add style check to unit test (#12)

Dockerfile

 FROM zasdfgbnm/pytorch-master
-RUN pacman -Sy --noconfirm python-sphinx python2-sphinx
+RUN pacman -Sy --noconfirm python-sphinx python2-sphinx flake8
 COPY . /torchani
 RUN cd torchani && pip install .
 RUN cd torchani && pip2 install .

benchmark/ani_benchmark.py

-from benchmark import Benchmark
-import torchani
-
-
-class ANIBenchmark(Benchmark):
-
-    def __init__(self, device):
-        super(ANIBenchmark, self).__init__(device)
-        self.aev_computer = torchani.SortedAEV(device=device)
-        self.model = torchani.ModelOnAEV(
-            self.aev_computer, benchmark=True, derivative=True, from_nc=None)
-
-    def oneByOne(self, coordinates, species):
-        conformations = coordinates.shape[0]
-        coordinates = coordinates.to(self.device)
-        for i in range(conformations):
-            c = coordinates[i:i+1, :, :]
-            self.model(c, species)
-        ret = {
-            'aev': self.model.timers['aev'],
-            'energy': self.model.timers['nn'],
-            'force': self.model.timers['derivative']
-        }
-        self.model.reset_timers()
-        return ret
-
-    def inBatch(self, coordinates, species):
-        coordinates = coordinates.to(self.device)
-        self.model(coordinates, species)
-        ret = {
-            'aev': self.model.timers['aev'],
-            'energy': self.model.timers['nn'],
-            'force': self.model.timers['derivative']
-        }
-        self.model.reset_timers()
-        return ret

benchmark/benchmark.py

-import numpy
+import torchani
 
 
-class Benchmark:
-    """Abstract class for benchmarking ANI implementations"""
+class ANIBenchmark:
 
     def __init__(self, device):
-        self.device = device
+        super(ANIBenchmark, self).__init__(device)
+        self.aev_computer = torchani.SortedAEV(device=device)
+        self.model = torchani.ModelOnAEV(
+            self.aev_computer, benchmark=True, derivative=True, from_nc=None)
 
     def oneByOne(self, coordinates, species):
-        """Benchmarking the given dataset of computing energies and forces one at a time
-
-        Parameters
-        ----------
-        coordinates : numpy.ndarray
-            Array of shape (conformations, atoms, 3)
-        species : list
-            List of species for this molecule. The length of the list must be the same as
-            atoms in the molecule.
-
-        Returns
-        -------
-        dict
-            Dictionary storing the times for computing AEVs, energies and forces, in seconds.
-            The dictionary should contain the following keys:
-            aev : the time used to compute AEVs from coordinates with given neighbor list.
-            energy : the time used to compute energies, when the AEVs are given.
-            force : the time used to compute forces, when the energies and AEVs are given.
-        """
-        # return { 'neighborlist': 0, 'aev': 0, 'energy': 0, 'force': 0 }
-        raise NotImplementedError('subclass must implement this method')
+        conformations = coordinates.shape[0]
+        coordinates = coordinates.to(self.device)
+        for i in range(conformations):
+            c = coordinates[i:i+1, :, :]
+            self.model(c, species)
+        ret = {
+            'aev': self.model.timers['aev'],
+            'energy': self.model.timers['nn'],
+            'force': self.model.timers['derivative']
+        }
+        self.model.reset_timers()
+        return ret
 
     def inBatch(self, coordinates, species):
-        """Benchmarking the given dataset of computing energies and forces in batch mode
-
-        The signature of this function is the same as `oneByOne`"""
-        # return { 'neighborlist': 0, 'aev': 0, 'energy': 0, 'force': 0 }
-        raise NotImplementedError('subclass must implement this method')
+        coordinates = coordinates.to(self.device)
+        self.model(coordinates, species)
+        ret = {
+            'aev': self.model.timers['aev'],
+            'energy': self.model.timers['nn'],
+            'force': self.model.timers['derivative']
+        }
+        self.model.reset_timers()
+        return ret

benchmark/generate_data.py

@@ -2,7 +2,6 @@ from ase import Atoms
 from ase.calculators.tip3p import TIP3P, rOH, angleHOH
 from ase.md import Langevin
 import ase.units as units
-from ase.io.trajectory import Trajectory
 import numpy
 import h5py
 from rdkit import Chem
@@ -10,14 +9,12 @@ from rdkit.Chem import AllChem
 # from asap3 import EMT
 from ase.calculators.emt import EMT
 from multiprocessing import Pool
-from tqdm import tqdm, tqdm_notebook, trange
-tqdm.monitor_interval = 0
+from tqdm import tqdm, trange
 from selected_system import mols, mol_file
 
-import functools
-
 conformations = 1024
 T = 30
+tqdm.monitor_interval = 0
 
 fw = h5py.File("waters.hdf5", "w")
 fm = h5py.File(mol_file, "w")
@@ -96,7 +93,7 @@ if __name__ == '__main__':
     print('done with molecules')
 
     with Pool() as p:
-        p.starmap(waterbox, [(10, 10, 10, 0), (20, 20, 10,
-                                               1), (30, 30, 30, 2), (40, 40, 40, 3)])
+        p.starmap(waterbox, [(10, 10, 10, 0), (20, 20, 10, 1),
+                             (30, 30, 30, 2), (40, 40, 40, 3)])
         print(list(fw.keys()))
         print('done with water boxes')

benchmark/selected_system.py

-mols = {
-    '20': [
-        'COC(=O)c1ccc([N+](=O)[O-])cc1',
-        'O=c1nnc2ccccc2n1CO',
-        'CCc1ccc([N+](=O)[O-])cc1',
-        'Nc1ccc(c2cnco2)cc1',
-        'COc1ccc(N)c(N)c1',
-        'O=C(O)CNc1ccccc1',
-        'NC(=O)NNc1ccccc1',
-        'Cn1c(=O)oc(=O)c2ccccc12',
-        'CC(=O)Nc1ccc(O)cc1',
-        'COc1ccc(CC#N)cc1'
-    ],
-    '50': [
-        'O=[N+]([O-])c1ccc(NN=Cc2ccc(C=NNc3ccc([N+](=O)[O-])cc3[N+](=O)[O-])cc2)c([N+](=O)[O-])c1',
-        'CCCCCc1nccnc1OCC(C)(C)CC(C)C',
-        'CC(C)(C)c1ccc(N(C(=O)c2ccccc2)C(C)(C)C)cc1',
-        'CCCCCCCCCCCOC(=O)Nc1ccccc1',
-        'CC(=O)NCC(CN1CCCC1)(c1ccccc1)c1ccccc1',
-        'CCCCCc1cnc(C)c(OCC(C)(C)CCC)n1',
-        'CCCCCCCCCCCCN1CCOC(=O)C1',
-        'CCCCOc1ccc(C=Nc2ccc(CCCC)cc2)cc1',
-        'CC1CC(C)C(=NNC(=O)N)C(C(O)CC2CC(=O)NC(=O)C2)C1',
-        'CCCCCOc1ccc(C=Nc2ccc(C(=O)OCC)cc2)cc1'
-    ],
-    '10': [
-        'N#CCC(=O)N',
-        'N#CCCO',
-        'O=C1NC(=O)C(=O)N1',
-        'COCC#N',
-        'N#CCNC=O',
-        'ON=CC=NO',
-        'NCC(=O)O',
-        'NC(=O)CO',
-        'N#Cc1ccco1',
-        'C=CC(=O)N'
-    ],
-    '4,5,6': [
-        'C',
-        'C#CC#N',
-        'C=C',
-        'CC#N',
-        'C#CC#C',
-        'O=CC#C',
-        'C#C'
-    ],
-    '100': [
-        'CC(C)C[C@@H](C(=O)O)NC(=O)C[C@@H]([C@H](CC1CCCCC1)NC(=O)CC[C@@H]([C@H](Cc2ccccc2)NC(=O)OC(C)(C)C)O)O',
-        'CC(C)(C)OC(=O)N[C@@H](Cc1ccccc1)[C@@H](CN[C@@H](Cc2ccccc2)C(=O)N[C@@H](CCC(=O)O)C(=O)N[C@@H](Cc3ccccc3)C(=O)N)O',
-        'CC(C)(C)OC(=O)N[C@@H](Cc1ccccc1)[C@H](CN[C@@H](Cc2ccccc2)C(=O)N[C@@H](CCC(=O)O)C(=O)N[C@@H](Cc3ccccc3)C(=O)N)O',
-        'CC[C@H](c1ccc(cc1)O)[C@H](c2ccc(cc2)O)C(=O)OCCCCCCCCOC(=O)C(c3ccc(cc3)O)C(CC)c4ccc(cc4)O',
-        'CC/C(=C\\CC[C@H](C)C[C@@H](C)CC[C@@H]([C@H](C)C(=O)C[C@H]([C@H](C)[C@@H](C)OC(=O)C[C@H](/C(=C(\\C)/C(=O)O)/C(=O)O)O)O)O)/C=C/C(=O)O',
-        'CC[C@H](C)[C@H]1C(=O)NCCCOc2ccc(cc2)C[C@@H](C(=O)N1)NC(=O)[C@@H]3Cc4ccc(cc4)OCCCCC(=O)N[C@H](C(=O)N3)C(C)C',
-        'CC(C)(C)CC(C)(C)c1ccc(cc1)OCCOCCOCCOCCOCCOCCOCCOCCOCCO',
-        'CCOC(=O)CC[C@H](C[C@@H]1CCNC1=O)NC(=O)[C@H](Cc2ccccc2)NC(=O)[C@H](CCC(=O)OC(C)(C)C)NC(=O)OCc3ccccc3',
-        'C[C@]12CC[C@@H]3c4ccc(cc4CC[C@H]3[C@@H]1C[C@@H]([C@@H]2O)CCCCCCCCC(=O)OC[C@@H]5[C@H]([C@H]([C@@H](O5)n6cnc7c6ncnc7N)O)O)O',
-        'c1cc(ccc1CCc2c[nH]c3c2C(=O)NC(=N3)N)C(=O)N[C@@H](CCC(=O)N[C@@H](CCC(=O)N[C@@H](CCC(=O)N[C@H](CCC(=O)O)C(=O)O)C(=O)O)C(=O)O)C(=O)O'
-    ],
-    '305': [
-        '[H]/N=C(/N)\\NCCC[C@H](C(=O)N[C@H]([C@@H](C)O)C(=O)N[C@H](Cc1ccc(cc1)O)C(=O)NCCCC[C@@H](C(=O)NCCCC[C@@H](C(=O)NCC(=O)O)NC(=O)[C@H](CCCCNC(=O)[C@@H](Cc2ccc(cc2)O)NC(=O)[C@@H]([C@@H](C)O)NC(=O)[C@@H](CCCN/C(=N\\[H])/N)N)NC(=O)[C@@H](Cc3ccc(cc3)O)NC(=O)[C@@H]([C@@H](C)O)NC(=O)[C@@H](CCCN/C(=N\\[H])/N)N)NC(=O)[C@@H](Cc4ccc(cc4)O)NC(=O)[C@@H]([C@@H](C)O)NC(=O)[C@@H](CCCN/C(=N\\[H])/N)N)N'
-    ]
-}
-
-mol_file = "molecules.hdf5"

benchmark/to_xyz.py

-from selected_system import mols, mol_file
-import h5py
-import os
-
-fm = h5py.File(os.path.join(mol_file), "r")
-
-for i in mols:
-    print('number of atoms:', i)
-    smiles = mols[i]
-    for s in smiles:
-        key = s.replace('/', '_')
-        filename = i
-        with open('benchmark_xyz/' + filename + '.xyz', 'w') as fxyz:
-            coordinates = fm[key][()]
-            species = fm[key].attrs['species'].split()
-            conformations = coordinates.shape[0]
-            atoms = len(species)
-            for i in range(conformations):
-                fxyz.write('{}\n{}\n'.format(
-                    atoms, 'smiles:{}\tconformation:{}'.format(s, i)))
-                for j in range(atoms):
-                    ss = species[j]
-                    xyz = coordinates[i, j, :]
-                    x = xyz[0]
-                    y = xyz[1]
-                    z = xyz[2]
-                    fxyz.write('{} {} {} {}\n'.format(ss, x, y, z))
-            break

codefresh.yml

@@ -10,4 +10,5 @@ steps:
   unit-tests:
     image: '${{build-torchani}}'
     commands:
+      - flake8
       - python setup.py test
\ No newline at end of file

examples/ANI-1x-training-Adam.py

@@ -3,14 +3,11 @@ import torchani
 import torchani.data
 import math
 import timeit
-import itertools
-import os
 import sys
 import pickle
 from tensorboardX import SummaryWriter
 from tqdm import tqdm
-from common import *
-import sys
+from common import get_or_create_model, Averager, evaluate
 import json
 
 chunk_size = 256
@@ -27,11 +24,14 @@ with open('data/dataset.dat', 'rb') as f:
         testing, chunk_size, batch_chunks)
 
     training_dataloader = torch.utils.data.DataLoader(
-        training, batch_sampler=training_sampler, collate_fn=torchani.data.collate)
+        training, batch_sampler=training_sampler,
+        collate_fn=torchani.data.collate)
     validation_dataloader = torch.utils.data.DataLoader(
-        validation, batch_sampler=validation_sampler, collate_fn=torchani.data.collate)
+        validation, batch_sampler=validation_sampler,
+        collate_fn=torchani.data.collate)
     testing_dataloader = torch.utils.data.DataLoader(
-        testing, batch_sampler=testing_sampler, collate_fn=torchani.data.collate)
+        testing, batch_sampler=testing_sampler,
+        collate_fn=torchani.data.collate)
 
 writer = SummaryWriter('runs/adam-{}'.format(sys.argv[1]))
 
@@ -48,8 +48,8 @@ def subset_rmse(subset_dataloader):
         for molecule_id in batch:
             _species = subset_dataloader.dataset.species[molecule_id]
             coordinates, energies = batch[molecule_id]
-            coordinates = coordinates.to(aev_computer.device)
-            energies = energies.to(aev_computer.device)
+            coordinates = coordinates.to(model.aev_computer.device)
+            energies = energies.to(model.aev_computer.device)
             count, squared_error = evaluate(
                 model, coordinates, energies, _species)
             squared_error = squared_error.item()
@@ -73,13 +73,15 @@ best_validation_rmse = math.inf
 best_epoch = 0
 start = timeit.default_timer()
 while True:
-    for batch in tqdm(training_dataloader, desc='epoch {}'.format(epoch), total=len(training_sampler)):
+    for batch in tqdm(training_dataloader,
+                      desc='epoch {}'.format(epoch),
+                      total=len(training_sampler)):
         a = Averager()
         for molecule_id in batch:
             _species = training.species[molecule_id]
             coordinates, energies = batch[molecule_id]
-            coordinates = coordinates.to(aev_computer.device)
-            energies = energies.to(aev_computer.device)
+            coordinates = coordinates.to(model.aev_computer.device)
+            energies = energies.to(model.aev_computer.device)
             count, squared_error = evaluate(
                 model, coordinates, energies, _species)
             a.add(count, squared_error / len(_species))

examples/ANI-1x-training.py

@@ -3,14 +3,10 @@ import torchani
 import torchani.data
 import math
 import timeit
-import itertools
-import os
-import sys
 import pickle
 from tensorboardX import SummaryWriter
 from tqdm import tqdm
-from common import *
-from copy import deepcopy
+from common import get_or_create_model, Averager, evaluate
 
 chunk_size = 256
 batch_chunks = 1024 // chunk_size
@@ -26,11 +22,14 @@ with open('data/dataset.dat', 'rb') as f:
         testing, chunk_size, batch_chunks)
 
     training_dataloader = torch.utils.data.DataLoader(
-        training, batch_sampler=training_sampler, collate_fn=torchani.data.collate)
+        training, batch_sampler=training_sampler,
+        collate_fn=torchani.data.collate)
     validation_dataloader = torch.utils.data.DataLoader(
-        validation, batch_sampler=validation_sampler, collate_fn=torchani.data.collate)
+        validation, batch_sampler=validation_sampler,
+        collate_fn=torchani.data.collate)
     testing_dataloader = torch.utils.data.DataLoader(
-        testing, batch_sampler=testing_sampler, collate_fn=torchani.data.collate)
+        testing, batch_sampler=testing_sampler,
+        collate_fn=torchani.data.collate)
 
 writer = SummaryWriter()
 
@@ -47,8 +46,8 @@ def subset_rmse(subset_dataloader):
         for molecule_id in batch:
             _species = subset_dataloader.dataset.species[molecule_id]
             coordinates, energies = batch[molecule_id]
-            coordinates = coordinates.to(aev_computer.device)
-            energies = energies.to(aev_computer.device)
+            coordinates = coordinates.to(model.aev_computer.device)
+            energies = energies.to(model.aev_computer.device)
             count, squared_error = evaluate(coordinates, energies, _species)
             squared_error = squared_error.item()
             a.add(count, squared_error)
@@ -71,13 +70,14 @@ best_validation_rmse = math.inf
 best_epoch = 0
 start = timeit.default_timer()
 while True:
-    for batch in tqdm(training_dataloader, desc='epoch {}'.format(epoch), total=len(training_sampler)):
+    for batch in tqdm(training_dataloader, desc='epoch {}'.format(epoch),
+                      total=len(training_sampler)):
         a = Averager()
         for molecule_id in batch:
             _species = training.species[molecule_id]
             coordinates, energies = batch[molecule_id]
-            coordinates = coordinates.to(aev_computer.device)
-            energies = energies.to(aev_computer.device)
+            coordinates = coordinates.to(model.aev_computer.device)
+            energies = energies.to(model.aev_computer.device)
             count, squared_error = evaluate(
                 model, coordinates, energies, _species)
             a.add(count, squared_error / len(_species))

examples/analyze-gradients.py

@@ -23,7 +23,8 @@ hyperparams = [  # (chunk size, batch chunks)
 ]
 
 for chunk_size, batch_chunks in hyperparams:
-    with open('data/avg-{}-{}.dat'.format(chunk_size, batch_chunks), 'rb') as f:
+    with open('data/avg-{}-{}.dat'.format(chunk_size, batch_chunks),
+              'rb') as f:
         ag, agsqr = pickle.load(f)
         variance = torch.sum(agsqr) - torch.sum(ag**2)
         stddev = torch.sqrt(variance).item()

examples/batch-gradient-variance.py

@@ -3,18 +3,16 @@ import torch
 import torchani
 import configs
 import torchani.data
-import math
 from tqdm import tqdm
-import itertools
-import os
 import pickle
+from common import get_or_create_model, Averager, evaluate
 
-
+device = configs.device
 if len(sys.argv) >= 2:
-    configs.device = torch.device(sys.argv[1])
-from common import *
+    device = torch.device(sys.argv[1])
 
 ds = torchani.data.load_dataset(configs.data_path)
+model = get_or_create_model('/tmp/model.pt', device=device)
 # just to conveniently zero grads
 optimizer = torch.optim.Adam(model.parameters())
 
@@ -31,8 +29,8 @@ def batch_gradient(batch):
     for molecule_id in batch:
         _species = ds.species[molecule_id]
         coordinates, energies = batch[molecule_id]
-        coordinates = coordinates.to(aev_computer.device)
-        energies = energies.to(aev_computer.device)
+        coordinates = coordinates.to(model.aev_computer.device)
+        energies = energies.to(model.aev_computer.device)
         a.add(*evaluate(coordinates, energies, _species))
     mse = a.avg()
     optimizer.zero_grad()
@@ -59,7 +57,8 @@ def compute(chunk_size, batch_chunks):
         agsqr.add(1, g**2)
     ag = ag.avg()
     agsqr = agsqr.avg()
-    with open('data/avg-{}-{}.dat'.format(chunk_size, batch_chunks), 'wb') as f:
+    filename = 'data/avg-{}-{}.dat'.format(chunk_size, batch_chunks)
+    with open(filename, 'wb') as f:
         pickle.dump((ag, agsqr), f)
 
 

examples/common.py

 import torchani
 import torch
 import os
-from configs import benchmark, device
+import configs
 
 
 class Averager:
@@ -18,17 +18,15 @@ class Averager:
         return self.subtotal / self.count
 
 
-aev_computer = torchani.SortedAEV(benchmark=benchmark, device=device)
-
-
 def celu(x, alpha):
     return torch.where(x > 0, x, alpha * (torch.exp(x/alpha)-1))
 
 
 class AtomicNetwork(torch.nn.Module):
 
-    def __init__(self):
+    def __init__(self, aev_computer):
         super(AtomicNetwork, self).__init__()
+        self.aev_computer = aev_computer
         self.output_length = 1
         self.layer1 = torch.nn.Linear(384, 128).type(
             aev_computer.dtype).to(aev_computer.device)
@@ -51,16 +49,17 @@ class AtomicNetwork(torch.nn.Module):
         return y
 
 
-def get_or_create_model(filename):
+def get_or_create_model(filename, benchmark=False, device=configs.device):
+    aev_computer = torchani.SortedAEV(benchmark=benchmark, device=device)
     model = torchani.ModelOnAEV(
         aev_computer,
         reducer=torch.sum,
         benchmark=benchmark,
         per_species={
-            'C': AtomicNetwork(),
-            'H': AtomicNetwork(),
-            'N': AtomicNetwork(),
-            'O': AtomicNetwork(),
+            'C': AtomicNetwork(aev_computer),
+            'H': AtomicNetwork(aev_computer),
+            'N': AtomicNetwork(aev_computer),
+            'O': AtomicNetwork(aev_computer),
         })
     if os.path.isfile(filename):
         model.load_state_dict(torch.load(filename))

examples/configs.py

 import torch
-benchmark = False
 data_path = 'data/ANI-1x_complete.h5'
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

examples/energy_force.py

+# flake8: noqa
 import torch
 import torchani
 

examples/training-benchmark.py

@@ -2,18 +2,17 @@ import torch
 import torchani
 import torchani.data
 import tqdm
-import math
 import timeit
 import configs
 import functools
-configs.benchmark = True
-from common import *
+from common import get_or_create_model, Averager, evaluate
 
 ds = torchani.data.load_dataset(configs.data_path)
 sampler = torchani.data.BatchSampler(ds, 256, 4)
 dataloader = torch.utils.data.DataLoader(
-    ds, batch_sampler=sampler, collate_fn=torchani.data.collate, num_workers=20)
-model = get_or_create_model('/tmp/model.pt')
+    ds, batch_sampler=sampler,
+    collate_fn=torchani.data.collate, num_workers=20)
+model = get_or_create_model('/tmp/model.pt', True)
 optimizer = torch.optim.Adam(model.parameters(), amsgrad=True)
 
 
@@ -47,20 +46,20 @@ for batch in tqdm.tqdm(dataloader, total=len(sampler)):
     for molecule_id in batch:
         _species = ds.species[molecule_id]
         coordinates, energies = batch[molecule_id]
-        coordinates = coordinates.to(aev_computer.device)
-        energies = energies.to(aev_computer.device)
+        coordinates = coordinates.to(model.aev_computer.device)
+        energies = energies.to(model.aev_computer.device)
         a.add(*evaluate(model, coordinates, energies, _species))
     optimize_step(a)
 
 elapsed = round(timeit.default_timer() - start, 2)
-print('Radial terms:', aev_computer.timers['radial terms'])
-print('Angular terms:', aev_computer.timers['angular terms'])
-print('Terms and indices:', aev_computer.timers['terms and indices'])
-print('Combinations:', aev_computer.timers['combinations'])
-print('Mask R:', aev_computer.timers['mask_r'])
-print('Mask A:', aev_computer.timers['mask_a'])
-print('Assemble:', aev_computer.timers['assemble'])
-print('Total AEV:', aev_computer.timers['total'])
+print('Radial terms:', model.aev_computer.timers['radial terms'])
+print('Angular terms:', model.aev_computer.timers['angular terms'])
+print('Terms and indices:', model.aev_computer.timers['terms and indices'])
+print('Combinations:', model.aev_computer.timers['combinations'])
+print('Mask R:', model.aev_computer.timers['mask_r'])
+print('Mask A:', model.aev_computer.timers['mask_a'])
+print('Assemble:', model.aev_computer.timers['assemble'])
+print('Total AEV:', model.aev_computer.timers['total'])
 print('NN:', model.timers['nn'])
 print('Total Forward:', model.timers['forward'])
 print('Total Backward:', timer['backward'])

tests/test_aev.py

 import torch
-import numpy
 import torchani
 import unittest
 import os
@@ -15,7 +14,8 @@ class TestAEV(unittest.TestCase):
         self.aev = torchani.SortedAEV(dtype=dtype, device=torch.device('cpu'))
         self.tolerance = 1e-5
 
-    def _test_molecule(self, coordinates, species, expected_radial, expected_angular):
+    def _test_molecule(self, coordinates, species, expected_radial,
+                       expected_angular):
         radial, angular = self.aev(coordinates, species)
         radial_diff = expected_radial - radial
         radial_max_error = torch.max(torch.abs(radial_diff)).item()

tests/test_benchmark.py

@@ -6,7 +6,8 @@ import copy
 
 class TestBenchmark(unittest.TestCase):
 
-    def setUp(self, dtype=torchani.default_dtype, device=torchani.default_device):
+    def setUp(self, dtype=torchani.default_dtype,
+              device=torchani.default_device):
         self.dtype = dtype
         self.device = device
         self.conformations = 100

tests/test_data.py

 import torchani
 import unittest
-import copy
 import tempfile
 import os
 import torch
@@ -28,7 +27,8 @@ class TestDataset(unittest.TestCase):
         l2 = 0
         for f in os.listdir(self.data_path):
             f = os.path.join(self.data_path, f)
-            if os.path.isfile(f) and (f.endswith('.h5') or f.endswith('.hdf5')):
+            if os.path.isfile(f) and \
+               (f.endswith('.h5') or f.endswith('.hdf5')):
                 for j in pyanitools.anidataloader(f):
                     l2 += j['energies'].shape[0]
         # compute data length using iterator
@@ -46,7 +46,8 @@ class TestDataset(unittest.TestCase):
         l2 = 0
         for f in os.listdir(self.data_path):
             f = os.path.join(self.data_path, f)
-            if os.path.isfile(f) and (f.endswith('.h5') or f.endswith('.hdf5')):
+            if os.path.isfile(f) and \
+               (f.endswith('.h5') or f.endswith('.hdf5')):
                 for j in pyanitools.anidataloader(f):
                     conformations = j['energies'].shape[0]
                     l2 += ceil(conformations / chunksize)
@@ -102,12 +103,12 @@ class TestDataset(unittest.TestCase):
 
     def _testMolSizes(self, ds):
         for i in range(len(ds)):
-            l = bisect(ds.cumulative_sizes, i)
+            left = bisect(ds.cumulative_sizes, i)
             moli = ds[i][0].item()
             for j in range(len(ds)):
-                l2 = bisect(ds.cumulative_sizes, j)
+                left2 = bisect(ds.cumulative_sizes, j)
                 molj = ds[j][0].item()
-                if l == l2:
+                if left == left2:
                     self.assertEqual(moli, molj)
                 else:
                     if moli == molj:

tests/test_energies.py

 import torch
-import numpy
 import torchani
 import unittest
 import os
@@ -12,7 +11,8 @@ N = 97
 
 class TestEnergies(unittest.TestCase):
 
-    def setUp(self, dtype=torchani.default_dtype, device=torchani.default_device):
+    def setUp(self, dtype=torchani.default_dtype,
+              device=torchani.default_device):
         self.tolerance = 5e-5
         self.aev_computer = torchani.SortedAEV(
             dtype=dtype, device=torch.device('cpu'))

tests/test_forces.py

 import torch
-import numpy
 import torchani
 import unittest
 import os
@@ -11,7 +10,8 @@ N = 97
 
 class TestForce(unittest.TestCase):
 
-    def setUp(self, dtype=torchani.default_dtype, device=torchani.default_device):
+    def setUp(self, dtype=torchani.default_dtype,
+              device=torchani.default_device):
         self.tolerance = 1e-5
         self.aev_computer = torchani.SortedAEV(
             dtype=dtype, device=torch.device('cpu'))