add setup.py

setup.py

@@ -0,0 +1,20 @@
+import setuptools
+from setuptools import setup, find_packages
+
+setup(name='smiles_transformer',
+      version='1.0.0',
+      description='Original implementation of the paper "SMILES Transformer: Pre-trained Molecular Fingerprint for Low Data Drug Discovery"',
+      long_description=open('README.md').read(),
+      author='Shion Honda',
+      author_email='https://twitter.com/shion_honda',
+      url='https://github.com/DSPsleeporg/smiles-transformer',
+      license='MIT',
+      install_requires=[
+          'numpy',
+          'pandas',
+          'torch',
+          'tqdm',
+      ],
+      python_requires='>=3',
+      packages=['smiles_transformer']
+      )

smiles_transformer/__init__.py

@@ -0,0 +1 @@
+from . import *

smiles_transformer/build_corpus.py

@@ -2,7 +2,7 @@
 import pandas as pd
 from tqdm import tqdm
 
-from utils import split
+from .utils import split
 
 def main():
     parser = argparse.ArgumentParser(description='Build a corpus file')

smiles_transformer/dataset.py

@@ -3,8 +3,8 @@
 import torch
 from torch.utils.data import Dataset, DataLoader
 
-from enumerator import SmilesEnumerator
-from utils import split
+from .enumerator import SmilesEnumerator
+from .utils import split
 
 PAD = 0
 MAX_LEN = 220

smiles_transformer/pretrain_rnn.py

@@ -12,8 +12,8 @@
 from torch.autograd import Variable
 from torch.nn.utils import clip_grad_norm_
 from torch.nn import functional as F
-from build_vocab import WordVocab
-from dataset import Seq2seqDataset
+from .build_vocab import WordVocab
+from .dataset import Seq2seqDataset
 
 PAD = 0
 UNK = 1

smiles_transformer/pretrain_trfm.py

@@ -12,8 +12,8 @@
 from torch.utils.data import DataLoader
 from tqdm import tqdm
 
-from build_vocab import WordVocab
-from dataset import Seq2seqDataset
+from .build_vocab import WordVocab
+from .dataset import Seq2seqDataset
 
 PAD = 0
 UNK = 1

smiles_transformer/sample.py

@@ -1,4 +1,4 @@
-from enumerator import SmilesEnumerator
+from .enumerator import SmilesEnumerator
 
 sme = SmilesEnumerator()
 smiles = ''