--- a/pylearn/algorithms/stopper.py	Fri Aug 20 09:28:39 2010 -0400
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,131 +0,0 @@
-import time
-"""Early stopping iterators
-
-The idea here is to supply early-stopping heuristics that can be used in the
-form:
-
-    stopper = SomeEarlyStopper()
-
-    for i in stopper():
-        # train from data
-        if i.set_score:
-            i.score = validation_score
-
-
-So far I only have one heuristic, so maybe this won't scale.
-"""
-
-class Stopper(object):
-
-    def train(self, data, update_rows_fn, update, validate, save=None):
-        """Return the best model trained on data
-
-        Parameters:
-        data - a thing that accepts getitem(<list of int64>), or a tuple of such things
-        update_rows_fn - fn : int --> <list or tensor of int>
-        update - fn: update an internal model from elements of data
-        validate - fn: evaluate an internal model based on elements of data
-        save - fn: return a copy of the internal model
-
-        The body of this function exhausts the <self> iterator, and trains a
-        model using early stopping in the process.
-        """
-
-        best = None
-        for stp in self:
-            i = stp.iter
-
-            # call update on some training set rows
-            t_rows = update_rows_fn(i)
-            if isinstance(data, (tuple, list)):
-                update(*[d[t_rows] for d in data])
-            else:
-                update(data[t_rows])
-
-            if stp.set_score:
-                stp.score = validate()
-                if (stp.score < stp.best_score) and save:
-                    best = save()
-        return best
-
-    def find_min(self, step, check, save):
-        best = None
-        for stp in self:
-            step()
-            if stp.set_score:
-                stp.score = check()
-                if (stp.score < stp.best_score) and save:
-                    best = (save(), stp.iter, stp.score)
-        return best
-
-class ICML08Stopper(Stopper):
-    @staticmethod
-    def icml08(ntrain, batchsize):
-        """Some setting similar to what I used for ICML08 submission"""
-        #TODO: what did I actually use? put that in here.
-        return ICML08Stopper(30*ntrain/batchsize,
-                ntrain/batchsize, 0.96, 2.0, 100000000)
-
-    def __init__(self, i_wait, v_int, min_improvement, patience, hard_limit, hard_time_limit=None):
-        self.initial_wait = i_wait
-        self.set_score_interval = v_int
-        self.min_improvement = min_improvement
-        self.patience = patience
-        self.hard_limit = hard_limit
-        self.hard_limit_seconds = hard_time_limit
-        self.start_time = time.time()
-
-        self.best_score = float('inf')
-        self.best_iter = -1
-        self.iter = -1
-
-        self.set_score = False
-        self.score = None
-
-    def __iter__(self):
-        return self
-
-    E_set_score = 'when iter.set_score is True, caller must assign a score to iter.score'
-    def next(self):
-
-        #print 'ICML08 stopper, were doing a next'
-
-        if self.set_score: #left over from last time
-            if self.score is None:
-                raise Exception(ICML08Stopper.E_set_score)
-            if self.score < (self.best_score * self.min_improvement):
-                (self.best_score, self.best_iter) = (self.score, self.iter)
-            self.score = None #un-set it
-
-
-        starting = self.iter < self.initial_wait
-        waiting = self.iter < (self.patience * self.best_iter)
-        if self.hard_limit_seconds != None:
-            times_up = (time.time() - self.start_time) > self.hard_limit_seconds
-        else: times_up = False
-        if (starting or waiting) and not times_up:
-            # continue to iterate
-            self.iter += 1
-            if self.iter == self.hard_limit:
-                raise StopIteration
-            self.set_score = (self.iter % self.set_score_interval == 0)
-            return self
-
-        raise StopIteration
-
-class NStages(ICML08Stopper):
-    """Run for a fixed number of steps, checking validation set every so
-    often."""
-    def __init__(self, hard_limit, v_int):
-        ICML08Stopper.__init__(self, hard_limit, v_int, 1.0, 1.0, hard_limit)
-
-    #TODO: could optimize next() function. Most of what's in ICML08Stopper.next()
-    #is not necessary
-
-def geometric_patience(i_wait, v_int, min_improvement, patience, hard_limit):
-    return ICML08Stopper(i_wait, v_int, min_improvement, patience, hard_limit)
-
-def nstages(hard_limit, v_int):
-    return ICML08Stopper(hard_limit, v_int, 1.0, 1.0, hard_limit)
-
-