def gather_weight_data(wtype):
# get the results
results = {} # maps |V| to ResultAccumulator
ds = DataSet.read_from_file(WeightResult, WeightResult.get_path_to(wtype))
for data in ds.dataset.values():
result = results.get(data.input().num_verts)
if result is None:
result = ResultAccumulator(data.mst_weight)
results[data.input().num_verts] = result
else:
result.add_data(data.mst_weight)
try:
# open a file to output to
fh = open(DATA_PATH + wtype + '.dat', 'w')
# compute relevant stats and output them
print >> fh, '#|V|\tLower\tAverage\tUpper (Lower/Upper from 99% CI)'
keys = results.keys()
keys.sort()
for num_verts in keys:
r = results[num_verts]
r.compute_stats()
if len(r.values) > 1:
print >> fh, '%u\t%.3f\t%.3f\t%.3f\t%u' % (
num_verts, r.lower99, r.mean, r.upper99, len(r.values))
fh.close()
return 0
except IOError, e:
print sys.stderr, "failed to write file: " + str(e)
return -1
def gather_weight_data(wtype):
# get the results
results = {} # maps |V| to ResultAccumulator
ds = DataSet.read_from_file(WeightResult, WeightResult.get_path_to(wtype))
for data in ds.dataset.values():
result = results.get(data.input().num_verts)
if result is None:
result = ResultAccumulator(data.mst_weight)
results[data.input().num_verts] = result
else:
result.add_data(data.mst_weight)
try:
# open a file to output to
fh = open(DATA_PATH + wtype + '.dat', 'w')
# compute relevant stats and output them
print >> fh, '#|V|\tLower\tAverage\tUpper (Lower/Upper from 99% CI)'
keys = results.keys()
keys.sort()
for num_verts in keys:
r = results[num_verts]
r.compute_stats()
if len(r.values) > 1:
print >> fh, '%u\t%.3f\t%.3f\t%.3f\t%u' % (num_verts, r.lower99, r.mean, r.upper99, len(r.values))
fh.close()
return 0
except IOError, e:
print sys.stderr, "failed to write file: " + str(e)
return -1
except ExtractInputFooterError, e:
raise CheckerError(
"run test error: unable to extract the input footer for %s: %s" %
(rel_input_graph, str(e)))
# log the result
if for_time:
data = PerfResult(ti.num_verts, ti.num_edges, ti.seed, rev, trial_num,
time_sec, mst_weight)
try:
DataSet.add_data_to_log_file(data)
except DataError, e:
print >> sys.stderr, "Unable to log result to file %s (was trying to log %s): %s" % (
data.get_path(), str(data), str(e))
else:
data = WeightResult(ti.dims, ti.num_verts, ti.seed, rev, trial_num,
mst_weight)
try:
DataSet.add_data_to_log_file(data)
except DataError, e:
print >> sys.stderr, "Unable to log result to file %s (was trying to log %s): %s" % (
data.get_path(), str(data), str(e))
def test_mst(is_test_perf, mst_binary, input_graph, out, do_log, rev,
trial_num):
trial_num = -1 if not do_log else trial_num
benchmark(mst_binary, input_graph, out, rev, trial_num, is_test_perf)
__input_graph_to_cleanup = None
__files_to_cleanup = []
def main():
usage = """usage: %prog [options]
Searches for missing results and uses run_test.py to collect it."""
parser = OptionParser(usage)
parser.add_option("-i", "--input_graph",
metavar="FILE",
help="restrict the missing data check to the specified input graph")
parser.add_option("-l", "--inputs-list-file",
metavar="FILE",
help="collect data for all inputs in the specified log file")
parser.add_option("--list-only",
action="store_true", default=False,
help="only list missing data (do not collect it)")
parser.add_option("-n", "--num-runs",
type="int", default="1",
help="number of desired runs per revision-input combination [default: 1]")
parser.add_option("-r", "--rev",
help="restrict the missing data check to the specified revision, or 'all' [default: current]")
group = OptionGroup(parser, "Data Collection Options")
group.add_option("-p", "--performance",
action="store_true", default=True,
help="collect performance data (this is the default)")
group.add_option("-c", "--correctness",
action="store_true", default=False,
help="collect correctness data")
parser.add_option_group(group)
group2 = OptionGroup(parser, "Weight (Part II) Data Collection Options")
group2.add_option("-v", "--num_vertices",
metavar="V", type="int", default=0,
help="collect weight data for V vertices (requires -d or -e)")
group2.add_option("-d", "--dims",
metavar="D", type="int", default=0,
help="collect weight data for randomly positioned vertices in D-dimensional space (requires -v)")
group2.add_option("-e", "--edge",
action="store_true", default=False,
help="collect weight data for random uniform edge weights in the range (0, 1] (requires -v)")
parser.add_option_group(group2)
(options, args) = parser.parse_args()
if len(args) > 0:
parser.error("too many arguments")
if options.num_runs < 1:
parser.error("-n must be at least 1")
input_solns = None
# prepare for a weight data collection
num_on = 0
weight_test = False
if options.num_vertices > 0:
weight_test = True
if options.input_graph or options.inputs_list_file:
parser.error('-i, -l, and -v are mutually exclusive')
if options.dims > 0:
num_on += 1
wtype = 'loc%u' % options.dims
if options.edge:
num_on += 1
wtype = 'edge'
if num_on == 0:
parser.error('-v requires either -d or -e be specified too')
if options.num_runs > 1:
options.num_runs = 1
print 'warning: -v truncates the number of runs to 1 (weight should not change b/w runs)'
input_path = InputSolution.get_path_to(15, options.dims, 0.0, 1.0)
print 'reading inputs to run on from ' + input_path
input_solns = DataSet.read_from_file(InputSolution, input_path)
revs = [None] # not revision-specific (assuming our alg is correct)
get_results_for_rev = lambda _ : DataSet.read_from_file(WeightResult, WeightResult.get_path_to(wtype))
collect_missing_data = collect_missing_weight_data
elif options.dims > 0 or options.edge:
parser.error('-v is required whenever -d or -e is used')
# handle -i, -l: collect data for a particular graph(s)
if options.input_graph and options.inputs_list_file:
parser.error('-i and -l are mutually exclusive')
if options.input_graph is not None:
try:
i = extract_input_footer(options.input_graph)
except ExtractInputFooterError, e:
parser.error(e)
input_solns = DataSet({0:InputSolution(i.prec,i.dims,i.min,i.max,i.num_verts,i.num_edges,i.seed)})
