4 ms·

eli5: what can you use these for? what are some examples of when you'd use something like this...

fnordsensei 11y ago
The idea is that you can define a transformation separate from its use and then reuse it in many contexts. Some of the win sits in decoupling what you are doing from when and where you are doing it. Rich Hickey wrote about them on Cognitect's blog a while back: http://blog.cognitect.com/blog/2014/8/6/transducers-are-coming http://blog.cognitect.com/blog/2014/8/6/transducers-are-comi... The author refers to the test suite for examples: https://github.com/cognitect-labs/transducers-python/blob/master/tests/transducer_tests.py https://github.com/cognitect-labs/transducers-python/blob/ma...
- ufo 11y ago
  I always had a hard time figuring out how transducers are different "stream transformation" functions like map and filter. I know that they are more complicated/powerful than that but I can't quite point that out precisely.
  - deleted 11y ago
    [deleted]
  - fnordsensei 11y ago
    You can transform map or filter into a transducer by decoupling the transformation from the data. When you call map, you usually call it on some data, or some stream. When you create a transducer, and you call for example (map inc), you're saying "whenever this transducer is applied, on whatever it is applied to, increase each unit by one". You can lug around the transformation itself as if it were an object, almost, separate from the data it's meant to transform.
    - ufo 11y ago
      How is that different from just partially applying/currying the map?
      - closed 11y ago
        A key component to transducers is to realize that map, filter, take, can all be expressed as special cases of the reduce function. So a curried map function can be expressed as a reduction operation, for example, in python you could do.. # map that adds 1 to each element # same as curried map(sub1)([1,2,3]) sub1 = lambda x: x - 1 map(sub1, [1,2,3]) # same outcome using reduce reduce(lambda acc, x: acc + [sub1(x)], [1,2,3], []) Notice that when writing a map operation using the reduce function, we've put two pieces of logic on each step. (1) we have a transformation called sub1, and (2) we have a step operation (returning an array with the transformed x appended to it). We can use this same logic to create filters with an if statement.. lt2 = lambda x: x < 2 filter(lt2, [1,2,3]) reduce(lambda acc, x: acc + [x] if lt2(x) else acc, [1,2,3], []) A critical thing to notice in each of these cases is that if we ran a curried filter and map (with transformation / filtering functions already passed), then they would loop over the data twice. However, if you look at the example on this github repo, the want to * take 3 elements from the sequence * perform a transformation (map operation) to those elements Notice that in this case take is being performed before the map, but you could imagine doing something like * map items from geometric series to float * filter items that are greater than .15 * take 3 of the remaining items How would you do that with map and filter functions without looping over the entire (inexhaustible) sequence? This is the beauty of transformers and transducers, they provide a composable approach to filtering, transforming, and taking elements from a (possibly infinite) sequence. Of course, all of this could be done in a for loop, but often it introduces complexity.. # assume geom_series is the generator from the github example take = 3 for el in geom_series: if float(el) < .15: result.append(el) if len(result) == take: break However, this for loop is using the same ideas: we have a transformation / if (predicate) operation, and step operation to append the data we want. The problem transducers try to solve is writing a series of these types of operations compactly and cleary as a pipeline of functions (erm, transformers), so those functions can be replaced/moved around quickly, and the pipelines can be combined together simply. A good article that covers tranducers in javascript (sorry!) is here: http://simplectic.com/blog/2014/transducers-explained-1/ http://simplectic.com/blog/2014/transducers-explained-1/
deleted 11y ago
[deleted]

The idea is that you can define a transformation separate from its use and then reuse it in many contexts. Some of the win sits in decoupling what you are doing from when and where you are doing it. Rich Hickey wrote about them on Cognitect's blog a while back: http://blog.cognitect.com/blog/2014/8/6/transducers-are-coming http://blog.cognitect.com/blog/2014/8/6/transducers-are-comi... The author refers to the test suite for examples: https://github.com/cognitect-labs/transducers-python/blob/master/tests/transducer_tests.py https://github.com/cognitect-labs/transducers-python/blob/ma...

ufo 11y ago
I always had a hard time figuring out how transducers are different "stream transformation" functions like map and filter. I know that they are more complicated/powerful than that but I can't quite point that out precisely.
- deleted 11y ago
  [deleted]
- fnordsensei 11y ago
  You can transform map or filter into a transducer by decoupling the transformation from the data. When you call map, you usually call it on some data, or some stream. When you create a transducer, and you call for example (map inc), you're saying "whenever this transducer is applied, on whatever it is applied to, increase each unit by one". You can lug around the transformation itself as if it were an object, almost, separate from the data it's meant to transform.
  - ufo 11y ago
    How is that different from just partially applying/currying the map?
    - closed 11y ago
      A key component to transducers is to realize that map, filter, take, can all be expressed as special cases of the reduce function. So a curried map function can be expressed as a reduction operation, for example, in python you could do.. # map that adds 1 to each element # same as curried map(sub1)([1,2,3]) sub1 = lambda x: x - 1 map(sub1, [1,2,3]) # same outcome using reduce reduce(lambda acc, x: acc + [sub1(x)], [1,2,3], []) Notice that when writing a map operation using the reduce function, we've put two pieces of logic on each step. (1) we have a transformation called sub1, and (2) we have a step operation (returning an array with the transformed x appended to it). We can use this same logic to create filters with an if statement.. lt2 = lambda x: x < 2 filter(lt2, [1,2,3]) reduce(lambda acc, x: acc + [x] if lt2(x) else acc, [1,2,3], []) A critical thing to notice in each of these cases is that if we ran a curried filter and map (with transformation / filtering functions already passed), then they would loop over the data twice. However, if you look at the example on this github repo, the want to * take 3 elements from the sequence * perform a transformation (map operation) to those elements Notice that in this case take is being performed before the map, but you could imagine doing something like * map items from geometric series to float * filter items that are greater than .15 * take 3 of the remaining items How would you do that with map and filter functions without looping over the entire (inexhaustible) sequence? This is the beauty of transformers and transducers, they provide a composable approach to filtering, transforming, and taking elements from a (possibly infinite) sequence. Of course, all of this could be done in a for loop, but often it introduces complexity.. # assume geom_series is the generator from the github example take = 3 for el in geom_series: if float(el) < .15: result.append(el) if len(result) == take: break However, this for loop is using the same ideas: we have a transformation / if (predicate) operation, and step operation to append the data we want. The problem transducers try to solve is writing a series of these types of operations compactly and cleary as a pipeline of functions (erm, transformers), so those functions can be replaced/moved around quickly, and the pipelines can be combined together simply. A good article that covers tranducers in javascript (sorry!) is here: http://simplectic.com/blog/2014/transducers-explained-1/ http://simplectic.com/blog/2014/transducers-explained-1/

deleted 11y ago

[deleted]