def run_combine(setts, fn_b, i, counts_cmp, pool):
fn_basis = "%s-%s" % (fn_b, i)
k = numpy.random.randint(setts["k_low"], setts["k_up"]+1)
patt_fn = []
combine_seqs = {}
patterns_list = []
offset_t0 = 0
prev_span = 0
t0_list = []
for ii in numpy.random.choice(len(pool), size=k):
fn_sub_basis = re.sub("_ds.txt", "", pool[ii])
seqs = readSequence({"filename": fn_sub_basis+"_ds.txt", "SEP": " "})
ds = DataSequence(seqs)
pcsH = load_pc(fn_sub_basis+"_pcH.txt", ds)
patt_fn.append(fn_sub_basis)
next_t0 = offset_t0 + numpy.random.randint(numpy.ceil(prev_span*setts["t0_low"]), numpy.ceil(prev_span*setts["t0_up"])+1)
t0_list.append(next_t0)
for ev, seq in seqs.items():
if ev not in combine_seqs:
combine_seqs[ev] = set()
combine_seqs[ev].update(seq+next_t0)
for (p, pt0, pE) in pcsH.getPatterns():
patterns_list.append((p, pt0+next_t0, pE))
offset_t0 = next_t0
prev_span = ds.getTend()
comb_setts = {"t0s": t0_list, "patt_fn": patt_fn, "k": k}
combine_ss = dict([(ev, numpy.array(sorted(s))) for (ev, s) in combine_seqs.items()])
ds = DataSequence(combine_ss)
pcH = PatternCollection(patterns_list)
writeSYNTHin(ds, pcH, fn_basis)
#### mine data sequence
SXPS = SyntheXPS()
mine_seqs(ds, fn_basis, writePCout_fun=SXPS.addPC)
pcF = SXPS.getPC()
#####
out_v, stats, results = compare_pcs(ds, pcH, pcF)
writeSYNTHout(setts, ds, pcH, pcF, out_v, fn_basis, save_pc=True, comb_setts=comb_setts)
counts_cmp[out_v] = counts_cmp.get(out_v, 0)+1
if out_v != 0 : #== -1:
print "RUN %s\tcl: %f vs. %f\t%d >> %s" % ( i, stats[0]["cl"], stats[1]["cl"], out_v, CMP_OUT_CODES[out_v])
return stats[0]["cl"], stats[1]["cl"], ds.codeLengthResiduals()
def prepare_synthetic_seq(patts_dets, bck_dets):
Hpatts = []
Hoccs = set()
for patt_dets in patts_dets:
patt, occs = prepare_pattern(**patt_dets)
Hoccs.update(occs)
Hpatts.append(patt)
tmp = sorted(Hoccs)
T_first, T_last = (tmp[0][0], tmp[-1][0])
for bck_det in bck_dets:
occs = prepare_bck(T_first, T_last, len(tmp), **bck_det)
Hoccs.update(occs)
ds = DataSequence(sorted(Hoccs))
ev_to_num = ds.getEvToNum()
for (Ptree, tp0, Ep) in Hpatts:
Ptree.mapEvents(ev_to_num)
pc_org = PatternCollection(Hpatts)
return pc_org, ds
def run_one(setts, fn_b, i, counts_cmp):
fn_basis = "%s-%s" % (fn_b, i)
if os.path.isfile(fn_basis+"_pcH.txt"):
ds = DataSequence(readSequence({"filename": fn_basis+"_ds.txt", "SEP": " "}))
pcH = load_pc(fn_basis+"_pcH.txt", ds)
else:
#### generate data sequence
k = setts["level"]
Rs = []
if k == 1:
Rs = [numpy.random.randint(int(.66*setts["nb_occs"]), setts["nb_occs"])]
elif k > 1:
mm = int(numpy.floor((setts["nb_occs"]/(1.*numpy.prod(range(1,k+1))))**(1./k)))
if mm < 3:
nn = int(numpy.floor(setts["nb_occs"]**(1./k)))
if nn < 3:
Rs = [3 for kk in range(k)]
else:
xx = [(.33*nn, nn+1) for kk in range(k)]
Rs = [numpy.random.randint(max(3, numpy.ceil(.33*nn)), nn+1) for kk in range(k)][::-1]
else:
xx = [((kk+.33)*mm, (kk+1.)*mm) for kk in range(k)]
Rs = [numpy.random.randint(max(3, numpy.ceil((kk+.33)*mm)), (kk+1)*mm+1) for kk in range(k)][::-1]
Ps = [numpy.random.randint(setts["p_down"], setts["p_up"])]
for kk in range(1,k):
prev = Ps[-1]*.5*Rs[kk-1]
if not setts.get("overlap", False):
prev = Ps[-1]*Rs[kk-1]
tt = numpy.random.randint(prev, prev+100)
ik = 0
while any([tt % p == 0 for p in Ps]) and ik < 100:
ik += 1
tt = numpy.random.randint(prev, prev+100)
Ps.append(tt)
patts_dets = [{"inner": setts["inner"], "t0": 0, "Rs": Rs, "Ps": Ps, "noise_lvl": setts["noise_lvl"], "noise_dens": setts["noise_dens"]}]
bck_dets = []
for (e, c) in setts.get("add_noise", []):
bck_dets.append({"event": e, "nb_occs": c})
print patts_dets, bck_dets, numpy.prod(Rs)
pcH, ds = prepare_synthetic_seq(patts_dets, bck_dets)
writeSYNTHin(ds, pcH, fn_basis)
#####
save_pcF = True
if os.path.isfile(fn_basis+"_pcF.txt"):
pcF = load_pc(fn_basis+"_pcF.txt", ds)
save_pcF = False
else:
#### mine data sequence
SXPS = SyntheXPS()
mine_seqs(ds, fn_basis, writePCout_fun=SXPS.addPC)
pcF = SXPS.getPC()
#####
if os.path.isfile(fn_basis+"_summary.txt"):
out_v = -1
with open(fn_basis+"_summary.txt") as fp:
out_v = int(fp.readline().split()[0])
stats = {0: {"cl": pcH.codeLength(ds)}, 1: {"cl": pcF.codeLength(ds)}}
else:
out_v, stats, results = compare_pcs(ds, pcH, pcF)
writeSYNTHout(setts, ds, pcH, pcF, out_v, fn_basis, save_pc=save_pcF)
counts_cmp[out_v] = counts_cmp.get(out_v, 0)+1
if out_v != 0 : #== -1:
print "RUN %s\tcl: %f vs. %f\t%d >> %s" % ( i, stats[0]["cl"], stats[1]["cl"], out_v, CMP_OUT_CODES[out_v])
return stats[0]["cl"], stats[1]["cl"], ds.codeLengthResiduals()
"max_len": 3000
}
# fparams = {"filename": "/home/egalbrun/TKTL/misc/itrami/per-pat/data/traces/prepared/trace_bugzilla_1_data.dat",
# "timestamp": False, "events": ["*"], "min_len": 30, "max_len": 3000}
# seqs = readSequence(fparams)
seq = []
for i in range(15):
seq.append(i * 400 + numpy.arange(0, 20, 2))
seqs = {0: numpy.hstack(seq)}
for ci, seq in seqs.items():
dets = None
ds = DataSequence({"a": seq})
print "------------"
if len(seq) < 30:
print "SEQUENCE %s: (%d)\t %s" % (ci, len(seq), seq)
else:
print "SEQUENCE %s: (%d)" % (ci, len(seq))
data_details = ds.getDetails()
results = run_test(seq, "a", data_details, dets)
for result in results:
nb_cycl = len([c for c in result["cycles"] if c.get("cp") > 0])
nb_res = numpy.sum(
[len(c["occs"]) for c in result["cycles"] if c.get("cp") <= 0])
print "%s:\tCL=%f nC=%d nR=%d RT=%f" % (
result["meth"], result["CL"], nb_cycl, nb_res, result["RT"])