def test_ca(self):
X, y = data1()
self.assertAlmostEqual(solution.CA(y, [[1, 0]] * len(y)), 0.5)
self.assertAlmostEqual(solution.CA(y, [[0.5, 1]] * len(y)), 0.5)
self.assertAlmostEqual(solution.CA(y[:4], [[0.4, 0.6]] * len(y[:4])),
0.0)
self.assertAlmostEqual(solution.CA(y[4:], [[0.4, 0.6]] * len(y[4:])),
1.0)
self.assertAlmostEqual(solution.CA(y[:6], [[0.4, 0.6]] * len(y[:6])),
2 / 6.)
self.assertAlmostEqual(
solution.CA(y, [[0, 1], [0.2, 0.8], [0, 1], [0, 1], [1, 0],
[0.6, 0.4], [1, 0], [1, 0]]), 0.0)
pylab.show()
X, y = solution.load('reg.data')
learner = solution.LogRegLearner(lambda_=0.)
classifier = learner(X, y)
draw_decision(X, y, classifier, 0, 1)
learner = solution.LogRegLearner(lambda_=0.01)
classifier = learner(X, y)
draw_decision(X, y, classifier, 0, 1)
learner = solution.LogRegLearner(lambda_=0.3)
classifier = learner(X, y)
draw_decision(X, y, classifier, 0, 1)
for lam in [0.0, 0.0001, 0.0005, 0.001, 0.01, 0.05, 0.1, 0.2, 0.3, 0.5]:
learner = solution.LogRegLearner(lambda_=lam)
res = solution.test_cv(learner, X, y)
res1 = solution.test_learning(learner, X, y)
print("Tocnost cv:", round(solution.CA(y, res),
3)) # argumenta sta pravi razredi, napovedani
print("Tocnost learning:",
round(solution.CA(y, res1),
3)) # argumenta sta pravi razredi, napovedani
print("\n")
def test_logreg_noreg_learning_ca(self):
X, y = data1()
logreg = solution.LogRegLearner(lambda_=0)
pred = solution.test_learning(logreg, X, y)
ca = solution.CA(y, pred)
self.assertAlmostEqual(ca, 1.)
# Part 3 test_cv:
X, y = s.load('reg.data')
lambdas = [10, 1, 0.5, 0.1, 0.075, 0.05, 0.03, 0.01, 0.001, 0.0001, 0.]
lambdas_ca = {l: 0 for l in lambdas}
for i in range(1,21):
for l in lambdas:
learner = s.LogRegLearner(lambda_=l)
res = s.test_cv(learner, X, y, seed=i)
ca = s.CA(y, res)
lambdas_ca[l] += ca
for l in lambdas_ca.keys():
print('Lambda:', l)
print('Tocnost', lambdas_ca[l]/20)
# # Part 3 test_learning:
#
# X, y = s.load('reg.data')
#
# lambdas = [10, 1, 0.5, 0.1, 0.05, 0.03, 0.01, 0.001, 0.0001, 0.]
#
# lambdas_ca = {l: 0 for l in lambdas}