Datasets:
Tasks:
Text Retrieval
Modalities:
Text
Formats:
json
Sub-tasks:
document-retrieval
Languages:
Slovak
Size:
10K - 100K
Tags:
text-retrieval
DOI:
License:
File size: 2,088 Bytes
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import pandas as pd
def count_map(actual_list, predicted_list, count_results):
"""
Funtion for count MAP metric
:param actual_list: actual results from annotated datatest
:param predicted_list: predicted results from searched engine
:return: map value in question MAP value
"""
# Initialization empty list for results
questions = []
k_stops = []
frequencies = []
mAPs = []
# For every question
for q, (actual, predicted) in enumerate(zip(actual_list, predicted_list), 1):
k_stop = None # initialization value who stop cycle for this question
ap_sum = 0 # initialization sum AP on question
count = 0 # count value k_stop
# Loop throw values k
for x, pred_value in enumerate(predicted[:count_results], 1):
act_set = set(actual)
pred_set = set(predicted[:x])
precision_at_k = len(act_set & pred_set) / x
if pred_value in actual:
rel_k = 1
else:
rel_k = 0
ap_sum += precision_at_k * rel_k
# If we have found all the relevant values and we don't have k_stop yet, we stop
if len(act_set) == ap_sum and k_stop is None:
k_stop = x
count += 1
# If we haven't reached k_stop by 15, we set it to 15
if k_stop is None:
k_stop = count_results
# Count mAP for question
ap_q = ap_sum / len(actual)
# Save results to list
questions.append(q)
k_stops.append(k_stop)
frequencies.append(count)
mAPs.append(round(ap_q, 2))
# Create DataFrame from results
df_results = pd.DataFrame({'Question': questions, 'k_stop': k_stops, 'Frequency': frequencies, 'mAP': mAPs})
# Count total mAP
total_mAP = round(df_results['mAP'].mean(), 2)
print(f"MAP Metric for {count_results} is: {total_mAP}")
k_stop_counts = df_results['k_stop'].value_counts()
print(f"Count of K_stop \n{k_stop_counts}")
return df_results, round(total_mAP,2)
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