def run(self):
print >> sys.stderr, "\nLoading data set <%s>" % self.datafile
full_data = amf.data.load(self.datafile)
###
print >> sys.stderr, "\nTesting FM Training Time"
total = len([ x for x in self.each_combo()])
count = 0
for (reg, params), tsize in self.each_combo():
print >> sys.stderr, " %d/%d " % (count, total),
count += 1
label = labelize(reg, params)
for i in xrange(FM_TRAINING_ITERATIONS):
dataset, = amf.data.random_subsets(full_data, [tsize])
fm = amf.ForwardMapping(reg, params, self.num_dep)
t = misc.StopWatch()
fm.train(dataset)
t = t.read()
#print >> sys.stderr, ' Regression <%s> took %.2f seconds to train on tsize=<%d>' \
# % (label, t, tsize)
self.results.fm_report(label, tsize, FMTRAININGTIME, t)
###
print >> sys.stderr, "\nCreating training sets and validation set"
sets = amf.data.random_subsets(full_data, self.training_sizes + [self.validation_size])
training_sets = sets[:-1]
validation_set = sets[-1]
###
print >> sys.stderr, "\nTesting the accuracy and speed of the forward mapping"
total = len([ x for x in self.each_combo()])
count = 0
for (reg, params), tsize in self.each_combo():
print >> sys.stderr, " %d/%d " % (count, total),
count += 1
label = labelize(reg, params)
for i in xrange(FM_QUERY_ITERATIONS):
#print >> sys.stderr, " Testing <%s> with dataset of size %d" % (label, tsize)
dataset, val = amf.data.random_subsets(full_data, [tsize, self.validation_size])
# might as well measure the training time here, too
fm = amf.ForwardMapping(reg, params, self.num_dep)
t = misc.StopWatch()
fm.train(dataset)
t = t.read()
#print >> sys.stderr, " Regression <%s> took %.2f seconds to train on tsize=<%d>" \
# % (label, t, tsize)
self.results.fm_report(label, tsize, FMTRAININGTIME, t)
#print >> sys.stderr, " Running queryies..."
for config, yval in val:
t = misc.StopWatch()
p = fm.predict(config)
t = t.read()
deviation = tuple( pv - yvalv for pv, yvalv in zip(p, yval) )
self.results.fm_report(label, tsize, FMACCURACY, deviation)
self.results.fm_report(label, tsize, FMQUERYTIME, t)
#print >> sys.stderr, " Done"
#print >> sys.stderr, " Average Error:", \
# self.results.average(label, tsize, FMACCURACY, m = (lambda l : tuple( abs(p) for p in l )) )
#print >> sys.stderr, " Average Query Time:", \
# self.results.average(label, tsize, FMQUERYTIME)
###
# use the largest training size for this experiment
tsize = max(self.training_sizes)
# use kNN for this experiment
dataset, validation = amf.data.random_subsets(full_data, [tsize, self.validation_size])
fm = amf.ForwardMapping(amf.regression.kNN, [], self.num_dep)
fm.train(dataset)
print >> sys.stderr, "\nTesting RM Training Time, Accuracy and Query Time"
total = len(self.rm_granularities)
count = 0
rm = None
for gran in self.rm_granularities:
#print >> sys.stderr, "\n\n GRANULARITY %d !!!" % gran
print >> sys.stderr, " %d/%d " % (count, total),
count += 1
for i in xrange(RM_TRAINING_ITERATIONS):
del rm
# Training Time Experiment
t = misc.StopWatch()
# FIXME, this should be a configuration, instead of a manual change!
rm = amf.ReverseMapping(fm, [(0.0, 1.0), (0.0, 1.0)], gran)
t = t.read()
#print >> sys.stderr, "took %.2f seconds to train" % t
self.results.rm_report(gran, RMTRAININGTIME, t)
# Error vs. Forward Mapping
for s in rm.simplexes:
# find the average config
configs = [ p[0] for p in s.corners]
avg_config = misc.col_average(configs)
# find the average value of the SLPs
slps = [ p[1] for p in s.corners ]
avg_values = misc.col_average(slps)
deviations = misc.list_sub(avg_values, fm.predict(avg_config))
#print >> sys.stderr, "deviated %s from the fm" % repr(deviations)
self.results.rm_report(gran, RMACCURACYFM, deviations)
# Error vs. ABM
for config, slp in validation:
# this only does one SLP right now! FIXME
dist = rm.distance_to(0, config, slp)
if dist != None:
#print >> sys.stderr, "configuration %s with value %s is %.3f away from the RM" \
# % (repr(config), repr(slp[0]), dist)
self.results.rm_report(gran, RMACCURACYABM, dist)
# Query Time
for config, slp in validation:
t = misc.StopWatch()
rm.all_intersections(slp)
t = t.read()
#print >> sys.stderr, "query too %.2f seconds" % t
self.results.rm_report(gran, RMQUERYTIME, t)
###
print >> sys.stderr, "\nDone"
def run(self):
print >> sys.stderr, "\nLoading data set <%s>" % self.datafile
full_data = amf.data.load(self.datafile)
###
# use the largest training size for this experiment
tsize = max(self.training_sizes)
# use kNN for this experiment
print>> sys.stderr, "\nGenerating Data Sets"
dataset, validation = amf.data.random_subsets(full_data, [tsize, self.validation_size])
print >> sys.stderr, "\n Training the forward mapping"
fm = amf.ForwardMapping(amf.regression.kNN, [], self.num_dep)
fm.train(dataset)
print >> sys.stderr, "\nTesting RM Training Time, Accuracy and Query Time"
total = len(self.rm_granularities)
count = 0
rm = None
for gran in self.rm_granularities:
#print >> sys.stderr, "\n\n GRANULARITY %d !!!" % gran
print >> sys.stderr, " %d/%d " % (count, total),
count += 1
for i in xrange(RM_TRAINING_ITERATIONS):
del rm
# Training Time Experiment
t = misc.StopWatch()
# FIXME, this should be a configuration, instead of a manual change!
rm = amf.ReverseMapping(fm, [(0.0, 1.0), (0.0, 1.0), (0.0, 1.0), (0.0, 1.0), (0.0, 1.0)], gran)
t = t.read()
#print >> sys.stderr, "took %.2f seconds to train" % t
num_simplexes = len(rm.simplexes)
subset = [ rm.simplexes[random.randint(0, num_simplexes-1)] for trolololo in range(200) ]
# Error vs. Forward Mapping
for s in subset:
# find the average config
configs = [ p[0] for p in s.corners]
avg_config = misc.col_average(configs)
# find the average value of the SLPs
slps = [ p[1] for p in s.corners ]
avg_values = misc.col_average(slps)
deviations = misc.list_sub(avg_values, fm.predict(avg_config))
#print >> sys.stderr, "deviated %s from the fm" % repr(deviations)
self.results.rm_report(gran, RMACCURACYFM, deviations)
print 'testing query time'
# Query Time
for config, slp in validation:
t = misc.StopWatch()
rm.all_intersections(slp)
t = t.read()
#print >> sys.stderr, "query too %.2f seconds" % t
self.results.rm_report(gran, RMQUERYTIME, t)
###
print >> sys.stderr, "\nDone"
