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Unverified Commit 704bcaf6 authored by Hongzhi (Steve), Chen's avatar Hongzhi (Steve), Chen Committed by GitHub
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examples (#5323) 


Co-authored-by: default avatarUbuntu <ubuntu@ip-172-31-28-63.ap-northeast-1.compute.internal>
parent 6bc82161
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examples/pytorch/metapath2vec/test.py
View file @ 704bcaf6
......@@ -3,7 +3,7 @@ from sklearn.linear_model import LogisticRegression
from sklearn.metrics import f1_score
if __name__ == "__main__":
if __name__ == "__main__":
venue_count = 133
author_count = 246678
experiment_times = 1
......@@ -25,7 +25,7 @@ if __name__ == "__main__":
file.readline()
print("read line by line")
for line in file:
embed = line.strip().split(' ')
embed = line.strip().split(" ")
if embed[0] in check_venue:
venue_embed_dict[embed[0]] = []
for i in range(1, len(embed), 1):
......@@ -34,7 +34,7 @@ if __name__ == "__main__":
author_embed_dict[embed[0]] = []
for j in range(1, len(embed), 1):
author_embed_dict[embed[0]].append(float(embed[j]))
#get venue embeddings
# get venue embeddings
print("reading finished")
venues = list(venue_embed_dict.keys())
authors = list(author_embed_dict.keys())
......@@ -68,11 +68,11 @@ if __name__ == "__main__":
# split data into training and testing
print("splitting")
venue_split = int(venue_count * percent)
venue_training = venue_embedding[:venue_split,:]
venue_testing = venue_embedding[venue_split:,:]
venue_training = venue_embedding[:venue_split, :]
venue_testing = venue_embedding[venue_split:, :]
author_split = int(author_count * percent)
author_training = author_embedding[:author_split,:]
author_testing = author_embedding[author_split:,:]
author_training = author_embedding[:author_split, :]
author_testing = author_embedding[author_split:, :]
# split label into training and testing
venue_label = []
venue_true = []
......@@ -94,15 +94,27 @@ if __name__ == "__main__":
author_true = np.array(author_true)
file.close()
print("beging predicting")
clf_venue = LogisticRegression(random_state=0, solver="lbfgs", multi_class="multinomial").fit(venue_training,venue_label)
clf_venue = LogisticRegression(
random_state=0, solver="lbfgs", multi_class="multinomial"
).fit(venue_training, venue_label)
y_pred_venue = clf_venue.predict(venue_testing)
clf_author = LogisticRegression(random_state=0, solver="lbfgs", multi_class="multinomial").fit(author_training,author_label)
clf_author = LogisticRegression(
random_state=0, solver="lbfgs", multi_class="multinomial"
).fit(author_training, author_label)
y_pred_author = clf_author.predict(author_testing)
macro_average_venue += f1_score(venue_true, y_pred_venue, average="macro")
micro_average_venue += f1_score(venue_true, y_pred_venue, average="micro")
macro_average_author += f1_score(author_true, y_pred_author, average="macro")
micro_average_author += f1_score(author_true, y_pred_author, average="micro")
print(macro_average_venue/float(experiment_times))
print(micro_average_venue/float(experiment_times))
macro_average_venue += f1_score(
venue_true, y_pred_venue, average="macro"
)
micro_average_venue += f1_score(
venue_true, y_pred_venue, average="micro"
)
macro_average_author += f1_score(
author_true, y_pred_author, average="macro"
)
micro_average_author += f1_score(
author_true, y_pred_author, average="micro"
)
print(macro_average_venue / float(experiment_times))
print(micro_average_venue / float(experiment_times))
print(macro_average_author / float(experiment_times))
print(micro_average_author / float(experiment_times))
examples/pytorch/mixhop/main.py
View file @ 704bcaf6
......@@ -4,15 +4,15 @@ import argparse
import copy
import random
import dgl
import dgl.function as fn
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from tqdm import trange
import dgl
import dgl.function as fn
from dgl.data import CiteseerGraphDataset, CoraGraphDataset, PubmedGraphDataset
from tqdm import trange
class MixHopConv(nn.Module):
......@@ -83,7 +83,6 @@ class MixHopConv(nn.Module):
max_j = max(self.p) + 1
outputs = []
for j in range(max_j):
if j in self.p:
output = self.weights[str(j)](feats)
outputs.append(output)
......
examples/pytorch/model_zoo/geometric/coarsening.py
View file @ 704bcaf6
......@@ -104,7 +104,6 @@ def HEM(W, levels, rid=None):
print("Heavy Edge Matching coarsening with Xavier version")
for _ in range(levels):
# CHOOSE THE WEIGHTS FOR THE PAIRING
# weights = ones(N,1) # metis weights
weights = degree # graclus weights
......@@ -186,7 +185,6 @@ def HEM_one_level(rr, cc, vv, rid, weights):
if marked[nid]:
tval = 0.0
else:
# First approach
if 2 == 1:
tval = vv[rs + jj] * (
......@@ -230,7 +228,6 @@ def compute_perm(parents):
indices.append(list(range(M_last)))
for parent in parents[::-1]:
# Fake nodes go after real ones.
pool_singeltons = len(parent)
......
examples/pytorch/model_zoo/geometric/mnist.py
View file @ 704bcaf6
import argparse
import time
import dgl
import networkx as nx
import numpy as np
import torch
......@@ -8,14 +10,12 @@ import torch.nn as nn
import torch.nn.functional as F
from coarsening import coarsen
from coordinate import get_coordinates, z2polar
from grid_graph import grid_graph
from torch.utils.data import DataLoader
from torchvision import datasets, transforms
import dgl
from dgl.data import load_data, register_data_args
from dgl.nn.pytorch.conv import ChebConv, GMMConv
from dgl.nn.pytorch.glob import MaxPooling
from grid_graph import grid_graph
from torch.utils.data import DataLoader
from torchvision import datasets, transforms
argparser = argparse.ArgumentParser("MNIST")
argparser.add_argument(
......
examples/pytorch/multigpu/multi_gpu_graph_prediction.py
View file @ 704bcaf6
import argparse
import dgl
import dgl.nn as dglnn
import torch
import torch.distributed as dist
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from dgl.data import AsGraphPredDataset
from dgl.dataloading import GraphDataLoader
from ogb.graphproppred import DglGraphPropPredDataset, Evaluator
from ogb.graphproppred.mol_encoder import AtomEncoder, BondEncoder
from tqdm import tqdm
import dgl
import dgl.nn as dglnn
from dgl.data import AsGraphPredDataset
from dgl.dataloading import GraphDataLoader
class MLP(nn.Module):
def __init__(self, in_feats):
......
examples/pytorch/multigpu/multi_gpu_node_classification.py
View file @ 704bcaf6
import argparse
import os
import dgl.nn as dglnn
import torch
import torch.distributed as dist
import torch.multiprocessing as mp
......@@ -8,10 +10,6 @@ import torch.nn as nn
import torch.nn.functional as F
import torchmetrics.functional as MF
import tqdm
from ogb.nodeproppred import DglNodePropPredDataset
from torch.nn.parallel import DistributedDataParallel
import dgl.nn as dglnn
from dgl.data import AsNodePredDataset
from dgl.dataloading import (
DataLoader,
......@@ -19,6 +17,8 @@ from dgl.dataloading import (
NeighborSampler,
)
from dgl.multiprocessing import shared_tensor
from ogb.nodeproppred import DglNodePropPredDataset
from torch.nn.parallel import DistributedDataParallel
class SAGE(nn.Module):
......
examples/pytorch/mvgrl/graph/dataset.py
View file @ 704bcaf6
......@@ -3,13 +3,13 @@ import os
import re
from collections import Counter
import dgl
import networkx as nx
import numpy as np
import torch as th
from scipy.linalg import fractional_matrix_power, inv
import dgl
from dgl.data import DGLDataset
from scipy.linalg import fractional_matrix_power, inv
""" Compute Personalized Page Ranking"""
......@@ -137,7 +137,6 @@ def process(dataset):
def load(dataset):
basedir = os.path.dirname(os.path.abspath(__file__))
datadir = os.path.join(basedir, "data", dataset)
......
examples/pytorch/mvgrl/graph/main.py
View file @ 704bcaf6
import argparse
import warnings
import dgl
import torch as th
from dataset import load
import dgl
from dgl.dataloading import GraphDataLoader
warnings.filterwarnings("ignore")
......@@ -66,7 +66,6 @@ def collate(samples):
if __name__ == "__main__":
# Step 1: Prepare data =================================================================== #
dataset = load(args.dataname)
......
examples/pytorch/mvgrl/graph/model.py
View file @ 704bcaf6
import torch as th
import torch.nn as nn
from utils import local_global_loss_
from dgl.nn.pytorch import GraphConv
from dgl.nn.pytorch.glob import SumPooling
from utils import local_global_loss_
class MLP(nn.Module):
......
examples/pytorch/mvgrl/graph/utils.py
View file @ 704bcaf6
......@@ -61,7 +61,6 @@ def get_negative_expectation(q_samples, average=True):
def local_global_loss_(l_enc, g_enc, graph_id):
num_graphs = g_enc.shape[0]
num_nodes = l_enc.shape[0]
......@@ -71,7 +70,6 @@ def local_global_loss_(l_enc, g_enc, graph_id):
neg_mask = th.ones((num_nodes, num_graphs)).to(device)
for nodeidx, graphidx in enumerate(graph_id):
pos_mask[nodeidx][graphidx] = 1.0
neg_mask[nodeidx][graphidx] = 0.0
......
examples/pytorch/mvgrl/node/dataset.py
View file @ 704bcaf6
""" Code adapted from https://github.com/kavehhassani/mvgrl """
import dgl
import networkx as nx
import numpy as np
import scipy.sparse as sp
import torch as th
from scipy.linalg import fractional_matrix_power, inv
from sklearn.preprocessing import MinMaxScaler
import dgl
from dgl.data import CiteseerGraphDataset, CoraGraphDataset, PubmedGraphDataset
from dgl.nn import APPNPConv
from scipy.linalg import fractional_matrix_power, inv
from sklearn.preprocessing import MinMaxScaler
def preprocess_features(features):
......
examples/pytorch/mvgrl/node/main.py
View file @ 704bcaf6
......@@ -8,7 +8,7 @@ import torch.nn as nn
warnings.filterwarnings("ignore")
from dataset import process_dataset
from model import MVGRL, LogReg
from model import LogReg, MVGRL
parser = argparse.ArgumentParser(description="mvgrl")
......
examples/pytorch/mvgrl/node/main_sample.py
View file @ 704bcaf6
......@@ -2,16 +2,16 @@ import argparse
import random
import warnings
import dgl
import numpy as np
import torch as th
import torch.nn as nn
import dgl
warnings.filterwarnings("ignore")
from dataset import process_dataset, process_dataset_appnp
from model import MVGRL, LogReg
from model import LogReg, MVGRL
parser = argparse.ArgumentParser(description="mvgrl")
......
examples/pytorch/node2vec/main.py
View file @ 704bcaf6
import time
from dgl.sampling import node2vec_random_walk
from model import Node2vecModel
from utils import load_graph, parse_arguments
from dgl.sampling import node2vec_random_walk
def time_randomwalk(graph, args):
"""
......@@ -49,7 +49,6 @@ def train_node2vec(graph, eval_set, args):
if __name__ == "__main__":
args = parse_arguments()
graph, eval_set = load_graph(args.dataset)
......
examples/pytorch/node2vec/model.py
View file @ 704bcaf6
import torch
import torch.nn as nn
from sklearn.linear_model import LogisticRegression
from torch.utils.data import DataLoader
from dgl.sampling import node2vec_random_walk
from sklearn.linear_model import LogisticRegression
from torch.utils.data import DataLoader
class Node2vec(nn.Module):
......@@ -255,7 +255,6 @@ class Node2vecModel(object):
eval_steps=-1,
device="cpu",
):
self.model = Node2vec(
g,
embedding_dim,
......
examples/pytorch/node2vec/utils.py
View file @ 704bcaf6
......@@ -23,7 +23,6 @@ def load_graph(name):
eval_set = [(nodes_train, y_train), (nodes_val, y_val)]
elif name.startswith("ogbn"):
dataset = DglNodePropPredDataset(name)
graph, y = dataset[0]
split_nodes = dataset.get_idx_split()
......
examples/pytorch/ogb/cluster-gat/main.py
View file @ 704bcaf6
......@@ -2,6 +2,9 @@ import argparse
import time
from functools import partial
import dgl
import dgl.nn.pytorch as dglnn
import numpy as np
import torch as th
import torch.nn as nn
......@@ -12,9 +15,6 @@ from ogb.nodeproppred import DglNodePropPredDataset
from sampler import ClusterIter, subgraph_collate_fn
from torch.utils.data import DataLoader
import dgl
import dgl.nn.pytorch as dglnn
class GAT(nn.Module):
def __init__(
......
examples/pytorch/ogb/cluster-gat/partition_utils.py
View file @ 704bcaf6
from time import time
import numpy as np
import dgl
import numpy as np
from dgl import backend as F
from dgl.transforms import metis_partition
......
examples/pytorch/ogb/cluster-sage/main.py
View file @ 704bcaf6
......@@ -3,6 +3,10 @@ import time
import traceback
from functools import partial
import dgl
import dgl.function as fn
import dgl.nn.pytorch as dglnn
import numpy as np
import torch as th
import torch.multiprocessing as mp
......@@ -10,15 +14,11 @@ import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import tqdm
from dgl.data import RedditDataset
from ogb.nodeproppred import DglNodePropPredDataset
from sampler import ClusterIter, subgraph_collate_fn
from torch.utils.data import DataLoader
import dgl
import dgl.function as fn
import dgl.nn.pytorch as dglnn
from dgl.data import RedditDataset
#### Neighbor sampler
......
examples/pytorch/ogb/cluster-sage/partition_utils.py
View file @ 704bcaf6
from time import time
import numpy as np
import dgl
import numpy as np
from dgl import backend as F
from dgl.transforms import metis_partition
......
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