Evaluation_CMC.py

import pandas as pd
from matplotlib import pyplot as plt


def return_first_one_prob(prob):
    for index in range(len(prob)):
        if prob[index] == 1.0:
            return index


def evaluate(dataset):
    x = pd.read_csv(dataset)
    real = x.REAL

    scores = x.SCORES
    for i in range(len(scores)):
        scores[i] = scores[i][1:-1]
        scores[i] = scores[i].split()
        for j in range(len(scores[i])):
            scores[i][j] = float(scores[i][j])

    for i in range(len(scores)):
        for j in range(len(scores[0])):
            scores[i][j] = (scores[i][j], j)

    # RANK
    ranks = len(scores[0])
    CMS = dict()
    c = 0
    for k in range(ranks):
        CMS[k + 1] = c
        for i in range(len(real)):
            s_scores = sorted(scores[i], reverse=True)
            if s_scores[k][1] == real[i]:
                CMS[k + 1] += 1
                c += 1
        CMS[k + 1] = CMS[k + 1] / len(real)

    prob = [0] + (list(CMS.values()))

    index_first_prob_one = return_first_one_prob(prob)

    plt.figure()
    plt.plot(list(range(ranks + 1)), prob)
    plt.plot(index_first_prob_one, prob[index_first_prob_one], "x",
             label="Probability = 1.0 at rank " + str(index_first_prob_one))
    plt.axvline(index_first_prob_one, color='r', linestyle=":", linewidth='1')
    # plt.xlim([0.0, 5.0])
    plt.ylim([0.0, 1.05])
    plt.xlabel('Ranks')
    plt.ylabel('Prob. of identification')
    plt.title('Cumulative Match Characteristic')
    plt.grid()
    plt.legend(loc="lower right")
    plt.savefig('plot/CMC/CMC.svg', dpi=1200)
    plt.clf()

    plt.figure()
    plt.plot(list(range(1, 6)), prob[1:6])
    plt.plot(list(range(1, 6)), prob[1:6], 'x')

    plt.ylim([0.8530, 0.88])
    plt.xlim([0.8, 5.5])
    plt.xlabel('Ranks')
    plt.ylabel('Prob. of identification')
    plt.title('Cumulative Match Characteristic')
    plt.grid()
    plt.legend(loc="lower right")
    for label, xi, yi in zip(prob[1:6], list(range(1, 6)), prob[1:6]):
        plt.annotate("{:.3f}".format(label), xy=(xi, yi), xytext=(0, -12), textcoords='offset points')

    plt.savefig('plot/CMC/CMC_at_rank_5.svg', dpi=1200)
    plt.clf()

    print("Score at rank 1 (Also Called Recognition Rate): ", CMS[1])
    print("Score at rank 2: ", CMS[2])
    print("Score at rank 3: ", CMS[3])
    print("Score at rank 3: ", CMS[4])
    print("Score at rank 5: ", CMS[5])


if __name__ == '__main__':
    evaluate("datasets/predictions_dataset.csv")