def test_net_will_predict_each(self):
net = create_net()
result = train_and_check(net)
stats = result.get_statistics()
self.assertGreater(stats[0], 1)
self.assertGreater(stats[1], 1)
self.assertGreater(stats[2], 1)
def test_net_will_verify_bl2(self):
for _ in range(0, 5):
net = create_net()
train_and_check(net, league='bl2')
(verified, _) = verify(net, league='bl2', factor=0.85)
if verified:
break
self.assertTrue(verified)
def test_after_serialize_same(self):
net1 = create_net()
result1 = train_and_check(net1)
string = as_string(net1)
net2 = from_string(string)
result2 = train_and_check(net2, train_set=[])
self.assertEqual(result1.get_performance(), result2.get_performance())
def test_save_load_from_file(self):
net1 = create_net()
result1 = train_and_check(net1)
filename = './prediction/pickles/test.pickle'
save_net(net1, filename)
net2 = load_net(filename)
result2 = train_and_check(net2, train_set=[])
self.assertEqual(result1.get_performance(), result2.get_performance())
def test_training_improves(self):
for _ in range(0, 3):
result_1 = train_and_check(self.net, [], '2016')
if result_1.get_performance() >= 50:
self.net = create_net()
else:
break
result_2 = train_and_check(self.net, ['2015'], '2016')
self.assertGreater(result_2.get_performance(), result_1.get_performance())
self.is_in_range(result_2.get_performance(), 45, range_value=5)
stats = result_2.get_statistics()
self.assertGreater(stats[0], 2)
self.assertGreater(stats[1], 5)
self.assertGreater(stats[2], 3)
def main():
x_axis = []
y_axis = []
start = 5
n = 10
for i in range(start, start + n + 1):
net = create_net(hidden_layer=i)
x_axis.append(i)
result = train_and_check(net)
verified = verify(net)
print 'Executed with', i, 'hidden layers:', result, 'verified', verified
y_axis.append(result.get_performance())
show_plot(x_axis, y_axis, start + n)
def main():
x_axis = []
y_axis = []
step = 0.01
n = 20
for i in range(0, n + 1):
alpha = step * i
net = create_net(alpha=alpha)
x_axis.append(alpha)
result = train_and_check(net)
verified = verify(net, delta=1)
print 'Executed with alpha', i * step, ':', result, 'verified', verified
y_axis.append(result.get_performance())
show_plot(x_axis, y_axis, n * step)
def main():
x_axis = []
y_axis = []
n = 15
for i in range(0, n + 1):
net = create_net()
x_axis.append(i + 1)
untrained = train_and_check(net, [])
trained = train_and_check(net)
verified = verify(net, delta=2)
print 'Execution {0}:'.format(i+1), untrained.get_performance(),\
'=>', trained, 'verified:', verified
y_axis.append(trained.get_performance())
show_plot(x_axis, y_axis, n)
def main():
x_axis = []
y_axis = []
seasons = [
'2006', '2007', '2008', '2009', '2010', '2011', '2012', '2013', '2014',
'2015'
]
n = 25
step = 1
net = create_net(alpha=0.1)
for i in range(step, n + 1, step):
x_axis.append(i)
result = train_and_check(net, max_iterations=step, train_set=seasons)
verified = verify(net, delta=1)
print 'Executed with ', i, 'iterations:', result, 'verified:', verified
y_axis.append(result.get_performance())
show_plot(x_axis, y_axis, n)
def setUp(self):
self.net = create_net()
self.trainer = NetTrainer(None)