--- a
+++ b/deepdta-toy/arguments.py
@@ -0,0 +1,130 @@
+import argparse
+import os
+
+
+
+def argparser():
+  parser = argparse.ArgumentParser()
+  # for model
+  parser.add_argument(
+      '--seq_window_lengths',
+      type=int,
+      nargs='+',
+      help='Space seperated list of motif filter lengths. (ex, --window_lengths 4 8 12)'
+  )
+  parser.add_argument(
+      '--smi_window_lengths',
+      type=int,
+      nargs='+',
+      help='Space seperated list of motif filter lengths. (ex, --window_lengths 4 8 12)'
+  )
+  parser.add_argument(
+      '--num_windows',
+      type=int,
+      nargs='+',
+      help='Space seperated list of the number of motif filters corresponding to length list. (ex, --num_windows 100 200 100)'
+  )
+  parser.add_argument(
+      '--num_hidden',
+      type=int,
+      default=0,
+      help='Number of neurons in hidden layer.'
+  )
+  parser.add_argument(
+      '--num_classes',
+      type=int,
+      default=0,
+      help='Number of classes (families).'
+  )
+  parser.add_argument(
+      '--max_seq_len',
+      type=int,
+      default=0,
+      help='Length of input sequences.'
+  )
+  parser.add_argument(
+      '--max_smi_len',
+      type=int,
+      default=0,
+      help='Length of input sequences.'
+  )
+  # for learning
+  parser.add_argument(
+      '--learning_rate',
+      type=float,
+      default=0.001,
+      help='Initial learning rate.'
+  )
+  parser.add_argument(
+      '--num_epoch',
+      type=int,
+      default=100,
+      help='Number of epochs to train.'
+  )
+  parser.add_argument(
+      '--batch_size',
+      type=int,
+      default=256,
+      help='Batch size. Must divide evenly into the dataset sizes.'
+  )
+  parser.add_argument(
+      '--train_path',
+      type=str,
+      default='/data/DTC/',
+      help='Directory for input data.'
+  )
+  parser.add_argument(
+      '--test_path',
+      type=str,
+      default='',
+      help='Directory for input data.'
+  )
+  parser.add_argument(
+      '--problem_type',
+      type=int,
+      default=1,
+      help='Type of the prediction problem (1-4)'
+  )
+  parser.add_argument(
+      '--isLog',
+      type=int,
+      default=0,
+      help='Convert the values to log10^9'
+  )
+  parser.add_argument(
+      '--binary_th',
+      type=float,
+      default=0.0,
+      help='Threshold to split data into binary classes'
+  )
+
+  parser.add_argument(
+      '--checkpoint_path',
+      type=str,
+      default='',
+      help='Path to write checkpoint file.'
+  )
+  parser.add_argument(
+      '--log_dir',
+      type=str,
+      default='/tmp',
+      help='Directory for log data.'
+  )
+
+
+
+  FLAGS, unparsed = parser.parse_known_args()
+
+  # check validity
+  #assert( len(FLAGS.window_lengths) == len(FLAGS.num_windows) )
+
+  return FLAGS
+
+
+
+
+def logging(msg, FLAGS):
+  fpath = os.path.join( FLAGS.log_dir, "log.txt" )
+  with open( fpath, "a" ) as fw:
+    fw.write("%s\n" % msg)
+  #print(msg)