def cluster_by_grs(target, source, env):
import graphmod as gm
args = source[-1].read()
verb_map = {}
gr_map = {}
instances = gm.Instances()
gm.load_instances(source[0].rstr(), instances)
for ii in range(len(instances)):
verb = instances.get_name("verb_lemma", instances.at(ii)["verb_lemma"][0])
grs = [instances.get_name("gr", x) for x in instances.at(ii)["gr"]]
verb_map[verb] = verb_map.get(verb, len(verb_map))
for gr in grs:
gr_map[gr] = gr_map.get(gr, len(gr_map))
data = numpy.zeros(shape=(len(verb_map), len(gr_map)))
for ii in range(len(instances)):
verb = instances.get_name("verb_lemma", instances.at(ii)["verb_lemma"][0])
verb_id = verb_map[verb]
grs = [instances.get_name("gr", x) for x in instances.at(ii)["gr"]]
gr_ids = [gr_map[x] for x in grs]
for gr in gr_ids:
data[verb_id, gr] += 1
data = numpy.transpose(data.T / data.sum(1))
tres = numpy.asarray(rcluster.clusGap(numpy2ri(data), FUN=stats.kmeans, K_max=30, B=500).rx2("Tab"))
gaps = tres[:, 2]
err = tres[:, 3]
best = rcluster.maxSE(numpy2ri(gaps), numpy2ri(err), method="globalmax")
res = stats.kmeans(numpy2ri(data), centers=best)
verbs = dict([(v, k) for k, v in verb_map.iteritems()])
ofd = meta_open(target[0].rstr(), "w")
for c in set(res.rx2("cluster")):
ofd.write(" ".join([verbs[i] for i, a in enumerate(res.rx2("cluster")) if a == c]) + "\n")
return None
def cluster_by_valex(target, source, env):
import graphmod as gm
args = source[-1].read()
target_verbs = set()
instances = gm.Instances()
gm.load_instances(source[0].rstr(), instances)
for vid in range(instances.get_size("verb_lemma")):
target_verbs.add(instances.get_name("verb_lemma", vid))
data = {}
scfs = {}
verbs = {}
for fname in sorted(glob(os.path.join("%s/lex-%s" % (env["VALEX_LEXICON"], args["lexicon"]), "*"))):
verb = os.path.basename(fname).split(".")[0]
if verb not in target_verbs:
continue
data[verb] = {}
for m in re.finditer(r":CLASSES \((.*?)\).*\n.*FREQCNT (\d+)", meta_open(fname).read()):
scf = int(m.group(1).split()[0])
count = int(m.group(2))
scfs[scf] = scfs.get(scf, 0) + count
verbs[verb] = verbs.get(verb, 0) + count
data[verb][scf] = count
ddata = numpy.zeros(shape=(len(verbs), len(scfs)))
verbs = sorted(verbs)
scfs = sorted(scfs)
for row, verb in enumerate(verbs):
for col, scf in enumerate(scfs):
ddata[row, col] = data[verb].get(scf, 0)
data = numpy.transpose(ddata.T / ddata.sum(1))
tres = numpy.asarray(rcluster.clusGap(numpy2ri(data), FUN=stats.kmeans, K_max=30, B=500).rx2("Tab"))
gaps = tres[:, 2]
err = tres[:, 3]
best = rcluster.maxSE(numpy2ri(gaps), numpy2ri(err), method="globalmax")
res = stats.kmeans(numpy2ri(data), centers=best)
ofd = meta_open(target[0].rstr(), "w")
for c in set(res.rx2("cluster")):
ofd.write(" ".join([verbs[i] for i, a in enumerate(res.rx2("cluster")) if a == c]) + "\n")
return None
def run_scf(target, source, env):
import graphmod as gm
args = source[-1].read()
parameters = args["parameters"]
nodes = {}
factors = {}
variables = {}
instances = gm.Instances()
gm.load_instances(source[0].rstr(), instances)
if "features" in args:
instances.transform("argument", args["features"])
graph = gm.Graph()
if args.get("model", "") == "multi":
variables["alpha"] = gm.ContinuousVectorVariable(parameters["scf"], parameters["alpha"])
variables["beta"] = gm.ContinuousVectorVariable(instances.get_size("argument"), parameters["beta"])
vxf = []
for v in range(instances.get_size("verb_lemma")):
variables["VERB%dxFRAME" % v] = gm.ContinuousVectorVariable([1.0 / parameters["scf"] for x in range(parameters["scf"])])
vxf.append(variables["VERB%dxFRAME" % v])
factors["VERB%dxFRAME" % v] = gm.DirichletMultinomial(variables["alpha"], variables["VERB%dxFRAME" % v])
variables["VERBxFRAME"] = gm.TiledContinuousVectorVariable(gm.ContinuousVectorVariableVector(vxf))
fxa = []
for f in range(parameters["scf"]):
variables["FRAME%dxARG" % f] = gm.ContinuousVectorVariable([1.0 / instances.get_size("argument") for x in range(instances.get_size("argument"))])
vxf.append(variables["FRAME%dxARG" % f])
factors["FRAME%dxARG" % f] = gm.DirichletMultinomial(variables["beta"], variables["FRAME%dxARG" % f])
variables["FRAMExARG"] = gm.TiledContinuousVectorVariable(gm.ContinuousVectorVariableVector(fxa))
for instance in range(len(instances)):
verb_id = instances.at(instance)("verb_lemma")[0]
vname = "instance%d_verb" % (instance)
variables[vname] = gm.DiscreteScalarVariable(verb_id)
variables[vname].set_support(instances.get_size("verb_lemma"))
variables[vname].set_name("verb")
iname = "instance%d_scf" % (instance)
variables[iname] = gm.DiscreteScalarVariable()
variables[iname].set_support(parameters["scf"])
variables[iname].set_name("scf")
fnameA = "instance%d_fA" % (instance)
factors[fnameA] = gm.MultinomialCategorical(variables["VERB0xFRAME"], variables[iname])
#factors[fnameA] = gm.MultinomialCategorical(variables["VERB0xFRAME"], variables[vname], variables[iname])
for i, val in enumerate(instances.at(instance)("argument")):
oname = "instance%d_observation%d" % (instance, i)
variables[oname] = gm.DiscreteScalarVariable(val)
variables[oname].set_support(instances.get_size("argument"))
variables[oname].set_name("argument")
fnameB = "instance%d_fB_%d" % (instance, i)
factors[fnameB] = gm.MultinomialCategorical(variables["FRAME0xARG"], variables[oname])
#factors[fnameB] = gm.DirichletMultinomialMixture(variables["beta"], variables[iname], variables[oname])
if args.get("model", "") == "pymulti":
variables["alpha"] = gm.ContinuousScalarVariable(parameters["alpha"])
variables["gamma"] = gm.ContinuousScalarVariable(parameters["gamma"])
variables["beta"] = gm.ContinuousVectorVariable(instances.get_size("argument"), parameters["beta"])
for instance in range(len(instances)):
verb_id = instances.at(instance)("verb_lemma")[0]
vname = "instance%d_verb" % (instance)
variables[vname] = gm.DiscreteScalarVariable(verb_id)
variables[vname].set_support(instances.get_size("verb_lemma"))
variables[vname].set_name("verb")
iname = "instance%d_scf" % (instance)
variables[iname] = gm.DiscreteScalarVariable()
variables[iname].set_support(parameters["scf"])
variables[iname].set_name("scf")
fnameA = "instance%d_fA" % (instance)
factors[fnameA] = gm.DirichletMultinomialMixture(variables["alpha"], variables[vname], variables[iname])
for i, val in enumerate(instances.at(instance)("argument")):
oname = "instance%d_observation%d" % (instance, i)
variables[oname] = gm.DiscreteScalarVariable(val)
variables[oname].set_support(instances.get_size("argument"))
variables[oname].set_name("argument")
fnameB = "instance%d_fB_%d" % (instance, i)
factors[fnameB] = gm.DirichletMultinomialMixture(variables["beta"], variables[iname], variables[oname])
elif args.get("model", "") == "beta":
variables["alpha"] = gm.ContinuousVectorVariable(parameters["scf"], parameters["alpha"])
betas = []
for i in range(instances.get_size("argument")):
variables["beta%d" % i] = gm.ContinuousVectorVariable(2, parameters["beta"])
betas.append(variables["beta%d" % i])
variables["beta"] = gm.TiledContinuousVectorVariable(gm.ContinuousVectorVariableVector(betas))
for instance in range(len(instances)):
verb_id = instances.at(instance)("verb_lemma")[0]
vname = "instance%d_verb" % (instance)
variables[vname] = gm.DiscreteScalarVariable(verb_id)
variables[vname].set_support(instances.get_size("verb_lemma"))
variables[vname].set_name("verb")
iname = "instance%d_scf" % (instance)
variables[iname] = gm.DiscreteScalarVariable()
variables[iname].set_support(parameters["scf"])
variables[iname].set_name("scf")
fnameA = "instance%d_fA" % (instance)
factors[fnameA] = gm.DirichletMultinomialMixture(variables["alpha"], variables[vname], variables[iname])
oname = "instance%d_observation" % (instance)
variables[oname] = gm.DiscreteMapVariable(instances.at(instance)("argument"))
variables[oname].set_support(instances.get_size("argument"))
variables[oname].set_name("argument")
fnameB = "instance%d_fB" % (instance)
factors[fnameB] = gm.BetaBinomialMixture(variables["beta"], variables[iname], variables[oname])
for variable in variables.values():
graph.add_variable(variable)
for factor in factors.values():
graph.add_factor(factor)
logging.error("Compiling graph...")
graph.compile()
logging.error("Creating counter...")
counts = graph.get_counts_object()
print len(graph.get_variables()), len(graph.get_factors())
samples = numpy.empty(shape=(instances.get_size("verb_lemma"), parameters["scf"], args["samples"]))
logging.error("Starting sampling...")
return None
#for v in variables.values():
# if not gm.BaseVariable_get_fixed(v):
# print v
# v.sample(counts)
#return None
for i in range(args["burnins"]):
graph.sample(counts)
#print nodes["alpha"].get_value()
logging.error("Burnin #%s", i + 1)
#print numpy.asarray(variables["alpha"].get_value())
print numpy.asarray(counts("scf"))
#print numpy.asarray(counts("scf", "argument"))
#ll = sampler.log_likelihood(graph)
#logging.error("LL = %f", ll)
#print sampler.get_counts("scf")
#print nodes["alpha"]
#print nodes["beta"]
variables["alpha"].set_fixed(False)
#variables["beta"].set_fixed(False)
for i in range(args["samples"]):
graph.sample(counts)
logging.error("Sample #%s", i + 1)
#logging.error("alpha=%s, beta=%s", variables["alpha"].get_value()[0], variables["beta"].get_value()[0])
#ll = sampler.log_likelihood(graph)
#logging.error("LL = %f", ll)
#print sampler.get_counts("scf")
#print nodes["beta"]
samples[:, :, i] = numpy.asarray(counts("verb", "scf"))
row_names = [instances.get_name("verb_lemma", x) for x in range(samples.shape[0])]
pickle.dump((row_names, samples), meta_open(target[0].rstr(), "w"))
return None
