I may have led in that direction, and I know R only passingly, not well.
But my understanding is that thinking of a data structure that gets parsed
by an evaluator, e.g. "do a linear regression with this structure (and a
DataFrame)" is better than a lambda.
I'm sure it's possible to describe this with a function, but the Patsy
documentation provides something that is probably more helpful:
from patsy import ModelDesc, Term, EvalFactorform1 =
ModelDesc([Term([EvalFactor("y")])],
[Term([]),
Term([EvalFactor("a")]),
Term([EvalFactor("a"), EvalFactor("b")]),
Term([EvalFactor("np.log(x)")])
])
Compare to what you get from parsing the above formula:
form2 = ModelDesc.from_formula("y ~ a + a:b + np.log(x)")
So given those two equivalent structures, we might call, identically:
decision_tree(form1, data=my_df)
decision_tree(form2, data=my_df)
I think if I were writing a high-level data structure rather than a simple
parse tree, I might do something more like:
{'dependent': ['y'],
'independent': ['a', Combine('a', 'b'), np.log(x)]}
But whatever the exact structure, basically the syntax just is a
mini-language to say which names go in which structural places for an
evaluator.
On Mon, Feb 24, 2020 at 3:43 PM Serhiy Storchaka <storch...@gmail.com>
wrote:
> 24.02.20 22:02, Guido van Rossum пише:
> > Hm, that's actually an interesting take. Can you compare it to the kind
> > of "quoting" that happens in a lambda? Is there some kind of translation
> > of the OP's original example (Lottery ~ Literacy + Wealth + Region) to a
> > lambda involving those words?
>
> I think that a named function is more appropriate than a lambda, because
> we need also the name of the output parameter:
>
> def Lottery(Literacy, Wealth, Region):
>
> And the most known application of such technique is fixtures in pytest.
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