Move and rename files

Author: emalgorithm

training/__init__.py

@@ -2,6 +2,12 @@
 
 
 def compute_rouge_scores(pred_seq, target_seq):
+    """
+    :param pred_seq: Predicted sequence
+    :param target_seq: Target sequence
+    :return: a pair (rouge_2, rouge_l) containing the rouge-2 and rouge-l scores given pred_seq
+    and target_seq
+    """
     rouge = Rouge()
     pred_seq_str = ' '.join([str(x) for x in pred_seq])
     target_seq = ' '.join([str(x) for x in target_seq])

metrics.py renamed to training/evaluation_util.py

@@ -1,8 +1,8 @@
 import os
 import argparse
 
-from models.lstm_to_lstm import Seq2Seq
-from models.lstm_to_lstm_full_training import train_iters
+from models.full_model import FullModel
+from training.train_model import train_iters
 from models.lstm_encoder import LSTMEncoder
 from models.lstm_decoder import LSTMDecoder
 from data_processing.data_util import prepare_tokens, prepare_data
@@ -33,10 +33,10 @@ def main():
             graph_encoder = GATEncoder(hidden_size, hidden_size)
         else:
             graph_encoder = GCNEncoder(hidden_size, hidden_size)
-        model = Seq2Seq(encoder=encoder, graph_encoder=graph_encoder, decoder=decoder,
-                        device=opt.device)
+        model = FullModel(encoder=encoder, graph_encoder=graph_encoder, decoder=decoder,
+                          device=opt.device)
     else:
-        model = Seq2Seq(encoder=encoder, decoder=decoder, device=opt.device)
+        model = FullModel(encoder=encoder, decoder=decoder, device=opt.device)
 
     train_iters(model, opt.iterations, pairs, print_every=opt.print_every, model_dir=model_dir,
                 lang=lang, graph=opt.graph)

train.py renamed to training/train.py

@@ -1,13 +1,14 @@
 from __future__ import unicode_literals, print_function, division
 import random
-from data_processing import tensors_from_pair_tokens, plot_loss, tensors_from_pair_tokens_graph
+from data_processing.data_util import tensors_from_pair_tokens, plot_loss, \
+    tensors_from_pair_tokens_graph
 
 import torch
 import torch.nn as nn
 from torch import optim
 from sklearn.metrics import f1_score
 import numpy as np
-from metrics import compute_rouge_scores
+from training.evaluation_util import compute_rouge_scores
 import pickle