[Misc] Black auto fix. (#4652)

Co-authored-by: Steve <ubuntu@ip-172-31-34-29.ap-northeast-1.compute.internal>

examples/pytorch/jtnn/jtnn/mol_tree_nx.py

-import dgl
-import rdkit.Chem as Chem
-from .chemutils import get_clique_mol, tree_decomp, get_mol, get_smiles, \
-                       set_atommap, enum_assemble_nx, decode_stereo
 import numpy as np
+import rdkit.Chem as Chem
+
+import dgl
+
+from .chemutils import (
+    decode_stereo,
+    enum_assemble_nx,
+    get_clique_mol,
+    get_mol,
+    get_smiles,
+    set_atommap,
+    tree_decomp,
+)
+
 
 class DGLMolTree(object):
     def __init__(self, smiles):
@@ -38,11 +48,13 @@ class DGLMolTree(object):
         # The clique with atom ID 0 becomes root
         if root > 0:
             for attr in self.nodes_dict[0]:
-                self.nodes_dict[0][attr], self.nodes_dict[root][attr] = \
-                        self.nodes_dict[root][attr], self.nodes_dict[0][attr]
+                self.nodes_dict[0][attr], self.nodes_dict[root][attr] = (
+                    self.nodes_dict[root][attr],
+                    self.nodes_dict[0][attr],
+                )
 
-        src = np.zeros((len(edges) * 2,), dtype='int')
-        dst = np.zeros((len(edges) * 2,), dtype='int')
+        src = np.zeros((len(edges) * 2,), dtype="int")
+        dst = np.zeros((len(edges) * 2,), dtype="int")
         for i, (_x, _y) in enumerate(edges):
             x = 0 if _x == root else root if _x == 0 else _x
             y = 0 if _y == root else root if _y == 0 else _y
@@ -53,10 +65,12 @@ class DGLMolTree(object):
         self.graph = dgl.graph((src, dst), num_nodes=len(cliques))
 
         for i in self.nodes_dict:
-            self.nodes_dict[i]['nid'] = i + 1
+            self.nodes_dict[i]["nid"] = i + 1
             if self.graph.out_degrees(i) > 1:  # Leaf node mol is not marked
-                set_atommap(self.nodes_dict[i]['mol'], self.nodes_dict[i]['nid'])
-            self.nodes_dict[i]['is_leaf'] = (self.graph.out_degrees(i) == 1)
+                set_atommap(
+                    self.nodes_dict[i]["mol"], self.nodes_dict[i]["nid"]
+                )
+            self.nodes_dict[i]["is_leaf"] = self.graph.out_degrees(i) == 1
 
     def treesize(self):
         return self.graph.number_of_nodes()
@@ -65,49 +79,65 @@ class DGLMolTree(object):
         node = self.nodes_dict[i]
 
         clique = []
-        clique.extend(node['clique'])
-        if not node['is_leaf']:
-            for cidx in node['clique']:
-                original_mol.GetAtomWithIdx(cidx).SetAtomMapNum(node['nid'])
+        clique.extend(node["clique"])
+        if not node["is_leaf"]:
+            for cidx in node["clique"]:
+                original_mol.GetAtomWithIdx(cidx).SetAtomMapNum(node["nid"])
 
         for j in self.graph.successors(i).numpy():
             nei_node = self.nodes_dict[j]
-            clique.extend(nei_node['clique'])
-            if nei_node['is_leaf']: # Leaf node, no need to mark
+            clique.extend(nei_node["clique"])
+            if nei_node["is_leaf"]:  # Leaf node, no need to mark
                 continue
-            for cidx in nei_node['clique']:
+            for cidx in nei_node["clique"]:
                 # allow singleton node override the atom mapping
-                if cidx not in node['clique'] or len(nei_node['clique']) == 1:
+                if cidx not in node["clique"] or len(nei_node["clique"]) == 1:
                     atom = original_mol.GetAtomWithIdx(cidx)
-                    atom.SetAtomMapNum(nei_node['nid'])
+                    atom.SetAtomMapNum(nei_node["nid"])
 
         clique = list(set(clique))
         label_mol = get_clique_mol(original_mol, clique)
-        node['label'] = Chem.MolToSmiles(Chem.MolFromSmiles(get_smiles(label_mol)))
-        node['label_mol'] = get_mol(node['label'])
+        node["label"] = Chem.MolToSmiles(
+            Chem.MolFromSmiles(get_smiles(label_mol))
+        )
+        node["label_mol"] = get_mol(node["label"])
 
         for cidx in clique:
             original_mol.GetAtomWithIdx(cidx).SetAtomMapNum(0)
 
-        return node['label']
+        return node["label"]
 
     def _assemble_node(self, i):
-        neighbors = [self.nodes_dict[j] for j in self.graph.successors(i).numpy()
-                     if self.nodes_dict[j]['mol'].GetNumAtoms() > 1]
-        neighbors = sorted(neighbors, key=lambda x: x['mol'].GetNumAtoms(), reverse=True)
-        singletons = [self.nodes_dict[j] for j in self.graph.successors(i).numpy()
-                      if self.nodes_dict[j]['mol'].GetNumAtoms() == 1]
+        neighbors = [
+            self.nodes_dict[j]
+            for j in self.graph.successors(i).numpy()
+            if self.nodes_dict[j]["mol"].GetNumAtoms() > 1
+        ]
+        neighbors = sorted(
+            neighbors, key=lambda x: x["mol"].GetNumAtoms(), reverse=True
+        )
+        singletons = [
+            self.nodes_dict[j]
+            for j in self.graph.successors(i).numpy()
+            if self.nodes_dict[j]["mol"].GetNumAtoms() == 1
+        ]
         neighbors = singletons + neighbors
 
         cands = enum_assemble_nx(self.nodes_dict[i], neighbors)
 
         if len(cands) > 0:
-            self.nodes_dict[i]['cands'], self.nodes_dict[i]['cand_mols'], _ = list(zip(*cands))
-            self.nodes_dict[i]['cands'] = list(self.nodes_dict[i]['cands'])
-            self.nodes_dict[i]['cand_mols'] = list(self.nodes_dict[i]['cand_mols'])
+            (
+                self.nodes_dict[i]["cands"],
+                self.nodes_dict[i]["cand_mols"],
+                _,
+            ) = list(zip(*cands))
+            self.nodes_dict[i]["cands"] = list(self.nodes_dict[i]["cands"])
+            self.nodes_dict[i]["cand_mols"] = list(
+                self.nodes_dict[i]["cand_mols"]
+            )
         else:
-            self.nodes_dict[i]['cands'] = []
-            self.nodes_dict[i]['cand_mols'] = []
+            self.nodes_dict[i]["cands"] = []
+            self.nodes_dict[i]["cand_mols"] = []
 
     def recover(self):
         for i in self.nodes_dict:

examples/pytorch/jtnn/jtnn/mpn.py

+import rdkit.Chem as Chem
 import torch
 import torch.nn as nn
-import rdkit.Chem as Chem
 import torch.nn.functional as F
-from .chemutils import get_mol
+
 import dgl
-from dgl import mean_nodes, line_graph
 import dgl.function as DGLF
+from dgl import line_graph, mean_nodes
 
-ELEM_LIST = ['C', 'N', 'O', 'S', 'F', 'Si', 'P', 'Cl', 'Br', 'Mg', 'Na', 'Ca',
-             'Fe', 'Al', 'I', 'B', 'K', 'Se', 'Zn', 'H', 'Cu', 'Mn', 'unknown']
+from .chemutils import get_mol
+
+ELEM_LIST = [
+    "C",
+    "N",
+    "O",
+    "S",
+    "F",
+    "Si",
+    "P",
+    "Cl",
+    "Br",
+    "Mg",
+    "Na",
+    "Ca",
+    "Fe",
+    "Al",
+    "I",
+    "B",
+    "K",
+    "Se",
+    "Zn",
+    "H",
+    "Cu",
+    "Mn",
+    "unknown",
+]
 
 ATOM_FDIM = len(ELEM_LIST) + 6 + 5 + 4 + 1
 BOND_FDIM = 5 + 6
 MAX_NB = 6
 
+
 def onek_encoding_unk(x, allowable_set):
     if x not in allowable_set:
         x = allowable_set[-1]
     return [x == s for s in allowable_set]
 
+
 def atom_features(atom):
-    return (torch.Tensor(onek_encoding_unk(atom.GetSymbol(), ELEM_LIST) 
-            + onek_encoding_unk(atom.GetDegree(), [0,1,2,3,4,5]) 
-            + onek_encoding_unk(atom.GetFormalCharge(), [-1,-2,1,2,0])
-            + onek_encoding_unk(int(atom.GetChiralTag()), [0,1,2,3])
-            + [atom.GetIsAromatic()]))
+    return torch.Tensor(
+        onek_encoding_unk(atom.GetSymbol(), ELEM_LIST)
+        + onek_encoding_unk(atom.GetDegree(), [0, 1, 2, 3, 4, 5])
+        + onek_encoding_unk(atom.GetFormalCharge(), [-1, -2, 1, 2, 0])
+        + onek_encoding_unk(int(atom.GetChiralTag()), [0, 1, 2, 3])
+        + [atom.GetIsAromatic()]
+    )
+
 
 def bond_features(bond):
     bt = bond.GetBondType()
     stereo = int(bond.GetStereo())
-    fbond = [bt == Chem.rdchem.BondType.SINGLE, bt == Chem.rdchem.BondType.DOUBLE, bt == Chem.rdchem.BondType.TRIPLE, bt == Chem.rdchem.BondType.AROMATIC, bond.IsInRing()]
-    fstereo = onek_encoding_unk(stereo, [0,1,2,3,4,5])
-    return (torch.Tensor(fbond + fstereo))
+    fbond = [
+        bt == Chem.rdchem.BondType.SINGLE,
+        bt == Chem.rdchem.BondType.DOUBLE,
+        bt == Chem.rdchem.BondType.TRIPLE,
+        bt == Chem.rdchem.BondType.AROMATIC,
+        bond.IsInRing(),
+    ]
+    fstereo = onek_encoding_unk(stereo, [0, 1, 2, 3, 4, 5])
+    return torch.Tensor(fbond + fstereo)
+
 
 def mol2dgl_single(smiles):
     n_edges = 0
@@ -61,8 +98,11 @@ def mol2dgl_single(smiles):
         bond_x.append(features)
     graph = dgl.graph((bond_src, bond_dst), num_nodes=n_atoms)
     n_edges += n_bonds
-    return graph, torch.stack(atom_x), \
-            torch.stack(bond_x) if len(bond_x) > 0 else torch.zeros(0)
+    return (
+        graph,
+        torch.stack(atom_x),
+        torch.stack(bond_x) if len(bond_x) > 0 else torch.zeros(0),
+    )
 
 
 class LoopyBPUpdate(nn.Module):
@@ -73,10 +113,10 @@ class LoopyBPUpdate(nn.Module):
         self.W_h = nn.Linear(hidden_size, hidden_size, bias=False)
 
     def forward(self, nodes):
-        msg_input = nodes.data['msg_input']
-        msg_delta = self.W_h(nodes.data['accum_msg'])
+        msg_input = nodes.data["msg_input"]
+        msg_delta = self.W_h(nodes.data["accum_msg"])
         msg = F.relu(msg_input + msg_delta)
-        return {'msg': msg}
+        return {"msg": msg}
 
 
 class GatherUpdate(nn.Module):
@@ -87,9 +127,9 @@ class GatherUpdate(nn.Module):
         self.W_o = nn.Linear(ATOM_FDIM + hidden_size, hidden_size)
 
     def forward(self, nodes):
-        m = nodes.data['m']
+        m = nodes.data["m"]
         return {
-            'h': F.relu(self.W_o(torch.cat([nodes.data['x'], m], 1))),
+            "h": F.relu(self.W_o(torch.cat([nodes.data["x"], m], 1))),
         }
 
 
@@ -121,7 +161,7 @@ class DGLMPN(nn.Module):
         mol_graph = self.run(mol_graph, mol_line_graph)
 
         # TODO: replace with unbatch or readout
-        g_repr = mean_nodes(mol_graph, 'h')
+        g_repr = mean_nodes(mol_graph, "h")
 
         self.n_samples_total += n_samples
         self.n_nodes_total += n_nodes
@@ -134,32 +174,38 @@ class DGLMPN(nn.Module):
         n_nodes = mol_graph.number_of_nodes()
 
         mol_graph.apply_edges(
-            func=lambda edges: {'src_x': edges.src['x']},
+            func=lambda edges: {"src_x": edges.src["x"]},
         )
         mol_line_graph.ndata.update(mol_graph.edata)
 
         e_repr = mol_line_graph.ndata
-        bond_features = e_repr['x']
-        source_features = e_repr['src_x']
+        bond_features = e_repr["x"]
+        source_features = e_repr["src_x"]
 
         features = torch.cat([source_features, bond_features], 1)
         msg_input = self.W_i(features)
-        mol_line_graph.ndata.update({
-            'msg_input': msg_input,
-            'msg': F.relu(msg_input),
-            'accum_msg': torch.zeros_like(msg_input),
-        })
-        mol_graph.ndata.update({
-            'm': bond_features.new(n_nodes, self.hidden_size).zero_(),
-            'h': bond_features.new(n_nodes, self.hidden_size).zero_(),
-        })
+        mol_line_graph.ndata.update(
+            {
+                "msg_input": msg_input,
+                "msg": F.relu(msg_input),
+                "accum_msg": torch.zeros_like(msg_input),
+            }
+        )
+        mol_graph.ndata.update(
+            {
+                "m": bond_features.new(n_nodes, self.hidden_size).zero_(),
+                "h": bond_features.new(n_nodes, self.hidden_size).zero_(),
+            }
+        )
 
         for i in range(self.depth - 1):
-            mol_line_graph.update_all(DGLF.copy_u('msg', 'msg'), DGLF.sum('msg', 'accum_msg'))
+            mol_line_graph.update_all(
+                DGLF.copy_u("msg", "msg"), DGLF.sum("msg", "accum_msg")
+            )
             mol_line_graph.apply_nodes(self.loopy_bp_updater)
 
         mol_graph.edata.update(mol_line_graph.ndata)
-        mol_graph.update_all(DGLF.copy_e('msg', 'msg'), DGLF.sum('msg', 'm'))
+        mol_graph.update_all(DGLF.copy_e("msg", "msg"), DGLF.sum("msg", "m"))
         mol_graph.apply_nodes(self.gather_updater)
 
         return mol_graph

examples/pytorch/jtnn/jtnn/nnutils.py

+import os
+
 import torch
 import torch.nn as nn
-import os
+
 import dgl
 
 
 def cuda(x):
-    if torch.cuda.is_available() and not os.getenv('NOCUDA', None):
-        return x.to(torch.device('cuda'))   # works for both DGLGraph and tensor
+    if torch.cuda.is_available() and not os.getenv("NOCUDA", None):
+        return x.to(torch.device("cuda"))  # works for both DGLGraph and tensor
     else:
         return x
 
@@ -22,27 +24,28 @@ class GRUUpdate(nn.Module):
         self.W_h = nn.Linear(2 * hidden_size, hidden_size)
 
     def update_zm(self, node):
-        src_x = node.data['src_x']
-        s = node.data['s']
-        rm = node.data['accum_rm']
+        src_x = node.data["src_x"]
+        s = node.data["s"]
+        rm = node.data["accum_rm"]
         z = torch.sigmoid(self.W_z(torch.cat([src_x, s], 1)))
         m = torch.tanh(self.W_h(torch.cat([src_x, rm], 1)))
         m = (1 - z) * s + z * m
-        return {'m': m, 'z': z}
+        return {"m": m, "z": z}
 
     def update_r(self, node, zm=None):
-        dst_x = node.data['dst_x']
-        m = node.data['m'] if zm is None else zm['m']
+        dst_x = node.data["dst_x"]
+        m = node.data["m"] if zm is None else zm["m"]
         r_1 = self.W_r(dst_x)
         r_2 = self.U_r(m)
         r = torch.sigmoid(r_1 + r_2)
-        return {'r': r, 'rm': r * m}
+        return {"r": r, "rm": r * m}
 
     def forward(self, node):
         dic = self.update_zm(node)
         dic.update(self.update_r(node, zm=dic))
         return dic
 
+
 def tocpu(g):
     src, dst = g.edges()
     src = src.cpu()

examples/pytorch/jtnn/vaetrain_dgl.py

+import math
+import random
+import sys
+from collections import deque
+from optparse import OptionParser
+
+import rdkit
 import torch
 import torch.nn as nn
 import torch.optim as optim
 import torch.optim.lr_scheduler as lr_scheduler
-from torch.utils.data import DataLoader
-
-import math, random, sys
-from optparse import OptionParser
-from collections import deque
-import rdkit
 import tqdm
-
 from jtnn import *
+from torch.utils.data import DataLoader
+
+torch.multiprocessing.set_sharing_strategy("file_system")
 
-torch.multiprocessing.set_sharing_strategy('file_system')
 
 def worker_init_fn(id_):
     lg = rdkit.RDLogger.logger()
     lg.setLevel(rdkit.RDLogger.CRITICAL)
+
+
 worker_init_fn(None)
 
 parser = OptionParser()
-parser.add_option("-t", "--train", dest="train", default='train', help='Training file name')
-parser.add_option("-v", "--vocab", dest="vocab", default='vocab', help='Vocab file name')
+parser.add_option(
+    "-t", "--train", dest="train", default="train", help="Training file name"
+)
+parser.add_option(
+    "-v", "--vocab", dest="vocab", default="vocab", help="Vocab file name"
+)
 parser.add_option("-s", "--save_dir", dest="save_path")
 parser.add_option("-m", "--model", dest="model_path", default=None)
 parser.add_option("-b", "--batch", dest="batch_size", default=40)
@@ -31,7 +39,7 @@ parser.add_option("-d", "--depth", dest="depth", default=3)
 parser.add_option("-z", "--beta", dest="beta", default=1.0)
 parser.add_option("-q", "--lr", dest="lr", default=1e-3)
 parser.add_option("-T", "--test", dest="test", action="store_true")
-opts,args = parser.parse_args()
+opts, args = parser.parse_args()
 
 dataset = JTNNDataset(data=opts.train, vocab=opts.vocab, training=True)
 vocab = dataset.vocab
@@ -55,7 +63,10 @@ else:
             nn.init.xavier_normal(param)
 
 model = cuda(model)
-print("Model #Params: %dK" % (sum([x.nelement() for x in model.parameters()]) / 1000,))
+print(
+    "Model #Params: %dK"
+    % (sum([x.nelement() for x in model.parameters()]) / 1000,)
+)
 
 optimizer = optim.Adam(model.parameters(), lr=lr)
 scheduler = lr_scheduler.ExponentialLR(optimizer, 0.9)
@@ -64,6 +75,7 @@ scheduler.step()
 MAX_EPOCH = 100
 PRINT_ITER = 20
 
+
 def train():
     dataset.training = True
     dataloader = DataLoader(
@@ -73,17 +85,18 @@ def train():
         num_workers=4,
         collate_fn=JTNNCollator(vocab, True),
         drop_last=True,
-            worker_init_fn=worker_init_fn)
+        worker_init_fn=worker_init_fn,
+    )
 
     for epoch in range(MAX_EPOCH):
-        word_acc,topo_acc,assm_acc,steo_acc = 0,0,0,0
+        word_acc, topo_acc, assm_acc, steo_acc = 0, 0, 0, 0
 
         for it, batch in enumerate(tqdm.tqdm(dataloader)):
             model.zero_grad()
             try:
                 loss, kl_div, wacc, tacc, sacc, dacc = model(batch, beta)
             except:
-                print([t.smiles for t in batch['mol_trees']])
+                print([t.smiles for t in batch["mol_trees"]])
                 raise
             loss.backward()
             optimizer.step()
@@ -99,20 +112,34 @@ def train():
                 assm_acc = assm_acc / PRINT_ITER * 100
                 steo_acc = steo_acc / PRINT_ITER * 100
 
-                print("KL: %.1f, Word: %.2f, Topo: %.2f, Assm: %.2f, Steo: %.2f, Loss: %.6f" % (
-                    kl_div, word_acc, topo_acc, assm_acc, steo_acc, loss.item()))
-                word_acc,topo_acc,assm_acc,steo_acc = 0,0,0,0
+                print(
+                    "KL: %.1f, Word: %.2f, Topo: %.2f, Assm: %.2f, Steo: %.2f, Loss: %.6f"
+                    % (
+                        kl_div,
+                        word_acc,
+                        topo_acc,
+                        assm_acc,
+                        steo_acc,
+                        loss.item(),
+                    )
+                )
+                word_acc, topo_acc, assm_acc, steo_acc = 0, 0, 0, 0
                 sys.stdout.flush()
 
-            if (it + 1) % 1500 == 0: #Fast annealing
+            if (it + 1) % 1500 == 0:  # Fast annealing
                 scheduler.step()
                 print("learning rate: %.6f" % scheduler.get_lr()[0])
-                torch.save(model.state_dict(),
-                           opts.save_path + "/model.iter-%d-%d" % (epoch, it + 1))
+                torch.save(
+                    model.state_dict(),
+                    opts.save_path + "/model.iter-%d-%d" % (epoch, it + 1),
+                )
 
         scheduler.step()
         print("learning rate: %.6f" % scheduler.get_lr()[0])
-        torch.save(model.state_dict(), opts.save_path + "/model.iter-" + str(epoch))
+        torch.save(
+            model.state_dict(), opts.save_path + "/model.iter-" + str(epoch)
+        )
+
 
 def test():
     dataset.training = False
@@ -123,12 +150,13 @@ def test():
         num_workers=0,
         collate_fn=JTNNCollator(vocab, False),
         drop_last=True,
-            worker_init_fn=worker_init_fn)
+        worker_init_fn=worker_init_fn,
+    )
 
     # Just an example of molecule decoding; in reality you may want to sample
     # tree and molecule vectors.
     for it, batch in enumerate(dataloader):
-        gt_smiles = batch['mol_trees'][0].smiles
+        gt_smiles = batch["mol_trees"][0].smiles
         print(gt_smiles)
         model.move_to_cuda(batch)
         _, tree_vec, mol_vec = model.encode(batch)
@@ -136,21 +164,28 @@ def test():
         smiles = model.decode(tree_vec, mol_vec)
         print(smiles)
 
-if __name__ == '__main__':
+
+if __name__ == "__main__":
     if opts.test:
         test()
     else:
         train()
 
-    print('# passes:', model.n_passes)
-    print('Total # nodes processed:', model.n_nodes_total)
-    print('Total # edges processed:', model.n_edges_total)
-    print('Total # tree nodes processed:', model.n_tree_nodes_total)
-    print('Graph decoder: # passes:', model.jtmpn.n_passes)
-    print('Graph decoder: Total # candidates processed:', model.jtmpn.n_samples_total)
-    print('Graph decoder: Total # nodes processed:', model.jtmpn.n_nodes_total)
-    print('Graph decoder: Total # edges processed:', model.jtmpn.n_edges_total)
-    print('Graph encoder: # passes:', model.mpn.n_passes)
-    print('Graph encoder: Total # candidates processed:', model.mpn.n_samples_total)
-    print('Graph encoder: Total # nodes processed:', model.mpn.n_nodes_total)
-    print('Graph encoder: Total # edges processed:', model.mpn.n_edges_total)
+    print("# passes:", model.n_passes)
+    print("Total # nodes processed:", model.n_nodes_total)
+    print("Total # edges processed:", model.n_edges_total)
+    print("Total # tree nodes processed:", model.n_tree_nodes_total)
+    print("Graph decoder: # passes:", model.jtmpn.n_passes)
+    print(
+        "Graph decoder: Total # candidates processed:",
+        model.jtmpn.n_samples_total,
+    )
+    print("Graph decoder: Total # nodes processed:", model.jtmpn.n_nodes_total)
+    print("Graph decoder: Total # edges processed:", model.jtmpn.n_edges_total)
+    print("Graph encoder: # passes:", model.mpn.n_passes)
+    print(
+        "Graph encoder: Total # candidates processed:",
+        model.mpn.n_samples_total,
+    )
+    print("Graph encoder: Total # nodes processed:", model.mpn.n_nodes_total)
+    print("Graph encoder: Total # edges processed:", model.mpn.n_edges_total)

examples/pytorch/label_propagation/main.py

 import argparse
+
 import torch
+
 import dgl
-from dgl.data import CoraGraphDataset, CiteseerGraphDataset, PubmedGraphDataset
+from dgl.data import CiteseerGraphDataset, CoraGraphDataset, PubmedGraphDataset
 from dgl.nn import LabelPropagation
 
 
 def main():
     # check cuda
-    device = f'cuda:{args.gpu}' if torch.cuda.is_available() and args.gpu >= 0 else 'cpu'
+    device = (
+        f"cuda:{args.gpu}"
+        if torch.cuda.is_available() and args.gpu >= 0
+        else "cpu"
+    )
 
     # load data
-    if args.dataset == 'Cora':
+    if args.dataset == "Cora":
         dataset = CoraGraphDataset()
-    elif args.dataset == 'Citeseer':
+    elif args.dataset == "Citeseer":
         dataset = CiteseerGraphDataset()
-    elif args.dataset == 'Pubmed':
+    elif args.dataset == "Pubmed":
         dataset = PubmedGraphDataset()
     else:
-        raise ValueError('Dataset {} is invalid.'.format(args.dataset))
+        raise ValueError("Dataset {} is invalid.".format(args.dataset))
 
     g = dataset[0]
     g = dgl.add_self_loop(g)
 
-    labels = g.ndata.pop('label').to(device).long()
+    labels = g.ndata.pop("label").to(device).long()
 
     # load masks for train / test, valid is not used.
-    train_mask = g.ndata.pop('train_mask')
-    test_mask = g.ndata.pop('test_mask')
+    train_mask = g.ndata.pop("train_mask")
+    test_mask = g.ndata.pop("test_mask")
 
     train_idx = torch.nonzero(train_mask, as_tuple=False).squeeze().to(device)
     test_idx = torch.nonzero(test_mask, as_tuple=False).squeeze().to(device)
@@ -37,19 +43,21 @@ def main():
     lp = LabelPropagation(args.num_layers, args.alpha)
     logits = lp(g, labels, mask=train_idx)
 
-    test_acc = torch.sum(logits[test_idx].argmax(dim=1) == labels[test_idx]).item() / len(test_idx)
+    test_acc = torch.sum(
+        logits[test_idx].argmax(dim=1) == labels[test_idx]
+    ).item() / len(test_idx)
     print("Test Acc {:.4f}".format(test_acc))
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     """
     Label Propagation Hyperparameters
     """
-    parser = argparse.ArgumentParser(description='LP')
-    parser.add_argument('--gpu', type=int, default=0)
-    parser.add_argument('--dataset', type=str, default='Cora')
-    parser.add_argument('--num-layers', type=int, default=10)
-    parser.add_argument('--alpha', type=float, default=0.5)
+    parser = argparse.ArgumentParser(description="LP")
+    parser.add_argument("--gpu", type=int, default=0)
+    parser.add_argument("--dataset", type=str, default="Cora")
+    parser.add_argument("--num-layers", type=int, default=10)
+    parser.add_argument("--alpha", type=float, default=0.5)
 
     args = parser.parse_args()
     print(args)

examples/pytorch/lda/example_20newsgroups.py

@@ -17,49 +17,49 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from time import time
-import matplotlib.pyplot as plt
 import warnings
+from time import time
 
+import matplotlib.pyplot as plt
 import numpy as np
 import scipy.sparse as ss
 import torch
-import dgl
-from dgl import function as fn
-
-from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
-from sklearn.decomposition import NMF, LatentDirichletAllocation
+from lda_model import LatentDirichletAllocation as LDAModel
 from sklearn.datasets import fetch_20newsgroups
+from sklearn.decomposition import NMF, LatentDirichletAllocation
+from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
 
-from lda_model import LatentDirichletAllocation as LDAModel
+import dgl
+from dgl import function as fn
 
 n_samples = 2000
 n_features = 1000
 n_components = 10
 n_top_words = 20
-device = 'cuda'
+device = "cuda"
+
 
 def plot_top_words(model, feature_names, n_top_words, title):
     fig, axes = plt.subplots(2, 5, figsize=(30, 15), sharex=True)
     axes = axes.flatten()
     for topic_idx, topic in enumerate(model.components_):
-        top_features_ind = topic.argsort()[:-n_top_words - 1:-1]
+        top_features_ind = topic.argsort()[: -n_top_words - 1 : -1]
         top_features = [feature_names[i] for i in top_features_ind]
         weights = topic[top_features_ind]
 
         ax = axes[topic_idx]
         ax.barh(top_features, weights, height=0.7)
-        ax.set_title(f'Topic {topic_idx +1}',
-                     fontdict={'fontsize': 30})
+        ax.set_title(f"Topic {topic_idx +1}", fontdict={"fontsize": 30})
         ax.invert_yaxis()
-        ax.tick_params(axis='both', which='major', labelsize=20)
-        for i in 'top right left'.split():
+        ax.tick_params(axis="both", which="major", labelsize=20)
+        for i in "top right left".split():
             ax.spines[i].set_visible(False)
         fig.suptitle(title, fontsize=40)
 
     plt.subplots_adjust(top=0.90, bottom=0.05, wspace=0.90, hspace=0.3)
     plt.show()
 
+
 # Load the 20 newsgroups dataset and vectorize it. We use a few heuristics
 # to filter out useless terms early on: the posts are stripped of headers,
 # footers and quoted replies, and common English words, words occurring in
@@ -67,43 +67,50 @@ def plot_top_words(model, feature_names, n_top_words, title):
 
 print("Loading dataset...")
 t0 = time()
-data, _ = fetch_20newsgroups(shuffle=True, random_state=1,
-                             remove=('headers', 'footers', 'quotes'),
-                             return_X_y=True)
+data, _ = fetch_20newsgroups(
+    shuffle=True,
+    random_state=1,
+    remove=("headers", "footers", "quotes"),
+    return_X_y=True,
+)
 data_samples = data[:n_samples]
-data_test = data[n_samples:2*n_samples]
+data_test = data[n_samples : 2 * n_samples]
 print("done in %0.3fs." % (time() - t0))
 
 # Use tf (raw term count) features for LDA.
 print("Extracting tf features for LDA...")
-tf_vectorizer = CountVectorizer(max_df=0.95, min_df=2,
-                                max_features=n_features,
-                                stop_words='english')
+tf_vectorizer = CountVectorizer(
+    max_df=0.95, min_df=2, max_features=n_features, stop_words="english"
+)
 t0 = time()
 tf_vectorizer.fit(data)
 tf = tf_vectorizer.transform(data_samples)
 tt = tf_vectorizer.transform(data_test)
 
 tf_feature_names = tf_vectorizer.get_feature_names()
-tf_uv = [(u,v)
-         for u,v,e in zip(tf.tocoo().row, tf.tocoo().col, tf.tocoo().data)
-         for _ in range(e)]
-tt_uv = [(u,v)
-         for u,v,e in zip(tt.tocoo().row, tt.tocoo().col, tt.tocoo().data)
-         for _ in range(e)]
+tf_uv = [
+    (u, v)
+    for u, v, e in zip(tf.tocoo().row, tf.tocoo().col, tf.tocoo().data)
+    for _ in range(e)
+]
+tt_uv = [
+    (u, v)
+    for u, v, e in zip(tt.tocoo().row, tt.tocoo().col, tt.tocoo().data)
+    for _ in range(e)
+]
 print("done in %0.3fs." % (time() - t0))
 print()
 
 print("Preparing dgl graphs...")
 t0 = time()
-G = dgl.heterograph({('doc','topic','word'): tf_uv}, device=device)
-Gt = dgl.heterograph({('doc','topic','word'): tt_uv}, device=device)
+G = dgl.heterograph({("doc", "topic", "word"): tf_uv}, device=device)
+Gt = dgl.heterograph({("doc", "topic", "word"): tt_uv}, device=device)
 print("done in %0.3fs." % (time() - t0))
 print()
 
 print("Training dgl-lda model...")
 t0 = time()
-model = LDAModel(G.num_nodes('word'), n_components)
+model = LDAModel(G.num_nodes("word"), n_components)
 model.fit(G)
 print("done in %0.3fs." % (time() - t0))
 print()
@@ -113,20 +120,27 @@ print(f"dgl-lda testing perplexity {model.perplexity(Gt):.3f}")
 
 word_nphi = np.vstack([nphi.tolist() for nphi in model.word_data.nphi])
 plot_top_words(
-    type('dummy', (object,), {'components_': word_nphi}),
-    tf_feature_names, n_top_words, 'Topics in LDA model')
+    type("dummy", (object,), {"components_": word_nphi}),
+    tf_feature_names,
+    n_top_words,
+    "Topics in LDA model",
+)
 
 print("Training scikit-learn model...")
 
-print('\n' * 2, "Fitting LDA models with tf features, "
-      "n_samples=%d and n_features=%d..."
-      % (n_samples, n_features))
-lda = LatentDirichletAllocation(n_components=n_components, max_iter=5,
-                                learning_method='online',
-                                learning_offset=50.,
+print(
+    "\n" * 2,
+    "Fitting LDA models with tf features, "
+    "n_samples=%d and n_features=%d..." % (n_samples, n_features),
+)
+lda = LatentDirichletAllocation(
+    n_components=n_components,
+    max_iter=5,
+    learning_method="online",
+    learning_offset=50.0,
     random_state=0,
     verbose=1,
-                               )
+)
 t0 = time()
 lda.fit(tf)
 print("done in %0.3fs." % (time() - t0))

examples/pytorch/lda/lda_model.py

@@ -17,8 +17,17 @@
 # limitations under the License.
 
 
-import os, functools, warnings, torch, collections, dgl, io
-import numpy as np, scipy as sp
+import collections
+import functools
+import io
+import os
+import warnings
+
+import numpy as np
+import scipy as sp
+import torch
+
+import dgl
 
 try:
     from functools import cached_property
@@ -37,17 +46,21 @@ class EdgeData:
 
     @property
     def loglike(self):
-        return (self.src_data['Elog'] + self.dst_data['Elog']).logsumexp(1)
+        return (self.src_data["Elog"] + self.dst_data["Elog"]).logsumexp(1)
 
     @property
     def phi(self):
         return (
-            self.src_data['Elog'] + self.dst_data['Elog'] - self.loglike.unsqueeze(1)
+            self.src_data["Elog"]
+            + self.dst_data["Elog"]
+            - self.loglike.unsqueeze(1)
         ).exp()
 
     @property
     def expectation(self):
-        return (self.src_data['expectation'] * self.dst_data['expectation']).sum(1)
+        return (
+            self.src_data["expectation"] * self.dst_data["expectation"]
+        ).sum(1)
 
 
 class _Dirichlet:
@@ -55,10 +68,13 @@ class _Dirichlet:
         self.prior = prior
         self.nphi = nphi
         self.device = nphi.device
-        self._sum_by_parts = lambda map_fn: functools.reduce(torch.add, [
-            map_fn(slice(i, min(i+_chunksize, nphi.shape[1]))).sum(1)
+        self._sum_by_parts = lambda map_fn: functools.reduce(
+            torch.add,
+            [
+                map_fn(slice(i, min(i + _chunksize, nphi.shape[1]))).sum(1)
                 for i in list(range(0, nphi.shape[1], _chunksize))
-        ])
+            ],
+        )
 
     def _posterior(self, _ID=slice(None)):
         return self.prior + self.nphi[:, _ID]
@@ -68,8 +84,9 @@ class _Dirichlet:
         return self.nphi.sum(1) + self.prior * self.nphi.shape[1]
 
     def _Elog(self, _ID=slice(None)):
-        return torch.digamma(self._posterior(_ID)) - \
-               torch.digamma(self.posterior_sum.unsqueeze(1))
+        return torch.digamma(self._posterior(_ID)) - torch.digamma(
+            self.posterior_sum.unsqueeze(1)
+        )
 
     @cached_property
     def loglike(self):
@@ -105,9 +122,15 @@ class _Dirichlet:
 
     @cached_property
     def Bayesian_gap(self):
-        return 1. - self._sum_by_parts(lambda s: self._Elog(s).exp())
-
-    _cached_properties = ["posterior_sum", "loglike", "n", "cdf", "Bayesian_gap"]
+        return 1.0 - self._sum_by_parts(lambda s: self._Elog(s).exp())
+
+    _cached_properties = [
+        "posterior_sum",
+        "loglike",
+        "n",
+        "cdf",
+        "Bayesian_gap",
+    ]
 
     def clear_cache(self):
         for name in self._cached_properties:
@@ -117,27 +140,29 @@ class _Dirichlet:
                 pass
 
     def update(self, new, _ID=slice(None), rho=1):
-        """ inplace: old * (1-rho) + new * rho """
+        """inplace: old * (1-rho) + new * rho"""
         self.clear_cache()
         mean_change = (self.nphi[:, _ID] - new).abs().mean().tolist()
 
-        self.nphi *= (1 - rho)
+        self.nphi *= 1 - rho
         self.nphi[:, _ID] += new * rho
         return mean_change
 
 
 class DocData(_Dirichlet):
-    """ nphi (n_docs by n_topics) """
+    """nphi (n_docs by n_topics)"""
+
     def prepare_graph(self, G, key="Elog"):
-        G.nodes['doc'].data[key] = getattr(self, '_'+key)().to(G.device)
+        G.nodes["doc"].data[key] = getattr(self, "_" + key)().to(G.device)
 
     def update_from(self, G, mult):
-        new = G.nodes['doc'].data['nphi'] * mult
+        new = G.nodes["doc"].data["nphi"] * mult
         return self.update(new.to(self.device))
 
 
 class _Distributed(collections.UserList):
-    """ split on dim=0 and store on multiple devices  """
+    """split on dim=0 and store on multiple devices"""
+
     def __init__(self, prior, nphi):
         self.prior = prior
         self.nphi = nphi
@@ -146,36 +171,38 @@ class _Distributed(collections.UserList):
     def split_device(self, other, dim=0):
         split_sections = [x.shape[0] for x in self.nphi]
         out = torch.split(other, split_sections, dim)
-        return [y.to(x.device) for x,y in zip(self.nphi, out)]
+        return [y.to(x.device) for x, y in zip(self.nphi, out)]
 
 
 class WordData(_Distributed):
-    """ distributed nphi (n_topics by n_words), transpose to/from graph nodes data """
+    """distributed nphi (n_topics by n_words), transpose to/from graph nodes data"""
+
     def prepare_graph(self, G, key="Elog"):
-        if '_ID' in G.nodes['word'].data:
-            _ID = G.nodes['word'].data['_ID']
+        if "_ID" in G.nodes["word"].data:
+            _ID = G.nodes["word"].data["_ID"]
         else:
             _ID = slice(None)
 
-        out = [getattr(part, '_'+key)(_ID).to(G.device) for part in self]
-        G.nodes['word'].data[key] = torch.cat(out).T
-
+        out = [getattr(part, "_" + key)(_ID).to(G.device) for part in self]
+        G.nodes["word"].data[key] = torch.cat(out).T
 
     def update_from(self, G, mult, rho):
-        nphi = G.nodes['word'].data['nphi'].T * mult
+        nphi = G.nodes["word"].data["nphi"].T * mult
 
-        if '_ID' in G.nodes['word'].data:
-            _ID = G.nodes['word'].data['_ID']
+        if "_ID" in G.nodes["word"].data:
+            _ID = G.nodes["word"].data["_ID"]
         else:
             _ID = slice(None)
 
-        mean_change = [x.update(y, _ID, rho)
-            for x, y in zip(self, self.split_device(nphi))]
+        mean_change = [
+            x.update(y, _ID, rho) for x, y in zip(self, self.split_device(nphi))
+        ]
         return np.mean(mean_change)
 
 
-class Gamma(collections.namedtuple('Gamma', "concentration, rate")):
-    """ articulate the difference between torch gamma and numpy gamma """
+class Gamma(collections.namedtuple("Gamma", "concentration, rate")):
+    """articulate the difference between torch gamma and numpy gamma"""
+
     @property
     def shape(self):
         return self.concentration
@@ -218,20 +245,23 @@ class LatentDirichletAllocation:
     (NIPS 2010).
     [2] Reactive LDA Library blogpost by Yingjie Miao for a similar Gibbs model
     """
+
     def __init__(
-        self, n_words, n_components,
+        self,
+        n_words,
+        n_components,
         prior=None,
         rho=1,
-        mult={'doc': 1, 'word': 1},
-        init={'doc': (100., 100.), 'word': (100., 100.)},
-        device_list=['cpu'],
+        mult={"doc": 1, "word": 1},
+        init={"doc": (100.0, 100.0), "word": (100.0, 100.0)},
+        device_list=["cpu"],
         verbose=True,
     ):
         self.n_words = n_words
         self.n_components = n_components
 
         if prior is None:
-            prior = {'doc': 1./n_components, 'word': 1./n_components}
+            prior = {"doc": 1.0 / n_components, "word": 1.0 / n_components}
         self.prior = prior
 
         self.rho = rho
@@ -244,46 +274,46 @@ class LatentDirichletAllocation:
 
         self._init_word_data()
 
-
     def _init_word_data(self):
         split_sections = np.diff(
-            np.linspace(0, self.n_components, len(self.device_list)+1).astype(int)
+            np.linspace(0, self.n_components, len(self.device_list) + 1).astype(
+                int
+            )
         )
         word_nphi = [
-            Gamma(*self.init['word']).sample((s, self.n_words), device)
+            Gamma(*self.init["word"]).sample((s, self.n_words), device)
             for s, device in zip(split_sections, self.device_list)
         ]
-        self.word_data = WordData(self.prior['word'], word_nphi)
-
+        self.word_data = WordData(self.prior["word"], word_nphi)
 
     def _init_doc_data(self, n_docs, device):
-        doc_nphi = Gamma(*self.init['doc']).sample(
-            (n_docs, self.n_components), device)
-        return DocData(self.prior['doc'], doc_nphi)
-
+        doc_nphi = Gamma(*self.init["doc"]).sample(
+            (n_docs, self.n_components), device
+        )
+        return DocData(self.prior["doc"], doc_nphi)
 
     def save(self, f):
         for w in self.word_data:
             w.clear_cache()
-        torch.save({
-            'prior': self.prior,
-            'rho': self.rho,
-            'mult': self.mult,
-            'init': self.init,
-            'word_data': [part.nphi for part in self.word_data],
-        }, f)
-
+        torch.save(
+            {
+                "prior": self.prior,
+                "rho": self.rho,
+                "mult": self.mult,
+                "init": self.init,
+                "word_data": [part.nphi for part in self.word_data],
+            },
+            f,
+        )
 
     def _prepare_graph(self, G, doc_data, key="Elog"):
         doc_data.prepare_graph(G, key)
         self.word_data.prepare_graph(G, key)
 
-
     def _e_step(self, G, doc_data=None, mean_change_tol=1e-3, max_iters=100):
-        """_e_step implements doc data sampling until convergence or max_iters
-        """
+        """_e_step implements doc data sampling until convergence or max_iters"""
         if doc_data is None:
-            doc_data = self._init_doc_data(G.num_nodes('doc'), G.device)
+            doc_data = self._init_doc_data(G.num_nodes("doc"), G.device)
 
         G_rev = G.reverse()  # word -> doc
         self.word_data.prepare_graph(G_rev)
@@ -291,65 +321,76 @@ class LatentDirichletAllocation:
         for i in range(max_iters):
             doc_data.prepare_graph(G_rev)
             G_rev.update_all(
-                lambda edges: {'phi': EdgeData(edges.src, edges.dst).phi},
-                dgl.function.sum('phi', 'nphi')
+                lambda edges: {"phi": EdgeData(edges.src, edges.dst).phi},
+                dgl.function.sum("phi", "nphi"),
             )
-            mean_change = doc_data.update_from(G_rev, self.mult['doc'])
+            mean_change = doc_data.update_from(G_rev, self.mult["doc"])
             if mean_change < mean_change_tol:
                 break
 
         if self.verbose:
-            print(f"e-step num_iters={i+1} with mean_change={mean_change:.4f}, "
-                  f"perplexity={self.perplexity(G, doc_data):.4f}")
+            print(
+                f"e-step num_iters={i+1} with mean_change={mean_change:.4f}, "
+                f"perplexity={self.perplexity(G, doc_data):.4f}"
+            )
 
         return doc_data
 
-
     transform = _e_step
 
-
     def predict(self, doc_data):
         pred_scores = [
             # d_exp @ w._expectation()
-            (lambda x: x @ w.nphi + x.sum(1, keepdims=True) * w.prior)
-            (d_exp / w.posterior_sum.unsqueeze(0))
+            (lambda x: x @ w.nphi + x.sum(1, keepdims=True) * w.prior)(
+                d_exp / w.posterior_sum.unsqueeze(0)
+            )
             for (d_exp, w) in zip(
                 self.word_data.split_device(doc_data._expectation(), dim=1),
-                self.word_data)
+                self.word_data,
+            )
         ]
         x = torch.zeros_like(pred_scores[0], device=doc_data.device)
         for p in pred_scores:
             x += p.to(x.device)
         return x
 
-
     def sample(self, doc_data, num_samples):
-        """ draw independent words and return the marginal probabilities,
+        """draw independent words and return the marginal probabilities,
         i.e., the expectations in Dirichlet distributions.
         """
+
         def fn(cdf):
             u = torch.rand(cdf.shape[0], num_samples, device=cdf.device)
             return torch.searchsorted(cdf, u).to(doc_data.device)
 
         topic_ids = fn(doc_data.cdf)
         word_ids = torch.cat([fn(part.cdf) for part in self.word_data])
-        ids = torch.gather(word_ids, 0, topic_ids) # pick components by topic_ids
+        ids = torch.gather(
+            word_ids, 0, topic_ids
+        )  # pick components by topic_ids
 
         # compute expectation scores on sampled ids
-        src_ids = torch.arange(
-            ids.shape[0], dtype=ids.dtype, device=ids.device
-            ).reshape((-1, 1)).expand(ids.shape)
-        unique_ids, inverse_ids = torch.unique(ids, sorted=False, return_inverse=True)
+        src_ids = (
+            torch.arange(ids.shape[0], dtype=ids.dtype, device=ids.device)
+            .reshape((-1, 1))
+            .expand(ids.shape)
+        )
+        unique_ids, inverse_ids = torch.unique(
+            ids, sorted=False, return_inverse=True
+        )
 
-        G = dgl.heterograph({('doc','','word'): (src_ids.ravel(), inverse_ids.ravel())})
-        G.nodes['word'].data['_ID'] = unique_ids
+        G = dgl.heterograph(
+            {("doc", "", "word"): (src_ids.ravel(), inverse_ids.ravel())}
+        )
+        G.nodes["word"].data["_ID"] = unique_ids
         self._prepare_graph(G, doc_data, "expectation")
-        G.apply_edges(lambda e: {'expectation': EdgeData(e.src, e.dst).expectation})
-        expectation = G.edata.pop('expectation').reshape(ids.shape)
+        G.apply_edges(
+            lambda e: {"expectation": EdgeData(e.src, e.dst).expectation}
+        )
+        expectation = G.edata.pop("expectation").reshape(ids.shape)
 
         return ids, expectation
 
-
     def _m_step(self, G, doc_data):
         """_m_step implements word data sampling and stores word_z stats.
         mean_change is in the sense of full graph with rho=1.
@@ -357,26 +398,25 @@ class LatentDirichletAllocation:
         G = G.clone()
         self._prepare_graph(G, doc_data)
         G.update_all(
-            lambda edges: {'phi': EdgeData(edges.src, edges.dst).phi},
-            dgl.function.sum('phi', 'nphi')
+            lambda edges: {"phi": EdgeData(edges.src, edges.dst).phi},
+            dgl.function.sum("phi", "nphi"),
         )
         self._last_mean_change = self.word_data.update_from(
-            G, self.mult['word'], self.rho)
+            G, self.mult["word"], self.rho
+        )
 
         if self.verbose:
             print(f"m-step mean_change={self._last_mean_change:.4f}, ", end="")
-            Bayesian_gap = np.mean([
-                part.Bayesian_gap.mean().tolist() for part in self.word_data
-            ])
+            Bayesian_gap = np.mean(
+                [part.Bayesian_gap.mean().tolist() for part in self.word_data]
+            )
             print(f"Bayesian_gap={Bayesian_gap:.4f}")
 
-
     def partial_fit(self, G):
         doc_data = self._e_step(G)
         self._m_step(G, doc_data)
         return self
 
-
     def fit(self, G, mean_change_tol=1e-3, max_epochs=10):
         for i in range(max_epochs):
             if self.verbose:
@@ -387,7 +427,6 @@ class LatentDirichletAllocation:
                 break
         return self
 
-
     def perplexity(self, G, doc_data=None):
         """ppl = exp{-sum[log(p(w1,...,wn|d))] / n}
         Follows Eq (15) in Hoffman et al., 2010.
@@ -398,45 +437,50 @@ class LatentDirichletAllocation:
         # compute E[log p(docs | theta, beta)]
         G = G.clone()
         self._prepare_graph(G, doc_data)
-        G.apply_edges(lambda edges: {'loglike': EdgeData(edges.src, edges.dst).loglike})
-        edge_elbo = (G.edata['loglike'].sum() / G.num_edges()).tolist()
+        G.apply_edges(
+            lambda edges: {"loglike": EdgeData(edges.src, edges.dst).loglike}
+        )
+        edge_elbo = (G.edata["loglike"].sum() / G.num_edges()).tolist()
         if self.verbose:
-            print(f'neg_elbo phi: {-edge_elbo:.3f}', end=' ')
+            print(f"neg_elbo phi: {-edge_elbo:.3f}", end=" ")
 
         # compute E[log p(theta | alpha) - log q(theta | gamma)]
         doc_elbo = (doc_data.loglike.sum() / doc_data.n.sum()).tolist()
         if self.verbose:
-            print(f'theta: {-doc_elbo:.3f}', end=' ')
+            print(f"theta: {-doc_elbo:.3f}", end=" ")
 
         # compute E[log p(beta | eta) - log q(beta | lambda)]
         # The denominator n for extrapolation perplexity is undefined.
         # We use the train set, whereas sklearn uses the test set.
-        word_elbo = (
-            sum([part.loglike.sum().tolist() for part in self.word_data])
-            / sum([part.n.sum().tolist() for part in self.word_data])
-            )
+        word_elbo = sum(
+            [part.loglike.sum().tolist() for part in self.word_data]
+        ) / sum([part.n.sum().tolist() for part in self.word_data])
         if self.verbose:
-            print(f'beta: {-word_elbo:.3f}')
+            print(f"beta: {-word_elbo:.3f}")
 
         ppl = np.exp(-edge_elbo - doc_elbo - word_elbo)
-        if G.num_edges()>0 and np.isnan(ppl):
+        if G.num_edges() > 0 and np.isnan(ppl):
             warnings.warn("numerical issue in perplexity")
         return ppl
 
 
 def doc_subgraph(G, doc_ids):
     sampler = dgl.dataloading.MultiLayerFullNeighborSampler(1)
-    _, _, (block,) = sampler.sample(G.reverse(), {'doc': torch.as_tensor(doc_ids)})
+    _, _, (block,) = sampler.sample(
+        G.reverse(), {"doc": torch.as_tensor(doc_ids)}
+    )
     B = dgl.DGLHeteroGraph(
-        block._graph, ['_', 'word', 'doc', '_'], block.etypes
+        block._graph, ["_", "word", "doc", "_"], block.etypes
     ).reverse()
-    B.nodes['word'].data['_ID'] = block.nodes['word'].data['_ID']
+    B.nodes["word"].data["_ID"] = block.nodes["word"].data["_ID"]
     return B
 
 
-if __name__ == '__main__':
-    print('Testing LatentDirichletAllocation ...')
-    G = dgl.heterograph({('doc', '', 'word'): [(0, 0), (1, 3)]}, {'doc': 2, 'word': 5})
+if __name__ == "__main__":
+    print("Testing LatentDirichletAllocation ...")
+    G = dgl.heterograph(
+        {("doc", "", "word"): [(0, 0), (1, 3)]}, {"doc": 2, "word": 5}
+    )
     model = LatentDirichletAllocation(n_words=5, n_components=10, verbose=False)
     model.fit(G)
     model.transform(G)
@@ -454,4 +498,4 @@ if __name__ == '__main__':
         f.seek(0)
         print(torch.load(f))
 
-    print('Testing LatentDirichletAllocation passed!')
+    print("Testing LatentDirichletAllocation passed!")

examples/pytorch/line_graph/train.py

@@ -6,11 +6,12 @@ Author's implementation: https://github.com/joanbruna/GNN_community
 """
 
 from __future__ import division
-import time
 
 import argparse
+import time
 from itertools import permutations
 
+import gnn
 import numpy as np
 import torch as th
 import torch.nn.functional as F
@@ -18,37 +19,51 @@ import torch.optim as optim
 from torch.utils.data import DataLoader
 
 from dgl.data import SBMMixtureDataset
-import gnn
 
 parser = argparse.ArgumentParser()
-parser.add_argument('--batch-size', type=int, help='Batch size', default=1)
-parser.add_argument('--gpu', type=int, help='GPU index', default=-1)
-parser.add_argument('--lr', type=float, help='Learning rate', default=0.001)
-parser.add_argument('--n-communities', type=int, help='Number of communities', default=2)
-parser.add_argument('--n-epochs', type=int, help='Number of epochs', default=100)
-parser.add_argument('--n-features', type=int, help='Number of features', default=16)
-parser.add_argument('--n-graphs', type=int, help='Number of graphs', default=10)
-parser.add_argument('--n-layers', type=int, help='Number of layers', default=30)
-parser.add_argument('--n-nodes', type=int, help='Number of nodes', default=10000)
-parser.add_argument('--optim', type=str, help='Optimizer', default='Adam')
-parser.add_argument('--radius', type=int, help='Radius', default=3)
-parser.add_argument('--verbose', action='store_true')
+parser.add_argument("--batch-size", type=int, help="Batch size", default=1)
+parser.add_argument("--gpu", type=int, help="GPU index", default=-1)
+parser.add_argument("--lr", type=float, help="Learning rate", default=0.001)
+parser.add_argument(
+    "--n-communities", type=int, help="Number of communities", default=2
+)
+parser.add_argument(
+    "--n-epochs", type=int, help="Number of epochs", default=100
+)
+parser.add_argument(
+    "--n-features", type=int, help="Number of features", default=16
+)
+parser.add_argument("--n-graphs", type=int, help="Number of graphs", default=10)
+parser.add_argument("--n-layers", type=int, help="Number of layers", default=30)
+parser.add_argument(
+    "--n-nodes", type=int, help="Number of nodes", default=10000
+)
+parser.add_argument("--optim", type=str, help="Optimizer", default="Adam")
+parser.add_argument("--radius", type=int, help="Radius", default=3)
+parser.add_argument("--verbose", action="store_true")
 args = parser.parse_args()
 
-dev = th.device('cpu') if args.gpu < 0 else th.device('cuda:%d' % args.gpu)
+dev = th.device("cpu") if args.gpu < 0 else th.device("cuda:%d" % args.gpu)
 K = args.n_communities
 
 training_dataset = SBMMixtureDataset(args.n_graphs, args.n_nodes, K)
-training_loader = DataLoader(training_dataset, args.batch_size,
-                             collate_fn=training_dataset.collate_fn, drop_last=True)
+training_loader = DataLoader(
+    training_dataset,
+    args.batch_size,
+    collate_fn=training_dataset.collate_fn,
+    drop_last=True,
+)
 
 ones = th.ones(args.n_nodes // K)
-y_list = [th.cat([x * ones for x in p]).long().to(dev) for p in permutations(range(K))]
+y_list = [
+    th.cat([x * ones for x in p]).long().to(dev) for p in permutations(range(K))
+]
 
 feats = [1] + [args.n_features] * args.n_layers + [K]
 model = gnn.GNN(feats, args.radius, K).to(dev)
 optimizer = getattr(optim, args.optim)(model.parameters(), lr=args.lr)
 
+
 def compute_overlap(z_list):
     ybar_list = [th.max(z, 1)[1] for z in z_list]
     overlap_list = []
@@ -58,15 +73,20 @@ def compute_overlap(z_list):
         overlap_list.append(overlap)
     return sum(overlap_list) / len(overlap_list)
 
+
 def from_np(f, *args):
     def wrap(*args):
-        new = [th.from_numpy(x) if isinstance(x, np.ndarray) else x for x in args]
+        new = [
+            th.from_numpy(x) if isinstance(x, np.ndarray) else x for x in args
+        ]
         return f(*new)
+
     return wrap
 
+
 @from_np
 def step(i, j, g, lg, deg_g, deg_lg, pm_pd):
-    """ One step of training. """
+    """One step of training."""
     g = g.to(dev)
     lg = lg.to(dev)
     deg_g = deg_g.to(dev).unsqueeze(1)
@@ -77,7 +97,10 @@ def step(i, j, g, lg, deg_g, deg_lg, pm_pd):
     t_forward = time.time() - t0
 
     z_list = th.chunk(z, args.batch_size, 0)
-    loss = sum(min(F.cross_entropy(z, y) for y in y_list) for z in z_list) / args.batch_size
+    loss = (
+        sum(min(F.cross_entropy(z, y) for y in y_list) for z in z_list)
+        / args.batch_size
+    )
     overlap = compute_overlap(z_list)
 
     optimizer.zero_grad()
@@ -88,6 +111,7 @@ def step(i, j, g, lg, deg_g, deg_lg, pm_pd):
 
     return loss, overlap, t_forward, t_backward
 
+
 @from_np
 def inference(g, lg, deg_g, deg_lg, pm_pd):
     g = g.to(dev)
@@ -99,9 +123,11 @@ def inference(g, lg, deg_g, deg_lg, pm_pd):
     z = model(g, lg, deg_g, deg_lg, pm_pd)
 
     return z
+
+
 def test():
-    p_list =[6, 5.5, 5, 4.5, 1.5, 1, 0.5, 0]
-    q_list =[0, 0.5, 1, 1.5, 4.5, 5, 5.5, 6]
+    p_list = [6, 5.5, 5, 4.5, 1.5, 1, 0.5, 0]
+    q_list = [0, 0.5, 1, 1.5, 4.5, 5, 5.5, 6]
     N = 1
     overlap_list = []
     for p, q in zip(p_list, q_list):
@@ -112,31 +138,38 @@ def test():
         overlap_list.append(compute_overlap(th.chunk(z, N, 0)))
     return overlap_list
 
+
 n_iterations = args.n_graphs // args.batch_size
 for i in range(args.n_epochs):
     total_loss, total_overlap, s_forward, s_backward = 0, 0, 0, 0
     for j, [g, lg, deg_g, deg_lg, pm_pd] in enumerate(training_loader):
-        loss, overlap, t_forward, t_backward = step(i, j, g, lg, deg_g, deg_lg, pm_pd)
+        loss, overlap, t_forward, t_backward = step(
+            i, j, g, lg, deg_g, deg_lg, pm_pd
+        )
 
         total_loss += loss
         total_overlap += overlap
         s_forward += t_forward
         s_backward += t_backward
 
-        epoch = '0' * (len(str(args.n_epochs)) - len(str(i)))
-        iteration = '0' * (len(str(n_iterations)) - len(str(j)))
+        epoch = "0" * (len(str(args.n_epochs)) - len(str(i)))
+        iteration = "0" * (len(str(n_iterations)) - len(str(j)))
         if args.verbose:
-            print('[epoch %s%d iteration %s%d]loss %.3f | overlap %.3f'
-                  % (epoch, i, iteration, j, loss, overlap))
+            print(
+                "[epoch %s%d iteration %s%d]loss %.3f | overlap %.3f"
+                % (epoch, i, iteration, j, loss, overlap)
+            )
 
-    epoch = '0' * (len(str(args.n_epochs)) - len(str(i)))
+    epoch = "0" * (len(str(args.n_epochs)) - len(str(i)))
     loss = total_loss / (j + 1)
     overlap = total_overlap / (j + 1)
     t_forward = s_forward / (j + 1)
     t_backward = s_backward / (j + 1)
-    print('[epoch %s%d]loss %.3f | overlap %.3f | forward time %.3fs | backward time %.3fs'
-          % (epoch, i, loss, overlap, t_forward, t_backward))
+    print(
+        "[epoch %s%d]loss %.3f | overlap %.3f | forward time %.3fs | backward time %.3fs"
+        % (epoch, i, loss, overlap, t_forward, t_backward)
+    )
 
     overlap_list = test()
-    overlap_str = ' - '.join(['%.3f' % overlap for overlap in overlap_list])
-    print('[epoch %s%d]overlap: %s' % (epoch, i, overlap_str))
+    overlap_str = " - ".join(["%.3f" % overlap for overlap in overlap_list])
+    print("[epoch %s%d]overlap: %s" % (epoch, i, overlap_str))

examples/pytorch/mixhop/main.py

@@ -2,16 +2,18 @@
 
 import argparse
 import copy
+import random
+
+import numpy as np
 import torch
-import torch.optim as optim
 import torch.nn as nn
-import numpy as np
-import random
+import torch.optim as optim
+from tqdm import trange
+
 import dgl
 import dgl.function as fn
+from dgl.data import CiteseerGraphDataset, CoraGraphDataset, PubmedGraphDataset
 
-from dgl.data import CoraGraphDataset, CiteseerGraphDataset, PubmedGraphDataset
-from tqdm import trange
 
 class MixHopConv(nn.Module):
     r"""
@@ -44,13 +46,16 @@ class MixHopConv(nn.Module):
     batchnorm: bool, optional
         If True, use batch normalization. Defaults: ``False``.
     """
-    def __init__(self,
+
+    def __init__(
+        self,
         in_dim,
         out_dim,
         p=[0, 1, 2],
         dropout=0,
         activation=None,
-                 batchnorm=False):
+        batchnorm=False,
+    ):
         super(MixHopConv, self).__init__()
         self.in_dim = in_dim
         self.out_dim = out_dim
@@ -66,9 +71,9 @@ class MixHopConv(nn.Module):
             self.bn = nn.BatchNorm1d(out_dim * len(p))
 
         # define weight dict for each power j
-        self.weights = nn.ModuleDict({
-            str(j): nn.Linear(in_dim, out_dim, bias=False) for j in p
-        })
+        self.weights = nn.ModuleDict(
+            {str(j): nn.Linear(in_dim, out_dim, bias=False) for j in p}
+        )
 
     def forward(self, graph, feats):
         with graph.local_scope():
@@ -84,9 +89,9 @@ class MixHopConv(nn.Module):
                     outputs.append(output)
 
                 feats = feats * norm
-                graph.ndata['h'] = feats
-                graph.update_all(fn.copy_u('h', 'm'), fn.sum('m', 'h'))
-                feats = graph.ndata.pop('h')
+                graph.ndata["h"] = feats
+                graph.update_all(fn.copy_u("h", "m"), fn.sum("m", "h"))
+                feats = graph.ndata.pop("h")
                 feats = feats * norm
 
             final = torch.cat(outputs, dim=1)
@@ -101,8 +106,10 @@ class MixHopConv(nn.Module):
 
             return final
 
+
 class MixHop(nn.Module):
-    def __init__(self,
+    def __init__(
+        self,
         in_dim,
         hid_dim,
         out_dim,
@@ -111,7 +118,8 @@ class MixHop(nn.Module):
         input_dropout=0.0,
         layer_dropout=0.0,
         activation=None,
-                 batchnorm=False):
+        batchnorm=False,
+    ):
         super(MixHop, self).__init__()
         self.in_dim = in_dim
         self.hid_dim = hid_dim
@@ -127,23 +135,33 @@ class MixHop(nn.Module):
         self.dropout = nn.Dropout(self.input_dropout)
 
         # Input layer
-        self.layers.append(MixHopConv(self.in_dim,
+        self.layers.append(
+            MixHopConv(
+                self.in_dim,
                 self.hid_dim,
                 p=self.p,
                 dropout=self.input_dropout,
                 activation=self.activation,
-                                      batchnorm=self.batchnorm))
+                batchnorm=self.batchnorm,
+            )
+        )
 
         # Hidden layers with n - 1 MixHopConv layers
         for i in range(self.num_layers - 2):
-            self.layers.append(MixHopConv(self.hid_dim * len(args.p),
+            self.layers.append(
+                MixHopConv(
+                    self.hid_dim * len(args.p),
                     self.hid_dim,
                     p=self.p,
                     dropout=self.layer_dropout,
                     activation=self.activation,
-                                          batchnorm=self.batchnorm))
+                    batchnorm=self.batchnorm,
+                )
+            )
 
-        self.fc_layers = nn.Linear(self.hid_dim * len(args.p), self.out_dim, bias=False)
+        self.fc_layers = nn.Linear(
+            self.hid_dim * len(args.p), self.out_dim, bias=False
+        )
 
     def forward(self, graph, feats):
         feats = self.dropout(feats)
@@ -154,41 +172,42 @@ class MixHop(nn.Module):
 
         return feats
 
+
 def main(args):
     # Step 1: Prepare graph data and retrieve train/validation/test index ============================= #
     # Load from DGL dataset
-    if args.dataset == 'Cora':
+    if args.dataset == "Cora":
         dataset = CoraGraphDataset()
-    elif args.dataset == 'Citeseer':
+    elif args.dataset == "Citeseer":
         dataset = CiteseerGraphDataset()
-    elif args.dataset == 'Pubmed':
+    elif args.dataset == "Pubmed":
         dataset = PubmedGraphDataset()
     else:
-        raise ValueError('Dataset {} is invalid.'.format(args.dataset))
+        raise ValueError("Dataset {} is invalid.".format(args.dataset))
 
     graph = dataset[0]
     graph = dgl.add_self_loop(graph)
 
     # check cuda
     if args.gpu >= 0 and torch.cuda.is_available():
-        device = 'cuda:{}'.format(args.gpu)
+        device = "cuda:{}".format(args.gpu)
     else:
-        device = 'cpu'
+        device = "cpu"
 
     # retrieve the number of classes
     n_classes = dataset.num_classes
 
     # retrieve labels of ground truth
-    labels = graph.ndata.pop('label').to(device).long()
+    labels = graph.ndata.pop("label").to(device).long()
 
     # Extract node features
-    feats = graph.ndata.pop('feat').to(device)
+    feats = graph.ndata.pop("feat").to(device)
     n_features = feats.shape[-1]
 
     # retrieve masks for train/validation/test
-    train_mask = graph.ndata.pop('train_mask')
-    val_mask = graph.ndata.pop('val_mask')
-    test_mask = graph.ndata.pop('test_mask')
+    train_mask = graph.ndata.pop("train_mask")
+    val_mask = graph.ndata.pop("val_mask")
+    test_mask = graph.ndata.pop("test_mask")
 
     train_idx = torch.nonzero(train_mask, as_tuple=False).squeeze().to(device)
     val_idx = torch.nonzero(val_mask, as_tuple=False).squeeze().to(device)
@@ -197,7 +216,8 @@ def main(args):
     graph = graph.to(device)
 
     # Step 2: Create model =================================================================== #
-    model = MixHop(in_dim=n_features,
+    model = MixHop(
+        in_dim=n_features,
         hid_dim=args.hid_dim,
         out_dim=n_classes,
         num_layers=args.num_layers,
@@ -205,7 +225,8 @@ def main(args):
         input_dropout=args.input_dropout,
         layer_dropout=args.layer_dropout,
         activation=torch.tanh,
-                   batchnorm=True)
+        batchnorm=True,
+    )
 
     model = model.to(device)
     best_model = copy.deepcopy(model)
@@ -218,7 +239,7 @@ def main(args):
     # Step 4: training epoches =============================================================== #
     acc = 0
     no_improvement = 0
-    epochs = trange(args.epochs, desc='Accuracy & Loss')
+    epochs = trange(args.epochs, desc="Accuracy & Loss")
 
     for _ in epochs:
         # Training using a full graph
@@ -228,7 +249,9 @@ def main(args):
 
         # compute loss
         train_loss = loss_fn(logits[train_idx], labels[train_idx])
-        train_acc = torch.sum(logits[train_idx].argmax(dim=1) == labels[train_idx]).item() / len(train_idx)
+        train_acc = torch.sum(
+            logits[train_idx].argmax(dim=1) == labels[train_idx]
+        ).item() / len(train_idx)
 
         # backward
         opt.zero_grad()
@@ -240,16 +263,21 @@ def main(args):
 
         with torch.no_grad():
             valid_loss = loss_fn(logits[val_idx], labels[val_idx])
-            valid_acc = torch.sum(logits[val_idx].argmax(dim=1) == labels[val_idx]).item() / len(val_idx)
+            valid_acc = torch.sum(
+                logits[val_idx].argmax(dim=1) == labels[val_idx]
+            ).item() / len(val_idx)
 
         # Print out performance
-        epochs.set_description('Train Acc {:.4f} | Train Loss {:.4f} | Val Acc {:.4f} | Val loss {:.4f}'.format(
-            train_acc, train_loss.item(), valid_acc, valid_loss.item()))
+        epochs.set_description(
+            "Train Acc {:.4f} | Train Loss {:.4f} | Val Acc {:.4f} | Val loss {:.4f}".format(
+                train_acc, train_loss.item(), valid_acc, valid_loss.item()
+            )
+        )
 
         if valid_acc < acc:
             no_improvement += 1
             if no_improvement == args.early_stopping:
-                print('Early stop.')
+                print("Early stop.")
                 break
         else:
             no_improvement = 0
@@ -260,34 +288,74 @@ def main(args):
 
     best_model.eval()
     logits = best_model(graph, feats)
-    test_acc = torch.sum(logits[test_idx].argmax(dim=1) == labels[test_idx]).item() / len(test_idx)
+    test_acc = torch.sum(
+        logits[test_idx].argmax(dim=1) == labels[test_idx]
+    ).item() / len(test_idx)
 
     print("Test Acc {:.4f}".format(test_acc))
     return test_acc
 
+
 if __name__ == "__main__":
     """
     MixHop Model Hyperparameters
     """
-    parser = argparse.ArgumentParser(description='MixHop GCN')
+    parser = argparse.ArgumentParser(description="MixHop GCN")
 
     # data source params
-    parser.add_argument('--dataset', type=str, default='Cora', help='Name of dataset.')
+    parser.add_argument(
+        "--dataset", type=str, default="Cora", help="Name of dataset."
+    )
     # cuda params
-    parser.add_argument('--gpu', type=int, default=-1, help='GPU index. Default: -1, using CPU.')
+    parser.add_argument(
+        "--gpu", type=int, default=-1, help="GPU index. Default: -1, using CPU."
+    )
     # training params
-    parser.add_argument('--epochs', type=int, default=2000, help='Training epochs.')
-    parser.add_argument('--early-stopping', type=int, default=200, help='Patient epochs to wait before early stopping.')
-    parser.add_argument('--lr', type=float, default=0.5, help='Learning rate.')
-    parser.add_argument('--lamb', type=float, default=5e-4, help='L2 reg.')
-    parser.add_argument('--step-size', type=int, default=40, help='Period of learning rate decay.')
-    parser.add_argument('--gamma', type=float, default=0.01, help='Multiplicative factor of learning rate decay.')
+    parser.add_argument(
+        "--epochs", type=int, default=2000, help="Training epochs."
+    )
+    parser.add_argument(
+        "--early-stopping",
+        type=int,
+        default=200,
+        help="Patient epochs to wait before early stopping.",
+    )
+    parser.add_argument("--lr", type=float, default=0.5, help="Learning rate.")
+    parser.add_argument("--lamb", type=float, default=5e-4, help="L2 reg.")
+    parser.add_argument(
+        "--step-size",
+        type=int,
+        default=40,
+        help="Period of learning rate decay.",
+    )
+    parser.add_argument(
+        "--gamma",
+        type=float,
+        default=0.01,
+        help="Multiplicative factor of learning rate decay.",
+    )
     # model params
-    parser.add_argument("--hid-dim", type=int, default=60, help='Hidden layer dimensionalities.')
-    parser.add_argument("--num-layers", type=int, default=4, help='Number of GNN layers.')
-    parser.add_argument("--input-dropout", type=float, default=0.7, help='Dropout applied at input layer.')
-    parser.add_argument("--layer-dropout", type=float, default=0.9, help='Dropout applied at hidden layers.')
-    parser.add_argument('--p', nargs='+', type=int, help='List of powers of adjacency matrix.')
+    parser.add_argument(
+        "--hid-dim", type=int, default=60, help="Hidden layer dimensionalities."
+    )
+    parser.add_argument(
+        "--num-layers", type=int, default=4, help="Number of GNN layers."
+    )
+    parser.add_argument(
+        "--input-dropout",
+        type=float,
+        default=0.7,
+        help="Dropout applied at input layer.",
+    )
+    parser.add_argument(
+        "--layer-dropout",
+        type=float,
+        default=0.9,
+        help="Dropout applied at hidden layers.",
+    )
+    parser.add_argument(
+        "--p", nargs="+", type=int, help="List of powers of adjacency matrix."
+    )
 
     parser.set_defaults(p=[0, 1, 2])
 
@@ -304,7 +372,7 @@ if __name__ == "__main__":
 
     mean = np.around(np.mean(acc_lists_top, axis=0), decimals=3)
     std = np.around(np.std(acc_lists_top, axis=0), decimals=3)
-    print('Total acc: ', acc_lists)
-    print('Top 50 acc:', acc_lists_top)
-    print('mean', mean)
-    print('std', std)
+    print("Total acc: ", acc_lists)
+    print("Top 50 acc:", acc_lists_top)
+    print("mean", mean)
+    print("std", std)