async def test_dataflow_usage_example(self):
# Write out shouldi dataflow
orig = self.mktempfile() + ".json"
pathlib.Path(orig).write_text(json.dumps(self.DATAFLOW.export()))
# Import from feature/git
transform_to_repo = Operation.load("dffml.mapping.create")
lines_of_code_by_language, lines_of_code_to_comments = list(
load(
"dffml_feature_git.feature.operations:lines_of_code_by_language",
"dffml_feature_git.feature.operations:lines_of_code_to_comments",
relative=relative_path("..", "..", "feature", "git"),
))
# Create new dataflow
override = DataFlow.auto(
transform_to_repo,
lines_of_code_by_language,
lines_of_code_to_comments,
)
# TODO Modify and compare against yaml in docs example
# Write out override dataflow
created = self.mktempfile() + ".json"
pathlib.Path(created).write_text(json.dumps(override.export()))
# Merge the two
with contextlib.redirect_stdout(self.stdout):
await CLI.cli("dataflow", "merge", orig, created)
DataFlow._fromdict(**json.loads(self.stdout.getvalue()))
async def test_run(self):
self.required_plugins("dffml-config-yaml", "dffml-model-scratch")
# Load get_single and model_predict
get_single = Operation.load("get_single")
model_predict = list(load("dffml.operation.model:model_predict"))[0]
# Create new dataflow from operations
dataflow = DataFlow.auto(get_single, model_predict)
# Add the seed inputs
dataflow.seed.append(
Input(
value=[
definition.name
for definition in model_predict.op.outputs.values()
],
definition=get_single.inputs["spec"],
))
# Write out the dataflow
dataflow_yaml = pathlib.Path(self.mktempfile() + ".yaml")
async with BaseConfigLoader.load("yaml").withconfig(
{}) as configloader:
async with configloader() as loader:
dataflow_yaml.write_bytes(await loader.dumpb(
dataflow.export(linked=True)))
# TODO Figure out how nested model config options will work
# print(dataflow_yaml.read_text())
return
async def setUp(self):
await super().setUp()
# Use shouldi's dataflow for tests
self.DATAFLOW = list(load("shouldi.cli:DATAFLOW"))[0]
