import numpy as np
import matplotlib.pyplot as plt
c = 1
pos = c * 50
neg = 50
def entropy(P, N):
if P == 0 or N == 0:
return 0
p = P / (P + N)
n = N / (P + N)
return -p * np.log2(p) - n * np.log2(n)
def gini(P, N):
p = P / (P + N)
n = N / (P + N)
return 4 * p * n
def dkm(P, N):
p = P / (P + N)
n = N / (P + N)
return 2 * np.sqrt(p * n)
def minacc(P, N):
p = P / (P + N)
n = N / (P + N)
return min(p, n)
def metric(tp, fp, m):
if tp + fp == 0:
return 0
Pos = pos
Neg = neg
N = Pos + Neg
TP = tp
FP = fp
FN = Pos - TP
TN = Neg - FP
if m == 'accuracy': return (TP + TN) / N
if m == 'wracc': return TP / N - (TP + FP) * (TP + FN) / N ** 2
if m == 'confirmation':
num = ((TP + FP) * (FP + TN)) / N ** 2 - FP / N
den = np.sqrt((TP + FP) * (FP + TN)) / N - ((TP + FP) * (FP + TN)) / N ** 2
return num / den if den != 0 else 0
if m == 'generality': return (TP + FP) / N
if m == 'precision': return TP / (TP + FP)
if m == 'laplace-precision': return (TP + 10) / (TP + FP + 20)
if m == 'f-measure': return 2 * TP / (2 * TP + FP + FN)
if m == 'g-measure': return TP / (FP + Pos)
if m == 'precision*recall': return TP ** 2 / ((TP + FP) * (TP + FN))
if m == 'avg-precision-recall': return TP / (2 * (TP + FP)) + TP / (2 * (TP + FN))
if m == 'aucsplit': return (TP * Neg + Pos * TN) / (2 * Pos * Neg)
if m == 'balanced-aucsplit': return TP / Pos - FP / Neg
if m == 'chi2': return (TP * TN - FP * FN) ** 2 / ((TP + FP) * (TP + FN) * (FP + TN) * (FN + TN))
if m == 'info-gain': return entropy(Pos, Neg) - (TP + FP) / N * entropy(TP, FP) - (FN + TN) / N * entropy(FN, TN)
if m == 'gini': return gini(Pos, Neg) - (TP + FP) / N * gini(TP, FP) - (FN + TN) / N * gini(FN, TN)
if m == 'dkm': return dkm(Pos, Neg) - (TP + FP) / N * dkm(TP, FP) - (FN + TN) / N * dkm(FN, TN)
if m == 'entropy': return entropy(TP, FP) / 2
if m == 'giniimp': return gini(TP, FP)
if m == 'dkmimp': return dkm(TP, FP)
if m == 'minacc': return minacc(TP, FP)
def rocgrid():
fig, ax = plt.subplots(figsize=(6, 6))
ax.set_xlim(0, neg)
ax.set_ylim(0, pos)
ax.set_xticks([])
ax.set_yticks([])
ax.set_xlabel("Negatives")
ax.set_ylabel("Positives")
ax.grid(which='both', linestyle='--', linewidth=0.5, color='gray', alpha=0.5)
return ax
def contour1(ax, m, col, lty, tp, fp):
v = metric(tp, fp, m)
r = min(2 - 2 * v, 1)
color = (r, v, 0)
ax.plot(fp, tp, marker='o', color=color, markersize=8)
if tp == 0 or fp == 0:
ax.plot([0, fp], [0, tp], linestyle=lty, color=color, linewidth=2)
return
x = np.arange(0, fp + 1)
y = np.arange(0, tp + 1)
z = np.zeros((len(y), len(x)))
for i in range(len(x)):
for j in range(len(y)):
z[j, i] = metric(y[j], x[i], m)
cs = ax.contour(x, y, z, levels=[v], colors=[color], linestyles=[lty])
save = False
colour1 = "red" if not save else "black"
colour2 = "blue" if not save else "black"
ax = rocgrid()
d = 1
method = 'precision'
colour = 'black'
p = 20
n = 0
ax.arrow(50 - d, 50 - d, n + d - (50 - d), p + d - (50 - d), head_width=0.8, head_length=1.5, fc='violet', ec='violet', linewidth=2)
contour1(ax, method, 'green', 'solid', p, n)
contour1(ax, method, colour, 'dotted', 10, 30)
contour1(ax, method, colour, 'dotted', 20, 20)
contour1(ax, method, colour, 'dotted', 0, 40)
contour1(ax, method, colour, 'dotted', 50, 10)
contour1(ax, method, colour, 'dotted', 50, 30)
contour1(ax, method, colour, 'dotted', 0, 20)
contour1(ax, method, colour, 'dotted', 30, 40)
contour1(ax, method, colour, 'dotted', 20, 10)
plt.title('ROC Grid - Métrica: ' + method)
plt.tight_layout()
plt.show()
