def test_read_test_data_bunch(read_data_set, test_params, expected): data_bunch = read_test_data_bunch(read_data_set, test_params) assert data_bunch.test == expected["test"] # # mock_state_dict = {} # mock_remote_dir = {} # # def mock_store_artifact_locally(data, directory, filename): # data["directory"]=directory # data["filename"]=filename # # def mock_copy_from_local_to_remote(source_dir, target_dir, filename, overwrite, delete_source): # target_dir["source_dir"]=source_dir # target_dir["filename"]=filename # target_dir["overwrite"]=overwrite # target_dir["delete_source"]=delete_source # # local_dir = "./local" # filename = "abc.txt" # overwrite_remote = True # keep_local = False # # expected_local = { # "directory": local_dir, # "filename": filename # } # expected_remote = { # "source_dir": local_dir, # "filename": filename, # "overwrite": overwrite_remote, # "delete_source": not keep_local # } # # # @pytest.mark.parametrize("store_artifact_locally, copy_from_local_to_remote, data, local_dir, filename, remote_dir, overwrite_remote, keep_local, expected_local, expected_remote", # [(mock_store_artifact_locally, mock_copy_from_local_to_remote, mock_state_dict, local_dir, filename, mock_remote_dir, overwrite_remote, keep_local, expected_local, expected_remote)]) # def test_store_artifacts(store_artifact_locally, copy_from_local_to_remote, data, local_dir, filename, remote_dir, overwrite_remote, keep_local, expected_local, expected_remote): # # store_artifacts(store_artifact_locally, copy_from_local_to_remote, data, local_dir, # filename, remote_dir, overwrite_remote, keep_local) # # assert expected_local == data # assert expected_remote == remote_dir # TODO
# a function for evaluating keras metrics evaluate = get_and_log(keras_containers.ModelEvaluators, config["init"]["evaluate"]["name"]) # a function that predictions using a keras model predict = get_and_log(keras_containers.PredictionFunctions, config["init"]["predict"]["name"]) # ## Execution # # Here we use the providers defined above to execute various tasks # ### Get source data data_bunch_source = tasks.read_test_data_bunch( read_source_data_set, **config["exec"]["read_source_data"]["params"]) print("Source data read using following parameters: \n") print_dict(config["exec"]["read_source_data"]["params"]) print("Read data_bunch consists of: \n") print_data_bunch(data_bunch_source) # ### Load Model # ##### Get custom loss function if config["init"]["get_loss_function"]["name"] == "get_custom_loss": loss = get_loss_function(**config["exec"]["get_loss_function"]["params"]) custom_objects = {loss.__name__: loss} else: custom_objects = None
def test_read_test_data_bunch(read_data_set, test_params, expected): data_bunch = read_test_data_bunch(read_data_set, test_params) assert data_bunch.test == expected["test"]