Operation Model

class dffml.operation.model.ModelPredictConfig(model: dffml.model.model.Model)

no_enforce_immutable()

By default, all properties of a config object are immutable. If you would like to mutate immutable properties, you must explicitly call this method using it as a context manager.

Examples

>>> from dffml import config
>>>
>>> @config
... class MyConfig:
...     C: int
>>>
>>> config = MyConfig(C=2)
>>> with config.no_enforce_immutable():
...     config.C = 1

async dffml.operation.model.model_predict(self, features: Dict[str, Any]) → Dict[str, Any]

Predict using dffml models.

Parameters:

features (dict) – A dictionary contaning feature name and feature value.

Returns:

A dictionary containing prediction.

Return type:

dict

Examples

The following example shows how to use model_predict.

>>> import asyncio
>>> from dffml import *
>>>
>>> slr_model = SLRModel(
...     features=Features(Feature("Years", int, 1)),
...     predict=Feature("Salary", int, 1),
...     location="tempdir",
... )
>>> dataflow = DataFlow(
...     operations={
...         "prediction_using_model": model_predict,
...         "get_single": GetSingle,
...     },
...     configs={"prediction_using_model": ModelPredictConfig(model=slr_model)},
... )
>>> dataflow.seed.append(
...     Input(
...         value=[model_predict.op.outputs["prediction"].name],
...         definition=GetSingle.op.inputs["spec"],
...     )
... )
>>>
>>> async def main():
...     await train(
...         slr_model,
...         {"Years": 0, "Salary": 10},
...         {"Years": 1, "Salary": 20},
...         {"Years": 2, "Salary": 30},
...         {"Years": 3, "Salary": 40},
...     )
...     inputs = [
...        Input(
...            value={"Years": 4}, definition=model_predict.op.inputs["features"],
...        )
...     ]
...     async for ctx, results in MemoryOrchestrator.run(dataflow, inputs):
...         print(results)
>>>
>>> asyncio.run(main())
{'model_predictions': {'Salary': {'confidence': 1.0, 'value': 50}}}