Full Code of bdy9527/SDCN for AI

Repository: bdy9527/SDCN
Branch: master
Commit: da6bb007b7d0
Files: 40
Total size: 140.3 MB

Directory structure:
gitextract_62qf39zu/

├── GNN.py
├── LICENSE
├── README.md
├── calcu_graph.py
├── data/
│   ├── acm.pkl
│   ├── acm.txt
│   ├── acm_label.txt
│   ├── cite.pkl
│   ├── cite_label.txt
│   ├── dblp.pkl
│   ├── dblp.txt
│   ├── dblp_label.txt
│   ├── evaluation.py
│   ├── hhar.pkl
│   ├── hhar_label.txt
│   ├── pretrain.py
│   ├── reut.pkl
│   ├── reut_label.txt
│   ├── usps.pkl
│   ├── usps.txt
│   └── usps_label.txt
├── evaluation.py
├── graph/
│   ├── acm_graph.txt
│   ├── cite_graph.txt
│   ├── dblp_graph.txt
│   ├── hhar10_graph.txt
│   ├── hhar1_graph.txt
│   ├── hhar3_graph.txt
│   ├── hhar5_graph.txt
│   ├── reut10_graph.txt
│   ├── reut1_graph.txt
│   ├── reut3_graph.txt
│   ├── reut5_graph.txt
│   ├── usps10_graph.txt
│   ├── usps1_graph.txt
│   ├── usps3_graph.txt
│   └── usps5_graph.txt
├── requirements.txt
├── sdcn.py
└── utils.py

================================================
FILE CONTENTS
================================================

================================================
FILE: GNN.py
================================================
import math
import torch
import torch.nn.functional as F
from torch.nn.parameter import Parameter
from torch.nn.modules.module import Module


class GNNLayer(Module):
    def __init__(self, in_features, out_features):
        super(GNNLayer, self).__init__()
        self.in_features = in_features
        self.out_features = out_features
        self.weight = Parameter(torch.FloatTensor(in_features, out_features))
        torch.nn.init.xavier_uniform_(self.weight)

    def forward(self, features, adj, active=True):
        support = torch.mm(features, self.weight)
        output = torch.spmm(adj, support)
        if active:
            output = F.relu(output)
        return output



================================================
FILE: LICENSE
================================================
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================================================
FILE: README.md
================================================
# SDCN
Structural Deep Clustering Network

# Paper
https://arxiv.org/abs/2002.01633

https://github.com/461054993/SDCN/blob/master/SDCN.pdf

# Dataset
Due to the limitation of file size, the complete data can be found in 

Baidu Netdisk:

graph: 链接:https://pan.baidu.com/s/1MEWr1KyrtBQndVNy8_y2Lw  密码:opc1

data: 链接:https://pan.baidu.com/s/1kqoWlElbWazJyrTdv1sHNg  密码:1gd4

Google Drive:

graph: https://drive.google.com/file/d/10rnVwIAuVRczmZJSX7mpSTR0-HVnMWLh/view?usp=sharing

data: https://drive.google.com/file/d/1VjH6xqt82GaQwwiy-4O2GedMgQMLN6dm/view?usp=sharing

# Code
```
python sdcn.py --name [usps|hhar|reut|acm|dblp|cite]
```

# Q&A
- Q: Why do not use distribution Q to supervise distribution P directly?<br>
  A: The reasons are two-fold: 1) Previous method has considered to use the clustering assignments as pseudo labels to re-train the encoder in a supervised manner, i.e., [deepCluster](https://arxiv.org/abs/1807.05520). However, in experiment, we find that the gradient of cross-entropy loss is too violent to prevent the embedding spaces from disturbance. 2) Although we can replace the cross-entropy loss with KL divergence, there is still a problem that we worried about, that is, there is no clustering information. The original intention of our research on deep clustering is to integrate the objective of clustering into the powerful representation ability of deep learning. Therefore, we introduce the distribution P to increase the cohesion of clustering performance, the details can be found in [DEC](http://www.jmlr.org/proceedings/papers/v48/xieb16.pdf).

- Q: How to apply SDCN to other datasets?<br>
  A: In general, if you want to apply our model to other datasets, three steps are required.
  1) Construct the KNN graph based on the similarity of features. Details can be found in ```calcu_graph.py```.
  2) Pretrain the autoencoder and save the pre-trained model. Details can be found in ```data/pretrain.py```.
  3) Replace the args in ```sdcn.py``` and run the code.

# Reference
If you make advantage of the SDCN model in your research, please cite the following in your manuscript:
```
@inproceedings{sdcn2020,
  author    = {Deyu Bo and
               Xiao Wang and
               Chuan Shi and
               Meiqi Zhu and
               Emiao Lu and
               Peng Cui},
  title     = {Structural Deep Clustering Network},
  booktitle = {{WWW}},
  pages     = {1400--1410},
  publisher = {{ACM} / {IW3C2}},
  year      = {2020}
}
```


================================================
FILE: calcu_graph.py
================================================
import pandas as pd
import numpy as np
import h5py
from sklearn.metrics.pairwise import cosine_similarity as cos
from sklearn.metrics import pairwise_distances as pair
from sklearn.preprocessing import normalize

topk = 10

def construct_graph(features, label, method='heat'):
    fname = 'graph/reut10_graph.txt'
    num = len(label)
    dist = None

    if method == 'heat':
        dist = -0.5 * pair(features) ** 2
        dist = np.exp(dist)
    elif method == 'cos':
        features[features > 0] = 1
        dist = np.dot(features, features.T)
    elif method == 'ncos':
        features[features > 0] = 1
        features = normalize(features, axis=1, norm='l1')
        dist = np.dot(features, features.T)

    inds = []
    for i in range(dist.shape[0]):
        ind = np.argpartition(dist[i, :], -(topk+1))[-(topk+1):]
        inds.append(ind)

    f = open(fname, 'w')
    counter = 0
    A = np.zeros_like(dist)
    for i, v in enumerate(inds):
        mutual_knn = False
        for vv in v:
            if vv == i:
                pass
            else:
                if label[vv] != label[i]:
                    counter += 1
                f.write('{} {}\n'.format(i, vv))
    f.close()
    print('error rate: {}'.format(counter / (num * topk)))

'''
f = h5py.File('data/usps.h5', 'r')
train = f.get('train')
test = f.get('test')
X_tr = train.get('data')[:]
y_tr = train.get('target')[:]
X_te = test.get('data')[:]
y_te = test.get('target')[:]
f.close()
usps = np.concatenate((X_tr, X_te)).astype(np.float32)
label = np.concatenate((y_tr, y_te)).astype(np.int32)
'''

'''
hhar = np.loadtxt('data/hhar.txt', dtype=float)
label = np.loadtxt('data/hhar_label.txt', dtype=int)
'''

reut = np.loadtxt('data/reut.txt', dtype=float)
label = np.loadtxt('data/reut_label.txt', dtype=int)

construct_graph(reut, label, 'ncos')


================================================
FILE: data/acm.pkl
================================================
[File too large to display: 16.9 MB]

================================================
FILE: data/acm.txt
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[File too large to display: 10.8 MB]

================================================
FILE: data/acm_label.txt
================================================
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6


================================================
FILE: data/dblp.pkl
================================================
[File too large to display: 11.0 MB]

================================================
FILE: data/dblp.txt
================================================
[File too large to display: 11.6 MB]

================================================
FILE: data/dblp_label.txt
================================================
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================================================
FILE: data/evaluation.py
================================================
import numpy as np
from munkres import Munkres, print_matrix
from sklearn.metrics.cluster import normalized_mutual_info_score as nmi_score
from sklearn.metrics import adjusted_rand_score as ari_score
from scipy.optimize import linear_sum_assignment as linear
from sklearn import metrics


def cluster_acc(y_true, y_pred):
    y_true = y_true - np.min(y_true)

    l1 = list(set(y_true))
    numclass1 = len(l1)

    l2 = list(set(y_pred))
    numclass2 = len(l2)

    ind = 0
    if numclass1 != numclass2:
        for i in l1:
            if i in l2:
                pass
            else:
                y_pred[ind] = i
                ind += 1

    l2 = list(set(y_pred))
    numclass2 = len(l2)

    if numclass1 != numclass2:
        print('error')
        return

    cost = np.zeros((numclass1, numclass2), dtype=int)
    for i, c1 in enumerate(l1):
        mps = [i1 for i1, e1 in enumerate(y_true) if e1 == c1]
        for j, c2 in enumerate(l2):
            mps_d = [i1 for i1 in mps if y_pred[i1] == c2]
            cost[i][j] = len(mps_d)

    # match two clustering results by Munkres algorithm
    m = Munkres()
    cost = cost.__neg__().tolist()
    indexes = m.compute(cost)

    # get the match results
    new_predict = np.zeros(len(y_pred))
    for i, c in enumerate(l1):
        # correponding label in l2:
        c2 = l2[indexes[i][1]]

        # ai is the index with label==c2 in the pred_label list
        ai = [ind for ind, elm in enumerate(y_pred) if elm == c2]
        new_predict[ai] = c

    acc = metrics.accuracy_score(y_true, new_predict)
    f1_macro = metrics.f1_score(y_true, new_predict, average='macro')
    precision_macro = metrics.precision_score(y_true, new_predict, average='macro')
    recall_macro = metrics.recall_score(y_true, new_predict, average='macro')
    f1_micro = metrics.f1_score(y_true, new_predict, average='micro')
    precision_micro = metrics.precision_score(y_true, new_predict, average='micro')
    recall_micro = metrics.recall_score(y_true, new_predict, average='micro')
    return acc, f1_macro


def eva(y_true, y_pred, epoch=0):
    acc, f1 = cluster_acc(y_true, y_pred)
    nmi = nmi_score(y_true, y_pred, average_method='arithmetic')
    ari = ari_score(y_true, y_pred)
    print(epoch, ':acc {:.4f}'.format(acc), ', nmi {:.4f}'.format(nmi), ', ari {:.4f}'.format(ari),
            ', f1 {:.4f}'.format(f1))



================================================
FILE: data/hhar.pkl
================================================
[File too large to display: 11.9 MB]

================================================
FILE: data/hhar_label.txt
================================================
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================================================
FILE: data/pretrain.py
================================================
import numpy as np
import h5py
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.parameter import Parameter
from torch.utils.data import DataLoader
from torch.optim import Adam, SGD
from torch.nn import Linear
from torch.utils.data import Dataset
from sklearn.cluster import KMeans
from evaluation import eva

#torch.cuda.set_device(3)


class AE(nn.Module):

    def __init__(self, n_enc_1, n_enc_2, n_enc_3, n_dec_1, n_dec_2, n_dec_3,
                 n_input, n_z):
        super(AE, self).__init__()
        self.enc_1 = Linear(n_input, n_enc_1)
        self.enc_2 = Linear(n_enc_1, n_enc_2)
        self.enc_3 = Linear(n_enc_2, n_enc_3)
        self.z_layer = Linear(n_enc_3, n_z)

        self.dec_1 = Linear(n_z, n_dec_1)
        self.dec_2 = Linear(n_dec_1, n_dec_2)
        self.dec_3 = Linear(n_dec_2, n_dec_3)
        self.x_bar_layer = Linear(n_dec_3, n_input)

    def forward(self, x):
        enc_h1 = F.relu(self.enc_1(x))
        enc_h2 = F.relu(self.enc_2(enc_h1))
        enc_h3 = F.relu(self.enc_3(enc_h2))
        z = self.z_layer(enc_h3)

        dec_h1 = F.relu(self.dec_1(z))
        dec_h2 = F.relu(self.dec_2(dec_h1))
        dec_h3 = F.relu(self.dec_3(dec_h2))
        x_bar = self.x_bar_layer(dec_h3)

        return x_bar, z


class LoadDataset(Dataset):
    def __init__(self, data):
        self.x = data

    def __len__(self):
        return self.x.shape[0]

    def __getitem__(self, idx):
        return torch.from_numpy(np.array(self.x[idx])).float(), \
               torch.from_numpy(np.array(idx))


def adjust_learning_rate(optimizer, epoch):
    lr = 0.001 * (0.1 ** (epoch // 20))
    for param_group in optimizer.param_groups:
        param_group['lr'] = lr


def pretrain_ae(model, dataset, y):
    train_loader = DataLoader(dataset, batch_size=256, shuffle=True)
    print(model)
    optimizer = Adam(model.parameters(), lr=1e-3)
    for epoch in range(30):
        # adjust_learning_rate(optimizer, epoch)
        for batch_idx, (x, _) in enumerate(train_loader):
            x = x.cuda()

            x_bar, _ = model(x)
            loss = F.mse_loss(x_bar, x)

            optimizer.zero_grad()
            loss.backward()
            optimizer.step()

        with torch.no_grad():
            x = torch.Tensor(dataset.x).cuda().float()
            x_bar, z = model(x)
            loss = F.mse_loss(x_bar, x)
            print('{} loss: {}'.format(epoch, loss))           
            kmeans = KMeans(n_clusters=4, n_init=20).fit(z.data.cpu().numpy())
            eva(y, kmeans.labels_, epoch)

        torch.save(model.state_dict(), 'dblp1.pkl')

model = AE(
        n_enc_1=500,
        n_enc_2=500,
        n_enc_3=2000,
        n_dec_1=2000,
        n_dec_2=500,
        n_dec_3=500,
        n_input=334,
        n_z=10,).cuda()

x = np.loadtxt('dblp.txt', dtype=float)
y = np.loadtxt('dblp_label.txt', dtype=int)

dataset = LoadDataset(x)
pretrain_ae(model, dataset, y)


================================================
FILE: data/reut.pkl
================================================
[File too large to display: 17.4 MB]

================================================
FILE: data/reut_label.txt
================================================
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================================================
FILE: data/usps.pkl
================================================
[File too large to display: 10.7 MB]

================================================
FILE: data/usps.txt
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[File too large to display: 20.4 MB]

================================================
FILE: data/usps_label.txt
================================================
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================================================
FILE: evaluation.py
================================================
import numpy as np
from munkres import Munkres, print_matrix
from sklearn.metrics.cluster import normalized_mutual_info_score as nmi_score
from sklearn.metrics import adjusted_rand_score as ari_score
from scipy.optimize import linear_sum_assignment as linear
from sklearn import metrics


def cluster_acc(y_true, y_pred):
    y_true = y_true - np.min(y_true)

    l1 = list(set(y_true))
    numclass1 = len(l1)

    l2 = list(set(y_pred))
    numclass2 = len(l2)

    ind = 0
    if numclass1 != numclass2:
        for i in l1:
            if i in l2:
                pass
            else:
                y_pred[ind] = i
                ind += 1

    l2 = list(set(y_pred))
    numclass2 = len(l2)

    if numclass1 != numclass2:
        print('error')
        return

    cost = np.zeros((numclass1, numclass2), dtype=int)
    for i, c1 in enumerate(l1):
        mps = [i1 for i1, e1 in enumerate(y_true) if e1 == c1]
        for j, c2 in enumerate(l2):
            mps_d = [i1 for i1 in mps if y_pred[i1] == c2]
            cost[i][j] = len(mps_d)

    # match two clustering results by Munkres algorithm
    m = Munkres()
    cost = cost.__neg__().tolist()
    indexes = m.compute(cost)

    # get the match results
    new_predict = np.zeros(len(y_pred))
    for i, c in enumerate(l1):
        # correponding label in l2:
        c2 = l2[indexes[i][1]]

        # ai is the index with label==c2 in the pred_label list
        ai = [ind for ind, elm in enumerate(y_pred) if elm == c2]
        new_predict[ai] = c

    acc = metrics.accuracy_score(y_true, new_predict)
    f1_macro = metrics.f1_score(y_true, new_predict, average='macro')
    precision_macro = metrics.precision_score(y_true, new_predict, average='macro')
    recall_macro = metrics.recall_score(y_true, new_predict, average='macro')
    f1_micro = metrics.f1_score(y_true, new_predict, average='micro')
    precision_micro = metrics.precision_score(y_true, new_predict, average='micro')
    recall_micro = metrics.recall_score(y_true, new_predict, average='micro')
    return acc, f1_macro


def eva(y_true, y_pred, epoch=0):
    acc, f1 = cluster_acc(y_true, y_pred)
    nmi = nmi_score(y_true, y_pred, average_method='arithmetic')
    ari = ari_score(y_true, y_pred)
    print(epoch, ':acc {:.4f}'.format(acc), ', nmi {:.4f}'.format(nmi), ', ari {:.4f}'.format(ari),
            ', f1 {:.4f}'.format(f1))



================================================
FILE: graph/acm_graph.txt
================================================
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