def get_pmi_model(wc_dir, noun):
pmi_model = features.PMINounModel()
filename = pmi_model.lemma_to_filename(noun, wc_dir)
try:
os.stat(filename)
except OSError:
return None
pmi_model._load_from_pmi_file(filename)
return pmi_model
def generate_cv_old_recursive(cv_entry, cv_entries_dict, input_dir,
output_dir):
concept = cv_entry.concept
# check if it exists on disk already
pmi_model = features.PMINounModel()
filename = pmi_model.lemma_to_filename(concept, output_dir)
try:
os.stat(filename)
pmi_model._load_from_pmi_file(filename)
return pmi_model
except OSError:
pass
# since it doesn't exist on disk, compute it recursively
wv_models = []
for word in cv_entry.wv:
word_pmi_model = get_pmi_model(input_dir, word)
if word_pmi_model:
wv_models.append(word_pmi_model)
for word1, word2 in cv_entry.wvi:
word1_pmi_model = get_pmi_model(input_dir, word1)
word2_pmi_model = get_pmi_model(input_dir, word2)
if word1_pmi_model and word2_pmi_model:
wv_models.append(word1_pmi_model.intersection(word2_pmi_model))
for parent, weight in cv_entry.p:
parent_cv_entry = cv_entries_dict[parent]
parent_pmi_model = generate_cv(parent_cv_entry, cv_entries_dict,
input_dir, output_dir)
parent_pmi_model.scale(weight)
wv_models.append(parent_pmi_model)
concept_pmi_model = features.PMINounModel()
concept_pmi_model.noun = concept
for model in wv_models:
concept_pmi_model.union_max(model)
concept_pmi_model.save_to_file(output_dir)
return concept_pmi_model
def get_pmi_model_OLD1(wc_dir, noun):
if cached_pmi_models.has_key(noun):
return cached_pmi_models[noun]
else:
pmi_model = features.PMINounModel()
filename = pmi_model.lemma_to_filename(noun, wc_dir)
try:
os.stat(filename)
except OSError:
return None
pmi_model._load_from_pmi_file(filename)
cached_pmi_models[noun] = pmi_model
return pmi_model
def get_feature_set(source_dir):
print 'Loading models...'
pmi_models = []
realcount = 0
feature_set = set()
for n, pmi_filename in enumerate(glob.glob('%s/*.pmi.bz2' % source_dir)):
print 'Loading:', n, pmi_filename
pmi_model = features.PMINounModel()
pmi_model._load_from_pmi_file(pmi_filename)
for section in pmi_model.sections:
for feature in pmi_model.__dict__[section]:
if (section, feature) not in feature_set:
feature_set.add((section, feature))
return feature_set
def generate_cv_entries(cv_entries_dict, input_dir, output_dir):
nm = features.PMINounModel()
for concept in cv_entries_dict:
filename = nm.lemma_to_filename(concept, output_dir)
concept_exists_already = False
try:
os.stat(filename)
concept_exists_already = True
except OSError:
pass
if not concept_exists_already:
generate_cv(cv_entries_dict[concept], cv_entries_dict, input_dir,
output_dir)
def make_db(source_dir, c, feature_map_toidx, concept_map_toidx):
for n, pmi_filename in enumerate(glob.glob('%s/*.pmi.bz2' % source_dir)):
print 'Loading:', n, pmi_filename
pmi_model = features.PMINounModel()
pmi_model._load_from_pmi_file(pmi_filename)
if pmi_model.high_fcount < 40:
continue
concept = pmi_model.noun
for section in pmi_model.sections:
for feature in pmi_model.__dict__[section]:
pmi = pmi_model.__dict__[section][feature]
feature_str = featuremap.feature_to_str(feature)
feature_idx = feature_map_toidx[(section, feature_str)]
concept_idx = concept_map_toidx[concept]
c.execute('insert into words values (?, ?, ?)',
(concept_idx, feature_idx, pmi))
def load_concept_model(concept):
try:
model = features.PMINounModel(concept, base_dir)
return model
except IOError:
return None
def generalize_and_save_pmi(source_dir, dest_dir):
for pmi_filename in glob.glob('%s/*.pmi' % source_dir):
pmi_model = features.PMINounModel()
pmi_model._load_from_pmi_file(pmi_filename)
generalize_pmi_nounmodel(pmi_model)
pmi_model.save_to_file(dest_dir)
