OptWok-0.3/ 0000755 ڛ |-00000000000 12127457270 017545 5 ustar cheng.ong UNIMELB\Domain Users 0000000 0000000 OptWok-0.3/COPYING 0000644 ڛ |-00000104513 11731540564 020603 0 ustar cheng.ong UNIMELB\Domain Users 0000000 0000000 GNU GENERAL PUBLIC LICENSE
Version 3, 29 June 2007
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.
OptWok-0.3/examples/ 0000755 ڛ |-00000000000 12127457270 021363 5 ustar cheng.ong UNIMELB\Domain Users 0000000 0000000 OptWok-0.3/examples/__init__.py 0000644 ڛ |-00000000000 12127456646 023470 0 ustar cheng.ong UNIMELB\Domain Users 0000000 0000000 OptWok-0.3/examples/classifier_demo.py 0000644 ڛ |-00000024572 12055542136 025073 0 ustar cheng.ong UNIMELB\Domain Users 0000000 0000000 """
Some code for testing and also as a demo
"""
from optwok.classifier import SVMPrimal, OneNormSVM, CapOneNormSVM, ZeroNormSVM
from optwok.classifier import RLSVM2, RLSVM1, RLSVMC1, RLSVM0
from optwok.classifier import OneNormSVM_dense
from optwok.evaluation import accuracy
from optwok.simdata import create_data
from optwok.testtools import plot_2d_pred, plot_2d
import pylab
import numpy
from pprint import pprint
from numpy import dot, eye, array, sign, sum, arange
from numpy.linalg import norm, cholesky, LinAlgError, eig, svd
def example_cloud_2d():
"""Use 2d gaussian clouds for training, plot the resulting classifier"""
numpy.random.seed(1)
(train_ex, train_lab, test_ex, test_labels) = create_data(num_train=200, num_test=10, num_feat=2,
num_noise_feat=0, width=0.7, frac_flip=0.2)
mu = 1.0
reg_param = 1.0
verbose = False
kdict = {'name':'linear', 'param': 0}
#kdict = {'name':'poly', 'param': 3, 'inhomog': True}
# Train
svm = SVMPrimal(reg_param, kdict, verbose=verbose)
svm.train(train_ex, train_lab)
rlsvm = RLSVM2(reg_param, kdict, mu, rand_init=False, verbose=verbose)
rlsvm.train(train_ex, train_lab)
# Predict on grid
plot_limit = 4.0
axis_limit = [-plot_limit, plot_limit, -plot_limit, plot_limit]
x1 = numpy.linspace(-plot_limit, plot_limit, 100)
x2 = numpy.linspace(-plot_limit, plot_limit, 100)
x,y = numpy.meshgrid(x1,x2)
pred_ex = numpy.array((numpy.ravel(x), numpy.ravel(y)))
svmout = svm.predict(pred_ex)
svmout.shape = (100,100)
rlsvmout = rlsvm.predict(pred_ex)
rlsvmout.shape = (100,100)
print('=====================================================================')
print('SVM Primal objective: %f' % svm.primal_obj)
print('SVM weights: ')
print(svm.w)
print('SVM bias: %f' % svm.b)
print('=====================================================================')
print('RLSVM Primal objective: %f' % rlsvm.primal_obj)
print('RLSVM weights: ')
print(rlsvm.w)
print('RLSVM bias: %f' % rlsvm.b)
print('Number of passes through data: %d' % rlsvm.num_iter)
plot_2d_pred(x, y, svmout, train_ex, train_lab, axis_limit, 'svm')
plot_2d_pred(x, y, rlsvmout, train_ex, train_lab, axis_limit, 'rlsvm')
pylab.show()
def demo_helper(classifier, train_ex, train_labels, test_ex, test_labels):
"""Helper script to train, predict and report performance"""
classifier.train(train_ex, train_labels)
svmout = classifier.predict(test_ex)
acc = accuracy(svmout, test_labels)
print('Accuracy:\t\t\t %1.2f' % acc)
#print('Margin:\t\t\t\t %f' % classifier.margin)
#print('Empirical Risk:\t\t\t %f' % sum(classifier.emp_risk))
#print('Primal Objective:\t\t %f' % classifier.primal_obj)
#print(numpy.where(abs(classifier.w)>classifier.epsilon)[0])
#print('Fraction of features used:\t %1.4f' % classifier.frac_feat)
#print('Fraction support vectors:\t %1.4f' % classifier.frac_sv)
#print('Total fraction:\t\t\t %1.4f' % classifier.frac_total)
pprint(classifier.perf)
def demo_svm(reg_param, kdict, verbose, train_ex, train_labels, test_ex, test_labels):
"""Create a SVM, train and predict"""
print('=== Running SVM ===')
svm = SVMPrimal(reg_param, kdict, verbose=verbose)
demo_helper(svm, train_ex, train_labels, test_ex, test_labels)
def demo_onenormsvm(reg_param, kdict, verbose, train_ex, train_labels, test_ex, test_labels):
"""Create a one norm SVM, train and predict"""
print('=== Running one norm SVM ===')
svm = OneNormSVM(reg_param, kdict, verbose=verbose)
demo_helper(svm, train_ex, train_labels, test_ex, test_labels)
def demo_onenormsvm_dense(reg_param, kdict, verbose, train_ex, train_labels, test_ex, test_labels):
"""Create a one norm SVM, train and predict"""
print('=== Running one norm SVM with dense matrices ===')
svm = OneNormSVM_dense(reg_param, kdict, verbose=verbose)
demo_helper(svm, train_ex, train_labels, test_ex, test_labels)
def demo_caponenormsvm(reg_param, kdict, a, verbose, train_ex, train_labels, test_ex, test_labels):
"""Create a capped one norm SVM, train and predict"""
print('=== Running cap one norm SVM ===')
svm = CapOneNormSVM(reg_param, kdict, a, rand_init=False, verbose=verbose)
demo_helper(svm, train_ex, train_labels, test_ex, test_labels)
def demo_zeronormsvm(reg_param, kdict, a, verbose, train_ex, train_labels, test_ex, test_labels):
"""Create a one norm SVM, train and predict"""
print('=== Running zero norm SVM ===')
svm = ZeroNormSVM(reg_param, kdict, a, rand_init=False, verbose=verbose)
demo_helper(svm, train_ex, train_labels, test_ex, test_labels)
def demo_rlsvm2(reg_param, kdict, mu, verbose, train_ex, train_labels, test_ex, test_labels):
"""Create an RLSVM, train and predict"""
print('=== Running RLSVM2 ===')
rlsvm = RLSVM2(reg_param, kdict, mu, rand_init=False, verbose=verbose)
demo_helper(rlsvm, train_ex, train_labels, test_ex, test_labels)
def demo_rlsvm1(reg_param, kdict, mu, verbose, train_ex, train_labels, test_ex, test_labels):
"""Create an RLSVM, train and predict"""
print('=== Running RLSVM1 ===')
rlsvm = RLSVM1(reg_param, kdict, mu, rand_init=False, verbose=verbose)
demo_helper(rlsvm, train_ex, train_labels, test_ex, test_labels)
def demo_rlsvmc1(reg_param, kdict, mu, a, verbose, train_ex, train_labels, test_ex, test_labels):
"""Create an RLSVM, train and predict"""
print('=== Running RLSVMC1 ===')
rlsvm = RLSVMC1(reg_param, kdict, mu, a, rand_init=False, verbose=verbose)
demo_helper(rlsvm, train_ex, train_labels, test_ex, test_labels)
def demo_rlsvm0(reg_param, kdict, mu, a, verbose, train_ex, train_labels, test_ex, test_labels):
"""Create an RLSVM, train and predict"""
print('=== Running RLSVM0 ===')
rlsvm = RLSVM0(reg_param, kdict, mu, a, rand_init=False, verbose=verbose)
demo_helper(rlsvm, train_ex, train_labels, test_ex, test_labels)
def example_toy():
"""Training and classification using two gaussian clouds"""
#numpy.random.seed(1)
(train_ex, train_labels, test_ex, test_labels) = create_data(num_train=200,
num_test=1000, num_feat=5,
num_noise_feat = 45,
width=1.5, frac_flip=0.1,
symm_flip=True)
mu = 1.0
a = 4.0 # the exponent in zero norm
cap = 0.5 # the cap in capped one norm
reg_param = 1.0
verbose = False
#kdict = {'name':'poly', 'param': 2, 'inhomog': True}
kdict = {'name':'linear', 'param': 0}
demo_svm(reg_param, kdict, verbose, train_ex, train_labels, test_ex, test_labels)
demo_onenormsvm(reg_param, kdict, verbose, train_ex, train_labels, test_ex, test_labels)
demo_caponenormsvm(reg_param, kdict, cap, verbose, train_ex, train_labels, test_ex, test_labels)
demo_zeronormsvm(reg_param, kdict, a, verbose, train_ex, train_labels, test_ex, test_labels)
demo_rlsvm2(reg_param, kdict, mu, verbose, train_ex, train_labels, test_ex, test_labels)
demo_rlsvm1(reg_param, kdict, mu, verbose, train_ex, train_labels, test_ex, test_labels)
demo_rlsvmc1(reg_param, kdict, mu, cap, verbose, train_ex, train_labels, test_ex, test_labels)
demo_rlsvm0(reg_param, kdict, mu, a, verbose, train_ex, train_labels, test_ex, test_labels)
def example_debug():
"""Tiny example to try to check global optimality"""
plot_limit = 4.0
axis_limit = [-plot_limit, plot_limit, -plot_limit, plot_limit]
train_ex = numpy.array([[-1.0, -1.0, -1.0, -1.0, -1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
[-1.0, 0.5, 0.0, -0.5, 1.0, 0.5, -0.5, 0.0, 1.0, -1.0]])
train_lab = numpy.array([-1., -1., -1., -1., 1., 1., 1., 1.,1.,1.])
x1 = numpy.linspace(-plot_limit, plot_limit, 100)
x2 = numpy.linspace(-plot_limit, plot_limit, 100)
x,y = numpy.meshgrid(x1,x2)
pred_ex = numpy.array((numpy.ravel(x), numpy.ravel(y)))
verbose = False
mu = 1.0
C = 1.0
kdict = {'name':'linear', 'param': 0}
svm = OneNormSVM(C, kdict, verbose=verbose)
svm.train(train_ex, train_lab)
print('=====================================================================')
print('OneNormSVM objective: %f' % svm.primal_obj)
print('SVM weights: ')
print(svm.w)
print('SVM bias: %f' % svm.b)
print('=====================================================================')
svmout = svm.predict(pred_ex)
svmout.shape = (100,100)
del svm
svm = RLSVM1(C, kdict, mu, verbose=verbose)
svm.train(train_ex, train_lab)
print('=====================================================================')
print('RLSVM1 Primal objective: %f' % svm.primal_obj)
print('RLSVM1 weights: ')
print(svm.w)
print('RLSVM1 bias: %f' % svm.b)
print('Number of passes through data: %d' % svm.num_iter)
print('=====================================================================')
rlsvmout = svm.predict(pred_ex)
rlsvmout.shape = (100,100)
del svm
plot_2d_pred(x, y, svmout, train_ex, train_lab, axis_limit, 'svm')
plot_2d_pred(x, y, rlsvmout, train_ex, train_lab, axis_limit, 'dcsvm')
pylab.show()
def example_sparse():
"""Training and classification using two gaussian clouds,
use sparse classifiers
"""
#numpy.random.seed(1)
(train_ex, train_labels, test_ex, test_labels) = create_data(num_train=200,
num_test=1000, num_feat=5,
num_noise_feat = 45,
width=1.5, frac_flip=0.1,
symm_flip=True)
mu = 1.0
a = 4.0 # the exponent in zero norm
cap = 0.5 # the cap in capped one norm
reg_param = 1.0
verbose = False
#kdict = {'name':'poly', 'param': 2, 'inhomog': True}
kdict = {'name':'linear', 'param': 0}
demo_onenormsvm_dense(reg_param, kdict, verbose, train_ex, train_labels, test_ex, test_labels)
demo_onenormsvm(reg_param, kdict, verbose, train_ex, train_labels, test_ex, test_labels)
if __name__ == '__main__':
example_debug()
example_toy()
example_cloud_2d()
#example_sparse()
OptWok-0.3/examples/contextucb_demo.py 0000644 ڛ |-00000006036 12127456765 025134 0 ustar cheng.ong UNIMELB\Domain Users 0000000 0000000 """
Demo to show how to use the contextual bandits optimization problem
using the upper confidence bound Gaussian process
"""
from pylab import show
from numpy import ones, eye
from optwok.kernel import GaussKernel
from optwok.testtools import list2matrix
from optwok.io_pickle import load
from optwok.gaussian_proc import GaussianProcess
from optwok import datatools
from optwok.context_ucb import expt_cucb, expt_cucb_batch
from optwok.context_ucb import plot_min_regret, plot_average_regret
from examples.gaussianproc_demo import plot_prediction
def demo_expt_cucb():
raw_data = load('tests/gaussproc_06_20_0.1.pkl')
data = datatools.Data(raw_data.examples, raw_data.context, raw_data.labels)
print(data.examples(1))
num_context = 6
reg_param = 0.1
width = 0.5
num_epoch = 10
Ky = list2matrix([[0,1],[2],[3,4,5]], neg_corr=True)
print(Ky)
gp = GaussianProcess(GaussKernel, Ky, kparam=width, reg_param=reg_param)
gp.train(raw_data.examples[:,:1], raw_data.context[:1], raw_data.labels[:1])
expt_cucb(data, gp, 'expt_cucb.pkl', num_context, num_epoch)
Ky = ones((num_context, num_context))
gp = GaussianProcess(GaussKernel, Ky, kparam=width, reg_param=reg_param)
gp.train(raw_data.examples[:,:1], raw_data.context[:1], raw_data.labels[:1])
expt_cucb(data, gp, 'expt_cucb_cat.pkl', num_context, num_epoch)
Ky = eye(num_context)
gp = GaussianProcess(GaussKernel, Ky, kparam=width, reg_param=reg_param)
gp.train(raw_data.examples[:,:1], raw_data.context[:1], raw_data.labels[:1])
expt_cucb(data, gp, 'expt_cucb_ind.pkl', num_context, num_epoch)
plot_min_regret('expt_cucb.pkl')
plot_min_regret('expt_cucb_cat.pkl')
plot_min_regret('expt_cucb_ind.pkl')
plot_average_regret(['expt_cucb.pkl','expt_cucb_cat.pkl','expt_cucb_ind.pkl'],
['CGP-UCB','merge','ignore'])
def demo_expt_cucb_batch():
raw_data = load('tests/gaussproc_06_20_0.1.pkl')
data = datatools.Data(raw_data.examples, raw_data.context, raw_data.labels)
num_context = 6
reg_param = 0.1
width = 0.5
num_epoch = 3
batch_size = 3
Ky = list2matrix([[0,1],[2],[3,4,5]], neg_corr=True)
print(Ky)
gp = GaussianProcess(GaussKernel, Ky, kparam=width, reg_param=reg_param)
gp.train(raw_data.examples[:,:1], raw_data.context[:1], raw_data.labels[:1])
expt_cucb_batch(data, gp, 'expt_cucb_kbest.pkl', num_context, num_epoch,
batch_size=batch_size, mode='kbest')
del gp
gp = GaussianProcess(GaussKernel, Ky, kparam=width, reg_param=reg_param)
gp.train(raw_data.examples[:,:1], raw_data.context[:1], raw_data.labels[:1])
expt_cucb_batch(data, gp, 'expt_cucb_batch.pkl', num_context, num_epoch,
batch_size=batch_size)
del gp
plot_min_regret('expt_cucb_batch.pkl')
plot_min_regret('expt_cucb_kbest.pkl')
plot_average_regret(['expt_cucb_batch.pkl','expt_cucb_kbest.pkl'], ['greedy','kbest'])
if __name__ == '__main__':
demo_expt_cucb()
demo_expt_cucb_batch()
show()
OptWok-0.3/examples/emil_demo.py 0000644 ڛ |-00000004402 12127456755 023676 0 ustar cheng.ong UNIMELB\Domain Users 0000000 0000000 """
Some code for testing and also as a usage demo
See bioweb.me/emil for benchmark experiments
"""
from numpy import r_
from numpy.random import seed
from matplotlib.pyplot import show
from optwok.mil.mi_data import EllipsoidData, MIMeanData
from optwok.mil.utility import create_perm, permfile_name
from optwok.mil.mi_classifier import MeanSVM
from optwok.mil.mi_classifier_emil import eMILDcaSocp, eMILDcaBfgs
from examples.emil_synthetic_ellipsoids import random_ellipsoids
from examples.emil_plotting import plot_ellipsoids, plot_hyperplane
def example_ellipsoid_2d():
"""Create some random ellipsoids, train and plot the resulting classifier"""
reg_param = 1.2
num_pos = 16
num_neg = 10
num_ex = num_pos + num_neg
num_feat = 2
seed(1)
# Create some data
perm_desc = 'example'
perm_dataset = 'synthetic_ellipsoid_2d'
create_perm('.', perm_dataset, perm_desc, 10, num_ex)
means, covariances, labels = random_ellipsoids(num_neg=num_neg, num_pos=num_pos,
n=num_feat, mean_radius=7)
data = EllipsoidData(means, covariances, labels)
meandata = MIMeanData(means, labels)
train_data, test_data = data.get_train_pred_data(permfile=permfile_name('.', perm_dataset, perm_desc), perm_idx=1,
model_selection=False, frac_ms=0.5, fold_cv=None, num_cv=None)
print('Training mean SVM for initialization')
meanclassifier = MeanSVM()
mean_train_data, _ = meandata.get_train_pred_data(permfile=permfile_name('.', perm_dataset, perm_desc), perm_idx=1,
model_selection=False, frac_ms=0.5, fold_cv=None, num_cv=None)
meanclassifier.train(mean_train_data, reg_param)
classifier = eMILDcaBfgs()
print('Training eMIL')
classifier.train(train_data, reg_param, huber_alpha=0.1, v0=r_[meanclassifier.w, meanclassifier.b])
print('Predicting')
pred = classifier.predict(test_data)
print(pred)
print(test_data.labels)
# Plot training
plot_ellipsoids(train_data.means, train_data.covariances, train_data.labels, color=True)
plot_hyperplane(classifier.w, classifier.b)
show()
if __name__ == '__main__':
example_ellipsoid_2d()
OptWok-0.3/examples/emil_plotting.py 0000644 ڛ |-00000004632 12127232011 024570 0 ustar cheng.ong UNIMELB\Domain Users 0000000 0000000 '''
Created on 05.09.2012
@author: kgabriel
'''
from numpy import linalg
from sys import float_info
from math import atan2
from matplotlib.patches import Ellipse
from numpy import array, logical_not, sqrt, pi
from numpy.linalg import norm
from matplotlib.pyplot import plot, xlim, gca, Polygon
def plot_ellipsoids(q, S, y, color=False):
''' plot 2d-ellipsoids '''
for qi, Si, yi in zip(q, S, y):
plot_ellipse(qi, Si, label=yi, color=color)
def plot_ellipse(q, Q, edge = 'k', face = 'none', scale = 1, label=None, color=False, alpha=0.7):
# Plots the ellipse defined by:
U, s , _ = linalg.svd(Q)
# print 's0: ', s[0]
# print 's1: ', s[1]
orient = atan2(U[1, 0], U[0, 0]) * 180 / pi
ellipsePlot = Ellipse(xy = q, width = 2 * scale * sqrt(s[0]),
height = 2 * scale * sqrt(s[1]), angle = orient, facecolor = face,
edgecolor = edge)
ax = gca()
ax.set_aspect('equal')
if label is None:
l = '.'
elif label > 0:
l = '+'
c = 'g'
else:
l = 'o'
c = 'r'
if color:
ellipsePlot.set_facecolor(c)
ellipsePlot.set_alpha(alpha)
ax.add_patch(ellipsePlot);
plot(q[0], q[1], 'k' + l)
return ellipsePlot
def plot_vector(origin, vector, color='k'):
''' plot vector from origin '''
dest = origin + vector
plot([origin[0], dest[0]], [origin[1], dest[1]], '-', color = color)
plot(origin[0], origin[1], 'o', color = color)
plot(dest[0], dest[1], 'x', color = color)
def plot_hyperplane(w,b,lcolor='black',pcolor='green',lstyle='solid',alpha=0.5):
''' plot hyperplane defined by w and b, plus margin 1/|w| '''
xl = array(xlim())
if w[1] == 0:
w[1] = float_info.min
y = (-w[0] * xl - b) / w[1]
plot(xl, y, color=lcolor, linestyle=lstyle)
margin = 1 / norm(w)
patch = Polygon([[xl[0], y[0] - margin], [xl[0], y[0] + margin], [xl[1], y[1] + margin], [xl[1], y[1]-margin]],
fill=True, closed=True, color=pcolor,alpha=alpha)
gca().add_patch(patch)
def plot_hyperplane_nomargin(w, b):
# plot hyperplane w'x + b = 0 in 2d
xl = array(xlim())
plot(xl, (-w[0] * xl - b) / w[1])
def plot_points(p, y):
p, y = array(p), array(y)
pos = y > 0
plot(p[pos,0], p[pos,1], 'go')
plot(p[logical_not(pos),0], p[logical_not(pos),1], 'ro')
OptWok-0.3/examples/emil_synthetic_ellipsoids.py 0000644 ڛ |-00000003311 12127250143 027170 0 ustar cheng.ong UNIMELB\Domain Users 0000000 0000000 '''
Created on 05.09.2012
@author: kgabriel
'''
from __future__ import division
from scipy import random
from numpy import diag, ones, dot, pi, cos, sin, array
from numpy.random import rand
def random_ellipsoids(num_pos, num_neg, n, var_pos=1, var_neg=1, mean_radius=2, axisp=False):
''' generate num_pos positive and num_neg negative random
ellipsoids of dimensionality n '''
offset_p = random.randn(n) + 1
offset_n = random.randn(n) - 1
qp = random.randn(num_pos, n) * var_pos + offset_p
qn = random.randn(num_neg, n) * var_neg + offset_n
S = []
for _ in range(num_pos + num_neg):
#print 'next random covariance...'
if not axisp:
S.append(random_covariance_matrix(n, mean_radius))
else:
S.append(diag(rand(n))*mean_radius)
return array(list(qp) + list(qn)), array(S), array(list(ones(num_pos))+ list(-1*ones(num_neg)))
def random_covariance_matrix(n, scale=1):
''' generate random covariance matrix of dimension n '''
# Use Householder reflections to build orthonormal matrix and then C = QSQ'
# I = eye(n)
# Q = I
# K = 2
# for i in range(K*n*n):
# v = random.rand(n)
# v = v / linalg.norm(v)
# P = I - outer(v,v)
# Q = dot(P, Q)
#
# S = diag(random.rand(n))
#
# C = dot(Q,dot(S, Q.transpose()))
# Use C=Q'Q
Q = random.rand(n,n)
C = dot(Q, Q.transpose())
if n==2:
if rand(1) > 0.5:
theta = 0
else:
theta = pi/2
C = dot(dot(rotate(theta).T,C),rotate(theta))
return scale * C
def rotate(theta):
return array([[cos(theta), -sin(theta)], [sin(theta), cos(theta)]])
OptWok-0.3/examples/gaussianproc_demo.py 0000644 ڛ |-00000007461 12055534750 025446 0 ustar cheng.ong UNIMELB\Domain Users 0000000 0000000 """
Some code for testing Gaussian process regression, also as a demo
"""
import os
from matplotlib.pyplot import plot, fill, show, axis, figure
from numpy import concatenate, arange, ones, sqrt
from numpy import flatnonzero
from optwok.testtools import pair2vec, flatten_list, list2matrix
from optwok.kernel import GaussKernel
from optwok.io_pickle import load
from optwok.gaussian_proc import GaussianProcess, GaussianProcessScalar
def _plot_1D_gp(data_ex, data_lab, test_range, preds, pred_var, colour):
plot(test_range.flatten(), preds, colour, hold=True)
sd = 2.0*sqrt(pred_var)
var_x = concatenate((test_range.flatten(), test_range.flatten()[::-1]))
var_y = concatenate((preds+sd, (preds-sd)[::-1]))
fill(var_x, var_y, edgecolor='w', facecolor=colour, alpha=0.3)
plot(data_ex, data_lab, colour+'o')
def plot_prediction(X, Z, Y, gp):
"""Plot the data points, the predictions and the predictive variance"""
colour_list = ['b','g','r','c','m','k']
min_val = min(X)
max_val = max(X)
test_range = arange(min_val-1, max_val+1, 0.1)
num_test = len(test_range)
test_range.shape = (1,num_test)
num_context = int(max(Z))+1
for cur_cont in range(num_context):
(preds, pred_var) = gp.predict((test_range, cur_cont*ones(num_test)))
data_ex = X[flatnonzero(Z==cur_cont)]
data_lab = Y[flatnonzero(Z==cur_cont)]
_plot_1D_gp(data_ex, data_lab, test_range, preds, pred_var, colour_list[cur_cont])
def generate_toy_data(num_ex=10, domain=5, noise_level=0.1, mix_list=[[0,1],[2]]):
"""Generate a multi-output Gaussian process dataset with dependencies between outputs."""
from optwok.simdata import gp_gen_pair, save_gp_context_data
num_class = len(flatten_list(mix_list))
dataname = 'gaussproc_%02d_%d_%1.1f' % (num_class, num_ex, noise_level)
print('Generating %s' % dataname)
(X,Z,Y) = gp_gen_pair(num_ex, 1, domain, noise_level, mix_list)
save_gp_context_data(X, Z, Y, dataname, data_dir='tests')
def toy_data_gp(num_ex=10, noise_level=0.1, mix_list=[[0,1],[2]]):
"""Estimate and plot the 1D Gaussian process"""
num_class = len(flatten_list(mix_list))
data = load('tests/gaussproc_%02d_%d_%1.1f.pkl' % (num_class, num_ex, noise_level))
#Ky = list2matrix(mix_list, noise=0.1) # so that can observe difference bewteen 0 and 1
Ky = list2matrix(mix_list)
print('Estimating mean and variance of Gaussian Process')
gp = GaussianProcess(GaussKernel, Ky, reg_param=0.1)
gp.train(data.examples, data.context, data.labels)
plot_prediction(data.examples.flatten(), data.context, data.labels, gp)
def toy_data_gp_scalar(num_ex=10, num_class=3, noise_level=0.1):
"""Estimate and plot the 1D scalar output GP"""
colour_list = ['b','g','r','c','m','k']
test_ex = arange(-1, 6, 0.1)
num_test = len(test_ex)
test_ex.shape = (1,num_test)
data = load('tests/gaussproc_%02d_%d_%1.1f.pkl' % (num_class, num_ex, noise_level))
for context in range(num_class):
gp = GaussianProcessScalar(GaussKernel, reg_param=0.1)
idx = flatnonzero(data.context == context)
gp.train(data.examples[:,idx], data.labels[idx])
(preds, pred_var) = gp.predict(test_ex)
_plot_1D_gp(data.examples[:,idx].flatten(), data.labels[idx],
test_ex, preds, pred_var, colour_list[context])
if __name__ == '__main__':
num_ex = 20
#generate_toy_data(num_ex, mix_list=[[0,1],[2],[3,4,5]])
toy_data_gp(num_ex, mix_list=[[0,1],[2],[3,4,5]])
print('mix_list=[[0,1],[2],[3,4,5]]')
figure()
toy_data_gp(num_ex, mix_list=[[0,1,2,3,4,5]])
print('mix_list=[[0,1,2,3,4,5]]')
figure()
toy_data_gp(num_ex, mix_list=[[0],[1],[2],[3],[4],[5]])
print('mix_list=[[0],[1],[2],[3],[4],[5]]')
figure()
toy_data_gp_scalar(num_ex, 6)
print('scalar GP')
show()
OptWok-0.3/examples/kernelopt_demo.py 0000644 ڛ |-00000014720 12055551176 024750 0 ustar cheng.ong UNIMELB\Domain Users 0000000 0000000 """Demo for learning the output kernel"""
from numpy import vstack
from matplotlib.pyplot import matshow, gray, colorbar, savefig, show
from optwok.kernelopt import KernelOptLS, KernelOptLin
def show_kernel(kmat):
"""Use matshow with gray colormap"""
matshow(kmat)
gray()
colorbar()
def show_kernel_dev(kmat):
"""Use matshow with a nice colormap"""
minval = min(kmat.flatten())
maxval = max(kmat.flatten())
absval = max([abs(minval),abs(maxval)])
near_zero = 0.1*absval
if minval < 0.0:
zeroval = -minval/(2.0*absval)
else:
zeroval = 0.0
small_neg = zeroval-near_zero
small_pos = min([1.0, zeroval+near_zero])
if small_neg >= 0.0:
blueval = (small_neg-minval)/(small_neg + absval)
assert(blueval>=0.0 and blueval<=1.0)
print(blueval)
cdict = {'red': ((0.0, blueval, blueval),
(small_neg, 1.0, 1.0),
(zeroval, 1.0, 1.0),
(small_pos, 1.0, 1.0),
(1.0, 1.0, 1.0)),
'green': ((0.0, 0.8-blueval, 0.8-blueval),
(small_neg, 1.0, 1.0),
(zeroval, 1.0, 1.0),
(small_pos, 1.0, 1.0),
(1.0, 0.2, 0.2)),
'blue': ((0.0, 1-blueval, 1-blueval),
(small_neg, 1.0, 1.0),
(zeroval, 1.0, 1.0),
(small_pos, 1.0, 1.0),
(1.0, 0.0, 0.0))
}
else:
small_neg = 0.0
cdict = {'red': ((0.0, 1.0, 1.0),
(small_neg, 1.0, 1.0),
(zeroval, 1.0, 1.0),
(small_pos, 1.0, 1.0),
(1.0, 1.0, 1.0)),
'green': ((0.0, 1.0, 1.0),
(small_neg, 1.0, 1.0),
(zeroval, 1.0, 1.0),
(small_pos, 1.0, 1.0),
(1.0, 0.2, 0.2)),
'blue': ((0.0, 1.0, 1.0),
(small_neg, 1.0, 1.0),
(zeroval, 1.0, 1.0),
(small_pos, 1.0, 1.0),
(1.0, 0.2, 0.2))
}
blue_red = LinearSegmentedColormap('BlueRedZero', cdict)
matshow(kmat, cmap=blue_red)
colorbar()
def test_kernelopt():
"""Small test for class KernelOpt"""
from numpy.random import randn
X = randn(10,50)
Kx = array(matrix(X).T*matrix(X))
alpha = randn(50,4)
print(normalize_unit_diag(matrix(alpha).T*matrix(Kx)*matrix(alpha)))
kern_mach = KernelOpt()
kern_mach.classifier.Kx = Kx
print(kern_mach.solve_opt(alpha))
def _create_test_data(mix_list=[[0,1],[2],[3],[4]],
num_point=[100, 50, 50, 50],
num_feat=20, edge_length=3):
"""Create an multiclass dataset with mixed classes"""
from optwok.simdata import multi_cloudgen_mix
num_blobs = len(mix_list)
train_data = multi_cloudgen_mix(num_point, num_feat, num_blobs, edge_length, mix_list)
(metadata, examples, labels) = train_data
return (metadata, examples, labels)
def test_kerneloptls():
"""Small test for class KernelOptLS"""
from optwok.kernel import LinearKernel
(metadata, examples, labels) = _create_test_data()
print(metadata)
svm = KernelOptLS(reg_param=1000.0, kernel=LinearKernel(), verbose=True)
svm.train(examples, labels)
train_out = svm.predict(examples)
print(vstack([train_out, labels]))
show_kernel(svm.Ky)
savefig('testKy.pdf')
def test_gp(mix_list=[[0,1],[2]]):
"""Small test for class KernelOptLS,
for Gaussian process regression"""
from optwok.kernel import LinearKernel
from optwok.simdata import gp_gen
from optwok.evaluation import rmse
(examples, labels) = gp_gen(100, mix_list)
svm = KernelOptLSGP(reg_param=10.0, kernel=LinearKernel(), verbose=True)
svm.train(examples, labels)
train_out = svm.predict(examples)
#print(vstack([train_out, labels]))
print('Error:')
print(rmse(train_out, labels))
show_kernel(svm.Ky)
def test_reg_path_Ky():
"""The regularization path"""
from optwok.kernel import LinearKernel
(metadata, examples, labels) = _create_test_data()
print(metadata)
svm = KernelOptLS(kernel=LinearKernel(), verbose=True)
svm.train_reg_path(examples, labels)
for ix in range(svm.all_Ky.shape[2]):
show_kernel(normalize_unit_diag(svm.all_Ky[:,:,ix]))
title(ix)
def test_reg_path_acc():
"""Check accuracy for whole regularization path"""
from optwok.kernel import LinearKernel
from optwok.evaluation import accuracy
from pylab import semilogx, xlabel, ylabel, legend
(metadata, examples, labels) = _create_test_data()
print(metadata)
svm = KernelOptLS(kernel=LinearKernel(), verbose=True)
svm.train_reg_path(examples, labels)
(metadata, examples, labels) = _create_test_data()
all_preds = svm.predict_reg_path(examples)
all_acc = zeros(len(svm.all_reg_param))
for ix in range(all_preds.shape[1]):
all_acc[ix] = accuracy(all_preds[:,ix], labels)
svm.reg_param = 1.0
svm.infer_sv(eye(svm.num_classes))
diag_acc = accuracy(svm.predict(examples), labels)
semilogx(svm.all_reg_param, all_acc, hold=True, label='learn Ky')
semilogx(svm.all_reg_param, all_acc, 'rx')
semilogx([svm.all_reg_param[0],svm.all_reg_param[-1]], [diag_acc, diag_acc], 'g-', label='identity')
xlabel('regularization parameter')
ylabel('accuracy (higher is better)')
legend()
def test_kerneloptlin():
"""Small test for class KernelOptLS"""
from optwok.kernel import LinearKernel
(metadata, examples, labels) = _create_test_data()
print(metadata)
svm = KernelOptLS(reg_param=1000.0, kernel=LinearKernel(), verbose=True)
svm.train(examples, labels)
train_out = svm.predict(examples)
print(train_out.shape)
print(vstack([train_out, labels]))
show_kernel(svm.Ky)
savefig('testKy.pdf')
svm = KernelOptLin(reg_param=1000.0, verbose=True)
svm.train(examples, labels)
train_out = svm.predict(examples)
print(train_out.shape)
print(vstack([train_out, labels]))
show_kernel(svm.Ky)
savefig('testLin.pdf')
if __name__ == '__main__':
#test_kerneloptls()
#test_reg_path_Ky()
#test_reg_path_acc()
#test_gp()
test_kerneloptlin()
show()
OptWok-0.3/examples/multiclass_demo.py 0000644 ڛ |-00000010443 12055534622 025120 0 ustar cheng.ong UNIMELB\Domain Users 0000000 0000000 """
Some code for testing and also as a demo
"""
import pylab
from numpy import eye, vstack, ones, matrix
from numpy import nonzero, abs, zeros, sum, sqrt, unique
from optwok.simdata import multi_cloudgen, multi_cloudgen_mix
from optwok.testtools import plot_confusion
from optwok.multiclass import StructPredictor
from optwok.evaluation import accuracy, accuracy_mix
from optwok.kernelopt import KernelOpt
from optwok.kernel import JointKernel, LinearKernel, GaussKernel
from optwok.kernel import normalize_unit_diag
def demo_identity_Ky():
print('demo_identity_Ky')
num_point = 100
num_feat = 10
num_class = 3
edge_length = 3
train_data = multi_cloudgen(num_point, num_feat, num_class, edge_length)
(metadata, examples, labels) = train_data
print(metadata)
kernel = JointKernel(LinearKernel(), num_classes=num_class)
svm = StructPredictor(1.0, kernel)
svm.train(examples, labels)
train_out = svm.predict(examples)
train_acc = accuracy(train_out, labels)
print('Training accuracy:\t %1.2f' % train_acc)
test_data = multi_cloudgen(num_point, num_feat, num_class, edge_length)
(metadata, test_ex, test_lab) = test_data
svmout = svm.predict(test_ex)
acc = accuracy(svmout, test_lab)
print('Test accuracy:\t\t %1.2f' % acc)
def demo_merged_classes():
"""The examples come from different Gaussians, but have the same label."""
print('demo_merged_classes')
num_point = 100
num_feat = 10
num_class = 4
edge_length = 3
mix_list = [[0,1],[2],[3]]
train_data = multi_cloudgen_mix(num_point, num_feat, num_class, edge_length, mix_list)
(metadata, examples, labels) = train_data
print(metadata)
kernel = JointKernel(GaussKernel(), num_classes=3)
svm = StructPredictor(1.0, kernel)
svm.train(examples, labels)
train_out = svm.predict(examples)
train_acc = accuracy(train_out, labels)
print('Training accuracy:\t %1.2f' % train_acc)
test_data = multi_cloudgen_mix(num_point, num_feat, num_class, edge_length, mix_list)
(metadata, test_ex, test_lab) = test_data
svmout = svm.predict(test_ex)
acc = accuracy(svmout, test_lab)
print('Test accuracy:\t\t %1.2f' % acc)
def demo_split_classes():
"""The examples from the same Gaussians may have different labels."""
print('demo_split_classes')
num_point = 100
num_feat = 6
num_class = 3
edge_length = 2
mix_list = [[0,1],[2],[3]]
Ky = eye(4)
Ky[0,1] = 0.99
Ky[1,0] = 0.99
print(Ky)
train_data = multi_cloudgen_mix(num_point, num_feat, num_class, edge_length, mix_list)
(metadata, examples, labels) = train_data
print(metadata)
test_data = multi_cloudgen_mix(num_point, num_feat, num_class, edge_length, mix_list)
(metadata, test_ex, test_lab) = test_data
kernel = JointKernel(LinearKernel(), num_classes=4)
print('============== With identity Ky ==============')
svm = StructPredictor(1.0, kernel)
svm.train(examples, labels)
train_out = svm.predict(examples)
#print(vstack([train_out, labels]))
print('Training accuracy:\t\t %1.2f' % accuracy(train_out, labels))
print('Training accuracy (mix):\t %1.2f' % accuracy_mix(train_out, labels, mix_list))
svmout = svm.predict(test_ex)
#print(vstack([svmout, test_lab]))
print('Test accuracy:\t\t\t %1.2f' % accuracy(svmout, test_lab))
print('Test accuracy (mix):\t\t %1.2f' % accuracy_mix(svmout, test_lab, mix_list))
Ktarget = normalize_unit_diag(matrix(svm.alpha).T*matrix(svm.kernel.Kx.kmat)*matrix(svm.alpha))
print(Ktarget)
print('============== With mix Ky ==============')
svm.kernel.Ky = Ky
svm.train(examples, labels)
train_out = svm.predict(examples)
#print(vstack([train_out, labels]))
print('Training accuracy:\t\t %1.2f' % accuracy(train_out, labels))
print('Training accuracy (mix):\t %1.2f' % accuracy_mix(train_out, labels, mix_list))
svmout = svm.predict(test_ex)
#print(vstack([svmout, test_lab]))
print('Test accuracy:\t\t\t %1.2f' % accuracy(svmout, test_lab))
print('Test accuracy (mix):\t\t %1.2f' % accuracy_mix(svmout, test_lab, mix_list))
print(normalize_unit_diag(matrix(svm.alpha).T*matrix(svm.kernel.Kx.kmat)*matrix(svm.alpha)))
if __name__ == '__main__':
demo_identity_Ky()
demo_merged_classes()
demo_split_classes()
OptWok-0.3/examples/named_array_demo.py 0000644 ڛ |-00000005551 12075664762 025241 0 ustar cheng.ong UNIMELB\Domain Users 0000000 0000000 from optwok.named_array import NArray
import subprocess
if __name__ == '__main__':
res = NArray(['CV','Task','Algorithm'])
print('Initial conditions')
print(res.ndim)
print(res.dim_names)
print(res.shape)
res.add_keys(range(5), 'CV')
print('Added 5 CV elements')
print(res.shape)
for task in ['A','B','A']:
res.add_key(task, 'Task')
for alg in ['svm','mtl','ky']:
res.add_key(alg, 'Algorithm')
print('Added tasks and methods')
print(res.shape)
print('Still nothing inside')
print(res.content)
# Note that you can fill in values in any order
values = range(5,100)
val_ix = 0
for cv in range(3):
for task in ['A','B']:
for alg in ['ky','svm','mtl']:
res.fill_value(values[val_ix], [task, cv, alg],
['Task','CV','Algorithm'])
val_ix += 1
print('After CV=%d' % cv)
print(res.content)
res2 = NArray(['CV','Task','Algorithm'])
res2.add_keys(range(5), 'CV')
res2.add_keys(['A','B'], 'Task')
res2.add_keys(['svm','mtl','ky'], 'Algorithm')
for cv in [2,3,4]:
for task in ['A','B']:
for alg in ['ky','svm','mtl']:
res.fill_value(values[val_ix], [task, cv, alg],
['Task','CV','Algorithm'])
val_ix += 1
print(res2.content)
print('Merging')
res.merge(res2)
del res2
print(res.content)
print('Get some entries')
print(res.get_value(['B','mtl',2], ['Task','Algorithm','CV']))
print(res.get_value(['mtl',2,'B'], ['Algorithm','CV','Task']))
print(res.get_value([0,'A','ky'], ['CV', 'Task', 'Algorithm']))
print('Get all keys')
print(res.keys('Task'))
print(res.keys('Algorithm'))
print('Get a slice')
print(res.slice('Task','B'))
print('A thinner slice')
print(res.slice(['Task','Algorithm'],['B','svm']))
print('Bad slices')
try:
print(res.slice('Task','Z'))
except IndexError as msg:
print(msg)
try:
print(res.slice('Tas','B'))
except IndexError as msg:
print(msg)
try:
print(res.slice(['Task','Alg'],['B','svm']))
except IndexError as msg:
print(msg)
try:
print(res.slice(['Task','Algorithm'],['Z','svm']))
except IndexError as msg:
print(msg)
print('Saving and loading')
res.save('na_demo.json')
new_res = NArray(['dummy'])
new_res.load('na_demo.json')
print('Get all keys')
print(new_res.keys('Task'))
print(new_res.keys('Algorithm'))
print('Get a slice')
print(new_res.slice('Task','B'))
print('A thinner slice')
print(new_res.slice(['Task','Algorithm'],['B','svm']))
print('Save in matlab format')
new_res.savemat('na_demo.mat')
subprocess.check_call('rm na_demo.json na_demo.mat', shell=True)
OptWok-0.3/examples/score_demo.py 0000644 ڛ |-00000002474 12055534135 024057 0 ustar cheng.ong UNIMELB\Domain Users 0000000 0000000 from numpy import array
from optwok.evaluation import accuracy, balanced_accuracy
from optwok.evaluation import stats_empirical, stats_binormal
def _demo_show(tpr, fpr, ppv, auc, apr):
from pylab import plot, figure, axis, title, xlabel, ylabel
print('AUC=%f, APR=%f' % (auc, apr))
figure()
if len(tpr) < 100:
plot(fpr, tpr, 'bo', hold=True)
plot(fpr, tpr, 'b-')
axis([0,1,0,1])
xlabel('False positive rate')
ylabel('True positive rate')
title('ROC')
figure()
if len(ppv) < 100:
plot(tpr, ppv, 'bo', hold=True)
plot(tpr, ppv, 'b-')
axis([0,1,0,1])
ylabel('Positive predictive value')
xlabel('True positive rate')
title('PRC')
def demo_stats_binary():
from numpy.random import rand
from pylab import show
labels = array([-1, 1., -1., 1., -1., -1., -1., 1., -1., -1.])
output = array([-0.4, 0.9, 0.4, 0.3, -0.1, -0.6, -0.7, -0.2, -1., -0.9,])
acc = accuracy(output, labels)
balacc = balanced_accuracy(output, labels)
print('ACC=%f, BAL=%f' % (acc, balacc))
tpr, fpr, ppv, auc, apr = stats_empirical(output, labels)
_demo_show(tpr, fpr, ppv, auc, apr)
tpr, fpr, ppv, auc, apr = stats_binormal(output, labels)
_demo_show(tpr, fpr, ppv, auc, apr)
show()
if __name__ == '__main__':
demo_stats_binary()
OptWok-0.3/examples/socp_demo.py 0000644 ڛ |-00000001075 12055534115 023702 0 ustar cheng.ong UNIMELB\Domain Users 0000000 0000000 '''
Created on 16.12.2011
@author: kgabriel
'''
from optwok.socp import SOCP
from numpy import array
if __name__ == '__main__':
socp = SOCP(3, verbose = True)
socp.set_objective(array([-2, 1, 5.]))
A = array([[-13., 3, 5], [-12, 12, -6]])
b = array([-3, -2.])
c = array([-12., -6, 5])
d = -12
socp.add_ineq_constraints(A, b, c, d)
A = array([[-3., 6, 2], [1, 9, 2], [-1, -19, 3]])
b = array([0, 3, -42.])
c = array([-3, 6, -10.])
d = 27
socp.add_ineq_constraints(A, b, c, d)
socp.solve()
print(socp.solution)
OptWok-0.3/optwok/ 0000755 ڛ |-00000000000 12127457270 021070 5 ustar cheng.ong UNIMELB\Domain Users 0000000 0000000 OptWok-0.3/optwok/__init__.py 0000644 ڛ |-00000000000 11731540564 023166 0 ustar cheng.ong UNIMELB\Domain Users 0000000 0000000 OptWok-0.3/optwok/classifier.py 0000644 ڛ |-00000102166 12055565522 023574 0 ustar cheng.ong UNIMELB\Domain Users 0000000 0000000 """Implementation of the optimization methods for the
empirical loss + regularizer
framework, for binary clssification.
The algorithms are primal variable algorithms.
We consider the following losses:
- hinge loss
- ramp loss
We consider the following regularizers:
- l2
- l1
- capped l1
- l0 (approximated by exponent)
"""
from time import time
import numpy
from numpy.random import randn
from numpy import matrix, array, vstack, hstack
from numpy import zeros, ones, transpose, nonzero
from numpy import sign, eye, where, dot, diag
from numpy.linalg import norm
from numpy import newaxis, outer, inner, mean, dot
from optwok.sip import SILP, SILPS, SIQP
from optwok.poly_feature import get_poly_features
from optwok.kernel import KernelFactory
from optwok.classifier_base import Classifier, ConvexClassifier
from optwok.classifier_base import Loss, HingeLoss, HingeLossKernel
from optwok.classifier_base import HuberHingeLoss, RampLoss, SlantLoss
from optwok.classifier_base import Regularizer
from optwok.classifier_base import Reg_l2, Reg_l1, Reg_l1cap, Reg_l0
from optwok.classifier_base import Reg_elastic_net, Reg_graph_net
from cvxopt import sparse
from cvxopt import matrix as cm
class SVMPrimal(ConvexClassifier):
"""
Standard l2 regularized SVM, in the primal
"""
def __init__(self, reg_param, kdict, verbose=False):
ConvexClassifier.__init__(self, reg_param, kdict, verbose)
self.loss = HingeLoss()
self.regularizer = Reg_l2()
def solve_opt(self):
"""Solve the SVM QP
Use cvxopt qp solver which expects the following format:
min_x 1/2 x'Px + q'x
st Gx <= h
Ax = b
We organise the variables of the SVM as (w,b,xi).
"""
yvec = self.y.copy()
yvec.shape = (len(self.y), 1)
if self.verbose: print('Solving QP')
n = len(self.y)
d = self.train_ex.shape[0]
m = n+d+1
svm = SIQP(m, self.verbose)
P = zeros((m, m))
P[:d,:d] = 2.0*self.reg_param*eye(d)
q = vstack([zeros((d+1,1)), ones((n,1))])
svm.set_objective(P,q)
svm.add_ineq_constraints(hstack([zeros((n,d+1)), -eye(n)]), zeros((n,1)))
G = hstack([-yvec*transpose(self.train_ex), -yvec, -eye(n)])
svm.add_ineq_constraints(G, -ones((n,1)))
svm.solve()
if svm.optimal:
self.w = svm.solution[:d]
self.w.shape = (d,1)
self.b = svm.solution[d]
class SVMDual(ConvexClassifier):
"""
Standard l2 regularized SVM, in the dual, not finished yet. TODO: test time, arbitrary kernel
"""
def __init__(self, reg_param, kdict, verbose=False):
ConvexClassifier.__init__(self, reg_param, kdict, verbose)
self.loss = HingeLoss()
self.regularizer = Reg_l2()
self.kernel = KernelFactory(kdict)
self.alpha = array([])
def train(self, train_ex, train_lab):
"""Wrapper to convert data to feature matrix,
and solve the optimization"""
self.train_ex = train_ex
self.kernel.compute(train_ex)
self.y = train_lab
t_start = time()
self.solve_opt()
t_end = time()
# Collect statistics about optimization
self.train_time = t_end-t_start
def predict(self, pred_ex):
"""Wrapper to do prediction"""
Kpred = self.kernel.compute_test(pred_ex)
Ymat = diag(self.y)
scores = dot(dot(Kpred.T,Ymat),self.alpha) + self.b
return scores
def solve_opt(self):
"""Solve the SVM QP
Use cvxopt qp solver which expects the following format:
min_x 1/2 x'Px + q'x
st Gx <= h
Ax = b
"""
n = len(self.y)
qp = SIQP(n, use_mosek = True)
q = -ones((n, 1), dtype = 'double')
ll = outer(self.y, self.y)
#K = inner(self.train_ex, self.train_ex)
K = self.kernel.kmat
P = ll * K
qp.set_objective(P, q)
qp.add_eq_constraints(array([self.y]), array([[0]]))
qp.add_ineq_constraints(eye(n), (self.reg_param * ones(n))[..., newaxis])
qp.add_ineq_constraints(-1. * eye(n), zeros((n, 1)))
qp.solve(warn = True)
self.alpha = qp.solution
sel = (qp.solution > self.epsilon) & (qp.solution < self.reg_param-self.epsilon)
pred = dot(dot(K[sel,:],diag(self.y)),self.alpha)
b = self.y[sel] - pred
self.b = mean(b)
class OneNormSVM_dense(ConvexClassifier):
"""
One norm SVM
"""
def __init__(self, reg_param, kdict, verbose=False):
ConvexClassifier.__init__(self, reg_param, kdict, verbose)
self.loss = HingeLoss()
self.regularizer = Reg_l1()
def solve_opt(self):
"""Solve the linear program corresponding to the one norm SVM.
Use cvxopt lp solver which expects the following format:
min_x c'x
st Gx <= h
Ax = b
We organise the variables of the SVM as (w,b,xi,eta).
"""
yvec = self.y.copy()
yvec.shape = (len(self.y), 1)
if self.verbose: print('Solving LP')
n = len(self.y)
d = self.train_ex.shape[0]
m = n+2*d+1
svm = SILP(m, self.verbose)
svm.set_objective(vstack([zeros((d+1,1)), ones((n,1)),
self.reg_param*ones((d,1))]))
G = hstack([-yvec*transpose(self.train_ex), -yvec, -eye(n), zeros((n,d))])
svm.add_ineq_constraints(G, -ones((n,1)))
svm.add_ineq_constraints(hstack([zeros((n,d+1)),-eye(n),zeros((n,d))]), zeros((n,1)))
svm.add_ineq_constraints(hstack([zeros((d,d+1)),zeros((d,n)),-eye(d)]), zeros((d,1)))
svm.add_ineq_constraints(hstack([eye(d), zeros((d,n+1)), -eye(d)]), zeros((d,1)))
svm.add_ineq_constraints(hstack([-eye(d), zeros((d,n+1)), -eye(d)]), zeros((d,1)))
svm.solve()
if svm.optimal:
self.w = svm.solution[:d]
self.w.shape = (d,1)
self.b = svm.solution[d]
class OneNormSVM(ConvexClassifier):
"""
One norm SVM, with sparse matrices as constraints
Should behave exactly the same as the non sparse version
"""
def __init__(self, reg_param, kdict, verbose=False):
ConvexClassifier.__init__(self, reg_param, kdict, verbose)
self.loss = HingeLoss()
self.regularizer = Reg_l1()
def solve_opt(self):
"""Solve the linear program corresponding to the one norm SVM.
Use cvxopt lp solver which expects the following format:
min_x c'x
st Gx <= h
Ax = b
We organise the variables of the SVM as (w,b,xi,eta).
"""
yvec = self.y.copy()
yvec.shape = (len(self.y), 1)
if self.verbose: print('Solving LP')
n = len(self.y)
d = self.train_ex.shape[0]
m = n+2*d+1
svm = SILPS(m, self.verbose)
svm.set_objective(vstack([zeros((d+1,1)), ones((n,1)),
self.reg_param*ones((d,1))]))
G = sparse(cm(hstack([-yvec*transpose(self.train_ex), -yvec, -eye(n), zeros((n,d))])))
svm.add_ineq_constraints(G, -ones((n,1)))
svm.add_ineq_constraints(sparse(cm(hstack([zeros((n,d+1)),-eye(n),zeros((n,d))]))), zeros((n,1)))
svm.add_ineq_constraints(sparse(cm(hstack([zeros((d,d+1)),zeros((d,n)),-eye(d)]))), zeros((d,1)))
svm.add_ineq_constraints(sparse(cm(hstack([eye(d), zeros((d,n+1)), -eye(d)]))), zeros((d,1)))
svm.add_ineq_constraints(sparse(cm(hstack([-eye(d), zeros((d,n+1)), -eye(d)]))), zeros((d,1)))
svm.solve()
if svm.optimal:
self.w = svm.solution[:d]
self.w.shape = (d,1)
self.b = svm.solution[d]
class DiffConvexClassifier(Classifier):
"""
Classifiers which have difference of convex training methods
"""
def __init__(self, reg_param, kdict, verbose=False, rand_init=False):
Classifier.__init__(self, reg_param, kdict, verbose)
self.rand_init = rand_init # Use SVM or random initialization
self.num_iter = 0
self.max_iter = 100 # Maximum number of DC iterations
self.min_diff = 1e-2 # Stopping criteria, change in parameters
self.all_objective = [] # The objective value for all iterations
@property
def perf(self):
"""Summary of results"""
perf = super(DiffConvexClassifier, self).perf
perf['num_iter'] = self.num_iter
perf['all_objective'] = self.all_objective
return perf
def _init_param(self, norm):
"""Initialize values of w and b.
The parameter denotes whether to use l1 or l2 norm.
"""
assert(norm == 1 or norm==2)
if self.rand_init:
self.num_iter = 0
if self.verbose: print('Random initialization')
w = self.reg_param*randn(self.train_ex.shape[0],1)
b = -randn()
else:
self.num_iter = 1
if self.verbose: print('SVM initialization')
if norm == 1:
svm = OneNormSVM(self.reg_param, self.kdict, self.verbose)
elif norm == 2:
svm = SVMPrimal(self.reg_param, self.kdict, self.verbose)
# train computes the feature mapping explicitly
svm.train_ex = self.train_ex
svm.y = self.y
svm.solve_opt()
(w, b) = (svm.w, svm.b)
return (w, b)
def _subgradient_concave_ramp(self, mu):
"""Compute the subgradient of the concave part of the ramp loss"""
(clipped, sloped, correct) = self.loss.get_loss_case(self.y*self.train_out)
#sw = zeros(self.train_ex.shape[0])
#sb = zeros((1,1))
#for idx in clipped:
# sw += -self.y[idx]*self.train_ex[:,idx]
# sb += -self.y[idx]
sw = numpy.sum(-self.y[clipped]*self.train_ex[:,clipped], axis=1)
sw.shape = (self.train_ex.shape[0], 1)
sb = sum(-self.y[clipped])
sw /= mu
sb /= mu
return (sw, sb)
def _subgradient_concave_cap_l1(self, a):
"""Compute the subgradient of the concave part of the capped l1 norm"""
grad = sign(self.w)
grad[nonzero(abs(self.w) < a)[0]] = 0.0
return grad
def _subgradient_concave_l0(self, a):
"""Compute the subgradient of the concave part of the l0 approximation"""
return a*sign(self.w)*(1.0 - numpy.exp(-a*abs(self.w)))
class CapOneNormSVM(DiffConvexClassifier):
"""
SVM with capped one norm regularizer.
"""
def __init__(self, reg_param, kdict, a=1.0, verbose=False, rand_init=False):
DiffConvexClassifier.__init__(self, reg_param, kdict, verbose, rand_init)
self.a = a # The value of the cap
self.loss = HingeLoss()
self.regularizer = Reg_l1cap(a)
#@property
#def margin(self):
# """The value of the regularizer, specialized because of parameter a"""
# return self.regularizer(self.w, self.a)
def _solve_linprog(self, u):
"""Solve the linear program corresponding to the convex
part of the capped one norm SVM.
Use cvxopt lp solver which expects the following format:
min_x c'x
st Gx <= h
Ax = b
We organise the variables of the SVM as (w,b,xi,eta).
"""
yvec = self.y.copy()
yvec.shape = (len(self.y), 1)
if self.verbose: print('Solving LP')
n = len(self.y)
d = self.train_ex.shape[0]
m = n+2*d+1
svm = SILPS(m, self.verbose)
svm.set_objective(vstack([-self.reg_param*u, zeros((1,1)), ones((n,1)),
self.reg_param*ones((d,1))]))
G = sparse(cm(hstack([-yvec*transpose(self.train_ex), -yvec, -eye(n), zeros((n,d))])))
svm.add_ineq_constraints(G, -ones((n,1)))
svm.add_ineq_constraints(sparse(cm(hstack([zeros((n,d+1)),-eye(n),zeros((n,d))]))), zeros((n,1)))
svm.add_ineq_constraints(sparse(cm(hstack([zeros((d,d+1)),zeros((d,n)),-eye(d)]))), zeros((d,1)))
svm.add_ineq_constraints(sparse(cm(hstack([eye(d), zeros((d,n+1)), -eye(d)]))), zeros((d,1)))
svm.add_ineq_constraints(sparse(cm(hstack([-eye(d), zeros((d,n+1)), -eye(d)]))), zeros((d,1)))
svm.solve()
if svm.optimal:
self.w = svm.solution[:d]
self.w.shape = (d,1)
self.b = svm.solution[d]
def _solve_linprog_dense(self, u):
"""Solve the linear program corresponding to the convex
part of the capped one norm SVM.
Uses dense matrices.
Use cvxopt lp solver which expects the following format:
min_x c'x
st Gx <= h
Ax = b
We organise the variables of the SVM as (w,b,xi,eta).
"""
yvec = self.y.copy()
yvec.shape = (len(self.y), 1)
if self.verbose: print('Solving LP')
n = len(self.y)
d = self.train_ex.shape[0]
m = n+2*d+1
svm = SILP(m, self.verbose)
svm.set_objective(vstack([-self.reg_param*u, zeros((1,1)), ones((n,1)),
self.reg_param*ones((d,1))]))
G = hstack([-yvec*transpose(self.train_ex), -yvec, -eye(n), zeros((n,d))])
svm.add_ineq_constraints(G, -ones((n,1)))
svm.add_ineq_constraints(hstack([zeros((n,d+1)),-eye(n),zeros((n,d))]), zeros((n,1)))
svm.add_ineq_constraints(hstack([zeros((d,d+1)),zeros((d,n)),-eye(d)]), zeros((d,1)))
svm.add_ineq_constraints(hstack([eye(d), zeros((d,n+1)), -eye(d)]), zeros((d,1)))
svm.add_ineq_constraints(hstack([-eye(d), zeros((d,n+1)), -eye(d)]), zeros((d,1)))
svm.solve()
if svm.optimal:
self.w = svm.solution[:d]
self.w.shape = (d,1)
self.b = svm.solution[d]
def solve_opt(self):
"""Use a DCA to solve the ramp loss SVM"""
if self.verbose: print('Solving DCA')
(self.w, self.b) = self._init_param(1)
self.all_objective.append(self.primal_obj)
u = self._subgradient_concave_cap_l1(self.a)
diff_param = numpy.inf
while (self.num_iter < self.max_iter)\
and (diff_param > self.min_diff):
self._solve_linprog(u)
old_u = u
u = self._subgradient_concave_cap_l1(self.a)
diff_param = sum(abs(old_u-u))
self.num_iter += 1
self.all_objective.append(self.primal_obj)
class ZeroNormSVM(DiffConvexClassifier):
"""
SVM with zero norm regularizer. Uses exponential approximation.
"""
def __init__(self, reg_param, kdict, a=4.0, verbose=False, rand_init=False):
DiffConvexClassifier.__init__(self, reg_param, kdict, verbose, rand_init)
self.a = a # The exponent in approximation
self.loss = HingeLoss()
self.regularizer = Reg_l0(a)
#@property
#def margin(self):
# """The value of the regularizer, specialized because of parameter a"""
# return self.regularizer(self.w, self.a)
def _solve_linprog(self, u):
"""Solve the linear program corresponding to the convex
part of the zero norm SVM.
Use cvxopt lp solver which expects the following format:
min_x c'x
st Gx <= h
Ax = b
We organise the variables of the SVM as (w,b,xi,eta).
"""
yvec = self.y.copy()
yvec.shape = (len(self.y), 1)
if self.verbose: print('Solving LP')
n = len(self.y)
d = self.train_ex.shape[0]
m = n+2*d+1
svm = SILPS(m, self.verbose)
svm.set_objective(vstack([-self.reg_param*u, zeros((1,1)), ones((n,1)),
self.reg_param*self.a*ones((d,1))]))
G = sparse(cm(hstack([-yvec*transpose(self.train_ex), -yvec, -eye(n), zeros((n,d))])))
svm.add_ineq_constraints(G, -ones((n,1)))
svm.add_ineq_constraints(sparse(cm(hstack([zeros((n,d+1)),-eye(n),zeros((n,d))]))), zeros((n,1)))
svm.add_ineq_constraints(sparse(cm(hstack([zeros((d,d+1)),zeros((d,n)),-eye(d)]))), zeros((d,1)))
svm.add_ineq_constraints(sparse(cm(hstack([eye(d), zeros((d,n+1)), -eye(d)]))), zeros((d,1)))
svm.add_ineq_constraints(sparse(cm(hstack([-eye(d), zeros((d,n+1)), -eye(d)]))), zeros((d,1)))
svm.solve()
if svm.optimal:
self.w = svm.solution[:d]
self.w.shape = (d,1)
self.b = svm.solution[d]
def _solve_linprog_dense(self, u):
"""Solve the linear program corresponding to the convex
part of the zero norm SVM.
Uses dense matrices.
Use cvxopt lp solver which expects the following format:
min_x c'x
st Gx <= h
Ax = b
We organise the variables of the SVM as (w,b,xi,eta).
"""
yvec = self.y.copy()
yvec.shape = (len(self.y), 1)
if self.verbose: print('Solving LP')
n = len(self.y)
d = self.train_ex.shape[0]
m = n+2*d+1
svm = SILP(m, self.verbose)
svm.set_objective(vstack([-self.reg_param*u, zeros((1,1)), ones((n,1)),
self.reg_param*self.a*ones((d,1))]))
G = hstack([-yvec*transpose(self.train_ex), -yvec, -eye(n), zeros((n,d))])
svm.add_ineq_constraints(G, -ones((n,1)))
svm.add_ineq_constraints(hstack([zeros((n,d+1)),-eye(n),zeros((n,d))]), zeros((n,1)))
svm.add_ineq_constraints(hstack([zeros((d,d+1)),zeros((d,n)),-eye(d)]), zeros((d,1)))
svm.add_ineq_constraints(hstack([eye(d), zeros((d,n+1)), -eye(d)]), zeros((d,1)))
svm.add_ineq_constraints(hstack([-eye(d), zeros((d,n+1)), -eye(d)]), zeros((d,1)))
svm.solve()
if svm.optimal:
self.w = svm.solution[:d]
self.w.shape = (d,1)
self.b = svm.solution[d]
def solve_opt(self):
"""Use a DCA to solve the ramp loss SVM"""
if self.verbose: print('Solving DCA')
(self.w, self.b) = self._init_param(1)
self.all_objective.append(self.primal_obj)
u = self._subgradient_concave_l0(self.a)
diff_param = numpy.inf
while (self.num_iter < self.max_iter)\
and (diff_param > self.min_diff):
self._solve_linprog(u)
old_u = u
u = self._subgradient_concave_l0(self.a)
diff_param = sum(abs(old_u-u))
self.num_iter += 1
self.all_objective.append(self.primal_obj)
class RLSVM2(DiffConvexClassifier):
"""
Ramp Loss SVM with l2 regularizer.
"""
def __init__(self, reg_param, kdict, mu, verbose=False, rand_init=False):
DiffConvexClassifier.__init__(self, reg_param, kdict, verbose, rand_init)
self.mu = mu # the slope of the ramp
self.loss = RampLoss(mu)
self.regularizer = Reg_l2()
def _solve_quadprog(self, sw, sb):
"""Solve the quadratic program given by
linearizing the concave part
Use cvxopt qp solver which expects the following format:
min_x 1/2 x'Px + q'x
st Gx <= h
Ax = b
We organise the variables of the SVM as (w,b,xi).
"""
yvec = self.y.copy()
yvec.shape = (len(self.y), 1)
if self.verbose: print('Solving QP')
n = len(self.y)
d = self.train_ex.shape[0]
m = n+d+1
svm = SIQP(m, self.verbose)
P = zeros((m,m))
P[:d,:d] = 2.0*self.reg_param*eye(d)
q = vstack([-sw, -sb, ones((n,1))/self.mu])
svm.set_objective(P, q)
svm.add_ineq_constraints(hstack([zeros((n,d+1)),-eye(n)]), zeros((n,1)))
G = hstack([-yvec*transpose(self.train_ex), -yvec, -eye(n)])
svm.add_ineq_constraints(G, -self.mu*ones((n,1)))
svm.solve()
if svm.optimal:
self.w = svm.solution[:d]
self.w.shape = (d,1)
self.b = svm.solution[d]
def solve_opt(self):
"""Use a DCA to solve the ramp loss SVM"""
if self.verbose: print('Solving DCA')
(self.w, self.b) = self._init_param(2)
self.all_objective.append(self.primal_obj)
(sw, sb) = self._subgradient_concave_ramp(self.mu)
#print(sw, sb)
diff_param = numpy.inf
while (self.num_iter < self.max_iter)\
and (diff_param > self.min_diff):
self._solve_quadprog(sw, sb)
#print('weights')
#print(self.w, self.b)
(old_sw, old_sb) = (sw, sb)
(sw, sb) = self._subgradient_concave_ramp(self.mu)
#print(sw, sb)
diff_param = sum(abs(old_sw-sw)) + abs(old_sb-sb)
#print(diff_param)
self.num_iter += 1
self.all_objective.append(self.primal_obj)
class RLSVM1(DiffConvexClassifier):
"""
Ramp Loss SVM with l1 regularizer.
"""
def __init__(self, reg_param, kdict, mu, verbose=False, rand_init=False):
DiffConvexClassifier.__init__(self, reg_param, kdict, verbose, rand_init)
self.mu = mu # the slope of the ramp
self.loss = RampLoss(mu)
self.regularizer = Reg_l1()
def _solve_linprog(self, sw, sb):
"""Solve the linear program corresponding to the convex part
of the ramp loss SVM with l1 penalty.
Use cvxopt lp solver which expects the following format:
min_x c'x
st Gx <= h
Ax = b
We organise the variables of the SVM as (w,b,xi,eta).
"""
yvec = self.y.copy()
yvec.shape = (len(self.y), 1)
if self.verbose: print('Solving LP')
n = len(self.y)
d = self.train_ex.shape[0]
m = n+2*d+1
svm = SILPS(m, self.verbose)
svm.set_objective(vstack([-sw, -sb, ones((n,1))/self.mu,
self.reg_param*ones((d,1))]))
G = sparse(cm(hstack([-yvec*transpose(self.train_ex), -yvec, -eye(n), zeros((n,d))])))
svm.add_ineq_constraints(G, -self.mu*ones((n,1)))
svm.add_ineq_constraints(sparse(cm(hstack([zeros((n,d+1)),-eye(n),zeros((n,d))]))), zeros((n,1)))
svm.add_ineq_constraints(sparse(cm(hstack([zeros((d,d+1)),zeros((d,n)),-eye(d)]))), zeros((d,1)))
svm.add_ineq_constraints(sparse(cm(hstack([eye(d), zeros((d,n+1)), -eye(d)]))), zeros((d,1)))
svm.add_ineq_constraints(sparse(cm(hstack([-eye(d), zeros((d,n+1)), -eye(d)]))), zeros((d,1)))
svm.solve()
if svm.optimal:
self.w = svm.solution[:d]
self.w.shape = (d,1)
self.b = svm.solution[d]
def _solve_linprog_dense(self, sw, sb):
"""Solve the linear program corresponding to the convex part
of the ramp loss SVM with l1 penalty.
Uses dense matrices
Use cvxopt lp solver which expects the following format:
min_x c'x
st Gx <= h
Ax = b
We organise the variables of the SVM as (w,b,xi,eta).
"""
yvec = self.y.copy()
yvec.shape = (len(self.y), 1)
if self.verbose: print('Solving LP')
n = len(self.y)
d = self.train_ex.shape[0]
m = n+2*d+1
svm = SILP(m, self.verbose)
svm.set_objective(vstack([-sw, -sb, ones((n,1))/self.mu,
self.reg_param*ones((d,1))]))
G = hstack([-yvec*transpose(self.train_ex), -yvec, -eye(n), zeros((n,d))])
svm.add_ineq_constraints(G, -self.mu*ones((n,1)))
svm.add_ineq_constraints(hstack([zeros((n,d+1)),-eye(n),zeros((n,d))]), zeros((n,1)))
svm.add_ineq_constraints(hstack([zeros((d,d+1)),zeros((d,n)),-eye(d)]), zeros((d,1)))
svm.add_ineq_constraints(hstack([eye(d), zeros((d,n+1)), -eye(d)]), zeros((d,1)))
svm.add_ineq_constraints(hstack([-eye(d), zeros((d,n+1)), -eye(d)]), zeros((d,1)))
svm.solve()
if svm.optimal:
self.w = svm.solution[:d]
self.w.shape = (d,1)
self.b = svm.solution[d]
def solve_opt(self):
"""Use a DCA to solve the ramp loss SVM with one norm penalty"""
if self.verbose: print('Solving DCA')
(self.w, self.b) = self._init_param(1)
self.all_objective.append(self.primal_obj)
(sw, sb) = self._subgradient_concave_ramp(self.mu)
diff_param = numpy.inf
while (self.num_iter < self.max_iter)\
and (diff_param > self.min_diff):
self._solve_linprog(sw, sb)
(old_sw, old_sb) = (sw, sb)
(sw, sb) = self._subgradient_concave_ramp(self.mu)
diff_param = sum(abs(old_sw-sw)) + abs(old_sb-sb)
self.num_iter += 1
self.all_objective.append(self.primal_obj)
class RLSVMC1(DiffConvexClassifier):
"""
Ramp Loss SVM with capped l1 regularizer.
"""
def __init__(self, reg_param, kdict, mu, a=1.0, verbose=False, rand_init=False):
DiffConvexClassifier.__init__(self, reg_param, kdict, verbose, rand_init)
self.mu = mu # the slope of the ramp
self.a = a # The value of the cap
self.loss = RampLoss(mu)
self.regularizer = Reg_l1cap(a)
#@property
#def margin(self):
# """The value of the regularizer, specialized because of parameter a"""
# return self.regularizer(self.w, self.a)
def _solve_linprog(self, sw, sb, u):
"""Solve the linear program corresponding to the convex part
of the ramp loss SVM with capped l1 penalty.
Use cvxopt lp solver which expects the following format:
min_x c'x
st Gx <= h
Ax = b
We organise the variables of the SVM as (w,b,xi,eta).
"""
yvec = self.y.copy()
yvec.shape = (len(self.y), 1)
if self.verbose: print('Solving LP')
n = len(self.y)
d = self.train_ex.shape[0]
m = n+2*d+1
svm = SILPS(m, self.verbose)
svm.set_objective(vstack([-sw-self.reg_param*u, -sb, ones((n,1))/self.mu,
self.reg_param*ones((d,1))]))
G = sparse(cm(hstack([-yvec*transpose(self.train_ex), -yvec, -eye(n), zeros((n,d))])))
svm.add_ineq_constraints(G, -self.mu*ones((n,1)))
svm.add_ineq_constraints(sparse(cm(hstack([zeros((n,d+1)),-eye(n),zeros((n,d))]))), zeros((n,1)))
svm.add_ineq_constraints(sparse(cm(hstack([zeros((d,d+1)),zeros((d,n)),-eye(d)]))), zeros((d,1)))
svm.add_ineq_constraints(sparse(cm(hstack([eye(d), zeros((d,n+1)), -eye(d)]))), zeros((d,1)))
svm.add_ineq_constraints(sparse(cm(hstack([-eye(d), zeros((d,n+1)), -eye(d)]))), zeros((d,1)))
svm.solve()
if svm.optimal:
self.w = svm.solution[:d]
self.w.shape = (d,1)
self.b = svm.solution[d]
def _solve_linprog_dense(self, sw, sb, u):
"""Solve the linear program corresponding to the convex part
of the ramp loss SVM with capped l1 penalty.
Uses dense matrices
Use cvxopt lp solver which expects the following format:
min_x c'x
st Gx <= h
Ax = b
We organise the variables of the SVM as (w,b,xi,eta).
"""
yvec = self.y.copy()
yvec.shape = (len(self.y), 1)
if self.verbose: print('Solving LP')
n = len(self.y)
d = self.train_ex.shape[0]
m = n+2*d+1
svm = SILP(m, self.verbose)
svm.set_objective(vstack([-sw-self.reg_param*u, -sb, ones((n,1))/self.mu,
self.reg_param*ones((d,1))]))
G = hstack([-yvec*transpose(self.train_ex), -yvec, -eye(n), zeros((n,d))])
svm.add_ineq_constraints(G, -self.mu*ones((n,1)))
svm.add_ineq_constraints(hstack([zeros((n,d+1)),-eye(n),zeros((n,d))]), zeros((n,1)))
svm.add_ineq_constraints(hstack([zeros((d,d+1)),zeros((d,n)),-eye(d)]), zeros((d,1)))
svm.add_ineq_constraints(hstack([eye(d), zeros((d,n+1)), -eye(d)]), zeros((d,1)))
svm.add_ineq_constraints(hstack([-eye(d), zeros((d,n+1)), -eye(d)]), zeros((d,1)))
svm.solve()
if svm.optimal:
self.w = svm.solution[:d]
self.w.shape = (d,1)
self.b = svm.solution[d]
def solve_opt(self):
"""Use a DCA to solve the ramp loss SVM with capped l1 regularizer"""
if self.verbose: print('Solving DCA')
(self.w, self.b) = self._init_param(1)
self.all_objective.append(self.primal_obj)
(sw, sb) = self._subgradient_concave_ramp(self.mu)
u = self._subgradient_concave_cap_l1(self.a)
diff_param = numpy.inf
while (self.num_iter < self.max_iter)\
and (diff_param > self.min_diff):
self._solve_linprog(sw, sb, u)
(old_sw, old_sb, old_u) = (sw, sb, u)
(sw, sb) = self._subgradient_concave_ramp(self.mu)
u = self._subgradient_concave_cap_l1(self.a)
diff_param = sum(abs(old_sw-sw)) + abs(old_sb-sb) + sum(abs(old_u-u))
self.num_iter += 1
self.all_objective.append(self.primal_obj)
class RLSVM0(DiffConvexClassifier):
"""
Ramp Loss SVM with l0 regularizer.
"""
def __init__(self, reg_param, kdict, mu, a=4.0, verbose=False, rand_init=False):
DiffConvexClassifier.__init__(self, reg_param, kdict, verbose, rand_init)
self.mu = mu # the slope of the ramp
self.a = a # The exponent in approximation
self.loss = RampLoss(mu)
self.regularizer = Reg_l0(a)
#@property
#def margin(self):
# """The value of the regularizer, specialized because of parameter a"""
# return self.regularizer(self.w, self.a)
def _solve_linprog(self, sw, sb, u):
"""Solve the linear program corresponding to the convex part
of the ramp loss SVM with l0 penalty.
Use cvxopt lp solver which expects the following format:
min_x c'x
st Gx <= h
Ax = b
We organise the variables of the SVM as (w,b,xi,eta).
"""
yvec = self.y.copy()
yvec.shape = (len(self.y), 1)
if self.verbose: print('Solving LP')
n = len(self.y)
d = self.train_ex.shape[0]
m = n+2*d+1
svm = SILPS(m, self.verbose)
svm.set_objective(vstack([-sw-self.reg_param*u, -sb, ones((n,1))/self.mu,
self.reg_param*self.a*ones((d,1))]))
G = sparse(cm(hstack([-yvec*transpose(self.train_ex), -yvec, -eye(n), zeros((n,d))])))
svm.add_ineq_constraints(G, -self.mu*ones((n,1)))
svm.add_ineq_constraints(sparse(cm(hstack([zeros((n,d+1)),-eye(n),zeros((n,d))]))), zeros((n,1)))
svm.add_ineq_constraints(sparse(cm(hstack([zeros((d,d+1)),zeros((d,n)),-eye(d)]))), zeros((d,1)))
svm.add_ineq_constraints(sparse(cm(hstack([eye(d), zeros((d,n+1)), -eye(d)]))), zeros((d,1)))
svm.add_ineq_constraints(sparse(cm(hstack([-eye(d), zeros((d,n+1)), -eye(d)]))), zeros((d,1)))
svm.solve()
if svm.optimal:
self.w = svm.solution[:d]
self.w.shape = (d,1)
self.b = svm.solution[d]
def _solve_linprog_dense(self, sw, sb, u):
"""Solve the linear program corresponding to the convex part
of the ramp loss SVM with l0 penalty.
Uses dense matrices
Use cvxopt lp solver which expects the following format:
min_x c'x
st Gx <= h
Ax = b
We organise the variables of the SVM as (w,b,xi,eta).
"""
yvec = self.y.copy()
yvec.shape = (len(self.y), 1)
if self.verbose: print('Solving LP')
n = len(self.y)
d = self.train_ex.shape[0]
m = n+2*d+1
svm = SILP(m, self.verbose)
svm.set_objective(vstack([-sw-self.reg_param*u, -sb, ones((n,1))/self.mu,
self.reg_param*self.a*ones((d,1))]))
G = hstack([-yvec*transpose(self.train_ex), -yvec, -eye(n), zeros((n,d))])
svm.add_ineq_constraints(G, -self.mu*ones((n,1)))
svm.add_ineq_constraints(hstack([zeros((n,d+1)),-eye(n),zeros((n,d))]), zeros((n,1)))
svm.add_ineq_constraints(hstack([zeros((d,d+1)),zeros((d,n)),-eye(d)]), zeros((d,1)))
svm.add_ineq_constraints(hstack([eye(d), zeros((d,n+1)), -eye(d)]), zeros((d,1)))
svm.add_ineq_constraints(hstack([-eye(d), zeros((d,n+1)), -eye(d)]), zeros((d,1)))
svm.solve()
if svm.optimal:
self.w = svm.solution[:d]
self.w.shape = (d,1)
self.b = svm.solution[d]
def solve_opt(self):
"""Use a DCA to solve the ramp loss SVM with zero norm (approx) regularizer"""
if self.verbose: print('Solving DCA')
(self.w, self.b) = self._init_param(1)
self.all_objective.append(self.primal_obj)
(sw, sb) = self._subgradient_concave_ramp(self.mu)
u = self._subgradient_concave_l0(self.a)
diff_param = numpy.inf
while (self.num_iter < self.max_iter)\
and (diff_param > self.min_diff):
self._solve_linprog(sw, sb, u)
(old_sw, old_sb, old_u) = (sw, sb, u)
(sw, sb) = self._subgradient_concave_ramp(self.mu)
u = self._subgradient_concave_l0(self.a)
diff_param = sum(abs(old_sw-sw)) + abs(old_sb-sb) + sum(abs(old_u-u))
self.num_iter += 1
self.all_objective.append(self.primal_obj)
OptWok-0.3/optwok/classifier_base.py 0000644 ڛ |-00000027533 12055566021 024565 0 ustar cheng.ong UNIMELB\Domain Users 0000000 0000000 """Base classes for classifiers"""
from time import time
import numpy
from numpy import array, matrix, zeros, nonzero
class Classifier(object):
"""
Implementation of the optimization methods for the
empirical loss + regularizer
framework, for binary clssification.
We only consider linear classifiers.
"""
def __init__(self, reg_param=1.0, kdict=None, verbose=False):
assert(reg_param > 0.0)
self.reg_param = reg_param # Tradeoff regularizer vs loss
self.regularizer = None
self.loss = None
self.kdict = kdict # kernel
self.train_ex = array([]) # training examples
self.y = array([]) # labels
self.train_time = 0.0 # time used for training
self.w = array([]) # weight
self.b = array([]) # bias
self.epsilon = 1e-6 # how small is zero?
self.verbose = verbose # how much output
@property
def num_examples(self):
return len(self.y)
@property
def num_feat(self):
return self.train_ex.shape[0]
@property
def train_out(self):
"""Output of the classifier on the training set"""
return self.get_svm_out(self.train_ex)
@property
def margin(self):
"""The value of the regularizer"""
return self.regularizer.margin(self.w)
@property
def emp_risk(self):
"""A vector of the empirical loss"""
return self.loss.get_emp_risk(self.y*self.train_out)
@property
def primal_obj(self):
"""Directly compute the primal objective function."""
if self.verbose:
print('Margin: %f, Risk: %f' % (self.margin,
self.reg_param*sum(self.emp_risk)))
obj = self.margin + self.reg_param*sum(self.emp_risk)
return obj
@property
def max_weight(self):
"""The maximum absolute value of the weight."""
return max(abs(self.w))[0]
@property
def frac_sv(self):
"""The fraction of examples used as support vectors."""
return self.loss.get_frac_sv(self.y*self.train_out)
@property
def nonzero_feat(self):
"""The features corresponding to non-zero parameters"""
return numpy.where(abs(self.w)>self.epsilon)[0]
@property
def frac_feat(self):
"""The fraction of features used in the final classifier,
i.e. the number of non-zero parameters"""
return float(len(self.nonzero_feat))/float(len(self.w))
@property
def frac_total(self):
"""The fraction of training data used in the final classifier,
taking both features and support vectors into account."""
num_sv = float(self.loss.get_num_sv(self.y*self.train_out))
num_feat = float(len(numpy.where(abs(self.w)>self.epsilon)[0]))
return (num_sv*num_feat) / (float(self.train_ex.shape[1])*float(len(self.w)))
@property
def perf(self):
"""Summary of results"""
perf = {'max_weight': self.max_weight,
'nonzero_feat': self.nonzero_feat,
'frac_feat': self.frac_feat,
'frac_sv': self.frac_sv,
'frac_total': self.frac_total,
'train_time': self.train_time,
'objective': self.primal_obj,
}
return perf
def get_svm_out(self, X):
"""Compute K*alpha+b"""
return array(matrix(self.w).T*matrix(X) + self.b).flatten()
def solve_opt(self):
"""solve_opt should the implemented in child class"""
pass
def train(self, train_ex, train_lab):
"""Wrapper to convert data to feature matrix,
and solve the optimization"""
self.train_ex = compute_features(train_ex, self.kdict)
self.y = train_lab
t_start = time()
self.solve_opt()
t_end = time()
# Collect statistics about optimization
self.train_time = t_end-t_start
if self.verbose: print('%f support vectors' % self.frac_sv)
if self.verbose: print('Primal Objective: %f' % self.primal_obj)
def predict(self, pred_ex):
"""Wrapper to do prediction"""
return self.get_svm_out(compute_features(pred_ex, self.kdict))
class ConvexClassifier(Classifier):
"""
Classifiers which have convex training methods
"""
def __init__(self, reg_param, kdict, verbose):
Classifier.__init__(self, reg_param, kdict, verbose)
self.num_iter = 1
class Loss(object):
"""A loss function, for classification"""
epsilon = 0.01
def gradient(self, w):
"""The vector of gradients"""
raise NotImplementedError
class HingeLoss(Loss):
"""
Standard SVM loss
"""
def get_emp_risk(self, t):
"""Compute the empirical risk, for each example."""
(sloped, correct) = self.get_loss_case(t)
emp_risk = zeros(len(t))
emp_risk[sloped] = 1.0 - t[sloped]
return emp_risk
def get_num_sv(self, t):
"""Estimate the number of support vectors"""
(sloped, correct) = self.get_loss_case(t)
return len(sloped)
def get_frac_sv(self, t):
"""Estimate the fraction of support vectors"""
num_sv = self.get_num_sv(t)
frac_sv = float(num_sv)/float(len(t))
return frac_sv
def get_loss_case(self, t):
"""Detect whether we are on the slope,
or classified correctly (larger than margin)."""
sloped = numpy.where(t <= 1.0+self.epsilon)[0]
correct = numpy.where(t > 1.0+self.epsilon)[0]
return (sloped, correct)
def gradient(self, x, y, t):
"""The gradient of a single example"""
if t <= 1.:
return -y*x
else:
return zeros(x.shape)
class HingeLossKernel(Loss):
"""
Standard SVM loss, with matrix inner product
"""
def __init__(self, M):
self.M = M
class HuberHingeLoss(Loss):
"""
Huberized hinge loss, to make things differentiable
"""
def __init__(self, delta=0.5):
"""The loss is quadratic in window delta"""
self.delta = delta
def get_loss_case(self, t):
"""Detect the three cases,
correct, curved, sloped
"""
sloped = numpy.where(t <= 1.0-self.delta)[0]
curved = numpy.where(abs(1-t)<=self.delta)[0]
correct = numpy.where(t > 1.0+self.delta)[0]
return (sloped, curved, correct)
def gradient(self, x, y, t):
"""The gradient of a single example"""
if t <= 1.0-self.delta:
return -y*x
elif t > 1.0+self.delta:
return zeros(x.shape)
else:
return (-0.5*y/self.delta)*(1+self.delta-t)*x
class RampLoss(Loss):
"""
Truncated hinge loss
"""
def __init__(self, mu):
"""The parameter mu controls the slope between [0,2]"""
assert((mu > 0.0) and (mu <= 1.0))
self.mu = mu
def get_emp_risk(self, t):
"""Compute the empirical risk, for each example."""
(clipped, sloped, correct) = self.get_loss_case(t)
emp_risk = zeros(len(t))
emp_risk[clipped] = self.mu + 1.0
emp_risk[sloped] = 1.0 - (t[sloped]/self.mu)
return emp_risk
def get_num_sv(self, t):
"""Estimate the number of support vectors"""
(clipped, sloped, correct) = self.get_loss_case(t)
return len(sloped)
def get_frac_sv(self, t):
"""Estimate the fraction of support vectors"""
num_sv = self.get_num_sv(t)
frac_sv = float(num_sv)/float(len(t))
return frac_sv
def get_loss_case(self, t):
"""Detect whether we are at saturation, on the slope,
or classified correctly (larger than margin)."""
clipped = numpy.where(t<-(self.mu*self.mu)-self.epsilon)[0]
lb = numpy.where(t>=-(self.mu*self.mu)-self.epsilon, 1, 0)
ub = numpy.where(t<=self.mu+self.epsilon, 1, 0)
sloped = numpy.where((lb+ub)>1)[0]
correct = numpy.where(t>self.mu+self.epsilon)[0]
return (clipped, sloped, correct)
class SlantLoss(RampLoss):
"""
Hinge loss with double kink
"""
def get_emp_risk(self, t):
"""Compute the empirical risk, for each example."""
(clipped, sloped, correct) = self.get_loss_case(t)
emp_risk = zeros(len(t))
emp_risk[clipped] = 0.5*(3.0-t[clipped])
emp_risk[sloped] = 1.0 - t[sloped]
return emp_risk
class Regularizer(object):
"""
A regularizer class
"""
def margin(self, w):
"""The value of the regularizer, intuitively the width of the margin"""
raise NotImplementedError
def gradient(self, w):
"""The vector of gradients"""
raise NotImplementedError
class Reg_l2(Regularizer):
"""The l2 norm of a vector"""
def margin(self, w):
"""The value of the regularizer, intuitively the width of the margin"""
return 0.5*numpy.linalg.norm(w)*numpy.linalg.norm(w)
def gradient(self, w):
"""The vector of gradients"""
return w
class Reg_l1(Regularizer):
"""The l1 norm of a vector"""
def margin(self, w):
"""The value of the regularizer, intuitively the width of the margin"""
return sum(abs(w))
def gradient(self, w):
"""The vector of gradients"""
return sign(w)
class Reg_l1cap(Regularizer):
"""The l1 norm of a vector"""
def __init__(self, a=1.0):
"""'a' is the value of the cap"""
assert(a > 0.0)
self.a = a
def margin(self, w, a=None):
"""The value of the regularizer, intuitively the width of the margin"""
if a is not None:
assert(a > 0.0)
self.a = a
cap_l1 = abs(w)
cap_l1[nonzero(cap_l1 > self.a)[0]] = self.a
return sum(cap_l1)
def gradient(self, w, a=None):
"""The vector of gradients"""
if a is not None:
assert(a > 0.0)
self.a = a
cap_l1 = abs(w)
grad = sign(w)
grad[nonzero(cap_l1 > self.a)[0]] = 0.
return grad
class Reg_l0(Regularizer):
"""The l1 norm of a vector"""
def __init__(self, a=1.0):
"""'a' is the coefficient of the exponent"""
assert(a > 0.0)
self.a = a
def margin(self, w, a=None):
"""The value of the regularizer, intuitively the width of the margin"""
if a is not None:
assert(a > 0.0)
self.a = a
return sum(1.0 - numpy.exp(-self.a*abs(w)))
def gradient(self, w, a=None):
"""The vector of gradients"""
if a is not None:
assert(a > 0.0)
self.a = a
return -self.a*sign(w)*numpy.exp(-self.a*abs(w))
class Reg_elastic_net(Regularizer):
"""The elastic net regularizer (l1+l2)"""
def margin(self, w):
"""The value of the regularizer"""
return 0.5*numpy.linalg.norm(w)*numpy.linalg.norm(w) + sum(abs(w))
def gradient(self, w):
"""The vector of gradients"""
return w + sign(w)
class Reg_graph_net(Regularizer):
"""The elastic net regularizer, with graph laplacian in l2"""
def __init__(self, M):
self.M = M
def margin(self, w):
"""The value of the regularizer"""
return 0.5*dot(dot(w,self.M),w) + sum(abs(w))
def gradient(self, w):
"""The vector of gradients"""
return dot(self.M,w) + sign(w)
def compute_features(train_ex, kdict):
"""Explicitly compute the feature space of the kernel"""
if kdict['name'] == 'linear':
return train_ex
elif kdict['name'] == 'poly':
if kdict['inhomog'] == True:
train_ex = vstack([train_ex, ones((1,train_ex.shape[1]))])
phi = get_poly_features(train_ex, kdict['param'])
return phi
else:
print('Explicit feature construction for kernel %s not implemented' % kdict['name'])
OptWok-0.3/optwok/context_ucb.py 0000644 ڛ |-00000021303 12055557045 023757 0 ustar cheng.ong UNIMELB\Domain Users 0000000 0000000 """Contextual Gaussian Process Upper Confidence Bound"""
import os
import copy
from numpy import delete, arange, kron, unique, array
from numpy import argmax, argsort
from numpy import log, log2, log10, zeros, ones, matrix, eye, sqrt
from numpy import max, min, mean
from numpy import flatnonzero
from numpy.random import randint, permutation, randn
from matplotlib.pyplot import figure, plot, legend, title, show
from matplotlib.pyplot import xlabel, ylabel, axis, savefig
from optwok import datatools
from io_pickle import load, save
class BanditResults(object):
"""A container to collect the data for a
contextual bandit experiment"""
def __init__(self, verbose=False):
"""Does nothing except set verbosity"""
self.verbose = verbose
pass
def init_data(self, data, num_context, num_epoch):
"""Make a copy of the data and initialize results"""
self.data = copy.deepcopy(data)
self.r_sum = zeros(num_context*(num_epoch-1)+1)
self.r_sum[0] = data.max_labels[0] - data.min_label
self.r_max = zeros((num_context, num_epoch))
for context in range(num_context):
self.r_max[context,0] = self.data.max_labels[context]
self.selection = zeros((num_context, num_epoch))
self.observed = zeros((num_context, num_epoch))
self.num_expt = 0
def do_experiment(self, context, epoch, pred_idx, pred_val=None):
"""Simulate an experiment"""
y = self.data.observe(pred_idx, context)
regret = self.data.max_labels[context] - y
self.r_sum[self.num_expt+1] = self.r_sum[self.num_expt] + regret
self.num_expt += 1
self.r_max[context, epoch] = min([self.r_max[context, epoch-1], regret])
self.selection[context, epoch] = pred_idx
self.observed[context, epoch] = y
if pred_val:
if self.verbose:
print('chose = %d, UCB = %f, observation = %f' % (pred_idx, pred_val, y))
if pred_val < y:
print('=== Observation %f higher than UCB %f!' % (y, pred_val))
return y
def save_result(self, filename):
"""Save the results to file with pickle"""
save(filename, (self.r_sum, self.r_max, self.selection, self.observed))
def load_result(self, filename):
"""Initialize results from file"""
(self.r_sum, self.r_max, self.selection, self.observed) = load(filename)
def compute_upper_bound(preds, pred_var, epoch, num_data):
"""Compute the upper confidence bound"""
beta = 2.0
upper_bound = preds + beta*log(num_data*(epoch+1.0))*sqrt(pred_var)
diff = max([(max(upper_bound) - min(upper_bound))/abs(mean(upper_bound)),0.01])
# add some random noise
upper_bound += 0.01*diff*randn(num_data)
diff = (max(upper_bound) - min(upper_bound))/abs(mean(upper_bound))
if diff < 1e-5:
print('==== UCB very flat!')
print('Context=%d, epoch=%d' % (context, epoch))
print(max(upper_bound), min(upper_bound), mean(upper_bound), diff)
return upper_bound
def ucb(examples, context, gp, epoch, verbose):
"""Upper confidence bound algorithm with context"""
num_data = examples.shape[1]
(preds, pred_var) = gp.predict((examples, context*ones(num_data)))
upper_bound = compute_upper_bound(preds, pred_var, epoch, num_data)
max_possible = argmax(upper_bound)
if verbose:
print('Preds(max)=%f, pred_sd(max)=%f' % (preds[max_possible], sqrt(pred_var[max_possible])))
#plot(examples.flatten(), preds, 'b.')
#plot(examples.flatten(), upper_bound, 'r.')
#plot(examples[:,max_possible], preds[max_possible], 'go')
#show()
return max_possible, upper_bound[max_possible]
def ucb_kbest(examples, context, gp, epoch, batch_size, verbose):
"""Predict the k best experiments from the GP"""
num_data = examples.shape[1]
(preds, pred_var) = gp.predict((examples, context*ones(num_data)))
upper_bound = compute_upper_bound(preds, pred_var, epoch, num_data)
idx = argsort(upper_bound)
kbest = idx[-batch_size:]
return kbest
def ucb_batch(examples, context, gp, epoch, batch_size, verbose):
"""Predict small batches, using ucb, of examples to test"""
local_gp = copy.deepcopy(gp)
local_gp.reg_param /= float(batch_size)
all_pred_idx = []
for batch_idx in range(batch_size):
(pred_idx, pred_val) = ucb(examples, context, local_gp,
epoch, verbose)
all_pred_idx.append(pred_idx)
local_gp.update(examples[:,pred_idx], context, pred_val)
return all_pred_idx
def expt_cucb(data, gp, savefile, num_context=3, num_epoch=15, verbose=False):
"""Estimate the learning curves for ucb"""
print(savefile)
results = BanditResults()
results.init_data(data, num_context, num_epoch)
for context in range(num_context):
for epoch in range(1,num_epoch):
if verbose:
print('Context=%d, epoch=%d' % (context, epoch))
(pred_idx, pred_val) = ucb(data.examples(context), context, gp, epoch, verbose)
y = results.do_experiment(context, epoch, pred_idx, pred_val)
gp.update(data.examples(context)[:,pred_idx], context, y)
results.save_result(savefile)
def expt_cucb_batch(data, gp, savefile, num_context=3, num_epoch=15,
batch_size=5, mode='greedy', verbose=False):
"""Estimate the learning curves for batch ucb"""
print(savefile)
results = BanditResults()
results.init_data(data, num_context, (num_epoch-1)*batch_size+1)
for context in range(num_context):
for epoch in range(1,num_epoch):
if verbose:
print('Context=%d, epoch=%d' % (context, epoch))
if mode == 'greedy':
all_pred_idx = ucb_batch(data.examples(context),
context, gp, epoch*batch_size, batch_size, verbose)
elif mode == 'kbest':
all_pred_idx = ucb_kbest(data.examples(context),
context, gp, epoch*batch_size, batch_size, verbose)
elif mode == 'random':
idx = permutation(len(data.examples(context)))
all_pred_idx = idx[:batch_size]
for pred_idx in all_pred_idx:
y = results.do_experiment(context, epoch, pred_idx)
gp.update(data.examples(context)[:,pred_idx], context, y)
results.save_result(savefile)
def plot_min_regret(filename, leg_list=None):
print(filename)
(r_sum, r_max, selection, observed) = load(filename)
(num_context, num_epoch) = r_max.shape
if leg_list is None:
leg_list = map(str,range(num_context))
for ix, task in enumerate(leg_list):
pos = flatnonzero(observed[ix,:]>0)
print('%s:%d unique positives from %d' % (task, len(unique(selection[ix,pos])), num_epoch))
figure()
plot(range(0,num_epoch),r_max.T, hold=True)
#plot(range(0,num_epoch),r_max.T, 'k.')
plot([0,num_epoch],[0,0], 'k--')
legend(leg_list, loc=4)
title('minimum regret')
xlabel('number of rounds')
#axis([0,num_epoch-1,-3,3])
savefig('%s_min.pdf' % filename[:-4], dpi=300, bbox_inches='tight')
def plot_average_regret(file_list, leg_list):
"""Plot all average regrets,
assuming that the file and legend order is the same.
"""
colours = ['b','r','g','c','m','k','y']
assert len(file_list) == len(leg_list)
figure()
for (fidx, file_name) in enumerate(file_list):
(r_sum, r_max, selection, observed) = load(file_name)
(num_context, num_epoch) = r_max.shape
plot(arange(1,len(r_sum)+1), r_sum/(arange(1,len(r_sum)+1)), colours[fidx])
#for ix in range(1,num_context):
# plot([(num_epoch-1)*ix,(num_epoch-1)*ix], [0,5], 'k:')
legend(leg_list, loc=1)
title('average regret')
xlabel('number of rounds')
#axis([1,num_context*(num_epoch-1), 1, 4])
savefig('%s_avg.pdf' % file_list[0][:-4], dpi=300, bbox_inches='tight')
def plot_average_regret_old(f_learn, f_cat, f_ind):
figure()
(r_sum, r_max, selection, observed) = load(f_learn)
(num_context, num_epoch) = r_max.shape
plot(arange(1,len(r_sum)+1), r_sum/(arange(1,len(r_sum)+1)), 'b')
(r_sum, r_max, selection, observed) = load(f_cat)
plot(arange(1,len(r_sum)+1), r_sum/(arange(1,len(r_sum)+1)), 'r')
(r_sum, r_max, selection, observed) = load(f_ind)
plot(arange(1,len(r_sum)+1), r_sum/(arange(1,len(r_sum)+1)), 'g')
#for ix in range(1,num_context):
# plot([(num_epoch-1)*ix,(num_epoch-1)*ix], [0,5], 'k:')
legend(['CGP-UCB','merge','ignore'], loc=1)
title('average regret')
xlabel('number of rounds')
#axis([1,num_context*(num_epoch-1), 1, 4])
savefig('%s_avg.pdf' % f_learn[:-4], dpi=300, bbox_inches='tight')
OptWok-0.3/optwok/datatools.py 0000644 ڛ |-00000012034 12055531443 023427 0 ustar cheng.ong UNIMELB\Domain Users 0000000 0000000 """Data management"""
from numpy import array, transpose, mean
from numpy import arange, hstack, kron, ones, eye
from numpy import sqrt, concatenate, zeros, log10
from numpy.random import permutation, seed
from numpy import flatnonzero
from numpy.linalg import norm
class Data(object):
"""A container for contextual bandit data"""
def __init__(self, examples, contexts, labels):
self.num_context = int(max(contexts))+1
self._examples = []
self._contexts = []
self._labels = []
self.min_label = 10000
self.max_labels = -10000*ones(self.num_context)
for con in range(self.num_context):
idx = flatnonzero(contexts==con)
self._examples.append(examples[:,idx])
self._contexts.append(contexts[idx])
self._labels.append(labels[idx])
self.min_label = min(self.min_label, min(labels[:,idx]))
self.max_labels[con] = max(labels[:,idx])
def __repr__(self):
return str((self._examples, self._contexts, self._labels))
def remove(self, idx, context):
"""Delete data item indexed by idx"""
self._examples[context] = delete(self._examples[context], idx, 1)
self._contexts[context] = delete(self._contexts[context], idx)
self._labels[context] = delete(self._labels[context], idx)
def examples(self, context):
"""Return all active data for given context"""
return self._examples[context]
def observe(self, idx, context):
"""Return the label for idx"""
return self._labels[context][idx]
def set_label(self, idx, context, val):
"""Set the value of label"""
self._labels[context][idx] = val
def data_stats(all_data, task_list=None, num_cv=5):
"""Print some statistics about each dataset"""
if task_list:
all_tasks = task_list
else:
all_tasks = all_data.keys()
for task in all_tasks:
data = all_data[task]
print('Number of examples in %s : %d' % (task, data.nof_peptides))
def _get_data(data, splits, subsample, num_feat):
examples = zeros((num_feat,0))
labels = []
for cv in splits:
# Normalize to unit length
new_ex = transpose(array(data.encoded_peptides[cv]))
for ix in range(new_ex.shape[1]):
new_ex[:,ix] /= norm(new_ex[:,ix])
examples = hstack([examples, new_ex])
labels.extend(-log10(data.ic50s[cv])+log10(500))
examples = array(examples)
labels = array(labels)
if subsample:
idx = flatnonzero(labels<-1.6)
perm = permutation(len(idx))
subidx = idx[perm[:20]]
idx = concatenate([flatnonzero(labels>=-1.6),subidx])
examples = examples[:,idx]
labels = labels[idx]
return examples, labels
def get_data(all_data, splits, all_task=None, subsample=False, num_feat=45):
"""Concatenate data from the splits,
randomly subsample 20 examples from labels < -1.6
"""
if all_task is None:
return _get_data(all_data, splits, subsample, num_feat)
else:
examples = zeros((num_feat,0))
labels = array([])
contexts = array([])
for (task_id, task) in enumerate(all_task):
data = all_data[task]
(new_ex, new_lab) = _get_data(data, splits, subsample, num_feat)
examples = hstack([examples, new_ex])
labels = hstack([labels, new_lab])
contexts = concatenate([contexts, task_id*ones(len(new_lab))])
return (examples, contexts, labels)
def get_split(data, split, num_cv=5, verbose=False):
"""Concatenate the data not in 'split' into the test set.
'split' is the training set.
"""
test_idx = range(num_cv)
test_idx.remove(split)
(train_ex, train_lab) = get_data(data, [split])
(test_ex, test_lab) = get_data(data, test_idx)
if verbose:
print('Split %d' % split)
print('features=%d, num_train=%d, num_test=%d'\
% (train_ex.shape[0], train_ex.shape[1], test_ex.shape[1]))
return train_ex, train_lab, test_ex, test_lab
def get_multitask(all_tasks, all_data, split):
"""Treat each task as a different label"""
train_ex = []
train_lab_list = []
test_ex = []
test_lab_list = []
num_train = 0
num_test = 0
for task in all_tasks:
data = all_data[task]
tre, trl, tee, tel = get_split(data, split)
num_train += len(trl)
num_test += len(tel)
train_ex.append(tre)
train_lab_list.append(trl)
test_ex.append(tee)
test_lab_list.append(tel)
train_ex = hstack(train_ex)
test_ex = hstack(test_ex)
# Construct the matrix of labels
train_lab = zeros((len(all_tasks), num_train))
idx = 0
for (ix, trl) in enumerate(train_lab_list):
train_lab[ix, idx:idx+len(trl)] = trl
idx += len(trl)
test_lab = zeros((len(all_tasks), num_test))
idx = 0
for (ix, tel) in enumerate(test_lab_list):
test_lab[ix, idx:idx+len(tel)] = tel
idx += len(tel)
return train_ex, train_lab, test_ex, test_lab
OptWok-0.3/optwok/evaluation.py 0000644 ڛ |-00000020221 11756655247 023621 0 ustar cheng.ong UNIMELB\Domain Users 0000000 0000000 from numpy import sign, nonzero, equal, zeros, mean, std
from numpy import array, concatenate, sqrt, diag, matrix
from numpy import log, pi, trace
from numpy.linalg import det, pinv
from numpy import sum, arange, nan, unique, argsort, isnan, cumsum
from scipy import stats
def confusionMatrix(labels_test, labels_predicted):
"""Compute the matrix of predictions versus labels"""
if len(labels_test) != len(labels_predicted):
return 0
TP = 0; FP = 0; TN = 0; FN = 0
for i in range(0, len(labels_test)):
if labels_test[i] == 0 or labels_predicted[i] == 0:
return 0
if labels_test[i] > 0:
if labels_predicted[i] > 0: TP += 1
else: FN +=1
else:
if labels_predicted[i] > 0: FP += 1
else: TN += 1
return (TP, TN, FP, FN)
def accuracy(output, labels_test):
"""How many correct predictions?"""
if max(labels_test) > 1:
return accuracy_multiclass(output, labels_test)
else:
TP, TN, FP, FN = confusionMatrix(labels_test, sign(output))
return float(TP + TN) / (TP + TN + FP + FN)
def balanced_accuracy(output, labels):
"""How many correct predictions?, normalized by number of positives and negatives"""
assert all(unique(labels)==array([-1,1])), 'Binary classification only'
TP, TN, FP, FN = confusionMatrix(labels, sign(output))
return 0.5*TP/(TP+FN) + 0.5*TN/(FP+TN)
def balanced_accuracy_multitask(output, labels):
"""Balanced accuracy applied to each task separately"""
assert output.shape == labels.shape, 'Predictions and labels have different shape'
num_task = output.shape[0]
balacc = zeros(num_task)
for ix in range(num_task):
lab_idx = nonzero(labels[ix,:])
balacc[ix] = balanced_accuracy(output[ix,lab_idx].flatten(), labels[ix,lab_idx].flatten())
return balacc
def accuracy_multiclass(output, labels_test):
"""Multiclass accuracy"""
int_out = map(int, output)
int_lab = map(int, labels_test)
return 1.0*len(nonzero(equal(int_out, int_lab))[0])/len(output)
def accuracy_mix(output, labels_test, mix_list):
"""Multiclass accuracy, but allowing class confusions,
as defined by mix_list"""
assert(output.shape == labels_test.shape)
int_out = map(int, output)
new_out = zeros(len(int_out), dtype=int)
for cid in range(len(mix_list)):
idx = array([], dtype=int)
for mix in mix_list[cid]:
idx = concatenate([idx, nonzero(equal(int_out, mix))[0]])
new_out[idx] = cid
int_lab = map(int, labels_test)
new_lab = zeros(len(int_lab), dtype=int)
for cid in range(len(mix_list)):
idx = array([], dtype=int)
for mix in mix_list[cid]:
idx = concatenate([idx, nonzero(equal(int_lab, mix))[0]])
new_lab[idx] = cid
return 1.0*len(nonzero(equal(new_out, new_lab))[0])/len(output)
def rmse(output, labels):
"""Root mean squared error"""
if output.ndim == 1:
num_ex = len(output)
else:
num_ex = output.shape[1]
return sqrt(diag(matrix(output - labels)*matrix(output-labels).T))/num_ex
def rmse_multitask(output, labels):
"""rmse for each task separately"""
assert output.shape == labels.shape, 'Predictions and labels have different shape'
num_task = output.shape[0]
error = zeros(num_task)
for ix in range(num_task):
lab_idx = nonzero(labels[ix,:])
error[ix] = rmse(output[ix,lab_idx].flatten(), labels[ix,lab_idx].flatten())
return error
def differential_entropy(K):
"""The differential entropy for a multivariate normal distribution.
Assume K is a positive semidefinite matrix."""
d = K.shape[0]
return (d/2)*(1+log(2*pi)) + 0.5*log(det(K))
def relative_entropy(K):
"""The relative entropy for a multivariate normal distribution,
compared to another Gaussian with the same mean and identity covariance.
"""
d = K.shape[0]
return 0.5*(log(det(pinv(K))) + trace(K) - d)
def trapz(x, y, ylim0, ylim1):
"""Trapezoidal rule for integrating
the curve defined by x-y pairs.
Assume x is in the range [0,1]
and y is constant to the ends of the interval
"""
assert len(x) == len(y), 'x and y need to be of same length'
x = concatenate([x, array([0.0, 1.0])])
y = concatenate([y, array([ylim0, ylim1])])
sort_idx = argsort(x)
sx = x[sort_idx]
sy = y[sort_idx]
area = 0.0
for ix in range(len(x)-1):
area += 0.5*(sx[ix+1]-sx[ix])*(sy[ix+1]+sy[ix])
return area
def stats_empirical(output, labels):
"""Compute some statistics for binary predictions.
tpr - true positive rate (recall)
fpr - false positive rate
ppv - positive predictive value (precision)
auc - area under the ROC curve
ap - area under the precision-recall curve (average precision)
"""
assert len(output)==len(labels), 'Predictions and labels have different lengths'
assert all(unique(labels)==array([-1,1])), 'Labels are not binary {-1,+1}'
# Sort true labels according to predictions in ascending order
n = len(output)
sort_idx = argsort(output)
sorted_labels = labels[sort_idx]
tpr = []
fpr = []
ppv = []
for thres in range(n+1):
tp = 1.0*sum(sorted_labels[thres:]>0)
fn = 1.0*sum(sorted_labels[:thres]>0)
tn = 1.0*sum(sorted_labels[:thres]<0)
fp = 1.0*sum(sorted_labels[thres:]<0)
if tp+fn > 0.0:
tpr.append(tp/(tp+fn))
else:
tpr.append(nan)
if fp+tn > 0.0:
fpr.append(fp/(fp+tn))
else:
fpr.append(nan)
if tp+fp > 0.0:
ppv.append(tp/(tp+fp))
else:
ppv.append(nan)
tpr = array(tpr)
fpr = array(fpr)
ppv = array(ppv)
auc = trapz(fpr, tpr, 0.0, 1.0)
idx = -isnan(ppv)
apr = trapz(tpr[idx], ppv[idx], 1.0, 0.0)
return tpr, fpr, ppv, auc, apr
def stats_binormal(output, labels, step=0.001):
"""Compute some statistics for binary predictions.
tpr - true positive rate (recall)
fpr - false positive rate
ppv - positive predictive value (precision)
auc - area under the ROC curve
ap - area under the precision-recall curve (average precision)
Use the binormal assumption.
step gives the smoothness of curve.
"""
assert len(output)==len(labels), 'Predictions and labels have different lengths'
assert all(unique(labels)==array([-1,1])), 'Labels are not binary {-1,+1}'
# Estimate the binormal parameters
pos = output[labels>0]
neg = output[labels<0]
mu_pos = mean(pos)
mu_neg = mean(neg)
std_pos = std(pos)
std_neg = std(neg)
alpha = 1.0*len(pos)/len(output)
# Sanity checks
assert mu_pos > mu_neg, 'positive Gaussian is not to the right of negative'
assert (std_pos>0) and (std_neg>0), 'Variance is zero'
# Use Gaussian cdf to estimate scores
thres = arange(mu_neg-5.0*std_neg, mu_pos+5*std_pos, step)
tp = alpha*(1.0-stats.norm.cdf(thres, mu_pos, std_pos))
fp = (1.0-alpha)*(1.0-stats.norm.cdf(thres, mu_neg, std_neg))
fn = alpha*stats.norm.cdf(thres, mu_pos, std_pos)
tn = (1.0-alpha)*stats.norm.cdf(thres, mu_neg, std_neg)
tpr = tp/(tp+fn)
fpr = fp/(fp+tn)
ppv = tp/(tp+fp)
A = (mu_pos-mu_neg)/std_pos
B = std_neg/std_pos
auc = stats.norm.cdf(A/sqrt(1+B*B))
apr = trapz(tpr, ppv, 1.0, 0.0)
return tpr, fpr, ppv, auc, apr
def stats_binormal_multitask(output, labels):
"""stats_binormal applied to each row"""
assert output.shape == labels.shape, 'Predictions and labels have different shape'
num_task = output.shape[0]
tpr = []
fpr = []
ppv = []
auc = zeros(num_task)
apr = zeros(num_task)
for ix in range(num_task):
lab_idx = nonzero(labels[ix,:])
ctpr, cfpr, cppv, auc[ix], apr[ix] = stats_binormal(output[ix,lab_idx].flatten(),
labels[ix,lab_idx].flatten())
tpr.append(ctpr)
fpr.append(cfpr)
ppv.append(cppv)
return tpr, fpr, ppv, auc, apr
def auc(output, labels):
"""The area under the ROC curve,
estimated using the binormal approximation
"""
tpr, fpr, ppv, auc, apr = stats_empirical(output, labels)
return auc
OptWok-0.3/optwok/expttools.py 0000644 ڛ |-00000036652 12120324207 023501 0 ustar cheng.ong UNIMELB\Domain Users 0000000 0000000
"""Some scripts to help run machine learning experiments.
Based on Hans Peter Langtangen's scripts for multipleloop
"""
import re, operator
import sys
import types
import time
multiprocessing_present = True
try:
import multiprocessing
except ImportError as detail:
print("Error importing multiprocessing. Local computing limited to one CPU.")
print("Please install python2.6 or the backport of the multiprocessing package")
print(detail)
multiprocessing_present = False
# Turn off multiprocessing
#multiprocessing_present = False
drmaa_present = True
pythongrid_present = False
try:
import drmaa
except ImportError as detail:
print("Error importing drmaa. Limited to local computing.")
print("See http://code.google.com/p/drmaa-python/""")
print(detail)
drmaa_present = False
if drmaa_present:
pythongrid_present = True
try:
import pythongrid
except ImportError as detail:
print("Error importing pythongrid. Limited to local computing.")
print("See http://bitbucket.org/chengsoon.ong/pythongrid/overview/")
print(detail)
pythongrid_present = False
# Turn off pythongrid
#pythongrid_present = False
from optwok.expttools_helper import _wrap_kwargs
from optwok.expttools_helper import _pickle_method, _unpickle_method
python_version = sys.version_info[:2]
if python_version >= (3, 0):
import copyreg as cr
else:
import copy_reg as cr
cr.pickle(types.MethodType, _pickle_method, _unpickle_method)
def apply_to_combination(function_handle, param, fixed=dict(),
verbose=False, multiproc=True, cluster=False,
check_result=None, lsf_param=None, blocking=True, addpath=[]):
"""Apply the function defined by function handle to the
tensor product of the parameters defined by dictionary param.
param is a dictionary of lists, each key defining the
keyword argument to be passed to the function. The function
is applied to all combinations of all the settings.
The dictionary defined by fixed is concatenated to the end
of the keywords.
Set blocking to False if you don't want to wait for all jobs to finish on the cluster
addpath is a list of directories you want to have added to pythonpath before the execution of the job,
the current directory for instance.
"""
assert(param.keys() != fixed.keys())
# Convert single parameters into singletons
param = dict([(item[0], [item[1]]) if not isinstance(item[1],list) else item for item in param.iteritems()])
all_values = [(name, input2values(param[name])) for name in param]
(params, names, varied) = combine(all_values)
function_inputs = []
for cur_param in params:
kwargs = fixed.copy()
kwargs.update(dict((names[ix], cur_param[ix]) for ix in range(len(names))))
if verbose:
print('apply_to_combination')
print(function_handle)
print(kwargs)
print('====================')
function_inputs.append(kwargs)
if drmaa_present and cluster:
jobs = []
for kwargs in function_inputs:
create_job = True
if check_result:
create_job = not check_result(**kwargs)
if create_job:
job = pythongrid.LSFJob(function_handle, kwlist=kwargs, cleanup=True)
#print('adding ', addpath, ' in optwok atc')
job.addpath = addpath
if lsf_param:
job.wall_time_h = lsf_param['wall_time_h']
job.wall_time_m = lsf_param['wall_time_m']
job.memory = lsf_param['memory']
jobs.append(job)
print('Processing %d jobs' % len(jobs))
time.sleep(5)
(sid, jobids) = pythongrid.submit_jobs(jobs)
function_outputs = []
if blocking:
processed_jobs = pythongrid.collect_jobs(sid, jobids, jobs, wait=True)
for job in processed_jobs:
function_outputs.append(job.ret)
elif multiprocessing_present and multiproc:
#cpus = multiprocessing.Pool(processes=3)
cpus = multiprocessing.Pool()
for fins in function_inputs:
fins['function_handle'] = function_handle
function_outputs = cpus.map(_wrap_kwargs, function_inputs)
else:
function_outputs = []
for kwargs in function_inputs:
cur_out = function_handle(**kwargs)
function_outputs.append(cur_out)
return function_outputs
def input2values(s):
"""
Translate a string s with multiple loop syntax into
a list of single values (for the corresponding parameter).
Multiple loop syntax:
'-1 & -3.4 & 20 & 70 & [0:10,1.3] & [0:10] & 11.76'
That is, & is delimiter between different values, [0:10,1.3]
generates a loop from 0 up to and including 10 with steps 1.3,
[0:10] generates the integers 1,2,...,10.
Interactive session::
>>> input2values('-1 & -3.4 & 20 & 70 & [0:10,1.3] & [0:10] & 11.76')
[-1, -3.3999999999999999, 20, 70, 0, 1.3, 2.6000000000000001,
3.9000000000000004, 5.2000000000000002, 6.5, 7.7999999999999998,
9.0999999999999996, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11.76]
>>> p = {'w': '[0.7:1.3,0.1]', 'b': '1 & 0.3 & 0', 'func': 'y & siny'}
>>> print(input2values(p['w']))
[0.69999999999999996, 0.79999999999999993, 0.89999999999999991,
0.99999999999999989, 1.0999999999999999, 1.2, 1.3]
>>> print(input2values(p['b']))
[1, 0.29999999999999999, 0]
>>> print(input2values(p['func']))
['y', 'siny']
>>> prm_values = [(name, input2values(p[name])) for name in p]
>>> prm_values
[('b', [1, 0.29999999999999999, 0]),
('func', ['y', 'siny']),
('w', [0.69999999999999996, 0.79999999999999993, 0.89999999999999991,
0.99999999999999989, 1.0999999999999999, 1.2, 1.3])]
"""
if not isinstance(s, type('')):
return s
items = s.split('&')
values = []
for i in items:
i = i.strip() # white space has no meaning
# is i a loop?
m = re.search(r'\[(.+):([^,]+),?(.*)\]',i)
if m:
# the group are numbers, take eval to get right type
start = eval(m.group(1))
stop = eval(m.group(2))
try:
incr = m.group(3).strip()
incr_op = operator.add
if incr[0] == '*':
incr_op = operator.mul
incr = incr[1:]
elif incr[0] == '+' or incr[0] == '-':
incr = incr[1:]
incr = eval(incr)
except:
incr = 1
r = start
while (r <= stop and start <= stop) or \
(r >= stop and start >= stop):
values.append(r)
r = incr_op(r, incr)
else:
# just an ordinary item, convert i to right type:
values.append(str2obj(i))
# return list only if there are more than one item:
if len(values) == 1:
return values[0]
else:
return values
def _outer(a, b):
"""
Return the outer product/combination of two lists.
a is a multi- or one-dimensional list,
b is a one-dimensional list, tuple, NumPy array or scalar (new parameter)
Return: outer combination 'all'.
The function is to be called repeatedly::
all = _outer(all, p)
"""
all = []
if not isinstance(a, list):
raise TypeError('a must be a list')
if isinstance(b, (float,int,complex,type(''))): b = [b] # scalar?
if len(a) == 0:
# first call:
for j in b:
all.append([j])
else:
for j in b:
for i in a:
if not isinstance(i, list):
raise TypeError('a must be list of list')
# note: i refers to a list; i.append(j) changes
# the underlying list (in a), which is not what
# we want, we need a copy, extend the copy, and
# add to all
k = i + [j] # extend previous prms with new one
all.append(k)
return all
def combine(prm_values):
"""
@param prm_values: nested list of
(parameter_name, list_of_parameter_values)
or dictionary
prm_values[parameter_name] = list_of_parameter_values
@type prm_values: list or dict
@return: (all,names,varied) where
- all contains all combinations (experiments)
all[i] is the list of individual parameter values in
experiment no i
- names contains a list of all parameter names
- varied holds a list of parameter names that are varied
(i.e. where there is more than one value of the parameter)
Code example::
>>> dx = array([1.0/2**k for k in range(2,5)])
>>> dt = 3*dx; dt = dt[:-1]
>>> p = {'dx': dx, 'dt': dt}
>>> p
{'dt': [ 0.75 , 0.375,], 'dx': [ 0.25 , 0.125 , 0.0625,]}
>>> all, names, varied = combine(p)
>>> all
[[0.75, 0.25], [0.375, 0.25], [0.75, 0.125], [0.375, 0.125],
[0.75, 0.0625], [0.375, 0.0625]]
"""
if isinstance(prm_values, dict):
# turn dict into list [(name,values),(name,values),...]:
prm_values = [(name, prm_values[name]) \
for name in prm_values]
all = []
varied = []
for name, values in prm_values:
all = _outer(all, values)
if isinstance(values, list) and len(values) > 1:
varied.append(name)
names = [name for name, values in prm_values]
return all, names, varied
def dump(all, names, varied):
e = 1
for experiment in all:
print('Experiment %4d:' % e,)
for name, value in zip(names, experiment):
print('%s:' % name, value,)
print('')# newline
e += 1 # experiment counter
for experiment in all:
cmd = ' '.join(['-'+name+' '+repr(value) for \
name, value in zip(names, experiment)])
print(cmd)
def options(all, names, prefix='--'):
"""
Return a list of command-line options.
@param all: all[i] holds a list of parameter values in experiment no i
@param names: names[i] holds name of parameter no. i
@return: cmd[i] holds -name value pairs of all parameters in
experiment no. i
"""
cmd = []
for experiment in all:
cmd.append(' '.join([prefix + name + ' ' + repr(str2obj(value)) \
for name, value in zip(names, experiment)]))
return cmd
def varied_parameters(parameters, varied, names):
"""
@param names: names of parameters.
@param parameters: values of parameters.
@param varied: subset of names (the parameters that are varied elsewhere).
@return: a list of the items in parameters whose names are listed
in varied.
An example may help to show the idea. Assume we have three parametes
named 'a', 'b', and 'c'. Their values are 1, 5, and 3, i.e.,
'a' is 1, 'b' is 5, and 'c' is 3. In a loop elsewhere we assume
that 'a' and 'c' are varied while 'b' is fixed. This function
returns a list of the parameter values that correspond to varied
parameters, i.e., [1,3] in this case, corresponding to the names
'a' and 'c'::
>>> parameters = [1,5,3]
>>> names = ['a','b','c']
>>> varied = ['a','c']
>>> varied_parameteres(parameters, varied, names)
[1,3]
"""
indices_varied = [names.index(i) for i in varied]
varied_parameters = [parameters[i] for i in indices_varied]
return varied_parameters
def remove(condition, all, names):
"""
Remove experiments that fulfill a boolean condition.
Example:
all = remove('w < 1.0 and p = 1.2) or (q in (1,2,3) and f < 0.1', all, names)
(names of the parametes must be used)
"""
import copy
for ex in copy.deepcopy(all): # iterate over a copy of all!
c = condition
for n in names: # replace names by actual values
#print('replace "%s" by "%s"' % (n, repr(ex[names.index(n)])))
c = c.replace(n, repr(ex[names.index(n)]))
# note the use of repr: strings must be quoted
#print('remove ',remove)
if eval(c): # if condition
all.remove(ex)
return all # modified list
def str2obj(s, globals=globals(), locals=locals()):
"""
Turn string s into the corresponding object.
eval(s) normally does this, but if s is just a string ready
from file, GUI or the command-line, eval will not work when
s really represents a string:
>>> eval('some string')
Traceback (most recent call last):
SyntaxError: unexpected EOF while parsing
It tries to parse 'some string' as Python code.
In this function we try to eval(s), and if it works, we
return that object. If it does not work, s probably has
meaning as a string, and we return just s.
Examples::
>>> from misc import str2obj
>>> s = str2obj('0.3')
>>> print(s, type(s))
0.3
>>> s = str2obj('3')
>>> print(s, type(s))
3
>>> s = str2obj('(1,8)')
>>> print(s, type(s))
(1, 8)
>>> s = str2obj('some string')
>>> s
'some string'
If the name of a user defined function, class or instance is
sent to str2obj, one must also provide locals() and globals()
dictionaries as extra arguments. Otherwise, str2obj will not
know how to "eval" the string and produce the right object
(user defined types are not known inside str2obj).
Here is an example::
>>> def myf(x):
... return 1+x
...
>>> class A:
... pass
...
>>> a = A()
>>>
>>> s = str2obj('myf')
>>> print(s, type(s) # now s is simply the string 'myf')
myf
>>> # provide locals and globals such that we get the function myf:
>>> s = str2obj('myf', locals(), globals())
>>> print(s, type(s))
>>> s = str2obj('a', locals(), globals())
>>> print(s, type(s))
<__main__.A instance at 0xb70f6fcc>
Caveat: if the string argument is the name of a valid Python
class (type), that class will be returned. For example,
>>> str2obj('list') # returns class list
You can normally safely apply eval on the output of this function.
"""
try:
s = eval(s, globals, locals)
return s
except:
return s
def _inner_func(a=1.0, b=2.0, x=3.0):
"""For use with _test1()"""
return (a+b-x)
def _test1():
"""Test apply_to_combination"""
from optwok.expttools import _inner_func
params = {'a': [-0.2, 0.2, 1.0],
'b': [0.3, 3.2],
}
fixed_param = {'x': 1.3}
print("Testing apply_to_combination")
print("Single CPU")
results = apply_to_combination(_inner_func, params, fixed=fixed_param,
multiproc=False, cluster=False)
print(results)
del results
if multiprocessing_present:
print("multiprocessing")
results = apply_to_combination(_inner_func, params, fixed=fixed_param,
multiproc=True, cluster=False)
print(results)
del results
if pythongrid_present:
print("cluster")
results = apply_to_combination(_inner_func, params, fixed=fixed_param,
multiproc=False, cluster=True)
print(results)
del results
if __name__ == '__main__':
_test1()
OptWok-0.3/optwok/expttools_helper.py 0000644 ڛ |-00000002560 12055603331 025034 0 ustar cheng.ong UNIMELB\Domain Users 0000000 0000000 """Helper functions for expttools"""
import sys
python_version = sys.version_info[:2]
def _wrap_kwargs(kwargs):
"""Small helper function to enable Pool"""
function_handle = kwargs['function_handle']
del kwargs['function_handle']
return function_handle(**kwargs)
if python_version >= (3, 0):
def _pickle_method(method):
func_name = method.__func__.__name__
obj = method.__self__
cls = method.__self__.__class__
return _unpickle_method, (func_name, obj, cls)
def _unpickle_method(func_name, obj, cls):
for cls in cls.mro():
try:
func = cls.__dict__[func_name]
except KeyError:
pass
else:
break
return func.__get__(obj, cls)
else:
def _pickle_method(method):
"""A workaround for inability to pickle instance methods"""
func_name = method.im_func.__name__
obj = method.im_self
cls = method.im_class
return _unpickle_method, (func_name, obj, cls)
def _unpickle_method(func_name, obj, cls):
"""A workaround for inability to pickle instance methods"""
for cls in cls.mro():
try:
func = cls.__dict__[func_name]
except KeyError:
pass
else:
break
return func.__get__(obj, cls)
OptWok-0.3/optwok/gauss_kernel.py 0000644 ڛ |-00000007045 11754625156 024137 0 ustar cheng.ong UNIMELB\Domain Users 0000000 0000000
class Kernel(object):
"""A wrapper to define the interfaces for kernels"""
def __init__(self, normalize=True):
self.train_ex = None
self.test_ex = None
self.km = None
self.normalize = normalize # not implemented yet
def init_data(self, train_ex, test_ex):
"""Copy data to internal variables"""
self.train_ex = train_ex.copy()
self.test_ex = test_ex.copy()
def get_kernel_matrix(self, split='train'):
pass
class LinearKernel(Kernel):
"""Standard scalar product"""
def __init__(self, normalize=True):
Kernel.__init__(self, normalize)
def get_kernel_matrix(self, split='train'):
if split == 'train':
return array(matrix(self.train_ex).T*matrix(self.train_ex))
elif split == 'test':
return array(matrix(self.train_ex).T*matrix(self.test_ex))
class GaussianKernel(Kernel):
"""Gaussian Kernel with scalar width
k(x,y) = exp(-width * ||x-y||^2)
"""
def __init__(self, width=1.0, normalize=True):
Kernel.__init__(self, normalize)
self.width = width
def get_kernel_matrix(self, split='train'):
if split == 'train':
m = self.train_ex.shape[1]
a = self.train_ex
unitvec = ones(m,1)
dot_a = sum(self.width*a*a,1)
K = matrix(sqrt(self.width)*a).T * matrix(sqrt(self.width)*a)
for ix in range(m):
K[:,ix] = exp(2*K[:,ix] - transpose(dot_a) - dot_a[ix]*unitvec)
elif split == 'test':
a = self.train_ex
m = a.shape[1]
b = self.test_ex
n = b.shape[1]
unitvec = ones(m,1)
dot_a = sum(self.width*a*a,1)
dot_a = sum(self.width*a*a,1)
K = matrix(sqrt(self.width)*a).T * matrix(sqrt(self.width)*b)
for ix in range(n):
K[:,ix] = exp(2*K[:,ix] - transpose(dot_a) - dot_b[ix]*unitvec)
return K
def get_kernel_matrix_slow(self, split='train'):
"""Implement the double for loop"""
if split == 'train':
m = self.train_ex.shape[1]
K = zeros(m,m)
for ix in range(m):
for iy in range(ix,m):
K[ix,iy] = exp(-self.width * norm(self.train_ex[:,ix]
- self.train_ex[:,iy])**2)
K[iy,ix] = K[ix,iy]
elif split == 'test':
m = self.train_ex.shape[1]
n = self.test_ex.shape[1]
K = zeros(m,n)
for ix in range(m):
for iy in range(n):
K[ix,iy] = exp(-self.width * norm(self.train_ex[:,ix]
- self.test_ex[:,iy])**2)
return K
class StudentKernel(Kernel):
"""Kernel based on the unnormalized Student T distribution.
k(x,y) = (1+(||x-y||^2)/nu)^(-(nu+1)/2)
"""
pass
class IntegratedGaussianKernel(Kernel):
"""Integral of the Gaussian kernel with respect to the width.
k(x,y) = sqrt(2)*sigma*exp(-1/2*sigma^2 ||x-y||^2)
- sqrt(pi)*||x-y||*erfc(-1/sqrt(2)*sigma ||x-y||)
"""
pass
class GaussianCovKernel(Kernel):
"""Gaussian kernel with the full covariance function.
k(x,y) = exp(- (x-y).T * Cov * (x-y))
Consider also different shrinkage approaches.
"""
class MixGaussianKernel(Kernel):
"""A finite sum of Gaussian Kernels"""
pass
class TaylorGaussianKernel(Kernel):
"""A infinite sum of Gaussian Kernels"""
pass
OptWok-0.3/optwok/gaussian_proc.py 0000644 ڛ |-00000013177 12055531247 024305 0 ustar cheng.ong UNIMELB\Domain Users 0000000 0000000 """Gaussian process regression"""
from numpy import array, matrix, kron, diag, ones, real, dot, ndarray
from numpy import zeros, eye, hstack, concatenate, isnan, sum, log, pi, inner, outer, ones_like, trace
from numpy.linalg import solve, inv, cholesky, lstsq
from numpy.linalg import eig
from scipy.linalg.matfuncs import sqrtm
from optwok.kernel import ContextKernel
from numpy.core.numeric import newaxis
#import ipdb
# TODO: general kernel function
# gp.update to rank one updates
class GaussianProcess(object):
"""Gaussian Process regression,
for objects which are pairs of (data, context)"""
def __init__(self, Ks, Kz, kparam = None, reg_param = 1.0):
if kparam is not None:
self.Ks = Ks(kparam)
self.Ks_pred = Ks(kparam)
else:
self.Ks = Ks()
self.Ks_pred = Ks()
self.Kz = Kz
self.Ktrain = ContextKernel(self.Ks, self.Kz)
self.Kpred = ContextKernel(self.Ks_pred, self.Kz)
self.train_ex = array([])
self.train_cont = array([], dtype = int)
self.train_lab = array([])
self.reg_param = reg_param
self.num_train = 0
self.num_context = Kz.shape[0]
self.alpha = array([])
self.L = array([])
def train(self, train_ex, train_cont, train_lab):
"""Learn the parameters.
train_ex is a tuple containing (example, context)
"""
self.train_ex = train_ex
self.train_cont = array(train_cont, dtype = int)
self.train_lab = array(train_lab)
self.num_train = self.train_ex.shape[1]
self._update()
def _update(self):
"""Computations to estimate GP parameters"""
self.Ktrain.compute(self.train_ex, self.train_cont)
#self.L = real(sqrtm(self.Ktrain.kmat + self.reg_param*eye(self.num_train)))
self.L = cholesky(self.Ktrain.kmat + self.reg_param * eye(self.num_train))
(x, residues, rank, sing) = lstsq(self.L, self.train_lab)
(self.alpha, residues, rank, sing) = lstsq(self.L.T, x)
def update(self, orig_ex, new_cont, new_lab):
"""Include the new example into training data"""
new_ex = orig_ex.copy()
new_ex.shape = (len(new_ex), 1)
self.train_ex = hstack([self.train_ex, new_ex])
self.train_cont = concatenate([self.train_cont, array([new_cont], dtype = int)])
self.train_lab = concatenate([self.train_lab, array([new_lab])])
self.num_train += 1
assert len(self.train_cont) == self.num_train
self._update()
def predict(self, pred_data):
"""Estimate the outputs"""
pred_ex = pred_data[0]
pred_cont = array(pred_data[1], dtype = int)
num_pred = pred_ex.shape[1]
self.Kpred.compute(pred_ex, pred_cont)
if self.num_train == 0:
scores = zeros(num_pred)
pred_var = diag(self.Kpred.kmat)
else:
Kmat_cross = self.Ktrain.compute_test(pred_ex, pred_cont)
scores = dot(Kmat_cross.T, self.alpha).flatten()
(v, residues, rank, sing) = lstsq(self.L, Kmat_cross)
pred_var = diag(self.Kpred.kmat - dot(v.T, v))
if not (pred_var > 0).all():
print('=== Predictive variance negative!')
#ipdb.set_trace()
return scores, pred_var
class GaussianProcessScalar(object):
"""Gaussian Process regression"""
def __init__(self, Ks, kparam = None, reg_param = 1.0, m=0):
''' Ks: Kernel Object, kparam: Kernel Parameters, reg_param: noise in the data,
m: constant mean '''
if kparam is not None:
self.Ktrain = Ks(kparam)
self.Kpred = Ks(kparam)
else:
self.Ktrain = Ks()
self.Kpred = Ks()
self.train_ex = array([])
self.train_lab = array([])
self.reg_param = reg_param
self.num_train = 0
self.inv_cov = matrix([])
self.alpha = array([])
self.L = matrix([])
self.m = m
self.kparam = kparam
def train(self, train_ex, train_lab):
"""Learn the parameters."""
self.train_ex = train_ex
self.train_lab = matrix(train_lab)
try:
self.num_train = self.train_ex.shape[1]
except IndexError: # 1-dimensional
self.num_train = self.train_ex.shape[0]
self.train_ex = self.train_ex[newaxis, :]
self.train_lab.shape = (self.num_train, 1)
self.Ktrain.compute(self.train_ex)
self.L = cholesky(self.Ktrain.kmat + self.reg_param * eye(self.num_train))
self.alpha = solve(self.L.T, solve(self.L, self.train_lab - self.m))
def predict(self, pred_ex):
"""Estimate the outputs"""
try:
num_pred = pred_ex.shape[1]
except IndexError: # 1-d
pred_ex = pred_ex[newaxis, :]
self.Kpred.compute(pred_ex)
Kmat_cross = self.Ktrain.compute_test(pred_ex)
scores = array(Kmat_cross.T * self.alpha)
v = matrix(solve(self.L, Kmat_cross))
pred_var = diag(self.Kpred.kmat - v.T * v)
return self.m + scores.flatten(), pred_var
def nl_likelihood(self):
return array((self.train_lab - self.m).T.dot(self.alpha)/2 + sum(log(diag(self.L))) + self.num_train * log(2*pi)/2)[0][0]
def grad_nl_likelihood(self):
Q = solve(self.L, eye(self.num_train)) / self.reg_param - outer(self.alpha, self.alpha)
dnoise = self.reg_param * trace(Q)
dm = dot(ones_like(self.train_ex),array(self.alpha))[0][0]
grad =[dnoise, dm]
dkparam = self.Ktrain.compute_grad()
for dkp in dkparam:
grad.append(sum(sum(Q*dkp))/2)
return array(grad)
OptWok-0.3/optwok/io_pickle.py 0000644 ڛ |-00000003011 12055561344 023371 0 ustar cheng.ong UNIMELB\Domain Users 0000000 0000000 """A Matlab like interface for loading and saving objects.
Has an additional benefit of collecting all instances of pickle code here."""
import sys
import bz2
import subprocess
import os
python_version = sys.version_info[:2]
if python_version >= (3, 0):
from pickle import load as pload
from pickle import dump as pdump
else:
from cPickle import load as pload
from cPickle import dump as pdump
def save(filename, myobj):
"""save the myobj to filename using pickle"""
try:
f = bz2.BZ2File(filename, 'wb')
except IOError:
sys.stderr.write('Trying local path...')
try:
f = bz2.BZ2File(os.path.join(os.getcwd(), filename))
except IOError:
sys.stderr.write('File ' + filename + ' cannot be written\n')
return
pdump(myobj, f, protocol = 2)
f.close()
def load(filename):
"""load the myobj from filename using pickle"""
try:
f = bz2.BZ2File(filename, 'rb')
except IOError:
sys.stderr.write('File ' + filename + ' cannot be read\n')
return
myobj = pload(f)
f.close()
return myobj
def convert_v6(input_filename, output_filename):
""" starts matlab and converts a structure to a matlab v6 structure """
cmd = "matlab -nojvm -nodisplay -r \"load %s; save -v6 '%s'; exit\"" % (input_filename, output_filename)
obj = subprocess.Popen(cmd, shell = True, stdout = subprocess.PIPE, stderr = subprocess.PIPE)
out, err = obj.communicate()
assert err == '', 'An error occured!\n%s' % err
OptWok-0.3/optwok/kernel.py 0000644 ڛ |-00000004520 11731540564 022722 0 ustar cheng.ong UNIMELB\Domain Users 0000000 0000000 """Implement some positive semidefinite kernels."""
from numpy import array, matrix
from numpy import zeros, ones, eye, diag
from numpy import average, exp, kron, sum
from numpy import sqrt, divide, mean, multiply
#try:
# from optwok.kernelbase import Kernel, JointKernel, ContextKernel
#except ImportError:
# from optwok.kernel_pp import Kernel, JointKernel, ContextKernel
from optwok.kernel_pp import *
#####################################################################
# Some miscellaneaous functions for kernel matrix operations
#####################################################################
def sqr_dist(a,b):
"""Compute the square distance between vectors"""
assert(a.shape[0] == b.shape[0])
if len(a.shape)>1:
# a is a matrix
dot_a = sum(a*a, axis=0).T
dot_b = sum(b*b ,axis=0).T
unitvec = ones(dot_a.shape)
D = array(2.0*matrix(a).T*matrix(b))
for ix,bval in enumerate(dot_b):
D[:,ix] = dot_a - D[:,ix] + kron(bval,unitvec)
else:
D = zeros((len(a),len(b)))
for (ixa, cur_a) in enumerate(a):
for (ixb, cur_b) in enumerate(b):
D[ixa,ixb] = (cur_a-cur_b)*(cur_a-cur_b)
return D
def center(K):
"""Center the kernel matrix, such that the mean (in feature space) is zero."""
one_mat = matrix(ones(K.shape))
one_vec = matrix(ones((K.shape[0],1)))
row_sum = matrix(mean(K,axis=0)).T
R = K - row_sum * one_vec.T - one_vec * row_sum.T +\
mean(row_sum.A)*one_mat
return R
def normalize_variance(K):
"""Normalize the kernel matrix, such that the variance (in feature space) is 1"""
one_vec = matrix(ones((K.shape[0],1)))
inv_sqrt_diag = divide(one_vec, matrix(sqrt(diag(K))).T)
KN = multiply(kron(one_vec.T,inv_sqrt_diag),K)
KN = multiply(kron(one_vec,inv_sqrt_diag.T),K)
return KN
def normalize_unit_diag(K):
"""Normalize the kernel matrix, such that all diagonal entries are 1."""
Kii = diag(K)
Kii.shape = (len(Kii),1)
return divide(K, sqrt(matrix(Kii)*matrix(Kii).T))
def kernel2distance(K):
"""Convert the kernel matrix into the corresponding Euclidean distance."""
# TODO: Vectorize
D = zeros(K.shape)
for ix in range(K.shape[0]):
for iy in range(K.shape[1]):
D[ix,iy] = sqrt(K[ix,ix] + K[iy,iy] - 2*K[ix,iy])
return D
OptWok-0.3/optwok/kernel_pp.py 0000644 ڛ |-00000024436 12055533446 023432 0 ustar cheng.ong UNIMELB\Domain Users 0000000 0000000 """Implement some positive semidefinite kernels.
These are in pure python. For the cython implementation, see kernelbase.pyx
"""
from numpy import array, matrix, max, ix_
from numpy import zeros, ones, eye, diag, empty
from numpy import average, exp, kron, sum
from numpy import sqrt, divide, mean, multiply
from numpy import pi, sin, cos
from numpy import flatnonzero
class Kernel(object):
"""A Mercer kernel. Note that if Cython is available, then this is not used."""
def __init__(self):
self.kmat = array([])
self.examples = None
def __getitem__(self, idx):
assert(len(idx)==2)
return self.kmat[idx[0], idx[1]]
def __repr__(self):
rep = '<' + self.__class__.__name__ + ' instance>'
return rep
def dump(self) :
"""
returns a string that can be used to construct an equivalent object
"""
kstr = self.__module__ + '.' + self.__class__.__name__ + '(' + \
self.construction_params() + ')'
return kstr
def construction_params(self):
raise NotImplementedError
def compute(self, examples):
"""Evaluate the kernel function and store the value"""
raise NotImplementedError
def compute_test(self, test_ex):
"""Evaluate the kernel function on test examples"""
raise NotImplementedError
class JointKernel(object):
"""A kernel on input and output pairs."""
def __init__(self, input_kernel, output_kmat=None, num_classes=-1):
self.Kx = input_kernel
if output_kmat is not None:
self.Ky = output_kmat
else:
assert(num_classes > 1)
self.Ky = eye(num_classes)
self.examples = None
def __repr__(self) :
rep = '<' + self.__class__.__name__ + ' instance>'
return rep
def __getitem__(self, idx):
"""Simulate matrix element.
Order is assumed to be examples first then data.
"""
assert(len(idx)==4)
return self.Kx[idx[0], idx[1]]*self.Ky[idx[2], idx[3]]
@property
def kmat(self):
"""Simulate the whole kernel matrix"""
return kron(self.Kx.kmat, self.Ky)
def compute(self, examples):
self.examples = examples
self.Kx.compute(examples)
def compute_test(self, test_ex):
"""Returns a non-square matrix, of num_train by num_test size."""
Kx = self.Kx.compute_test(test_ex)
return kron(Kx, self.Ky)
class ContextKernel(Kernel):
"""A kernel of (data, context) pairs,
mathematically the same as the JointKernel,
but does not evaluate all combinations"""
def __init__(self, input_kernel, output_kmat):
self.Kx = input_kernel
self.num_context = output_kmat.shape[0]
self.Ky = output_kmat
self.examples = None
self.num_examples = 0
self.context = None
def __repr__(self) :
rep = '<' + self.__class__.__name__ + ' instance>'
return rep
def __getitem__(self, idx):
"""Simulate matrix element.
Order is assumed to be (example,context,example,context)
"""
assert(len(idx)==4)
return self.Kx[idx[0], idx[2]]*self.Ky[idx[1], idx[3]]
@property
def kmat(self):
"""Simulate the whole kernel matrix"""
K = zeros((self.num_examples, self.num_examples))
for con_x in range(self.num_context):
for con_y in range(self.num_context):
ix = flatnonzero(self.context == con_x)
iy = flatnonzero(self.context == con_y)
if len(ix)>0 and len(iy)>0:
Ky_val = self.Ky[self.context[ix[0]], self.context[iy[0]]]
if abs(Ky_val) > 0.0:
K[ix_(ix,iy)] = self.Kx[ix_(ix,iy)]*Ky_val
K[ix_(iy,ix)] = self.Kx[ix_(iy,ix)]*Ky_val
return K
@property
def kmat_forloop(self):
"""Simulate the whole kernel matrix"""
K = zeros((self.num_examples, self.num_examples))
for ix in range(self.num_examples):
for iy in range(ix, self.num_examples):
val = self.Kx[ix,iy]*self.Ky[self.context[ix], self.context[iy]]
K[ix,iy] = val
K[iy,ix] = val
return K
def compute(self, examples, context):
self.examples = examples
self.context = context
self.num_examples = examples.shape[1]
self.Kx.compute(examples)
def compute_test(self, examples, context):
"""Returns a non-square matrix, of num_train by num_test size."""
assert self.examples is not None
assert self.context is not None
num_test = examples.shape[1]
Kx_cross = self.Kx.compute_test(examples)
Kmat_cross = zeros((self.num_examples, num_test))
for con_x in range(self.num_context):
for con_y in range(self.num_context):
ix = flatnonzero(self.context == con_x)
iy = flatnonzero(context == con_y)
if len(ix)>0 and len(iy)>0:
Ky_val = self.Ky[self.context[ix[0]], context[iy[0]]]
if abs(Ky_val) > 0.0:
Kmat_cross[ix_(ix,iy)] = Kx_cross[ix_(ix,iy)]*Ky_val
return Kmat_cross
def compute_test_forloop(self, examples, context):
"""Returns a non-square matrix, of num_train by num_test size."""
assert self.examples is not None
assert self.context is not None
num_test = examples.shape[1]
Kx_cross = self.Kx.compute_test(examples)
Kmat_cross = zeros((self.num_examples, num_test))
for ix in range(self.num_examples):
for iy in range(num_test):
Kmat_cross[ix,iy] = Kx_cross[ix,iy]*self.Ky[self.context[ix], context[iy]]
return Kmat_cross
class LinearKernel(Kernel):
"""Standard dot product"""
def __init__(self):
Kernel.__init__(self)
def construction_params(self):
return ''
def compute(self, examples):
"""Evaluate the kernel function and store the value"""
self.examples = examples
self.kmat = array(matrix(examples).T*matrix(examples))
def compute_test(self, test_ex):
"""Evaluate the kernel function on test examples"""
if test_ex.ndim == 1:
test_ex.shape = (len(test_ex),1)
return array(matrix(self.examples).T*matrix(test_ex))
class SEKernel(Kernel):
''' Squared Exponential Kernel '''
def sqr_dist(self, a, b):
"""Compute the square distance between vectors"""
assert(a.shape[0] == b.shape[0])
if len(a.shape) > 1:
# a is a matrix
dot_a = sum(a * a, axis = 0).T
dot_b = sum(b * b , axis = 0).T
unitvec = ones(dot_a.shape)
D = array(2.0 * matrix(a).T * matrix(b))
for ix, bval in enumerate(dot_b):
D[:, ix] = dot_a - D[:, ix] + kron(bval, unitvec)
else:
D = zeros((len(a), len(b)))
for (ixa, cur_a) in enumerate(a):
for (ixb, cur_b) in enumerate(b):
D[ixa, ixb] = (cur_a - cur_b) * (cur_a - cur_b)
return D
class GaussKernel(SEKernel):
"""Gaussian rbf kernel"""
def __init__(self, gamma=1.0):
Kernel.__init__(self)
self.gamma = gamma
def __repr__(self) :
rep = '<' + self.__class__.__name__ + ' instance>\n'
rep += 'gamma : ' + str(self.gamma)
return rep
def constructionParams(self) :
return 'gamma = ' + str(self.gamma)
def compute(self, examples):
"""Evaluate the kernel function and store the value"""
self.examples = examples
self.kmat = exp(-self.gamma * self.sqr_dist(examples, examples))
def compute_test(self, test_ex):
"""Evaluate the kernel function on test examples"""
if test_ex.ndim == 1:
test_ex.shape = (len(test_ex),1)
return exp(-self.gamma * self.sqr_dist(self.examples, test_ex))
class PeriodicKernel(SEKernel):
""" Periodic kernel
k(x,y) = theta * exp( -2*sin^2( pi*||x-y||/P )/lamb^2 )
"""
def __init__(self, theta=1.0, lamb=1.1, P=pi):
Kernel.__init__(self)
self.theta = theta
self.lamb = lamb
self.P = P
def __repr__(self) :
rep = '<' + self.__class__.__name__ + ' instance>\n'
rep += 'theta : ' + str(self.theta)
rep += ', lambda : ' + str(self.lamb)
rep += ', P : ' + str(self.P)
return rep
def constructionParams(self) :
return 'theta = ' + str(self.theta) + ', lambda : ' + str(self.lamb) + ', P : ' + str(self.P)
def compute_pk(self, ex1, ex2):
''' Evaluate kernel for ex1 and ex2 '''
K = sin(pi * sqrt(self.sqr_dist(ex1, ex2)) / self.P) / self.lamb
K = K * K
return self.theta * exp(-2 * K)
def compute(self, examples):
"""Evaluate the kernel function and store the value"""
self.examples = examples
self.kmat = self.compute_pk(examples, examples)
def compute_test(self, test_ex):
"""Evaluate the kernel function on test examples"""
if test_ex.ndim == 1:
test_ex.shape = (len(test_ex), 1)
return self.compute_pk(self.examples, test_ex)
def compute_grad(self):
K = pi * sqrt(self.sqr_dist(self.examples, self.examples)) / self.P
R = sin(K) / self.lamb
sKs = R * R
dtheta = 2 * self.theta * exp(-2*sKs)
dP = 4 * self.theta / self.lamb * exp(-2 * R*R) * R * cos(K) * K
dlamb = 4 * self.theta * exp(-2*sKs) * sKs
return dtheta, dlamb, dP
class CustomKernel(Kernel):
"""Just a positive semidefinite matrix"""
def __init__(self):
Kernel.__init__(self)
def compute(self, examples):
"""Assume input is a kernel matrix.
Copy the training kernel matrix"""
self.kmat = examples
def compute_test(self, test_ex):
"""Assume input is a kernel matrix.
Just return the test kernel"""
return test_ex
def KernelFactory(kdict):
"""Some if then statements to map between
the strings describing the kernels to the actual implementation
"""
if kdict['name'] == 'gauss':
return GaussKernel(kdict['param'])
if kdict['name'] == 'custom':
return CustomKernel()
else:
print('%s not parsed properly in KernelFactory' % kdict['name'])
OptWok-0.3/optwok/kernelbase.pyx 0000644 ڛ |-00000021463 11731540564 023752 0 ustar cheng.ong UNIMELB\Domain Users 0000000 0000000 # cython: profile=True
"""Implement a Cython interface."""
from numpy import array
import numpy as np
cimport numpy as np
np.import_array()
cimport cython
DTYPE = np.float
ctypedef np.float_t DTYPE_t
@cython.boundscheck(False) # turn off bounds-checking for entire function
cdef getitem2(np.ndarray[DTYPE_t, ndim=2, negative_indices=False] kmat, Py_ssize_t xi, Py_ssize_t xj):
"""Fast version of __getitem__ with 2 arguments"""
return kmat[xi, xj]
@cython.boundscheck(False)
cdef getitem4(np.ndarray[DTYPE_t, ndim=2, negative_indices=False] Kx,
np.ndarray[DTYPE_t, ndim=2, negative_indices=False] Ky,
Py_ssize_t xi, Py_ssize_t xj, Py_ssize_t yi, Py_ssize_t yj):
"""Fast version of __getitem__ with 4 arguments"""
return Kx[xi, xj]*Ky[yi, yj]
@cython.boundscheck(False)
cdef DTYPE_t getprod(np.ndarray[DTYPE_t, ndim=2, negative_indices=False] Kx,
np.ndarray[DTYPE_t, ndim=2, negative_indices=False] Ky,
np.ndarray[Py_ssize_t, ndim=1, negative_indices=False] context,
Py_ssize_t ix, Py_ssize_t iy):
"""product of input and output kernels"""
cdef DTYPE_t val = Kx[ix,iy]*Ky[context[ix], context[iy]]
return val
@cython.boundscheck(False)
cdef DTYPE_t getprod_cross(np.ndarray[DTYPE_t, ndim=2, negative_indices=False] Kx,
np.ndarray[DTYPE_t, ndim=2, negative_indices=False] Ky,
np.ndarray[Py_ssize_t, ndim=1, negative_indices=False] context,
np.ndarray[Py_ssize_t, ndim=1, negative_indices=False] context_cross,
Py_ssize_t ix, Py_ssize_t iy):
"""product of input and output kernels"""
cdef DTYPE_t val = Kx[ix,iy]*Ky[context[ix], context_cross[iy]]
return val
class Kernel(object):
"""A Mercer kernel, with Cython interface for getitem"""
def __init__(self):
self.kmat = array([], dtype=DTYPE)
self.examples = None
def __getitem__(self, idx):
assert(len(idx)==2)
(xi,xj) = idx
return getitem2(self.kmat, xi, xj)
def __repr__(self):
rep = '<' + self.__class__.__name__ + ' instance>'
return rep
def dump(self) :
"""
returns a string that can be used to construct an equivalent object
"""
kstr = self.__module__ + '.' + self.__class__.__name__ + '(' + \
self.construction_params() + ')'
return kstr
def construction_params(self):
raise NotImplementedError
def compute(self, examples):
"""Evaluate the kernel function and store the value"""
raise NotImplementedError
def compute_test(self, test_ex):
"""Evaluate the kernel function on test examples"""
raise NotImplementedError
class JointKernel(object):
"""A kernel on input and output pairs."""
def __init__(self, input_kernel, output_kmat=None, num_classes=-1):
self.Kx = input_kernel
if output_kmat is not None:
self.Ky = output_kmat
else:
assert(num_classes > 1)
self.Ky = np.eye(num_classes)
self.examples = None
def __repr__(self) :
rep = '<' + self.__class__.__name__ + ' instance>'
return rep
def __getitem__(self, idx):
"""Simulate matrix element.
Order is assumed to be examples first then data.
"""
assert(len(idx)==4)
(xi, xj, yi, yj) = idx
return getitem4(self.Kx.kmat, self.Ky, xi, xj, yi, yj)
@property
def kmat(self):
"""Simulate the whole kernel matrix"""
return np.kron(self.Kx.kmat, self.Ky)
def compute(self, examples):
self.examples = examples
self.Kx.compute(examples)
def compute_test(self, test_ex):
"""Returns a non-square matrix, of num_train by num_test size."""
Kx = self.Kx.compute_test(test_ex)
return np.kron(Kx, self.Ky)
class ContextKernel(Kernel):
"""A kernel of (data, context) pairs,
mathematically the same as the JointKernel,
but does not evaluate all combinations"""
def __init__(self, input_kernel, output_kmat):
self.Kx = input_kernel
self.num_classes = output_kmat.shape[0]
self.Ky = output_kmat
self.examples = None
self.num_examples = 0
self.context = None
def __repr__(self) :
rep = '<' + self.__class__.__name__ + ' instance>'
return rep
def __getitem__(self, idx):
"""Simulate matrix element.
Order is assumed to be (example,context,example,context)
"""
assert(len(idx)==4)
(xi, yi, xj, yj) = idx
return getitem4(self.Kx.kmat, self.Ky, xi, xj, yi, yj)
@property
def kmat(self):
"""Simulate the whole kernel matrix"""
cdef Py_ssize_t ix
cdef Py_ssize_t iy
cdef DTYPE_t val
cdef np.ndarray K = np.zeros((self.num_examples, self.num_examples), dtype=DTYPE)
for ix in range(self.num_examples):
for iy in range(ix, self.num_examples):
#val = self.Kx[ix,iy]*self.Ky[self.context[ix], self.context[iy]]
val = getprod(self.Kx.kmat, self.Ky, self.context, ix, iy)
K[ix,iy] = val
K[iy,ix] = val
return K
def compute(self, examples, context):
self.examples = examples
self.context = context
self.num_examples = examples.shape[1]
self.Kx.compute(examples)
def compute_test(self, examples, context):
"""Returns a non-square matrix, of num_train by num_test size."""
assert self.examples is not None
assert self.context is not None
cdef Py_ssize_t num_test = examples.shape[1]
cdef np.ndarray[DTYPE_t, ndim=2, negative_indices=False] Kx_cross = self.Kx.compute_test(examples)
cdef np.ndarray[DTYPE_t, ndim=2, negative_indices=False] Kmat_cross = np.empty((self.num_examples, num_test),dtype=DTYPE)
cdef Py_ssize_t ix
cdef Py_ssize_t iy
for ix in range(self.num_examples):
for iy in range(num_test):
Kmat_cross[ix,iy] = Kx_cross[ix,iy]*self.Ky[self.context[ix], context[iy]]
#Kmat_cross[ix,iy] = getprod_cross(Kx_cross, self.Ky, self.context, context, ix, iy)
return Kmat_cross
class LinearKernel(Kernel):
"""Standard dot product"""
def __init__(self):
Kernel.__init__(self)
def construction_params(self):
return ''
def compute(self, np.ndarray[DTYPE_t, ndim=2] examples):
"""Evaluate the kernel function and store the value"""
self.examples = examples
self.kmat = np.dot(examples.T, examples)
def compute_test(self, np.ndarray[DTYPE_t, ndim=2] test_ex):
"""Evaluate the kernel function on test examples"""
cdef np.ndarray[DTYPE_t, ndim=2, negative_indices=False] k_cross = np.dot(self.examples.T, test_ex)
return k_cross
class GaussKernel(Kernel):
"""Gaussian rbf kernel"""
def __init__(self, gamma=1.0):
Kernel.__init__(self)
self.gamma = gamma
def __repr__(self) :
rep = '<' + self.__class__.__name__ + ' instance>\n'
rep += 'gamma : ' + str(self.gamma)
return rep
def constructionParams(self) :
return 'gamma = ' + str(self.gamma)
def compute(self, examples):
"""Evaluate the kernel function and store the value"""
self.examples = examples
self.kmat = np.exp(-self.gamma * self.sqr_dist(examples, examples))
def compute_test(self, test_ex):
"""Evaluate the kernel function on test examples"""
if test_ex.ndim == 1:
test_ex.shape = (len(test_ex),1)
return np.exp(-self.gamma * self.sqr_dist(self.examples, test_ex))
def sqr_dist(a,b):
"""Compute the square distance between vectors"""
assert(a.shape[0] == b.shape[0])
if len(a.shape)>1:
# a is a matrix
dot_a = np.sum(a*a, axis=0).T
dot_b = np.sum(b*b ,axis=0).T
unitvec = np.ones(dot_a.shape)
D = np.array(2.0*np.matrix(a).T*np.matrix(b))
for ix,bval in enumerate(dot_b):
D[:,ix] = dot_a - D[:,ix] + np.kron(bval,unitvec)
else:
D = np.zeros((len(a),len(b)))
for (ixa, cur_a) in enumerate(a):
for (ixb, cur_b) in enumerate(b):
D[ixa,ixb] = (cur_a-cur_b)*(cur_a-cur_b)
return D
class CustomKernel(Kernel):
"""Just a positive semidefinite matrix"""
def __init__(self):
Kernel.__init__(self)
def compute(self, examples):
"""Assume input is a kernel matrix.
Copy the training kernel matrix"""
self.kmat = examples
def compute_test(self, test_ex):
"""Assume input is a kernel matrix.
Just return the test kernel"""
return test_ex
OptWok-0.3/optwok/kernelopt.py 0000644 ڛ |-00000041567 12055556203 023456 0 ustar cheng.ong UNIMELB\Domain Users 0000000 0000000 """Optimize over the choice of kernel.
For learning the kernel over labels.
"""
from time import time
from numpy import zeros, ones, eye, sign
from numpy import matrix, array, diag, trace
from numpy import vstack, unique, maximum, argmax
from numpy.linalg import eig, norm, solve, lstsq, inv
from numpy import diag, sqrt, divide, logspace, kron
from numpy import min, max, transpose, real
from scipy.linalg.matfuncs import sqrtm
from matplotlib.pyplot import matshow, title, colorbar, savefig, show, gray
from matplotlib.colors import LinearSegmentedColormap
from cvxopt import matrix as cm
from cvxopt.solvers import sdp
from optwok.multiclass import StructPredictor
from optwok.multiclass import idx2binvec
from optwok.kernel import normalize_unit_diag, JointKernel
from optwok.io_pickle import load, save
from optwok.mat_blas import solve_sylvester
class KernelOptBase(object):
"""Base class for learning the kernel on labels"""
def __init__(self, reg_param=1.0, verbose=False):
assert(reg_param > 0.0)
self.reg_param = reg_param # Tradeoff regularizer vs loss
self.all_reg_param = logspace(-4,4,30)
self.verbose = verbose # how much output
self.Ky = array([]) # kernel on labels
self.all_Ky = None # label kernel for whole reg path
def train(self, train_ex, train_lab):
raise NotImplementedError
def predict(self, pred_ex):
raise NotImplementedError
def save_Ky(self, dataname, work_dir='.'):
"""Save the learned label kernel,
as pickle and pdf image"""
if self.all_Ky is None:
save('%s/%s_Ky.pkl' % (work_dir, dataname), self.Ky)
else:
save('%s/%s_Ky.pkl' % (work_dir, dataname), self.all_Ky)
show_kernel(self.Ky)
title(dataname)
savefig('%s/%s_Ky.pdf' % (work_dir, dataname))
def normalize_unit_diag(self):
"""Normalize Ky to unit diagonal, and appropriately scale alpha"""
diag_Ky = diag(self.Ky)
diag_Ky.shape = (self.num_classes, 1)
self.Ky = divide(self.Ky, sqrt(matrix(diag_Ky)*matrix(diag_Ky).T))
diag_Ky.shape = (1, self.num_classes)
diag_Ky = sqrt(diag_Ky)
self.alpha = self.alpha*diag_Ky
def _set_dimensions(self, train_lab):
"""Infer the number of examples and classes"""
if len(train_lab.shape) == 1:
self.y = idx2binvec(train_lab)
self.num_train = len(train_lab)
self.num_classes = len(unique(train_lab))
assert(self.y.shape == (self.num_train, self.num_classes))
else:
self.y = train_lab.T
(self.num_classes, self.num_train) = train_lab.shape
class KernelOptLS(KernelOptBase):
"""Optimize the kernel on the labels,
with the least squares loss, and also Frobenious norm regularizer.
Uses slycot for solving the sylvester equation"""
def __init__(self, reg_param=1.0, kernel=None, verbose=False):
KernelOptBase.__init__(self, reg_param, verbose)
self.max_iter = 1000 # maximum number of iterations
self.rel_tol = 0.01 # relative stopping tolerance
self.near_zero = 1e-5 # initialize a positive definite matrix
self.train_ex = array([]) # training examples
self.train_lab = array([]) # labels in original format
self.y = array([]) # labels
self.num_classes = 0 # number of classes
self.num_train = 0 # number of training examples
#self.objective = [] # objective value at each iteration
self.Kx = kernel # kernel on examples
self.alpha = array([]) # support vector coefficients
def train(self, train_ex, train_lab, init_Ky=None):
"""Wrapper to convert data to feature matrix,
and solve the optimization"""
self.train_ex = train_ex
self.train_lab = train_lab
self._set_dimensions(train_lab)
self.Kx.compute(train_ex)
if init_Ky is not None:
self.Ky = init_Ky
else:
#self.Ky = zeros((self.num_classes,self.num_classes))
self.Ky = self.near_zero*eye(self.num_classes)
# shrink Ky towards this matrix
#bias = matrix(ones((self.num_classes,self.num_classes)))
#bias = matrix(eye(self.num_classes))
residual = norm(self.y)
min_tol = self.rel_tol * residual
num_iter = 0
print('Learning the kernel by coordinate descent')
while residual > min_tol and num_iter < self.max_iter:
num_iter += 1
self.alpha = solve_sylvester(self.Kx.kmat, self.Ky/self.reg_param,
self.y/self.reg_param)
#self.objective.append((trace(matrix(self.alpha).T*matrix(self.y))+
# norm(self.Ky)**2)/2.0)
X = matrix(self.Kx.kmat)*matrix(self.alpha)
#P = 0.5*X.T*matrix(self.alpha)-matrix(self.Ky)+bias
P = 0.5*X.T*matrix(self.alpha)-matrix(self.Ky)
P = (P+P.T)/2.0
Q = matrix(solve(X.T*X+self.reg_param*eye(self.num_classes), P))
#(Q, res, xrank, s) = lstsq(X.T*X+self.reg_param*eye(self.num_classes), P)
Q = (Q+Q.T)/2.0
self.Ky += self.reg_param*Q
yhat = X*matrix(self.Ky)
residual = norm(yhat+self.reg_param*self.alpha-self.y)
if self.verbose:
print('Iteration: %d: residual = %f' % (num_iter, residual))
#self.normalize_unit_diag()
print('Number of iterations: %d' % num_iter)
if self.verbose:
print('Kernel on labels')
print(self.Ky)
def predict(self, pred_ex, get_max=True):
"""Wrapper to do prediction.
returns argmax of scores"""
Ktest = self.Kx.compute_test(pred_ex)
scores = array((matrix(self.Ky)*matrix(self.alpha).T*matrix(Ktest)).T)
if get_max:
return argmax(scores, axis=1)
else:
return transpose(scores)
def train_reg_path(self, train_ex, train_lab, all_reg_param=None):
"""Solve for the whole regularization path"""
if all_reg_param is not None:
self.all_reg_param = all_reg_param
self._set_dimensions(train_lab)
self.all_Ky = zeros((self.num_classes, self.num_classes, len(self.all_reg_param)))
self.Ky = zeros((self.num_classes, self.num_classes))
for (idx_reg, reg) in reversed(list(enumerate(self.all_reg_param))):
self.reg_param = reg
self.train(train_ex, train_lab, self.Ky)
self.all_Ky[:,:,idx_reg] = self.Ky.copy()
# Use the middle of the regularization range as default
self.Ky = self.all_Ky[:,:,round(self.all_Ky.shape[2]/2)]
def infer_sv(self, Ky=None):
"""Compute the support vector coefficients for a given Ky"""
if Ky is not None:
self.Ky = Ky
self.alpha = solve_sylvester(self.Kx.kmat, self.Ky/self.reg_param,
self.y/self.reg_param)
def predict_reg_path(self, pred_ex, ident_Ky=False):
"""Compute prediction for each value in regularization path.
Use learned Ky and retrain to get the corresponding
support vector coefficients.
"""
all_preds = zeros((pred_ex.shape[1],len(self.all_reg_param)))
for (idx_reg, reg) in reversed(list(enumerate(self.all_reg_param))):
print('Predicting with reg param = %e' % reg)
self.reg_param = reg
if ident_Ky:
self.infer_sv(eye(self.num_classes))
else:
self.infer_sv(self.all_Ky[:,:,idx_reg])
all_preds[:,idx_reg] = self.predict(pred_ex)
return all_preds
class KernelOptLin(KernelOptBase):
"""Optimize the kernel on the labels,
with the least squares loss, and also Frobenious norm regularizer.
Uses slycot for solving the sylvester equation
Use linear kernel on the inputs
"""
def __init__(self, reg_param=1.0, verbose=False):
KernelOptBase.__init__(self, reg_param, verbose)
self.max_iter = 1000 # maximum number of iterations
self.rel_tol = 0.01 # relative stopping tolerance
self.near_zero = 1e-5 # initialize a positive definite matrix
self.train_ex = array([]) # training examples
self.train_lab = array([]) # labels in original format
self.y = array([]) # labels
self.num_feat = 0 # number of features
self.num_classes = 0 # number of classes
self.num_train = 0 # number of training examples
#self.objective = [] # objective value at each iteration
self.alpha = array([]) # support vector coefficients
def train(self, train_ex, train_lab, init_Ky=None):
"""Wrapper to convert data to feature matrix,
and solve the optimization"""
self.train_ex = train_ex
self.train_lab = train_lab
self.num_feat = train_ex.shape[0]
self._set_dimensions(train_lab)
if init_Ky is not None:
self.Ky = init_Ky
else:
self.Ky = self.near_zero*eye(self.num_classes)
residual = norm(self.y)
min_tol = self.rel_tol * residual
num_iter = 0
print('Learning the kernel by coordinate descent')
while residual > min_tol and num_iter < self.max_iter:
num_iter += 1
K = array(matrix(self.train_ex).T*matrix(self.train_ex))
self.alpha = solve_sylvester(K, self.Ky/self.reg_param,
self.y/self.reg_param)
X = K*matrix(self.alpha)
P = 0.5*X.T*matrix(self.alpha)-matrix(self.Ky)
P = (P+P.T)/2.0
Q = matrix(solve(X.T*X+self.reg_param*eye(self.num_classes), P))
Q = (Q+Q.T)/2.0
self.Ky += self.reg_param*Q
yhat = X*matrix(self.Ky)
residual = norm(yhat+self.reg_param*self.alpha-self.y)
if self.verbose:
print('Iteration: %d: residual = %f' % (num_iter, residual))
print('Number of iterations: %d' % num_iter)
if self.verbose:
print('Kernel on labels')
print(self.Ky)
def predict(self, pred_ex, get_max=True):
"""Wrapper to do prediction.
returns argmax of scores"""
Ktest = matrix(self.train_ex.T)*matrix(pred_ex)
scores = array((matrix(self.Ky)*matrix(self.alpha).T*Ktest).T)
if get_max:
return argmax(scores, axis=1)
else:
return transpose(scores)
class KernelOptLSGP(KernelOptLS):
"""Optimize the kernel on the labels,
with the least squares loss, and also Frobenious norm regularizer.
Uses slycot for solving the sylvester equation.
Multiple output regression, also estimate predictive variance.
"""
def __init__(self, reg_param=1.0, kernel=None, verbose=False):
KernelOptLS.__init__(self, reg_param, kernel(), verbose)
self.test_kernel = JointKernel(kernel(), self.Ky)
def predict(self, pred_ex):
"""Wrapper to do prediction and also estimate predictive variance."""
num_test = pred_ex.shape[1]
Ktest = self.Kx.compute_test(pred_ex)
scores = array((matrix(self.Ky)*matrix(self.alpha).T*matrix(Ktest)).T)
self.test_kernel.Ky = self.Ky
self.test_kernel.compute(pred_ex)
A = matrix(inv(kron(self.Kx.kmat,self.Ky)\
+self.reg_param*eye(self.num_train*self.num_classes)))
k_star = matrix(kron(Ktest, self.Ky))
pred_var = diag(self.test_kernel.kmat - k_star.T*A*k_star)
pred_var.shape = (num_test, self.num_classes)
return scores.T, pred_var.T
def predict_reg_path(self, pred_ex, ident_Ky=False):
"""Compute prediction for each value in regularization path.
Use learned Ky and retrain to get the corresponding
support vector coefficients.
"""
all_preds = zeros((self.num_classes, pred_ex.shape[1], len(self.all_reg_param)))
for (idx_reg, reg) in reversed(list(enumerate(self.all_reg_param))):
print('Predicting with reg param = %e' % reg)
self.reg_param = reg
if ident_Ky:
self.infer_sv(eye(self.num_classes))
else:
self.infer_sv(self.all_Ky[:,:,idx_reg])
all_preds[:,:,idx_reg] = self.predict(pred_ex)
return all_preds
class KernelOpt(object):
"""Learn the kernel on the labels
Uses semidefinite programming with cvxopt
"""
def __init__(self, reg_param=1.0, kernel=None, verbose=False):
self.train_time = 0.0 # time used for training
self.max_iter = 3 # maximum number of iterations
self.verbose = verbose # how much output
# Pointer to the classifier object
self.classifier = StructPredictor(reg_param, kernel, verbose)
def solve_opt(self, alpha):
"""Solve the optimization problem using an SDP.
max tr(alpha' Kx alpha Ky)
st diag(Ky) = I
Ky > 0
cvxopt takes the problem as:
sdp(c[, Gl, hl[, Gs, hs[, A, b]]])
minimize c'x
subject to Gl x + sl = hl
sl >= 0
Ax = b
Gk x + vec(sk) = vec(hk)
sk > 0
Gl and hl define the inequality constraints
Gs and hs are lists, containing Gk and hk respectively, representing the LMIs.
The inequality > mean positive semidefinite.
We consider the dual form:
max -hl'zl - sum_k tr(hk zk) - b'y
st Gl'zl + sum_k Gk'vec(zk) + A'y + c = 0
zl >= 0
zk > 0
"""
n = alpha.shape[1] # the number of classes
m = n**2 # the size of Ky
# TODO: How do we regularize?
h = [cm(-array(normalize_unit_diag(matrix(alpha).T*self.classifier.Kx*matrix(alpha))))]
#h = [cm(-array(matrix(alpha).T*self.classifier.Kx*matrix(alpha)))]
#print(-h[0])
A = zeros((m,n))
for icol in range(n):
A[(n+1)*icol,icol] = 1.0
G = [cm(A)]
c = cm(-ones((n,1)))
print('Solving SDP')
sol = sdp(c, Gs=G, hs=h, solver='dsdp')
Ky = array(sol['zs'][0])
Ky = Ky + Ky.T - diag(Ky.diagonal())
#Ky = maximum(Ky, 0.0)
#print(trace(cm(Ky)*h[0]))
return Ky
def solve_opt_abspos(self, alpha):
"""Solve the optimization problem using an SDP.
Also require that entries in Ky are positive.
cvxopt takes the problem as:
sdp(c[, Gl, hl[, Gs, hs[, A, b]]])
minimize c'x
subject to Gl x + sl = hl
sl >= 0
Ax = b
Gk x + vec(sk) = vec(hk)
sk > 0
Gl and hl define the inequality constraints
Gs and hs are lists, containing Gk and hk respectively, representing the LMIs.
The inequality > mean positive semidefinite.
We consider the dual form:
max -hl'zl - sum_k tr(hk zk) - b'y
st Gl'zl + sum_k Gk'vec(zk) + A'y + c = 0
zl >= 0
zk > 0
"""
n = alpha.shape[1] # the number of classes
m = n**2 # the size of Ky
nm = n+m # The number of variables in the SDP
# TODO: How do we regularize?
#h = [cm(-array(normalize_unit_diag(matrix(alpha).T*self.classifier.Kx*matrix(alpha))))]
#h = [cm(-array(matrix(alpha).T*self.classifier.Kx*matrix(alpha)))]
F = zeros((nm, nm))
F[:n,:n] = -array(normalize_unit_diag(matrix(alpha).T*self.classifier.Kx*matrix(alpha)))
h = [cm(F)]
#print(-h[0])
Fk = zeros((nm**2,nm))
for icol in range(n):
Fk[(nm+1)*icol,icol] = 1.0
for irow in range(n):
for icol in range(n):
Fk[nm*irow+icol,n+n*irow+icol] = 1.0
Fk[n*nm+n+(nm+1)*(n*irow+icol),n+n*irow+icol] = -1.0
G = [cm(Fk)]
c = cm(vstack([-ones((n,1)), zeros((m,1))]))
print('Solving SDP')
sol = sdp(c, Gs=G, hs=h, solver='dsdp')
Ky = array(sol['zs'][0][:n,:n])
Ky = Ky + Ky.T - diag(Ky.diagonal())
#Ky = maximum(Ky, 0.0)
#print(trace(cm(Ky)*h[0]))
return Ky
def train(self, train_ex, train_lab):
"""Wrapper to convert data to feature matrix,
and solve the optimization"""
self.classifier.train(train_ex, train_lab)
for ix in range(self.max_iter):
print('Iteration: %d' % ix)
self.classifier.Ky = self.solve_opt(self.classifier.alpha)
self.classifier.solve_opt()
#print(self.classifier.Ky)
#print(eig(self.classifier.Ky)[0])
def predict(self, pred_ex):
"""Wrapper to do prediction."""
return self.classifier.predict(pred_ex)
OptWok-0.3/optwok/mat_blas.py 0000644 ڛ |-00000001023 12055550713 023214 0 ustar cheng.ong UNIMELB\Domain Users 0000000 0000000 """Collection of routines for solving matrix linear equations"""
from numpy.linalg import eigh
from numpy import dot, outer, ones
def solve_sylvester(A,B,C,symm=False):
"""Solve the Sylvester equation:
AXB + X = C
where A and B are symmetric real valued,
by using the eigenvalue decomposition
"""
LA, U = eigh(A)
LB, V = eigh(B)
frac = dot(dot(U.T,C),V)/(outer(LA,LB)+ones((len(LA),len(LB))))
sol = dot(dot(U,frac),V.T)
if symm:
return 0.5*(sol+sol.T)
else:
return sol
OptWok-0.3/optwok/mil/ 0000755 ڛ |-00000000000 12127457270 021651 5 ustar cheng.ong UNIMELB\Domain Users 0000000 0000000 OptWok-0.3/optwok/mil/__init__.py 0000644 ڛ |-00000000000 12115570207 023741 0 ustar cheng.ong UNIMELB\Domain Users 0000000 0000000 OptWok-0.3/optwok/mil/mi_classifier.py 0000644 ڛ |-00000004645 12127232011 025025 0 ustar cheng.ong UNIMELB\Domain Users 0000000 0000000 '''
Created on 21.02.2013
@author: Gabriel
'''
from __future__ import division
from numpy import dot, sign, array, squeeze, repeat
from sklearn.svm import SVC
class MIClassifier(object):
''' Multiple Instance Learning Classifier '''
def train(self, traindata, reg_param, v0 = None, verbose = False, *args, **kwargs):
self.traindata = traindata
self.reg_param = reg_param
self.solve_opt(v0 = v0, verbose = verbose, *args, **kwargs)
def predict(self, preddata):
''' implement classifier '''
y = []
for pd in preddata.iter():
if preddata.sparse:
yi = self.one_predict_sparse(pd)
else:
yi = self.one_predict(pd)
y.append(yi)
return sign(array(y))
def read_data(self, data):
''' Return midata object in corresponding format, by calling .to_$ on data '''
raise NotImplementedError
class MeanSVM(MIClassifier):
''' SVM on the means for MIL '''
def one_predict(self, data):
''' predict one datapoint '''
return dot(self.w,data['mean']) + self.b
def read_data(self, data):
''' Read data and convert to EllipsoidData '''
return data.to_means()
def solve_opt(self, v0, verbose):
svm = SVC(C=1/self.reg_param, kernel='linear')
q, y = array(self.traindata.means), array(self.traindata.labels)
svm.fit(q, y)
self.w, self.b, self.primal_obj = squeeze(svm.coef_), svm.intercept_[0], None
class InstanceSVM(MIClassifier):
''' Naive instance level SVM for MIL '''
def one_predict(self, data):
''' predict one datapoint '''
return dot(self.w,data['mean']) + self.b
return reduce(max,[dot(self.w,inst)+self.b for inst in data['bag']])
def read_data(self, data):
''' Read data and convert to EllipsoidData '''
return data.to_instances()
def solve_opt(self, v0, verbose):
svm = SVC(C=1/self.lamb, kernel='linear')
q = array([inst for qi in self.bags for inst in qi]) # flatten out all instances
y = array([repeat(yi,len(qi)) for yi, qi in zip(self.labels,self.bags)]).flatten() # repeat labels for all instances
svm.fit(q, y)
self.w, self.b, self.primal_obj = squeeze(self.svm.coef_), self.svm.intercept_[0], None
OptWok-0.3/optwok/mil/mi_classifier_emil.py 0000644 ڛ |-00000027642 12127232011 026035 0 ustar cheng.ong UNIMELB\Domain Users 0000000 0000000 '''
Created on 04.03.2013
@author: Gabriel
'''
from __future__ import division
from scipy.optimize.lbfgsb import fmin_l_bfgs_b
from scipy.misc import logsumexp
from numpy.linalg import norm, cholesky
from sys import float_info
from optwok.socp import SOCP
from numpy.random import rand
from scipy.sparse import issparse
from numpy import sqrt, dot, zeros, array, squeeze, ones, r_, amax, eye, exp, diag
from optwok.mil.mi_classifier import MIClassifier
from optwok.mil.mi_loss import HingeLoss, HuberizedHingeLoss
from scipy.sparse import csc_matrix
class eMIL(MIClassifier):
''' Ellipsoidal Multiple Instance Learning Classifier '''
def one_predict_sparse(self, sparsedata):
''' Ellipsoidal MI classifier for one sparse ellipsoid '''
w = csc_matrix(self.w)
return (w*sparsedata['mean'].transpose()).A[0][0] + self.b \
+ sqrt((w * sparsedata['covariance'] * w.transpose()).A[0][0])
def one_predict(self, data):
''' Ellipsoidal MI classifier for one ellipsoid'''
quad_form = dot(self.w,dot(data['covariance'],self.w))
return dot(self.w,data['mean']) + self.b + sqrt(quad_form)
def read_data(self, data):
''' Read data and convert to EllipsoidData '''
return data.to_ellipsoids()
def cost_ellipsoid(self, w, b, qi, Si):
''' Loss per ellipsoid'''
if issparse(Si):
return (w*qi.transpose()).A[0][0] + b + sqrt((w * Si * w.transpose()).A[0][0])
else:
quad_form = dot(w,dot(Si,w))
return dot(w,qi) + b + sqrt(quad_form)
class eMILDca(eMIL):
''' Ellipsoidal Multiple Instance Learning Classifier,
Solve minimization with DCA'''
def __init__(self):
self.set_opt_param(1e10, 100)
def solve_opt(self, v0, verbose, *args, **kwargs):
self.w, self.b, self.primal_obj = self.min_dca(v0=v0, verbose=verbose, *args, **kwargs)
def set_opt_param(self, factr, maxiter):
self.maxiter = maxiter
self.factr = factr
def check_conv(self, v_old, v_new, f_old, f_new, iteration):
return abs((f_old-f_new)/max([1,abs(f_old),abs(f_new)])) < self.factr * float_info.epsilon \
or iteration > self.maxiter
def loss(self, w, b):
''' compute the loss for hyperplane (w,b) and bags with labels y and regularization parameter lamb '''
lossf = HingeLoss()
loss = 0
if self.traindata.sparse:
w = csc_matrix(w)
for qi, si, yi in zip(self.traindata.means,self.traindata.covariances,self.traindata.labels): # sum over hinge loss for all distances
fi = yi*(self.cost_ellipsoid(w,b,qi,si))
loss += lossf.loss(fi)
if self.traindata.sparse:
w = squeeze(w.A)
# regularize with 2-norm squared:
ww = dot(w,w)
loss = loss + self.reg_param * ww
return loss
def min_dca(self, v0=None, verbose=False, check_convergence=None, *args, **kwargs):
''' Minimize with the CCCP, needs solve_dca_subproblem(w_k ,v0,verbose) defined
check_convergence(v_old, v_new, f_old, f_new, iter) returns false to continue'''
if check_convergence is None:
check_convergence = self.check_conv
if v0 is None:
v = r_[rand(self.traindata.means[0].shape[0]), rand(1)]
else:
v = v0
converged = False
i = 0
while not converged:
v_old = v
v, f = self.solve_dca_subproblem(v[:-1],v0=v, verbose=verbose)
i += 1
f_old = self.loss(v_old[:-1],v_old[-1])
f_new = self.loss(v[:-1],v[-1])
if verbose:
print 'current sub-problem loss: ', f
print 'full loss: %f, old full loss: %f' %(f_new, f_old)
print 'v diff: ', norm(v_old-v)
converged = check_convergence(v_old, v, f_old, f_new, i)
return v[:-1], v[-1], f
class eMILDcaBfgs(eMILDca):
''' Ellipsoidal Multiple Instance Learning Classifier,
Solve minimization with DCA and subproblem with bfgs and soft_max'''
def check_convergence(self, v_old, v_new, f_old, f_new, iteration ):
return self.check_conv(v_old, v_new, f_old, f_new, iteration) \
or norm(v_old-v_new)< self.factr * float_info.epsilon
def loss_and_grad_dca(self, v, wk):
''' gradient and loss at w, b for the DCA loss'''
w = v[:-1]
b = v[-1]
beta = 18
loss = 0
gradw = 0
gradb = 0
for qi, Pi, yi in zip(self.traindata.means, self.traindata.covariances, self.traindata.labels):
quad_form = sqrt(dot(w,dot(Pi,w)))
pred = quad_form + dot(w,qi) + b
pred_grad = qi + dot(Pi,w)/quad_form
if yi > 0: #positive
smgp = self._soft_max_grad(-1+pred,beta)
loss += self._soft_max(-1+pred,beta)
gradw += smgp * pred_grad
gradb += smgp
# Linearization of concave part
conc_linw = -qi - dot(Pi, wk) / sqrt(dot(wk,dot(Pi,wk)))
loss += dot(w,conc_linw) - b
gradw += conc_linw
gradb += -1
else: #negative
smgn = self._soft_max_grad(1+pred,beta)
loss += self._soft_max(1+pred,beta)
gradw += smgn * pred_grad
gradb += smgn
# Regularizer
loss += self.reg_param * dot(w,w)
gradw += self.reg_param * 2 * w
return loss, r_[gradw, gradb]
def solve_dca_subproblem(self, wk,v0,verbose):
return self.solve_dca_bfgs(wk,v0,verbose)
def solve_dca_bfgs(self, wk, v0, verbose=False):
''' Solve the same problem as solve_dca_socp, but instead of converting to a SOCP,
use the soft max instead of the hinge loss '''
factr = 1e7
pgtol = 1e-05
v, f, info = fmin_l_bfgs_b(func = self.loss_and_grad_dca, x0 = v0, approx_grad = False,
m=100, fprime = None, args = (wk,), factr = factr, pgtol=pgtol)
if verbose:
print 'loss: ', f
print 'norm of gradient: ', norm(info['grad'])
print 'warnflag: ', info['warnflag']
print 'stop reason: ', info['task']
print 'funcalls: ', info['funcalls']
return v, f
def _soft_max(self,x,beta):
''' relax max(0,x) by 1/b (e^(b*x)+1) '''
return 1/beta * logsumexp([beta*x, 0])
def _soft_max_grad(self,x, beta):
''' return the gradient of the soft max
(need to multiply this by the gradient of x...'''
y = beta*x
return exp(y-logsumexp([y,0]))
class eMILDcaSocp(eMILDca):
''' Ellipsoidal Multiple Instance Learning Classifier
Solve minimization with DCA and subproblem as SOCP'''
#@memoized
def chol_S(self,i):
''' return A, where A'A = Si '''
return cholesky(self.traindata.covariances[i]).T
def solve_dca_subproblem(self, wk,v0,verbose):
return self.solve_dca_socp(wk,verbose)
def solve_dca_socp(self, wk, verbose=False):
'''
x = [w, b, xi, theta],
'''
num_vars = self.traindata.num_feat + 1 + self.traindata.num_ex + 1
num_feat = self.traindata.num_feat
num_ex = self.traindata.num_ex
socp = SOCP(num_vars= num_vars, verbose=verbose, use_mosek=True)
pb_ind = array(self.traindata.labels) > 0
q = array(self.traindata.means)
P = array(self.traindata.covariances)
# Linearization of concave part
conc_linw = 0
for qi, Pi in zip(q[pb_ind], P[pb_ind]):
conc_linw += -qi - dot(Pi, wk) / sqrt(dot(wk,dot(Pi,wk)))
conc_linb = -sum(pb_ind)
# f = r_[conc_linw, conc_linb, ones(mibag.m), mibag.lamb]
f = r_[zeros(num_vars-1),1] # only z
socp.set_objective(f)
# Constraints for original objective + regularization
q = r_[-conc_linw, -conc_linb, -ones(num_ex), 1]/2
A = zeros((num_feat + 1,num_vars))
A[0,:] = -q
A[1:,:num_feat] = diag(ones(num_feat) * sqrt(self.reg_param/2))
d = 1/2
b = zeros(num_feat+1)
b[0] = 1/2
socp.add_ineq_constraints(A, b, q, d)
for i, e in enumerate(zip(self.traindata.means, self.traindata.labels)):
qi, yi = e
A_i = zeros((num_feat, num_vars))
A_i[:,:num_feat] = self.chol_S(i) # Ai
b_i = zeros(num_feat)
c_i = r_[-qi,-1,zeros(num_ex+1)]
c_i[num_feat+1+i] = 1 # xi
d_i = yi
socp.add_ineq_constraints(A_i, b_i, c_i, d_i)
# Constraint for positive slack
A_0 = zeros((num_vars, num_vars))
A_0[num_feat+1:-1,num_feat+1:-1] = -1*eye(num_ex)
socp.set_component_ineq_constraints(A_0, zeros(num_vars))
socp.solve(warn=verbose)
return socp.solution[:num_feat+1], socp.cur_obj
class eMILBfgs(eMIL):
''' Ellipsoidal Multiple Instance Learning Classifier,
Minimize with bfgs'''
def solve_opt(self, v0, verbose, huber_delta, *args, **kwargs):
self.lossf = HuberizedHingeLoss(huber_delta)
self.w, self.b, self.primal_obj = self.min_bfgs(v0=v0, verbose=verbose, *args, **kwargs)
def loss_and_grad_onepm(self, v):
return self.loss_and_grad(v[:-1],v[-1])
def loss_and_grad(self, w,b):
''' compute the loss for hyperplane (w,b) and bag with labels y and regularization parameter lamb
also compute the gradient with respect to (w,b)'''
loss = 0
gradw, gradb = 0, 0
nw = norm(w)
if self.traindata.sparse:
w = csc_matrix(w)
for qi, si, yi in zip(self.traindata.means,self.traindata.covariances,self.traindata.labels): # sum over hinge loss for all distances
fi = yi*(self.cost_ellipsoid(w,b,qi,si))
loss += self.lossf.loss(fi)
gradwi, gradbi = self.lossf.grad(fi, qi, si, yi, w, nw)
gradb += gradbi
gradw += gradwi
if self.traindata.sparse:
w = squeeze(w.A)
# regularize with 2-norm squared:
ww = dot(w,w)
gradw = gradw + self.reg_param * 2* w
loss = loss + self.reg_param * ww
return loss, r_[gradw, gradb]
def min_bfgs(self,v0=None, verbose=False):
''' Find optimal hyperplane for the balls (q,s) with labels y and the regularizer parameter lamb '''
if v0 is None:
w0 = ones(amax(self.traindata.num_feat))
w0 = w0 / norm(w0)
v0 = r_[w0, 0]
if verbose:
disp = 1
else:
disp = 0
if self.traindata.sparse:
factr = 1e7
pgtol = 1e-04
else:
factr = 1e7
pgtol = 1e-05
v, f, info = fmin_l_bfgs_b(func = self.loss_and_grad_onepm, x0 = v0, approx_grad = False,
m=100, fprime = None, args = (), factr = factr, pgtol=pgtol)
if verbose:
print 'loss: ', f
print 'norm of gradient: ', norm(info['grad'])
print 'warnflag: ', info['warnflag']
print 'stop reason: ', info['task']
print 'funcalls: ', info['funcalls']
return v[:-1], v[-1], f
OptWok-0.3/optwok/mil/mi_data.py 0000644 ڛ |-00000005142 12127232011 023603 0 ustar cheng.ong UNIMELB\Domain Users 0000000 0000000 '''
Created on 28.02.2013
@author: Gabriel
'''
from __future__ import division
from optwok.mldata import ClassificationData
from scipy.sparse import issparse
from numpy import squeeze, array
class MIData(ClassificationData):
def iter(self):
for i in range(self.num_ex):
yield self.ith_data(i)
class EllipsoidData(MIData):
''' Class for ellipsoids for multiple instance learning '''
def __init__(self, means, covariances, labels):
self.means = means
self.covariances = covariances
self.labels = labels
if issparse(self.means[0]):
self.sparse = True
else:
self.sparse = False
def split_off(self, idx, id_str):
rbd = EllipsoidData(self.means[idx], self.covariances[idx], self.labels[idx])
rbd.id_str = id_str
rbd.idx = idx
return rbd
def ith_data(self, i):
return {'mean': self.means[i], 'covariance': self.covariances[i], 'label':self.labels[i]}
@property
def num_feat(self):
return max(self.means[0].shape)
class MultipleInstanceData(MIData):
''' Class for multiple instance bags: bags is a list of lists of instances
label is on a bag level '''
def __init__(self, bags, labels):
self.labels = labels
if issparse(bags[0][0]):
self.bags = [[squeeze(inst.A) for inst in qi] for qi in bags]
else:
self.bags = bags
self.sparse = False
def split_off(self, idx, id_str):
rbd = MultipleInstanceData(self.bags[idx], self.labels[idx])
rbd.id_str = id_str
rbd.idx = idx
return rbd
def ith_data(self, i):
return {'bag': self.bags[i], 'label':self.labels[i]}
@property
def num_feat(self):
return max(self.bags[0][0].shape)
class MIMeanData(MIData):
''' Class for means of multiple instance bags '''
def __init__(self, means, labels):
self.labels = labels
if issparse(means[0]):
self.means = [squeeze(qi.A) for qi in means]
else:
self.means = means
self.sparse = False
def split_off(self, idx, id_str):
rbd = MIMeanData(self.means[idx], self.labels[idx])
rbd.id_str = id_str
rbd.idx = idx
return rbd
def ith_data(self, i):
return {'mean': self.means[i], 'label':self.labels[i]}
@property
def num_feat(self):
return max(self.means[0].shape) OptWok-0.3/optwok/mil/mi_loss.py 0000644 ڛ |-00000003552 12115570207 023666 0 ustar cheng.ong UNIMELB\Domain Users 0000000 0000000 '''
Created on 04.03.2013
@author: Gabriel
'''
from __future__ import division
from numpy import squeeze, sqrt, dot
from scipy.sparse import issparse
class eMILLoss(object):
def gradf(self, qi, Si, yi, w, nw):
if issparse(Si):
return -yi*(squeeze((qi+ (Si*w.transpose()).transpose()/sqrt((w*Si*w.transpose()).A[0,0])).A))
else:
quad_form = dot(w,dot(Si,w))
return -yi*(qi + dot(Si,w)/sqrt(quad_form))
class HingeLoss(eMILLoss):
def grad(self, fi, qi, Si, yi, w, nw):
if 1 <= fi:
gradbi, gradwi = 0, 0
else:
gradbi, gradwi = -yi, self.gradf(qi,Si,yi,w,nw)
return gradwi, gradbi
def loss(self, x):
return max(0,1-x)
class HuberizedHingeLoss(eMILLoss):
''' hinge without the kink, delta >= 0 parametrizes how "squared" the loss is
if delta is zero the original hinge loss is recovered
'''
def __init__(self, delta):
self.delta = delta
def grad(self, fi, qi, Si, yi, w, nw):
if fi <= 1:
if fi > 1 - self.delta:
fid = (1-fi) / self.delta
gradbi = fid * -yi
gradwi = fid * self.gradf(qi, Si, yi, w, nw)
else:
gradbi = -yi
gradwi = self.gradf(qi, Si, yi, w, nw)
else:
gradbi, gradwi = 0, 0
return gradwi, gradbi
def loss(self, x):
if x <= 1:
if x > 1 - self.delta: # 1 - delta < x <= 1
return ((1-x) **2) / (2*self.delta)
else: # x <= 1 - delta
return 1 - x - self.delta/2
else: # x > 1
return 0 OptWok-0.3/optwok/mil/utility.py 0000644 ڛ |-00000010566 12120324207 023720 0 ustar cheng.ong UNIMELB\Domain Users 0000000 0000000 '''
Created on 06.03.2013
@author: Gabriel
'''
from __future__ import division
import os
from numpy import ndarray, array
from numpy.random import rand, permutation
try:
import jsonpickle.handlers
from jsonpickle.handlers import BaseHandler
from jsonpickle import encode, decode, set_encoder_options
except ImportError as imperr:
print imperr, ": json dump and load won't work"
from types import FunctionType
import string
import datetime as dt
import inspect
from os.path import join as pjoin, isfile
import numpy
dtformat = '%Y-%m-%d_%H-%M-%S'
dformat = '%Y-%m-%d'
class NumpyFloatHandler(jsonpickle.handlers.BaseHandler):
def flatten(self, obj, data):
"""
Converts and round to float and encode
"""
return round(obj,12)
jsonpickle.handlers.registry.register(numpy.float, NumpyFloatHandler)
jsonpickle.handlers.registry.register(numpy.float32, NumpyFloatHandler)
jsonpickle.handlers.registry.register(numpy.float64, NumpyFloatHandler)
class NumpyHandler(BaseHandler):
def flatten(self,obj,data):
data['array'] = obj.__repr__()
return data
def restore(self,obj):
return eval(obj['array'])
jsonpickle.handlers.registry.register(ndarray,NumpyHandler)
set_encoder_options('json',indent=4)
def ensure_dir(f):
d = os.path.dirname(f)
if d <> '':
if not os.path.exists(d):
os.makedirs(d)
def name_experiment(*args,**kwargs):
''' Create a string for an experiment '''
fname = slugify('-'.join(['-'.join(map(strify,args)), '-'.join(map(strify_item,sorted(kwargs.items())))]))
return fname
def check_result(path, result_desc):
''' Check if result file exists:
path: path to result folder
result_desc: string that identifies the experiment result file
'''
return isfile(pjoin(path, result_desc))
def jappend(fname):
if fname[-5:] <> '.json':
fname = fname + '.json'
return fname
def jexists(fname):
fnamej = jappend(fname)
return isfile(fnamej)
def jdump(fname, obj):
ensure_dir(fname)
fname = jappend(fname)
with open(fname, 'w') as f:
f.write(encode(obj))
def jload(fname):
fname = jappend(fname)
with open(fname, 'r') as f:
ftxt = f.read()
return(decode(ftxt))
def create_perm(path, dataset, description, n_perm, num_ex):
''' create n_perm permutations for dataset with num_ex examples and save to file '''
p = []
for i in range(n_perm):
p += [permutation(num_ex).tolist()]
fname = permfile_name(path, dataset, description)
jdump(fname, p)
def permfile_name(path, dataset, description):
return pjoin(path, description + '_' + dataset)
def slugify(filename):
''' convert a string into a valid filename '''
valid_chars = "-_.() %s%s" % (string.ascii_letters, string.digits)
return ''.join(c for c in filename if c in valid_chars)
def strify_item(item):
''' strify dict items '''
return "%s(%s)" %(strify(item[0]),strify(item[1]))
def strify(anything):
''' Recursive application of str to anything s.t. it qualifies as a filename '''
if isinstance(anything, list) or isinstance(anything, tuple):
return '_'.join(map(strify, anything))
elif isinstance(anything, dict):
return '_'.join(map(strify_item, anything.items()))
elif isinstance(anything, ndarray):
raise TypeError('Can not convert numpy array to string')
elif isinstance(anything, FunctionType):
if anything.__name__ == '':
return str(rand(1)[0])
else:
return anything.__name__
elif isinstance(anything, dt.datetime):
return anything.strftime(dtformat)
elif isinstance(anything, dt.date):
return anything.strftime(dformat)
elif inspect.isclass(anything):
return anything.__name__
else:
return str(anything)
def _test_jdump():
a = array([[1,2],[3,4]])
obj = {'a': a, 'b': 4.3}
objs = encode(obj)
print objs
objl = decode(objs)
print objl
jdump('test.txt', obj)
print jload('test.txt')
def _test_strify():
class FooBar:
pass
a=[FooBar, [1,2,3]]
print FooBar.__name__
print strify(a)
if __name__ == '__main__':
_test_strify() OptWok-0.3/optwok/mldata.py 0000644 ڛ |-00000045305 12120324207 022675 0 ustar cheng.ong UNIMELB\Domain Users 0000000 0000000 #!/usr/bin/env python
"""Classes to encapsulate the idea of a dataset in machine learning,
including file access. Currently this focuses on reading and writing
transparently to different file formats.
A dataset is modeled as an (example,label) tuple, each of which is an array.
The base class doesn't know how to split, so just returns one array.
The classes currently implemented use three
different ways of iterating through files:
- CSV uses the python module csv's iterator
- Libsvm uses a hand crafted while loop
- ARFF always reads the whole file, and does a slice
- FASTA uses a hand crafted while loop that behaves like a generator
The class DatasetFileARFF is in mldata-arff.py.
"""
#############################################################################################
# #
# This program is free software; you can redistribute it and/or modify #
# it under the terms of the GNU General Public License as published by #
# the Free Software Foundation; either version 3 of the License, or #
# (at your option) any later version. #
# #
# This program is distributed in the hope that it will be useful, #
# but WITHOUT ANY WARRANTY; without even the implied warranty of #
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the #
# GNU General Public License for more details. #
# #
# You should have received a copy of the GNU General Public License #
# along with this program; if not, see http://www.gnu.org/licenses #
# or write to the Free Software Foundation, Inc., 51 Franklin Street, #
# Fifth Floor, Boston, MA 02110-1301 USA #
# #
#############################################################################################
import sys
import numpy
from numpy import array, concatenate, unique, setdiff1d
import csv
from optwok.mil.utility import jdump, jload
try:
import arff
have_arff = True
except ImportError:
have_arff = False
python_version = sys.version_info[:2]
if python_version >= (3, 0):
from io import FileIO as BUILTIN_FILE_TYPE
else:
BUILTIN_FILE_TYPE = file
class DatasetFileBase(BUILTIN_FILE_TYPE):
"""A Base class defining barebones and common behaviour
"""
def __init__(self,filename,extype):
"""Just the normal file __init__,
followed by the specific class corresponding to the file extension.
"""
self.extype = extype
self.filename = filename
def readlines(self,idx=None):
"""Read the lines defined by idx (a numpy array).
Default is read all lines.
"""
if idx is None:
data = self.readlines()
else:
data = self.readlines()[idx]
#itertools.islice(open('tempx.txt'), 11, 12).next()
#file("filename").readlines()[11]
#linecache.getline( filename, lineno[, module_globals])
return data
def writelines(self,data,idx=None):
"""Write the lines defined by idx (a numpy array).
Default is write all lines.
data is assumed to be a numpy array.
"""
if idx is None:
self.writelines(data)
else:
self.writelines(data[idx])
class DatasetFileCSV(DatasetFileBase):
"""Comma Seperated Values file.
Labels are in the first column.
"""
def __init__(self, filename, extype, verbose=False):
self.verbose=verbose
DatasetFileBase.__init__(self,filename,extype)
def readlines(self,idx=None):
"""Read from file and split data into examples and labels"""
reader = csv.reader(open(self.filename,'r'), delimiter=',', quoting=csv.QUOTE_NONE)
labels = []
examples = []
for ix,line in enumerate(reader):
if idx is None or ix in idx:
labels.append(float(line[0]))
if self.extype == 'vec':
examples.append(array(map(float,line[1:])))
elif self.extype == 'seq':
examples.append(line[1:][0])
elif self.extype == 'mseq':
examples.append(array(line[1:]))
if self.extype == 'vec':
examples = array(examples).T
if self.verbose: print('%d features, %d examples' % examples.shape)
elif self.extype == 'seq':
if self.verbose: print('sequence length = %d, %d examples' % (len(examples[0]),len(examples)))
elif self.extype == 'mseq':
printstr = 'sequence lengths = '
for seq in examples[0]:
printstr += '%d, ' % len(seq)
printstr += '%d examples' % len(examples)
if self.verbose: print(printstr)
return (examples,array(labels))
def writelines(self,examples,labels,idx=None):
"""Merge the examples and labels and write to file"""
if idx==None:
idx = range(len(labels))
if self.extype == 'seq':
data = zip(labels[idx],list(array(examples)[idx]))
if self.extype == 'mseq':
data = []
for ix,curlab in enumerate(labels):
data.append([curlab]+list(examples[ix]))
elif self.extype == 'vec':
data = []
for ix,curlab in enumerate(labels):
data.append(concatenate((array([curlab]),examples[:,ix].T)))
fp = open(self.filename,'w')
writer = csv.writer(fp,delimiter=',',quoting=csv.QUOTE_NONE)
for ix in idx:
writer.writerow(data[ix])
fp.close()
class DatasetFileLibsvm(DatasetFileBase):
"""LibSVM format, where the first column is assumed to be the label,
and the entries are in sparse matrix format."""
def __init__(self, filename, extype='vec', verbose=False):
assert(extype=='vec')
self.verbose=verbose
DatasetFileBase.__init__(self,filename,extype)
def readlines(self,idx=None):
"""Read from file and split data into examples and labels"""
if idx is not None:
raise NotImplementedError
infile = open(self.filename, 'r')
num_dim = 0
parsed = []
for line in infile:
(pline, max_idx) = self._parse_line(line)
parsed.append(pline)
num_dim = max(num_dim, max_idx)
infile.close()
labels = []
if self.verbose: print('%d features, %d examples' % (num_dim, len(parsed)))
examples = numpy.zeros((int(num_dim), len(parsed)))
for ix in range(len(parsed)):
labels.append(parsed[ix]['label'])
for v in parsed[ix]['variables']:
examples[int(v[0])-1,ix] = float(v[1])
return (examples,array(labels))
def _parse_line(self, line):
"""Parse a LibSVM input line and return attributes."""
items = line.split()
label = int(items[0])-1
variables = []
max_idx = 0
for entry in items[1:]:
val = entry.split(':')
max_idx = max(max_idx, int(val[0]))
variables.append((int(val[0]),float(val[1])))
return ({'label':label, 'variables':variables}, max_idx)
def writelines(self, examples, labels, idx=None):
"""Merge the examples and labels and write to file"""
if idx==None:
idx = range(len(labels))
num_feat = examples.shape[0]
fp = open(self.filename,'w')
for ix in idx:
fp.write('%d ' % (labels[ix]+1))
for ix_feat in range(num_feat):
if examples[ix_feat, ix] != 0.0:
fp.write('%d:%f '% (ix_feat+1, examples[ix_feat, ix]))
fp.write('\n')
fp.close()
class DatasetFileFASTA(DatasetFileBase):
"""Fasta format file, labels are in the comment after keyword 'label'.
label=1
label=-1
"""
def __init__(self, filename, extype):
if extype != 'seq':
print('Can only write fasta file for sequences!')
raise IOError
DatasetFileBase.__init__(self,filename,extype)
self.fp = None
def readlines(self,idx=None):
"""Read from file and split data into examples and labels"""
self.fp = open(self.filename,'r')
line = self.fp.readline()
examples = []
labels = []
ix = 0
while True:
if not line : break
(ex,lab,line) = self.readline(line)
if idx is None or ix in idx:
examples.append(ex)
labels.append(lab)
ix += 1
self.fp.close()
print('sequence length = %d, %d examples' % (len(examples[0]),len(examples)))
return (examples,array(labels))
def writelines(self,examples,labels,idx=None,linelen=60):
"""Write the examples and labels and write to file"""
if idx==None:
idx = range(len(labels))
fp = open(self.filename,'w')
for ix in idx:
fp.write('> %d label=%d\n'%(ix,round(labels[ix])))
for lineidx in xrange(0, len(examples[ix]), linelen):
fp.write(examples[ix][lineidx:lineidx+linelen] + '\n')
fp.close()
def readline(self,line):
"""Reads a fasta entry and returns the label and the sequence"""
if line[0] == '' : return
assert(line[0] == '>')
# Use list comprehension to get the integer that comes after label=
a = line.split()
label = float([b.split('=')[1] for b in a if b.split('=')[0]=='label'][0])
lines = []
line = self.fp.readline()
while True:
if not line : break
if line[0] == ">": break
#Remove trailing whitespace, and any internal spaces
lines.append(line.rstrip().replace(" ",""))
line = self.fp.readline()
return (''.join(lines),label,line)
def init_datasetfile(filename, extype):
"""A factory that returns the appropriate class based on the file extension.
recognised file extensions
- .csv : Comma Separated Values
- .tab : General tabular data
- .arff : Attribute-Relation File Format (weka)
- .fa : Fasta file format (seq only)
- .fasta: same as above.
Since the file type does not determine what type of data is actually being used,
the user has to supply the example type.
extype can be ('vec','seq','mseq')
vec - array of floats
seq - single sequence
mseq - multiple sequences
"""
allowedtypes = ('vec','seq','mseq')
assert(extype in allowedtypes)
# map the file extensions to the relevant classes
_format2dataset = {'csv' : DatasetFileCSV,
'fa' : DatasetFileFASTA,
'fasta' : DatasetFileFASTA,
'libsvm' : DatasetFileLibsvm,
}
if have_arff:
from esvm.mldata_arff import DatasetFileARFF
_format2dataset['arff'] = DatasetFileARFF
extension = detect_extension(filename)
return _format2dataset[extension](filename, extype)
def detect_extension(filename):
"""Get the file extension"""
#if filename.count('.') > 1:
# print('WARNING: %s has more than one . using last one' % filename)
detect_ext = filename.split('.')[-1]
known_ext = ['csv','fasta','fa','libsvm']
if have_arff:
known_ext.append('arff')
if detect_ext not in known_ext:
print('WARNING: %s is an unknown file extension, defaulting to csv' % detect_ext)
detect_ext = 'csv'
return detect_ext
def convert(infile,outfile,extype):
"""Copy data from infile to outfile, possibly converting the file format."""
fp1 = init_datasetfile(infile,extype)
(examples,labels) = fp1.readlines()
fp2 = init_datasetfile(outfile,extype)
fp2.writelines(examples,labels)
class DatasetBase(object):
"""Encapsulate the data as well as permutations.
This is to ensure consistent splitting of training and test sets.
"""
def __init__(self):
self.name = ''
self.filename = ''
self.examples = None
self.labels = None
self.num_class = -1
self.perm_filename = ''
self.perms = numpy.array([]) # all the permutations
self.perm_idx = -1
self.data_dir = ''
self.frac_train = 0.7
def get_id_str(self, split_idx, split_type, fold=None, num_cv=None):
self.perm_idx = split_idx
return self._param2string(split_idx, split_type, fold, num_cv)
def get_perm(self, split_idx, split_type, fold=None, num_cv=None):
"""Returns the indices of the training and test examples."""
assert(split_type=='val' or split_type=='test')
self.perm_idx = split_idx
perm = self.perms[split_idx]
split_train = int(self.frac_train*self.num_examples)
if split_type == 'val':
perm_train = perm[:split_train]
idx_pred = perm_train[fold::num_cv]
idx_train = []
for idx in perm_train:
if not (idx in idx_pred):
idx_train.append(idx)
idx_train = numpy.array(idx_train)
elif split_type == 'test':
idx_train = perm[:split_train]
idx_pred = perm[split_train:]
id_str = self._param2string(split_idx, split_type, fold, num_cv)
return (idx_train, idx_pred, id_str)
def generate_perm(self, num_perm=50):
"""Generate permutations of the index of the examples"""
self.perms = []
for iperm in range(num_perm):
self.perms.append(permutation(self.num_examples))
self.perms = array(self.perms)
def _param2string(self, split_idx, split_type, fold, num_cv):
"""Generate part of file name the split"""
return param2string(split_idx, split_type, fold, num_cv, self.frac_train)
def param2string(split_idx, split_type, fold=None, num_cv=None, frac_train=None):
"""Generate part of file name the split"""
if split_type == 'test':
id_str = '%d_r%1.2f' % (split_idx, float(frac_train))
elif split_type == 'val':
id_str = '%d_cv%d:%d' % (split_idx, fold, num_cv)
return id_str
class ClassificationData(DatasetBase):
''' Dataset for classification '''
def __init__(self):
self.labels = array([])
def split_off(self, idx, id_str):
raise NotImplementedError
def load_perm(self, permfile, i):
''' load permutation i for task '''
p = jload(permfile)
return array(p[i])
def get_perm(self, permfile, perm_idx, model_selection, frac_ms=None, fold_cv=None, num_cv=None):
""" Returns the indices of the training and test examples.
model_selection: True: This is model selection
False: This is test
frac_ms: Fraction of model selection / test split (set to 1 for simple cv schemes)
fold: None (when model_selection is False) or number of current cross validation fold
num_cv: None (when model_selection is False) or total number of cross validation folds
"""
perm = self.load_perm(permfile, perm_idx)
self.perm_idx = perm_idx
split_train = int(frac_ms*self.num_ex)
if model_selection: # use cross validation for model selection
perm_train = perm[:split_train]
idx_pred = perm_train[fold_cv::num_cv]
idx_train = setdiff1d(perm_train, idx_pred)
else:
idx_train = perm[:split_train]
idx_pred = perm[split_train:]
id_str = self._str_perm(permfile, perm_idx, model_selection, frac_ms, fold_cv, num_cv)
return (idx_train, idx_pred, id_str)
def get_train_pred_data(self, permfile, perm_idx, model_selection, frac_ms, fold_cv, num_cv):
(idx_train, idx_pred, id_str) = self.get_perm(permfile, perm_idx, model_selection, frac_ms, fold_cv, num_cv)
traindata = self.split_off(idx_train, id_str)
preddata = self.split_off(idx_pred, id_str)
return traindata, preddata
def _str_perm(self, permfile, perm_idx, model_selection, frac_ms, fold_cv, num_cv):
test = '_validation' if not model_selection else ''
cvstr = '_%d_of_%d-fold_cv' % (fold_cv, num_cv) if model_selection else ''
return "perm_ms_%s_perm_idx_%d%s%s" % (permfile, perm_idx, test, cvstr)
@property
def num_ex(self):
return len(self.labels)
class Dataset(DatasetBase):
"""Encapsulate the data as well as permutations.
This is to ensure consistent splitting of training and test sets.
Initialize dataset from a file.
"""
def __init__(self, name, data_file=None, perm_file=None,
data_dir='', frac_train=0.7, read_data=True):
"""Load the data into memory"""
self.name = name
self.data_dir = data_dir
self.frac_train = frac_train
if data_file:
self.filename = data_file
else:
self.filename = '%s/%s.csv' % (self.data_dir, name)
data = init_datasetfile(self.filename,'vec')
if read_data:
(self.examples, self.labels) = data.readlines()
self.num_class = len(unique(self.labels))
if perm_file:
self.perm_filename = perm_file
else:
self.perm_filename = '%s/%s_perm.txt' % (self.data_dir, name)
if read_data:
self.perms = numpy.loadtxt(self.perm_filename, dtype=int, delimiter=' ')
@property
def num_examples(self):
return self.examples.shape[1]
@property
def num_features(self):
return self.examples.shape[0]
def generate_perm(datafile, permfile, num_perm=50):
"""Read in the data in datafile, and generate a file
with permutations of the index of the examples.
"""
fp1 = init_datasetfile(datafile, 'vec')
(examples,labels) = fp1.readlines()
num_examples = len(labels)
fp2 = open(permfile, 'w')
for iperm in range(num_perm):
cur_mix = permutation(num_examples)
fp2.write(' '.join(map(str, cur_mix.tolist())))
fp2.write('\n')
fp2.close()
OptWok-0.3/optwok/mldata_pytables.py 0000644 ڛ |-00000010125 12051075316 024577 0 ustar cheng.ong UNIMELB\Domain Users 0000000 0000000 """Classes to encapsulate the idea of a dataset in machine learning,
including file access. Similar to mldata.py.
The file format is a pytables zlib compressed hdf5 file.
A dataset is modeled as an (example,label) tuple, each of which is an array.
The idea is to avoid loading the whole dataset into memory.
"""
from os.path import isfile
import numpy
from numpy import array, unique
from numpy.random import permutation
from tables import openFile
class Dataset(object):
"""Encapsulate the data as well as permutations.
This is to ensure consistent splitting of training and test sets.
"""
def __init__(self, name, data_file=None, perm_file=None,
data_dir='', frac_train=0.7):
"""Initialize file names and parameters"""
self.name = name
self.data_dir = data_dir
self.frac_train = frac_train
if data_file:
self.filename = data_file
else:
self.filename = '%s/%s_mat.h5' % (self.data_dir, name)
self.data = openFile(self.filename, 'r')
self.examples = self.data.root.examples
self.labels = self.data.root.labels
self.num_class = len(unique(array(self.labels)))
self._get_set_permfile(perm_file)
@property
def num_examples(self):
return self.examples.shape[1]
@property
def num_features(self):
return self.examples.shape[0]
def _generate_perm(self, num_perm):
"""Generate permutations of the index of the examples"""
self.perms = []
for iperm in range(num_perm):
self.perms.append(permutation(self.num_examples))
self.perms = array(self.perms)
def _get_set_permfile(self, perm_file, num_perm=50):
"""If a permutation file exists, load it.
Otherwise, generate it and save one.
Sets self.perm_filename and self.perms
"""
if perm_file:
self.perm_filename = perm_file
else:
self.perm_filename = '%s/%s_perm.txt' % (self.data_dir, self.name)
if isfile(self.perm_filename):
self.perms = numpy.loadtxt(self.perm_filename, dtype=int, delimiter=' ')
else:
self._generate_perm(num_perm)
fp = open(self.perm_filename, 'w')
for iperm in range(num_perm):
cur_mix = permutation(self.num_examples)
fp.write(' '.join(map(str, cur_mix.tolist())))
fp.write('\n')
fp.close()
def get_id_str(self, split_idx, split_type, fold=None, num_cv=None):
self.perm_idx = split_idx
return self._param2string(split_idx, split_type, fold, num_cv)
def get_perm(self, split_idx, split_type, fold=None, num_cv=None):
"""Returns the indices of the training and test examples."""
assert(split_type=='val' or split_type=='test')
self.perm_idx = split_idx
perm = self.perms[split_idx]
split_train = int(self.frac_train*self.num_examples)
if split_type == 'val':
perm_train = perm[:split_train]
idx_pred = perm_train[fold::num_cv]
idx_train = []
for idx in perm_train:
if not (idx in idx_pred):
idx_train.append(idx)
idx_train = numpy.array(idx_train)
elif split_type == 'test':
idx_train = perm[:split_train]
idx_pred = perm[split_train:]
id_str = self._param2string(split_idx, split_type, fold, num_cv)
return (idx_train, idx_pred, id_str)
def close(self):
"""Close the HDF5 file"""
self.data.close()
def _param2string(self, split_idx, split_type, fold, num_cv):
"""Generate part of file name the split"""
return param2string(split_idx, split_type, fold, num_cv, self.frac_train)
def param2string(split_idx, split_type, fold=None, num_cv=None, frac_train=None):
"""Generate part of file name the split"""
if split_type == 'test':
id_str = '%d_r%1.2f' % (split_idx, float(frac_train))
elif split_type == 'val':
id_str = '%d_cv%d:%d' % (split_idx, fold, num_cv)
return id_str
OptWok-0.3/optwok/multiclass.py 0000644 ڛ |-00000011613 12055530734 023621 0 ustar cheng.ong UNIMELB\Domain Users 0000000 0000000 """Implementation of a multiclass SVM in the dual.
Uses the vector formulation of multiclass SVM,
and not one-vs-one or one-vs-all.
"""
from time import time
from numpy.random import randn
from numpy import matrix, array, vstack, hstack
from numpy import zeros, ones, transpose, nonzero
from numpy import sign, eye, unique, kron, argmax
from numpy import trace
from optwok.kernel import kernel2distance
from optwok.sip import SIQP
class MulticlassSVM(object):
"""Base class for multiclass support vector machine,
allowing a kernel on labels"""
def __init__(self, reg_param, kernel, verbose=False):
assert(reg_param > 0.0)
self.reg_param = reg_param # Tradeoff regularizer vs loss
self.kernel = kernel # kernel
self.train_ex = array([]) # training examples
self.y = array([]) # labels
self.scale = 1.0 # the trace of Ktrain
self.train_time = 0.0 # time used for training
self.alpha = array([]) # weight vector
self.num_train = 0 # number of examples
self.num_classes = 0 # number of classes
self.epsilon = 1e-6 # how small is zero?
self.verbose = verbose # how much output
@property
def train_out(self):
"""Output of the classifier on the training set.
return a column vector of scores for each example."""
return array((matrix(self.kernel.Ky)*matrix(self.alpha).T*matrix(self.kernel.Kx.kmat)).T)
def solve_opt(self):
"""To be implemented in children"""
raise NotImplementedError
def train(self, train_ex, train_lab):
"""Wrapper to convert data to feature matrix,
and solve the optimization"""
self.train_ex = train_ex
self.y = train_lab
self.num_train = len(self.y)
self.num_classes = len(unique(self.y))
self.kernel.compute(train_ex)
t_start = time()
self.solve_opt()
t_end = time()
# Collect statistics about optimization
self.train_time = t_end-t_start
if self.verbose: print('Trained for %f seconds' % self.train_time)
def predict(self, pred_ex):
"""Wrapper to do prediction.
Compute K*alpha,
return a column vector of scores for each example
"""
Ktest = self.kernel.Kx.compute_test(pred_ex)
scores = array((matrix(self.kernel.Ky)*matrix(self.alpha).T*matrix(Ktest)).T)
return argmax(scores, axis=1)
class StructPredictor(MulticlassSVM):
"""Structured output SVM in the dual"""
def __init__(self, reg_param, kernel, verbose=False):
MulticlassSVM.__init__(self, reg_param, kernel, verbose)
def solve_opt(self):
"""Solve the multiclass SVM QP
Use cvxopt qp solver which expects the following format:
min_x 1/2 x'Px + q'x
st Gx <= h
Ax = b
alpha has num_train rows and num_classes columns
alphavec = self.alpha.T.reshape((m,1), order='F')
"""
m = self.num_train*self.num_classes
# class indicator
oneY = zeros((self.num_train, self.num_classes))
for ix in range(self.num_train):
oneY[ix, self.y[ix]] = 1.0
oneY.shape = (m,1)
# Margin term for margin rescaling
distance = kernel2distance(self.kernel.Ky)
distY = zeros((self.num_train, self.num_classes))
for ix in range(self.num_train):
distY[ix,:] = distance[self.y[ix],:]
distY.shape = (m,1)
# Set up the optimization problem
svm = SIQP(m, self.verbose)
#svm.set_objective(kron(self.Kx, self.Ky), -oneY)
#svm.set_objective(self.kernel.kmat, -oneY)
svm.set_objective(self.kernel.kmat, distY)
D = eye(m)
idx = nonzero(oneY)[0]
D[idx,idx] = -1.0
svm.add_ineq_constraints(D, zeros((m,1)))
UB = zeros((m,m))
UB[idx,idx] = 1.0
svm.add_ineq_constraints(UB, self.reg_param*ones((m,1)))
# sum_u alpha_iu = 0 for all i
for ix in range(self.num_train):
cur_const = zeros((1,m))
cur_const[0,self.num_classes*ix:self.num_classes*(ix+1)] = 1.0
svm.add_eq_constraints(cur_const, zeros((1,1)))
# sum_i alpha_iu = 0 for all u
#for ix in range(self.num_classes):
# class_idx = zeros((1,self.num_classes))
# class_idx[0,ix] = 1.0
# svm.add_eq_constraints(kron(ones((1,self.num_train)), class_idx), zeros((1,1)))
svm.solve()
self.alpha = svm.solution.reshape((self.num_train, self.num_classes))
def idx2binvec(y):
"""Convert a vector y with class labels 0,1,...
to a binary matrix with corresponding 1 in column label"""
num_class = len(unique(y))
Y = zeros((len(y),num_class))
for ix in range(len(y)):
Y[ix,y[ix]] = 1.0
return Y
OptWok-0.3/optwok/named_array.py 0000644 ڛ |-00000020633 12075701775 023734 0 ustar cheng.ong UNIMELB\Domain Users 0000000 0000000 """A thin wrapper around numpy arrays such that dimensions
and coordinates can be named. This makes it easier to access
slices and elements of the array without resorting to a
particular convention.
The idea is to have dictionary like interfaces.
and have the array expand as needed.
The code is not designed for efficiency, but ease of use.
"""
import os
from numpy import array, ones, zeros, empty
from numpy import flatnonzero
import json
class NArray(object):
"""A thin wrapper around numpy arrays such that dimensions
and coordinates can be named. This makes it easier to access
slices and elements of the array without resorting to a
particular convention.
The idea is to have dictionary like interfaces.
and have the array expand as needed.
The code is not designed for efficiency, but ease of use.
"""
def __init__(self, dim_names, max_elem=10, is_object=True):
self.dim_names = array(dim_names) # the name of each axis
self.elem_names = [] # list of list of names of each coordinate
self.max_elem = max_elem # size of the array
for ix in range(len(dim_names)):
self.elem_names.append([])
if is_object:
self.data = empty([max_elem for ix in dim_names], dtype=object) # the contents
else:
self.data = zeros([max_elem for ix in dim_names]) # the contents
@property
def ndim(self):
"""Number of dimensions"""
return self.data.ndim
@property
def shape(self):
"""The number of elements in each dimension"""
cur_shape = []
for names in self.elem_names:
cur_shape.append(len(names))
return tuple(cur_shape)
@property
def content(self):
"""Return the array corresponding to meaningful data"""
last = self.shape
slicer = ''
for ix in last:
slicer += ':%d,' % ix
slicer = slicer[:-1]
return eval('self.data['+slicer+']')
def keys(self, dim_name):
"""Return the list of keys"""
if dim_name not in self.dim_names:
raise KeyError('%s not found' % dim_name)
cur_dim = flatnonzero(self.dim_names==dim_name)[0]
return self.elem_names[cur_dim]
def num_keys(self, dim_name):
return len(self.keys(dim_name))
def add_key(self, elem_name, dim_name, warn=True):
"""Add element elem_name to dimension dim_name"""
cur_dim = flatnonzero(self.dim_names==dim_name)[0]
if len(self.elem_names[cur_dim]) >= self.max_elem:
raise IndexError('Exceeding size of array, allocate more using max_elem')
if elem_name in self.elem_names[cur_dim]:
if warn:
print('Element name %s already exists, not added' % elem_name)
else:
self.elem_names[cur_dim].append(elem_name)
def add_keys(self, elem_list, dim_name):
"""Add all elements in elem_list to dim_name"""
for elem in elem_list:
self.add_key(elem, dim_name)
def fill_value(self, value, elem_names, dim_names):
"""Insert value into the array
indexed by elem_names"""
assert len(elem_names)==len(dim_names)
assert len(dim_names)==self.ndim
pointer = ''
for (ix,dim) in enumerate(self.dim_names):
cur_dim = flatnonzero(array(dim_names)==dim)[0]
cur_elem = flatnonzero(array(self.elem_names[ix])==elem_names[cur_dim])[0]
if cur_elem >= self.max_elem:
raise IndexError('Exceeding size of array')
pointer += '%d,' % cur_elem
pointer = pointer[:-1]
exec('self.data['+pointer+'] = value')
def get_value(self, elem_names, dim_names):
"""Return the value from array"""
assert len(elem_names)==len(dim_names)
assert len(dim_names)==self.ndim
pointer = ''
for (ix,dim) in enumerate(self.dim_names):
cur_dim = flatnonzero(array(dim_names)==dim)[0]
cur_elem = flatnonzero(array(self.elem_names[ix])==elem_names[cur_dim])[0]
pointer += '%d,' % cur_elem
pointer = pointer[:-1]
exec('value = self.data['+pointer+']')
return locals()['value']
def _slice(self, dim_name, dim_value):
"""Return a slice of the array,
i.e. conditioning on dim_name = dim_value
Base case
"""
try:
cur_dim = flatnonzero(self.dim_names==dim_name)[0]
except IndexError:
msg = 'Dimension name unknown: %s not found in ' % dim_name
msg += str(self.dim_names)
raise IndexError(msg)
try:
cur_val = flatnonzero(array(self.elem_names[cur_dim])==dim_value)[0]
except IndexError:
msg = 'Index name %s not found in dimension %s\n' % (dim_value, dim_name)
msg += 'Known options:'
msg += str(self.elem_names[cur_dim])
raise IndexError(msg)
slicer = ''
for ix in range(self.ndim):
if ix == cur_dim:
slicer += str(cur_val)+','
else:
slicer += ':,'
slicer = slicer[:-1]
return eval('self.content['+slicer+']')
def slice(self, dim_name, dim_value):
"""Return a slice of the array,
i.e. conditioning on dim_name = dim_value
"""
if type(dim_name) == type(''):
return self._slice(dim_name, dim_value)
elif type(dim_name) == type(list()) and len(dim_name)==1:
return self._slice(dim_name[0], dim_value[0])
else:
assert len(dim_name)==len(dim_value)
cur_dim = []
cur_val = []
for ix in range(len(dim_name)):
try:
idx = flatnonzero(self.dim_names==dim_name[ix])[0]
except IndexError:
msg = 'Dimension name unknown: %s not found in ' % dim_name[ix]
msg += str(self.dim_names)
raise IndexError(msg)
cur_dim.append(idx)
try:
val = flatnonzero(array(self.elem_names[idx])==dim_value[ix])[0]
except IndexError:
msg = 'Index name %s not found in dimension %s\n' % (dim_value[ix], dim_name[ix])
msg += 'Known options:'
msg += str(self.elem_names[idx])
raise IndexError(msg)
cur_val.append(val)
slicer = ''
for ix in range(self.ndim):
if ix in cur_dim:
idx = flatnonzero(array(cur_dim)==ix)[0]
slicer += str(cur_val[idx])+','
else:
slicer += ':,'
slicer = slicer[:-1]
return eval('self.content['+slicer+']')
def save(self, file_name, merge=False):
"""Save the named array in json format"""
if merge and os.path.isfile(file_name):
cur = NArray(['dummy'])
cur.load(file_name)
self.merge(cur)
out = {}
out['dim_names'] = self.dim_names.tolist()
out['elem_names'] = self.elem_names
out['max_elem'] = max(self.shape)
out['content'] = self.content.tolist()
fp = open(file_name, 'w')
json.dump(out, fp, indent=4)
fp.close()
def load(self, file_name):
"""Load the named array from a file with json format"""
fp = open(file_name, 'r')
raw_data = json.load(fp)
fp.close()
self.dim_names = array(raw_data['dim_names'])
self.elem_names = raw_data['elem_names']
self.max_elem = raw_data['max_elem']
self.data = array(raw_data['content'])
def merge(self, other):
"""Overwrite non-zero elements in self"""
assert((self.dim_names == other.dim_names).all())
assert(self.elem_names == other.elem_names)
assert(self.max_elem == other.max_elem)
to_copy = self.content == 0.0
self.content[to_copy] = other.content[to_copy].copy()
def savemat(self, file_name):
"""Save the named array in matlab format"""
from scipy.io import savemat
out = {}
out['dim_names'] = self.dim_names.tolist()
out['elem_names'] = self.elem_names
out['max_elem'] = max(self.shape)
out['content'] = self.content.tolist()
savemat(file_name, out, oned_as='column')
OptWok-0.3/optwok/poly_feature.py 0000644 ڛ |-00000007441 12055525154 024143 0 ustar cheng.ong UNIMELB\Domain Users 0000000 0000000 try:
import scipy
except ImportError:
print('poly_feature.py: scipy not available')
import numpy
def partitions(p, d):
"""Generate integer partitions of p with maximum d terms"""
# base case of recursion: zero is the sum of the empty list
if p == 0:
yield ()
return
# modify partitions of n-1 to form partitions of n
for m in partitions(p-1, d):
if len(m) < d:
yield (1,) + m
if m and (len(m) < 2 or m[1] > m[0]):
yield (m[0] + 1,) + m[1:]
def get_subsets(d, k):
"""Get all subsets of size k from set of size d.
Assume items in d are integers from 0 to d-1.
"""
# check base cases
if k == 0 or d < k:
yield []
elif d == k:
yield range(d)
else:
# Use recursive formula based on binomial coeffecients:
# choose(d, k) = choose(d - 1, k - 1) + choose(d - 1, k)
for s in get_subsets(d-1, k-1):
s.append(d-1)
yield s
for s in get_subsets(d-1, k):
yield s
def compute_coeff(p,m):
"""Compute the coefficient for a polynomial kernel (of degree p)
feature given the partition vector m"""
denom = 1.0
for mi in m:
denom *= scipy.factorial(mi)
return numpy.sqrt(scipy.factorial(p)/denom)
def get_feature_map(p,d):
"""Explicit feature map for polynomial kernel
of degree p and original dimensions d"""
for m in partitions(p, d):
c = compute_coeff(p,m)
for ss in get_subsets(d, len(m)):
yield (c, m, ss)
def get_poly_features(X, p):
"""Return the explicit feature vector for the polynomial kernel"""
d = X.shape[0]
n = X.shape[1]
Phi = []
for ix in range(n):
example = X[:,ix]
cur_phi = []
for (c, m, ss) in get_feature_map(p, d):
value = 1.0
for ixm in range(len(m)):
value *= example[ss[ixm]]**m[ixm]
cur_phi.append(c*value)
Phi.append(cur_phi)
Phi = (numpy.array(Phi)).T
# How to normalize?
for ix in range(Phi.shape[1]):
Phi[:,ix] /= numpy.linalg.norm(Phi[:,ix])
return Phi
#####################################################################
# Test scripts
#####################################################################
def diff_kernel_matrix(train_ex, kdict):
"""Compute the difference between the kernel matrices between shogun
and this implementation"""
# shogun
from shogun.Features import RealFeatures
from shogun.Kernel import PolyKernel
sfeat = RealFeatures(train_ex)
kernel = PolyKernel(sfeat, sfeat, kdict['param'], kdict['inhomog'])
K_shogun = kernel.get_kernel_matrix()
# explicit feature map
if kdict['inhomog']:
X = numpy.vstack([train_ex, numpy.ones((1,train_ex.shape[1]))])
else:
X = train_ex
efeat = get_poly_features(X, kdict['param'])
K_feat = numpy.matrix(efeat).T*numpy.matrix(efeat)
# compare
if train_ex.shape[1] < 10:
print(K_shogun)
print(K_feat)
return numpy.sum(sum(numpy.abs(K_feat-K_shogun)))
def compare_with_shogun():
"""Compare the kernel matrix with shogun's"""
from esvm.datafuncs import cloudgen
#numpy.random.seed(1)
num_train = 100
num_feat = 3
frac_pos = 0.5
width = 1.5
(metadata, train_ex, train_labels) = cloudgen(num_train, num_feat, frac_pos, width)
for degree in range(1,7):
kdict = {'name':'poly', 'param': degree, 'inhomog': False}
print(kdict)
print('Absolute difference: %f' % diff_kernel_matrix(train_ex, kdict))
kdict = {'name':'poly', 'param': degree, 'inhomog': True}
print(kdict)
print('Absolute difference: %f' % diff_kernel_matrix(train_ex, kdict))
if __name__ == '__main__':
compare_with_shogun()
OptWok-0.3/optwok/robust_svm.py 0000644 ڛ |-00000007260 12124766721 023653 0 ustar cheng.ong UNIMELB\Domain Users 0000000 0000000 '''
Created on 21.02.2013
@author: Gabriel
'''
from optwok.socp import SOCPS
from numpy import r_, zeros, sqrt, ones, eye, diag, sign, array, dot
from optwok.mldata import ClassificationData
class RobustData(ClassificationData):
''' Dataset that has a covariance matrix associated with every data point '''
def __init__(self, examples, covariances, labels):
self.examples = examples
self.covariances = covariances
self.labels = labels
def split_off(self, idx, id_str):
rbd = RobustData(self.examples[idx], self.covariances[idx], self.labels[idx])
rbd.id_str = id_str
rbd.idx = idx
return rbd
def ith_data(self, i):
return {'example': self.examples[i], 'covariance': self.covariances[i], 'label':self.labels[i]}
def iter(self): # TODO: this should be in common MIData ancestor
for i in range(self.num_ex):
yield self.ith_data(i)
@property
def num_feat(self):
return self.examples[0].shape[0]
class RobustSVM():
def __init__(self, C, verbose=False):
self.verbose = verbose
self.C = C
def one_predict(self, data):
''' predict one datapoint '''
return dot(self.w,data['example']) + self.b
def predict(self, testset): # Could also be in common MIClassifier ancestor
''' implement classifier '''
y = []
for pd in testset.iter():
yi = self.one_predict(pd)
y.append(yi)
return sign(array(y))
def train(self, trainset):
self.train_ex = trainset.examples
self.train_cov = trainset.covariances
self.train_lab = trainset.labels
self.num_feat = trainset.num_feat
self.num_ex = trainset.num_ex
self.solve_opt()
def solve_opt(self):
'''
x = [w, b, xi, theta],
'''
ndim = self.num_feat
nobj = self.num_ex
num_vars = ndim + 1 + nobj + 1
socp = SOCPS(num_vars= num_vars, verbose=self.verbose, use_mosek=True)
f = r_[zeros(num_vars-1),1] # only theta
socp.set_objective(f)
# Constraints for original objective + regularization
# (see http://www-personal.umich.edu/~mepelman/teaching/IOE511/Handouts/511notes07-16.pdf for QCQP -> LPQP,
# http://retis.sssup.it/~bini/teaching/optim2010/cone_programming.pdf for LPQP -> SOCP)
c = r_[zeros(ndim+1),-ones(nobj)*self.C, 1]/2
A = zeros((ndim+1,num_vars))
#A[:ndim,:ndim] = eye(ndim,ndim) * sqrt(1/2)
A[0,:] = -c
A[1:,:ndim] = diag(ones(ndim) * sqrt(1/2))
d = 1/2
b = zeros(ndim+1)
b[0] = 1/2
socp.add_ineq_constraints(A, b, c, d)
for i, e in enumerate(zip(self.train_ex, self.train_lab, self.train_cov)):
qi, yi, Pi = e
A_i = zeros((ndim, num_vars))
A_i[:,:ndim] = Pi
b_i = zeros(ndim)
c_i = r_[yi*qi,yi,zeros(nobj+1)]
c_i[ndim+1+i] = 1 # xi
d_i = -1
socp.add_ineq_constraints(A_i, b_i, c_i, d_i)
# Constraint for positive slack
A_0 = zeros((num_vars, num_vars))
A_0[ndim+1:-1,ndim+1:-1] = -1*eye(nobj)
socp.set_component_ineq_constraints(A_0, zeros(num_vars))
socp.solve(warn=self.verbose)
self.w = socp.solution[:ndim]
self.b = socp.solution[ndim+1]
self.primal_obj = socp.cur_obj
OptWok-0.3/optwok/simdata.py 0000644 ڛ |-00000041254 12055562256 023073 0 ustar cheng.ong UNIMELB\Domain Users 0000000 0000000 """Some functions to generate simulated data"""
try:
from os import link
except ImportError:
# Hack for windows
from shutil import copy2
def link(src, dst):
copy2(src, dst)
from numpy import ones, concatenate, real
from numpy.random import randn, rand, permutation, randint, seed
from numpy import where, nonzero, sqrt, real
from numpy import zeros, eye, mean, kron, sign
from numpy import array, equal, linspace
from numpy import flatnonzero
from numpy.linalg import norm
from optwok.mldata import DatasetFileCSV
from optwok.testtools import flatten_list, list2matrix
from numpy import matrix, vstack, hstack, concatenate
from optwok.kernel import GaussKernel, JointKernel
from optwok.io_pickle import load, save
from optwok.mldata import DatasetBase
from scipy.linalg.matfuncs import sqrtm
#####################################################################
# Binary classification
#####################################################################
def cloudgen(numpoint, numfeat, numnoise, fracpos, width):
"""Generate two Gaussian point clouds, centered around one and minus one.
Gaussian clouds are in numfeat dimensions.
Generate uniform noise in numnoise dimensions.
"""
numpos = int(round(fracpos*numpoint))
numneg = numpoint - numpos
metadata = 'cloudgen(%d,%d,%d,%d,%3.2f)' % (numpos, numneg, numfeat, numnoise, width)
print(metadata)
datapos = ones((numfeat, numpos)) + width*randn(numfeat, numpos)
dataneg = -ones((numfeat, numneg)) + width*randn(numfeat, numneg)
noise = (2.0+width)*(rand(numnoise, numpos+numneg)
-0.5*ones((numnoise, numpos+numneg)))
#noise = sign(rand(numnoise, numpos+numneg)-0.5*ones((numnoise, numpos+numneg)))\
# + width*randn(numnoise, numpos+numneg)
pointcloud = 0.2*concatenate((concatenate((datapos,dataneg),axis=1),
noise), axis=0)
labels = concatenate((ones(numpos),-ones(numneg)))
return metadata, pointcloud, labels
def cloudgen_weak(numpoint, numfeat, numweak, fracpos, width):
"""Generate two Gaussian point clouds, centered around one and minus one.
Gaussian clouds are in numfeat dimensions.
Overlapping Gaussian clouds are in numweak dimensions.
"""
numpos = int(round(fracpos*numpoint))
numneg = numpoint - numpos
metadata = 'cloudgen_weak(%d,%d,%d,%d,%3.2f)' %\
(numpos, numneg, numfeat, numweak, width)
print(metadata)
strongpos = ones((numfeat, numpos)) + width*randn(numfeat, numpos)
strongneg = -ones((numfeat, numneg)) + width*randn(numfeat, numneg)
weakpos = 0.1*ones((numweak, numpos)) + 2.0*width*randn(numweak, numpos)
weakneg = -0.1*ones((numweak, numneg)) + 2.0*width*randn(numweak, numneg)
datapos = concatenate((strongpos, weakpos), axis=0)
dataneg = concatenate((strongneg, weakneg), axis=0)
pointcloud = 0.2*concatenate((datapos,dataneg),axis=1)
labels = concatenate((ones(numpos),-ones(numneg)))
return metadata, pointcloud, labels
def create_data(num_train=500, num_test=200, num_feat=10, num_noise_feat=10, width=0.8,
frac_pos=0.5, frac_flip=0.0, symm_flip=True):
"""Create mixture of Gaussians distribution"""
(metadata, train_ex, train_labels) = cloudgen(num_train, num_feat,
num_noise_feat, frac_pos, width)
(metadata, test_ex, test_labels) = cloudgen(num_test, num_feat,
num_noise_feat, frac_pos, width)
random_classification_noise(train_labels, frac_flip, symm_flip)
return (train_ex, train_labels, test_ex, test_labels)
def _get_hyperplane(num_feat, in_simplex, homogeneous, min_val=0.01):
"""Randomly create a hyperplane"""
if in_simplex:
w = rand(num_feat,1)
w += min_val+1.0/num_feat
w = w/sum(abs(w))
if homogeneous:
b = 0.0
else:
b = rand()
else:
w = rand(num_feat,1)-0.5
w = sign(w)*(abs(w)+min_val+1.0/num_feat)
w = w/norm(w)
if homogeneous:
b = 0.0
else:
b = 2.0*(rand()-0.5)
return w,b
def linear_separation(num_point, num_feat, num_noise, margin=0.5, homogeneous=True, in_simplex=False):
"""Generate uniformly distributed data in [-1,1]^d,
pick a random hyperplane and create labels according to it.
"""
assert(margin < 1.0 and margin >= 0.0)
margin /= float(num_feat)
w, b = _get_hyperplane(num_feat, in_simplex, homogeneous)
param = 'linear_separation(%d, %d, %d, %1.2f, %d)' %\
(num_point, num_feat, num_noise, margin, int(homogeneous))
metadata = (param, w, b)
data = 2.0*(rand(num_feat, int((2.0/(1.0-margin))*num_point))-0.5)
pred = array(matrix(w).T*matrix(data) + b)
to_keep = nonzero(abs(pred)>margin)[1]
data = data[:,to_keep]
data = data[:,:num_point]
# label based on w and b
pred = array(matrix(w).T*matrix(data) + b)
pos_idx = nonzero(pred>0.0)[1]
neg_idx = nonzero(pred<0.0)[1]
label = zeros(num_point)
label[pos_idx] = 1.0
label[neg_idx] = -1.0
# generate noise dimensions
if num_noise > 0:
noise = 2.0*(rand(num_noise, num_point)-0.5)
data = vstack([data, noise])
return metadata, data, label
def linear_separation_example():
from optwok.testtools import plot_2d
from matplotlib.pyplot import figure
metadata, data, label = linear_separation(200, 2, 0)
figure()
plot_2d(data, label)
metadata, data, label = linear_separation(200, 2, 0, margin=0.4, homogeneous=False)
figure()
plot_2d(data, label)
metadata, data, label = linear_separation(200, 1, 1, margin=0.5)
figure()
plot_2d(data, label)
metadata, data, label = linear_separation(200, 2, 0, in_simplex=True)
figure()
plot_2d(data, label)
metadata, data, label = linear_separation(200, 1, 1, in_simplex=True)
figure()
plot_2d(data, label)
#####################################################################
# Multiclass
#####################################################################
def multi_cloudgen(num_point, num_feat, num_class, edge_length):
"""Generate numclass number of Gaussian point clouds,
centered around the simplex of numclass."""
assert(num_feat >= num_class)
centers = zeros((num_feat, num_class))
centers[:num_class, :num_class] = (edge_length/sqrt(2))*eye(num_class)
midpoint = mean(centers,axis=1)
midpoint.shape = (num_feat,1)
centers = centers - kron(ones((1,num_class)),midpoint)
if type(num_point) == type(0):
num_point_list = []
for ix in range(num_class):
num_point_list.append(num_point)
num_point = num_point_list
metadata = 'multi_cloudgen(%s,%d,%d,%3.2f)' % (num_point, num_feat, num_class, edge_length)
pointcloud = zeros((num_feat,0))
labels = array([],dtype=int)
for ix in range(num_class):
cur_cloud = kron(ones((num_point[ix],1)),centers[:,ix]).T + randn(num_feat, num_point[ix])
pointcloud = concatenate((pointcloud, cur_cloud), axis=1)
labels = concatenate((labels, ix*ones(num_point[ix])))
pointcloud /= edge_length/sqrt(2)
return metadata, pointcloud, labels
def multi_cloudgen_mix(num_point, num_feat, num_class, edge_length, mix_list):
"""Generate num_class Gaussian point clouds,
but labels are mixed depending on mix_list.
"""
(metadata, pointcloud, labels) = multi_cloudgen(num_point, num_feat, num_class, edge_length)
metadata += str(mix_list)
new_labels = zeros(labels.shape, dtype=int)
if len(mix_list) == num_class:
# split classes
for cid in range(num_class):
idx = nonzero(equal(labels,cid))[0]
if len(mix_list[cid]) > 1:
new_labels[idx] = randint(min(mix_list[cid]), max(mix_list[cid])+1, len(idx))
else:
new_labels[idx] = mix_list[cid][0]
else:
# merge classes
assert(len(flatten_list(mix_list)) == num_class)
for cid in range(len(mix_list)):
idx = array([], dtype=int)
for mix in mix_list[cid]:
idx = concatenate([idx, nonzero(equal(labels, mix))[0]])
new_labels[idx] = cid
return metadata, pointcloud, new_labels
#####################################################################
# Simulated data for sparsity experiments
#####################################################################
def random_classification_noise(labels, frac_flip, symm_flip):
"""Generate random classification noise by flipping a proportion
of labels randomly.
"""
if frac_flip>0.0:
num_ex = len(labels)
if symm_flip:
flip_idx = permutation(num_ex)[:round(frac_flip*num_ex)]
labels[flip_idx] = -1.0*labels[flip_idx]
else:
flip_idx = permutation(num_ex)[:round(2.0*frac_flip*num_ex)]
labels[flip_idx] = -1.0*labels[where(labels[flip_idx]>0.0)]
def twoballs_filename(data_dir, num_examples, num_feat, num_noise_feat, frac_flip):
"""Generate filename to save data and permutations"""
data_filename = data_dir + '/twoballs_n=%d_%d:%d_rcn=%1.1f.csv'\
% (num_examples, num_feat, num_noise_feat, frac_flip)
perm_filename = data_dir + '/twoballs_n=%d_%d:%d_rcn=%1.1f_perm.txt'\
% (num_examples, num_feat, num_noise_feat, frac_flip)
return (data_filename, perm_filename)
def save_cloudgen(data_dir, num_noise_feat, frac_flip, symm_flip=True,
num_ex=100, num_feat=10, frac_pos=0.5, width=1.5, num_perms=50):
"""Generate two gaussians, with noise dimensions and
random classification noise, and save the dataset.
"""
assert(frac_flip>=0.0 and frac_flip<=0.5)
(metadata, examples, labels) = cloudgen(num_ex, num_feat, num_noise_feat,
frac_pos, width)
random_classification_noise(labels, frac_flip, symm_flip)
(data_filename, perm_filename) = twoballs_filename(data_dir, num_ex, num_feat, num_noise_feat, frac_flip)
outfile = DatasetFileCSV(data_filename, 'vec', verbose=True)
outfile.writelines(examples, labels)
handle = open(perm_filename, 'w')
for iperm in range(num_perms):
cur_mix = permutation(num_ex)
handle.write(' '.join(map(str, cur_mix.tolist())))
handle.write('\n')
handle.close()
def sparse_data(data_dir):
"""Generate data to investigate sparsity,
with respect to both features and support vectors."""
seed(1)
num_feat = 10
num_examples = [50, 100]
noise_feats = [0, 10, 100, 200]
rcns = [0.0, 0.1, 0.2, 0.3]
num_datasets = len(noise_feats)
assert(len(rcns)==num_datasets)
for num_ex in num_examples:
for num_noise_feat in noise_feats:
save_cloudgen(data_dir, num_noise_feat, 0.0, num_ex=num_ex, num_feat=num_feat)
(data_filename, perm_filename) = twoballs_filename(data_dir, num_ex, num_feat, 0, 0.0)
# Add random classification noise in the training
for rcn in rcns[1:]:
(df, pf) = twoballs_filename(data_dir, num_ex, num_feat, 0, rcn)
link(data_filename, df)
link(perm_filename, pf)
for ix in range(1,num_datasets):
(dfs, pfs) = twoballs_filename(data_dir, num_ex, num_feat, noise_feats[ix], 0.0)
(df, pf) = twoballs_filename(data_dir, num_ex, num_feat, noise_feats[ix], rcns[ix])
link(dfs, df)
link(pfs, pf)
#####################################################################
# Gaussian Process regression
#####################################################################
def mean_func1(x, shift=0):
from numpy import sin
x += shift
return 2*sin(x)+sin(3*x+1)
def mean_func2(x, shift=0):
from numpy import cos
x += shift
return 2*cos(x)+cos(3*x+1)
def gp_gen(num_point, num_dim, domain, noise_level, mix_list=[[0,1],[2]]):
"""Generate matrix variate normally distributed data"""
reg_param = 1e-8
num_class = len(flatten_list(mix_list))
X = domain*rand(num_dim, num_point)
#X = linspace(-domain, domain, num_point)
#X.shape = (num_dim, num_point)
Kx = GaussKernel(1.0)
Kx.compute(X)
Ky = list2matrix(mix_list, neg_corr=True)
K = JointKernel(Kx, Ky)
#imshow(K.kmat)
#colorbar()
L = real(sqrtm(0.5*(K.kmat)+reg_param*eye(num_point*num_class)).T)
#mu = vstack([mean_func1(X), mean_func1(X), mean_func2(X)])
mu = zeros((num_class, num_point))
Y = L*matrix(randn(num_point*num_class,1))
Y.shape = (num_point,num_class)
Y = real(Y.T)
Y += mu + noise_level*randn(num_class, num_point)
Y = array(Y)
return (X,Y)
def gp_gen_pair(num_pairs, num_dim, domain, noise_level, mix_list=[[0,1],[2]], pos_label=False):
"""Generate data from a multioutput Gaussian process,
The outputs are irregular, and are dependent on context Z"""
num_class = len(flatten_list(mix_list))
num_ex = num_class*num_pairs
num_ex = max(num_ex, num_pairs)
(X_full,Y_full) = gp_gen(num_ex, num_dim, domain, noise_level, mix_list)
X = zeros((X_full.shape[0],0))
Y = array([])
Z = array([])
for ix in range(num_class):
perm = permutation(num_ex)
X = hstack([X, X_full[:,perm[:num_pairs]]])
Y = hstack([Y, Y_full[ix,perm[:num_pairs]]])
Z = concatenate((Z, ix*ones(num_pairs)))
if pos_label:
min_y = min(Y) - 0.1
Y -= min_y
return (X,Z,Y)
def plot_missing_data():
"""A demo of gp_gen_pair"""
from matplotlib.pyplot import plot, show
colour_list = ['b','g','r','c','m','k']
(X,Z,Y) = gp_gen_pair(20, 1, 5, 0.1)
for zix in range(3):
cur_idx = flatnonzero(Z == zix)
plot(X[:,cur_idx].flatten(), Y[:,cur_idx].flatten(),
colour_list[zix]+'o', hold=True)
show()
def save_gp_data(x, y, dataname, data_dir='.'):
"""Save Gaussian process data"""
dataset = DatasetBase()
dataset.name = dataname
dataset.filename = dataset.name + '.pkl'
dataset.examples = x
dataset.labels = y
(dataset.num_features, dataset.num_examples) = x.shape
dataset.num_class = y.shape[0]
dataset.perm_filename = ''
dataset.perms = zeros((50, dataset.num_examples), dtype=int)
for ix in range(50):
dataset.perms[ix] = permutation(dataset.num_examples)
dataset.perm_idx = -1
dataset.data_dir = data_dir
dataset.frac_train = 0.7
save('%s/%s' % (data_dir, dataset.filename), dataset)
def save_gp_context_data(x, z, y, dataname, data_dir='.'):
"""Save Gaussian process with context (z) data"""
dataset = DatasetBase()
dataset.name = dataname
dataset.filename = dataset.name + '.pkl'
dataset.examples = x
dataset.context = z
dataset.labels = y
(dataset.num_features, dataset.num_examples) = x.shape
dataset.num_class = y.shape[0]
dataset.perm_filename = ''
dataset.perms = zeros((50, dataset.num_examples), dtype=int)
for ix in range(50):
dataset.perms[ix] = permutation(dataset.num_examples)
dataset.perm_idx = -1
dataset.data_dir = data_dir
dataset.frac_train = 0.7
save('%s/%s' % (data_dir, dataset.filename), dataset)
def gp_example():
"""Plot some points"""
from matplotlib.pyplot import plot, figure
num_ex = 200
domain = 5
noise_level = 0.3
(X,Y) = gp_gen(num_ex, 1, domain, noise_level)
dataname = 'gaussproc_03_%d_%1.1f' % (num_ex, noise_level)
save_gp_data(X, Y, dataname, data_dir='/local/cong/Data/muloutreg')
data = load('/local/cong/Data/muloutreg/gaussproc_03_%d_%1.1f.pkl' % (num_ex, noise_level))
figure()
plot(data.examples.T, data.labels.T, '+', hold=True)
#####################################################################
# learning with teacher
#####################################################################
def teacher_data(num_train=100, num_test=300, num_feat=5, num_feat_teach=10,
width=1.0):
"""Generate two Gaussians and then split features
into normal features and teacher features
"""
(metadata, examples, labels) = cloudgen(num_train+num_test, num_feat+num_feat_teach,
0, 0.5, width)
cur_mix = permutation(num_train+num_test)
examples = examples[:,cur_mix]
labels = labels[cur_mix]
train_ex = examples[:num_feat,:num_train]
teach_ex = examples[-num_feat_teach:,:num_train]
pred_ex = examples[:num_feat,-num_test:]
train_lab = labels[:num_train]
pred_lab = labels[-num_test:]
return (train_ex, teach_ex, train_lab, pred_ex, pred_lab)
if __name__ == '__main__':
data = teacher_data()
(train_ex, teach_ex, train_lab, test_ex, test_lab) = data
print(train_ex.shape)
print(teach_ex.shape)
print(train_lab.shape)
print(test_ex.shape)
print(test_lab.shape)
OptWok-0.3/optwok/sip.py 0000644 ڛ |-00000014555 12055533155 022244 0 ustar cheng.ong UNIMELB\Domain Users 0000000 0000000 """A collection of classes for semi-infinite programming"""
import cvxopt.solvers
from cvxopt import matrix as cm
from cvxopt import sparse as cs
from numpy import matrix, array, dot, vstack, hstack, kron
from numpy import zeros, ones, mean, std, sign, eye, nonzero
import sys
#sys.path.append('/cluster/home/infk/cong/Install/mosek/6/tools/platform/linux64x86/python/2')
have_mosek = True
try:
import mosek
except ImportError as error:
print(error)
have_mosek = False
class SIP(object):
"""A wrapper to cvxopt solver.
Allow an iterative addition of constraints to the original problem.
The cvxopt solver expects the following format:
min_x c'x
st Gx <= h
Ax = b
min_x 1/2 x'Px + q'x
st Gx <= h
Ax = b
"""
def __init__(self, num_vars, verbose=False, use_mosek=False):
assert num_vars > 0.0, 'Need at least one variable'
self.num_vars = num_vars
self.stop_eps = 1e-7
self.old_w = None
self.P = cm(0.0, (num_vars, num_vars))
self.q = cm(0.0, (num_vars, 1))
self.G = cm(0.0, (0, num_vars))
self.h = cm(0.0, (0, 1))
self.A = cm(0.0, (0, num_vars))
self.b = cm(0.0, (0, 1))
self.solution = None
self.lag_eq = None
self.cur_obj = None
self.optimal = False
self.verbose = verbose
self.use_mosek = use_mosek
def add_ineq_constraints(self, G_new, h_new):
"""Concatenate the new inequality constraints"""
assert G_new.shape[1] == self.num_vars, 'Size mismatch in inequality constraint (lhs)'
assert h_new.shape[1] == 1, 'Size mismatch in inequality constraint (rhs)'
assert G_new.shape[0] == h_new.shape[0], 'Size mismatch in inequality constraint'
self.G = cm(vstack([array(self.G), G_new]))
self.h = cm(vstack([array(self.h, dtype = 'd'), array(h_new, dtype = 'd')]))
def add_eq_constraints(self, A_new, b_new):
"""Concatenate the new equality constraints"""
assert A_new.shape[1] == self.num_vars, 'Size mismatch in equality constraint (lhs)'
assert b_new.shape[1] == 1, 'Size mismatch in equality constraint (rhs)'
assert A_new.shape[0] == b_new.shape[0], 'Size mismatch in equality constraint'
self.A = cm(vstack([array(self.A), A_new]))
self.b = cm(vstack([array(self.b), b_new]))
def solve(self, warn=True):
"""Check for empty constraints then invoke solver"""
res = self.solve_cvxopt()
if self.verbose: print(res)
self.solution = array(res['x']).copy().flatten()
self.lag_eq = array(res['y']).copy().flatten()
self.cur_obj = res['primal objective']
if res['status'] == 'optimal':
self.optimal = True
else:
self.optimal = False
if warn:
print('sip.py: cvxopt did not converge to optimality')
print(res)
del res
class SIQP(SIP):
"""A wrapper to cvxopt QP solver."""
def __init__(self, num_vars, verbose=False, use_mosek=True):
SIP.__init__(self, num_vars, verbose, use_mosek)
def set_objective(self, P, q):
"""Set P and q"""
assert P.shape == (self.num_vars, self.num_vars), 'Size mismatch in quadratic objective term'
assert q.shape == (self.num_vars, 1), 'Size mismatch in linear objective term'
self.P = cm(P)
self.q = cm(q)
def solve_cvxopt(self):
"""Check for empty constraints then invoke solver"""
if self.verbose:
print('have mosek %s' % str(have_mosek))
print('use mosek %s' % str(self.use_mosek))
if have_mosek and self.use_mosek:
if not self.verbose:
cvxopt.solvers.options['MOSEK'] = {mosek.iparam.log:0}
res = cvxopt.solvers.qp(self.P, self.q,
self.G, self.h, self.A, self.b, 'mosek')
else:
if not self.verbose:
cvxopt.solvers.options['show_progress'] = False
res = cvxopt.solvers.qp(self.P, self.q, self.G, self.h, self.A, self.b)
return res
class SILP(SIP):
"""A wrapper to cvxopt LP solver."""
def __init__(self, num_vars, verbose=False, use_mosek=True):
SIP.__init__(self, num_vars, verbose, use_mosek)
def set_objective(self, q):
"""Set c"""
assert q.shape == (self.num_vars, 1), 'Size mismatch in linear objective term'
self.q = cm(q)
def solve_cvxopt(self):
"""Check for empty constraints then invoke solver"""
if self.verbose:
print('have mosek %s' % str(have_mosek))
print('use mosek %s' % str(self.use_mosek))
if have_mosek and self.use_mosek:
if not self.verbose:
cvxopt.solvers.options['MOSEK'] = {mosek.iparam.log:0}
res = cvxopt.solvers.lp(self.q,
self.G, self.h, self.A, self.b, 'mosek')
else:
if not self.verbose:
cvxopt.solvers.options['show_progress'] = False
res = cvxopt.solvers.lp(self.q, self.G, self.h, self.A, self.b)
return res
class SILPS(SILP):
"""A wrapper to cvxopt LP solver with sparse matrices for the constraints."""
def __init__(self, num_vars, verbose=False, use_mosek=True):
SILP.__init__(self, num_vars, verbose, use_mosek)
def add_ineq_constraints(self, G_new, h_new):
"""Concatenate the new inequality constraints"""
assert h_new.shape[1] == 1, 'Size mismatch in inequality constraint (rhs)'
assert G_new.size[1] == self.num_vars, 'Size mismatch in inequality constraint (lhs)'
assert G_new.size[0] == h_new.shape[0], 'Size mismatch in inequality constraint'
self.G = cs([self.G,G_new])
self.h = cm(vstack([array(self.h,dtype='d'), array(h_new,dtype='d')]))
def add_eq_constraints(self, A_new, b_new):
"""Concatenate the new equality constraints"""
assert A_new.size[1] == self.num_vars, 'Size mismatch in equality constraint (lhs)'
assert b_new.shape[1] == 1, 'Size mismatch in equality constraint (rhs)'
assert A_new.size[0] == b_new.shape[0], 'Size mismatch in equality constraint'
self.A = cs([self.A, A_new])
self.b = cm(vstack([array(self.b,dtype='d'), array(b_new,dtype='d')]))
OptWok-0.3/optwok/socp.py 0000644 ڛ |-00000007174 12115570207 022410 0 ustar cheng.ong UNIMELB\Domain Users 0000000 0000000 '''
Created on 16.12.2011
@author: kgabriel
'''
from numpy.core.numeric import newaxis
from optwok.sip import SIP
"""Class for second order cone programming"""
import cvxopt
import cvxopt.solvers
from cvxopt import matrix as cm, sparse as cs
from numpy import matrix, array, dot, vstack, hstack, kron
from numpy import zeros, ones, mean, std, sign, eye, nonzero
have_mosek = True
try:
print("using mosek")
import mosek
except:
print("not using mosek")
have_mosek = False
class SOCP(SIP):
''' A wrapper to cvxopt SOCP solver, converting the boyd notation into cvxopt matrices
boyd + component wise constraints:
min_x f'x
st ||A_i x + b_i|| <= c_i'x + d_i, i = 1 ... m
0 <= b_0 - A_0 x
Fx = g
cvxopt:
min_x c'x
st G_i x + s_i = h_i, i = 0 ... M
Ax = b
s_0 >= 0
s_i0 >= ||s_i1|| i = 1 ... M
'''
def __init__(self, num_vars, verbose = False, use_mosek=False):
SIP.__init__(self, num_vars, verbose, use_mosek=use_mosek)
self.G0 = None
self.h0 = None
self.G1 = []
self.h1 = []
def set_objective(self, f):
"""Set f"""
assert f.shape[0] == self.num_vars, 'Size mismatch in linear objective term'
self.c = cm(f)
def set_component_ineq_constraints(self, A_0, b_0):
''' Set the non-cone constraints
0 <= b_0 - A_0 x '''
assert A_0.shape == (b_0.shape[0], self.num_vars), 'Size mismatch in inequality constraint (lhs)'
assert b_0.shape[0] == self.num_vars
self.G0 = cm(A_0)
self.h0 = cm(b_0)
def add_ineq_constraints(self, A_i, b_i, c_i, d_i):
"""Add the new inequality constraints to the list"""
assert c_i.shape[0] == self.num_vars, 'Size mismatch in inequality constraint (rhs)'
assert A_i.shape == (b_i.shape[0], self.num_vars), 'Size mismatch in inequality constraint (lhs)'
G = cm(vstack((-c_i.T, -A_i)))
h = cm(vstack((d_i, b_i[:, newaxis])))
self.G1.append(G)
self.h1.append(h)
def solve_cvxopt(self):
"""Check for empty constraints then invoke solver"""
if have_mosek and self.use_mosek:
if not self.verbose:
cvxopt.solvers.options['MOSEK'] = {mosek.iparam.log:0}
res = cvxopt.solvers.socp(self.c,
self.G0, self.h0, self.G1, self.h1, solver = 'mosek')
else:
if not self.verbose:
cvxopt.solvers.options['show_progress'] = False
res = cvxopt.solvers.socp(self.c, self.G0, self.h0, self.G1, self.h1)
return res
class SOCPS(SOCP):
''' SOCP for sparse constraints
converts matrices/numpy arrays into sparse matrices'''
def set_component_ineq_constraints(self, A_0, b_0):
''' Set the non-cone constraints
0 <= b_0 - A_0 x '''
assert A_0.shape == (b_0.shape[0], self.num_vars), 'Size mismatch in inequality constraint (lhs)'
assert b_0.shape[0] == self.num_vars
self.G0 = cs(cm(A_0))
self.h0 = cm(b_0)
def add_ineq_constraints(self, A_i, b_i, c_i, d_i):
"""Add the new inequality constraints to the list"""
assert c_i.shape[0] == self.num_vars, 'Size mismatch in inequality constraint (rhs)'
assert A_i.shape == (b_i.shape[0], self.num_vars), 'Size mismatch in inequality constraint (lhs)'
G = cs(cm(vstack((-c_i.T, -A_i))))
h = cm(vstack((d_i, b_i[:, newaxis])))
self.G1.append(G)
self.h1.append(h)
OptWok-0.3/optwok/testtools.py 0000644 ڛ |-00000005550 12055564636 023514 0 ustar cheng.ong UNIMELB\Domain Users 0000000 0000000 """Some functions to help test the modules"""
from numpy import log, exp, nonzero
from numpy import linspace, square, median
from numpy import zeros, eye, sort, ix_
from optwok.kernel import sqr_dist, normalize_unit_diag
def plot_confusion(mix_list):
"""Plot the kernel matrix corresponding to a particular
mix_list denoting the mixing of labels."""
from matplotlib.pyplot import matshow, colorbar
Ky = list2matrix(mix_list)
matshow(Ky)
colorbar()
def list2matrix(mix_list, noise=0.0, neg_corr=False):
"""Create the corresponding {0,1} matrix to the mixture list"""
k = len(flatten_list(mix_list))
Ky = zeros((k,k))
off_diag = 1.0-noise
for block in mix_list:
Ky[ix_(block,block)] = off_diag
for ix in range(k):
Ky[ix,ix] = 1.0
if neg_corr:
num_ones = sum(sum(Ky))
neg_val = -num_ones/(k*k)
Ky[nonzero(Ky==0)] = neg_val
Ky = normalize_unit_diag(Ky+1e-5*eye(k))
return Ky
def flatten_list(general_list):
"""Convert a list of lists into one list. Works recursively."""
if isinstance(general_list, list):
return sum((flatten_list(x) for x in general_list), [])
else:
return [general_list]
def pair2vec(context, value):
"""Convert a pair of (context, value) into a vector
with value in element context.
Assume that context is in range()
"""
vec = zeros((max(context)+1, len(context)))
for (ix,cur) in enumerate(zip(context, value)):
vec[cur[0],ix] = cur[1]
return vec
def plot_2d(train_ex, train_lab):
"""Plot 2D data"""
from matplotlib.pyplot import plot
pos_lab = train_lab > 0.0
neg_lab = train_lab < 0.0
plot(train_ex[0,pos_lab], train_ex[1,pos_lab], 'r+')
plot(train_ex[0,neg_lab], train_ex[1,neg_lab], 'bx')
def plot_2d_pred(x, y, svmout, train_ex, train_lab, axis_limit, title_text):
"""Plot 2D data, along with prediction contours."""
from matplotlib.pyplot import figure, pcolor, contour, axis, title
figure()
pcolor(x, y, svmout)
contour(x, y, svmout, linewidths=1, colors='black', hold=True)
plot_2d(train_ex, train_lab)
axis(axis_limit)
title(title_text)
def get_gauss_widths(examples, num_widths, start_quantile=0.1, stop_quantile=0.9):
"""Return a candidate set of possible widths
for use in the Gaussian kernel.
"""
distance = sqr_dist(examples, examples)
# get the sorted distances (ignoring distances to self)
dm = sort(distance.flatten())
dm = square(dm[examples.shape[1]:])
if num_widths == 1:
widths = median(dm)
else:
# find points uniformly spaces in log scale
start_idx = round(start_quantile*len(dm))
stop_idx = round(stop_quantile*len(dm))
dm = log(dm)
widths = exp(linspace(dm[stop_idx],dm[start_idx],num_widths))
widths = 1/widths
print(widths)
return widths
OptWok-0.3/PKG-INFO 0000644 ڛ |-00000007722 12127457270 020652 0 ustar cheng.ong UNIMELB\Domain Users 0000000 0000000 Metadata-Version: 1.0
Name: OptWok
Version: 0.3
Summary: A collection of tools for prototyping optimization methods in machine learning.
Home-page: http://www.ong-home.my/optwok.html
Author: Cheng Soon Ong
Author-email: chengsoon.ong@unimelb.edu.au
License: GNU GPL version 3 or later
Description: ======
OptWok
======
A collection of python code to perform research in optimization. The
aim is to provide reusable components that can be quickly applied to
machine learning problems.
Dependencies
============
* python 2.5+
* cvxopt 1.0+ (for solving linear and quadratic programs)
* pythongrid (for using a cluster)
* cython 0.14.1 (for speeding up kernel computations)
* numpy, scipy, matplotlib
Description
===========
The projects currently prototyped:
- Ellipsoidal multiple instance learning
- difference of convex functions algorithms for sparse classfication
- learning the output kernel using block coordinate descent
- Gaussian process regression
- Contextual bandits upper confidence bound algorithm (using GP)
Please look at the examples directory for usage.
Ellipsoidal Multiple Instance Learning
--------------------------------------
- The code for eMIL is contained in mil.mi_classifier_emil
- emil_demo.py gives usage examples
- mil.mi_data provides containers for multiple instance learning data
Difference of convex functions algorithms for sparse classfication
------------------------------------------------------------------
- The workhorse is classifier.py, which implements classifiers for binary
classification in the primal. The optimization methods use the framework
empirical loss + regularizer.
- classifier_demo.py gives two usage examples.
- poly_feature.py computes the polynomial kernel features
explicitly.
- experiment_poly.py is an experimental framework using polynomial
kernels.
Learning the output kernel using block coordinate descent
---------------------------------------------------------
- The workhorse is kernelopt.py, which implements a framework for minimizing
the invex function for learning the kernel on outputs.
- multiclass_demo.py gives usage examples.
Utilities
---------
- sip.py is a wrapper around cvxopt to allow addition of constraints.
- evaluation.py calculates the accuracy
- mldata.py defines a machine learning dataset and their files
- expttools.py defines utilities to flatten nested for loops.
- io_pickle.py for loading and saving objects.
- testtools.py is some code to ease the demos.
- simdata.py contains code to generate toy datasets.
- kernel.py implements code for joint kernels
- kernelbase.pyx speeds up kernel.py with cython
Contributors
============
Cheng Soon Ong
Gabriel Krummenacher
License
=======
OptWok is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
OptWok is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with OptWok. If not, see .
Platform: UNKNOWN
OptWok-0.3/README 0000644 ڛ |-00000005705 12126463761 020435 0 ustar cheng.ong UNIMELB\Domain Users 0000000 0000000 ======
OptWok
======
A collection of python code to perform research in optimization. The
aim is to provide reusable components that can be quickly applied to
machine learning problems.
Dependencies
============
* python 2.5+
* cvxopt 1.0+ (for solving linear and quadratic programs)
* pythongrid (for using a cluster)
* cython 0.14.1 (for speeding up kernel computations)
* numpy, scipy, matplotlib
Description
===========
The projects currently prototyped:
- Ellipsoidal multiple instance learning
- difference of convex functions algorithms for sparse classfication
- learning the output kernel using block coordinate descent
- Gaussian process regression
- Contextual bandits upper confidence bound algorithm (using GP)
Please look at the examples directory for usage.
Ellipsoidal Multiple Instance Learning
--------------------------------------
- The code for eMIL is contained in mil.mi_classifier_emil
- emil_demo.py gives usage examples
- mil.mi_data provides containers for multiple instance learning data
Difference of convex functions algorithms for sparse classfication
------------------------------------------------------------------
- The workhorse is classifier.py, which implements classifiers for binary
classification in the primal. The optimization methods use the framework
empirical loss + regularizer.
- classifier_demo.py gives two usage examples.
- poly_feature.py computes the polynomial kernel features
explicitly.
- experiment_poly.py is an experimental framework using polynomial
kernels.
Learning the output kernel using block coordinate descent
---------------------------------------------------------
- The workhorse is kernelopt.py, which implements a framework for minimizing
the invex function for learning the kernel on outputs.
- multiclass_demo.py gives usage examples.
Utilities
---------
- sip.py is a wrapper around cvxopt to allow addition of constraints.
- evaluation.py calculates the accuracy
- mldata.py defines a machine learning dataset and their files
- expttools.py defines utilities to flatten nested for loops.
- io_pickle.py for loading and saving objects.
- testtools.py is some code to ease the demos.
- simdata.py contains code to generate toy datasets.
- kernel.py implements code for joint kernels
- kernelbase.pyx speeds up kernel.py with cython
Contributors
============
Cheng Soon Ong
Gabriel Krummenacher
License
=======
OptWok is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
OptWok is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with OptWok. If not, see .
OptWok-0.3/run_demo.sh 0000755 ڛ |-00000001730 12127457076 021721 0 ustar cheng.ong UNIMELB\Domain Users 0000000 0000000 #!/bin/bash
echo '===== Classifier Demo ====='
python examples/classifier_demo.py
echo '===== Context UCB Demo ====='
python examples/contextucb_demo.py
rm expt_cucb.pkl expt_cucb_cat.pkl expt_cucb_kbest.pkl
rm expt_cucb_batch.pkl expt_cucb_ind.pkl
rm expt_cucb_avg.pdf expt_cucb_cat_min.pdf expt_cucb_min.pdf
rm expt_cucb_batch_avg.pdf expt_cucb_ind_min.pdf
rm expt_cucb_batch_min.pdf expt_cucb_kbest_min.pdf
echo '===== Ellipsoidal MIL Demo ====='
python examples/emil_demo.py
rm example_synthetic_ellipsoid_2d.json
echo '===== Gaussian Process Regression Demo ====='
python examples/gaussianproc_demo.py
echo '===== Learning Output Kernel Demo ====='
python examples/kernelopt_demo.py
rm testKy.pdf testLin.pdf
echo '===== Multiclass Demo ====='
python examples/multiclass_demo.py
echo '===== Named Array Demo ====='
python examples/named_array_demo.py
echo '===== Score Demo ====='
python examples/score_demo.py
echo '===== SOCP Demo ====='
python examples/socp_demo.py
OptWok-0.3/setup.py 0000644 ڛ |-00000004036 12121244374 021253 0 ustar cheng.ong UNIMELB\Domain Users 0000000 0000000 #!/usr/bin/env python
import os
import numpy
# Use numpy version of distutils to get f2py
#from numpy.distutils.core import setup, Extension
from distutils.core import setup, Extension
from distutils.command.sdist import sdist
from glob import glob
try:
#from Cython.Build import cythonize
from Cython.Distutils import build_ext
have_cython = True
except ImportError:
have_cython = False
opt_modules = []
if have_cython:
# cython code is used to speed up kronecker product kernel
kron = Extension('optwok.kernelbase',
sources = ['optwok/kernelbase.pyx'],
include_dirs = [numpy.get_include(),'.'],
)
opt_modules.append(kron)
class sdist_hg(sdist):
"""Append Mercurial repository version if 'dev' option is used with sdist"""
user_options = sdist.user_options + [
('dev', None, "Add a dev marker")
]
def initialize_options(self):
sdist.initialize_options(self)
self.dev = 0
def run(self):
if self.dev:
suffix = '.dev%d' % self.get_tip_revision()
self.distribution.metadata.version += suffix
sdist.run(self)
def get_tip_revision(self, path=os.path.expanduser('~')+'/Mercurial/OptWok'):
from mercurial.hg import repository
from mercurial.ui import ui
from mercurial import node
repo = repository(ui(), path)
tip = repo.changelog.tip()
return repo.changelog.rev(tip)
setup(name = 'OptWok',
author = 'Cheng Soon Ong',
author_email = 'chengsoon.ong@unimelb.edu.au',
version = '0.3',
description = 'A collection of tools for prototyping optimization methods in machine learning.',
long_description = open('README').read(),
url = 'http://www.ong-home.my/optwok.html',
packages = ['optwok', 'optwok.mil'],
cmdclass = {'sdist': sdist_hg, 'build_ext': build_ext},
#ext_modules = cythonize(opt_modules),
ext_modules = opt_modules,
license = 'GNU GPL version 3 or later',
)
OptWok-0.3/tests/ 0000755 ڛ |-00000000000 12127457270 020707 5 ustar cheng.ong UNIMELB\Domain Users 0000000 0000000 OptWok-0.3/tests/__init__.py 0000644 ڛ |-00000000000 11731540564 023005 0 ustar cheng.ong UNIMELB\Domain Users 0000000 0000000 OptWok-0.3/tests/all_tests.py 0000644 ڛ |-00000000572 11731540564 023256 0 ustar cheng.ong UNIMELB\Domain Users 0000000 0000000 """
Run all tests
"""
import glob
import unittest
def create_test_suite():
test_file_strings = glob.glob('optwok/test/*_test.py')
module_strings = ['optwok.test.'+str[12:len(str)-3] for str in test_file_strings]
suites = [unittest.defaultTestLoader.loadTestsFromName(name) for name in module_strings]
testSuite = unittest.TestSuite(suites)
return testSuite
OptWok-0.3/tests/classifier_test.py 0000644 ڛ |-00000000371 11731540564 024444 0 ustar cheng.ong UNIMELB\Domain Users 0000000 0000000 import unittest
from examples.classifier_demo import example_toy, example_cloud_2d
class Demo(unittest.TestCase):
"""Run the demo for classifier"""
def test_demo(self):
example_toy()
if __name__ == '__main__':
unittest.main()
OptWok-0.3/tests/gaussproc_06_20_0.1.pkl 0000644 ڛ |-00000020100 11731540564 024601 0 ustar cheng.ong UNIMELB\Domain Users 0000000 0000000 BZh91AY&SY$ I t T5D OAOiOMhނ Ty zS5=0A2$اzGQz0$Sez=SyM
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+;yRcz0nq'2́oݻ0 LCl~_5\r]9BD.@1ć(KfX #W n5Я8/2vn{{\!Mjk&21-t{R7pd+e>ZeZK Qæo/` +Ԙ_?QذKgu8ۗ";4k13yd;*#:?
`v'~_[ AAUa\5lM{SXKGv4d}>Эُl>8-- +dνt{%N ]F-}!y nsyMyDQnOΑ &T>itB#GrE8P$ OptWok-0.3/tests/import_test.py 0000644 ڛ |-00000000731 11731540564 023632 0 ustar cheng.ong UNIMELB\Domain Users 0000000 0000000 """Checking whether all the external libraries are installed"""
try:
import cvxopt
print cvxopt.__file__
except ImportError:
print 'cvxopt not found'
try:
import pythongrid
print pythongrid.__file__
except ImportError:
print 'pythongrid not found'
try:
import cython
print cython.__file__
except ImportError:
print 'cython not found'
try:
import slycot
print slycot.__file__
except ImportError:
print 'slycot not found'
OptWok-0.3/tests/kernel_test.py 0000644 ڛ |-00000011266 11731540564 023605 0 ustar cheng.ong UNIMELB\Domain Users 0000000 0000000 # cython: profile=True
"""Test the kernel module, including speedups due to cython"""
import pstats, cProfile
from numpy import sum
from numpy.random import rand, randint, seed
from optwok.testtools import list2matrix
def test_kernel(kfunc, X):
print '=== Test Kernel ==='
kg = kfunc()
print kg
kg.compute(X)
print kg.kmat
for ix in range(X.shape[1]):
for iy in range(X.shape[1]):
print '(%d,%d) %f' % (ix, iy, kg[ix,iy])
def test_joint_kernel(kfunc, X):
print '=== Test Joint Kernel ==='
joint = JointKernel(kfunc(), num_classes=2)
print joint
print joint.Kx
joint.compute(X)
print joint.kmat
for xi in range(X.shape[1]):
for yi in range(2):
for xj in range(X.shape[1]):
for yj in range(2):
print 'Kx(%d,%d),Ky(%d,%d): %f' % (xi,xj,yi,yj, joint[xi,xj,yi,yj])
def test_prediction(kfunc, Xtrain, Xtest):
print '=== Test prediction ==='
kg = kfunc()
print kg
kg.compute(Xtrain)
print kg.compute_test(Xtest)
joint = JointKernel(kfunc(), num_classes=2)
joint.compute(Xtrain)
print joint.compute_test(Xtest)
def demo_context_kernel(kfunc, X, Z):
print '=== Test Context Kernel ==='
from optwok.testtools import list2matrix
ck = ContextKernel(kfunc(), list2matrix([[0],[1,2]], noise=0.2))
print ck
print ck.Kx
ck.compute(X,Z)
print ck.kmat
def _test_linear_kernel(kernel):
X1 = rand(45,5000)
X2 = rand(45,12000)
kernel.compute(X1)
kernel.kmat
kernel.compute_test(X1)
kernel.compute_test(X2)
def test_linear_kernel():
from optwok.kernelbase import LinearKernel
kernel = LinearKernel()
_test_linear_kernel(kernel)
def test_linear_kernel_pp():
from optwok.kernel_pp import LinearKernel
kernel = LinearKernel()
_test_linear_kernel(kernel)
def profile_linear_kernel():
cProfile.runctx('test_linear_kernel_pp()', globals(), locals(), 'kernel_pp.prof')
cProfile.runctx('test_linear_kernel()', globals(), locals(), 'kernelbase.prof')
s = pstats.Stats('kernel_pp.prof')
s.strip_dirs().sort_stats('cumulative').print_stats(10)
f = pstats.Stats('kernelbase.prof')
f.strip_dirs().sort_stats('cumulative').print_stats(10)
def _test_context_kernel(kernel):
num_feat = 45
num1 = 1000
num2 = 2000
seed(0)
X1 = rand(num_feat, num1)
c1 = randint(3, size=num1)
X2 = rand(num_feat, num2)
c2 = randint(3, size=num2)
kernel.compute(X1, c1)
kernel.kmat
kernel.compute_test(X1, c1)
kernel.compute_test(X2, c2)
def test_context_kernel():
from optwok.kernelbase import LinearKernel, ContextKernel
ck = ContextKernel(LinearKernel(), list2matrix([[0],[1,2]], noise=0.1))
_test_context_kernel(ck)
def test_context_kernel_pp():
from optwok.kernel_pp import LinearKernel, ContextKernel
ck = ContextKernel(LinearKernel(), list2matrix([[0],[1,2]], noise=0.1))
_test_context_kernel(ck)
def profile_context_kernel():
cProfile.runctx('test_context_kernel_pp()', globals(), locals(), 'kernel_pp.prof')
cProfile.runctx('test_context_kernel()', globals(), locals(), 'kernelbase.prof')
s = pstats.Stats('kernel_pp.prof')
s.strip_dirs().sort_stats('cumulative').print_stats(10)
f = pstats.Stats('kernelbase.prof')
f.strip_dirs().sort_stats('cumulative').print_stats(10)
def test_all():
"""Execute all the functions"""
X = rand(2,3)
print X
demo_context_kernel(LinearKernel, X, [0,1,2])
test_kernel(LinearKernel, X)
test_kernel(GaussKernel, X)
test_joint_kernel(LinearKernel, X)
test_joint_kernel(GaussKernel, X)
Xtest = rand(2,1)
test_prediction(LinearKernel, X, Xtest)
test_prediction(GaussKernel, X, Xtest)
if __name__ == '__main__':
from optwok.kernel_pp import LinearKernel, ContextKernel
kernel = ContextKernel(LinearKernel(), list2matrix([[0],[1,2]], noise=0.9))
num_feat = 45
num1 = 1000
num2 = 2000
seed(0)
X1 = rand(num_feat, num1)
c1 = randint(2, size=num1)
X2 = rand(num_feat, num2)
c2 = randint(3, size=num2)
kernel.compute(X1, c1)
print sum(abs(kernel.kmat - kernel.kmat_forloop))
print sum(abs(kernel.compute_test(X2,c2) - kernel.compute_test_forloop(X2,c2)))
cProfile.runctx('kernel.kmat;kernel.compute_test(X2,c2)',
globals(), locals(), 'kmat.prof')
cProfile.runctx('kernel.kmat_forloop;kernel.compute_test_forloop(X2,c2)',
globals(), locals(), 'kmat_forloop.prof')
s = pstats.Stats('kmat.prof')
s.strip_dirs().sort_stats('cumulative').print_stats(10)
f = pstats.Stats('kmat_forloop.prof')
f.strip_dirs().sort_stats('cumulative').print_stats(10)
OptWok-0.3/tests/multiclass_test.py 0000644 ڛ |-00000000623 11731540564 024500 0 ustar cheng.ong UNIMELB\Domain Users 0000000 0000000 import unittest
from examples.multiclass_demo import demo_identity_Ky, demo_merged_classes, demo_split_classes
class Demo(unittest.TestCase):
"""Run the demo for multiclass SVM"""
def test_identity(self):
demo_identity_Ky()
def test_merged(self):
demo_merged_classes()
def test_split(self):
demo_split_classes()
if __name__ == '__main__':
unittest.main()
OptWok-0.3/tests/run_tests.py 0000644 ڛ |-00000000317 11731540564 023307 0 ustar cheng.ong UNIMELB\Domain Users 0000000 0000000 """
Use unittest to run all tests in optwok/test
"""
import unittest
import optwok.test.all_tests
testSuite = optwok.test.all_tests.create_test_suite()
text_runner = unittest.TextTestRunner().run(testSuite)