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_linear_regression_persistence(self):
model = LinearRegression(['a', 'b', 'c'], fit_intercept=False)
model.fit(self.train_x, self.train_y)
desc = model.save()
new_model = LinearRegression.load(desc)
calculated_y = new_model.predict(self.predict_x)
expected_y = model.predict(self.predict_x)
np.testing.assert_array_almost_equal(calculated_y, expected_y)
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)
else:
raise ValueError(
'{0} is not currently supported in model loader.'.format(
model_name))