Attempt at fixing travis failures

deepchem/molnet/load_function/delaney_datasets.py

@@ -22,7 +22,7 @@ def create_dataset(self) -> Dataset:
     if not os.path.exists(dataset_file):
       dc.utils.data_utils.download_url(url=DELANEY_URL, dest_dir=self.data_dir)
     loader = dc.data.CSVLoader(
-        tasks=DELANEY_TASKS, feature_field="smiles", featurizer=self.featurizer)
+        tasks=self.tasks, feature_field="smiles", featurizer=self.featurizer)
     return loader.create_dataset(dataset_file, shard_size=8192)
 
 
@@ -79,6 +79,6 @@ def load_delaney(
      molecular structure." Journal of chemical information and computer
      sciences 44.3 (2004): 1000-1005.
   """
-  loader = _DelaneyLoader(featurizer, splitter, transformers, data_dir,
-                          save_dir, **kwargs)
-  return loader.load_dataset('delaney', DELANEY_TASKS, reload)
+  loader = _DelaneyLoader(featurizer, splitter, transformers, DELANEY_TASKS,
+                          data_dir, save_dir, **kwargs)
+  return loader.load_dataset('delaney', reload)

deepchem/molnet/load_function/molnet_loader.py

@@ -85,7 +85,8 @@ class _MolnetLoader(object):
   def __init__(self, featurizer: Union[dc.feat.Featurizer, str],
                splitter: Union[dc.splits.Splitter, str, None],
                transformer_generators: List[Union[TransformerGenerator, str]],
-               data_dir: Optional[str], save_dir: Optional[str], **kwargs):
+               tasks: List[str], data_dir: Optional[str],
+               save_dir: Optional[str], **kwargs):
     """Construct an object for loading a dataset.
 
     Parameters
@@ -102,6 +103,8 @@ def __init__(self, featurizer: Union[dc.feat.Featurizer, str],
       the Transformers to apply to the data.  Each one is specified by a
       TransformerGenerator or, as a shortcut, one of the names from
       dc.molnet.transformers.
+    tasks: List[str]
+      the names of the tasks in the dataset
     data_dir: str
       a directory to save the raw data in
     save_dir: str
@@ -124,21 +127,20 @@ def __init__(self, featurizer: Union[dc.feat.Featurizer, str],
         transformers[t.lower()] if isinstance(t, str) else t
         for t in transformer_generators
     ]
+    self.tasks = list(tasks)
     self.data_dir = data_dir
     self.save_dir = save_dir
     self.args = kwargs
 
   def load_dataset(
-      self, name: str, tasks: List[str], reload: bool
+      self, name: str, reload: bool
   ) -> Tuple[List[str], Tuple[Dataset, ...], List[dc.trans.Transformer]]:
     """Load the dataset.
 
     Parameters
     ----------
     name: str
       the name of the dataset, used to identify the directory on disk
-    tasks: List[str]
-      the names of the tasks in this dataset
     reload: bool
       if True, the first call for a particular featurizer and splitter will cache
       the datasets to disk, and subsequent calls will reload the cached datasets.
@@ -160,12 +162,12 @@ def load_dataset(
       if self.splitter is None:
         if os.path.exists(save_folder):
           transformers = dc.utils.data_utils.load_transformers(save_folder)
-          return tasks, (DiskDataset(save_folder),), transformers
+          return self.tasks, (DiskDataset(save_folder),), transformers
       else:
         loaded, all_dataset, transformers = dc.utils.data_utils.load_dataset_from_disk(
             save_folder)
         if all_dataset is not None:
-          return tasks, all_dataset, transformers
+          return self.tasks, all_dataset, transformers
 
     # Create the dataset
 
@@ -190,7 +192,7 @@ def load_dataset(
       if reload and isinstance(dataset, DiskDataset):
         dataset.move(save_folder)
         dc.utils.data_utils.save_transformers(save_folder, transformers)
-      return tasks, (dataset,), transformers
+      return self.tasks, (dataset,), transformers
 
     for transformer in transformers:
       train = transformer.transform(train)
@@ -200,7 +202,7 @@ def load_dataset(
         valid, DiskDataset) and isinstance(test, DiskDataset):
       dc.utils.data_utils.save_dataset_to_disk(save_folder, train, valid, test,
                                                transformers)
-    return tasks, (train, valid, test), transformers
+    return self.tasks, (train, valid, test), transformers
 
   def create_dataset(self) -> Dataset:
     """Subclasses must implement this to load the dataset."""