def get_and_log_mst_weight_from_checker(input_graph, force_recompute=False, inputslogfn=None):
"""Returns the a 2-tuple of (input, weight). If force_recompute is not
True, then it will check the input log cache to see if we already know the
answer first. Logs the result."""
ti = __get_ti(input_graph)
# load in the inputs in the category of input_graph
if inputslogfn is None:
logfn = InputSolution.get_path_to(ti.prec, ti.dims, ti.min, ti.max)
else:
logfn = inputslogfn
ds = DataSet.read_from_file(InputSolution, logfn)
if ds.dataset.has_key(ti):
input_soln = ds.dataset[ti]
do_log = True
# see if we already know the answer
if not force_recompute:
if input_soln.has_mst_weight():
return (ti, input_soln.mst_weight) # cache hit!
else:
# if we weren't tracking the input before, don't start now
do_log = False
# compute the answer and (if specified) save it
w = compute_mst_weight(input_graph)
if do_log:
if input_soln.update_mst_weight(w):
ds.save_to_file(logfn)
return (ti, w)
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)})
options.inputs_list_file_arg = '' if options.inputs_list_file is None else ' -l ' + options.inputs_list_file
collect_missing_data = lambda w,x,y,z: collect_missing_correctness_data(w,x,y,z,options.inputs_list_file_arg)
# make sure no more than 1 type of data collection was specified
if num_on > 1:
parser.error('at most one of -c, -d, and -e may be specified')
elif num_on == 0:
# prepare for a performance data collection (default if nothing else is specified)
get_results_for_rev = lambda rev : DataSet.read_from_file(PerfResult, PerfResult.get_path_to(rev))
collect_missing_data = collect_missing_performance_data
# prepare the inputs and revisions for non-weight data collection schemes
if options.num_vertices == 0:
# get all performance inputs if we are not collecting for a single graph
if input_solns is None:
input_path = InputSolution.get_path_to(1, 0, 0, 100000)
input_solns = DataSet.read_from_file(InputSolution, input_path)
# prepare the revisions to collect data for
if options.rev is not None:
if options.rev.lower() == 'all':
revs = get_tracked_revs()
else:
revs = [options.rev]
else:
revs = ['current'] # just use the current revision
# pull out just the Input object (results are keyed on these, not InputSolution)
inputs = [i.input() for i in input_solns.dataset.values()]
# collect the data!