On Monday, November 20, 2017 at 10:49:01 AM UTC-5, Jason wrote:
> a pipeline can be described as a sequence of functions that are applied to an
> input with each subsequent function getting the output of the preceding
> function:
>
> out = f6(f5(f4(f3(f2(f1(in))))))
>
> However this isn't very readable and does not support conditionals.
>
> Tensorflow has tensor-focused pipepines:
> fc1 = layers.fully_connected(x, 256, activation_fn=tf.nn.relu,
> scope='fc1')
> fc2 = layers.fully_connected(fc1, 256, activation_fn=tf.nn.relu,
> scope='fc2')
> out = layers.fully_connected(fc2, 10, activation_fn=None, scope='out')
>
> I have some code which allows me to mimic this, but with an implied parameter.
>
> def executePipeline(steps, collection_funcs = [map, filter, reduce]):
> results = None
> for step in steps:
> func = step[0]
> params = step[1]
> if func in collection_funcs:
> print func, params[0]
> results = func(functools.partial(params[0],
> *params[1:]), results)
> else:
> print func
> if results is None:
> results = func(*params)
> else:
> results = func(*(params+(results,)))
> return results
>
> executePipeline( [
> (read_rows, (in_file,)),
> (map, (lower_row, field)),
> (stash_rows, ('stashed_file', )),
> (map, (lemmatize_row, field)),
> (vectorize_rows, (field, min_count,)),
> (evaluate_rows, (weights, None)),
> (recombine_rows, ('stashed_file', )),
> (write_rows, (out_file,))
> ]
> )
>
> Which gets me close, but I can't control where rows gets passed in. In the
> above code, it is always the last parameter.
>
> I feel like I'm reinventing a wheel here. I was wondering if there's already
> something that exists?
Why do I want this? Because I'm tired of writing code that is locked away in a
bespoke function. I'd have an army of functions all slightly different in
functionality. I require flexibility in defining pipelines, and I don't want a
custom pipeline to require any low-level coding. I just want to feed a sequence
of functions to a script and have it process it. A middle ground between the
shell | operator and bespoke python code. Sure, I could write many binaries
bound by shell, but there are some things done far easier in python because of
its extensive libraries and it can exist throughout the execution of the
pipeline whereas any temporary persistence has to be though environment
variables or files.
Well after examining your feedback, it looks like Grapevine has 99% of the
concepts that I wanted to invent, even if the | operator seems a bit clunky. I
personally prefer the affluent interface convention. But this should work.
Kamaelia could also work, but it seems a little bit more grandiose.
Thanks everyone who chimed in!
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