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)