def main(preprocessed_node_path, argument_path, dictionary_path, tfidf_path):
preprocessed_node_path = Path(args.preprocessed_node_path)
argument_path = Path(args.argument_path)
dictionary_path = Path(args.dictionary_path)
tfidf_path = Path(args.tfidf_path)
#argument_generator_getter = lambda: utils.load(argument_path)
#argument_nodes_ids = set((
# node_id
# for argument in argument_generator_getter()
# for node_id in argument[0].values()))
# Use the set of ids to select only the relevant nodes
# (and not train nlp models on all documents).
#preprocessed_node_generator_getter = lambda : filter(
# lambda node: node['id'] in argument_nodes_ids,
# utils.load( preprocessed_node_path))
dictionary = pkl.load(dictionary_path.open('rb'))
#tfidf = text.fit_tfidf(preprocessed_node_generator_getter,
# dictionary,
# verbose = True)
tfidf = TfIdf()
tfidf.fit(dictionary.dictionary)
tfidf.save(tfidf_path)
