def test_get_xs_ys_consistent_wh(self):
xs = MomentRandomForestTrainTestModel.get_xs_from_results(
self.features, [0, 1, 2])
ys = MomentRandomForestTrainTestModel.get_ys_from_results(
self.features, [0, 1, 2])
xys = MomentRandomForestTrainTestModel.get_xys_from_results(
self.features, [0, 1, 2])
expected_ys = {'label': [2.5, 3.9, 5.0], 'content_id': [0, 1, 2]}
self.assertEqual(xs['dis_y'][0].shape, (1, 321, 481))
self.assertEqual(xs['dis_y'][1].shape, (1, 321, 481))
self.assertEqual(xs['dis_y'][2].shape, (1, 321, 481))
self.assertEqual(xs['dis_u'][0].shape, (1, 321, 481))
self.assertEqual(xs['dis_u'][1].shape, (1, 321, 481))
self.assertEqual(xs['dis_u'][2].shape, (1, 321, 481))
self.assertEqual(xs['dis_v'][0].shape, (1, 321, 481))
self.assertEqual(xs['dis_v'][1].shape, (1, 321, 481))
self.assertEqual(xs['dis_v'][2].shape, (1, 321, 481))
self.assertAlmostEqual(np.mean(xs['dis_y']),
128.26146851380497,
places=4)
self.assertEqual(list(ys['label']), expected_ys['label'])
self.assertEqual(list(ys['content_id']), expected_ys['content_id'])
self.assertAlmostEqual(np.mean(xys['dis_y']),
128.26146851380497,
places=4)
self.assertEqual(list(xys['label']), expected_ys['label'])
self.assertEqual(list(xys['content_id']), expected_ys['content_id'])
def test_get_xs_ys_inconsistent_wh(self):
xs = MomentRandomForestTrainTestModel.get_xs_from_results(
self.features, [0, 8])
ys = MomentRandomForestTrainTestModel.get_ys_from_results(
self.features, [0, 8])
xys = MomentRandomForestTrainTestModel.get_xys_from_results(
self.features, [0, 8])
expected_ys = {'label': [2.5, 4.7], 'content_id': [0, 13]}
self.assertEqual(xs['dis_y'][0].shape, (1, 321, 481))
self.assertEqual(xs['dis_y'][1].shape, (1, 486, 720))
self.assertEqual(xs['dis_u'][0].shape, (1, 321, 481))
self.assertEqual(xs['dis_u'][1].shape, (1, 486, 720))
self.assertEqual(xs['dis_v'][0].shape, (1, 321, 481))
self.assertEqual(xs['dis_v'][1].shape, (1, 486, 720))
self.assertAlmostEqual(np.mean(xs['dis_y'][0]),
160.617204551784,
places=4)
self.assertAlmostEqual(np.mean(xs['dis_y'][1]),
35.8163894604481,
places=4)
self.assertEqual(list(ys['label']), expected_ys['label'])
self.assertEqual(list(ys['content_id']), expected_ys['content_id'])
self.assertEqual(xys['dis_y'][0].shape, (1, 321, 481))
self.assertEqual(xys['dis_y'][1].shape, (1, 486, 720))
self.assertEqual(xys['dis_u'][0].shape, (1, 321, 481))
self.assertEqual(xys['dis_u'][1].shape, (1, 486, 720))
self.assertEqual(xys['dis_v'][0].shape, (1, 321, 481))
self.assertEqual(xys['dis_v'][1].shape, (1, 486, 720))
self.assertEqual(list(xys['label']), expected_ys['label'])
self.assertEqual(list(xys['content_id']), expected_ys['content_id'])
def test_get_xs_ys_inconsistent_wh(self):
xs = MomentRandomForestTrainTestModel.get_xs_from_results(self.features, [0, 8])
ys = MomentRandomForestTrainTestModel.get_ys_from_results(self.features, [0, 8])
xys = MomentRandomForestTrainTestModel.get_xys_from_results(self.features, [0, 8])
expected_ys = {'label': [2.5, 4.7], 'content_id': [0, 13]}
self.assertEqual(xs['dis_y'][0].shape, (1, 321, 481))
self.assertEqual(xs['dis_y'][1].shape, (1, 486, 720))
self.assertEqual(xs['dis_u'][0].shape, (1, 321, 481))
self.assertEqual(xs['dis_u'][1].shape, (1, 486, 720))
self.assertEqual(xs['dis_v'][0].shape, (1, 321, 481))
self.assertEqual(xs['dis_v'][1].shape, (1, 486, 720))
self.assertAlmostEqual(np.mean(xs['dis_y'][0]), 160.617204551784, places=4)
self.assertAlmostEqual(np.mean(xs['dis_y'][1]), 35.8163894604481, places=4)
self.assertEqual(list(ys['label']), expected_ys['label'])
self.assertEqual(list(ys['content_id']), expected_ys['content_id'])
self.assertEqual(xys['dis_y'][0].shape, (1, 321, 481))
self.assertEqual(xys['dis_y'][1].shape, (1, 486, 720))
self.assertEqual(xys['dis_u'][0].shape, (1, 321, 481))
self.assertEqual(xys['dis_u'][1].shape, (1, 486, 720))
self.assertEqual(xys['dis_v'][0].shape, (1, 321, 481))
self.assertEqual(xys['dis_v'][1].shape, (1, 486, 720))
self.assertEqual(list(xys['label']), expected_ys['label'])
self.assertEqual(list(xys['content_id']), expected_ys['content_id'])
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_get_xs_ys_consistent_wh(self):
xs = MomentRandomForestTrainTestModel.get_xs_from_results(self.features, [0, 1, 2])
ys = MomentRandomForestTrainTestModel.get_ys_from_results(self.features, [0, 1, 2])
xys = MomentRandomForestTrainTestModel.get_xys_from_results(self.features, [0, 1, 2])
expected_ys = {'label': [2.5, 3.9, 5.0], 'content_id': [0, 1, 2]}
self.assertEqual(xs['dis_y'][0].shape, (1, 321, 481))
self.assertEqual(xs['dis_y'][1].shape, (1, 321, 481))
self.assertEqual(xs['dis_y'][2].shape, (1, 321, 481))
self.assertEqual(xs['dis_u'][0].shape, (1, 321, 481))
self.assertEqual(xs['dis_u'][1].shape, (1, 321, 481))
self.assertEqual(xs['dis_u'][2].shape, (1, 321, 481))
self.assertEqual(xs['dis_v'][0].shape, (1, 321, 481))
self.assertEqual(xs['dis_v'][1].shape, (1, 321, 481))
self.assertEqual(xs['dis_v'][2].shape, (1, 321, 481))
self.assertAlmostEqual(np.mean(xs['dis_y']), 128.26146851380497, places=4)
self.assertEqual(list(ys['label']), expected_ys['label'])
self.assertEqual(list(ys['content_id']), expected_ys['content_id'])
self.assertAlmostEqual(np.mean(xys['dis_y']), 128.26146851380497, places=4)
self.assertEqual(list(xys['label']), expected_ys['label'])
self.assertEqual(list(xys['content_id']), expected_ys['content_id'])
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)
