Datasets:
Tasks:
Text Retrieval
Modalities:
Text
Formats:
json
Sub-tasks:
document-retrieval
Languages:
Slovak
Size:
10K - 100K
Tags:
text-retrieval
DOI:
License:
File size: 3,621 Bytes
a8a2b54 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 |
from math import log2
import pandas as pd
def filtering_annotated_dataset_for_eval_ndcg(filtered_annotated_dataset):
"""
Get from filtering annotated dataset only article id and category
next step save in dict and all save dict save in list
:param filtered_annotated_dataset: annotated dataset in json format
:return: list of dict for prepare datas for evaluation ndcg
"""
correct_answers = []
for i in range(0, len(filtered_annotated_dataset)):
correct_answer = {}
line = filtered_annotated_dataset[i]
for sublist in line["results"]:
correct_answer[sublist['answer_id']] = sublist['category']
correct_answers.append(correct_answer)
return correct_answers
def filter_and_parsing_assignment_article_id_to_category(predicted_list, correct_answers):
"""
Funtion to filter and parsing search_result id to category for evaluation ndcg
:param predicted_list: search results for evaluation ndcg
:param correct_answers: correct answers for evaluation ndcg from annotated dataset
:return: list with correct order with number of relevant category
"""
predictions = []
# for cycle in seared dataset
for i in range(0, len(predicted_list)):
prediction = []
# parsing data to tmp
line = predicted_list[i]
correct_answer = correct_answers[i]
# get on id and find same id in dict from annotated dataset
for j in range(0, len(line)):
# if article id in correct answers
if line[j] in correct_answer:
"""
acd correct order
if category 1 in ndcg is 3
if category 2 in ndcg is 2
if category 3 in ndcg is 1
else category in ndcg is 0
"""
if correct_answer[line[j]] == 1:
prediction.append(3)
elif correct_answer[line[j]] == 2:
prediction.append(2)
else:
# correct_answer[line[j]] == 3:
prediction.append(1)
else:
prediction.append(0)
# save get values
predictions.append(prediction)
return predictions
def count_ndcg(relevance, count_results):
"""
Function count ndcg
:param relevance: search results with value relevance
:return: total ndcg value
"""
df = pd.DataFrame()
df['Count Results'] = range(1, count_results + 1)
df['SUM NDCG'] = [0.00] * count_results
K = count_results
for line in relevance:
# sort items in 'relevance' from most relevant to less relevant
ideal_relevance = sorted(line, reverse=True)
dcg = 0
idcg = 0
ndcg_list = []
for k in range(1, K + 1):
# calculate rel_k values
rel_k = line[k - 1]
ideal_rel_k = ideal_relevance[k - 1]
# calculate dcg and idcg
dcg += rel_k / log2(1 + k)
idcg += ideal_rel_k / log2(1 + k)
if dcg == 0.00 and idcg == 0.00:
ndcg = 0
else:
# calculate ndcg
ndcg = dcg / idcg
df.at[k - 1, 'SUM NDCG'] += ndcg
# Create new column "NDCG" in dataFrame df
df['NDCG'] = round(df['SUM NDCG'] / len(relevance), 2)
print_ndcg = round(df.at[count_results - 1, 'NDCG'], 2)
print(f"NDCG Metric for {count_results} is: {print_ndcg} \n")
print(df)
sum_ndcg = df['NDCG'].sum()
ndcg_mean = sum_ndcg / K
return round(ndcg_mean, 2)
|