def test_train_save_load_predict(self):
print "test train, save, load and predict..."
xs = SklearnRandomForestTrainTestModel.get_xs_from_results(
self.features)
ys = SklearnRandomForestTrainTestModel.get_ys_from_results(
self.features)
xys = SklearnRandomForestTrainTestModel.get_xys_from_results(
self.features)
self.model = SklearnRandomForestTrainTestModel(
{
'norm_type': 'normalize',
'random_state': 0
}, None)
self.model.train(xys)
self.model.to_file(self.model_filename)
self.assertTrue(os.path.exists(self.model_filename))
loaded_model = SklearnRandomForestTrainTestModel.from_file(
self.model_filename, None)
result = loaded_model.evaluate(xs, ys)
self.assertAlmostEquals(result['RMSE'], 0.17634739353518517, places=4)
def test_train_save_load_predict_libsvmnusvr(self):
print "test libsvmnusvr train, save, load and predict..."
xs = LibsvmNusvrTrainTestModel.get_xs_from_results(self.features)
ys = LibsvmNusvrTrainTestModel.get_ys_from_results(self.features)
xys = LibsvmNusvrTrainTestModel.get_xys_from_results(self.features)
self.model = LibsvmNusvrTrainTestModel({'norm_type': 'normalize'},
None)
self.model.train(xys)
self.model.to_file(self.model_filename)
self.assertTrue(os.path.exists(self.model_filename))
self.assertTrue(os.path.exists(self.model_filename + '.model'))
loaded_model = LibsvmNusvrTrainTestModel.from_file(
self.model_filename, None)
result = self.model.evaluate(xs, ys)
self.assertAlmostEquals(result['RMSE'], 0.62263086620058783, places=4)
# loaded model generates slight numerical difference
result = loaded_model.evaluate(xs, ys)
self.assertAlmostEquals(result['RMSE'], 0.62263139871631323, places=4)
def test_train_save_load_predict_libsvmnusvr(self):
print "test libsvmnusvr train, save, load and predict..."
xs = LibsvmNusvrTrainTestModel.get_xs_from_results(self.features)
ys = LibsvmNusvrTrainTestModel.get_ys_from_results(self.features)
xys = LibsvmNusvrTrainTestModel.get_xys_from_results(self.features)
self.model = LibsvmNusvrTrainTestModel({'norm_type':'normalize'}, None)
self.model.train(xys)
self.model.to_file(self.model_filename)
self.assertTrue(os.path.exists(self.model_filename))
self.assertTrue(os.path.exists(self.model_filename + '.model'))
loaded_model = LibsvmNusvrTrainTestModel.from_file(self.model_filename, None)
result = self.model.evaluate(xs, ys)
self.assertAlmostEquals(result['RMSE'], 0.62263086620058783, places=4)
# loaded model generates slight numerical difference
result = loaded_model.evaluate(xs, ys)
self.assertAlmostEquals(result['RMSE'], 0.62263139871631323, places=4)
def test_train_save_load_predict(self):
print "test train, save, load and predict..."
xs = SklearnRandomForestTrainTestModel.get_xs_from_results(self.features)
ys = SklearnRandomForestTrainTestModel.get_ys_from_results(self.features)
xys = SklearnRandomForestTrainTestModel.get_xys_from_results(self.features)
self.model = SklearnRandomForestTrainTestModel({'norm_type':'normalize', 'random_state':0}, None)
self.model.train(xys)
self.model.to_file(self.model_filename)
self.assertTrue(os.path.exists(self.model_filename))
loaded_model = SklearnRandomForestTrainTestModel.from_file(self.model_filename, None)
result = loaded_model.evaluate(xs, ys)
self.assertAlmostEquals(result['RMSE'], 0.17634739353518517, places=4)
class TrainTestModelTest(unittest.TestCase):
def setUp(self):
train_dataset_path = config.ROOT + '/python/test/resource/test_image_dataset_diffdim.py'
train_dataset = import_python_file(train_dataset_path)
train_assets = read_dataset(train_dataset)
_, self.features = run_executors_in_parallel(
MomentNorefFeatureExtractor,
train_assets,
fifo_mode=True,
delete_workdir=True,
parallelize=True,
result_store=None,
optional_dict=None,
optional_dict2=None,
)
self.model_filename = config.ROOT + "/workspace/model/test_save_load.pkl"
def tearDown(self):
if hasattr(self, 'model'):
self.model.delete(self.model_filename)
def test_get_xs_ys(self):
xs = TrainTestModel.get_xs_from_results(self.features, [0, 1, 2])
self.assertEquals(len(xs['Moment_noref_feature_1st_score']), 3)
self.assertAlmostEquals(np.mean(xs['Moment_noref_feature_1st_score']),
128.26146851380497,
places=4)
self.assertEquals(len(xs['Moment_noref_feature_var_score']), 3)
self.assertAlmostEquals(np.mean(xs['Moment_noref_feature_var_score']),
1569.2395085695462,
places=4)
xs = TrainTestModel.get_xs_from_results(self.features)
self.assertEquals(len(xs['Moment_noref_feature_1st_score']), 9)
self.assertAlmostEquals(np.mean(xs['Moment_noref_feature_1st_score']),
111.59099599173773,
places=4)
self.assertEquals(len(xs['Moment_noref_feature_var_score']), 9)
self.assertAlmostEquals(np.mean(xs['Moment_noref_feature_var_score']),
1806.8620377229011,
places=4)
ys = TrainTestModel.get_ys_from_results(self.features, [0, 1, 2])
expected_ys = {
'label': np.array([2.5, 3.9, 5.0]),
'content_id': np.array([0, 1, 2])
}
self.assertTrue(all(ys['label'] == expected_ys['label']))
self.assertTrue(all(ys['content_id'] == expected_ys['content_id']))
def test_train_save_load_predict(self):
print "test train, save, load and predict..."
xs = SklearnRandomForestTrainTestModel.get_xs_from_results(
self.features)
ys = SklearnRandomForestTrainTestModel.get_ys_from_results(
self.features)
xys = SklearnRandomForestTrainTestModel.get_xys_from_results(
self.features)
self.model = SklearnRandomForestTrainTestModel(
{
'norm_type': 'normalize',
'random_state': 0
}, None)
self.model.train(xys)
self.model.to_file(self.model_filename)
self.assertTrue(os.path.exists(self.model_filename))
loaded_model = SklearnRandomForestTrainTestModel.from_file(
self.model_filename, None)
result = loaded_model.evaluate(xs, ys)
self.assertAlmostEquals(result['RMSE'], 0.17634739353518517, places=4)
def test_train_save_load_predict_libsvmnusvr(self):
print "test libsvmnusvr train, save, load and predict..."
xs = LibsvmNusvrTrainTestModel.get_xs_from_results(self.features)
ys = LibsvmNusvrTrainTestModel.get_ys_from_results(self.features)
xys = LibsvmNusvrTrainTestModel.get_xys_from_results(self.features)
self.model = LibsvmNusvrTrainTestModel({'norm_type': 'normalize'},
None)
self.model.train(xys)
self.model.to_file(self.model_filename)
self.assertTrue(os.path.exists(self.model_filename))
self.assertTrue(os.path.exists(self.model_filename + '.model'))
loaded_model = LibsvmNusvrTrainTestModel.from_file(
self.model_filename, None)
result = self.model.evaluate(xs, ys)
self.assertAlmostEquals(result['RMSE'], 0.62263086620058783, places=4)
# loaded model generates slight numerical difference
result = loaded_model.evaluate(xs, ys)
self.assertAlmostEquals(result['RMSE'], 0.62263139871631323, places=4)
def test_train_predict_libsvmnusvr(self):
print "test libsvmnusvr train and predict..."
# libsvmnusvr is bit exact to nusvr
xs = LibsvmNusvrTrainTestModel.get_xs_from_results(self.features)
ys = LibsvmNusvrTrainTestModel.get_ys_from_results(self.features)
xys = LibsvmNusvrTrainTestModel.get_xys_from_results(self.features)
model = LibsvmNusvrTrainTestModel({'norm_type': 'normalize'}, None)
model.train(xys)
result = model.evaluate(xs, ys)
self.assertAlmostEquals(result['RMSE'], 0.62263086620058783, places=4)
model = LibsvmNusvrTrainTestModel({'norm_type': 'clip_0to1'}, None)
model.train(xys)
result = model.evaluate(xs, ys)
self.assertAlmostEquals(result['RMSE'], 0.84243141087114626, places=4)
model = LibsvmNusvrTrainTestModel(
{
'norm_type': 'custom_clip_0to1',
'custom_clip_0to1_map': {
'Moment_noref_feature_1st_score': [0.0, 100.0],
},
}, None)
model.train(xys)
result = model.evaluate(xs, ys)
self.assertAlmostEquals(result['RMSE'], 0.84639162766546994, places=4)
model = LibsvmNusvrTrainTestModel({'norm_type': 'clip_minus1to1'},
None)
model.train(xys)
result = model.evaluate(xs, ys)
self.assertAlmostEquals(result['RMSE'], 0.8314352752340991, places=4)
model = LibsvmNusvrTrainTestModel({'norm_type': 'none'}, None)
model.train(xys)
result = model.evaluate(xs, ys)
self.assertAlmostEquals(result['RMSE'], 0.23294283650716496, places=4)
def test_train_predict_randomforest(self):
print "test random forest train and predict..."
# random forest don't need proper data normalization
xs = SklearnRandomForestTrainTestModel.get_xs_from_results(
self.features, [0, 1, 2])
ys = SklearnRandomForestTrainTestModel.get_ys_from_results(
self.features, [0, 1, 2])
xys = SklearnRandomForestTrainTestModel.get_xys_from_results(
self.features, [0, 1, 2])
model = SklearnRandomForestTrainTestModel(
{
'norm_type': 'normalize',
'random_state': 0
}, None)
model.train(xys)
result = model.evaluate(xs, ys)
self.assertAlmostEquals(result['RMSE'], 0.051804171170643766, places=4)
model = SklearnRandomForestTrainTestModel(
{
'norm_type': 'clip_0to1',
'random_state': 0
}, None)
model.train(xys)
result = model.evaluate(xs, ys)
self.assertAlmostEquals(result['RMSE'], 0.051804171170643752, places=4)
model = SklearnRandomForestTrainTestModel(
{
'norm_type': 'custom_clip_0to1',
'custom_clip_0to1_map': {
'Moment_noref_feature_1st_score': [0.0, 100.0],
},
'random_state': 0
}, None)
model.train(xys)
result = model.evaluate(xs, ys)
self.assertAlmostEquals(result['RMSE'], 0.051804171170643752, places=4)
model = SklearnRandomForestTrainTestModel(
{
'norm_type': 'clip_minus1to1',
'random_state': 0
}, None)
model.train(xys)
result = model.evaluate(xs, ys)
self.assertAlmostEquals(result['RMSE'], 0.051804171170643752, places=4)
model = SklearnRandomForestTrainTestModel(
{
'norm_type': 'none',
'random_state': 0
}, None)
model.train(xys)
result = model.evaluate(xs, ys)
self.assertAlmostEquals(result['RMSE'], 0.051804171170643752, places=4)
def test_train_predict_extratrees(self):
print "test extra trees train and predict..."
# extra trees don't need proper data normalization
xs = SklearnExtraTreesTrainTestModel.get_xs_from_results(
self.features, [0, 1, 2])
ys = SklearnExtraTreesTrainTestModel.get_ys_from_results(
self.features, [0, 1, 2])
xys = SklearnExtraTreesTrainTestModel.get_xys_from_results(
self.features, [0, 1, 2])
model = SklearnExtraTreesTrainTestModel(
{
'norm_type': 'normalize',
'random_state': 0
}, None)
model.train(xys)
result = model.evaluate(xs, ys)
self.assertAlmostEquals(result['RMSE'], 0.042867322777879642, places=4)
model = SklearnExtraTreesTrainTestModel(
{
'norm_type': 'clip_0to1',
'random_state': 0
}, None)
model.train(xys)
result = model.evaluate(xs, ys)
self.assertAlmostEquals(result['RMSE'], 0.042867322777879642, places=4)
model = SklearnExtraTreesTrainTestModel(
{
'norm_type': 'custom_clip_0to1',
'custom_clip_0to1_map': {
'Moment_noref_feature_1st_score': [0.0, 100.0],
},
'random_state': 0
}, None)
model.train(xys)
result = model.evaluate(xs, ys)
self.assertAlmostEquals(result['RMSE'], 0.042867322777879642, places=4)
model = SklearnExtraTreesTrainTestModel(
{
'norm_type': 'clip_minus1to1',
'random_state': 0
}, None)
model.train(xys)
result = model.evaluate(xs, ys)
self.assertAlmostEquals(result['RMSE'], 0.042867322777879642, places=4)
model = SklearnExtraTreesTrainTestModel(
{
'norm_type': 'none',
'random_state': 0
}, None)
model.train(xys)
result = model.evaluate(xs, ys)
self.assertAlmostEquals(result['RMSE'], 0.042867322777879642, places=4)
def test_train_predict_libsvmnusvr(self):
print "test libsvmnusvr train and predict..."
# libsvmnusvr is bit exact to nusvr
xs = LibsvmNusvrTrainTestModel.get_xs_from_results(self.features)
ys = LibsvmNusvrTrainTestModel.get_ys_from_results(self.features)
xys = LibsvmNusvrTrainTestModel.get_xys_from_results(self.features)
model = LibsvmNusvrTrainTestModel({'norm_type': 'normalize'}, None)
model.train(xys)
result = model.evaluate(xs, ys)
self.assertAlmostEquals(result['RMSE'], 0.62263086620058783, places=4)
model = LibsvmNusvrTrainTestModel({'norm_type': 'clip_0to1'}, None)
model.train(xys)
result = model.evaluate(xs, ys)
self.assertAlmostEquals(result['RMSE'], 0.84243141087114626, places=4)
model = LibsvmNusvrTrainTestModel(
{
'norm_type': 'custom_clip_0to1',
'custom_clip_0to1_map': {
'Moment_noref_feature_1st_score': [0.0, 100.0],
},
}, None)
model.train(xys)
result = model.evaluate(xs, ys)
self.assertAlmostEquals(result['RMSE'], 0.84639162766546994, places=4)
model = LibsvmNusvrTrainTestModel({'norm_type': 'clip_minus1to1'},
None)
model.train(xys)
result = model.evaluate(xs, ys)
self.assertAlmostEquals(result['RMSE'], 0.8314352752340991, places=4)
model = LibsvmNusvrTrainTestModel({'norm_type': 'none'}, None)
model.train(xys)
result = model.evaluate(xs, ys)
self.assertAlmostEquals(result['RMSE'], 0.23294283650716496, places=4)
class TrainTestModelTest(unittest.TestCase):
def setUp(self):
train_dataset_path = config.ROOT + '/python/test/resource/test_image_dataset_diffdim.py'
train_dataset = import_python_file(train_dataset_path)
train_assets = read_dataset(train_dataset)
_, self.features = run_executors_in_parallel(
MomentNorefFeatureExtractor,
train_assets,
fifo_mode=True,
delete_workdir=True,
parallelize=True,
result_store=None,
optional_dict=None,
optional_dict2=None,
)
self.model_filename = config.ROOT + "/workspace/model/test_save_load.pkl"
def tearDown(self):
if hasattr(self, 'model'):
self.model.delete(self.model_filename)
def test_get_xs_ys(self):
xs = TrainTestModel.get_xs_from_results(self.features, [0, 1, 2])
self.assertEquals(len(xs['Moment_noref_feature_1st_score']), 3)
self.assertAlmostEquals(np.mean(xs['Moment_noref_feature_1st_score']), 128.26146851380497, places=4)
self.assertEquals(len(xs['Moment_noref_feature_var_score']), 3)
self.assertAlmostEquals(np.mean(xs['Moment_noref_feature_var_score']), 1569.2395085695462, places=4)
xs = TrainTestModel.get_xs_from_results(self.features)
self.assertEquals(len(xs['Moment_noref_feature_1st_score']), 9)
self.assertAlmostEquals(np.mean(xs['Moment_noref_feature_1st_score']), 111.59099599173773, places=4)
self.assertEquals(len(xs['Moment_noref_feature_var_score']), 9)
self.assertAlmostEquals(np.mean(xs['Moment_noref_feature_var_score']), 1806.8620377229011, places=4)
ys = TrainTestModel.get_ys_from_results(self.features, [0, 1, 2])
expected_ys = {'label': np.array([2.5, 3.9, 5.0]),
'content_id': np.array([0, 1, 2])}
self.assertTrue(all(ys['label'] == expected_ys['label']))
self.assertTrue(all(ys['content_id'] == expected_ys['content_id']))
def test_train_save_load_predict(self):
print "test train, save, load and predict..."
xs = SklearnRandomForestTrainTestModel.get_xs_from_results(self.features)
ys = SklearnRandomForestTrainTestModel.get_ys_from_results(self.features)
xys = SklearnRandomForestTrainTestModel.get_xys_from_results(self.features)
self.model = SklearnRandomForestTrainTestModel({'norm_type':'normalize', 'random_state':0}, None)
self.model.train(xys)
self.model.to_file(self.model_filename)
self.assertTrue(os.path.exists(self.model_filename))
loaded_model = SklearnRandomForestTrainTestModel.from_file(self.model_filename, None)
result = loaded_model.evaluate(xs, ys)
self.assertAlmostEquals(result['RMSE'], 0.17634739353518517, places=4)
def test_train_save_load_predict_libsvmnusvr(self):
print "test libsvmnusvr train, save, load and predict..."
xs = LibsvmNusvrTrainTestModel.get_xs_from_results(self.features)
ys = LibsvmNusvrTrainTestModel.get_ys_from_results(self.features)
xys = LibsvmNusvrTrainTestModel.get_xys_from_results(self.features)
self.model = LibsvmNusvrTrainTestModel({'norm_type':'normalize'}, None)
self.model.train(xys)
self.model.to_file(self.model_filename)
self.assertTrue(os.path.exists(self.model_filename))
self.assertTrue(os.path.exists(self.model_filename + '.model'))
loaded_model = LibsvmNusvrTrainTestModel.from_file(self.model_filename, None)
result = self.model.evaluate(xs, ys)
self.assertAlmostEquals(result['RMSE'], 0.62263086620058783, places=4)
# loaded model generates slight numerical difference
result = loaded_model.evaluate(xs, ys)
self.assertAlmostEquals(result['RMSE'], 0.62263139871631323, places=4)
def test_train_predict_libsvmnusvr(self):
print "test libsvmnusvr train and predict..."
# libsvmnusvr is bit exact to nusvr
xs = LibsvmNusvrTrainTestModel.get_xs_from_results(self.features)
ys = LibsvmNusvrTrainTestModel.get_ys_from_results(self.features)
xys = LibsvmNusvrTrainTestModel.get_xys_from_results(self.features)
model = LibsvmNusvrTrainTestModel(
{'norm_type':'normalize'}, None)
model.train(xys)
result = model.evaluate(xs, ys)
self.assertAlmostEquals(result['RMSE'], 0.62263086620058783, places=4)
model = LibsvmNusvrTrainTestModel(
{'norm_type':'clip_0to1'}, None)
model.train(xys)
result = model.evaluate(xs, ys)
self.assertAlmostEquals(result['RMSE'], 0.84243141087114626, places=4)
model = LibsvmNusvrTrainTestModel(
{'norm_type': 'custom_clip_0to1',
'custom_clip_0to1_map': {
'Moment_noref_feature_1st_score': [0.0, 100.0],
},
}, None)
model.train(xys)
result = model.evaluate(xs, ys)
self.assertAlmostEquals(result['RMSE'], 0.84639162766546994, places=4)
model = LibsvmNusvrTrainTestModel(
{'norm_type':'clip_minus1to1'}, None)
model.train(xys)
result = model.evaluate(xs, ys)
self.assertAlmostEquals(result['RMSE'], 0.8314352752340991, places=4)
model = LibsvmNusvrTrainTestModel(
{'norm_type':'none'}, None)
model.train(xys)
result = model.evaluate(xs, ys)
self.assertAlmostEquals(result['RMSE'], 0.23294283650716496, places=4)
def test_train_predict_randomforest(self):
print "test random forest train and predict..."
# random forest don't need proper data normalization
xs = SklearnRandomForestTrainTestModel.get_xs_from_results(self.features, [0, 1, 2])
ys = SklearnRandomForestTrainTestModel.get_ys_from_results(self.features, [0, 1, 2])
xys = SklearnRandomForestTrainTestModel.get_xys_from_results(self.features, [0, 1, 2])
model = SklearnRandomForestTrainTestModel({'norm_type':'normalize',
'random_state': 0}, None)
model.train(xys)
result = model.evaluate(xs, ys)
self.assertAlmostEquals(result['RMSE'], 0.051804171170643766, places=4)
model = SklearnRandomForestTrainTestModel({'norm_type':'clip_0to1',
'random_state': 0}, None)
model.train(xys)
result = model.evaluate(xs, ys)
self.assertAlmostEquals(result['RMSE'], 0.051804171170643752, places=4)
model = SklearnRandomForestTrainTestModel(
{'norm_type': 'custom_clip_0to1',
'custom_clip_0to1_map': {
'Moment_noref_feature_1st_score': [0.0, 100.0],
},
'random_state': 0
}, None)
model.train(xys)
result = model.evaluate(xs, ys)
self.assertAlmostEquals(result['RMSE'], 0.051804171170643752, places=4)
model = SklearnRandomForestTrainTestModel({'norm_type':'clip_minus1to1',
'random_state': 0}, None)
model.train(xys)
result = model.evaluate(xs, ys)
self.assertAlmostEquals(result['RMSE'], 0.051804171170643752, places=4)
model = SklearnRandomForestTrainTestModel({'norm_type':'none', 'random_state': 0}, None)
model.train(xys)
result = model.evaluate(xs, ys)
self.assertAlmostEquals(result['RMSE'], 0.051804171170643752, places=4)
def test_train_predict_extratrees(self):
print "test extra trees train and predict..."
# extra trees don't need proper data normalization
xs = SklearnExtraTreesTrainTestModel.get_xs_from_results(self.features, [0, 1, 2])
ys = SklearnExtraTreesTrainTestModel.get_ys_from_results(self.features, [0, 1, 2])
xys = SklearnExtraTreesTrainTestModel.get_xys_from_results(self.features, [0, 1, 2])
model = SklearnExtraTreesTrainTestModel({'norm_type':'normalize',
'random_state': 0}, None)
model.train(xys)
result = model.evaluate(xs, ys)
self.assertAlmostEquals(result['RMSE'], 0.042867322777879642, places=4)
model = SklearnExtraTreesTrainTestModel({'norm_type':'clip_0to1',
'random_state': 0}, None)
model.train(xys)
result = model.evaluate(xs, ys)
self.assertAlmostEquals(result['RMSE'], 0.042867322777879642, places=4)
model = SklearnExtraTreesTrainTestModel(
{'norm_type': 'custom_clip_0to1',
'custom_clip_0to1_map': {
'Moment_noref_feature_1st_score': [0.0, 100.0],
},
'random_state': 0
}, None)
model.train(xys)
result = model.evaluate(xs, ys)
self.assertAlmostEquals(result['RMSE'], 0.042867322777879642, places=4)
model = SklearnExtraTreesTrainTestModel({'norm_type':'clip_minus1to1',
'random_state': 0}, None)
model.train(xys)
result = model.evaluate(xs, ys)
self.assertAlmostEquals(result['RMSE'], 0.042867322777879642, places=4)
model = SklearnExtraTreesTrainTestModel({'norm_type':'none', 'random_state': 0}, None)
model.train(xys)
result = model.evaluate(xs, ys)
self.assertAlmostEquals(result['RMSE'], 0.042867322777879642, places=4)
def test_train_predict_libsvmnusvr(self):
print "test libsvmnusvr train and predict..."
# libsvmnusvr is bit exact to nusvr
xs = LibsvmNusvrTrainTestModel.get_xs_from_results(self.features)
ys = LibsvmNusvrTrainTestModel.get_ys_from_results(self.features)
xys = LibsvmNusvrTrainTestModel.get_xys_from_results(self.features)
model = LibsvmNusvrTrainTestModel(
{'norm_type':'normalize'}, None)
model.train(xys)
result = model.evaluate(xs, ys)
self.assertAlmostEquals(result['RMSE'], 0.62263086620058783, places=4)
model = LibsvmNusvrTrainTestModel(
{'norm_type':'clip_0to1'}, None)
model.train(xys)
result = model.evaluate(xs, ys)
self.assertAlmostEquals(result['RMSE'], 0.84243141087114626, places=4)
model = LibsvmNusvrTrainTestModel(
{'norm_type': 'custom_clip_0to1',
'custom_clip_0to1_map': {
'Moment_noref_feature_1st_score': [0.0, 100.0],
},
}, None)
model.train(xys)
result = model.evaluate(xs, ys)
self.assertAlmostEquals(result['RMSE'], 0.84639162766546994, places=4)
model = LibsvmNusvrTrainTestModel(
{'norm_type':'clip_minus1to1'}, None)
model.train(xys)
result = model.evaluate(xs, ys)
self.assertAlmostEquals(result['RMSE'], 0.8314352752340991, places=4)
model = LibsvmNusvrTrainTestModel(
{'norm_type':'none'}, None)
model.train(xys)
result = model.evaluate(xs, ys)
self.assertAlmostEquals(result['RMSE'], 0.23294283650716496, places=4)
