[DGL-LifeSci] Release Preparation (CI, Docker, Conda build) (#1399)

* Update

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* add docs

* Fix style

* Fix lint

* Bug fix

* Fix test

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Co-authored-by: Minjie Wang <wmjlyjemaine@gmail.com>

apps/life_sci/python/dgllife/model/model_zoo/jtnn/jtnn_enc.py

-# pylint: disable=C0111, C0103, E1101, W0611, W0612
+# pylint: disable=C0111, C0103, E1101, W0611, W0612, W0221
 import numpy as np
 import torch
 import torch.nn as nn

apps/life_sci/python/dgllife/model/model_zoo/jtnn/jtnn_vae.py

-# pylint: disable=C0111, C0103, E1101, W0611, W0612, C0200
+# pylint: disable=C0111, C0103, E1101, W0611, W0612, C0200, W0221, E1102
 import copy
 import rdkit.Chem as Chem
 import torch

apps/life_sci/python/dgllife/model/model_zoo/jtnn/mpn.py

-# pylint: disable=C0111, C0103, E1101, W0611, W0612
+# pylint: disable=C0111, C0103, E1101, W0611, W0612, I1101, W0221
 # pylint: disable=redefined-outer-name
 import rdkit.Chem as Chem
 import torch

apps/life_sci/python/dgllife/model/model_zoo/jtnn/nnutils.py

-# pylint: disable=C0111, C0103, E1101, W0611, W0612
+# pylint: disable=C0111, C0103, E1101, W0611, W0612, W0221
 import os
 import torch
 import torch.nn as nn

apps/life_sci/python/dgllife/model/model_zoo/mgcn_predictor.py

 """MGCN"""
+# pylint: disable= no-member, arguments-differ, invalid-name
 import torch.nn as nn
 
 from ..gnn import MGCNGNN
@@ -6,6 +7,7 @@ from ..readout import MLPNodeReadout
 
 __all__ = ['MGCNPredictor']
 
+# pylint: disable=W0221
 class MGCNPredictor(nn.Module):
     """MGCN for for regression and classification on graphs.
 

apps/life_sci/python/dgllife/model/model_zoo/mlp_predictor.py

 """MLP for prediction on the output of readout."""
+# pylint: disable= no-member, arguments-differ, invalid-name
 import torch.nn as nn
 
+# pylint: disable=W0221
 class MLPPredictor(nn.Module):
     """Two-layer MLP for regression or soft classification
     over multiple tasks from graph representations.

apps/life_sci/python/dgllife/model/model_zoo/mpnn_predictor.py

 """MPNN"""
+# pylint: disable= no-member, arguments-differ, invalid-name
 import torch.nn as nn
 
 from dgl.nn.pytorch import Set2Set
@@ -7,6 +8,7 @@ from ..gnn import MPNNGNN
 
 __all__ = ['MPNNPredictor']
 
+# pylint: disable=W0221
 class MPNNPredictor(nn.Module):
     """MPNN for regression and classification on graphs.
 

apps/life_sci/python/dgllife/model/model_zoo/schnet_predictor.py

 """SchNet"""
+# pylint: disable= no-member, arguments-differ, invalid-name
 import torch.nn as nn
 
 from dgl.nn.pytorch.conv.cfconv import ShiftedSoftplus
@@ -8,6 +9,7 @@ from ..readout import MLPNodeReadout
 
 __all__ = ['SchNetPredictor']
 
+# pylint: disable=W0221
 class SchNetPredictor(nn.Module):
     """SchNet for regression and classification on graphs.
 

apps/life_sci/python/dgllife/model/model_zoo/wln_reaction_center.py

 """Weisfeiler-Lehman Network (WLN) for Reaction Center Prediction."""
+# pylint: disable= no-member, arguments-differ, invalid-name
 import dgl.function as fn
 import torch
 import torch.nn as nn
@@ -7,6 +8,7 @@ from ..gnn.wln import WLNLinear, WLN
 
 __all__ = ['WLNReactionCenter']
 
+# pylint: disable=W0221, E1101
 class WLNContext(nn.Module):
     """Attention-based context computation for each node.
 

apps/life_sci/python/dgllife/model/pretrain.py

 """Utilities for using pretrained models."""
+# pylint: disable= no-member, arguments-differ, invalid-name
 import os
 import torch
 import torch.nn.functional as F
@@ -58,6 +59,7 @@ def download_and_load_checkpoint(model_name, model, model_postfix,
 
     return model
 
+# pylint: disable=I1101
 def load_pretrained(model_name, log=True):
     """Load a pretrained model
 

apps/life_sci/python/dgllife/model/readout/attentivefp_readout.py

 """Readout for AttentiveFP"""
+# pylint: disable= no-member, arguments-differ, invalid-name
 import dgl
 import torch
 import torch.nn as nn
@@ -6,6 +7,7 @@ import torch.nn.functional as F
 
 __all__ = ['AttentiveFPReadout']
 
+# pylint: disable=W0221
 class GlobalPool(nn.Module):
     """One-step readout in AttentiveFP
 
@@ -88,7 +90,7 @@ class AttentiveFPReadout(nn.Module):
         super(AttentiveFPReadout, self).__init__()
 
         self.readouts = nn.ModuleList()
-        for t in range(num_timesteps):
+        for _ in range(num_timesteps):
             self.readouts.append(GlobalPool(feat_size, dropout))
 
     def forward(self, g, node_feats, get_node_weight=False):

apps/life_sci/python/dgllife/model/readout/mlp_readout.py

 """Readout for SchNet"""
+# pylint: disable= no-member, arguments-differ, invalid-name
 import dgl
 import torch.nn as nn
 
 __all__ = ['MLPNodeReadout']
 
+# pylint: disable=W0221
 class MLPNodeReadout(nn.Module):
     """MLP-based Readout.
 

apps/life_sci/python/dgllife/model/readout/weighted_sum_and_max.py

 """Apply weighted sum and max pooling to the node representations and concatenate the results."""
+# pylint: disable= no-member, arguments-differ, invalid-name
 import dgl
 import torch
 import torch.nn as nn
@@ -7,6 +8,7 @@ from dgl.nn.pytorch import WeightAndSum
 
 __all__ = ['WeightedSumAndMax']
 
+# pylint: disable=W0221
 class WeightedSumAndMax(nn.Module):
     r"""Apply weighted sum and max pooling to the node
     representations and concatenate the results.

apps/life_sci/python/dgllife/utils/complex_to_graph.py

 """Convert complexes into DGLHeteroGraphs"""
+# pylint: disable= no-member, arguments-differ, invalid-name
 import dgl.backend as F
 import numpy as np
 
@@ -50,6 +51,7 @@ def get_atomic_numbers(mol, indices):
         atomic_numbers.append(atom.GetAtomicNum())
     return atomic_numbers
 
+# pylint: disable=C0326
 def ACNN_graph_construction_and_featurization(ligand_mol,
                                               protein_mol,
                                               ligand_coordinates,
@@ -216,7 +218,7 @@ def ACNN_graph_construction_and_featurization(ligand_mol,
     protein_atomic_numbers = np.concatenate([
         protein_atomic_numbers, np.zeros(num_protein_atoms - len(protein_atom_indices_left))])
 
-    g.nodes['ligand_atom'].data['atomic_number'] =  F.reshape(F.zerocopy_from_numpy(
+    g.nodes['ligand_atom'].data['atomic_number'] = F.reshape(F.zerocopy_from_numpy(
         ligand_atomic_numbers.astype(np.float32)), (-1, 1))
     g.nodes['protein_atom'].data['atomic_number'] = F.reshape(F.zerocopy_from_numpy(
         protein_atomic_numbers.astype(np.float32)), (-1, 1))

apps/life_sci/python/dgllife/utils/early_stop.py

 """Early stopping"""
+# pylint: disable= no-member, arguments-differ, invalid-name
 import datetime
 import torch
 
 __all__ = ['EarlyStopping']
 
+# pylint: disable=C0103
 class EarlyStopping(object):
     """Early stop tracker
 
@@ -56,7 +58,7 @@ class EarlyStopping(object):
         bool
             Whether the new score is higher than the previous best score.
         """
-        return (score > prev_best_score)
+        return score > prev_best_score
 
     def _check_lower(self, score, prev_best_score):
         """Check if the new score is lower than the previous best score.
@@ -73,7 +75,7 @@ class EarlyStopping(object):
         bool
             Whether the new score is lower than the previous best score.
         """
-        return (score < prev_best_score)
+        return score < prev_best_score
 
     def step(self, score, model):
         """Update based on a new score.

apps/life_sci/python/dgllife/utils/eval.py

 """Evaluation of model performance."""
+# pylint: disable= no-member, arguments-differ, invalid-name
 import numpy as np
 import torch
 import torch.nn.functional as F
@@ -8,6 +9,7 @@ from sklearn.metrics import roc_auc_score
 
 __all__ = ['Meter']
 
+# pylint: disable=E1101
 class Meter(object):
     """Track and summarize model performance on a dataset for (multi-label) prediction.
 
@@ -252,12 +254,12 @@ class Meter(object):
         """
         if metric_name == 'r2':
             return self.pearson_r2(reduction)
-
-        if metric_name == 'mae':
+        elif metric_name == 'mae':
             return self.mae(reduction)
-
-        if metric_name == 'rmse':
+        elif metric_name == 'rmse':
             return self.rmse(reduction)
-
-        if metric_name == 'roc_auc_score':
+        elif metric_name == 'roc_auc_score':
             return self.roc_auc_score(reduction)
+        else:
+            raise ValueError('Expect metric_name to be "r2" or "mae" or "rmse" '
+                             'or "roc_auc_score", got {}'.format(metric_name))

apps/life_sci/python/dgllife/utils/featurizers.py

 """Node and edge featurization for molecular graphs."""
-import dgl.backend as F
+# pylint: disable= no-member, arguments-differ, invalid-name
 import itertools
+from collections import defaultdict
+import dgl.backend as F
 import numpy as np
 
-from collections import defaultdict
 from rdkit import Chem
 
 __all__ = ['one_hot_encoding',
@@ -302,6 +303,7 @@ def atom_implicit_valence(atom):
     """
     return [atom.GetImplicitValence()]
 
+# pylint: disable=I1101
 def atom_hybridization_one_hot(atom, allowable_set=None, encode_unknown=False):
     """One hot encoding for the hybridization of an atom.
 
@@ -601,7 +603,7 @@ class BaseAtomFeaturizer(object):
     Examples
     --------
 
-    >>> from dgl.data.life_sci import BaseAtomFeaturizer, atom_mass, atom_degree_one_hot
+    >>> from dgl.data.dgllife import BaseAtomFeaturizer, atom_mass, atom_degree_one_hot
     >>> from rdkit import Chem
 
     >>> mol = Chem.MolFromSmiles('CCO')
@@ -856,17 +858,17 @@ class BaseBondFeaturizer(object):
     Examples
     --------
 
-    >>> from dgl.data.life_sci import BaseBondFeaturizer, bond_type_one_hot, bond_is_in_ring
+    >>> from dgl.data.dgllife import BaseBondFeaturizer, bond_type_one_hot, bond_is_in_ring
     >>> from rdkit import Chem
 
     >>> mol = Chem.MolFromSmiles('CCO')
-    >>> bond_featurizer = BaseBondFeaturizer({'bond_type': bond_type_one_hot, 'in_ring': bond_is_in_ring})
+    >>> bond_featurizer = BaseBondFeaturizer({'type': bond_type_one_hot, 'ring': bond_is_in_ring})
     >>> bond_featurizer(mol)
-    {'bond_type': tensor([[1., 0., 0., 0.],
-                          [1., 0., 0., 0.],
-                          [1., 0., 0., 0.],
-                          [1., 0., 0., 0.]]),
-     'in_ring': tensor([[0.], [0.], [0.], [0.]])}
+    {'type': tensor([[1., 0., 0., 0.],
+                     [1., 0., 0., 0.],
+                     [1., 0., 0., 0.],
+                     [1., 0., 0., 0.]]),
+     'ring': tensor([[0.], [0.], [0.], [0.]])}
     """
     def __init__(self, featurizer_funcs, feat_sizes=None):
         self.featurizer_funcs = featurizer_funcs

apps/life_sci/python/dgllife/utils/mol_to_graph.py

 """Convert molecules into DGLGraphs."""
+# pylint: disable= no-member, arguments-differ, invalid-name
+from functools import partial
 import torch
 
 from dgl import DGLGraph
-from functools import partial
 from rdkit import Chem
 from rdkit.Chem import rdmolfiles, rdmolops
 from sklearn.neighbors import NearestNeighbors
@@ -16,6 +17,7 @@ __all__ = ['mol_to_graph',
            'mol_to_nearest_neighbor_graph',
            'smiles_to_nearest_neighbor_graph']
 
+# pylint: disable=I1101
 def mol_to_graph(mol, graph_constructor, node_featurizer, edge_featurizer, canonical_atom_order):
     """Convert an RDKit molecule object into a DGLGraph and featurize for it.
 
@@ -225,7 +227,8 @@ def mol_to_complete_graph(mol, add_self_loop=False,
     g : DGLGraph
         Complete DGLGraph for the molecule
     """
-    return mol_to_graph(mol, partial(construct_complete_graph_from_mol, add_self_loop=add_self_loop),
+    return mol_to_graph(mol,
+                        partial(construct_complete_graph_from_mol, add_self_loop=add_self_loop),
                         node_featurizer, edge_featurizer, canonical_atom_order)
 
 def smiles_to_complete_graph(smiles, add_self_loop=False,
@@ -321,6 +324,7 @@ def k_nearest_neighbors(coordinates, neighbor_cutoff, max_num_neighbors=None,
 
     return srcs, dsts, dists
 
+# pylint: disable=E1102
 def mol_to_nearest_neighbor_graph(mol,
                                   coordinates,
                                   neighbor_cutoff,

apps/life_sci/python/dgllife/utils/rdkit_utils.py

 """Utils for RDKit, mostly adapted from DeepChem
 (https://github.com/deepchem/deepchem/blob/master/deepchem)."""
+# pylint: disable= no-member, arguments-differ, invalid-name
 import warnings
 
 from functools import partial
@@ -7,16 +8,12 @@ from multiprocessing import Pool
 from rdkit import Chem
 from rdkit.Chem import AllChem
 
-try:
-    from StringIO import StringIO
-except ImportError:
-    from io import StringIO
-
-__all__ = ['get_mol_3D_coordinates',
+__all__ = ['get_mol_3d_coordinates',
            'load_molecule',
            'multiprocess_load_molecules']
 
-def get_mol_3D_coordinates(mol):
+# pylint: disable=W0702
+def get_mol_3d_coordinates(mol):
     """Get 3D coordinates of the molecule.
 
     Parameters
@@ -42,6 +39,7 @@ def get_mol_3D_coordinates(mol):
         warnings.warn('Unable to get conformation of the molecule.')
         return None
 
+# pylint: disable=E1101
 def load_molecule(molecule_file, sanitize=False, calc_charges=False,
                   remove_hs=False, use_conformation=True):
     """Load a molecule from a file.
@@ -80,8 +78,8 @@ def load_molecule(molecule_file, sanitize=False, calc_charges=False,
         supplier = Chem.SDMolSupplier(molecule_file, sanitize=False, removeHs=False)
         mol = supplier[0]
     elif molecule_file.endswith('.pdbqt'):
-        with open(molecule_file) as f:
-            pdbqt_data = f.readlines()
+        with open(molecule_file) as file:
+            pdbqt_data = file.readlines()
         pdb_block = ''
         for line in pdbqt_data:
             pdb_block += '{}\n'.format(line[:66])
@@ -109,7 +107,7 @@ def load_molecule(molecule_file, sanitize=False, calc_charges=False,
         return None, None
 
     if use_conformation:
-        coordinates = get_mol_3D_coordinates(mol)
+        coordinates = get_mol_3d_coordinates(mol)
     else:
         coordinates = None
 
@@ -151,7 +149,7 @@ def multiprocess_load_molecules(files, sanitize=False, calc_charges=False,
     """
     if num_processes == 1:
         mols_loaded = []
-        for i, f in enumerate(files):
+        for f in files:
             mols_loaded.append(load_molecule(
                 f, sanitize=sanitize, calc_charges=calc_charges,
                 remove_hs=remove_hs, use_conformation=use_conformation))

apps/life_sci/python/dgllife/utils/splitters.py

@@ -3,10 +3,8 @@
 We mostly adapt them from deepchem
 (https://github.com/deepchem/deepchem/blob/master/deepchem/splits/splitters.py).
 """
-import dgl.backend as F
-import numpy as np
-
-from dgl.data.utils import split_dataset, Subset
+# pylint: disable= no-member, arguments-differ, invalid-name
+# pylint: disable=E0611
 from collections import defaultdict
 from functools import partial
 from itertools import accumulate, chain
@@ -15,6 +13,10 @@ from rdkit.Chem import rdMolDescriptors
 from rdkit.Chem.rdmolops import FastFindRings
 from rdkit.Chem.Scaffolds import MurckoScaffold
 
+import dgl.backend as F
+import numpy as np
+from dgl.data.utils import split_dataset, Subset
+
 __all__ = ['ConsecutiveSplitter',
            'RandomSplitter',
            'MolecularWeightSplitter',
@@ -304,6 +306,7 @@ class RandomSplitter(object):
 
         return base_k_fold_split(partial(indices_split, indices=indices), dataset, k, log)
 
+# pylint: disable=I1101
 class MolecularWeightSplitter(object):
     """Sort molecules based on their weights and then split them."""
 
@@ -335,7 +338,7 @@ class MolecularWeightSplitter(object):
         for i, mol in enumerate(molecules):
             count_and_log('Computing molecular weight for compound',
                           i, len(molecules), log_every_n)
-            mws.append(Chem.rdMolDescriptors.CalcExactMolWt(mol))
+            mws.append(rdMolDescriptors.CalcExactMolWt(mol))
 
         return np.argsort(mws)
 
@@ -427,6 +430,7 @@ class MolecularWeightSplitter(object):
         return base_k_fold_split(partial(indices_split, indices=sorted_indices), dataset, k,
                                  log=(log_every_n is not None))
 
+# pylint: disable=W0702
 class ScaffoldSplitter(object):
     """Group molecules based on their Bemis-Murcko scaffolds and then split the groups.