Update (#1359)

apps/life_sci/dgllife/data/csv_dataset.py

@@ -43,9 +43,11 @@ class MoleculeCSVDataset(object):
         Whether to load the previously pre-processed dataset or pre-process from scratch.
         ``load`` should be False when we want to try different graph construction and
         featurization methods and need to preprocess from scratch. Default to True.
+    log_every : bool
+        Print a message every time ``log_every`` molecules are processed. Default to 1000.
     """
     def __init__(self, df, smiles_to_graph, node_featurizer, edge_featurizer,
-                 smiles_column, cache_file_path, task_names=None, load=True):
+                 smiles_column, cache_file_path, task_names=None, load=True, log_every=1000):
         self.df = df
         self.smiles = self.df[smiles_column].tolist()
         if task_names is None:
@@ -54,9 +56,9 @@ class MoleculeCSVDataset(object):
             self.task_names = task_names
         self.n_tasks = len(self.task_names)
         self.cache_file_path = cache_file_path
-        self._pre_process(smiles_to_graph, node_featurizer, edge_featurizer, load)
+        self._pre_process(smiles_to_graph, node_featurizer, edge_featurizer, load, log_every)
 
-    def _pre_process(self, smiles_to_graph, node_featurizer, edge_featurizer, load):
+    def _pre_process(self, smiles_to_graph, node_featurizer, edge_featurizer, load, log_every):
         """Pre-process the dataset
 
         * Convert molecules from smiles format into DGLGraphs
@@ -78,6 +80,8 @@ class MoleculeCSVDataset(object):
             Whether to load the previously pre-processed dataset or pre-process from scratch.
             ``load`` should be False when we want to try different graph construction and
             featurization methods and need to preprocess from scratch. Default to True.
+        log_every : bool
+            Print a message every time ``log_every`` molecules are processed.
         """
         if os.path.exists(self.cache_file_path) and load:
             # DGLGraphs have been constructed before, reload them
@@ -89,6 +93,7 @@ class MoleculeCSVDataset(object):
             print('Processing dgl graphs from scratch...')
             self.graphs = []
             for i, s in enumerate(self.smiles):
+                if (i + 1) % log_every == 0:
                     print('Processing molecule {:d}/{:d}'.format(i+1, len(self)))
                 self.graphs.append(smiles_to_graph(s, node_featurizer=node_featurizer,
                                                    edge_featurizer=edge_featurizer))

apps/life_sci/dgllife/data/pubchem_aromaticity.py

@@ -34,9 +34,11 @@ class PubChemBioAssayAromaticity(MoleculeCSVDataset):
         Whether to load the previously pre-processed dataset or pre-process from scratch.
         ``load`` should be False when we want to try different graph construction and
         featurization methods and need to pre-process from scratch. Default to True.
+    log_every : bool
+        Print a message every time ``log_every`` molecules are processed. Default to 1000.
     """
     def __init__(self, smiles_to_graph=smiles_to_bigraph,
-                 node_featurizer=None, edge_featurizer=None, load=True):
+                 node_featurizer=None, edge_featurizer=None, load=True, log_every=1000):
         self._url = 'dataset/pubchem_bioassay_aromaticity.csv'
         data_path = get_download_dir() + '/pubchem_bioassay_aromaticity.csv'
         download(_get_dgl_url(self._url), path=data_path)
@@ -44,4 +46,4 @@ class PubChemBioAssayAromaticity(MoleculeCSVDataset):
 
         super(PubChemBioAssayAromaticity, self).__init__(
             df, smiles_to_graph, node_featurizer, edge_featurizer, "cano_smiles",
-            "pubchem_aromaticity_dglgraph.bin", load=load)
+            "pubchem_aromaticity_dglgraph.bin", load=load, log_every=log_every)

apps/life_sci/dgllife/data/tox21.py

@@ -41,11 +41,14 @@ class Tox21(MoleculeCSVDataset):
         Whether to load the previously pre-processed dataset or pre-process from scratch.
         ``load`` should be False when we want to try different graph construction and
         featurization methods and need to preprocess from scratch. Default to True.
+    log_every : bool
+        Print a message every time ``log_every`` molecules are processed. Default to 1000.
     """
     def __init__(self, smiles_to_graph=smiles_to_bigraph,
                  node_featurizer=None,
                  edge_featurizer=None,
-                 load=True):
+                 load=True,
+                 log_every=1000):
         self._url = 'dataset/tox21.csv.gz'
         data_path = get_download_dir() + '/tox21.csv.gz'
         download(_get_dgl_url(self._url), path=data_path)
@@ -55,7 +58,8 @@ class Tox21(MoleculeCSVDataset):
         df = df.drop(columns=['mol_id'])
 
         super(Tox21, self).__init__(df, smiles_to_graph, node_featurizer, edge_featurizer,
-                                    "smiles", "tox21_dglgraph.bin", load=load)
+                                    "smiles", "tox21_dglgraph.bin",
+                                    load=load, log_every=log_every)
         self._weight_balancing()
 
     def _weight_balancing(self):