load_data and tokenize

.gitignore

@@ -1,2 +1,3 @@
 *~
 *.pyc
+venv/*

seq.py

@@ -1,21 +1,48 @@
+from numpy.lib.shape_base import split
 import pandas as pd
 import numpy as np
 import gensim
 import torch
 import pandas as pd
-import seaborn as sns
 from sklearn.model_selection import train_test_split
+from collections import Counter
+from torchtext.vocab import Vocab
+
+
+# Functions from jupyter
+def build_vocab(dataset):
+    counter = Counter()
+    for document in dataset:
+        counter.update(document)
+    return Vocab(counter, specials=['<unk>', '<pad>', '<bos>', '<eos>'])
+
+
+def data_process(dt, vocab):
+    return [torch.tensor([vocab['<bos>']] + [vocab[token] for token in document] + [vocab['<eos>']], dtype=torch.long) for document in dt]
+
+
+def labels_process(dt, vocab):
+    return [torch.tensor([0] + document + [0], dtype=torch.long) for document in dt]
 
 
 # Load data
-train = pd.read_csv('train/train.tsv', sep='\t', names=['labels', 'document'])
-Y_train = train['labels'].values
-X_train = train['document'].values
+def load_data():
+    train = pd.read_csv('train/train.tsv', sep='\t',
+                        names=['labels', 'document'])
+
+    Y_train = [y.split(sep=" ") for y in train['labels'].values]
+    X_train = [x.split(sep=" ") for x in train['document'].values]
+
+    dev = pd.read_csv('dev-0/in.tsv', sep='\t', names=['document'])
+    exp = pd.read_csv('dev-0/expected.tsv', sep='\t', names=['labels'])
+    X_dev = [x.split(sep=" ") for x in dev['document'].values]
+    Y_dev = [y.split(sep=" ") for y in exp['labels'].values]
 
     test = pd.read_csv('test-A/in.tsv', sep='\t', names=['document'])
     X_test = test['document'].values
 
-dev = pd.read_csv('dev-0/in.tsv', sep='\t', names=['document'])
-exp = pd.read_csv('dev-0/expected.tsv', sep='\t', names=['labels'])
-X_dev = dev['document'].values
-Y_dev = dev['labels'].values
+    return X_train, Y_train, X_dev, Y_dev, X_test
+
+
+if __name__ == "__main__":
+    X_train, Y_train, X_dev, Y_dev, X_test = load_data()