DFFML Issue 831. Need to make it so that config properties can be saved / loaded from file

a.py

import json
from dffml import *
from dffml.noasync import train, accuracy

SLRModel = Model.load("slr")
LinearRegressionModel = Model.load("scikitlr")

model1 = SLRModel(
    features=Features(Feature("Years", int, 1)),
    predict=Feature("Salary", int, 1),
    directory="slr-model",
)
model2 = LinearRegressionModel(
    features=Features(Feature("Years", int, 1)),
    predict=Feature("Salary", int, 1),
    directory="scikitlr",
)

from pprint import pprint

pprint(export(model1.config))
pprint(export(model2.config))


class ExampleLoadable(Model):
    async def __aexit__(self, exc_type, exc_value, traceback):
        await super().__aexit__(exc_type, exc_value, traceback)
        (self.config.directory / "a.json").write_text(
            json.dumps(export(self.config))
        )

    @classmethod
    def a(cls, directory):
        return cls(
            **{
                **json.loads((directory / "a.json").read_text()),
                **{"directory": directory},
            }
        )


train(
    model1,
    {"Years": 0, "Expertise": 1, "Trust": 0.1, "Salary": 10},
    {"Years": 1, "Expertise": 3, "Trust": 0.2, "Salary": 20},
    {"Years": 2, "Expertise": 5, "Trust": 0.3, "Salary": 30},
    {"Years": 3, "Expertise": 7, "Trust": 0.4, "Salary": 40},
)


print(
    "Accuracy:",
    accuracy(
        ExampleLoadable.a(model1.config.directory),
        {"Years": 4, "Expertise": 9, "Trust": 0.5, "Salary": 50},
        {"Years": 5, "Expertise": 11, "Trust": 0.6, "Salary": 60},
    ),
)