Turn functions with a callback into Python generators?
Question:
The Scipy minimization function (just to use as an example), has the option of adding a callback function at each step. So I can do something like,
def my_callback(x):
print x
scipy.optimize.fmin(func, x0, callback=my_callback)
Is there a way to use the callback function to create a generator version of fmin, so that I could do,
for x in my_fmin(func,x0):
print x
It seems like it might be possible with some combination of yields and sends, but I can quite think of anything.
Answers:
How about
data = []
scipy.optimize.fmin(func,x0,callback=data.append)
for line in data:
print line
If not, what exactly do you want to do with the generator’s data?
As pointed in the comments, you could do it in a new thread, using Queue. The drawback is that you’d still need some way to access the final result (what fmin returns at the end). My example below uses an optional callback to do something with it (another option would be to just yield it also, though your calling code would have to differentiate between iteration results and final results):
from thread import start_new_thread
from Queue import Queue
def my_fmin(func, x0, end_callback=(lambda x:x), timeout=None):
q = Queue() # fmin produces, the generator consumes
job_done = object() # signals the processing is done
# Producer
def my_callback(x):
q.put(x)
def task():
ret = scipy.optimize.fmin(func,x0,callback=my_callback)
q.put(job_done)
end_callback(ret) # "Returns" the result of the main call
# Starts fmin in a new thread
start_new_thread(task,())
# Consumer
while True:
next_item = q.get(True,timeout) # Blocks until an input is available
if next_item is job_done:
break
yield next_item
Update: to block the execution of the next iteration until the consumer has finished processing the last one, it’s also necessary to use task_done and join.
# Producer
def my_callback(x):
q.put(x)
q.join() # Blocks until task_done is called
# Consumer
while True:
next_item = q.get(True,timeout) # Blocks until an input is available
if next_item is job_done:
break
yield next_item
q.task_done() # Unblocks the producer, so a new iteration can start
Note that maxsize=1 is not necessary, since no new item will be added to the queue until the last one is consumed.
Update 2: Also note that, unless all items are eventually retrieved by this generator, the created thread will deadlock (it will block forever and its resources will never be released). The producer is waiting on the queue, and since it stores a reference to that queue, it will never be reclaimed by the gc even if the consumer is. The queue will then become unreachable, so nobody will be able to release the lock.
A clean solution for that is unknown, if possible at all (since it would depend on the particular function used in the place of fmin). A workaround could be made using timeout, having the producer raises an exception if put blocks for too long:
q = Queue(maxsize=1)
# Producer
def my_callback(x):
q.put(x)
q.put("dummy",True,timeout) # Blocks until the first result is retrieved
q.join() # Blocks again until task_done is called
# Consumer
while True:
next_item = q.get(True,timeout) # Blocks until an input is available
q.task_done() # (one "task_done" per "get")
if next_item is job_done:
break
yield next_item
q.get() # Retrieves the "dummy" object (must be after yield)
q.task_done() # Unblocks the producer, so a new iteration can start
Concept Use a blocking queue with maxsize=1 and a producer/consumer model.
The callback produces, then the next call to the callback will block on the full queue.
The consumer then yields the value from the queue, tries to get another value, and blocks on read.
The producer is the allowed to push to the queue, rinse and repeat.
Usage:
def dummy(func, arg, callback=None):
for i in range(100):
callback(func(arg+i))
# Dummy example:
for i in Iteratorize(dummy, lambda x: x+1, 0):
print(i)
# example with scipy:
for i in Iteratorize(scipy.optimize.fmin, func, x0):
print(i)
Can be used as expected for an iterator:
for i in take(5, Iteratorize(dummy, lambda x: x+1, 0)):
print(i)
Iteratorize class:
from thread import start_new_thread
from Queue import Queue
class Iteratorize:
"""
Transforms a function that takes a callback
into a lazy iterator (generator).
"""
def __init__(self, func, ifunc, arg, callback=None):
self.mfunc=func
self.ifunc=ifunc
self.c_callback=callback
self.q = Queue(maxsize=1)
self.stored_arg=arg
self.sentinel = object()
def _callback(val):
self.q.put(val)
def gentask():
ret = self.mfunc(self.ifunc, self.stored_arg, callback=_callback)
self.q.put(self.sentinel)
if self.c_callback:
self.c_callback(ret)
start_new_thread(gentask, ())
def __iter__(self):
return self
def next(self):
obj = self.q.get(True,None)
if obj is self.sentinel:
raise StopIteration
else:
return obj
Can probably do with some cleaning up to accept *args and **kwargs for the function being wrapped and/or the final result callback.
Generator as coroutine (no threading)
Let’s have FakeFtp with retrbinary function using callback being called with each successful read of chunk of data:
class FakeFtp(object):
def __init__(self):
self.data = iter(["aaa", "bbb", "ccc", "ddd"])
def login(self, user, password):
self.user = user
self.password = password
def retrbinary(self, cmd, cb):
for chunk in self.data:
cb(chunk)
Using simple callback function has disadvantage, that it is called repeatedly and the callback
function cannot easily keep context between calls.
Following code defines process_chunks generator, which will be able receiving chunks of data one
by one and processing them. In contrast to simple callback, here we are able to keep all the
processing within one function without losing context.
from contextlib import closing
from itertools import count
def main():
processed = []
def process_chunks():
for i in count():
try:
# (repeatedly) get the chunk to process
chunk = yield
except GeneratorExit:
# finish_up
print("Finishing up.")
return
else:
# Here process the chunk as you like
print("inside coroutine, processing chunk:", i, chunk)
product = "processed({i}): {chunk}".format(i=i, chunk=chunk)
processed.append(product)
with closing(process_chunks()) as coroutine:
# Get the coroutine to the first yield
coroutine.next()
ftp = FakeFtp()
# next line repeatedly calls `coroutine.send(data)`
ftp.retrbinary("RETR binary", cb=coroutine.send)
# each callback "jumps" to `yield` line in `process_chunks`
print("processed result", processed)
print("DONE")
To see the code in action, put the FakeFtp class, the code shown above and following line:
main()
into one file and call it:
$ python headsandtails.py
('inside coroutine, processing chunk:', 0, 'aaa')
('inside coroutine, processing chunk:', 1, 'bbb')
('inside coroutine, processing chunk:', 2, 'ccc')
('inside coroutine, processing chunk:', 3, 'ddd')
Finishing up.
('processed result', ['processed(0): aaa', 'processed(1): bbb', 'processed(2): ccc', 'processed(3): ddd'])
DONE
How it works
processed = [] is here just to show, the generator process_chunks shall have no problems to
cooperate with its external context. All is wrapped into def main(): to prove, there is no need to
use global variables.
def process_chunks() is the core of the solution. It might have one shot input parameters (not
used here), but main point, where it receives input is each yield line returning what anyone sends
via .send(data) into instance of this generator. One can coroutine.send(chunk) but in this example it is done via callback refering to this function callback.send.
Note, that in real solution there is no problem to have multiple yields in the code, they are
processed one by one. This might be used e.g. to read (and ignore) header of CSV file and then
continue processing records with data.
We could instantiate and use the generator as follows:
coroutine = process_chunks()
# Get the coroutine to the first yield
coroutine.next()
ftp = FakeFtp()
# next line repeatedly calls `coroutine.send(data)`
ftp.retrbinary("RETR binary", cb=coroutine.send)
# each callback "jumps" to `yield` line in `process_chunks`
# close the coroutine (will throw the `GeneratorExit` exception into the
# `process_chunks` coroutine).
coroutine.close()
Real code is using contextlib closing context manager to ensure, the coroutine.close() is
always called.
Conclusions
This solution is not providing sort of iterator to consume data from in traditional style “from
outside”. On the other hand, we are able to:
- use the generator “from inside”
- keep all iterative processing within one function without being interrupted between callbacks
- optionally use external context
- provide usable results to outside
- all this can be done without using threading
Credits: The solution is heavily inspired by SO answer Python FTP “chunk” iterator (without loading entire file into memory)
written by user2357112
Solution to handle non-blocking callbacks
The solution using threading and queue is pretty good, of high-performance and cross-platform, probably the best one.
Here I provide this not-too-bad solution, which is mainly for handling non-blocking callbacks, e.g. called from the parent function through threading.Thread(target=callback).start(), or other non-blocking ways.
import pickle
import select
import subprocess
def my_fmin(func, x0):
# open a process to use as a pipeline
proc = subprocess.Popen(['cat'], stdin=subprocess.PIPE, stdout=subprocess.PIPE)
def my_callback(x):
# x might be any object, not only str, so we use pickle to dump it
proc.stdin.write(pickle.dumps(x).replace(b'n', b'\n') + b'n')
proc.stdin.flush()
from scipy import optimize
optimize.fmin(func, x0, callback=my_callback)
# this is meant to handle non-blocking callbacks, e.g. called somewhere
# through `threading.Thread(target=callback).start()`
while select.select([proc.stdout], [], [], 0)[0]:
yield pickle.loads(proc.stdout.readline()[:-1].replace(b'\n', b'n'))
# close the process
proc.communicate()
Then you can use the function like this:
# unfortunately, `scipy.optimize.fmin`'s callback is blocking.
# so this example is just for showing how-to.
for x in my_fmin(lambda x: x**2, 3):
print(x)
Although This solution seems quite simple and readable, it’s not as high-performance as the threading and queue solution, because:
- Processes are much heavier than threadings.
- Passing data through pipe instead of memory is much slower.
Besides, it doesn’t work on Windows, because the select module on Windows can only handle sockets, not pipes and other file descriptors.
A variant of Frits’ answer, that:
- Supports
send to choose a return value for the callback
- Supports
throw to choose an exception for the callback
- Supports
close to gracefully shut down
- Does not compute a queue item until it is requested
The complete code with tests can be found on github
import queue
import threading
import collections.abc
class generator_from_callback(collections.abc.Generator):
def __init__(self, expr):
"""
expr: a function that takes a callback
"""
self._expr = expr
self._done = False
self._ready_queue = queue.Queue(1)
self._done_queue = queue.Queue(1)
self._done_holder = [False]
# local to avoid reference cycles
ready_queue = self._ready_queue
done_queue = self._done_queue
done_holder = self._done_holder
def callback(value):
done_queue.put((False, value))
cmd, *args = ready_queue.get()
if cmd == 'close':
raise GeneratorExit
elif cmd == 'send':
return args[0]
elif cmd == 'throw':
raise args[0]
def thread_func():
try:
cmd, *args = ready_queue.get()
if cmd == 'close':
raise GeneratorExit
elif cmd == 'send':
if args[0] is not None:
raise TypeError("can't send non-None value to a just-started generator")
elif cmd == 'throw':
raise args[0]
ret = expr(callback)
raise StopIteration(ret)
except BaseException as e:
done_holder[0] = True
done_queue.put((True, e))
self._thread = threading.Thread(target=thread_func)
self._thread.start()
def __next__(self):
return self.send(None)
def send(self, value):
if self._done_holder[0]:
raise StopIteration
self._ready_queue.put(('send', value))
is_exception, val = self._done_queue.get()
if is_exception:
raise val
else:
return val
def throw(self, exc):
if self._done_holder[0]:
raise StopIteration
self._ready_queue.put(('throw', exc))
is_exception, val = self._done_queue.get()
if is_exception:
raise val
else:
return val
def close(self):
if not self._done_holder[0]:
self._ready_queue.put(('close',))
self._thread.join()
def __del__(self):
self.close()
Which works as:
In [3]: def callback(f):
...: ret = f(1)
...: print("gave 1, got {}".format(ret))
...: f(2)
...: print("gave 2")
...: f(3)
...:
In [4]: i = generator_from_callback(callback)
In [5]: next(i)
Out[5]: 1
In [6]: i.send(4)
gave 1, got 4
Out[6]: 2
In [7]: next(i)
gave 2, got None
Out[7]: 3
In [8]: next(i)
StopIteration
For scipy.optimize.fmin, you would use generator_from_callback(lambda c: scipy.optimize.fmin(func, x0, callback=c))
For a super simple approach…
def callback_to_generator():
data = []
method_with_callback(blah, foo, callback=data.append)
for item in data:
yield item
- Yes, this isn’t good for large data
- Yes, this blocks on all items being processed first
- But it still might be useful for some use cases 🙂
Also thanks to @winston-ewert as this is just a small variant on his answer 🙂
The Scipy minimization function (just to use as an example), has the option of adding a callback function at each step. So I can do something like,
def my_callback(x):
print x
scipy.optimize.fmin(func, x0, callback=my_callback)
Is there a way to use the callback function to create a generator version of fmin, so that I could do,
for x in my_fmin(func,x0):
print x
It seems like it might be possible with some combination of yields and sends, but I can quite think of anything.
How about
data = []
scipy.optimize.fmin(func,x0,callback=data.append)
for line in data:
print line
If not, what exactly do you want to do with the generator’s data?
As pointed in the comments, you could do it in a new thread, using Queue. The drawback is that you’d still need some way to access the final result (what fmin returns at the end). My example below uses an optional callback to do something with it (another option would be to just yield it also, though your calling code would have to differentiate between iteration results and final results):
from thread import start_new_thread
from Queue import Queue
def my_fmin(func, x0, end_callback=(lambda x:x), timeout=None):
q = Queue() # fmin produces, the generator consumes
job_done = object() # signals the processing is done
# Producer
def my_callback(x):
q.put(x)
def task():
ret = scipy.optimize.fmin(func,x0,callback=my_callback)
q.put(job_done)
end_callback(ret) # "Returns" the result of the main call
# Starts fmin in a new thread
start_new_thread(task,())
# Consumer
while True:
next_item = q.get(True,timeout) # Blocks until an input is available
if next_item is job_done:
break
yield next_item
Update: to block the execution of the next iteration until the consumer has finished processing the last one, it’s also necessary to use task_done and join.
# Producer
def my_callback(x):
q.put(x)
q.join() # Blocks until task_done is called
# Consumer
while True:
next_item = q.get(True,timeout) # Blocks until an input is available
if next_item is job_done:
break
yield next_item
q.task_done() # Unblocks the producer, so a new iteration can start
Note that maxsize=1 is not necessary, since no new item will be added to the queue until the last one is consumed.
Update 2: Also note that, unless all items are eventually retrieved by this generator, the created thread will deadlock (it will block forever and its resources will never be released). The producer is waiting on the queue, and since it stores a reference to that queue, it will never be reclaimed by the gc even if the consumer is. The queue will then become unreachable, so nobody will be able to release the lock.
A clean solution for that is unknown, if possible at all (since it would depend on the particular function used in the place of fmin). A workaround could be made using timeout, having the producer raises an exception if put blocks for too long:
q = Queue(maxsize=1)
# Producer
def my_callback(x):
q.put(x)
q.put("dummy",True,timeout) # Blocks until the first result is retrieved
q.join() # Blocks again until task_done is called
# Consumer
while True:
next_item = q.get(True,timeout) # Blocks until an input is available
q.task_done() # (one "task_done" per "get")
if next_item is job_done:
break
yield next_item
q.get() # Retrieves the "dummy" object (must be after yield)
q.task_done() # Unblocks the producer, so a new iteration can start
Concept Use a blocking queue with
maxsize=1and a producer/consumer model.
The callback produces, then the next call to the callback will block on the full queue.
The consumer then yields the value from the queue, tries to get another value, and blocks on read.
The producer is the allowed to push to the queue, rinse and repeat.
Usage:
def dummy(func, arg, callback=None):
for i in range(100):
callback(func(arg+i))
# Dummy example:
for i in Iteratorize(dummy, lambda x: x+1, 0):
print(i)
# example with scipy:
for i in Iteratorize(scipy.optimize.fmin, func, x0):
print(i)
Can be used as expected for an iterator:
for i in take(5, Iteratorize(dummy, lambda x: x+1, 0)):
print(i)
Iteratorize class:
from thread import start_new_thread
from Queue import Queue
class Iteratorize:
"""
Transforms a function that takes a callback
into a lazy iterator (generator).
"""
def __init__(self, func, ifunc, arg, callback=None):
self.mfunc=func
self.ifunc=ifunc
self.c_callback=callback
self.q = Queue(maxsize=1)
self.stored_arg=arg
self.sentinel = object()
def _callback(val):
self.q.put(val)
def gentask():
ret = self.mfunc(self.ifunc, self.stored_arg, callback=_callback)
self.q.put(self.sentinel)
if self.c_callback:
self.c_callback(ret)
start_new_thread(gentask, ())
def __iter__(self):
return self
def next(self):
obj = self.q.get(True,None)
if obj is self.sentinel:
raise StopIteration
else:
return obj
Can probably do with some cleaning up to accept *args and **kwargs for the function being wrapped and/or the final result callback.
Generator as coroutine (no threading)
Let’s have FakeFtp with retrbinary function using callback being called with each successful read of chunk of data:
class FakeFtp(object):
def __init__(self):
self.data = iter(["aaa", "bbb", "ccc", "ddd"])
def login(self, user, password):
self.user = user
self.password = password
def retrbinary(self, cmd, cb):
for chunk in self.data:
cb(chunk)
Using simple callback function has disadvantage, that it is called repeatedly and the callback
function cannot easily keep context between calls.
Following code defines process_chunks generator, which will be able receiving chunks of data one
by one and processing them. In contrast to simple callback, here we are able to keep all the
processing within one function without losing context.
from contextlib import closing
from itertools import count
def main():
processed = []
def process_chunks():
for i in count():
try:
# (repeatedly) get the chunk to process
chunk = yield
except GeneratorExit:
# finish_up
print("Finishing up.")
return
else:
# Here process the chunk as you like
print("inside coroutine, processing chunk:", i, chunk)
product = "processed({i}): {chunk}".format(i=i, chunk=chunk)
processed.append(product)
with closing(process_chunks()) as coroutine:
# Get the coroutine to the first yield
coroutine.next()
ftp = FakeFtp()
# next line repeatedly calls `coroutine.send(data)`
ftp.retrbinary("RETR binary", cb=coroutine.send)
# each callback "jumps" to `yield` line in `process_chunks`
print("processed result", processed)
print("DONE")
To see the code in action, put the FakeFtp class, the code shown above and following line:
main()
into one file and call it:
$ python headsandtails.py
('inside coroutine, processing chunk:', 0, 'aaa')
('inside coroutine, processing chunk:', 1, 'bbb')
('inside coroutine, processing chunk:', 2, 'ccc')
('inside coroutine, processing chunk:', 3, 'ddd')
Finishing up.
('processed result', ['processed(0): aaa', 'processed(1): bbb', 'processed(2): ccc', 'processed(3): ddd'])
DONE
How it works
processed = [] is here just to show, the generator process_chunks shall have no problems to
cooperate with its external context. All is wrapped into def main(): to prove, there is no need to
use global variables.
def process_chunks() is the core of the solution. It might have one shot input parameters (not
used here), but main point, where it receives input is each yield line returning what anyone sends
via .send(data) into instance of this generator. One can coroutine.send(chunk) but in this example it is done via callback refering to this function callback.send.
Note, that in real solution there is no problem to have multiple yields in the code, they are
processed one by one. This might be used e.g. to read (and ignore) header of CSV file and then
continue processing records with data.
We could instantiate and use the generator as follows:
coroutine = process_chunks()
# Get the coroutine to the first yield
coroutine.next()
ftp = FakeFtp()
# next line repeatedly calls `coroutine.send(data)`
ftp.retrbinary("RETR binary", cb=coroutine.send)
# each callback "jumps" to `yield` line in `process_chunks`
# close the coroutine (will throw the `GeneratorExit` exception into the
# `process_chunks` coroutine).
coroutine.close()
Real code is using contextlib closing context manager to ensure, the coroutine.close() is
always called.
Conclusions
This solution is not providing sort of iterator to consume data from in traditional style “from
outside”. On the other hand, we are able to:
- use the generator “from inside”
- keep all iterative processing within one function without being interrupted between callbacks
- optionally use external context
- provide usable results to outside
- all this can be done without using threading
Credits: The solution is heavily inspired by SO answer Python FTP “chunk” iterator (without loading entire file into memory)
written by user2357112
Solution to handle non-blocking callbacks
The solution using threading and queue is pretty good, of high-performance and cross-platform, probably the best one.
Here I provide this not-too-bad solution, which is mainly for handling non-blocking callbacks, e.g. called from the parent function through threading.Thread(target=callback).start(), or other non-blocking ways.
import pickle
import select
import subprocess
def my_fmin(func, x0):
# open a process to use as a pipeline
proc = subprocess.Popen(['cat'], stdin=subprocess.PIPE, stdout=subprocess.PIPE)
def my_callback(x):
# x might be any object, not only str, so we use pickle to dump it
proc.stdin.write(pickle.dumps(x).replace(b'n', b'\n') + b'n')
proc.stdin.flush()
from scipy import optimize
optimize.fmin(func, x0, callback=my_callback)
# this is meant to handle non-blocking callbacks, e.g. called somewhere
# through `threading.Thread(target=callback).start()`
while select.select([proc.stdout], [], [], 0)[0]:
yield pickle.loads(proc.stdout.readline()[:-1].replace(b'\n', b'n'))
# close the process
proc.communicate()
Then you can use the function like this:
# unfortunately, `scipy.optimize.fmin`'s callback is blocking.
# so this example is just for showing how-to.
for x in my_fmin(lambda x: x**2, 3):
print(x)
Although This solution seems quite simple and readable, it’s not as high-performance as the threading and queue solution, because:
- Processes are much heavier than threadings.
- Passing data through pipe instead of memory is much slower.
Besides, it doesn’t work on Windows, because the select module on Windows can only handle sockets, not pipes and other file descriptors.
A variant of Frits’ answer, that:
- Supports
sendto choose a return value for the callback - Supports
throwto choose an exception for the callback - Supports
closeto gracefully shut down - Does not compute a queue item until it is requested
The complete code with tests can be found on github
import queue
import threading
import collections.abc
class generator_from_callback(collections.abc.Generator):
def __init__(self, expr):
"""
expr: a function that takes a callback
"""
self._expr = expr
self._done = False
self._ready_queue = queue.Queue(1)
self._done_queue = queue.Queue(1)
self._done_holder = [False]
# local to avoid reference cycles
ready_queue = self._ready_queue
done_queue = self._done_queue
done_holder = self._done_holder
def callback(value):
done_queue.put((False, value))
cmd, *args = ready_queue.get()
if cmd == 'close':
raise GeneratorExit
elif cmd == 'send':
return args[0]
elif cmd == 'throw':
raise args[0]
def thread_func():
try:
cmd, *args = ready_queue.get()
if cmd == 'close':
raise GeneratorExit
elif cmd == 'send':
if args[0] is not None:
raise TypeError("can't send non-None value to a just-started generator")
elif cmd == 'throw':
raise args[0]
ret = expr(callback)
raise StopIteration(ret)
except BaseException as e:
done_holder[0] = True
done_queue.put((True, e))
self._thread = threading.Thread(target=thread_func)
self._thread.start()
def __next__(self):
return self.send(None)
def send(self, value):
if self._done_holder[0]:
raise StopIteration
self._ready_queue.put(('send', value))
is_exception, val = self._done_queue.get()
if is_exception:
raise val
else:
return val
def throw(self, exc):
if self._done_holder[0]:
raise StopIteration
self._ready_queue.put(('throw', exc))
is_exception, val = self._done_queue.get()
if is_exception:
raise val
else:
return val
def close(self):
if not self._done_holder[0]:
self._ready_queue.put(('close',))
self._thread.join()
def __del__(self):
self.close()
Which works as:
In [3]: def callback(f):
...: ret = f(1)
...: print("gave 1, got {}".format(ret))
...: f(2)
...: print("gave 2")
...: f(3)
...:
In [4]: i = generator_from_callback(callback)
In [5]: next(i)
Out[5]: 1
In [6]: i.send(4)
gave 1, got 4
Out[6]: 2
In [7]: next(i)
gave 2, got None
Out[7]: 3
In [8]: next(i)
StopIteration
For scipy.optimize.fmin, you would use generator_from_callback(lambda c: scipy.optimize.fmin(func, x0, callback=c))
For a super simple approach…
def callback_to_generator():
data = []
method_with_callback(blah, foo, callback=data.append)
for item in data:
yield item
- Yes, this isn’t good for large data
- Yes, this blocks on all items being processed first
- But it still might be useful for some use cases 🙂
Also thanks to @winston-ewert as this is just a small variant on his answer 🙂