async def input_set(self, record: Record) -> List[Input]:
return ([
Input(
value=record.feature(feature.name),
definition=Definition(
name=feature.name,
primitive=str(feature.dtype()),
),
) for feature in self.parent.config.features
] + [
Input(
value=value,
definition=self.parent.config.dataflow.definitions[name],
) for value, name in self.parent.config.inputs
] + ([] if not self.parent.config.length else [
Input(
value=await self.sctx.length(),
definition=Definition(
name=self.parent.config.length,
primitive="int",
),
)
]) + ([] if not self.parent.config.record_def else [
Input(
value=record.key,
definition=Definition(
name=self.parent.config.record_def,
primitive="string",
),
)
]))
async def test_predict(self):
records: Dict[str, Record] = {
record.key: record.export()
async for record in self.sctx.records()
}
async with self.post(f"/model/{self.mlabel}/predict/0",
json=records) as r:
i: int = 0
response = await r.json()
for key, record_data in response["records"].items():
record = Record(key, data=record_data)
self.assertEqual(int(record.key), i)
self.assertEqual(
record.feature("by_ten"),
record.prediction("Salary").value / 10,
)
self.assertEqual(float(record.key),
record.prediction("Salary").confidence)
i += 1
self.assertEqual(i, self.num_records)
