def run(output_dir, feature_num, mode):
feature_num = int(feature_num)
taxo_res = TaxonomyResources(freq_fpaths, isa_fpaths)
taxo_features = TaxonomyFeatures(taxo_res, relations_fpath=train_relations_fpath)
ensure_dir(output_dir)
features = ["hyper_in_hypo_i","hypo2hyper_substract", "freq_substract", "in_weight_substract", "length_substract",
"hypo2hyper_s_substract","hypo2hyper_max2_substract"]
features = features[:feature_num]
if mode == "gridsearch":
# grid search is only supported for SVC
method = "SVC"
hc = SuperTaxi(join(output_dir, "SVC-grid-search"), method="SVC", features=features, overwrite=True)
clf = hc.grid_search_svc(taxo_features.relations, test=TEST_GRIDSEARCH)
return
for method in METHODS:
try:
classifier_dir = join(output_dir, method)
print("\n", method.upper(), "\n", "="*50)
hc = SuperTaxi(classifier_dir, method=method, features=features, overwrite=True)
if mode == "train":
clf = hc.train(taxo_features.relations)
hc._print_clf_info()
elif mode == "cv":
hc.crossval(taxo_features.relations)
else:
print("Error: unrecognised mode %s" % mode)
except:
print(format_exc())
def run(output_dir, feature_num, mode):
feature_num = int(feature_num)
taxo_res = TaxonomyResources(freq_fpaths, isa_fpaths)
taxo_features = TaxonomyFeatures(taxo_res, relations_fpath=train_relations_fpath)
ensure_dir(output_dir)
features = ["hyper_in_hypo_i","hypo2hyper_substract", "freq_substract", "in_weight_substract", "length_substract",
"hypo2hyper_s_substract","hypo2hyper_max2_substract"]
features = features[:feature_num]
if mode == "gridsearch":
# grid search is only supported for SVC
method = "SVC"
hc = SuperTaxi(join(output_dir, "SVC-grid-search"), method="SVC", features=features, overwrite=True)
clf = hc.grid_search_svc(taxo_features.relations, test=TEST_GRIDSEARCH)
return
for method in METHODS:
try:
classifier_dir = join(output_dir, method)
print "\n", method.upper(), "\n", "="*50
hc = SuperTaxi(classifier_dir, method=method, features=features, overwrite=True)
if mode == "train":
clf = hc.train(taxo_features.relations)
hc._print_clf_info()
elif mode == "cv":
hc.crossval(taxo_features.relations)
else:
print "Error: unrecognised mode %s" % mode
except:
print format_exc()
def predict_by_classifier(self, classifier_dir):
st = SuperTaxi(classifier_dir)
self._relations = st.predict(self._relations)
return st.meta["method"]
def predict_by_classifier(self, classifier_dir):
st = SuperTaxi(classifier_dir)
self._relations = st.predict(self._relations)
return st.meta["method"]
