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_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_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)
