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from sklearn.metrics import accuracy_score, cohen_kappa_score, root_mean_squared_error, f1_score
import numpy as np
def arredondar_notas(notas):
referencia = [0, 40, 80, 120, 160, 200]
novas_notas = []
for n in notas:
mais_prox = 1000
arredondado = -1
for r in referencia:
if abs(n - r) < mais_prox:
arredondado = r
mais_prox = abs(n - r)
novas_notas.append(arredondado)
return novas_notas
def calcular_div(notas1, notas2):
#calcula a divergencia horizontal: duas notas são divergentes se a diferença entre elas é maior que 80
div = 0
for n1, n2 in zip(notas1,notas2):
if abs(n1 - n2) > 80:
div += 1
return 100*div/len(notas1)
def calcular_agregado(dic_perf):
acc = dic_perf['ACC']*100
rmse = (200 - dic_perf['RMSE'])/2
qwk = dic_perf['QWK']*100
div = 100 - dic_perf['DIV']
#print(acc, rmse, qwk, div)
return (acc + rmse + qwk + div)/4
def calcular_resultados(y, y_hat):
ALL_LABELS = [0, 40, 80, 120, 160, 200]
ACC = accuracy_score(y, y_hat)
RMSE = root_mean_squared_error(y, y_hat )
QWK = cohen_kappa_score(y, y_hat, weights='quadratic', labels=ALL_LABELS)
DIV = calcular_div(y, y_hat)
macro_f1 = f1_score(
y,
y_hat,
average="macro",
labels=ALL_LABELS,
zero_division=np.nan,
)
weighted_f1 = f1_score(
y,
y_hat,
average="weighted",
labels=ALL_LABELS,
zero_division=np.nan,
)
if not isinstance(y, list):
y = y.tolist()
dic = {'ACC': ACC, 'RMSE': RMSE, 'QWK': QWK, 'DIV': DIV, 'F1-Macro': macro_f1, 'F1-Weighted': weighted_f1, 'y': y, 'y_hat': y_hat}
dic['Agregado'] = calcular_agregado(dic)
return dic |