metrics/mrr.py

def count_mrr(actual_list, predicted_list, count_results):
    """
    Function for calculate MRR metric
    :param actual_list:  actual results from annotated datatest
    :param predicted_list: predicted results from searched engine
    :return: None
    """
    # number of queries
    Q = len(actual_list)
    # calculate the reciprocal rank for each query
    cumulative_reciprocal = 0
    # initialize dictionary to store count of relevant results for each query
    relevant_results_count = {i: 0 for i in range(1, count_results + 1)}

    for i in range(Q):
        actual = actual_list[i]
        pred = predicted_list[i][:count_results]
        reciprocal_rank = 0
        for j, result in enumerate(pred, 1):
            if result in actual:
                reciprocal_rank = 1 / j
                relevant_results_count[j] += 1  # increment count of relevant results for this query
                break
        cumulative_reciprocal += reciprocal_rank
        # print(f"query #{i+1} = {1}/{j} = {reciprocal_rank}")

    # calculate MRR
    mrr = cumulative_reciprocal / Q

    # generate result
    print(f"MRR Metric for {count_results} is: {round(mrr, 2)}")

    # generate table of relevant results count
    print("Table of Relevant Results Count:")
    print("Position | Count")

    for position, count in relevant_results_count.items():
        print(f"{position:^9} | {count:^5}")