Mercurial > pylearn
view doc/v2_planning/plugin.py @ 1128:03b41a79bd60
coding_style: Replies to James' questions / comments
author | Olivier Delalleau <delallea@iro> |
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date | Wed, 15 Sep 2010 12:06:09 -0400 |
parents | 8cc324f388ba |
children | a1957faecc9b |
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import time from collections import defaultdict inf = float('inf') ################ ### SCHEDULE ### ################ class Schedule(object): def __add__(self, i): return OffsetSchedule(self, i) def __or__(self, s): return UnionSchedule(self, to_schedule(s)) def __and__(self, s): return IntersectionSchedule(self, to_schedule(s)) def __sub__(self, i): return OffsetSchedule(self, -i) def __ror__(self, s): return UnionSchedule(to_schedule(s), self) def __rand__(self, s): return IntersectionSchedule(to_schedule(s), self) def __invert__(self): return NegatedSchedule(self) def to_schedule(x): if x in (None, False): return never if x is True: return always elif isinstance(x, (list, tuple)): return reduce(UnionSchedule, x) else: return x class ScheduleMix(Schedule): __n__ = None def __init__(self, *subschedules): assert (not self.__n__) or len(subschedules) == self.__n__ self.subschedules = map(to_schedule, subschedules) class UnionSchedule(ScheduleMix): def __call__(self, t1, t2): return any(s(t1, t2) for s in self.subschedules) class IntersectionSchedule(ScheduleMix): def __call__(self, t1, t2): return all(s(t1, t2) for s in self.subschedules) class DifferenceSchedule(ScheduleMix): __n__ = 2 def __call__(self, t1, t2): return self.subschedules[0](t1, t2) and not self.subschedules[1](t1, t2) class NegatedSchedule(ScheduleMix): __n__ = 1 def __call__(self, t1, t2): return not self.subschedules[0](t1, t2) class OffsetSchedule(Schedule): def __init__(self, schedule, offset): self.schedule = schedule self.offset = offset def __call__(self, t1, t2): return self.schedule(t1 - self.offset, t2 - self.offset) class AlwaysSchedule(Schedule): def __call__(self, t1, t2): return True always = AlwaysSchedule() never = ~always class IntervalSchedule(Schedule): def __init__(self, step, repeat = inf): self.step = step self.upper_bound = step * (repeat - 1) def __call__(self, t1, t2): if t2 < 0 or t1 > self.upper_bound: return False diff = t2 - t1 t1m = t1 % self.step t2m = t2 % self.step return (diff >= self.step or t1m == 0 or t2m == 0 or t1m > t2m) each = lambda step, repeat = inf: each0(step, repeat) + step each0 = IntervalSchedule class RangeSchedule(Schedule): def __init__(self, low = None, high = None): self.low = low or -inf self.high = high or inf def __call__(self, t1, t2): return self.low <= t1 <= self.high \ or self.low <= t2 <= self.high inrange = RangeSchedule class ListSchedule(Schedule): def __init__(self, *schedules): self.schedules = schedules def __call__(self, t1, t2): for t in self.schedules: if t1 <= t <= t2: return True return False at = ListSchedule at_start = at(-inf) at_end = at(inf) ############## ### RUNNER ### ############## class scratchpad: pass # # ORIGINAL RUNNER, NO TIMELINES # def runner(master, plugins): # """ # master is a function which is in charge of the "this" object. It # is in charge of updating the t1, t2 and done fields, It must # take a single argument, this. # plugins is a list of (schedule, function) pairs. In-between each # execution of the master function, as well as at the very # beginning and at the very end, the schedule will be consulted # for the time range [t1, t2], and if there is a match, the # function will be called with this as the argument. The order # in which the functions are provided is respected. # Note: the reason why we use t1 and t2 instead of just t is that it # gives the master function the ability to run several iterations at # once without consulting any plugins. In that situation, t1 and t2 # represent a range, and the schedule must determine if there would # have been an event in that range (we do not distinguish between a # single event and multiple events). # For instance, if one is training using minibatches, one could set # t1 and t2 to the index of the lower and higher examples, and the # plugins' schedules would be given according to how many examples # were seen rather than how many minibatches were processed. # Another possibility is to use real time - t1 would be the time # before the execution of the master function, t2 the time after # (in, say, milliseconds). Then you can define plugins that run # every second or every minute, but only in-between two training # iterations. # """ # this = scratchpad() # this.t1 = -inf # this.t2 = -inf # this.started = False # this.done = False # while True: # for schedule, function in plugins: # if schedule(this.t1, this.t2): # function(this) # if this.done: # break # master(this) # this.started = True # if this.done: # break # this.t1 = inf # this.t2 = inf # for schedule, function in plugins: # if schedule(this.t1, this.t2): # function(this) def runner(main, plugins): """ :param main: A function which must take a single argument, ``this``. The ``this`` argument contains a settable ``done`` flag indicating whether the iterations should keep going or not, as well as a flag indicating whether this is the first time runner() is calling main(). main() may store whatever it wants in ``this``. It may also add one or more timelines in ``this.timelines[timeline_name]``, which plugins can exploit. :param plugins: A list of (schedule, timeline, function) tuples. In-between each execution of the main function, as well as at the very beginning and at the very end, the schedule will be consulted for the time range [t1, t2] from the appropriate timeline, and if there is a match, the function will be called with ``this`` as the argument. The order in which the functions are provided is respected. For any plugin, the timeline can be * 'iterations', where t1 == t2 == the iteration number * 'real_time', where t1 and t2 mark the start of the last loop and the start of the current loop, in seconds since the beginning of training (includes time spent in plugins) * 'algorithm_time', where t1 and t2 mark the start and end of the last iteration of the main function (does not include time spent in plugins) * A main function specific timeline. At the very beginning, the time for all timelines is -infinity, at the very end it is +infinity. """ start_time = time.time() this = scratchpad() this.timelines = defaultdict(lambda: [-inf, -inf]) realt = this.timelines['real_time'] algot = this.timelines['algorithm_time'] itert = this.timelines['iterations'] this.started = False this.done = False while True: for schedule, timeline, function in plugins: if schedule(*this.timelines[timeline]): function(this) if this.done: break t1 = time.time() main(this) t2 = time.time() if not this.started: realt[:] = [0, 0] algot[:] = [0, 0] itert[:] = [-1, -1] realt[:] = [realt[1], t2 - start_time] algot[:] = [algot[1], algot[1] + (t2 - t1)] itert[:] = [itert[0] + 1, itert[1] + 1] this.started = True if this.done: break this.timelines = defaultdict(lambda: [inf, inf]) for schedule, timeline, function in plugins: if schedule(*this.timelines[timeline]): function(this) ################ ### SHOWCASE ### ################ def main(this): if not this.started: this.error = 1.0 # note: runner will automatically set this.started to true else: this.error /= 1.1 def welcome(this): print "Let's start!" def print_iter(this): print "Now running iteration #%i" % this.timelines['iterations'][0] def print_error(this): print "The error rate is %s" % this.error def maybe_stop(this): thr = 0.01 if this.error < thr: print "Error is below the threshold: %s <= %s" % (this.error, thr) this.done = True def wait_a_bit(this): time.sleep(1./37) def printer(txt): def f(this): print txt return f def stop_this_madness(this): this.done = True def byebye(this): print "Bye bye!" runner(main = main, plugins = [# At the very beginning, print a welcome message (at_start, 'iterations', welcome), # Each iteration from 1 to 10 inclusive, OR each multiple of 10 # (except 0 - each() excludes 0, each0() includes it) # print the error (inrange(1, 10) | each(10), 'iterations', print_error), # Each multiple of 10, check for stopping condition (each(10), 'iterations', maybe_stop), # At iteration 1000, if we ever get that far, just stop (at(1000), 'iterations', stop_this_madness), # Wait a bit (each(1), 'iterations', wait_a_bit), # Print bonk each second of real time (each(1), 'real_time', printer('BONK')), # Print thunk each second of time in main() (main() # is too fast, so this does not happen for many # iterations) (each(1), 'algorithm_time', printer('THUNK')), # Announce the next iteration (each0(1), 'iterations', print_iter), # At the very end, display a message (at_end, 'iterations', byebye)])