metrics/ndcg.py

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)