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"]