def manual_eval():
# Load vocabularies.
sc_vocab_path = os.path.join(FLAGS.data_dir,
"vocab%d.nl" % FLAGS.sc_vocab_size)
tg_vocab_path = os.path.join(FLAGS.data_dir,
"vocab%d.cm.ast" % FLAGS.tg_vocab_size)
sc_vocab, rev_sc_vocab = data_utils.initialize_vocabulary(sc_vocab_path)
tg_vocab, rev_tg_vocab = data_utils.initialize_vocabulary(tg_vocab_path)
train_set, dev_set, test_set = load_data()
model = knn_model.KNNModel()
model.train(train_set)
eval_tools.manual_eval(model_name, test_set, rev_sc_vocab,
FLAGS, FLAGS.model_dir, num_eval=500)
def manual_eval(dataset, model_dir=None, decode_sig=None):
if model_dir is None:
model_subdir, decode_sig = graph_utils.get_decode_signature(FLAGS)
model_dir = os.path.join(FLAGS.model_root_dir, model_subdir)
print("(Manual) evaluating " + model_dir)
return eval_tools.manual_eval(model_dir, decode_sig, dataset, FLAGS, top_k=3, num_examples=100, verbose=True)
def manual_eval(dataset, prediction_path=None):
if prediction_path is None:
model_subdir, decode_sig = graph_utils.get_decode_signature(FLAGS)
model_dir = os.path.join(FLAGS.model_root_dir, model_subdir)
prediction_path = os.path.join(model_dir, 'predictions.{}.latest'.format(decode_sig))
print("(Manual) evaluating " + prediction_path)
return eval_tools.manual_eval(prediction_path, dataset, FLAGS, top_k=3, num_examples=100, verbose=True)
def manual_eval(dataset, num_eval):
_, decode_sig = graph_utils.get_decode_signature(FLAGS)
eval_tools.manual_eval(decode_sig, dataset, FLAGS, FLAGS.model_root_dir,
num_eval)