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quality.py
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quality.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
SVP Challenge Solver Command Line Client
"""
import copy
import logging
import pickle as pickler
import sys
from collections import OrderedDict
from fpylll.util import gaussian_heuristic
from g6k.algorithms.workout import workout
from g6k.siever import Siever
from g6k.utils.cli import parse_args, run_all, pop_prefixed_params
from g6k.utils.stats import SieveTreeTracer
from g6k.utils.util import load_svpchallenge_and_randomize, load_matrix_file, db_stats
def asvp_kernel(arg0, params=None, seed=None):
logger = logging.getLogger('asvp')
# Pool.map only supports a single parameter
if params is None and seed is None:
n, params, seed = arg0
else:
n = arg0
params = copy.copy(params)
load_matrix = params.pop("load_matrix")
pump_params = pop_prefixed_params("pump", params)
workout_params = pop_prefixed_params("workout", params)
verbose = params.pop("verbose")
if verbose:
workout_params["verbose"] = True
challenge_seed = params.pop("challenge_seed")
if load_matrix is None:
A, _ = load_svpchallenge_and_randomize(n, s=challenge_seed, seed=seed)
if verbose:
print("Loaded challenge dim %d" % n)
else:
A, _ = load_matrix_file(load_matrix)
if verbose:
print("Loaded file '%s'" % load_matrix)
g6k = Siever(A, params, seed=seed)
tracer = SieveTreeTracer(g6k, root_label=("svp-challenge", n), start_clocks=True)
gh = gaussian_heuristic([g6k.M.get_r(i, i) for i in range(n)])
flast = workout(g6k, tracer, 0, n, pump_params=pump_params, **workout_params)
tracer.exit()
stat = tracer.trace
f = workout_params["dim4free_min"]
gh2 = gaussian_heuristic([g6k.M.get_r(i, i) for i in range(f, n)])
quality = (gh * (n - f)) / (gh2 * n)
stat.data["quality"] = quality
print >> sys.stderr, g6k.M.B
return tracer.trace
def asvp():
"""
Run a Workout until 1.05-approx-SVP on matrices with dimensions in ``range(lower_bound, upper_bound, step_size)``.
"""
description = asvp.__doc__
args, all_params = parse_args(description,
load_matrix=None,
verbose=True,
challenge_seed=0,
workout__dim4free_min=30,
workout__dim4free_dec=2)
stats = run_all(asvp_kernel, all_params.values(),
lower_bound=args.lower_bound,
upper_bound=args.upper_bound,
step_size=args.step_size,
trials=args.trials,
workers=args.workers,
seed=args.seed)
inverse_all_params = OrderedDict([(v, k) for (k, v) in all_params.iteritems()])
for (n, params) in stats:
stat = stats[(n, params)]
cputime = sum([float(node["cputime"]) for node in stat])/len(stat)
walltime = sum([float(node["walltime"]) for node in stat])/len(stat)
quality = sum([float(node["quality"]) for node in stat])/len(stat)
avr_db, max_db = db_stats(stat)
fmt = "%48s :: m: %1d, n: %2d, cputime :%7.4fs, walltime :%7.4fs, quality : %2.4f" # noqa
logging.info(fmt % (inverse_all_params[params], params.threads, n, cputime, walltime, quality))
if args.pickle:
pickler.dump(stats, open("hkz-asvp-%d-%d-%d-%d.sobj" %
(args.lower_bound, args.upper_bound, args.step_size, args.trials), "wb"))
if __name__ == '__main__':
asvp()