# %% # 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 import torch import torch.nn as nn from transformers import ( AutoTokenizer, AutoModelForSequenceClassification, AutoModel, 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 # %% # construct the target id list # data_path = '../../../esAppMod_data_import/train.csv' data_path = '../esAppMod_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.lower() # Substitute digits with '#' # text = re.sub(r'\d+', '#', text) # standardize spacing text = re.sub(r'\s+', ' ', text).strip() 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 = '../esAppMod_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 MODEL_NAME = 'prajjwal1/bert-small' # 'prajjwal1/bert-small' 'bert-base-cased' 'distilbert-base-cased' tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, 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, # 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']) data_collator = DataCollatorWithPadding(tokenizer=tokenizer) tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME) bert_model = AutoModel.from_pretrained(MODEL_NAME) class BertForClassificationAndTriplet(nn.Module): def __init__(self, bert_model, num_classes): super().__init__() self.bert = bert_model self.classifier = nn.Linear(bert_model.config.hidden_size, num_classes) def forward(self, input_ids, attention_mask=None): outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask) cls_embeddings = outputs.last_hidden_state[:, 0, :] # CLS token logits = self.classifier(cls_embeddings) return cls_embeddings, logits model = BertForClassificationAndTriplet(bert_model, num_classes=len(label2id)) state_dict = torch.load('./checkpoint/classification.pt') model.load_state_dict(state_dict) 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, collate_fn=data_collator) 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['labels']) # Move to GPU if available input_ids = input_ids.to(device) attention_mask = attention_mask.to(device) # Perform inference with torch.no_grad(): cls, logits = model( input_ids, attention_mask) 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("results/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"results/classify.csv", index=False) # %% # reset file before writing to it with open("results/output.txt", "w") as f: print('', file=f) test()