def get_model_by_name(name):
"""
Get a model from database and reproduce it given the parameters saved
:param name: str - name of the model
:return: (None, None, str) | (None, dict, str) | (BaseModel, dict, None) - str is error message, dict is model's
parameters from DB, BaseModel is the instance of the model, might be ConvolutionalNeuralNetwork,
GaussianProcesses, SparseGaussianProcesses up to date...
"""
model_record, err = DBManager.get_model_by_name(name)
if model_record is None:
return None, None, err
if model_record.type == 'CNN':
cnn, err = ConvolutionalNeuralNetwork.new_from_json(
model_record.model_params, model_record.extra_params)
return cnn, model_record, None
elif model_record.type == 'FullGP':
full_gp, err = GaussianProcesses.new_from_json(
model_record.model_params, model_record.extra_params)
return full_gp, model_record, None
elif model_record.type == 'SparseGP':
sparse_gp, err = SparseGaussianProcesses.new_from_json(
model_record.model_params, model_record.extra_params)
return sparse_gp, model_record, None
return None, model_record, err
def test_model_to_json_load_from_json(self):
global full_gp
model_params, extra_params = full_gp.model_to_json()
model_params_dict = json.loads(model_params)
extra_params_dict = json.loads(extra_params)
assert model_params_dict['data']['kernel'] == full_gp.kernel.to_dict()
assert model_params_dict['data']['params'] == full_gp.model.param_array.tolist()
loaded_gp, msg = GaussianProcesses.new_from_json(model_params_dict, extra_params_dict)
assert msg is None
assert full_gp.stats == loaded_gp.stats
assert full_gp.kernel.to_dict() == loaded_gp.kernel.to_dict()
assert full_gp.model.param_array.tolist() == loaded_gp.model.param_array.tolist()