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annotate stopper.py @ 188:f01ac276c6fb

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added __contains__ to Dataset, added parent constructor call to ArrayDataSet
author James Bergstra <bergstrj@iro.umontreal.ca>
date Wed, 14 May 2008 14:49:08 -0400
parents 4090779e39a9
children bd728c83faff
rev   line source
178
James Bergstra <bergstrj@iro.umontreal.ca>
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1 """Early stopping iterators
James Bergstra <bergstrj@iro.umontreal.ca>
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2
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3 The idea here is to supply early-stopping heuristics that can be used in the
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4 form:
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5
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6 stopper = SomeEarlyStopper()
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7
James Bergstra <bergstrj@iro.umontreal.ca>
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8 for i in stopper():
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9 # train from data
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10 if i.set_score:
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11 i.score = validation_score
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12
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13
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14 So far I only have one heuristic, so maybe this won't scale.
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15 """
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16
James Bergstra <bergstrj@iro.umontreal.ca>
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17 class Stopper(object):
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18
James Bergstra <bergstrj@iro.umontreal.ca>
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19 def train(self, data, update_rows_fn, update, validate, save=None):
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20 """Return the best model trained on data
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21
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22 Parameters:
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23 data - a thing that accepts getitem(<list of int64>), or a tuple of such things
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24 update_rows_fn - fn : int --> <list or tensor of int>
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25 update - fn: update an internal model from elements of data
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26 validate - fn: evaluate an internal model based on elements of data
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27 save - fn: return a copy of the internal model
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28
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29 The body of this function exhausts the <self> iterator, and trains a
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30 model using early stopping in the process.
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31 """
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32
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33 best = None
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34 for stp in self:
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35 i = stp.iter
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36
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37 # call update on some training set rows
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38 t_rows = update_rows_fn(i)
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39 if isinstance(data, (tuple, list)):
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40 update(*[d[t_rows] for d in data])
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41 else:
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42 update(data[t_rows])
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43
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44 if stp.set_score:
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45 stp.score = validate()
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46 if (stp.score < stp.best_score) and save:
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47 best = save()
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48 return best
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49
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50
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51 class ICML08Stopper(Stopper):
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52 @staticmethod
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53 def icml08(ntrain, batchsize):
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54 """Some setting similar to what I used for ICML08 submission"""
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55 #TODO: what did I actually use? put that in here.
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56 return ICML08Stopper(30*ntrain/batchsize,
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57 ntrain/batchsize, 0.96, 2.0, 100000000)
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58
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59 def __init__(self, i_wait, v_int, min_improvement, patience, hard_limit):
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60 self.initial_wait = i_wait
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61 self.set_score_interval = v_int
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62 self.min_improvement = min_improvement
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63 self.patience = patience
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64 self.hard_limit = hard_limit
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65
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66 self.best_score = float('inf')
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67 self.best_iter = -1
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68 self.iter = -1
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69
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70 self.set_score = False
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71 self.score = None
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72
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73 def __iter__(self):
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74 return self
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75
James Bergstra <bergstrj@iro.umontreal.ca>
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76 E_set_score = 'when iter.set_score is True, caller must assign a score to iter.score'
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77 def next(self):
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78 if self.set_score: #left over from last time
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79 if self.score is None:
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80 raise Exception(ICML08Stopper.E_set_score)
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81 if self.score < (self.best_score * self.min_improvement):
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82 (self.best_score, self.best_iter) = (self.score, self.iter)
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83 self.score = None #un-set it
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84
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85
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86 starting = self.iter < self.initial_wait
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87 waiting = self.iter < (self.patience * self.best_iter)
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88 if starting or waiting:
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89 # continue to iterate
James Bergstra <bergstrj@iro.umontreal.ca>
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90 self.iter += 1
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91 if self.iter == self.hard_limit:
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92 raise StopIteration
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93 self.set_score = (self.iter % self.set_score_interval == 0)
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94 return self
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95
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96 raise StopIteration
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97
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98
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99 class NStages(ICML08Stopper):
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100 """Run for a fixed number of steps, checking validation set every so
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101 often."""
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102 def __init__(self, hard_limit, v_int):
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103 ICML08Stopper.__init__(self, hard_limit, v_int, 1.0, 1.0, hard_limit)
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104
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105 #TODO: could optimize next() function. Most of what's in ICML08Stopper.next()
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106 #is not necessary
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107
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108