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import ast
import xml.etree.ElementTree as ET
from datasets import Dataset
from opencompass.openicl.icl_evaluator import BaseEvaluator
from opencompass.registry import ICL_EVALUATORS, LOAD_DATASET
from ..base import BaseDataset
from .prompts import gcpPrompts
def q2text(q, p=gcpPrompts): # q is the data for the HP-hard question, p is the prompt
# print(q)
chromatic_number = q.split('\n')[0][-1] # last character of the first line
number_of_vertices = q.split('\n')[1].split(' ')[2] # third word of the second line
prompt_text = p['Intro'] + '\n' \
+ p['Initial_question'].format(max_vertices=number_of_vertices,max_colors=chromatic_number) + '\n' \
+ p['Output_content'] + '\n' \
+ p['Output_format'] + \
'\n The graph is below: \n'
for line in q.split('\n')[2:]:
vertex_list = line.split(' ')
this_line = 'Vertex {} is connected to vertex {}.'.format(vertex_list[1], vertex_list[2])
prompt_text += this_line + '\n'
return prompt_text
@LOAD_DATASET.register_module(force=True)
class hard_GCP_Dataset(BaseDataset):
@staticmethod
def load(path: str):
raw_data = []
data_path = path
all_data = []
for file_num in range(10):
with open(data_path + 'synthesized_data_GCP_{}.txt'.format(file_num)) as f:
data = f.read()
sample = data.split('\n\n')[:-1]
all_data += zip([file_num + 1] * len(sample), sample)
for (level, q) in all_data:
prompt = q2text(q)
raw_data.append({
'prompt': prompt,
'q': str(level) + '####\n' + q,
'level': level
})
dataset = Dataset.from_list(raw_data)
return dataset
@ICL_EVALUATORS.register_module(force=True)
class hard_GCP_Evaluator(BaseEvaluator):
def score(self, predictions, references):
assert len(predictions) == len(references)
result = {'pass': 0, 'fail': 0}
details = {}
for index, (q, output) in enumerate(zip(references, predictions)):
output_dict = {}
level = int(q.split('####\n')[0])
q = q.split('####\n')[-1]
output_dict['output'] = output
try:
output_dict['correctness'] = self.gcpCheck(q, output)
except Exception as e:
print(f'Check failed: {e}')
output_dict['correctness'] = False
output_dict['level'] = level
if output_dict['correctness']:
r = 'pass'
else:
r = 'fail'
result[r] += level
details[str(index)] = {'q': q, 'output': output, 'result': r}
result['score'] = result['pass'] / (result['pass'] + result['fail']) * 100
result['details'] = details
final_result = {'Weighted Accuracy': result['score']}
return final_result
def parse_xml_to_dict(self, xml_string):
try:
# Parse the XML string
root = ET.fromstring(xml_string)
# Find the 'final_answer' tag
final_answer_element = root.find('final_answer')
# Find the 'reasoning' tag
reasoning_element = root.find('reasoning')
except Exception:
try:
assert '<final_answer>' in xml_string
assert '</final_answer>' in xml_string
assert '<reasoning>' in xml_string
assert '</reasoning>' in xml_string
final_answer_start = xml_string.index('<final_answer>') + len('<final_answer>')
final_answer_end = xml_string.index('</final_answer>')
reasoning_start = xml_string.index('<reasoning>') + len('<reasoning>')
reasoning_end = xml_string.index('</reasoning>')
final_answer_element = xml_string[final_answer_start:final_answer_end]
reasoning_element = xml_string[reasoning_start:reasoning_end]
except Exception:
final_answer_element = ''
reasoning_element = ''
return final_answer_element, reasoning_element
def gcpCheck(self, dimacs_str, answer_str):
num_vertices, adjacency_list = self.read_dimacs_format(dimacs_str)
answer_colors = self.parse_answer(answer_str)
# print(adjacency_list)
# print(answer_colors)
# Check if all colors in the answer are valid
for vertex, neighbors in adjacency_list.items():
for neighbor in neighbors:
try:
if answer_colors[vertex] == answer_colors[neighbor]:
print(f'Invalid coloring: Vertex {vertex} and {neighbor} have the same color.')
return False
except:
print(f'Invalid input.') # dealing with hullucination
return False
print(f'Valid coloring found with {len(set(answer_colors.values()))} colors: {answer_colors}')
return True
def read_dimacs_format(self, dimacs_str):
lines = dimacs_str.strip().split('\n')
# Read the number of vertices and edges
p_line = next(line for line in lines if line.startswith('p'))
_, _, num_vertices, num_edges = p_line.split()
num_vertices, num_edges = int(num_vertices), int(num_edges)
# Create adjacency list
adjacency_list = {i: set() for i in range(1, num_vertices + 1)}
# Read the edges and ignore those that reference non-existing vertices
for line in lines:
if line.startswith('e'):
_, vertex1, vertex2 = line.split()
vertex1, vertex2 = int(vertex1), int(vertex2)
if vertex1 in adjacency_list and vertex2 in adjacency_list:
adjacency_list[vertex1].add(vertex2)
adjacency_list[vertex2].add(vertex1)
return num_vertices, adjacency_list
def parse_answer(self, llm_string):
# # Convert the answer string to a dictionary
# answer_dict = {}
# # Remove the braces and split the string by commas
# entries = answer_str.strip("}{").split(', ')
# for entry in entries:
# vertex, color = entry.split(':')
# answer_dict[int(vertex)] = color
# return answer_dict
all_answers, reasoning_element = self.parse_xml_to_dict(llm_string)
if all_answers == '':
return {}
elif all_answers is None:
return {}
else:
if isinstance(all_answers, str):
try:
all_answers = ast.literal_eval(all_answers)
except Exception:
try:
all_answers = ast.literal_eval('{' + all_answers + '}')
except Exception:
return {}
else:
all_answers = ast.literal_eval(all_answers.text)
# answer_dict = {}
# for pair in all_answers:
# vertex, color = pair.split(":")
# answer_dict[int(vertex)] = color
# convert key type to int
all_answers = {int(k): v for k, v in all_answers.items()}
return all_answers # answer_dict
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