def test_train_save_load_predict(self):
xs = MomentRandomForestTrainTestModel.get_xs_from_results(
self.features)
ys = MomentRandomForestTrainTestModel.get_ys_from_results(
self.features)
xys = MomentRandomForestTrainTestModel.get_xys_from_results(
self.features)
# using dis_y only
del xs['dis_u']
del xs['dis_v']
del xys['dis_u']
del xys['dis_v']
model = MomentRandomForestTrainTestModel({
'norm_type': 'normalize',
'random_state': 0
})
model.train(xys)
model.to_file(self.model_filename)
self.assertTrue(os.path.exists(self.model_filename))
loaded_model = TrainTestModel.from_file(self.model_filename)
result = loaded_model.evaluate(xs, ys)
self.assertAlmostEquals(result['RMSE'], 0.17634739353518517, places=4)
def test_train_predict_using_yuv(self):
xs = MomentRandomForestTrainTestModel.get_xs_from_results(self.features)
ys = MomentRandomForestTrainTestModel.get_ys_from_results(self.features)
xys = MomentRandomForestTrainTestModel.get_xys_from_results(self.features)
model = MomentRandomForestTrainTestModel({'norm_type':'normalize', 'random_state':0})
model.train(xys)
result = model.evaluate(xs, ys)
self.assertAlmostEquals(result['RMSE'], 0.51128487038576109, places=4)
def test_train_predict_using_yuv(self):
xs = MomentRandomForestTrainTestModel.get_xs_from_results(
self.features)
ys = MomentRandomForestTrainTestModel.get_ys_from_results(
self.features)
xys = MomentRandomForestTrainTestModel.get_xys_from_results(
self.features)
model = MomentRandomForestTrainTestModel({
'norm_type': 'normalize',
'random_state': 0
})
model.train(xys)
result = model.evaluate(xs, ys)
self.assertAlmostEquals(result['RMSE'], 0.51128487038576109, places=4)
def test_train_predict(self):
xs = MomentRandomForestTrainTestModel.get_xs_from_results(self.features)
ys = MomentRandomForestTrainTestModel.get_ys_from_results(self.features)
xys = MomentRandomForestTrainTestModel.get_xys_from_results(self.features)
# using dis_y only
del xs['dis_u']
del xs['dis_v']
del xys['dis_u']
del xys['dis_v']
model = MomentRandomForestTrainTestModel({'norm_type':'normalize', 'random_state':0})
model.train(xys)
result = model.evaluate(xs, ys)
self.assertAlmostEquals(result['RMSE'], 0.17634739353518517, places=4)
def test_train_predict(self):
xs = MomentRandomForestTrainTestModel.get_xs_from_results(
self.features)
ys = MomentRandomForestTrainTestModel.get_ys_from_results(
self.features)
xys = MomentRandomForestTrainTestModel.get_xys_from_results(
self.features)
# using dis_y only
del xs['dis_u']
del xs['dis_v']
del xys['dis_u']
del xys['dis_v']
model = MomentRandomForestTrainTestModel({
'norm_type': 'normalize',
'random_state': 0
})
model.train(xys)
result = model.evaluate(xs, ys)
self.assertAlmostEquals(result['RMSE'], 0.17634739353518517, places=4)
def test_train_save_load_predict(self):
xs = MomentRandomForestTrainTestModel.get_xs_from_results(self.features)
ys = MomentRandomForestTrainTestModel.get_ys_from_results(self.features)
xys = MomentRandomForestTrainTestModel.get_xys_from_results(self.features)
# using dis_y only
del xs['dis_u']
del xs['dis_v']
del xys['dis_u']
del xys['dis_v']
model = MomentRandomForestTrainTestModel({'norm_type':'normalize', 'random_state':0})
model.train(xys)
model.to_file(self.model_filename)
self.assertTrue(os.path.exists(self.model_filename))
loaded_model = TrainTestModel.from_file(self.model_filename)
result = loaded_model.evaluate(xs, ys)
self.assertAlmostEquals(result['RMSE'], 0.17634739353518517, places=4)
