def test_copy(self):
# train model #1
metric = Metric({"name": "logloss"})
nn = NeuralNetworkAlgorithm(self.params)
nn.fit(self.X, self.y)
y_predicted = nn.predict(self.X)
loss = metric(self.y, y_predicted)
# create model #2
nn2 = NeuralNetworkAlgorithm(self.params)
# model #2 is not initialized in constructor
self.assertTrue(nn2.model is None)
# do a copy and use it for predictions
nn2 = nn.copy()
self.assertEqual(type(nn), type(nn2))
y_predicted = nn2.predict(self.X)
loss2 = metric(self.y, y_predicted)
self.assertEqual(loss, loss2)
# fit model #1, there should be improvement in loss
nn.fit(self.X, self.y)
y_predicted = nn.predict(self.X)
loss3 = metric(self.y, y_predicted)
self.assertTrue(loss3 < loss)
# the loss of model #2 should not change
y_predicted = nn2.predict(self.X)
loss4 = metric(self.y, y_predicted)
assert_almost_equal(loss2, loss4)
def test_save_and_load(self):
metric = Metric({"name": "logloss"})
nn = NeuralNetworkAlgorithm(self.params)
nn.fit(self.X, self.y)
y_predicted = nn.predict(self.X)
loss = metric(self.y, y_predicted)
with tempfile.NamedTemporaryFile() as tmp:
nn.save(tmp.name)
json_desc = nn.get_params()
nn2 = NeuralNetworkAlgorithm(json_desc["params"])
nn2.load(tmp.name)
y_predicted = nn2.predict(self.X)
loss2 = metric(self.y, y_predicted)
assert_almost_equal(loss, loss2)
def test_save_and_load(self):
metric = Metric({"name": "logloss"})
nn = NeuralNetworkAlgorithm(self.params)
nn.fit(self.X, self.y)
y_predicted = nn.predict(self.X)
loss = metric(self.y, y_predicted)
filename = os.path.join(tempfile.gettempdir(), os.urandom(12).hex())
nn.save(filename)
json_desc = nn.get_params()
nn2 = NeuralNetworkAlgorithm(json_desc["params"])
nn2.load(filename)
#Finished with the file, delete it
os.remove(filename)
y_predicted = nn2.predict(self.X)
loss2 = metric(self.y, y_predicted)
assert_almost_equal(loss, loss2)
def test_fit_predict(self):
metric = Metric({"name": "logloss"})
nn = NeuralNetworkAlgorithm(self.params)
loss_prev = None
for _ in range(3):
nn.fit(self.X, self.y)
y_predicted = nn.predict(self.X)
loss = metric(self.y, y_predicted)
if loss_prev is not None:
self.assertTrue(loss + 0.000001 < loss_prev)
loss_prev = loss