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run_20News.py
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run_20News.py
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# -*- coding: utf-8 -*-
"""
Created on Sun Oct 9 13:25:23 2016
Script to run experiments on 20Newsgroup
@author: bo
"""
import sys
import numpy as np
import matplotlib.pyplot as plt
from sklearn import metrics
from sklearn.cluster import KMeans
from sklearn.manifold import SpectralEmbedding
from multi_layer_km import test_SdC, load_data
from cluster_acc import acc
K = 20
trials = 10
filename = '20News_ncw.pkl.gz'
path = '/home/bo/Data/20News/'
dataset = path+filename
### Perform KM
train_x, train_y = load_data(dataset)
print('KM started...')
results_KM = np.zeros((trials, 3))
km_model = KMeans(n_clusters=K, n_init=1)
for i in range(trials):
ypred = km_model.fit_predict(train_x)
nmi = metrics.normalized_mutual_info_score(train_y, ypred)
ari = metrics.adjusted_rand_score(train_y, ypred)
ac = acc(ypred, train_y)
results_KM[i] = np.array([nmi, ari, ac])
KM_mean = np.mean(results_KM, axis = 0)
KM_std = np.std(results_KM, axis = 0)
## Perform SC: first find the embeddings, then perform 10 KM
print('SC started...')
results_SC = np.zeros((trials, 3))
se_model = SpectralEmbedding(n_components=K, affinity='rbf', gamma = 0.01)
se_vec = se_model.fit_transform(train_x)
for i in range(trials):
ypred = km_model.fit_predict(se_vec)
nmi = metrics.normalized_mutual_info_score(train_y, ypred)
ari = metrics.adjusted_rand_score(train_y, ypred)
ac = acc(ypred, train_y)
results_SC[i] = np.array([nmi, ari, ac])
SC_mean = np.mean(results_SC, axis = 0)
SC_std = np.std(results_SC, axis = 0)
# Perform DCN
print('DCN started...')
config = {'Init': '',
'lbd': 10,
'beta': 1,
'output_dir': '20News',
'save_file': '20News_10.pkl.gz',
'pretraining_epochs': 10,
'pretrain_lr': .01,
'mu': 0.9,
'finetune_lr': 0.001,
'training_epochs': 50,
'dataset': dataset,
'batch_size': 20,
'nClass': K,
'hidden_dim': [250, 100, 20],
'diminishing': False}
results = []
for i in range(trials):
res_metrics = test_SdC(**config)
results.append(res_metrics)
results_SAEKM = np.zeros((trials, 3))
results_DCN = np.zeros((trials, 3))
N = config['training_epochs']/5
for i in range(trials):
results_SAEKM[i] = results[i][0]
results_DCN[i] = results[i][N]
SAEKM_mean = np.mean(results_SAEKM, axis = 0)
SAEKM_std = np.std(results_SAEKM, axis = 0)
DCN_mean = np.mean(results_DCN, axis = 0)
DCN_std = np.std(results_DCN, axis = 0)
color = ['b', 'g', 'r']
marker = ['o', '+', '*']
x = np.linspace(0, config['training_epochs'], num = config['training_epochs']/5 +1)
plt.figure(3)
plt.xlabel('Epochs')
for i in range(3):
y = res_metrics[:][:,i]
plt.plot(x, y, '-'+color[i]+marker[i], linewidth = 2)
plt.show()
plt.legend(['NMI', 'ARI', 'ACC'])
print >> sys.stderr, ('KM avg. NMI = {0:.2f}, ARI = {1:.2f}, ACC = {2:.2f}'.format(KM_mean[0],
KM_mean[1], KM_mean[2]) )
print >> sys.stderr, ('SC avg. NMI = {0:.2f}, ARI = {1:.2f}, ACC = {2:.2f}'.format(SC_mean[0],
SC_mean[1], SC_mean[2]) )
print >> sys.stderr, ('SAE+KM avg. NMI = {0:.2f}, ARI = {1:.2f}, ACC = {2:.2f}'.format(SAEKM_mean[0],
SAEKM_mean[1], SAEKM_mean[2]) )
print >> sys.stderr, ('DCN avg. NMI = {0:.2f}, ARI = {1:.2f}, ACC = {2:.2f}'.format(DCN_mean[0],
DCN_mean[1], DCN_mean[2]) )