Improve according to codefactor.io (#130)

README.md

 # <img src=https://raw.githubusercontent.com/aiqm/torchani/master/logo1.png width=180/>  Accurate Neural Network Potential on PyTorch
 
 [![Codefresh build status]( https://g.codefresh.io/api/badges/pipeline/zasdfgbnm/aiqm%2Ftorchani%2Ftorchani?branch=master&type=cf-1)]( https://g.codefresh.io/repositories/aiqm/torchani/builds?filter=trigger:build;branch:master;service:5babc52a8a90dc40a407b05f~torchani)
+[![CodeFactor](https://www.codefactor.io/repository/github/aiqm/torchani/badge/master)](https://www.codefactor.io/repository/github/aiqm/torchani/overview/master)
+[![codecov](https://codecov.io/gh/aiqm/torchani/branch/master/graph/badge.svg)](https://codecov.io/gh/aiqm/torchani)
 
 TorchANI is a pytorch implementation of ANI. It is currently under alpha release, which means, the API is not stable yet. If you find a bug of TorchANI, or have some feature request, feel free to open an issue on GitHub, or send us a pull request.
 

torchani/aev.py

@@ -16,10 +16,10 @@ def default_neighborlist(species, coordinates, cutoff):
     """Default neighborlist computer"""
 
     vec = coordinates.unsqueeze(2) - coordinates.unsqueeze(1)
-    """Shape (conformations, atoms, atoms, 3) storing Rij vectors"""
+    # vec has hape (conformations, atoms, atoms, 3) storing Rij vectors
 
     distances = vec.norm(2, -1)
-    """Shape (conformations, atoms, atoms) storing Rij distances"""
+    # distances has shape (conformations, atoms, atoms) storing Rij distances
 
     padding_mask = (species == -1).unsqueeze(1)
     distances = distances.masked_fill(padding_mask, math.inf)
@@ -267,7 +267,8 @@ class AEVComputer(torch.nn.Module):
             radial_terms.unsqueeze(-2) *
             mask_r.unsqueeze(-1).type(radial_terms.dtype)
         ).sum(-3)
-        """shape (conformations, atoms, present species, radial_length)"""
+        # present_radial_aevs has shape
+        # (conformations, atoms, present species, radial_length)
         radial_aevs = present_radial_aevs.flatten(start_dim=2)
 
         # assemble angular subaev

torchani/data/__init__.py

@@ -38,7 +38,7 @@ def split_batch(natoms, species, coordinates):
     natoms = natoms.tolist()
     counts = []
     for i in natoms:
-        if len(counts) == 0:
+        if not counts:
             counts.append([i, 1])
             continue
         if i == counts[-1][0]:

torchani/ignite.py

@@ -91,8 +91,7 @@ def MSELoss(key, per_atom=True):
     """Create MSE loss on the specified key."""
     if per_atom:
         return PerAtomDictLoss(key, torch.nn.MSELoss(reduction='none'))
-    else:
-        return DictLoss(key, torch.nn.MSELoss())
+    return DictLoss(key, torch.nn.MSELoss())
 
 
 class TransformedLoss(_Loss):

torchani/neurochem/__init__.py

@@ -526,10 +526,9 @@ class Trainer:
                         # There is no plan to support the "L2" settings in
                         # input file before AdamW get merged into pytorch.
                         raise NotImplementedError('L2 not supported yet')
-                        l2reg.append((0.5 * layer['l2valu'], module))
                     del layer['l2norm']
                     del layer['l2valu']
-                if len(layer) > 0:
+                if layer:
                     raise ValueError('unrecognized parameter in layer setup')
                 i = o
             atomic_nets[atom_type] = torch.nn.Sequential(*modules)
@@ -549,7 +548,7 @@ class Trainer:
             MSELoss('energies'),
             lambda x: 0.5 * (torch.exp(2 * x) - 1) + l2())
 
-        if len(params) > 0:
+        if params:
             raise ValueError('unrecognized parameter')
 
         self.global_epoch = 0

torchani/utils.py

@@ -161,8 +161,7 @@ class ChemicalSymbolsToInts:
 
     def __init__(self, all_species):
         self.rev_species = {}
-        for i in range(len(all_species)):
-            s = all_species[i]
+        for i, s in enumerate(all_species):
             self.rev_species[s] = i
 
     def __call__(self, species):