Different type of inputs by using UniMolRepr to generate embeddings.

[WzWang-2000]

When using unimol_tools, I found that this function can take a dictionary as input. However, when I tried to use it, I encountered the following error.

clf = UniMolRepr(data_type='molecule', remove_hs=False)
mol_dict = {}
mol_dict['atoms'] = atoms
mol_dict['coordinates'] = atoms_coord
clf.get_repr(mol_dict, return_atomic_reprs=True)
---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
Cell In[2], [line 9](vscode-notebook-cell:?execution_count=2&line=9)
      [7](vscode-notebook-cell:?execution_count=2&line=7) mol_dict['atoms'] = atoms
      [8](vscode-notebook-cell:?execution_count=2&line=8) mol_dict['coordinates'] = atoms_coord
----> [9](vscode-notebook-cell:?execution_count=2&line=9) clf.get_repr(mol_dict, return_atomic_reprs=True)

File [~/miniconda3/lib/python3.9/site-packages/unimol_tools-1.0.0-py3.9.egg/unimol_tools/predictor.py:84](https://vscode-remote+ssh-002dremote-002b207.vscode-resource.vscode-cdn.net/home/wwz2000/NeuralPLexer/~/miniconda3/lib/python3.9/site-packages/unimol_tools-1.0.0-py3.9.egg/unimol_tools/predictor.py:84), in UniMolRepr.get_repr(self, data, return_atomic_reprs)
     [82](file:///home/wwz2000/miniconda3/lib/python3.9/site-packages/unimol_tools-1.0.0-py3.9.egg/unimol_tools/predictor.py?line=81) else:
     [83](file:///home/wwz2000/miniconda3/lib/python3.9/site-packages/unimol_tools-1.0.0-py3.9.egg/unimol_tools/predictor.py?line=82)     raise ValueError('Unknown data type: {}'.format(type(data)))
---> [84](file:///home/wwz2000/miniconda3/lib/python3.9/site-packages/unimol_tools-1.0.0-py3.9.egg/unimol_tools/predictor.py?line=83) datahub = DataHub(data=data, 
     [85](file:///home/wwz2000/miniconda3/lib/python3.9/site-packages/unimol_tools-1.0.0-py3.9.egg/unimol_tools/predictor.py?line=84)                  task='repr', 
     [86](file:///home/wwz2000/miniconda3/lib/python3.9/site-packages/unimol_tools-1.0.0-py3.9.egg/unimol_tools/predictor.py?line=85)                  is_train=False, 
     [87](file:///home/wwz2000/miniconda3/lib/python3.9/site-packages/unimol_tools-1.0.0-py3.9.egg/unimol_tools/predictor.py?line=86)                  **self.params,
     [88](file:///home/wwz2000/miniconda3/lib/python3.9/site-packages/unimol_tools-1.0.0-py3.9.egg/unimol_tools/predictor.py?line=87)                 )
     [89](file:///home/wwz2000/miniconda3/lib/python3.9/site-packages/unimol_tools-1.0.0-py3.9.egg/unimol_tools/predictor.py?line=88) dataset = MolDataset(datahub.data['unimol_input'])
     [90](file:///home/wwz2000/miniconda3/lib/python3.9/site-packages/unimol_tools-1.0.0-py3.9.egg/unimol_tools/predictor.py?line=89) self.trainer = Trainer(task='repr')

File [~/miniconda3/lib/python3.9/site-packages/unimol_tools-1.0.0-py3.9.egg/unimol_tools/data/datahub.py:42](https://vscode-remote+ssh-002dremote-002b207.vscode-resource.vscode-cdn.net/home/wwz2000/NeuralPLexer/~/miniconda3/lib/python3.9/site-packages/unimol_tools-1.0.0-py3.9.egg/unimol_tools/data/datahub.py:42), in DataHub.__init__(self, data, is_train, save_path, **params)
     [40](file:///home/wwz2000/miniconda3/lib/python3.9/site-packages/unimol_tools-1.0.0-py3.9.egg/unimol_tools/data/datahub.py?line=39) self.multiclass_cnt = params.get('multiclass_cnt', None)
     [41](file:///home/wwz2000/miniconda3/lib/python3.9/site-packages/unimol_tools-1.0.0-py3.9.egg/unimol_tools/data/datahub.py?line=40) self.ss_method = params.get('target_normalize', 'none')
---> [42](file:///home/wwz2000/miniconda3/lib/python3.9/site-packages/unimol_tools-1.0.0-py3.9.egg/unimol_tools/data/datahub.py?line=41) self._init_data(**params)

File [~/miniconda3/lib/python3.9/site-packages/unimol_tools-1.0.0-py3.9.egg/unimol_tools/data/datahub.py:55](https://vscode-remote+ssh-002dremote-002b207.vscode-resource.vscode-cdn.net/home/wwz2000/NeuralPLexer/~/miniconda3/lib/python3.9/site-packages/unimol_tools-1.0.0-py3.9.egg/unimol_tools/data/datahub.py:55), in DataHub._init_data(self, **params)
     [44](file:///home/wwz2000/miniconda3/lib/python3.9/site-packages/unimol_tools-1.0.0-py3.9.egg/unimol_tools/data/datahub.py?line=43) def _init_data(self, **params):
...
     [70](file:///home/wwz2000/miniconda3/lib/python3.9/site-packages/unimol_tools-1.0.0-py3.9.egg/unimol_tools/data/datareader.py?line=69)     data = pd.DataFrame(data).rename(columns={smiles_col: 'SMILES'})
     [72](file:///home/wwz2000/miniconda3/lib/python3.9/site-packages/unimol_tools-1.0.0-py3.9.egg/unimol_tools/data/datareader.py?line=71) elif isinstance(data, list):
     [73](file:///home/wwz2000/miniconda3/lib/python3.9/site-packages/unimol_tools-1.0.0-py3.9.egg/unimol_tools/data/datareader.py?line=72)     # load from smiles list

KeyError: 'target'

I believe the issue may lie in the datareader.py file. Indenting the '_= data.pop('target')' line seems to resolve the problem, but I am unsure if this is the correct approach.

[Naplessss]

Thank you for pointing this out. This issue could potentially be due to the Python version in relation to data.pop. We will address and fix this issue as soon as possible. Alternatively, you are welcome to submit a pull request for this fix.

[emotionor]

We have fixed the bug in !214

data.pop('target', None) will return None, and will not raise an KeyError

Please make sure that the code you are currently using has been updated to the latest version.