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import json
import logging
def compute_metrics(attributes, total_sentences):
# Extract relevant information from attributes
all_relevant_sentence_keys = attributes.get("all_relevant_sentence_keys", [])
all_utilized_sentence_keys = attributes.get("all_utilized_sentence_keys", [])
sentence_support_information = attributes.get("sentence_support_information", [])
# Compute Context Relevance
context_relevance = len(all_relevant_sentence_keys) / total_sentences if total_sentences else 0
# Compute Context Utilization
context_utilization = len(all_utilized_sentence_keys) / total_sentences if total_sentences else 0
# Compute Completeness score
Ri = set(all_relevant_sentence_keys)
Ui = set(all_utilized_sentence_keys)
completeness_score = len(Ri & Ui) / len(Ri) if len(Ri) else 0
# Compute Adherence
adherence = all(info.get("fully_supported", False) for info in sentence_support_information)
#adherence = 1 if all(info.get("fully_supported", False) for info in sentence_support_information) else 0
return {
"Context Relevance": context_relevance,
"Context Utilization": context_utilization,
"Completeness Score": completeness_score,
"Adherence": adherence
}
def get_metrics(attributes, total_sentences):
if attributes.content:
#print(attributes)
result_content = attributes.content # Access the content attribute
# Extract the JSON part from the result_content
json_start = result_content.find("{")
json_end = result_content.rfind("}") + 1
json_str = result_content[json_start:json_end]
try:
result_json = json.loads(json_str)
# Compute metrics using the extracted attributes
metrics = compute_metrics(result_json, total_sentences)
logging.info(metrics)
return metrics
except json.JSONDecodeError as e:
logging.error(f"JSONDecodeError: {e}") |