def regularization_test():
with open("log_regular.txt", "w+") as f:
f.write("Regularization | lambda | Area | R2\n")
f.write("-" * 50 + "\n")
for reg in ['l2', 'l1']:
for lamb in [0.0001, 0.001, 0.01, 0.1, 1.0]:
logreg = LogReg(X, Y)
logreg.optimize(m=100,
epochs=5000,
eta=0.01,
regularization=reg,
lamb=lamb)
ypred_train = logreg.p_train
ypred_test = logreg.p_test
area = gain_chart(logreg.Y_test, ypred_test, plot=False)
R2 = prob_acc(logreg.Y_test, ypred_test, plot=False)
f.write(" %s | %g | %.4f | %.4f \n" %
(reg, lamb, area, R2))
f.write("-" * 50 + "\n")
import numpy as np
from read_data import get_data
import sys
sys.path.append("network/")
from logreg import LogReg
from metrics import gain_chart, prob_acc
#X, Y = get_data(normalized = False, standardized = False)
X, Y = get_data()
logreg = LogReg(X, Y)
logreg.optimize(m=100, epochs=5000,
eta=0.01) #, regularization='l2', lamb=0.0001)
ypred_train = logreg.p_train
ypred_test = logreg.p_test
gain_chart(logreg.Y_test, ypred_test)
prob_acc(logreg.Y_test, ypred_test)
def regularization_test():
with open("log_regular.txt", "w+") as f:
f.write("Regularization | lambda | Area | R2\n")
f.write("-" * 50 + "\n")
for reg in ['l2', 'l1']:
for lamb in [0.0001, 0.001, 0.01, 0.1, 1.0]:
logreg = LogReg(X, Y)
logreg.optimize(m=100,
