def main(src, fpath, dst, cvmap_file, cvn):
langs = [lang for lang in load_json_stream(open(src))]
flist = load_json_file(fpath)
cvmap = load_json_file(cvmap_file)
name2lang = {}
for lang in langs:
lang["annotation"]["features_orig"] = copy.copy(
lang["annotation"]["features"])
lang["catvect_orig"] = copy.copy(lang["catvect"])
lang["cv"] = cvn
name2lang[lang["annotation"]["name"]] = lang
name2fstruct = {}
for fstruct in flist:
name2fstruct[fstruct["annotation"]["name"]] = fstruct
for lname, fname in cvmap[cvn]:
lang = name2lang[lname]
fstruct = name2fstruct[fname]
lang["catvect"][fstruct["fid"]] = -1
del lang["annotation"]["features"][fname]
with open(dst, 'w') as f:
for lang in langs:
f.write("%s\n" % json.dumps(lang))
def main():
parser = ArgumentParser()
parser.add_argument("-s",
"--seed",
dest="seed",
metavar="INT",
type=int,
default=None,
help="random seed")
parser.add_argument("--random",
dest="random",
action="store_true",
default=False)
parser.add_argument("--freq",
dest="most_frequent",
action="store_true",
default=False)
parser.add_argument("langs", metavar="LANGS", default=None)
parser.add_argument("f1",
metavar="DUMMY_OR_LANGS_FILLED_OR_LANGS_HIDDEN",
default=None)
parser.add_argument("f2",
metavar="FLIST_OR_DUMMY_OR_LANGS_HIDDEN",
default=None)
args = parser.parse_args()
if args.seed is not None:
np.random.seed(args.seed)
random.seed(args.seed)
langs = list(load_json_stream(open(args.langs)))
if args.random:
flist = load_json_file(args.f2)
total, correct = eval_random(flist, langs)
elif args.most_frequent:
hidelist = list(load_json_stream(open(args.f1)))
flist = load_json_file(args.f2)
total, correct = eval_most_frequent(flist, hidelist, langs)
else:
filled_langs = list(load_json_stream(open(args.f1)))
total, correct = eval_mv(filled_langs, langs)
sys.stdout.write("%f\t%d\t%d\n" % (float(correct) / total, correct, total))
def main():
sys.stderr = codecs.getwriter("utf-8")(sys.stderr)
parser = ArgumentParser()
parser.add_argument("langs_all", metavar="INPUT", default=None)
parser.add_argument("flist", metavar="FLIST", default=None)
parser.add_argument("langs", metavar="OUTPUT", default=None)
args = parser.parse_args()
fid2struct = load_json_file(args.flist)
fsize = len(fid2struct)
fname = "Ongoing creolization of pidgins"
vnames = ["Not applicable (because the language is not a pidgin)", "Widespread"]
fstruct = None
for fstruct2 in fid2struct:
if fstruct2["name"] == fname:
fstruct = fstruct2
break
if not fstruct:
sys.stderr.write("No such feature found\n")
exit(1)
vids = []
for vname in vnames:
if vname not in fstruct["label2vid"]:
sys.stderr.write("No such feature value found\n")
exit(1)
vid = fstruct["label2vid"][vname]
vids.append(vid)
fid = str(fstruct["fid"])
sys.stderr.write("fid, vid: %s %s\n" % (fid, vids))
lang_total, lang_survived = 0, 0
with codecs.getwriter("utf-8")(open(args.langs, "w")) as out:
for lang in load_json_stream(open(args.langs_all)):
lang_total += 1
survived = True
if lang["source"] == "APiCS":
if fid in lang["apics_features"]:
if lang["apics_features"][fid][0][0] not in vids:
sys.stderr.write("remove %s (pidgins: %s)\n" % (lang["name"], lang["apics_features"][fid][0][0]))
survived = False
else:
sys.stderr.write("keep %s (feature missed)\n" % lang["name"])
# survived = False
if survived:
lang_survived += 1
out.write("%s\n" % json.dumps(lang))
sys.stderr.write("language thresholding: %d -> %d\n" % (lang_total, lang_survived))
def main():
sys.stdout = codecs.getwriter("utf-8")(sys.stdout)
sys.stderr = codecs.getwriter("utf-8")(sys.stderr)
parser = ArgumentParser()
parser.add_argument("--type", metavar="POINT_TYPE", default="theta")
parser.add_argument("--output", metavar="IMG", default=None)
parser.add_argument("dumps", metavar="DUMP", default=None)
parser.add_argument("langs", metavar="LANG", default=None)
parser.add_argument("flist", metavar="FLIST", default=None)
args = parser.parse_args()
burnin = 51
fid2struct = load_json_file(args.flist)
langs = {}
for lang in load_json_stream(open(args.langs)):
if lang["source"] == "APiCS":
langs[lang["name"]] = lang
# stats = np.zeros(len(bin_flist))
# points = []
fcount = defaultdict(int)
samples = 0
total = 0
rtotal = 0
stream = load_json_stream(open(args.dumps))
for i in xrange(burnin):
stream.next()
for dump in stream:
# lang_num = len(dump['mixlist'])
for creole in dump['mixlist']:
catvect = langs[creole["langname"]]["catvect_filled"]
total += 1
for j, val in enumerate(creole["assignments"]):
if val == 0:
rtotal += 1
vid = catvect[j]
flabel = fid2struct[j]["name"] + "\t" + fid2struct[j]["vid2label"][vid]
fcount[flabel] += 1
samples += 1
total = float(total)
rtotal = float(rtotal)
_sorted = sorted(fcount.keys(), key=lambda x: fcount[x], reverse=True)
cum = 0
for flabel in _sorted:
cum += fcount[flabel]
sys.stdout.write("%d\t%f\t%f\t%s\n" % (fcount[flabel], fcount[flabel] / total, cum / rtotal, flabel))
def main():
parser = ArgumentParser()
parser.add_argument("walslangs", metavar="WALS_LANGS", default=None)
parser.add_argument("walsfeatures", metavar="WALS_FEATURES", default=None)
parser.add_argument("apicslangs", metavar="APiCS_LANGS", default=None)
parser.add_argument("merged", metavar="MERGED", default=None)
parser.add_argument("flist", metavar="FLIST", default=None)
args = parser.parse_args()
wals_langs = {}
for lang in load_json_stream(open(args.walslangs)):
wals_langs[lang["name"]] = lang
fid2struct = load_json_file(args.walsfeatures)
apics_langs = {}
for lang in load_json_stream(open(args.apicslangs)):
apics_langs[lang["name"]] = lang
# count features used in apics
feature2count = defaultdict(float)
for name, lang in apics_langs.iteritems():
for wals_id, v in lang["features"].iteritems():
feature2count[wals_id] += 1
# shrink features
fid2struct2 = []
for struct in fid2struct:
if struct["wals_id"] in feature2count:
struct["idx"] = len(fid2struct2)
fid2struct2.append(struct)
fid2struct = fid2struct2
# shrink features property of each WALS language
for name in wals_langs.keys():
lang = wals_langs[name]
lang["source"] = "WALS"
lang["orig_features"] = copy.copy(lang["features"])
for wals_id in lang["features"].keys():
if wals_id not in feature2count:
del lang["features"][wals_id]
with codecs.getwriter("utf-8")(open(args.merged, 'w')) as f:
for _l in (apics_langs, wals_langs):
for name, lang in _l.iteritems():
f.write("%s\n" % json.dumps(lang))
with codecs.getwriter("utf-8")(open(args.flist, 'w')) as f:
f.write("%s\n" % json.dumps(fid2struct))
def main():
sys.stderr = codecs.getwriter("utf-8")(sys.stderr)
parser = ArgumentParser()
parser.add_argument("langs_in", metavar="LANGS_IN", default=None)
parser.add_argument("flist_in", metavar="FLIST_IN", default=None)
parser.add_argument("langs_out", metavar="LANGS_OUT", default=None)
args = parser.parse_args()
fid2struct = load_json_file(args.flist_in)
with codecs.getwriter("utf-8")(open(args.langs_out, 'w')) as f:
for lang in load_json_stream(open(args.langs_in)):
lang["catvect"] = create_cat_vect(fid2struct, lang["features"])
if "features_filled" in lang:
lang["catvect_filled"] = create_cat_vect(fid2struct, lang["features_filled"])
f.write("%s\n" % json.dumps(lang))
def main(orig, src, fpath, dst):
fid2struct = load_json_file(fpath)
with open(src) as fin:
fin.readline() # ignore the header
with codecs.getwriter("utf-8")(open(dst, 'w')) as fout:
for lang, l in zip(load_json_stream(open(orig)), fin):
lang["features_filled"] = {}
l = l.rstrip()
a = l.split("\t")
label = a.pop(0)
for fid, v in enumerate(a):
wals_id = fid2struct[fid]["wals_id"]
lang["features_filled"][wals_id] = int(v)
assert(wals_id not in lang["features"] or lang["features"][wals_id] == int(v))
fout.write("%s\n" % json.dumps(lang))
def main(orig, src, fpath, dst):
langs = list(load_json_stream(open(orig)))
flist = load_json_file(fpath)
for lang in langs:
lang["counted_features"] = [Counter() for feature in flist]
lang["annotation"]["features_filled"] = {}
for fstruct in flist:
lang["annotation"]["features_filled"][fstruct["annotation"]
["name"]] = -1
for fpath in glob.glob(src + ".*"):
sys.stderr.write("processing {}\n".format(fpath))
with open(fpath) as fin:
fin.readline() # ignore the header
for lang, l in zip(langs, fin):
l = l.rstrip()
a = l.split("\t")
label = a.pop(0)
for fid, v in enumerate(a):
lang["counted_features"][fid][int(v)] += 1
for lang in langs:
binsize = 0
xfreq = Counter()
for fid, (fstruct,
counts) in enumerate(zip(flist, lang["counted_features"])):
if fstruct["type"] == "bin":
size = 2
else:
size = len(fstruct["annotation"]["vid2label"])
name = fstruct["annotation"]["name"]
maxv, maxvv = -1, -1
for i in range(size):
xfreq[binsize + i] += counts[i]
# if lang["xfreq"][binsize+i] >= maxvv:
if counts[i] >= maxvv:
maxvv = counts[i]
maxv = i
lang["annotation"]["features_filled"][name] = maxv
binsize += size
del lang["counted_features"]
lang["xfreq"] = [xfreq[i] for i in range(binsize)]
with open(dst, 'w') as fout:
for lang in langs:
fout.write("%s\n" % json.dumps(lang))
def main(src, fpath, dst):
flist = load_json_file(fpath)
langs = list(load_json_stream(open(src, "r")))
counts = {}
for fstruct in flist:
if fstruct["type"] == "count":
counts[fstruct["fid"]] = fstruct
with open(dst, 'w') as f:
rv = "\t".join([fstruct["annotation"]["name"] for fstruct in flist])
f.write(rv + "\n")
for i, lang in enumerate(langs):
catvect = list(lang["catvect"])
f.write("L{}\t{}\n".format(
i, "\t".join(map(lambda x: str(x)
if x >= 0 else "NA", catvect))))
def main():
parser = ArgumentParser()
parser.add_argument("--cvout", metavar="FILE", default=None)
parser.add_argument("--detail", metavar="FILE", default=None)
parser.add_argument('aggregated', nargs="+")
args = parser.parse_args()
# TODO: update langs.filled.json
cv_results = {}
for agfile in args.aggregated:
results = load_json_file(agfile)
fid = results["fid"]
if "cv_result" in results:
if fid not in cv_results:
cv_results[fid] = { "cor": 0, "total": 0, "cvn": 0 }
cv_results[fid]["cvn"] += 1
cv_results[fid]["cor"] += results["cv_result"]["cor"]
cv_results[fid]["total"] += results["cv_result"]["total"]
cor, total = 0, 0
for k, result in cv_results.items():
cor += result["cor"]
total += result["total"]
if args.cvout is not None:
with open(args.cvout, "w") as f:
f.write("{:.6f}\t{}\t{}\n".format(cor / total, cor, total))
if args.detail is not None:
cv_summary = OrderedDict()
cv_summary["microaverage"] = cor / total
avgs = {}
for k, result in cv_results.items():
avgs[k] = result["cor"] / result["total"]
cv_summary["macroaverage"] = sum(avgs.values()) / len(avgs)
cv_summary["per_feature"] = OrderedDict()
for k in sorted(cv_results.keys()):
cv_summary["per_feature"][k] = OrderedDict()
cv_summary["per_feature"][k]["accuracy"] = cv_results[k]["cor"] / cv_results[k]["total"]
cv_summary["per_feature"][k]["cor"] = cv_results[k]["cor"]
cv_summary["per_feature"][k]["total"] = cv_results[k]["total"]
with open(args.detail, "w") as f:
f.write("%s\n" % json.dumps(cv_summary, indent=4))
def main(orig, src, fpath, dst):
flist = load_json_file(fpath)
with open(src) as fin:
fin.readline() # ignore the header
with open(dst, 'w') as fout:
for lang, line in zip(load_json_stream(open(orig)), fin):
line = line.rstrip()
a = line.split("\t")
a.pop(0) # lang id
catvect = list(map(lambda x: int(x), a))
lang["catvect_filled"] = catvect
lang["annotation"]["features_filled"] = {}
for fid, v in enumerate(catvect):
name = flist[fid]["annotation"]["name"]
lang["annotation"]["features_filled"][name] = v
assert (name not in lang["annotation"]["features"]
or lang["annotation"]["features"][name] == v)
fout.write("%s\n" % json.dumps(lang))
def main(src, fpath, dst, fpath2):
fid2struct = load_json_file(fpath)
with codecs.getwriter("utf-8")(open(dst, 'w')) as f:
for i, lang in enumerate(load_json_stream(open(src))):
rv = ""
for struct in fid2struct:
flen = len(struct["vid2label"])
_arr = ["0"] * flen
wals_id = struct["wals_id"]
v = lang["features_filled"][wals_id]
_arr[v] = "1"
rv += "".join(_arr)
lang["bin"] = rv
f.write("%s\n" % json.dumps(lang))
flist_bin = []
for struct in fid2struct:
name = struct["name"]
for v in struct["vid2label"]:
flist_bin.append("%s\t%s" % (name, v))
with codecs.getwriter("utf-8")(open(fpath2, 'w')) as f:
f.write("%s\n" % json.dumps(flist_bin))
def main():
sys.stderr = codecs.getwriter("utf-8")(sys.stderr)
parser = ArgumentParser()
parser.add_argument("--lthres", dest="lthres", metavar="FLOAT", type=float, default=0.0,
help="eliminate languages with higher rate of missing values [0,1]")
parser.add_argument("langs_all", metavar="INPUT", default=None)
parser.add_argument("flist", metavar="FLIST", default=None)
parser.add_argument("langs", metavar="OUTPUT", default=None)
args = parser.parse_args()
fid2struct = load_json_file(args.flist)
fsize = len(fid2struct)
sys.stderr.write("%d featurs\n" % fsize)
lang_total, lang_survived = 0, 0
with codecs.getwriter("utf-8")(open(args.langs, "w")) as out:
for lang in load_json_stream(open(args.langs_all)):
lang_total += 1
if float(len(lang["features"])) / fsize >= args.lthres:
lang_survived += 1
out.write("%s\n" % json.dumps(lang))
sys.stderr.write("language thresholding: %d -> %d\n" % (lang_total, lang_survived))
def main():
parser = ArgumentParser()
parser.add_argument("-s",
"--seed",
metavar="INT",
type=int,
default=None,
help="random seed")
parser.add_argument("--K", metavar="INT", type=int, default=3, help="K")
parser.add_argument("-i",
"--iter",
dest="_iter",
metavar="INT",
type=int,
default=1000,
help="# of iterations")
parser.add_argument("--output",
dest="output",
metavar="FILE",
default=None,
help="save the model to the specified path")
parser.add_argument('--bins', type=str, default=None)
parser.add_argument('--bins_iter', type=int, default=500)
parser.add_argument("langs", metavar="LANG", default=None)
parser.add_argument("flist", metavar="FLIST", default=None)
args = parser.parse_args()
sys.stderr.write("args\t{}\n".format(args))
if args.seed is not None:
np.random.seed(args.seed)
random.seed(args.seed)
flist = load_json_file(args.flist)
langs = list(load_json_stream(open(args.langs)))
mat, mvs = create_mat(langs, flist)
adm = Admixture(mat, flist, K=args.K, mvs=mvs)
ll = adm.logprob()
sys.stderr.write("iter 0: {}\n".format(ll))
ll_max = ll
for _iter in range(args._iter):
adm.sample()
ll = adm.logprob()
sys.stderr.write("iter {}: {}\n".format(_iter + 1, ll))
sys.stderr.flush()
if args.output is not None:
with open(args.output + ".current", "wb") as f:
obj = {"model": adm, "iter": _iter + 1, "ll": ll}
pickle.dump(obj, f)
if ll > ll_max:
ll_max = ll
shutil.copyfile(args.output + ".current", args.output + ".best")
if args.output is not None:
with open(args.output + ".final", "wb") as f:
obj = {"model": adm, "iter": _iter + 1, "ll": ll}
pickle.dump(obj, f)
if args.bins is not None:
# Zs = [np.copy(adm.Z)]
bins = [
np.apply_along_axis(lambda x: np.bincount(x, minlength=adm.K),
axis=1,
arr=adm.Z)
]
for i in range(args.bins_iter):
adm.sample()
# Zs.append(np.copy(adm.Z))
bins.append(
np.apply_along_axis(lambda x: np.bincount(x, minlength=adm.K),
axis=1,
arr=adm.Z))
# Zs = np.vstack(Zs)
# bins = np.apply_along_axis(lambda x: np.bincount(x, minlength=adm.K), axis=1, arr=Zs)
bins = np.dstack(bins).sum(axis=2)
with open(args.bins, 'w') as f:
f.write("{}\n".format(json.dumps(bins.tolist())))
def main():
sys.stderr = codecs.getwriter("utf-8")(sys.stderr)
parser = ArgumentParser()
parser.add_argument("-s", "--seed", dest="seed", metavar="INT", type=int, default=None,
help="random seed")
parser.add_argument("-i", "--iter", dest="_iter", metavar="INT", type=int, default=20000,
help="number of dimensions")
parser.add_argument("-t", "--type", dest="mtype", metavar="MODEL_TYPE", default="mono",
help="model type (mono or fact)")
parser.add_argument("--dump", default=None)
parser.add_argument("langs", metavar="LANG", default=None)
# parser.add_argument("fid2struct", metavar="FLIST", default=None)
parser.add_argument("flist", metavar="FLIST", default=None)
parser.add_argument("sources", metavar="SOURCES", default=None)
args = parser.parse_args()
if args.seed is not None:
np.random.seed(args.seed)
random.seed(args.seed)
creoles = {}
with codecs.getreader("utf-8")(open(args.sources)) as f:
for line in f:
line = line.rstrip()
creole, lexifier, substrate = line.split("\t")
creoles[creole] = {
"lexifier": lexifier,
"substrate": substrate,
}
langs = {}
for lang in load_json_stream(open(args.langs)):
if lang["source"] == "APiCS":
langs[lang["name"]] = lang
else:
langs[lang["wals_code"]] = lang
fid2struct = load_json_file(args.flist)
# **TODO** pass command-line args
if args.mtype == 'mono':
cm = CreoleMixtureDiscrete(fid2struct, alpha_a=1.0, alpha_u=1.0)
elif args.mtype == 'fact':
cm = CreoleMixtureDiscreteFactored(fid2struct, gamma_f=10.0, gamma_c=10.0, alpha_u=1.0)
else:
sys.stderr.write("unsupported model\n")
exit(1)
objs = []
for creole, obj in creoles.iteritems():
if creole not in langs:
sys.stderr.write("creole %s not found in the lang list\n" % creole)
continue
clang = langs[creole]
if obj["lexifier"] not in langs:
sys.stderr.write("lexifier %s not found in the lang list\n" % obj["lexifier"])
continue
llang = langs[obj["lexifier"]]
if obj["substrate"] not in langs:
sys.stderr.write("substrate %s not found in the lang list\n" % obj["substrate"])
continue
slang = langs[obj["substrate"]]
clang_cat = clang["catvect_filled"]
llang_cat = llang["catvect_filled"]
slang_cat = slang["catvect_filled"]
obj = cm.add_mix(clang_cat, llang_cat, slang_cat, langname=creole)
objs.append({
"obj": obj,
# "creole": evaluator.cat2bin(np.array(clang["catvect_filled"])),
# "lexifier": evaluator.cat2bin(np.array(llang["catvect_filled"])),
# "substrate": evaluator.cat2bin(np.array(slang["catvect_filled"])),
})
sys.stderr.write("%d creoles\n" % len(cm.mixlist))
cm.init_mcmc()
sys.stderr.write("0\tlog marginal: %f\n" % cm.log_marginal())
# print cm.niw.L
sys.stdout.write("%s\n" % cm.serialize(_iter=0))
temp = 2.0
tempstep = temp / (args._iter * 0.75)
for _iter in xrange(args._iter):
temp -= tempstep
if temp <= 0.1:
temp = 0.1
# print >>sys.stderr, temp
# cm.sample(temp=temp)
cm.sample(temp=1.0)
sys.stderr.write("%d\tlog marginal: %f\n" % (_iter + 1, cm.log_marginal()))
if (_iter + 1) % 100 == 0:
sys.stdout.write("%s\n" % cm.serialize(_iter=_iter + 1))
# print cm.niw.L
if args.dump:
if args.dump == "-":
f = codecs.getwriter("utf-8")(sys.stdout)
else:
f = codecs.getwriter("utf-8")(open(args.dump, "w"))
rv = []
for obj_base in objs:
obj = obj_base["obj"]
rv.append({
"creole": obj["creole"],
"lexifier": obj["lexifier"],
"substrate": obj["substrate"],
"assignments": obj["assignments"].tolist(),
})
f.write("%s\n" % json.dumps(rv))
f.close()
def main():
# sys.stderr = codecs.getwriter("utf-8")(sys.stderr)
parser = ArgumentParser()
parser.add_argument("-s", "--seed", dest="seed", metavar="INT", type=int, default=None,
help="random seed")
# parser.add_argument("--sidx", metavar="IDX", type=int, default=0,
# help="i-th sample of leaf states (-1: last sample)")
# parser.add_argument("--npriors", metavar="NODE_PRIORS", default=None, help="priors for nodes (json)")
parser.add_argument("-i", "--iter", dest="_iter", metavar="INT", type=int, default=1000,
help="# of iterations")
# parser.add_argument("--resume_if", action="store_true", default=False,
# help="resume training if the output exists")
parser.add_argument("model", metavar="FILE", default=None,
help="resume training from model dump")
parser.add_argument("flist", metavar="FLIST", default=None)
# parser.add_argument("trees", metavar="TREES", default=None, help="merged trees (pkl)")
parser.add_argument("langs", metavar="LANG", default=None) # **HACK**
# parser.add_argument("samples", metavar="SAMPLES", default=None, help="parameter states (json stream)")
parser.add_argument("out", metavar="OUT", default=None, help="out (pkl)")
args = parser.parse_args()
if args.seed is not None:
np.random.seed(args.seed)
random.seed(args.seed)
# if args.resume_if:
# if os.path.isfile(args.out + ".current"):
# args.resume = args.out + ".current"
# elif os.path.isfile(args.out + ".best"):
# args.resume = args.out + ".best"
# if args.resume:
flist = load_json_file(args.flist)
for fid, fnode in enumerate(flist):
if fnode["annotation"]["fullname"] == "81A Order of Subject, Object and Verb":
wals_id = fnode["annotation"]["name"]
T = fnode["size"] # len(fnode["vid2label"])
break
# "label2vid": {"1 SOV": 0, "2 SVO": 1, "3 VSO": 2, "6 OSV": 5, "4 VOS": 3, "5 OVS": 4, "7 No dominant order": 6}
# fval = 0
# j_start, T = ibp.bmap(fid)
sys.stderr.write("loading model from %s\n" % args.model)
spec = pickle.load(open(args.model, "rb"), encoding="latin-1")
trees = spec["trees"]
# HACK
from train_bin_ctmc import register_node
langs = list(load_json_stream(open(args.langs, "r")))
idx2id = {}
for i, lang in enumerate(langs):
if "glottocode" in lang:
idx2id[i] = lang["glottocode"] + ":" + lang["annotation"]["name"]
id2node = {}
glottocode2node = {}
for tree in trees:
register_node(tree, id2node, glottocode2node)
for i, lang in enumerate(langs):
if i in idx2id:
_id = idx2id[i]
if _id in id2node:
node = id2node[_id]
node.lang = lang
# sampler = spec["sampler"]
# if "logprob" not in spec:
# logprob = sampler.logprob(trees)
# else:
# logprob = spec["logprob"]
# sys.stderr.write("iter {}\t{}\n".format(spec["iter"], logprob))
# _start = spec["iter"] + 1
# else:
_start = 1
# trees = load(open(args.trees, 'rb'))
# # trees2 = []
# # for tree in trees:
# # if tree.is_isolate is False:
# # trees2.append(tree)
# # trees = trees2
# sys.stderr.write("{} trees\n".format(len(trees)))
# node_priors = None
# if args.npriors is not None:
# prior_specs = load_json_file(args.npriors)
# node_priors = create_node_priors(prior_specs)
# langs = list(load_json_stream(open(args.langs)))
# with open(args.samples, 'r') as f:
# for i, sample in enumerate(load_json_stream(f)):
# if i == args.sidx:
# break
for tree in trees:
update_state(tree, [wals_id])
sampler = CTMC_Sampler(1, states=[T], ctmc_scale=0.00005)
sampler.init_trees(trees, sample_dates=False)
sys.stderr.write("iter 0\t{}\n".format(sampler.logprob(trees)))
for _iter in six.moves.range(_start, args._iter + 1):
sampler.sample(_iter=_iter)
logprob = sampler.logprob(trees)
sys.stderr.write("iter {}\t{}\n".format(_iter, logprob))
with open(args.out + ".current", "wb") as f:
pickle.dump({ "sampler": sampler, "trees": trees, "iter": _iter, "logprob": logprob }, f)
with open(args.out + ".final", "wb") as f:
pickle.dump({ "sampler": sampler, "trees": trees, "iter": _iter, "logprob": logprob }, f)
def main():
parser = ArgumentParser()
parser.add_argument("--input", metavar="FILE", default=None)
parser.add_argument("--burnin", metavar="INT", type=int, default=0,
help="# of burn-in iterations")
parser.add_argument("--interval", metavar="INT", type=int, default=1,
help="pick up one per # samples")
parser.add_argument("--update", action="store_true", default=False,
help="update features (for MVI)")
parser.add_argument("langs", metavar="LANG", default=None)
parser.add_argument("flist", metavar="FLIST", default=None)
args = parser.parse_args()
flist = load_json_file(args.flist)
langs = list(load_json_stream(open(args.langs)))
P = len(flist)
L = len(langs)
count = 0
xfreq = []
for l in range(L):
xfreq.append([None] * P)
zfreq = None
if args.input is None or args.input == "-":
f = sys.stdin
elif args.input.endswith(".bz2"):
import bz2
f = bz2.open(args.input, "r")
else:
f = open(args.input, "r")
for langdat in load_json_stream(f):
sys.stderr.write("+")
sys.stderr.flush()
if langdat["iter"] >= args.burnin and langdat["iter"] % args.interval == 0:
count += 1
if zfreq is None:
zfreq = np.zeros((L, len(langdat["z"])), dtype=np.int32)
zfreq += np.array(langdat["z"]).T
for l in range(L):
for p in range(P):
v = langdat["x"][l][p]
if xfreq[l][p] is None:
if flist[p]["type"] == "bin":
xfreq[l][p] = [0, 0]
elif flist[p]["type"] == "cat":
xfreq[l][p] = [0] * flist[p]["size"]
elif flist[p]["type"] == "count":
xfreq[l][p] = Counter()
xfreq[l][p][v] += 1
if args.input is not None and args.input != "-":
f.close()
sys.stderr.write("\n")
for p in range(P):
if flist[p]["type"] == "count":
for l, lang in enumerate(langs):
maxv = max(xfreq[l][p].keys())
vlist = [0] * (maxv + 1)
for k, v in xfreq[l][p].items():
vlist[k] = v
xfreq[l][p] = vlist
for l, lang in enumerate(langs):
lang["count"] = count
lang["xfreq"] = xfreq[l]
lang["zfreq"] = zfreq[l].tolist()
if args.update:
for p, fnode in enumerate(flist):
v = int(np.argmax(np.array(lang["xfreq"][p])))
lang["catvect_filled"][p] = v
lang["annotation"]["features_filled"][fnode["annotation"]["name"]] = v
sys.stdout.write("{}\n".format(json.dumps(lang)))
def main():
# sys.stderr = codecs.getwriter("utf-8")(sys.stderr)
parser = ArgumentParser()
parser.add_argument("-s",
"--seed",
metavar="INT",
type=int,
default=None,
help="random seed")
parser.add_argument("--fid", metavar="INT", type=int, default=-1)
parser.add_argument("--only_alphas",
action="store_true",
default=False,
help="autologistic: ignore v and h")
parser.add_argument("--drop_hs",
action="store_true",
default=False,
help="autologistic: ignore v")
parser.add_argument("--burnin",
metavar="INT",
type=int,
default=1000,
help="# of iterations")
parser.add_argument("--samples",
metavar="INT",
type=int,
default=500,
help="save interval")
parser.add_argument("--interval",
metavar="INT",
type=int,
default=5,
help="sampling interval")
parser.add_argument("--alpha",
metavar="FLOAT",
type=float,
default=-1.0,
help="parameter alpha")
parser.add_argument("--K", metavar="INT", type=int, default=100, help="K")
parser.add_argument('--norm_sigma',
type=float,
default=5.0,
help='standard deviation of Gaussian prior for u')
parser.add_argument('--gamma_shape',
type=float,
default=1.0,
help='shape of Gamma prior for v and h')
parser.add_argument('--gamma_scale',
type=float,
default=0.001,
help='scale of Gamma prior for v and h')
parser.add_argument("--output",
dest="output",
metavar="FILE",
default=None,
help="save the model to the specified path")
parser.add_argument("--resume",
metavar="FILE",
default=None,
help="resume training from model dump")
parser.add_argument("--resume_if",
action="store_true",
default=False,
help="resume training if the output exists")
parser.add_argument("langs", metavar="LANG", default=None)
parser.add_argument("flist", metavar="FLIST", default=None)
parser.add_argument("aggregated", metavar="FLIST", default=None)
args = parser.parse_args()
sys.stderr.write("args\t{}\n".format(args))
if args.seed is not None:
np.random.seed(args.seed)
random.seed(args.seed)
flist = load_json_file(args.flist)
# offset = 0
# if args.resume_if:
# if os.path.isfile(args.output + ".current"):
# args.resume = args.output + ".current"
# elif os.path.isfile(args.output + ".best"):
# args.resume = args.output + ".best"
# if args.resume:
# sys.stderr.write("loading model from {}\n".format(args.resume))
# spec = pickle.load(open(args.resume, "rb"))
# mda = spec["model"]
# sys.stderr.write("iter {}\n".format(spec["iter"] + 1))
# if args.cv:
# eval_cvlist(mda)
# offset = spec["iter"] + 1
# else:
langs = list(load_json_stream(open(args.langs)))
sys.stderr.write("building hnet\n")
hnet = WeightedNeighborGraph(langs)
if args.fid >= 0:
fstruct = flist[args.fid]
vec, mvs, size = create_vec(langs, fstruct, args.fid)
al = CategoricalAutologistic(vec,
size,
hnet=hnet,
mvs=mvs,
only_alphas=args.only_alphas,
drop_hs=args.drop_hs,
norm_sigma=args.norm_sigma,
gamma_shape=args.gamma_shape,
gamma_scale=args.gamma_scale)
else:
als = []
for fid, fstruct in enumerate(flist):
vec, mvs, size = create_vec(langs, fstruct, fid)
al = CategoricalAutologistic(vec,
size,
hnet=hnet,
mvs=mvs,
only_alphas=args.only_alphas,
drop_hs=args.drop_hs,
norm_sigma=args.norm_sigma,
gamma_shape=args.gamma_shape,
gamma_scale=args.gamma_scale)
als.append(al)
al = CategoricalAutologisticGroup(als)
sys.stderr.write("iter 0\n")
offset = 0
for _iter in range(args.burnin):
al.sample()
offset += 1
# ll = mda.calc_loglikelihood()
sys.stderr.write("iter {}\n".format(offset))
sys.stderr.flush()
if args.output is not None:
with open(args.output, "wb") as f:
obj = {"model": al, "iter": offset}
pickle.dump(obj, f)
results = []
results.append(get_result(al))
while len(results) < args.samples:
for _iter in range(args.interval):
al.sample()
offset += 1
sys.stderr.write("iter {}\n".format(offset))
sys.stderr.flush()
results.append(get_result(al))
if args.aggregated == "-":
f = sys.stdout
else:
f = open(args.aggregated, "w")
aggregated = aggregate_results(results, al, flist, args.fid)
f.write("%s\n" % json.dumps(aggregated))
def main():
parser = ArgumentParser()
parser.add_argument("-s",
"--seed",
metavar="INT",
type=int,
default=None,
help="random seed")
parser.add_argument("--bias",
action="store_true",
default=False,
help="bias term in Z")
parser.add_argument("--only_alphas",
action="store_true",
default=False,
help="autologistic: ignore v and h")
parser.add_argument("--drop_hs",
action="store_true",
default=False,
help="autologistic: ignore h")
parser.add_argument("-i",
"--iter",
dest="_iter",
metavar="INT",
type=int,
default=1000,
help="# of iterations")
parser.add_argument("--save_interval",
metavar="INT",
type=int,
default=-1,
help="save interval")
parser.add_argument("--K", metavar="INT", type=int, default=100, help="K")
parser.add_argument('--norm_sigma',
type=float,
default=5.0,
help='standard deviation of Gaussian prior for u')
parser.add_argument('--gamma_shape',
type=float,
default=1.0,
help='shape of Gamma prior for v and h')
parser.add_argument('--gamma_scale',
type=float,
default=0.001,
help='scale of Gamma prior for v and h')
parser.add_argument("--hmc_l", metavar="INT", type=int, default=10)
parser.add_argument('--hmc_epsilon',
type=float,
default=0.05,
help='HMC epsilon')
parser.add_argument("--maxanneal", metavar="INT", type=int, default=0)
parser.add_argument("--output",
dest="output",
metavar="FILE",
default=None,
help="save the model to the specified path")
parser.add_argument("--resume",
metavar="FILE",
default=None,
help="resume training from model dump")
parser.add_argument("--resume_if",
action="store_true",
default=False,
help="resume training if the output exists")
parser.add_argument('--bins', type=str, default=None)
parser.add_argument('--bins_iter', type=int, default=100)
parser.add_argument("langs", metavar="LANG", default=None)
parser.add_argument("flist", metavar="FLIST", default=None)
args = parser.parse_args()
sys.stderr.write("args\t{}\n".format(args))
if args.seed is not None:
np.random.seed(args.seed)
random.seed(args.seed)
flist = load_json_file(args.flist)
offset = 0
if args.resume_if:
if os.path.isfile(args.output + ".current"):
args.resume = args.output + ".current"
elif os.path.isfile(args.output + ".best"):
args.resume = args.output + ".best"
if args.resume:
sys.stderr.write("loading model from {}\n".format(args.resume))
spec = pickle.load(open(args.resume, "rb"))
mda = spec["model"]
sys.stderr.write("iter {}: {}\n".format(spec["iter"] + 1, spec["ll"]))
offset = spec["iter"] + 1
else:
langs = list(load_json_stream(open(args.langs)))
mat, mvs = create_mat(langs, flist)
sys.stderr.write("building hnet\n")
hnet = WeightedNeighborGraph(langs)
mda = MatrixDecompositionAutologistic(
mat,
flist,
hnet=hnet,
K=args.K,
mvs=mvs,
bias=args.bias,
only_alphas=args.only_alphas,
drop_hs=args.drop_hs,
norm_sigma=args.norm_sigma,
# const_h = 0.03253780242472478,
gamma_shape=args.gamma_shape,
gamma_scale=args.gamma_scale,
hmc_l=args.hmc_l,
hmc_epsilon=args.hmc_epsilon)
mda.init_with_clusters()
sys.stderr.write("iter 0: {}\n".format(mda.calc_loglikelihood()))
ll_max = -np.inf
for _iter in range(offset, args._iter):
mda.sample(_iter=_iter, maxanneal=args.maxanneal)
ll = mda.calc_loglikelihood()
sys.stderr.write("iter {}: {}\n".format(_iter + 1, ll))
sys.stderr.flush()
if args.save_interval >= 0 and (_iter + 1) % args.save_interval == 0:
with open(args.output + ".{}".format(_iter), "wb") as f:
obj = {"model": mda, "iter": _iter, "ll": ll}
if args.output is not None:
with open(args.output + ".current", "wb") as f:
obj = {"model": mda, "iter": _iter, "ll": ll}
pickle.dump(obj, f)
if ll > ll_max:
ll_max = ll
shutil.copyfile(args.output + ".current", args.output + ".best")
if args.output is not None:
with open(args.output + ".final", "wb") as f:
obj = {"model": mda, "iter": _iter, "ll": ll}
pickle.dump(obj, f)
if args.bins is not None:
zmats = [np.copy(mda.zmat)]
wmats = [np.copy(mda.wmat)]
hkss = [np.copy(mda.hks)]
for i in range(args.bins_iter):
mda.sample()
zmats.append(np.copy(mda.zmat))
wmats.append(np.copy(mda.wmat))
hkss.append(np.copy(mda.hks))
avg_zmat = np.sum(zmats, axis=0) / float(len(zmats))
avg_wmat = np.sum(wmats, axis=0) / float(len(wmats))
avg_hks = np.sum(hkss, axis=0) / float(len(hkss))
with open(args.bins, 'w') as f:
f.write("{}\n".format(
json.dumps({
"avg_zmat": avg_zmat.tolist(),
"avg_wmat": avg_wmat.tolist(),
"avg_hks": avg_hks.tolist(),
})))
def main():
parser = ArgumentParser()
parser.add_argument("-s",
"--seed",
metavar="INT",
type=int,
default=None,
help="random seed")
parser.add_argument("--bias",
action="store_true",
default=False,
help="bias term in Z")
parser.add_argument("--only_alphas",
action="store_true",
default=False,
help="autologistic: ignore v and h")
parser.add_argument("--drop_vs",
action="store_true",
default=False,
help="autologistic: ignore h")
parser.add_argument("--drop_hs",
action="store_true",
default=False,
help="autologistic: ignore v")
parser.add_argument("-i",
"--iter",
dest="_iter",
metavar="INT",
type=int,
default=1000,
help="# of iterations")
parser.add_argument("--save_interval",
metavar="INT",
type=int,
default=-1,
help="save interval")
parser.add_argument("--K", metavar="INT", type=int, default=100, help="K")
parser.add_argument('--norm_sigma',
type=float,
default=5.0,
help='standard deviation of Gaussian prior for u')
parser.add_argument('--gamma_shape',
type=float,
default=1.0,
help='shape of Gamma prior for v and h')
parser.add_argument('--gamma_scale',
type=float,
default=0.001,
help='scale of Gamma prior for v and h')
parser.add_argument("--hmc_l", metavar="INT", type=int, default=10)
parser.add_argument('--hmc_epsilon',
type=float,
default=0.05,
help='HMC epsilon')
parser.add_argument("--maxanneal", metavar="INT", type=int, default=0)
parser.add_argument(
"--cv",
action="store_true",
default=False,
help=
"some features are intentionally hidden (but kept as \"catvect_orig\")"
)
parser.add_argument("--output",
dest="output",
metavar="FILE",
default=None,
help="save the model to the specified path")
parser.add_argument("--resume",
metavar="FILE",
default=None,
help="resume training from model dump")
parser.add_argument("--resume_if",
action="store_true",
default=False,
help="resume training if the output exists")
parser.add_argument("langs", metavar="LANG", default=None)
parser.add_argument("flist", metavar="FLIST", default=None)
args = parser.parse_args()
sys.stderr.write("args\t{}\n".format(args))
if args.seed is not None:
np.random.seed(args.seed)
random.seed(args.seed)
flist = load_json_file(args.flist)
offset = 0
if args.resume_if:
if os.path.isfile(args.output + ".current"):
args.resume = args.output + ".current"
elif os.path.isfile(args.output + ".best"):
args.resume = args.output + ".best"
if args.resume:
sys.stderr.write("loading model from {}\n".format(args.resume))
spec = pickle.load(open(args.resume, "rb"))
mda = spec["model"]
sys.stderr.write("iter {}: {}\n".format(spec["iter"] + 1, spec["ll"]))
if args.cv:
eval_cvlist(mda)
offset = spec["iter"] + 1
else:
langs = list(load_json_stream(open(args.langs)))
mat, mvs = create_mat(langs, flist)
sys.stderr.write("building vnet\n")
vnet = create_vnet(langs)
sys.stderr.write("building hnet\n")
hnet = create_hnet(langs)
mda = MatrixDecompositionAutologistic(mat,
flist,
vnet=vnet,
hnet=hnet,
K=args.K,
mvs=mvs,
bias=args.bias,
only_alphas=args.only_alphas,
drop_vs=args.drop_vs,
drop_hs=args.drop_hs,
norm_sigma=args.norm_sigma,
gamma_shape=args.gamma_shape,
gamma_scale=args.gamma_scale,
hmc_l=args.hmc_l,
hmc_epsilon=args.hmc_epsilon)
if args.cv:
mda.cvlist = create_cvlist(langs)
mda.init_with_clusters()
sys.stderr.write("iter 0: {}\n".format(mda.calc_loglikelihood()))
if args.cv:
eval_cvlist(mda)
ll_max = -np.inf
for _iter in range(offset, args._iter):
mda.sample(_iter=_iter, maxanneal=args.maxanneal)
ll = mda.calc_loglikelihood()
sys.stderr.write("iter {}: {}\n".format(_iter + 1, ll))
sys.stderr.flush()
if args.cv:
cv_result = eval_cvlist(mda)
sys.stderr.flush()
if args.save_interval >= 0 and (_iter + 1) % args.save_interval == 0:
with open(args.output + ".{}".format(_iter), "wb") as f:
obj = {"model": mda, "iter": _iter, "ll": ll}
if args.cv:
obj["cv_result"] = cv_result
pickle.dump(obj, f)
if args.output is not None:
with open(args.output + ".current", "wb") as f:
obj = {"model": mda, "iter": _iter, "ll": ll}
if args.cv:
obj["cv_result"] = cv_result
pickle.dump(obj, f)
if ll > ll_max:
ll_max = ll
shutil.copyfile(args.output + ".current", args.output + ".best")
if args.output is not None:
with open(args.output + ".final", "wb") as f:
obj = {"model": mda, "iter": _iter, "ll": ll}
if args.cv:
obj["cv_result"] = cv_result
pickle.dump(obj, f)
# -*- coding: utf-8 -*- import sys import json from json_utils import load_json_file, load_json_stream flist = load_json_file(sys.argv[1]) print(len(flist))
def main():
parser = ArgumentParser()
parser.add_argument("--lthres", dest="lthres", metavar="FLOAT", type=float, default=0.0,
help="eliminate trees with higher rate of missing values [0,1]")
parser.add_argument("--npriors", metavar="NODE_PRIORS", default=None, help="priors for nodes (json)")
parser.add_argument("langs", metavar="LANG", default=None)
parser.add_argument("tree", metavar="TREE", default=None)
parser.add_argument("out", metavar="OUTPUT", default=None)
args = parser.parse_args()
trees = load(open(args.tree, 'rb'))
langs = list(load_json_stream(open(args.langs)))
# a glottocode corresponds to one or more WALS languages
glotto_code2lang = {}
for lang in langs:
if "glottocode" in lang and lang["glottocode"]:
if lang["glottocode"] in glotto_code2lang:
glotto_code2lang[lang["glottocode"]].append(lang)
else:
glotto_code2lang[lang["glottocode"]] = [lang]
else:
sys.stderr.write(u"dropping lang without glottocode: {}\n".format(lang["annotation"]["name"]))
# try to keep nodes specified by priors
registered_codes = {}
if args.npriors is not None:
prior_specs = load_json_file(args.npriors)
for spec in prior_specs:
registered_codes[spec["glottocode"]] = True
for tree in trees:
remove_unclassified(tree)
for tree in trees:
attach_lang(tree, glotto_code2lang)
ucount, total = 0, 0
for code, langlist in glotto_code2lang.items():
for lang in langlist:
total += 1
if "used" in lang and lang["used"] == True:
ucount += 1
del lang["used"]
else:
sys.stderr.write("glottocode never appeared in trees: {}\n".format(code))
sys.stderr.write("{} (out of {}) languages in the trees\n".format(ucount, total))
trees2 = []
for tree in trees:
c = shrink_tree(tree, registered_codes)
if c > 0:
trees2.append(tree)
sys.stderr.write("# of trees: {} -> {}\n".format(len(trees), len(trees2)))
trees = trees2
if args.lthres > 0.0:
trees2 = []
for tree in trees:
c = get_feature_coverage(tree)
if c >= args.lthres:
trees2.append(tree)
sys.stderr.write("# of trees ({} thres): {} -> {}\n".format(args.lthres, len(trees), len(trees2)))
trees = trees2
isolate = 0
for tree in trees:
tree.is_root = True
tree.parent = None
if len(tree.children) <= 0:
tree.is_isolate = True
isolate += 1
tree.date = 0.0
tree.is_date_frozen = True
tree.is_state_frozen = True
else:
tree.is_isolate = False
tree.is_date_frozen = False
tree.is_state_frozen = False
for child in tree.children:
set_date(child)
sys.stderr.write("# of isolate trees: {} (out of {})\n".format(isolate, len(trees)))
# for tree in trees:
# sys.stdout.write(tree.name + "\n")
with open(args.out, "wb") as f:
dump(trees, f)
def main():
parser = ArgumentParser()
parser.add_argument('--min_ratio', type=float, default=0.05)
parser.add_argument("--model", default="adm")
parser.add_argument("tree", metavar="LANG", default=None)
parser.add_argument("bins", metavar="LANG", default=None)
args = parser.parse_args()
sys.stderr.write("args\t{}\n".format(args))
tree = load_json_file(args.tree)
leaves = get_leaves(tree, [])
min_size = len(leaves) * args.min_ratio
treeclusters = get_clusters(tree, min_size=min_size)
# print(treeclusters)
geotree = upgma(leaves, lambda x, y: np.sqrt((x["x"] - y["x"]) ** 2 + (x["y"] - y["y"]) ** 2))
geoclusters = get_geoclusters(geotree, leaves, min_size=min_size)
# print(geoclusters)
combinedclusters = merge_clusters(treeclusters, geoclusters)
bins = load_json_file(args.bins)
report = []
if args.model == "adm":
bins = np.array(bins, dtype=np.float64)
bins /= bins.sum(axis=1, keepdims=True)
thres = 0.1
while thres < 1.0:
K = bins.shape[1]
clusters = []
for k in range(K):
cluster = []
for lid, probs in enumerate(bins):
if probs[k] >= thres:
cluster.append(leaves[lid]["name"])
if len(cluster) > 0:
clusters.append(cluster)
if len(clusters) <= 0:
break
treescore = max_jaccard(clusters, treeclusters)
geoscore = max_jaccard(clusters, geoclusters)
combinedscore = max_jaccard(clusters, combinedclusters)
report.append({
"model": "adm",
"K": K,
"thres": thres,
"treescore": treescore,
"geoscore": geoscore,
"combinedscore": combinedscore,
})
sys.stderr.write("{}\t{}\t{}\t{}\n".format(thres, treescore, geoscore, combinedscore))
thres += 0.1
elif args.model == "mda":
cprobs = np.array(bins["avg_zmat"]) # K x L
thres = 0.1
while thres < 1.0:
K = cprobs.shape[0]
clusters = []
for k in range(K):
cluster = []
for lid, prob in enumerate(cprobs[k]):
if prob >= thres:
cluster.append(leaves[lid]["name"])
if len(cluster) > 0:
clusters.append(cluster)
if len(clusters) <= 0:
break
treescore = max_jaccard(clusters, treeclusters)
geoscore = max_jaccard(clusters, geoclusters)
combinedscore = max_jaccard(clusters, combinedclusters)
report.append({
"model": "mda",
"K": K,
"thres": thres,
"treescore": treescore,
"geoscore": geoscore,
"combinedscore": combinedscore,
})
sys.stderr.write("{}\t{}\t{}\t{}\n".format(thres, treescore, geoscore, combinedscore))
thres += 0.1
else:
raise NotImplementedError
print(json.dumps(report))
def main():
parser = ArgumentParser()
parser.add_argument("-s",
"--seed",
dest="seed",
metavar="INT",
type=int,
default=None,
help="random seed")
# parser.add_argument("--sidx", metavar="IDX", type=int, default=0,
# help="i-th sample of leaf states (-1: last sample)")
parser.add_argument("--npriors",
metavar="NODE_PRIORS",
default=None,
help="priors for nodes (json)")
parser.add_argument("-i",
"--iter",
dest="_iter",
metavar="INT",
type=int,
default=1000,
help="# of iterations")
parser.add_argument("--resume_if",
action="store_true",
default=False,
help="resume training if the output exists")
parser.add_argument("--resume",
metavar="FILE",
default=None,
help="resume training from model dump")
parser.add_argument("--save_interval",
metavar="INT",
type=int,
default=-1,
help="save interval")
# parser.add_argument("--merged", action="store_true", default=False,
# help="use merged langs instead of a single sample")
parser.add_argument("--has_bias",
action="store_true",
default=False,
help="0th item of z is a bias term (always 1)")
parser.add_argument("--surface",
dest="latent",
action="store_false",
default=True,
help="use surface features")
parser.add_argument(
"--surface_state_limit",
dest="max_states",
type=int,
default=-1,
help=
"maximum number of distinct states of surface features (trimming for speed)"
)
parser.add_argument("trees",
metavar="TREES",
default=None,
help="merged trees (pkl)")
parser.add_argument("langs", metavar="LANG", default=None)
# parser.add_argument("samples", metavar="SAMPLES", default=None, help="parameter states (json stream)")
parser.add_argument("out", metavar="OUT", default=None, help="out (pkl)")
args = parser.parse_args()
if args.seed is not None:
np.random.seed(args.seed)
random.seed(args.seed)
if args.resume_if:
if os.path.isfile(args.out + ".current"):
args.resume = args.out + ".current"
elif os.path.isfile(args.out + ".best"):
args.resume = args.out + ".best"
if args.resume:
sys.stderr.write("loading model from %s\n" % args.resume)
spec = load(open(args.resume, "rb"))
trees = spec["trees"]
sampler = spec["sampler"]
if "logprob" not in spec:
logprob = sampler.logprob(trees)
else:
logprob = spec["logprob"]
sys.stderr.write("iter {}\t{}\n".format(spec["iter"], logprob))
_start = spec["iter"] + 1
else:
_start = 1
trees = load(open(args.trees, 'rb'))
sys.stderr.write("{} trees\n".format(len(trees)))
node_priors = None
if args.npriors is not None:
prior_specs = load_json_file(args.npriors)
node_priors = create_node_priors(prior_specs)
langs = list(load_json_stream(open(args.langs, "r")))
# with open(args.samples, 'r') as f:
# if args.merged:
# sample = list(load_json_stream(f))
# else:
# for i, sample in enumerate(load_json_stream(f)):
# if i == args.sidx:
# break
K, states = attach_sample(trees,
langs,
node_priors,
has_bias=args.has_bias,
latent=args.latent,
max_states=args.max_states)
sampler = CTMC_Sampler(K, states=states, ctmc_scale=0.00005)
sampler.init_trees(trees)
sys.stderr.write("iter 0\t{}\n".format(sampler.logprob(trees)))
_iter = _start - 1
if args.save_interval >= 0 and _iter % args.save_interval == 0:
with open(args.out + ".{}".format(_iter), "wb") as f:
dump(
{
"sampler": sampler,
"trees": trees,
"iter": _iter,
"logprob": logprob
}, f)
for _iter in range(_start, args._iter + 1):
sampler.sample(_iter=_iter)
logprob = sampler.logprob(trees)
sys.stderr.write("iter {}\t{}\n".format(_iter, logprob))
if args.save_interval >= 0 and _iter % args.save_interval == 0:
with open(args.out + ".{}".format(_iter), "wb") as f:
dump(
{
"sampler": sampler,
"trees": trees,
"iter": _iter,
"logprob": logprob
}, f)
with open(args.out + ".current", "wb") as f:
dump(
{
"sampler": sampler,
"trees": trees,
"iter": _iter,
"logprob": logprob
}, f)
with open(args.out + ".final", "wb") as f:
dump(
{
"sampler": sampler,
"trees": trees,
"iter": _iter,
"logprob": logprob
}, f)
