def test_xgb_trainer_equal_classifier(self):
model1 = XGBClassifier(n_estimators=100,
learning_rate=0.1,
max_depth=3,
features=self.features,
random_state=42)
model2 = XGBTrainer(features=self.features,
objective='reg:logistic',
booster='gbtree',
tree_method='exact',
n_estimators=100,
learning_rate=0.1,
max_depth=3,
random_state=42)
y = np.where(self.y > 0, 1, 0)
model1.fit(self.x, y)
model2.fit(self.x, y)
predict1 = model1.predict(self.sample_x)
predict2 = model2.predict(self.sample_x)
predict2 = np.where(predict2 > 0.5, 1., 0.)
np.testing.assert_array_almost_equal(predict1, predict2)
def load_model(model_desc: dict) -> ModelBase:
model_name = model_desc['model_name']
model_name_parts = set(model_name.split('.'))
if 'ConstLinearModel' in model_name_parts:
return ConstLinearModel.load(model_desc)
elif 'LinearRegression' in model_name_parts:
return LinearRegression.load(model_desc)
elif 'LassoRegression' in model_name_parts:
return LassoRegression.load(model_desc)
elif 'LogisticRegression' in model_name_parts:
return LogisticRegression.load(model_desc)
elif 'RandomForestRegressor' in model_name_parts:
return RandomForestRegressor.load(model_desc)
elif 'RandomForestClassifier' in model_name_parts:
return RandomForestClassifier.load(model_desc)
elif 'XGBRegressor' in model_name_parts:
return XGBRegressor.load(model_desc)
elif 'XGBClassifier' in model_name_parts:
return XGBClassifier.load(model_desc)
elif 'XGBTrainer' in model_name_parts:
return XGBTrainer.load(model_desc)
elif 'NvSVR' in model_name_parts:
return NvSVRModel.load(model_desc)
else:
raise ValueError('{0} is not currently supported in model loader.'.format(model_name))
def test_composer_persistence(self):
freq = '5b'
universe = Universe('zz800')
batch = 4
neutralized_risk = ['SIZE']
risk_model = 'long'
pre_process = ['standardize', 'winsorize_normal']
post_process = ['standardize', 'winsorize_normal']
warm_start = 2
data_source = 'postgresql://*****:*****@server/dummy'
data_meta = DataMeta(freq=freq,
universe=universe,
batch=batch,
neutralized_risk=neutralized_risk,
risk_model=risk_model,
pre_process=pre_process,
post_process=post_process,
warm_start=warm_start,
data_source=data_source)
features = {'f1': 'closePrice', 'f2': 'openPrice'}
alpha_model = XGBClassifier(features=features)
composer = Composer(alpha_model=alpha_model,
data_meta=data_meta)
comp_desc = composer.save()
loaded_comp = Composer.load(comp_desc)
self._assert_composer_equal(composer, loaded_comp)
def test_xgb_classify_persistence(self):
model = XGBClassifier(features=self.features)
y = np.where(self.y > 0, 1, 0)
model.fit(self.x, y)
desc = model.save()
new_model = load_model(desc)
self.assertEqual(model.features, new_model.features)
np.testing.assert_array_almost_equal(model.predict(self.sample_x),
new_model.predict(self.sample_x))
np.testing.assert_array_almost_equal(model.importances,
new_model.importances)