Add VAE example

examples/job/train-vae.py

@@ -4,7 +4,7 @@
 # In[]:
 
 
-get_ipython().system('pip install tensorflow rdkit_pypi pymonad tqdm deepchem')
+get_ipython().system('pip install tensorflow rdkit_pypi pymonad tqdm deepchem pillow')
 
 
 # In[]:
@@ -23,6 +23,7 @@
 import rdkit.Chem
 import tqdm
 
+rdkit.RDLogger.DisableLog('rdApp.*')
 RANDOM_SEED = 42
 tqdm.tqdm.pandas()
 
@@ -33,6 +34,7 @@
 
 
 data = pd.read_csv("assets/curated-solubility-dataset.csv")
+data.to_pickle("vae_data.pkl")
 
 # Drop duplicates based on the InChI representation
 nondupes = data.drop_duplicates(subset="InChI")
@@ -43,7 +45,7 @@
 
 # # Data Augmentation
 
-# In[ ]:
+# In[]:
 
 
 augmented_data = list(smiles.copy())
@@ -74,7 +76,7 @@ def augment_smiles(smiles_string: str):
 
 # # Tokenization with DeepChem
 
-# In[ ]:
+# In[]:
 
 
 # DeepChem's SmileTokenizer uses the WordPiece transformer by HuggingFace (https://huggingface.co/transformers/tokenizer_summary.html), with the regular expression SMILES tokenization strategy developed by Schwaller, P. et al in https://doi.org/10.1039/c8sc02339e
@@ -93,7 +95,7 @@ def augment_smiles(smiles_string: str):
 tokenized_smiles
 
 
-# In[ ]:
+# In[]:
 
 
 # Next up, we'll remove SMILES with any unknown characters, since we don't want our generator putting those tokens in the output
@@ -108,13 +110,13 @@ def augment_smiles(smiles_string: str):
 
 # # Create the VAE
 
-# In[ ]:
+# In[]:
 
 
 from tensorflow.keras.layers import Input, Dense, Conv1D, Layer, Flatten, Reshape, Conv1DTranspose
 
 
-# In[ ]:
+# In[]:
 
 
 class Sampling(Layer):
@@ -129,7 +131,7 @@ def call(self, inputs):
         return z_mean + tf.exp(0.5 * z_log_var) * epsilon
 
 
-# In[ ]:
+# In[]:
 
 
 latent_dim = 1
@@ -147,7 +149,7 @@ def call(self, inputs):
 encoder.summary()
 
 
-# In[ ]:
+# In[]:
 
 
 latent_inputs = Input(shape=(latent_dim,))
@@ -161,7 +163,7 @@ def call(self, inputs):
 decoder.summary()
 
 
-# In[ ]:
+# In[]:
 
 
 class VAE(tf.keras.Model):
@@ -209,21 +211,22 @@ def train_step(self, data):
 
 # # Train the VAE
 
-# In[ ]:
+# In[]:
 
 
 # Reshape the data as needed, scale between 0 and 1
 train_data = np.array([i for i in tokenized_smiles])
 train_data = np.expand_dims(train_data, -1).astype("float32") / maxlen
-train_data.reshape(1,312,-1).shape
+train_data = train_data.reshape(-1,maxlen,1)
+train_data.shape
 
 
-# In[ ]:
+# In[]:
 
 
 vae = VAE(encoder, decoder)
 vae.compile(optimizer=tf.keras.optimizers.Adam())
-vae.fit(train_data, epochs=1000, batch_size=64)
+vae.fit(train_data, epochs=100, batch_size=64)
 
 
 # In[ ]:
@@ -254,8 +257,33 @@ def invert_tokenization(tokens):
 # In[ ]:
 
 
-predictions = (vae.decoder.predict([np.random.uniform(low=-10, high=10, size=100)])[:,:,0] * maxlen).astype(int)
-list(map(lambda tokenized: invert_tokenization(tokenized), predictions))
+predictions = (vae.decoder.predict([np.linspace(-100, 1000, 50)])[:,:,0] * maxlen).astype(int)
+pred_smiles = list(map(lambda tokenized: invert_tokenization(tokenized), predictions))
+
+
+# In[ ]:
+
+
+from rdkit.Chem.Draw import IPythonConsole
+from rdkit.Chem.Draw.MolDrawing import MolDrawing
+pred_mols = []
+for i in pred_smiles:
+    mol = rdkit.Chem.MolFromSmiles(i)
+    if mol:
+        pred_mols.append(mol)
+
+
+# In[ ]:
+
+
+img = rdkit.Chem.Draw.MolsToGridImage(pred_mols, subImgSize=[3000,3000])
+img
+
+
+# In[ ]:
+
+
+
 
 
 # In[ ]: