File size: 4,000 Bytes
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from typing import Any
def parse_response(response: str):
"""Parse the response.
Returns a parsed plan in a list of (city, stay_days) tuples.
Args:
response: Raw response from the model.
Returns:
Structured plan after parsing.
"""
pattern_visit = r'\d+-\d+'
pattern_flight = r'.*Day (\d+).*from (\w+) to (\w+)'
pattern_days = r'European cities for (\d+) days'
days, flights, flight_days = [], [], []
total_days = None
for piece in response.split('\n'):
days_match = re.findall(pattern_days, piece)
if days_match:
total_days = int(days_match[0])
visit_match = re.findall(pattern_visit, piece)
if visit_match:
days.append(visit_match[0])
end_day = int(visit_match[0].split('-')[1])
# Reach the end of the plan, stop to avoid parsing alternative plans.
if end_day == total_days:
break
flight_match = re.findall(pattern_flight, piece)
if flight_match:
flights.append(flight_match[0])
visit_cities, parsed_plan = [], []
for flight_day, begin_city, end_city in flights:
flight_days.append(int(flight_day))
if not visit_cities:
visit_cities.append(begin_city)
visit_cities.append(end_city)
else:
visit_cities.append(end_city)
if not days or not flights or not visit_cities:
return []
last_day = int(days[-1].split('-')[1])
flight_days = [1] + flight_days + [last_day]
for i, visit_city in enumerate(visit_cities):
city_stay = flight_days[i + 1] - flight_days[i] + 1
parsed_plan.append((visit_city, city_stay))
return parsed_plan
def compute_example_score(cities: str, durations: str, parsed_plan: list[Any]):
"""Compute the exact-match accuracy.
Compute the example-level exact_match score (0/1) given the parsed plan
and the ground truth in the format of durations and cities.
Args:
cities: The cities in the plan in the format of "city1**city2**city3".
durations: The durations of the stay in each city in the format of
"1**2**3".
parsed_plan: The parsed plan from the response.
Returns:
Exact-match accuracy of 0 (mismatched) or 1 (matched).
"""
stays = [x for x in cities.split('**') if x]
days = [int(x) for x in durations.split('**') if x]
num_stays = min(len(stays), len(parsed_plan))
num_match = 0
for i in range(num_stays):
if stays[i] == parsed_plan[i][0] and days[i] == parsed_plan[i][1]:
num_match += 1
else:
break
hard_score = 0.0 if num_match / len(stays) < 1.0 else 1.0
return hard_score
def compute_score(
cities: list[str], durations: list[str], responses: list[str]
):
"""Compute the sample-level exact-match accuracy.
Args:
cities: List of cities in the plan in the format of "city1**city2**city3".
durations: List of durations of the stay in each city in the format of
"1**2**3".
responses: The raw responses from the model.
Returns:
Exact-match score at the sample level.
"""
parsed_plans = [parse_response(response) for response in responses]
hard_scores = [
compute_example_score(city, duration, parsed_plan)
for city, duration, parsed_plan in zip(cities, durations, parsed_plans)
]
print([i for i, j in enumerate(hard_scores) if j == 1.0])
hard_acc = sum(hard_scores) / len(hard_scores)
return hard_acc
def trip_metric(data, preds):
cities, durations, responses = [], [], []
sample_count = 0
for item, pred in zip(data.values(), preds):
cities.append(item['cities'])
durations.append(item['durations'])
responses.append(pred)
sample_count += 1
hard_acc = compute_score(cities, durations, responses)
print(f'EM Accuracy of {sample_count} samples: {hard_acc}')
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