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mnist_local.py
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/
mnist_local.py
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# -*- coding: utf-8 -*-
# @Author: twankim
# @Date: 2017-02-24 17:46:51
# @Last Modified by: twankim
# @Last Modified time: 2018-03-09 22:14:34
import numpy as np
import time
import sys
import os
import argparse
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from ssac import weakSSAC
from ssac_org import SSAC
# from gen_data import genData
from utils import *
import cPickle as pickle
weak = "local"
delta = 0.99
base_dir= os.path.join('./results',weak+'_compare_mnist')
def main(args):
plotted = False
# Load MNIST 2500 subset
with open('dataset/mnist2500.pkl','rb') as fp:
dataset = pickle.load(fp)
rep = args.rep
i_plot = 0
# qs = [float(q) for q in args.qs.split(',')]
etas = [float(eta) for eta in args.etas.split(',')]
beta = args.beta
verbose = args.verbose
cs = [float(q) for q in args.cs.split(',')]
res_acc = np.zeros((rep,len(cs),len(etas))) # Accuracy of clustering
res_mean_acc = np.zeros((rep,len(cs),len(etas))) # Mean accuracy of clustering (per cluster)
# res_err = np.zeros((rep,len(qs),len(etas))) # Number of misclustered points
res_fail = np.zeros((rep,len(cs),len(etas))) # Number of Failure
res_acc_org = np.zeros((rep,len(cs),len(etas))) # Accuracy of clustering
res_mean_acc_org = np.zeros((rep,len(cs),len(etas))) # Mean accuracy of clustering (per cluster)
# res_err = np.zeros((rep,len(qs),len(etas))) # Number of misclustered points
res_fail_org = np.zeros((rep,len(cs),len(etas))) # Number of Failure
gammas = np.zeros(rep)
nus = np.zeros((rep,len(cs)))
rhos = np.zeros((rep,len(cs)))
digits = [str(int(dataset.dict_label[label])) for label in dataset.dict_label.keys()]
# Make directories to save results
if not os.path.exists(base_dir):
os.makedirs(base_dir)
res_dir = base_dir + '/{}'.format(
','.join(digits))
if not os.path.exists(res_dir):
os.makedirs(res_dir)
if verbose:
print "MNIST2500 Subset... Digits:{}".format(','.join(digits))
for i_rep in xrange(rep):
if verbose:
print "({}/{})... Testing Algorithm".format(i_rep+1,rep)
X = dataset.X
y_true = dataset.y
ris = dataset.ris
gamma = dataset.gamma
gammas[i_rep] = gamma
n = dataset.n
m = dataset.m
k = dataset.k
algo = weakSSAC(X,y_true,k,wtype=weak,ris=ris)
algo_org = SSAC(X,y_true,k,wtype=weak,ris=ris)
# Test SSAC algorithm for different c's and eta's (fix beta in this case)
for i_c,c_dist in enumerate(cs):
assert (c_dist>0.5) & (c_dist<=1.0), "c_dist must be in (0.5,1]"
nus[i_rep,i_c] = 1 + 2.0*(1-c_dist)
rhos[i_rep,i_c] = c_dist
# Calculate proper eta and beta based on parameters including delta
if verbose:
print " - Proper eta={}, beta={} (delta={})".format(
dataset.calc_eta(delta,weak=weak,nu=nus[i_rep,i_c],rho=rhos[i_rep,i_c]),
dataset.calc_beta(delta,weak=weak,nu=nus[i_rep,i_c],rho=rhos[i_rep,i_c]),
delta)
for i_eta,eta in enumerate(etas):
if verbose:
print " <Test: c_dist={}, eta={}, beta={}>".format(c_dist,eta,beta)
algo.set_params(eta,beta,rho=rhos[i_rep,i_c],nu=nus[i_rep,i_c])
algo_org.set_params(eta,beta,rho=rhos[i_rep,i_c],nu=nus[i_rep,i_c])
if not algo.fit():
# Algorithm has failed
res_fail[i_rep,i_c,i_eta] = 1
if not plotted:
i_plot = np.random.randint(i_rep+1,rep) # Index of experiment to plot the figure
if not algo_org.fit():
# Algorithm has failed
res_fail_org[i_rep,i_c,i_eta] = 1
# i_plot = np.random.randint(i_rep+1,rep) # Index of experiment to plot the figure
y_pred = algo.y
mpps = algo.mpps # Estimated cluster centers
# print " ... Clustering is done. Number of binary search steps = {}\n".format(algo.bs_num)
y_pred_org = algo_org.y
mpps_org = algo_org.mpps # Estimated cluster centers
# For evaluation & plotting, find best permutation of cluster assignment
y_pred_perm = find_permutation(dataset,algo)
y_pred_perm_org = find_permutation(dataset,algo_org)
# Calculate accuracy and mean accuracy
res_acc[i_rep,i_c,i_eta] = accuracy(y_true,y_pred_perm)
res_mean_acc[i_rep,i_c,i_eta] = mean_accuracy(y_true,y_pred_perm)
res_acc_org[i_rep,i_c,i_eta] = accuracy(y_true,y_pred_perm_org)
res_mean_acc_org[i_rep,i_c,i_eta] = mean_accuracy(y_true,y_pred_perm_org)
# # Calculate number of errors
# res_err[i_rep,i_c,i_eta] = error(y_true,y_pred_perm)
if (i_rep == i_plot) and (m<=2) and (not plotted):
if (i_eta==len(etas)-1) and (i_c==len(cs)-1):
plotted = True
list_classes = ['Not assigned']
for i in xrange(k):
list_classes.append('Digit {}'.format(i))
title = r"SSAC with {} weak oracle ($\eta={}, \beta={}, \nu={:.2f}, \rho={:.2f}$)".format(
weak,eta,beta,nus[i_rep,i_c],rhos[i_rep,i_c])
f_name = res_dir+'/fig_n{}_m{}_k{}_c{:03d}_e{:d}.png'.format(n,m,k,int(100*c_dist),int(eta))
plot_cluster(X,y_true,y_pred_perm,k,mpps,gamma,
title,f_name,verbose,
classes=list_classes)
# title_org = r"SSAC(original) with {} weak oracle ($\eta={}, \beta={}, \rho={:.2f}$)".format(
# weak,eta,beta,rhos[i_rep,i_c])
# f_name_org = res_dir+'/fig_org_n{}_m{}_k{}_c{:03d}_e{:d}.png'.format(n,m,k,int(100*c_dist),int(eta))
# plot_cluster(X,y_true,y_pred_perm_org,k,mpps_org,gamma,
# title_org,f_name_org,verbose)
# Write result as table
print_eval("Accuracy(%)",res_acc,etas,
res_dir+'/res_{}_n{}_m{}_k{}.csv'.format("acc",n,m,k),weak=weak,params=cs)
print_eval("Mean Accuracy(%)",res_mean_acc,etas,
res_dir+'/res_{}_n{}_m{}_k{}.csv'.format("meanacc",n,m,k),weak=weak,params=cs)
# print_eval("# Error(%)",res_err,qs,etas,
# res_dir+'/res_{}_n{}_m{}_k{}.csv'.format("err",n,m,k))
print_eval("# Failures",res_fail,etas,
res_dir+'/res_{}_n{}_m{}_k{}.csv'.format("fail",n,m,k),
is_sum=True,weak=weak,params=cs)
print_eval("Accuracy(%)",res_acc_org,etas,
res_dir+'/res_org_{}_n{}_m{}_k{}.csv'.format("acc",n,m,k),weak=weak,params=cs)
print_eval("Mean Accuracy(%)",res_mean_acc_org,etas,
res_dir+'/res_org_{}_n{}_m{}_k{}.csv'.format("meanacc",n,m,k),weak=weak,params=cs)
# print_eval("# Error(%)",res_err,qs,etas,
# res_dir+'/res_{}_n{}_m{}_k{}.csv'.format("err",n,m,k))
print_eval("# Failures",res_fail_org,etas,
res_dir+'/res_org_{}_n{}_m{}_k{}.csv'.format("fail",n,m,k),
is_sum=True,weak=weak,params=cs)
# if args.isplot:
# Plot Accuracy vs. eta
fig_name = res_dir+'/fig_{}_n{}_m{}_k{}.pdf'.format("acc",n,m,k)
plot_eval("Accuracy(%)",res_acc,etas,fig_name,weak=weak,params=cs,res_org=res_acc_org)
# Plot Mean Accuracy vs. eta
fig_name = res_dir+'/fig_{}_n{}_m{}_k{}.pdf'.format("meanacc",n,m,k)
plot_eval("Mean Accuracy(%)",res_mean_acc,etas,fig_name,weak=weak,params=cs,res_org=res_mean_acc_org)
# Plot Failure vs. eta
fig_name = res_dir+'/fig_{}_n{}_m{}_k{}.pdf'.format("fail",n,m,k)
plot_eval("# Failure",res_fail,etas,fig_name,is_sum=True,weak=weak,params=cs,res_org=res_fail_org)
# # Plot histogram of gammas
# fig_name = res_dir+'/fig_gamma_hist.pdf'
# plot_hist(gammas,args.min_gamma,args.max_gamma,fig_name)
if args.isplot:
plt.show()
def parse_args():
def str2bool(v):
return v.lower() in ('true', '1')
parser = argparse.ArgumentParser(description=
'Test Semi-Supervised Active Clustering with Weak Oracles: Random-weak model')
# parser.add_argument('-m', dest='m',
# help='Dimension of data points in synthetic data',
# default = 2, type = int)
parser.add_argument('-rep', dest='rep',
help='Number of experiments to repeat',
default = 1000, type = int)
parser.add_argument('-qs', dest='qs',
help='Probabilities q (not-sure with 1-q) ex) 0.7,0.85,1',
default = '0.7,0.85,1', type = str)
parser.add_argument('-etas', dest='etas',
help='etas: parameter for sampling (phase 1) ex) 10,50',
default = '2,5,10,20,30', type = str)
parser.add_argument('-beta', dest='beta',
help='beta: parameter for sampling (phase 2)',
default = 1, type = int)
parser.add_argument('-cs', dest='cs',
help='Fractions to set distance-weak parameters (0.5,1] ex) 0.7,0.85,1',
default = '0.6,0.8,1', type = str)
parser.add_argument('-isplot', dest='isplot',
help='plot the result: True/False',
default = False, type = str2bool)
parser.add_argument('-verbose', dest='verbose',
help='verbose: True/False',
default = False, type = str2bool)
args = parser.parse_args()
return args
if __name__ == '__main__':
args = parse_args()
print "Called with args:"
print args
sys.exit(main(args))