def process_simple(self,
ids=[],
datas=[],
series=[],
runfilter=None,
datafilter=None,
aggr=aggregate.concat):
fs = [metrics.metric(data) for data in datas]
runkey = lambda run: map(lambda m: metrics.evalmetric(m, run), series)
runsets = self.makerunsets(ids, runfilter, datafilter)
runseries = self.makerunsets(series, runfilter, datafilter)
print 'Data:'
print '\\begin{itemize}'
for d in datas:
print '\item %s' % d
print '\\end{itemize}'
print 'Series:'
print '\\begin{itemize}'
count = 0
for i, (key, runset) in enumerate(runseries):
print '\item S%s: %s' % (i + 1, ', '.join(
['%s: %s' % (s, k) for s, k in zip(series, key)]))
count += 1
print '\\end{itemize}'
print '\\begin{tabular}{|%s|}' % ('c' * len(ids) +
(('|' + ('c' * (len(datas))))))
grouped_runsets = []
for key, runset in runsets:
grouped_runsets.append(list(groupby_sorted(runset, key=runkey)))
for serieindex in range(count):
print '\hline'
print '\\multicolumn{%s}{|c|}{Id} & ' % len(
ids) + ' & ' + '\\multicolumn{%s}{|c|}{S%s}' % (len(datas),
serieindex)
print '\\\\'
print ' & ' + ' & '.join(list([str(data) for data in datas]) * 1)
print '\\\\'
print '\hline'
for grouped_runset, (key, runset) in zip(grouped_runsets, runsets):
gk, rungroup = grouped_runset[serieindex]
print ' & '.join([str(k).replace('\\', '') for k in key] \
+ [str(aggr(map(f,rungroup))).rjust(4) for f in fs]) \
+ '\n\\\\'
print '\hline'
print '\hline \n \\end{tabular}'
def filterruns(self, runfilter, datafilter):
runs = self.runs
if runfilter:
runs = filter(runfilter, self.runs)
if datafilter:
fdatafilter = lambda run: all([
metrics.evalmetric(k, run) == val
for k, val in datafilter.iteritems()
])
runs = filter(fdatafilter, runs)
self.runs = runs
def makerunsets(self, series, runfilter, datafilter, store=False):
runkey = lambda run: map(lambda m: metrics.evalmetric(m, run), series)
runs = self.runs
if runfilter:
runs = filter(runfilter, self.runs)
if datafilter:
fdatafilter = lambda run: all([
metrics.evalmetric(k, run) == val
for k, val in datafilter.iteritems()
])
runs = filter(fdatafilter, runs)
if len(series) == 0:
runsets = [(["run"], runs)]
elif store:
runsets = groupby_store(sorted(runs, key=runkey), runkey)
else:
runsets = itertools.groupby(sorted(runs, key=runkey), runkey)
return runsets
def process(self, ids=[], datas=[], series=[], runfilter=None, datafilter=None, aggr=aggregate.concat):
runsets = list(self.makerunsets(ids, runfilter, datafilter, True))
runseries = list(self.makerunsets(series, runfilter, datafilter, True))
runkey = lambda run: map(lambda m: metrics.evalmetric(m, run), series)
for i,(key,runserie) in enumerate(runseries):
print '#Serie %s: %s' % (i+1, ', '.join(['%s: %s' % (s,k) for s,k in zip(series,key)]))
for i,(key,runset) in enumerate(runsets):
print '#Runset %s: %s' % (i+1, ', '.join(['%s: %s' % (s,k) for s,k in zip(ids,key)]))
for runset in runsets:
self.handle_runset(runset, runseries, datas, aggr, runkey)
def process(self,
ids=[],
datas=[],
series=[],
runfilter=None,
datafilter=None,
aggr=aggregate.concat):
fs = [metrics.metric(data) for data in datas]
runkey = lambda run: map(lambda m: metrics.evalmetric(m, run), series)
runsets = self.makerunsets(ids, runfilter, datafilter)
runseries = self.makerunsets(series, runfilter, datafilter)
print 'Data:'
print '\\begin{itemize}'
for d in datas:
print '\item %s' % d
print '\\end{itemize}'
print 'Series:'
print '\\begin{itemize}'
count = 0
for i, (key, runset) in enumerate(runseries):
print '\item S%s: %s' % (i + 1, ', '.join(
['%s: %s' % (s, k) for s, k in zip(series, key)]))
count += 1
print '\\end{itemize}'
print '\\begin{tabular}{|%s|}' % ('c' * len(ids) +
(('|' + ('c' *
(len(datas)))) * count))
print '\hline'
print '\\multicolumn{%s}{|c|}{Id} & ' % len(ids) + ' & '.join([
'\\multicolumn{%s}{|c|}{S%s}' % (len(datas), i + 1)
for i in range(count)
])
print '\\\\'
print ' & ' + ' & '.join(list([str(data) for data in datas]) * count)
print '\\\\'
print '\hline'
# Rungroup: las 3 corridas de una determinada densidad
for key, runset in runsets:
print ' & '.join([str(k).replace('\\', '') for k in key] \
+ flatten([[str(aggr(map(f,rungroup))).rjust(4) for f in fs] for k,rungroup in groupby_sorted(runset, key=runkey)])) \
+ '\n\\\\'
print '\hline \n \\end{tabular}'
def makerow(run):
return tuple(
[metrics.evalmetric(metric, run) for metric in fields])