Merge pull request #4 from OpenDrugDiscovery/minor-fixes

Minor fixes

src/openqdc/datasets/base.py

@@ -3,6 +3,7 @@
 
 import numpy as np
 import torch
+from loguru import logger
 from sklearn.utils import Bunch
 from tqdm import tqdm
 
@@ -124,6 +125,7 @@ def collate_list(self, list_entries):
 
     def save_preprocess(self, data_dict):
         # save memmaps
+        logger.info("Preprocessing data and saving it to cache.")
         for key in self.data_keys:
             local_path = p_join(self.preprocess_path, f"{key}.mmap")
             out = np.memmap(local_path, mode="w+", dtype=data_dict[key].dtype, shape=data_dict[key].shape)
@@ -140,6 +142,7 @@ def save_preprocess(self, data_dict):
             push_remote(local_path)
 
     def read_preprocess(self):
+        logger.info("Reading preprocessed data")
         self.data = {}
         for key in self.data_keys:
             filename = p_join(self.preprocess_path, f"{key}.mmap")
@@ -172,14 +175,17 @@ def __len__(self):
     def __getitem__(self, idx: int):
         p_start, p_end = self.data["position_idx_range"][idx]
         input = self.data["atomic_inputs"][p_start:p_end]
-        z, c, positions = input[:, 0], input[:, 1], input[:, -3:]
-        z, c = z.astype(np.int32), c.astype(np.int32)
-        energies = self.data["energies"][idx]
+        z, c, positions, energies = (
+            np.array(input[:, 0], dtype=np.int32),
+            np.array(input[:, 1], dtype=np.int32),
+            np.array(input[:, -3:], dtype=np.float32),
+            np.array(self.data["energies"][idx], dtype=np.float32),
+        )
         name = self.data["name"]["uniques"][self.data["name"]["inv_indices"][idx]]
         subset = self.data["subset"]["uniques"][self.data["subset"]["inv_indices"][idx]]
 
         if "forces" in self.data:
-            forces = self.data["forces"][p_start:p_end]
+            forces = np.array(self.data["forces"][p_start:p_end], dtype=np.float32)
         else:
             forces = None
 

src/openqdc/datasets/geom.py

@@ -6,7 +6,7 @@
 from openqdc.datasets.base import BaseDataset
 from openqdc.utils import load_json, load_pkl
 from openqdc.utils.constants import MAX_ATOMIC_NUMBER
-from openqdc.utils.molecule import get_atomic_numuber_and_charge
+from openqdc.utils.molecule import get_atomic_number_and_charge
 
 
 def read_mol(mol_id, mol_dict, base_path, partition):
@@ -34,7 +34,7 @@ def read_mol(mol_id, mol_dict, base_path, partition):
     try:
         d = load_pkl(p_join(base_path, mol_dict["pickle_path"]), False)
         confs = d["conformers"]
-        x = get_atomic_numuber_and_charge(confs[0]["rd_mol"])
+        x = get_atomic_number_and_charge(confs[0]["rd_mol"])
         positions = np.array([cf["rd_mol"].GetConformer().GetPositions() for cf in confs])
         n_confs = positions.shape[0]
 

src/openqdc/datasets/molecule3d.py

@@ -9,13 +9,13 @@
 
 from openqdc.datasets.base import BaseDataset
 from openqdc.utils.constants import BOHR2ANG, MAX_ATOMIC_NUMBER
-from openqdc.utils.molecule import get_atomic_numuber_and_charge
+from openqdc.utils.molecule import get_atomic_number_and_charge
 
 
 def read_mol(mol, energy):
     smiles = dm.to_smiles(mol, explicit_hs=False)
     # subset = dm.to_smiles(dm.to_scaffold_murcko(mol, make_generic=True), explicit_hs=False)
-    x = get_atomic_numuber_and_charge(mol)
+    x = get_atomic_number_and_charge(mol)
     positions = mol.GetConformer().GetPositions() * BOHR2ANG
 
     res = dict(

src/openqdc/datasets/qmugs.py

@@ -7,7 +7,7 @@
 
 from openqdc.datasets.base import BaseDataset
 from openqdc.utils.constants import MAX_ATOMIC_NUMBER
-from openqdc.utils.molecule import get_atomic_numuber_and_charge
+from openqdc.utils.molecule import get_atomic_number_and_charge
 
 
 def read_mol(mol_dir):
@@ -19,7 +19,7 @@ def read_mol(mol_dir):
         return None
 
     smiles = dm.to_smiles(mols[0], explicit_hs=False)
-    x = get_atomic_numuber_and_charge(mols[0])[None, ...].repeat(n_confs, axis=0)
+    x = get_atomic_number_and_charge(mols[0])[None, ...].repeat(n_confs, axis=0)
     positions = np.array([mol.GetConformer().GetPositions() for mol in mols])
     props = [mol.GetPropsAsDict() for mol in mols]
     targets = np.array([[p[el] for el in QMugs.energy_target_names] for p in props])

src/openqdc/datasets/spice.py

@@ -7,14 +7,14 @@
 from openqdc.datasets.base import BaseDataset
 from openqdc.utils import load_hdf5_file
 from openqdc.utils.constants import BOHR2ANG, MAX_ATOMIC_NUMBER
-from openqdc.utils.molecule import get_atomic_numuber_and_charge
+from openqdc.utils.molecule import get_atomic_number_and_charge
 
 
 def read_record(r):
     smiles = r["smiles"].asstr()[0]
     subset = r["subset"][0].decode("utf-8")
     n_confs = r["conformations"].shape[0]
-    x = get_atomic_numuber_and_charge(dm.to_mol(smiles, add_hs=True))
+    x = get_atomic_number_and_charge(dm.to_mol(smiles, add_hs=True))
     positions = r["conformations"][:] * BOHR2ANG
 
     res = dict(

src/openqdc/utils/io.py

@@ -34,7 +34,7 @@ def push_remote(local_path, overwrite=True):
     return remote_path
 
 
-def pull_locally(local_path, overwrite=True):
+def pull_locally(local_path, overwrite=False):
     remote_path = local_path.replace(get_local_cache(), get_remote_cache())
     os.makedirs(os.path.dirname(local_path), exist_ok=True)
     if not os.path.exists(local_path) or overwrite:

src/openqdc/utils/molecule.py

@@ -14,6 +14,6 @@ def get_atomic_charge(mol: Chem.Mol):
     return np.array([atom.GetFormalCharge() for atom in mol.GetAtoms()])
 
 
-def get_atomic_numuber_and_charge(mol: Chem.Mol):
+def get_atomic_number_and_charge(mol: Chem.Mol):
     """Returns atoms number and charge for rdkit molecule"""
     return np.array([[atom.GetAtomicNum(), atom.GetFormalCharge()] for atom in mol.GetAtoms()])