F-beta score variation animation

fbeta_anim.py

#!/usr/bin/env python3

import imageio
import math
import matplotlib.pyplot as plt
import numpy as np
from pathlib import Path

def fscore(p, r, beta=1):
    if p == r == 0:
        return 0
    return (1+beta**2) *  (p * r) / ( (beta**2 * p) + r)

def populate_matrix(operator):
    steps = [x/100 for x in range(0, 101)]
    matrix = []
    for step_p in steps:
        row = []
        for step_r in steps:
            row.append(operator(step_p, step_r))
        matrix.append(list(row))
    return np.array(matrix)

def show_array(data, title='Title', labelx='recall', labely='precision', saveas=False, show=True):
    cmap = 'seismic'
    plt.figure(figsize=(9, 9))
    plt.imshow(data, cmap=cmap, interpolation='nearest', origin='lower')
    plt.title(title)
    plt.xlabel(labelx)
    plt.ylabel(labely)
    plt.colorbar(plt.pcolor(data, cmap=cmap))
    if saveas:
        plt.savefig(saveas)
    if show:
        plt.show()
    plt.close()

def fbeta_global(p,r):
    global global_beta
    return fscore(p,r,global_beta)

seq = 0
for i in range(-40, 41):
    global_beta = math.pow(2, i/10)

    f_ = populate_matrix(fbeta_global)
    show_array(f_, title=f'F-score, beta={global_beta:.3f}', saveas=f'fbeta_{seq:04}.png', show=False)
    seq += 1

image_path = Path('.')
images = list(image_path.glob('fbeta_????.png'))
images.sort()
image_list = []
for file_name in images:
    image_list.append(imageio.imread(file_name))

# get the reverse pass in there too
loop_back = image_list[1:-1]
loop_back.reverse()

looped_images = image_list + loop_back

imageio.mimwrite('fbeta.gif', looped_images, fps=12)