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import os
import json
import random
from utils import *
from pipeline import *
current_dir = os.path.dirname(os.path.abspath(__file__))
DATA_DIR = os.path.join(current_dir, "../data/datasets")
OUTPUT_DIR = os.path.join(current_dir, "results")
class BaseDataset:
def __init__(self):
pass
def __getitem__(self, idx):
return None
def __len__(self):
return None
def evaluate(self, idx, answer):
return None
class NERDataset(BaseDataset):
def __init__(self, name=None, task="NER", data_dir = f"{DATA_DIR}/CrossNER", output_dir = f"{OUTPUT_DIR}", train=False):
self.name = name
self.task = task
self.data_dir = data_dir
self.output_dir = output_dir
self.test_file = json.load(open(f"{data_dir}/train.json")) if train else json.load(open(f"{data_dir}/test.json"))
self.schema = str(json.load(open(f"{data_dir}/class.json")))
self.retry = 2
def evaluate(self, llm: BaseEngine, mode="", sample=None, random_sample=False, update_case=False):
# initialize
sample = len(self.test_file) if sample is None else sample
if random_sample:
test_file = random.sample(self.test_file, sample)
else:
test_file = self.test_file[:sample]
total_precision, total_recall, total_f1 = 0, 0, 0
num_items = 0
output_path = f"{self.output_dir}/{self.name}_{self.task}_{mode}_{llm.name}_sample{sample}.jsonl"
print("Results will be saved to: ", output_path)
# predict and evaluate
pipeline = Pipeline(llm=llm)
for item in test_file:
try:
# get prediction
num_items += 1
truth = list(item.items())[1]
truth = {truth[0]: truth[1]}
pred_set = set()
for attempt in range(self.retry):
pred_result, pred_detailed, _, _ = pipeline.get_extract_result(task=self.task, text=item['sentence'], constraint=self.schema, mode=mode, truth=truth, update_case=update_case)
try:
pred_result = pred_result['entity_list']
pred_set = dict_list_to_set(pred_result)
break
except Exception as e:
print(f"Failed to parse result: {pred_result}, retrying... Exception: {e}")
# evaluate
truth_result = item["entity_list"]
truth_set = dict_list_to_set(truth_result)
print(truth_set)
print(pred_set)
precision, recall, f1_score = calculate_metrics(truth_set, pred_set)
total_precision += precision
total_recall += recall
total_f1 += f1_score
pred_detailed["pred"] = pred_result
pred_detailed["truth"] = truth_result
pred_detailed["metrics"] = {"precision": precision, "recall": recall, "f1_score": f1_score}
res_detailed = {"id": num_items}
res_detailed.update(pred_detailed)
with open(output_path, 'a') as file:
file.write(json.dumps(res_detailed) + '\n')
except Exception as e:
print(f"Exception occured: {e}")
print(f"idx: {num_items}")
pass
# calculate overall metrics
if num_items > 0:
avg_precision = total_precision / num_items
avg_recall = total_recall / num_items
avg_f1 = total_f1 / num_items
overall_metrics = {
"total_items": num_items,
"average_precision": avg_precision,
"average_recall": avg_recall,
"average_f1_score": avg_f1
}
with open(output_path, 'a') as file:
file.write(json.dumps(overall_metrics) + '\n\n')
print(f"Overall Metrics:\nTotal Items: {num_items}\nAverage Precision: {avg_precision:.4f}\nAverage Recall: {avg_recall:.4f}\nAverage F1 Score: {avg_f1:.4f}")
else:
print("No items processed.")
class REDataset(BaseDataset):
def __init__(self, name=None, task="RE", data_dir = f"{DATA_DIR}/NYT11", output_dir = f"{OUTPUT_DIR}", train=False):
self.name = name
self.task = task
self.data_dir = data_dir
self.output_dir = output_dir
self.test_file = json.load(open(f"{data_dir}/train.json")) if train else json.load(open(f"{data_dir}/test.json"))
self.schema = str(json.load(open(f"{data_dir}/class.json")))
self.retry = 2
def evaluate(self, llm: BaseEngine, mode="", sample=None, random_sample=False, update_case=False):
# initialize
sample = len(self.test_file) if sample is None else sample
if random_sample:
test_file = random.sample(self.test_file, sample)
else:
test_file = self.test_file[:sample]
total_precision, total_recall, total_f1 = 0, 0, 0
num_items = 0
output_path = f"{self.output_dir}/{self.name}_{self.task}_{mode}_{llm.name}_sample{sample}.jsonl"
print("Results will be saved to: ", output_path)
# predict and evaluate
pipeline = Pipeline(llm=llm)
for item in test_file:
try:
# get prediction
num_items += 1
truth = list(item.items())[1]
truth = {truth[0]: truth[1]}
pred_set = set()
for attempt in range(self.retry):
pred_result, pred_detailed, _, _ = pipeline.get_extract_result(task=self.task, text=item['sentence'], constraint=self.schema, mode=mode, truth=truth, update_case=update_case)
try:
pred_result = pred_result['relation_list']
pred_set = dict_list_to_set(pred_result)
break
except Exception as e:
print(f"Failed to parse result: {pred_result}, retrying... Exception: {e}")
# evaluate
truth_result = item["relation_list"]
truth_set = dict_list_to_set(truth_result)
print(truth_set)
print(pred_set)
precision, recall, f1_score = calculate_metrics(truth_set, pred_set)
total_precision += precision
total_recall += recall
total_f1 += f1_score
pred_detailed["pred"] = pred_result
pred_detailed["truth"] = truth_result
pred_detailed["metrics"] = {"precision": precision, "recall": recall, "f1_score": f1_score}
res_detailed = {"id": num_items}
res_detailed.update(pred_detailed)
with open(output_path, 'a') as file:
file.write(json.dumps(res_detailed) + '\n')
except Exception as e:
print(f"Exception occured: {e}")
print(f"idx: {num_items}")
pass
# calculate overall metrics
if num_items > 0:
avg_precision = total_precision / num_items
avg_recall = total_recall / num_items
avg_f1 = total_f1 / num_items
overall_metrics = {
"total_items": num_items,
"average_precision": avg_precision,
"average_recall": avg_recall,
"average_f1_score": avg_f1
}
with open(output_path, 'a') as file:
file.write(json.dumps(overall_metrics) + '\n\n')
print(f"Overall Metrics:\nTotal Items: {num_items}\nAverage Precision: {avg_precision:.4f}\nAverage Recall: {avg_recall:.4f}\nAverage F1 Score: {avg_f1:.4f}")
else:
print("No items processed.") |