Minor improvements to molnet loader functions

deepchem/molnet/defaults.py

@@ -17,10 +17,11 @@
 logger = logging.getLogger(__name__)
 
 featurizers = {
-    'ECFP': dc.feat.CircularFingerprint(size=1024),
-    'GraphConv': dc.feat.ConvMolFeaturizer(),
-    'Weave': dc.feat.WeaveFeaturizer(),
-    'Raw': dc.feat.RawFeaturizer()
+    'ecfp': dc.feat.CircularFingerprint(size=1024),
+    'graphconv': dc.feat.ConvMolFeaturizer(),
+    'weave': dc.feat.WeaveFeaturizer(),
+    'raw': dc.feat.RawFeaturizer(),
+    'smiles2img': dc.feat.SmilesToImage(img_size=80, img_spec='std')
 }
 
 splitters = {

deepchem/molnet/load_function/delaney_datasets.py

@@ -68,9 +68,9 @@ def load_delaney(
     splitter = kwargs['split']
     logger.warning("'split' is deprecated.  Use 'splitter' instead.")
   if isinstance(featurizer, str):
-    featurizer = dc.molnet.defaults.featurizers[featurizer]
+    featurizer = dc.molnet.defaults.featurizers[featurizer.lower()]
   if isinstance(splitter, str):
-    splitter = dc.molnet.defaults.splitters[splitter]
+    splitter = dc.molnet.defaults.splitters[splitter.lower()]
   if data_dir is None:
     data_dir = DEFAULT_DIR
   if save_dir is None:
@@ -80,8 +80,8 @@ def load_delaney(
   # Try to reload cached datasets.
 
   if reload:
-    featurizer_name = str(featurizer.__class__.__name__)
-    splitter_name = str(splitter.__class__.__name__)
+    featurizer_name = str(featurizer)
+    splitter_name = str(splitter)
     if not move_mean:
       featurizer_name = featurizer_name + "_mean_unmoved"
     save_folder = os.path.join(save_dir, "delaney-featurized", featurizer_name,

deepchem/splits/splitters.py

@@ -1,6 +1,7 @@
 """
 Contains an abstract base class that supports chemically aware data splits.
 """
+import inspect
 import os
 import random
 import tempfile
@@ -270,7 +271,30 @@ def __str__(self) -> str:
     >>> str(dc.splits.RandomSplitter())
     'RandomSplitter'
     """
-    return self.__class__.__name__
+    args_spec = inspect.getfullargspec(self.__init__)  # type: ignore
+    args_names = [arg for arg in args_spec.args if arg != 'self']
+    args_num = len(args_names)
+    args_default_values = [None for _ in range(args_num)]
+    if args_spec.defaults is not None:
+      defaults = list(args_spec.defaults)
+      args_default_values[-len(defaults):] = defaults
+
+    override_args_info = ''
+    for arg_name, default in zip(args_names, args_default_values):
+      if arg_name in self.__dict__:
+        arg_value = self.__dict__[arg_name]
+        # validation
+        # skip list
+        if isinstance(arg_value, list):
+          continue
+        if isinstance(arg_value, str):
+          # skip path string
+          if "\\/." in arg_value or "/" in arg_value or '.' in arg_value:
+            continue
+        # main logic
+        if default != arg_value:
+          override_args_info += '_' + arg_name + '_' + str(arg_value)
+    return self.__class__.__name__ + override_args_info
 
   def __repr__(self) -> str:
     """Convert self to repr representation.
@@ -284,9 +308,22 @@ def __repr__(self) -> str:
     --------
     >>> import deepchem as dc
     >>> dc.splits.RandomSplitter()
-    RandomSplitter
+    RandomSplitter[]
     """
-    return self.__str__()
+    args_spec = inspect.getfullargspec(self.__init__)  # type: ignore
+    args_names = [arg for arg in args_spec.args if arg != 'self']
+    args_info = ''
+    for arg_name in args_names:
+      value = self.__dict__[arg_name]
+      # for str
+      if isinstance(value, str):
+        value = "'" + value + "'"
+      # for list
+      if isinstance(value, list):
+        threshold = get_print_threshold()
+        value = np.array2string(np.array(value), threshold=threshold)
+      args_info += arg_name + '=' + str(value) + ', '
+    return self.__class__.__name__ + '[' + args_info[:-2] + ']'
 
 
 class RandomSplitter(Splitter):