Best practice for operations on streams of text
James
I'm working on some NLP code - what I'm doing is passing a large
number of tokens through a number of filtering / processing steps.
The filters take a token as input, and may or may not yield a token as
a result. For example, I might have filters which lowercases the
input, filter out boring words and filter out duplicates chained
together.
I originally had code like this:
for t0 in token_stream:
for t1 in lowercase_token(t0):
for t2 in remove_boring(t1):
for t3 in remove_dupes(t2):
yield t3
Apart from being ugly as sin, I only get one token out as
StopIteration is raised before the whole token stream is consumed.
Any suggestions on an elegant way to chain together a bunch of
generators, with processing steps in between?
Thanks,
James
J Kenneth King
Co-routines my friends. Google will help you greatly in discovering
this processing wonder.
Gary Herron
> http://mail.python.org/mailman/listinfo/python-list
>
David Beazly has a very interesting talk on using generators for
building and linking together individual stream filters. Its very cool
and surprisingly eye-opening.
See "Generator Tricks for Systems Programmers" at
http://www.dabeaz.com/generators/
Gary Herron
MRAB
yield values on demand:
def lowercase_token(stream):
for t in stream:
yield t.lower()
def remove_boring(stream):
for t in stream:
if t not in boring:
yield t
def remove_dupes(stream):
seen = set()
for t in stream:
if t not in seen:
yield t
seen.add(t)
def compound_filter(token_stream):
stream = lowercase_token(token_stream)
stream = remove_boring(stream)
stream = remove_dupes(stream)
for t in stream(t):
yield t
Terry Reedy
> James wrote:
>> Hello all,
>> I'm working on some NLP code - what I'm doing is passing a large
>> number of tokens through a number of filtering / processing steps.
>>
>> The filters take a token as input, and may or may not yield a token as
>> a result. For example, I might have filters which lowercases the
>> input, filter out boring words and filter out duplicates chained
>> together.
>>
>> I originally had code like this:
>> for t0 in token_stream:
>> for t1 in lowercase_token(t0):
>> for t2 in remove_boring(t1):
>> for t3 in remove_dupes(t2):
>> yield t3
For that to work at all, the three functions would have to turn each
token into an iterable of 0 or 1 tokens. Hence the inner 'loops' would
execute 0 or 1 times. Better to return a token or None, and replace the
three inner 'loops' with three conditional statements (ugly too) or less
efficiently (due to lack of short circuiting),
t = remove_dupes(remove_boring(lowercase_token(t0)))
if t is not None: yield t
>> Apart from being ugly as sin, I only get one token out as
>> StopIteration is raised before the whole token stream is consumed.
That puzzles me. Your actual code must be slightly different from the
above and what I imagine the functions to be. But nevermind, because
>> Any suggestions on an elegant way to chain together a bunch of
>> generators, with processing steps in between?
MRAB's suggestion is the way to go. Your automatically get
short-circuiting because each generator only gets what is passed on.
And resuming a generator is much faster that re-calling a function.
> What you should be doing is letting the filters accept an iterator and
> yield values on demand:
>
> def lowercase_token(stream):
> for t in stream:
> yield t.lower()
>
> def remove_boring(stream):
> for t in stream:
> if t not in boring:
> yield t
>
> def remove_dupes(stream):
> seen = set()
> for t in stream:
> if t not in seen:
> yield t
> seen.add(t)
>
> def compound_filter(token_stream):
> stream = lowercase_token(token_stream)
> stream = remove_boring(stream)
> stream = remove_dupes(stream)
> for t in stream(t):
> yield t
I also recommend the Beazly reference Herron gave.
tjr
Beni Cherniavsky
> def compound_filter(token_stream):
> stream = lowercase_token(token_stream)
> stream = remove_boring(stream)
> stream = remove_dupes(stream)
> for t in stream(t):
> yield t
The last loop is superfluous. You can just do::
def compound_filter(token_stream):
stream = lowercase_token(token_stream)
stream = remove_boring(stream)
stream = remove_dupes(stream)
return stream
which is simpler and slightly more efficient. This works because from
the caller's perspective, a generator is just a function that returns
an iterator. It doesn't matter whether it implements the iterator
itself by containing ``yield`` statements, or shamelessly passes on an
iterator implemented elsewhere.