import pandas as pd


# recall@k function
def recall(actual, predicted, k):
    """
    Calculate recall for set results
    :param actual: actual results on question from annotated datatest
    :param predicted: predicted results on question from searched engine
    :param k: max results in set
    :return: recall value
    """

    # corrects results
    act_set = actual
    # search results (count edit k)
    pred_set = predicted[:k]

    # count and find same numbers
    common_elements = 0
    for item in act_set:
        if item in pred_set:
            common_elements += 1

    result = round(common_elements / float(len(act_set)), 2)

    return result


def count_recall(actual_list, predicted_list, count_results):
    """
    Calculate recall for search engine
    :param actual_list:  actual results from annotated datatest
    :param predicted_list: predicted results from searched engine
    :return: average recall value
    """

    # set values for parameter k
    k_start = 3
    k_end = count_results + 1

    # Initialization empty DataFrame
    df_recall = pd.DataFrame(index=range(3, count_results + 1))

    # For cycle go to every predicted questions
    for i, predicted_val in enumerate(predicted_list, 1):

        recalls = []

        #  Count recall for question
        for k in range(k_start, k_end):
            recall_val = recall(actual_list[i - 1], predicted_val, k)
            recalls.append(recall_val)

        df_temp = pd.DataFrame({f"Question {i}": recalls}, index=range(3, count_results + 1))
        df_recall = pd.concat([df_recall, df_temp], axis=1)

        df_recall[f"Question {i}"] = recalls

    # Calculate the average recall value for each number of questions
    average_recall = df_recall.mean(axis=1)

    # Print list the recall values for each question separately
    # print("Recall values for every question:")
    # print(df_recall)

    # set results two dots numbers
    pd.set_option('display.float_format', '{:.2f}'.format)

    # Print all mean recall
    # print(f"\nAll Mean Recall for {count_results} results :{round(average_recall.mean(), 2)}")

    print(f"Recall Metric for {count_results} is: {round(average_recall.iloc[-1], 2)}")
    return average_recall