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 breakdown(lexsample_fpath, output_dir):
ensure_dir(output_dir)
df = read_csv(lexsample_fpath, encoding='utf-8', delimiter="\t", error_bad_lines=False,
header=None, names=names, low_memory=False)
n = 0
q_levels = [0.1*x for x in range(9, 0, -1)]
q_values = [df["conf"].quantile(l) for l in q_levels]
q = zip(q_levels, q_values)
q_cur = q.pop()
df = df.sort(["conf"], ascending=1)
if REVERSE: df.conf = -df.conf
name = splitext(ntpath.basename(lexsample_fpath))[0]
for i, row in df.iterrows():
if row["conf"] >= q_cur[1]:
output_fpath = join(output_dir, name + "-" +"conf" + unicode(int(100*(1.0-q_cur[0]))) + ".csv")
print "Saving: %.3f %.3f %d %s" % (q_cur[0], q_cur[1], n, output_fpath)
df.to_csv(output_fpath, sep="\t", encoding="utf-8", float_format='%.3f', index=False, header=False)
if len(q) > 0: q_cur = q.pop()
else: break
if row["conf"] < q_cur[1]:
df.loc[i,"predict_sense_ids"] = -1
if n % 5000 == 0: print n
n += 1
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 __init__(self,
model_dir,
method="LogisticRegressionL2",
features=FEATURES,
k=100,
overwrite=False):
self.CLASSIFIER_FILE = "classifier"
self.KBEST_VOC_FILE = "kbest-voc.csv"
self.KBEST_FILE = "kbest.pkl"
self.META_FILE = "meta.json"
clf_fpath = join(model_dir, self.CLASSIFIER_FILE)
kbest_fpath = join(model_dir, self.KBEST_FILE)
self._model_dir = model_dir
self._meta_fpath = join(model_dir, self.META_FILE)
self._meta = {}
self._meta["method"] = method
self._meta["k"] = k
self._meta["features"] = features
if exists(model_dir) and exists(clf_fpath) and not overwrite:
# load the model
self._clf = joblib.load(clf_fpath)
self._meta = json.load(open(self._meta_fpath, "r"))
print("Metadata were loaded from:", self._meta_fpath)
else:
# model doesn't exist, or must be overwritten create a new one
ensure_dir(model_dir)
self.save_meta()
def __init__(self, model_dir, method="LogisticRegressionL2", features=FEATURES, k=100, overwrite=False):
self.CLASSIFIER_FILE = "classifier"
self.KBEST_VOC_FILE = "kbest-voc.csv"
self.KBEST_FILE = "kbest.pkl"
self.META_FILE = "meta.json"
clf_fpath = join(model_dir, self.CLASSIFIER_FILE)
kbest_fpath = join(model_dir, self.KBEST_FILE)
self._model_dir = model_dir
self._meta_fpath = join(model_dir, self.META_FILE)
self._meta = {}
self._meta["method"] = method
self._meta["k"] = k
self._meta["features"] = features
if exists(model_dir) and exists(clf_fpath) and not overwrite:
# load the model
self._clf = joblib.load(clf_fpath)
self._meta = json.load(open(self._meta_fpath, "r"))
print "Metadata were loaded from:", self._meta_fpath
else:
# model doesn't exist, or must be overwritten create a new one
ensure_dir(model_dir)
self.save_meta()
def __init__(self, babelnet_keys, babelnet_fpath="", freq_fpath="", normalized=True, divide_by_freq=False, force_api=False):
self._babelnet_keys = babelnet_keys
self._babelnet_dir = babelnet_fpath
ensure_dir(self._babelnet_dir)
self._normalized = normalized
self._force_api = force_api
self._freq = FreqDictionary(freq_fpath)
self._babelnet = self._load(babelnet_fpath, divide_by_freq=divide_by_freq) # format: (word->sense_id->{"bow" , "wnOffset"}
def __init__(self,
babelnet_keys,
babelnet_fpath="",
freq_fpath="",
normalized=True,
divide_by_freq=False,
force_api=False):
self._babelnet_keys = babelnet_keys
self._babelnet_dir = babelnet_fpath
ensure_dir(self._babelnet_dir)
self._normalized = normalized
self._force_api = force_api
self._freq = FreqDictionary(freq_fpath)
self._babelnet = self._load(
babelnet_fpath, divide_by_freq=divide_by_freq
) # format: (word->sense_id->{"bow" , "wnOffset"}
def breakdown(lexsample_fpath, output_dir):
ensure_dir(output_dir)
df = read_csv(lexsample_fpath,
encoding='utf-8',
delimiter="\t",
error_bad_lines=False,
header=None,
names=names,
low_memory=False)
n = 0
q_levels = [0.1 * x for x in range(9, 0, -1)]
q_values = [df["conf"].quantile(l) for l in q_levels]
q = zip(q_levels, q_values)
q_cur = q.pop()
df = df.sort(["conf"], ascending=1)
if REVERSE: df.conf = -df.conf
name = splitext(ntpath.basename(lexsample_fpath))[0]
for i, row in df.iterrows():
if row["conf"] >= q_cur[1]:
output_fpath = join(
output_dir, name + "-" + "conf" +
unicode(int(100 * (1.0 - q_cur[0]))) + ".csv")
print "Saving: %.3f %.3f %d %s" % (q_cur[0], q_cur[1], n,
output_fpath)
df.to_csv(output_fpath,
sep="\t",
encoding="utf-8",
float_format='%.3f',
index=False,
header=False)
if len(q) > 0: q_cur = q.pop()
else: break
if row["conf"] < q_cur[1]:
df.loc[i, "predict_sense_ids"] = -1
if n % 5000 == 0: print n
n += 1
