automatic_evaluation.py

"""
Michal Stromko Automating Evaluation Script Annotated Dataset
for task Semantic Evaluation
"""

import os
import sys
import json
from metrics import recall, mrr, map, ndcg
from filtering_parsing import filtering_annotated, filtering_predicted


def load_jsonl_file(file_path: str):
    """
    Load jsonl file
    :param file_path: path to the file
    :return: json data
    """
    data = []
    with open(file_path, "r", encoding="utf-8") as f:
        for line in f:
            data.append(json.loads(line))
    return data


def load_json_file(file_path: str):
    """
    Load json file
    :param file_path: path to the file
    :return: json data
    """
    with open(file_path, "r", encoding="utf-8") as f:
        data = json.load(f)
    return data


if __name__ == '__main__':
    # get file names
    f_annotated_final = sys.argv[1]
    f_predicted_final = sys.argv[2]

    # load files
    print("Loading datasets")
    final_annotated = load_jsonl_file(f_annotated_final)
    predicted = load_json_file(f_predicted_final)
    print("Loaded datasets")

    # filtering parsing annotated dataset
    filtered_annotated_dataset = filtering_annotated.first_filtering_annotated(final_annotated)

    filtering_annotated.counting_count_results(filtered_annotated_dataset)
    actual_results = filtering_annotated.second_filtering_annotated(filtered_annotated_dataset)

    # filtering and parsing predicted dataset
    predicted_results = filtering_predicted.prediction_filtering_dataset(predicted)

    # prepare dataset for ndcg evaluation
    correct_answers_ndcg = ndcg.filtering_annotated_dataset_for_eval_ndcg(filtered_annotated_dataset)
    predicted_ndcg = ndcg.filter_and_parsing_assignment_article_id_to_category(predicted_results, correct_answers_ndcg)

    print("\n")

    count_results = [5, 10, 15]

    # Evaluation Dataset
    print("Start evaluation")

    # count recall
    for i in range(0, len(count_results)):
        print(f"Count Recall Metric for {count_results[i]} results:")
        recall_value = recall.count_recall(actual_results, predicted_results, count_results[i])
        print(f"\nMean value Recall for every count questions: \n{recall_value}")
        print("\n")
    print("---------------------------------------------------------")

    # count mrr
    for i in range(0, len(count_results)):
        print(f"Count MRR Metric for {count_results[i]} results:")
        mrr.count_mrr(actual_results, predicted_results, count_results[i])
        print("\n ")
        print("---------------------------------------------------------")

    # count map
    for i in range(0, len(count_results)):
        print(f"Count MAP Metric for {count_results[i]} results:")
        results, total_mAP = map.count_map(actual_results, predicted_results, count_results[i])
        print("Results for individual questions:")
        print(results)

        print("\n")
        print("---------------------------------------------------------")

    # count ndcg
    for i in range(0, len(count_results)):
        print(f"Count NDCG Metric for {count_results[i]}:")
        ndcg_res = ndcg.count_ndcg(predicted_ndcg, count_results[i])
        print("\n")
    print("---------------------------------------------------------")

    print("Finish evaluation")