy.py b/analysis/entity_hierarchy.py new file mode 100644 index 0000000..937aa59 --- /dev/null +++ b/analysis/entity_hierarchy.py @@ -0,0 +1,95 @@ +# %% +import json +import pandas as pd + +########################################## +# %% + +# Load the JSON file +data_path = '../esAppMod/tca_entities.json' +with open(data_path, 'r') as file: + data = json.load(file) + +# Initialize an empty list to store the rows +rows = [] + +# %% +# Loop through all entities in the JSON +for entity in data["data"].items(): + entity_data = entity[1] + entity_id = entity_data['entity_id'] + entity_name = entity_data['entity_name'] + entity_type_id = entity_data['entity_type_id'] + entity_type_name = entity_data['entity_type_name'] + + # Add each mention and its entity_id to the rows list + rows.append( + { + 'id': entity_id, + 'name': entity_name, + 'type_id': entity_type_id, + 'type_name': entity_type_name + }) + +# Create a DataFrame from the rows +df = pd.DataFrame(rows) + +# %% +# df.to_csv('entity.csv', index=False) +df + +# %% +df['type_name'].value_counts() +# %% +df['type_id'].value_counts() + +# %% +name_list = df['name'].to_list() +# %% +name_list + +# %% +from scipy.cluster.hierarchy import dendrogram, linkage, fcluster +import numpy as np + +# %% +# Define labels +labels = name_list + +# Create a prefix-based distance matrix +def prefix_distance(label1, label2): + prefix1 = label1.split() + prefix2 = label2.split() + # Find common prefix length + common_prefix_length = len([w1 for w1, w2 in zip(prefix1, prefix2) if w1 == w2]) + # Distance is inversely proportional to common prefix length + return 1.0 / (common_prefix_length + 1) + +# Create a pairwise distance matrix +n = len(labels) +distance_matrix = np.zeros((n, n)) +for i in range(n): + for j in range(n): + distance_matrix[i, j] = prefix_distance(labels[i], labels[j]) + +# Perform hierarchical clustering +linkage_matrix = linkage(distance_matrix, method='average') + +# Visualize as a dendrogram +import matplotlib.pyplot as plt +dendrogram(linkage_matrix, labels=labels, leaf_rotation=90, leaf_font_size=2) +plt.title("Prefix-Based Clustering") +plt.show() + +# %% +linkage_matrix +# %% +# Extract flat clusters with a distance threshold +threshold = 0.5 +clusters = fcluster(linkage_matrix, t=threshold, criterion='distance') + +# Display clusters +for i, cluster_id in enumerate(clusters): + print(f"Label: {labels[i]}, Cluster ID: {cluster_id}") + +# %% diff --git a/analysis/error_analysis.py b/analysis/error_analysis.py new file mode 100644 index 0000000..bc066af --- /dev/null +++ b/analysis/error_analysis.py @@ -0,0 +1,71 @@ +# %% +import pandas as pd + +# %% +# import training file +data_path = '../data_import/train.csv' +train_df = pd.read_csv(data_path, skipinitialspace=True) + + +# import test file +data_path = '../data_import/test.csv' +test_df = pd.read_csv(data_path, skipinitialspace=True) + +# import entity file +data_path = '../data_import/entity.csv' +entity_df = pd.read_csv(data_path, skipinitialspace=True) +id2label = {} +for _, row in entity_df.iterrows(): + id2label[row['id']] = row['name'] + + + + + +# %% +data_path = '../train/class_bert_process/classification_prediction/exports/result.csv' +prediction_df = pd.read_csv(data_path) + +# %% +predicted_entity_list = [] +for element in prediction_df['class_prediction']: + predicted_entity_list.append(id2label[element]) + +prediction_df['predicted_name'] = predicted_entity_list +# %% +new_df = pd.concat((test_df, prediction_df ), axis=1) + +# %% +mismatch_mask = new_df['entity_id'] != new_df['class_prediction'] +mismatch_df = new_df[mismatch_mask] + + +# %% +# print the top 10 offending classes +print(mismatch_df['entity_id'].value_counts()[:10]) + +# %% +# Convert the whole dataframe as a string and display +# print the mismatch_df +print(mismatch_df.to_markdown()) + + +# %% +# let us see the test mentions +select_value = 434 +select_mask = mismatch_df['entity_id'] == select_value +mismatch_df[select_mask] + +# %% +# let us see the train mentions +select_value = 434 +select_mask = train_df['entity_id'] == select_value +train_df[select_mask] + + + +# %% +mismatch_df[select_mask]['class_prediction'].to_list() + +# %% +# %% diff --git a/analysis/utils.py b/analysis/utils.py new file mode 100644 index 0000000..98749be --- /dev/null +++ b/analysis/utils.py @@ -0,0 +1,81 @@ +import torch +from transformers import ( + AutoTokenizer, + AutoModelForSequenceClassification, + DataCollatorWithPadding, +) +import torch.nn.functional as F + + + +class Retriever: + def __init__(self, input_texts, model_checkpoint): + # we need to generate the embedding from list of input strings + self.embeddings = [] + self.inputs = input_texts + model_checkpoint = model_checkpoint + self.tokenizer = AutoTokenizer.from_pretrained(model_checkpoint, return_tensors="pt", clean_up_tokenization_spaces=True) + + model = AutoModelForSequenceClassification.from_pretrained(model_checkpoint) + self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + # device = "cpu" + model.to(self.device) + self.model = model.eval() + + + def make_embedding(self, batch_size=64): + all_embeddings = self.embeddings + input_texts = self.inputs + + for i in range(0, len(input_texts), batch_size): + batch_texts = input_texts[i:i+batch_size] + # Tokenize the input text + inputs = self.tokenizer(batch_texts, return_tensors="pt", padding=True, truncation=True, max_length=64) + input_ids = inputs.input_ids.to(self.device) + attention_mask = inputs.attention_mask.to(self.device) + + + # Pass the input through the encoder and retrieve the embeddings + with torch.no_grad(): + encoder_outputs = self.model(input_ids, attention_mask=attention_mask, output_hidden_states=True) + # get last layer + embeddings = encoder_outputs.hidden_states[-1] + # get cls token embedding + cls_embeddings = embeddings[:, 0, :] # Shape: (batch_size, hidden_size) + all_embeddings.append(cls_embeddings) + + # remove the batch list and makes a single large tensor, dim=0 increases row-wise + all_embeddings = torch.cat(all_embeddings, dim=0) + + self.embeddings = all_embeddings + +def cosine_similarity_chunked(batch1, batch2, chunk_size=1024): + device = 'cuda' + batch1_size = batch1.size(0) + batch2_size = batch2.size(0) + batch2.to(device) + + # Prepare an empty tensor to store results + cos_sim = torch.empty(batch1_size, batch2_size, device=device) + + # Process batch1 in chunks + for i in range(0, batch1_size, chunk_size): + batch1_chunk = batch1[i:i + chunk_size] # Get chunk of batch1 + + batch1_chunk.to(device) + # Expand batch1 chunk and entire batch2 for comparison + # batch1_chunk_exp = batch1_chunk.unsqueeze(1) # Shape: (chunk_size, 1, seq_len) + # batch2_exp = batch2.unsqueeze(0) # Shape: (1, batch2_size, seq_len) + batch2_norms = batch2.norm(dim=1, keepdim=True) + + + # Compute cosine similarity for the chunk and store it in the final tensor + # cos_sim[i:i + chunk_size] = F.cosine_similarity(batch1_chunk_exp, batch2_exp, dim=-1) + + # Compute cosine similarity by matrix multiplication and normalizing + sim_chunk = torch.mm(batch1_chunk, batch2.T) / (batch1_chunk.norm(dim=1, keepdim=True) * batch2_norms.T + 1e-8) + + # Store the results in the appropriate part of the final tensor + cos_sim[i:i + chunk_size] = sim_chunk + + return cos_sim \ No newline at end of file diff --git a/data_import/.gitignore b/data_import/.gitignore new file mode 100644 index 0000000..16f2dc5 --- /dev/null +++ b/data_import/.gitignore @@ -0,0 +1 @@ +*.csv \ No newline at end of file diff --git a/data_import/entity_label.py b/data_import/entity_label.py new file mode 100644 index 0000000..7e2bb0d --- /dev/null +++ b/data_import/entity_label.py @@ -0,0 +1,41 @@ +# %% +import json +import pandas as pd + +########################################## +# %% + +# Load the JSON file +data_path = '../esAppMod/tca_entities.json' +with open(data_path, 'r') as file: + data = json.load(file) + +# Initialize an empty list to store the rows +rows = [] + +# %% +# Loop through all entities in the JSON +for entity in data["data"].items(): + entity_data = entity[1] + entity_id = entity_data['entity_id'] + entity_name = entity_data['entity_name'] + entity_type_id = entity_data['entity_type_id'] + entity_type_name = entity_data['entity_type_name'] + + # Add each mention and its entity_id to the rows list + rows.append( + { + 'id': entity_id, + 'name': entity_name, + 'type_id': entity_type_id, + 'type_name': entity_type_name + }) + +# Create a DataFrame from the rows +df = pd.DataFrame(rows) + +# %% +df.to_csv('entity.csv', index=False) + + +# %% diff --git a/data_import/process_data.py b/data_import/process_data.py new file mode 100644 index 0000000..f6d3d2e --- /dev/null +++ b/data_import/process_data.py @@ -0,0 +1,85 @@ +# %% +import json +import pandas as pd + +########################################## +# %% +# import entity information + +# %% +data_path = 'entity.csv' +entity_df = pd.read_csv(data_path, skipinitialspace=True) +id2label = {} +for _, row in entity_df.iterrows(): + id2label[row['id']] = row['name'] + + +# Load the JSON file +data_path = '../esAppMod/train.json' +with open(data_path, 'r') as file: + data = json.load(file) + +# Initialize an empty list to store the rows +rows = [] + +# Loop through all entities in the JSON +for entity_key, entity_data in data["data"].items(): + mentions = entity_data["mentions"] + entity_id = entity_data["entity_id"] + entity_name = id2label[entity_id] + + # Add each mention and its entity_id to the rows list + for mention in mentions: + rows.append( + { + "mention": mention, + "entity_id": entity_id, + "entity_name": entity_name + }) + +# Create a DataFrame from the rows +train_df = pd.DataFrame(rows) + +train_class_set = set(train_df['entity_id'].to_list()) + +# %% +train_df.to_csv('train.csv', index=False) +########################################## +# %% +# Load the JSON file +data_path = '../esAppMod/infer.json' +with open(data_path, 'r') as file: + data = json.load(file) + +# Initialize an empty list to store the rows +rows = [] + +# Loop through all entities in the JSON +for entity_key, entity_data in data["data"].items(): + mention = entity_data["mention"] + entity_id = entity_data["entity_id"] + entity_name = id2label[entity_id] + + # Add each mention and its entity_id to the rows list + rows.append( + { + "mention": mention, + "entity_id": entity_id, + "entity_name": entity_name + }) + + + +# Create a DataFrame from the rows +test_df = pd.DataFrame(rows) + +test_class_set = (set(test_df['entity_id'].to_list())) + +# %% +test_df.to_csv('test.csv', index=False) + +# %% +# this shows that the training data can be found in the train set +test_class_set - train_class_set + +# %% diff --git a/esAppMod/infer.json b/esAppMod/infer.json new file 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of file diff --git a/train/class_bert_process/.gitignore b/train/class_bert_process/.gitignore new file mode 100644 index 0000000..2c8f0d6 --- /dev/null +++ b/train/class_bert_process/.gitignore @@ -0,0 +1,2 @@ +checkpoint* +tensorboard-log diff --git a/train/class_bert_process/classification_prediction/.gitignore b/train/class_bert_process/classification_prediction/.gitignore new file mode 100644 index 0000000..dbe1a9b --- /dev/null +++ b/train/class_bert_process/classification_prediction/.gitignore @@ -0,0 +1 @@ +exports \ No newline at end of file diff --git a/train/class_bert_process/classification_prediction/output.txt b/train/class_bert_process/classification_prediction/output.txt new file mode 100644 index 0000000..838eee8 --- /dev/null +++ b/train/class_bert_process/classification_prediction/output.txt @@ -0,0 +1,6 @@ + +******************************************************************************* +Accuracy: 0.79090 +F1 Score: 0.80996 +Precision: 0.88827 +Recall: 0.79090 diff --git a/train/class_bert_process/classification_prediction/predict.py b/train/class_bert_process/classification_prediction/predict.py new file mode 100644 index 0000000..36a5c7f --- /dev/null +++ b/train/class_bert_process/classification_prediction/predict.py @@ -0,0 +1,262 @@ +# %% + +# from datasets import load_from_disk +import os +import glob + +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 torch +from torch.utils.data import DataLoader + +from transformers import ( + AutoTokenizer, + AutoModelForSequenceClassification, + DataCollatorWithPadding, +) +import evaluate +import numpy as np +import pandas as pd +# import matplotlib.pyplot as plt +from datasets import Dataset, DatasetDict + +from tqdm import tqdm + +torch.set_float32_matmul_precision('high') + + +BATCH_SIZE = 256 + +# %% +data_path = '../../../data_import/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.upper() + + # 2. Remove punctuations + # text = re.sub(r'[^\w\s]', '', text) # Retains only alphanumeric and spaces + + # 3. Substitute digits with '#' + text = re.sub(r'\d', '#', text) + + return text + + + +# 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(): + desc = row['mention'] + desc = preprocess_text(desc) + index = row['entity_id'] + element = { + 'text' : desc, + 'label': label2id[index], # ensure labels starts from 0 + } + output_list.append(element) + + return output_list + + +def create_dataset(): + # train + data_path = '../../../data_import/test.csv' + test_df = pd.read_csv(data_path, skipinitialspace=True) + + + # combined_data = DatasetDict({ + # 'train': Dataset.from_list(process_df_to_dict(train_df)), + # }) + return Dataset.from_list(process_df_to_dict(test_df)) + + + +# %% + +def test(): + + test_dataset = create_dataset() + + # prepare tokenizer + + checkpoint_directory = f'../checkpoint' + # Use glob to find matching paths + # path is usually checkpoint_fold_1/checkpoint- + # we are guaranteed to save only 1 checkpoint from training + pattern = 'checkpoint-*' + model_checkpoint = glob.glob(os.path.join(checkpoint_directory, pattern))[0] + + tokenizer = AutoTokenizer.from_pretrained(model_checkpoint, return_tensors="pt", clean_up_tokenization_spaces=True) + # Define additional special tokens + # additional_special_tokens = ["", "", "", "", "", "", "", "", ""] + # Add the additional special tokens to the tokenizer + # tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens}) + + # %% + # compute max token length + max_length = 0 + for sample in test_dataset['text']: + # Tokenize the sample and get the length + input_ids = tokenizer(sample, truncation=False, add_special_tokens=True)["input_ids"] + length = len(input_ids) + + # Update max_length if this sample is longer + if length > max_length: + max_length = length + + print(max_length) + + # %% + + max_length = 128 + + # 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, + max_length=max_length, + # truncation=True, + padding='max_length' + ) + return model_inputs + + # map maps function to each "row" in the dataset + # aka the data in the immediate nesting + datasets = test_dataset.map( + preprocess_function, + batched=True, + num_proc=8, + remove_columns="text", + ) + + + datasets.set_format(type='torch', columns=['input_ids', 'attention_mask', 'label']) + + # %% temp + # tokenized_datasets['train'].rename_columns() + + # %% + # create data collator + + # data_collator = DataCollatorWithPadding(tokenizer=tokenizer, padding="max_length") + + # %% + # 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) + + 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 = model.eval() + + device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + model.to(device) + + pred_labels = [] + actual_labels = [] + + + dataloader = DataLoader(datasets, batch_size=BATCH_SIZE, shuffle=False) + for batch in tqdm(dataloader): + # Inference in batches + input_ids = batch['input_ids'] + attention_mask = batch['attention_mask'] + # save labels too + actual_labels.extend(batch['label']) + + + # Move to GPU if available + input_ids = input_ids.to(device) + attention_mask = attention_mask.to(device) + + # Perform inference + with torch.no_grad(): + logits = model( + input_ids, + attention_mask).logits + predicted_class_ids = logits.argmax(dim=1).to("cpu") + pred_labels.extend(predicted_class_ids) + + pred_labels = [tensor.item() for tensor in pred_labels] + + + # %% + from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score, confusion_matrix + y_true = actual_labels + y_pred = pred_labels + + # Compute metrics + accuracy = accuracy_score(y_true, y_pred) + average_parameter = 'weighted' + zero_division_parameter = 0 + f1 = f1_score(y_true, y_pred, average=average_parameter, zero_division=zero_division_parameter) + precision = precision_score(y_true, y_pred, average=average_parameter, zero_division=zero_division_parameter) + recall = recall_score(y_true, y_pred, average=average_parameter, zero_division=zero_division_parameter) + + with open("output.txt", "a") as f: + + print('*' * 80, file=f) + # Print the results + print(f'Accuracy: {accuracy:.5f}', file=f) + print(f'F1 Score: {f1:.5f}', file=f) + print(f'Precision: {precision:.5f}', file=f) + print(f'Recall: {recall:.5f}', file=f) + + # export result + label_list = [id2label[id] for id in pred_labels] + df = pd.DataFrame({ + 'class_prediction': pd.Series(label_list) + }) + + # we can save the t5 generation output here + df.to_csv(f"exports/result.csv", index=False) + + + + + + +# %% +# reset file before writing to it +with open("output.txt", "w") as f: + print('', file=f) + test() diff --git a/train/class_bert_process/train.py b/train/class_bert_process/train.py new file mode 100644 index 0000000..1a714ec --- /dev/null +++ b/train/class_bert_process/train.py @@ -0,0 +1,283 @@ +# %% + +# 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=5 + +# %% + +# import training file +data_path = '../../data_import/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.upper() + + # 2. Remove punctuations + # text = re.sub(r'[^\w\s]', '', text) # Retains only alphanumeric and spaces + + # 3. Substitute digits with '#' + text = re.sub(r'\d', '#', text) + + 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 = [] + all_processed.append(text) + + # Generate random shuffles + shuffled_variations = generate_random_shuffles(text, n_shuffles) + all_processed.extend(shuffled_variations) + + return all_processed + + +# 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'] + desc = row['mention'] + desc = preprocess_text(desc) + processed_descs = shuffle_text(desc, n_shuffles=SHUFFLES) + + for desc in processed_descs: + element = { + 'text' : desc, + 'label': label2id[index], # ensure labels starts from 0 + } + output_list.append(element) + + return output_list + + +def create_dataset(): + # train + data_path = '../../data_import/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 + + +# %% + +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' + tokenizer = AutoTokenizer.from_pretrained(model_checkpoint, return_tensors="pt", clean_up_tokenization_spaces=True) + # Define additional special tokens + # additional_special_tokens = [""] + # Add the additional special tokens to the tokenizer + # tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens}) + + 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, + max_length=max_length, + truncation=True, + padding=True + ) + 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", + ) + + # %% temp + # tokenized_datasets['train'].rename_columns() + + # %% + # 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-4, + per_device_train_batch_size=128, + per_device_eval_batch_size=128, + auto_find_batch_size=False, + ddp_find_unused_parameters=False, + weight_decay=0.01, + save_total_limit=1, + num_train_epochs=120, + 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, + 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() + + +# %% diff --git a/train/class_bert_simple/.gitignore b/train/class_bert_simple/.gitignore new file mode 100644 index 0000000..2c8f0d6 --- /dev/null +++ b/train/class_bert_simple/.gitignore @@ -0,0 +1,2 @@ +checkpoint* +tensorboard-log diff --git a/train/class_bert_simple/classification_prediction/.gitignore b/train/class_bert_simple/classification_prediction/.gitignore new file mode 100644 index 0000000..dbe1a9b --- /dev/null +++ b/train/class_bert_simple/classification_prediction/.gitignore @@ -0,0 +1 @@ +exports \ No newline at end of file diff --git a/train/class_bert_simple/classification_prediction/output.txt b/train/class_bert_simple/classification_prediction/output.txt new file mode 100644 index 0000000..2ecf38f --- /dev/null +++ b/train/class_bert_simple/classification_prediction/output.txt @@ -0,0 +1,6 @@ + +******************************************************************************* +Accuracy: 0.70070 +F1 Score: 0.73260 +Precision: 0.84815 +Recall: 0.70070 diff --git a/train/class_bert_simple/classification_prediction/predict.py b/train/class_bert_simple/classification_prediction/predict.py new file mode 100644 index 0000000..c39faed --- /dev/null +++ b/train/class_bert_simple/classification_prediction/predict.py @@ -0,0 +1,246 @@ +# %% + +# from datasets import load_from_disk +import os +import glob + +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 torch +from torch.utils.data import DataLoader + +from transformers import ( + AutoTokenizer, + AutoModelForSequenceClassification, + DataCollatorWithPadding, +) +import evaluate +import numpy as np +import pandas as pd +# import matplotlib.pyplot as plt +from datasets import Dataset, DatasetDict + +from tqdm import tqdm + +torch.set_float32_matmul_precision('high') + + +BATCH_SIZE = 256 + +# %% +data_path = '../../../data_import/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 + + +# %% + +# 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(): + desc = row['mention'] + index = row['entity_id'] + element = { + 'text' : f"{desc}", + 'label': label2id[index], # ensure labels starts from 0 + } + output_list.append(element) + + return output_list + + +def create_dataset(): + # train + data_path = '../../../data_import/test.csv' + test_df = pd.read_csv(data_path, skipinitialspace=True) + + + # combined_data = DatasetDict({ + # 'train': Dataset.from_list(process_df_to_dict(train_df)), + # }) + return Dataset.from_list(process_df_to_dict(test_df)) + + + +# %% + +def test(): + + test_dataset = create_dataset() + + # prepare tokenizer + + checkpoint_directory = f'../checkpoint' + # Use glob to find matching paths + # path is usually checkpoint_fold_1/checkpoint- + # we are guaranteed to save only 1 checkpoint from training + pattern = 'checkpoint-*' + model_checkpoint = glob.glob(os.path.join(checkpoint_directory, pattern))[0] + + tokenizer = AutoTokenizer.from_pretrained(model_checkpoint, return_tensors="pt", clean_up_tokenization_spaces=True) + # Define additional special tokens + # additional_special_tokens = ["", "", "", "", "", "", "", "", ""] + # Add the additional special tokens to the tokenizer + # tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens}) + + # %% + # compute max token length + max_length = 0 + for sample in test_dataset['text']: + # Tokenize the sample and get the length + input_ids = tokenizer(sample, truncation=False, add_special_tokens=True)["input_ids"] + length = len(input_ids) + + # Update max_length if this sample is longer + if length > max_length: + max_length = length + + print(max_length) + + # %% + + max_length = 128 + + # 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, + max_length=max_length, + # truncation=True, + padding='max_length' + ) + return model_inputs + + # map maps function to each "row" in the dataset + # aka the data in the immediate nesting + datasets = test_dataset.map( + preprocess_function, + batched=True, + num_proc=8, + remove_columns="text", + ) + + + datasets.set_format(type='torch', columns=['input_ids', 'attention_mask', 'label']) + + # %% temp + # tokenized_datasets['train'].rename_columns() + + # %% + # create data collator + + # data_collator = DataCollatorWithPadding(tokenizer=tokenizer, padding="max_length") + + # %% + # 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) + + 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 = model.eval() + + device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + model.to(device) + + pred_labels = [] + actual_labels = [] + + + dataloader = DataLoader(datasets, batch_size=BATCH_SIZE, shuffle=False) + for batch in tqdm(dataloader): + # Inference in batches + input_ids = batch['input_ids'] + attention_mask = batch['attention_mask'] + # save labels too + actual_labels.extend(batch['label']) + + + # Move to GPU if available + input_ids = input_ids.to(device) + attention_mask = attention_mask.to(device) + + # Perform inference + with torch.no_grad(): + logits = model( + input_ids, + attention_mask).logits + predicted_class_ids = logits.argmax(dim=1).to("cpu") + pred_labels.extend(predicted_class_ids) + + pred_labels = [tensor.item() for tensor in pred_labels] + + + # %% + from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score, confusion_matrix + y_true = actual_labels + y_pred = pred_labels + + # Compute metrics + accuracy = accuracy_score(y_true, y_pred) + average_parameter = 'weighted' + zero_division_parameter = 0 + f1 = f1_score(y_true, y_pred, average=average_parameter, zero_division=zero_division_parameter) + precision = precision_score(y_true, y_pred, average=average_parameter, zero_division=zero_division_parameter) + recall = recall_score(y_true, y_pred, average=average_parameter, zero_division=zero_division_parameter) + + with open("output.txt", "a") as f: + + print('*' * 80, file=f) + # Print the results + print(f'Accuracy: {accuracy:.5f}', file=f) + print(f'F1 Score: {f1:.5f}', file=f) + print(f'Precision: {precision:.5f}', file=f) + print(f'Recall: {recall:.5f}', file=f) + + # export result + label_list = [id2label[id] for id in pred_labels] + df = pd.DataFrame({ + 'class_prediction': pd.Series(label_list) + }) + + # we can save the t5 generation output here + df.to_csv(f"exports/result.csv", index=False) + + + + + + +# %% +# reset file before writing to it +with open("output.txt", "w") as f: + print('', file=f) + test() diff --git a/train/class_bert_simple/train.py b/train/class_bert_simple/train.py new file mode 100644 index 0000000..ddfab85 --- /dev/null +++ b/train/class_bert_simple/train.py @@ -0,0 +1,200 @@ +# %% + +# 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 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') + +# %% + +# import training file +data_path = '../../data_import/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 + +# %% + +# 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(): + desc = row['mention'] + index = row['entity_id'] + element = { + 'text' : f"{desc}", + 'label': label2id[index], # ensure labels starts from 0 + } + output_list.append(element) + + return output_list + + +def create_dataset(): + # train + data_path = '../../data_import/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 + + +# %% + +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' + tokenizer = AutoTokenizer.from_pretrained(model_checkpoint, return_tensors="pt", clean_up_tokenization_spaces=True) + # Define additional special tokens + # additional_special_tokens = [""] + # Add the additional special tokens to the tokenizer + # tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens}) + + 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, + max_length=max_length, + truncation=True, + padding=True + ) + 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", + ) + + # %% temp + # tokenized_datasets['train'].rename_columns() + + # %% + # 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-4, + per_device_train_batch_size=64, + 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=250, + 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, + 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() + + +# %% diff --git a/train/seq2seq_t5_simple/.gitignore b/train/seq2seq_t5_simple/.gitignore new file mode 100644 index 0000000..d943a39 --- /dev/null +++ b/train/seq2seq_t5_simple/.gitignore @@ -0,0 +1,2 @@ +checkpoint* +tensorboard-log \ No newline at end of file diff --git a/train/seq2seq_t5_simple/mapping_prediction/.gitignore b/train/seq2seq_t5_simple/mapping_prediction/.gitignore new file mode 100644 index 0000000..e9ebfc9 --- /dev/null +++ b/train/seq2seq_t5_simple/mapping_prediction/.gitignore @@ -0,0 +1,2 @@ +__pycache__ +exports/ diff --git a/train/seq2seq_t5_simple/mapping_prediction/inference.py b/train/seq2seq_t5_simple/mapping_prediction/inference.py new file mode 100644 index 0000000..618b3f8 --- /dev/null +++ b/train/seq2seq_t5_simple/mapping_prediction/inference.py @@ -0,0 +1,150 @@ +import torch +from torch.utils.data import DataLoader +from transformers import ( + T5TokenizerFast, + AutoModelForSeq2SeqLM, +) +import os +from tqdm import tqdm +from datasets import Dataset +import numpy as np + +os.environ['TOKENIZERS_PARALLELISM'] = 'false' + + +class Inference(): + tokenizer: T5TokenizerFast + model: torch.nn.Module + dataloader: DataLoader + + def __init__(self, checkpoint_path): + self._create_tokenizer() + self._load_model(checkpoint_path) + + + def _create_tokenizer(self): + # %% + # load tokenizer + self.tokenizer = T5TokenizerFast.from_pretrained("t5-small", return_tensors="pt", clean_up_tokenization_spaces=True) + # Define additional special tokens + # additional_special_tokens = ["", "", "", "", "", "", "SIG", "UNIT", "DATA_TYPE"] + # Add the additional special tokens to the tokenizer + # self.tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens}) + + def _load_model(self, checkpoint_path: str): + # load model + # Define the directory and the pattern + model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint_path) + model = torch.compile(model) + # set model to eval + self.model = model.eval() + + + + + def prepare_dataloader(self, input_df, batch_size, max_length): + """ + *arguments* + - input_df: input dataframe containing fields 'tag_description', 'thing', 'property' + - batch_size: the batch size of dataloader output + - max_length: length of tokenizer output + """ + print("preparing dataloader") + # convert each dataframe row into a dictionary + # outputs a list of dictionaries + + def _process_df(df): + output_list = [] + for _, row in df.iterrows(): + desc = row['mention'] + label = row['entity_name'] + element = { + 'input' : desc, + 'output': label + } + + output_list.append(element) + + return output_list + + def _preprocess_function(example): + input = example['input'] + target = example['output'] + # text_target sets the corresponding label to inputs + # there is no need to create a separate 'labels' + model_inputs = self.tokenizer( + input, + text_target=target, + max_length=max_length, + return_tensors="pt", + padding='max_length', + truncation=True, + ) + return model_inputs + + test_dataset = Dataset.from_list(_process_df(input_df)) + + + # map maps function to each "row" in the dataset + # aka the data in the immediate nesting + datasets = test_dataset.map( + _preprocess_function, + batched=True, + num_proc=1, + remove_columns=test_dataset.column_names, + ) + # datasets = _preprocess_function(test_dataset) + datasets.set_format(type='torch', columns=['input_ids', 'attention_mask', 'labels']) + + # create dataloader + self.dataloader = DataLoader(datasets, batch_size=batch_size) + + + def generate(self): + device = torch.device('cuda:1' if torch.cuda.is_available() else 'cpu') + MAX_GENERATE_LENGTH = 128 + + pred_generations = [] + pred_labels = [] + + print("start generation") + for batch in tqdm(self.dataloader): + # Inference in batches + input_ids = batch['input_ids'] + attention_mask = batch['attention_mask'] + # save labels too + pred_labels.extend(batch['labels']) + + + # Move to GPU if available + input_ids = input_ids.to(device) + attention_mask = attention_mask.to(device) + self.model.to(device) + + # Perform inference + with torch.no_grad(): + outputs = self.model.generate(input_ids, + attention_mask=attention_mask, + max_length=MAX_GENERATE_LENGTH) + + # Decode the output and print the results + pred_generations.extend(outputs.to("cpu")) + + + + # %% + def process_tensor_output(tokens): + predictions = self.tokenizer.decode(tokens, skip_special_tokens=True) + return predictions + + # decode prediction labels + def decode_preds(tokens_list): + prediction_list = [] + for tokens in tokens_list: + predicted_seq = process_tensor_output(tokens) + prediction_list.append(predicted_seq) + return prediction_list + + prediction_list = decode_preds(pred_generations) + return prediction_list + diff --git a/train/seq2seq_t5_simple/mapping_prediction/output.txt b/train/seq2seq_t5_simple/mapping_prediction/output.txt new file mode 100644 index 0000000..dd5d228 --- /dev/null +++ b/train/seq2seq_t5_simple/mapping_prediction/output.txt @@ -0,0 +1,2 @@ + +Accuracy for fold: 0.5846658466584665 diff --git a/train/seq2seq_t5_simple/mapping_prediction/predict.py b/train/seq2seq_t5_simple/mapping_prediction/predict.py new file mode 100644 index 0000000..12b931e --- /dev/null +++ b/train/seq2seq_t5_simple/mapping_prediction/predict.py @@ -0,0 +1,62 @@ + +import pandas as pd +import os +import glob +from inference import Inference + +checkpoint_directory = '../' + +BATCH_SIZE = 512 + +def infer(): + print(f"Inference for data") + # import test data + data_path = '../../../data_import/test.csv' + df = pd.read_csv(data_path, skipinitialspace=True) + + + ########################################## + # run inference + # checkpoint + # Use glob to find matching paths + directory = os.path.join(checkpoint_directory, f'checkpoint') + # Use glob to find matching paths + # path is usually checkpoint- + # we are guaranteed to save only 1 checkpoint from training + pattern = 'checkpoint-*' + checkpoint_path = glob.glob(os.path.join(directory, pattern))[0] + + + infer = Inference(checkpoint_path) + infer.prepare_dataloader(df, batch_size=BATCH_SIZE, max_length=128) + prediction_list = infer.generate() + + # add labels too + # thing_actual_list, property_actual_list = decode_preds(pred_labels) + # Convert the list to a Pandas DataFrame + df_out = pd.DataFrame({ + 'predictions': prediction_list + }) + # df_out['p_thing_correct'] = df_out['p_thing'] == df_out['thing'] + # df_out['p_property_correct'] = df_out['p_property'] == df_out['property'] + df = pd.concat([df, df_out], axis=1) + + # we can save the t5 generation output here + df.to_csv(f"exports/result.csv", index=False) + + # here we want to evaluate mapping accuracy within the valid in mdm data only + condition_correct = df['predictions'] == df['entity_name'] + pred_correct_proportion = sum(condition_correct)/len(df) + + # write output to file output.txt + with open("output.txt", "a") as f: + print(f'Accuracy for fold: {pred_correct_proportion}', file=f) + +########################################### +# execute + +# reset file before writing to it +with open("output.txt", "w") as f: + print('', file=f) + +infer() diff --git a/train/seq2seq_t5_simple/train.py b/train/seq2seq_t5_simple/train.py new file mode 100644 index 0000000..8edb0fe --- /dev/null +++ b/train/seq2seq_t5_simple/train.py @@ -0,0 +1,190 @@ +# %% + +# 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 torch +from transformers import ( + T5TokenizerFast, + AutoModelForSeq2SeqLM, + DataCollatorForSeq2Seq, + Seq2SeqTrainer, + EarlyStoppingCallback, + Seq2SeqTrainingArguments +) +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') + +# %% +# outputs a list of dictionaries +def process_df_to_dict(df): + output_list = [] + for _, row in df.iterrows(): + desc = row['mention'] + label = row['entity_name'] + element = { + 'input' : desc, + 'output': label + } + output_list.append(element) + + return output_list + + +def create_dataset(): + # train + data_path = f"../../data_import/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 + + +# function to perform training for a given fold +def train(): + save_path = f'checkpoint' + split_datasets = create_dataset() + + # prepare tokenizer + + model_checkpoint = "t5-small" + tokenizer = T5TokenizerFast.from_pretrained(model_checkpoint, return_tensors="pt", clean_up_tokenization_spaces=True) + # Define additional special tokens + # additional_special_tokens = ["", "", "", "", "", "", "", "", ""] + # Add the additional special tokens to the tokenizer + # tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens}) + + max_length = 120 + + # given a dataset entry, run it through the tokenizer + def preprocess_function(example): + input = example['input'] + target = example['output'] + # text_target sets the corresponding label to inputs + # there is no need to create a separate 'labels' + model_inputs = tokenizer( + input, + text_target=target, + max_length=max_length, + truncation=True, + padding=True + ) + 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=split_datasets["train"].column_names, + ) + + # https://github.com/huggingface/transformers/pull/28414 + # model_checkpoint = "google/t5-efficient-tiny" + # device_map set to auto to force it to load contiguous weights + # model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint, device_map='auto') + + model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint) + # important! after extending tokens vocab + model.resize_token_embeddings(len(tokenizer)) + + data_collator = DataCollatorForSeq2Seq(tokenizer, model=model) + metric = evaluate.load("sacrebleu") + + + def compute_metrics(eval_preds): + preds, labels = eval_preds + # In case the model returns more than the prediction logits + if isinstance(preds, tuple): + preds = preds[0] + + decoded_preds = tokenizer.batch_decode(preds, + skip_special_tokens=False) + + # Replace -100s in the labels as we can't decode them + labels = np.where(labels != -100, labels, tokenizer.pad_token_id) + decoded_labels = tokenizer.batch_decode(labels, + skip_special_tokens=False) + + # Remove tokens from decoded predictions and labels + decoded_preds = [pred.replace(tokenizer.pad_token, '').strip() for pred in decoded_preds] + decoded_labels = [[label.replace(tokenizer.pad_token, '').strip()] for label in decoded_labels] + + # Some simple post-processing + # decoded_preds = [pred.strip() for pred in decoded_preds] + # decoded_labels = [[label.strip()] for label in decoded_labels] + # print(decoded_preds, decoded_labels) + + result = metric.compute(predictions=decoded_preds, references=decoded_labels) + return {"bleu": result["score"]} + + + # Generation Config + # from transformers import GenerationConfig + gen_config = model.generation_config + gen_config.max_length = 64 + + # compile + # model = torch.compile(model, backend="inductor", dynamic=True) + + + # Trainer + + args = Seq2SeqTrainingArguments( + 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=64, + 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=80, + predict_with_generate=True, + bf16=True, + push_to_hub=False, + generation_config=gen_config, + remove_unused_columns=False, + ) + + + trainer = Seq2SeqTrainer( + model, + args, + train_dataset=tokenized_datasets["train"], + data_collator=data_collator, + tokenizer=tokenizer, + 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() +