Support transforming Transformers into stateful transforms

[jnothman]

basically we need some kind of mixin or wrapper that does:

class transformer_to_patsy:
    def __init__(self, transformer, use_partial_fit=False):
        self.fitted = False
        self.use_partial_fit = use_partial_fit
        self.transformer = transformer

    def memorize_chunk(self, X):
        if self.use_partial_fit:
            self.transformer.partial_fit(X)
        elif self.fitted:
            raise RuntimeError
        else:
            self.fitted = True
            self.transformer.fit(X)

    def memorize_finish(self):
        pass

    def transform(self, X):
        return self.transformer.transform()

Then you can define quantiletransform = transformer_to_patsy(QuantileTransformer()) and use quantiletransform(mycategories) in a formula.

[jnothman]

Function composition would also implicitly yield a pipeline. Of course hyperparameter search over transformers would become pretty impossible, but still this seems worth supporting.

[jnothman]

hmm. we'll need some cloning in there at some point too...