247 lines
6.8 KiB
Python
247 lines
6.8 KiB
Python
# %%
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# from datasets import load_from_disk
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import os
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import glob
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os.environ['NCCL_P2P_DISABLE'] = '1'
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os.environ['NCCL_IB_DISABLE'] = '1'
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os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
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os.environ["CUDA_VISIBLE_DEVICES"] = "0,1,2,3"
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import torch
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from torch.utils.data import DataLoader
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from transformers import (
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AutoTokenizer,
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AutoModelForSequenceClassification,
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DataCollatorWithPadding,
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)
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import evaluate
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import numpy as np
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import pandas as pd
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# import matplotlib.pyplot as plt
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from datasets import Dataset, DatasetDict
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from tqdm import tqdm
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torch.set_float32_matmul_precision('high')
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BATCH_SIZE = 256
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# %%
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data_path = '../../../data_import/train.csv'
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train_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 = train_df['entity_id'].to_list()
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target_id_list = sorted(list(set(entity_ids)))
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# %%
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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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# %%
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# outputs a list of dictionaries
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# processes dataframe into lists of dictionaries
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# each element maps input to output
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# input: tag_description
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# output: class label
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def process_df_to_dict(df):
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output_list = []
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for _, row in df.iterrows():
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desc = row['mention']
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index = row['entity_id']
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element = {
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'text' : f"{desc}",
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'label': label2id[index], # ensure labels starts from 0
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}
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output_list.append(element)
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return output_list
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def create_dataset():
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# train
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data_path = '../../../data_import/test.csv'
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test_df = pd.read_csv(data_path, skipinitialspace=True)
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# combined_data = DatasetDict({
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# 'train': Dataset.from_list(process_df_to_dict(train_df)),
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# })
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return Dataset.from_list(process_df_to_dict(test_df))
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# %%
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def test():
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test_dataset = create_dataset()
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# prepare tokenizer
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checkpoint_directory = f'../checkpoint'
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# Use glob to find matching paths
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# path is usually checkpoint_fold_1/checkpoint-<step number>
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# we are guaranteed to save only 1 checkpoint from training
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pattern = 'checkpoint-*'
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model_checkpoint = glob.glob(os.path.join(checkpoint_directory, pattern))[0]
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tokenizer = AutoTokenizer.from_pretrained(model_checkpoint, return_tensors="pt", clean_up_tokenization_spaces=True)
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# Define additional special tokens
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# additional_special_tokens = ["<THING_START>", "<THING_END>", "<PROPERTY_START>", "<PROPERTY_END>", "<NAME>", "<DESC>", "<SIG>", "<UNIT>", "<DATA_TYPE>"]
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# Add the additional special tokens to the tokenizer
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# tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens})
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# %%
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# compute max token length
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max_length = 0
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for sample in test_dataset['text']:
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# Tokenize the sample and get the length
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input_ids = tokenizer(sample, truncation=False, add_special_tokens=True)["input_ids"]
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length = len(input_ids)
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# Update max_length if this sample is longer
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if length > max_length:
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max_length = length
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print(max_length)
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# %%
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max_length = 128
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# given a dataset entry, run it through the tokenizer
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def preprocess_function(example):
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input = example['text']
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# text_target sets the corresponding label to inputs
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# there is no need to create a separate 'labels'
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model_inputs = tokenizer(
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input,
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max_length=max_length,
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# truncation=True,
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padding='max_length'
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)
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return model_inputs
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# map maps function to each "row" in the dataset
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# aka the data in the immediate nesting
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datasets = test_dataset.map(
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preprocess_function,
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batched=True,
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num_proc=8,
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remove_columns="text",
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)
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datasets.set_format(type='torch', columns=['input_ids', 'attention_mask', 'label'])
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# %% temp
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# tokenized_datasets['train'].rename_columns()
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# %%
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# create data collator
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# data_collator = DataCollatorWithPadding(tokenizer=tokenizer, padding="max_length")
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# %%
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# compute metrics
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# metric = evaluate.load("accuracy")
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#
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#
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# def compute_metrics(eval_preds):
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# preds, labels = eval_preds
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# preds = np.argmax(preds, axis=1)
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# return metric.compute(predictions=preds, references=labels)
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model = AutoModelForSequenceClassification.from_pretrained(
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model_checkpoint,
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num_labels=len(target_id_list),
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id2label=id2label,
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label2id=label2id)
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# important! after extending tokens vocab
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model.resize_token_embeddings(len(tokenizer))
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model = model.eval()
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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model.to(device)
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pred_labels = []
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actual_labels = []
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dataloader = DataLoader(datasets, batch_size=BATCH_SIZE, shuffle=False)
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for batch in tqdm(dataloader):
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# Inference in batches
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input_ids = batch['input_ids']
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attention_mask = batch['attention_mask']
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# save labels too
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actual_labels.extend(batch['label'])
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# Move to GPU if available
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input_ids = input_ids.to(device)
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attention_mask = attention_mask.to(device)
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# Perform inference
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with torch.no_grad():
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logits = model(
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input_ids,
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attention_mask).logits
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predicted_class_ids = logits.argmax(dim=1).to("cpu")
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pred_labels.extend(predicted_class_ids)
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pred_labels = [tensor.item() for tensor in pred_labels]
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# %%
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from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score, confusion_matrix
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y_true = actual_labels
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y_pred = pred_labels
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# Compute metrics
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accuracy = accuracy_score(y_true, y_pred)
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average_parameter = 'weighted'
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zero_division_parameter = 0
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f1 = f1_score(y_true, y_pred, average=average_parameter, zero_division=zero_division_parameter)
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precision = precision_score(y_true, y_pred, average=average_parameter, zero_division=zero_division_parameter)
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recall = recall_score(y_true, y_pred, average=average_parameter, zero_division=zero_division_parameter)
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with open("output.txt", "a") as f:
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print('*' * 80, file=f)
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# Print the results
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print(f'Accuracy: {accuracy:.5f}', file=f)
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print(f'F1 Score: {f1:.5f}', file=f)
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print(f'Precision: {precision:.5f}', file=f)
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print(f'Recall: {recall:.5f}', file=f)
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# export result
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label_list = [id2label[id] for id in pred_labels]
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df = pd.DataFrame({
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'class_prediction': pd.Series(label_list)
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})
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# we can save the t5 generation output here
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df.to_csv(f"exports/result.csv", index=False)
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# %%
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# reset file before writing to it
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with open("output.txt", "w") as f:
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print('', file=f)
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test()
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