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bahsic.py
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bahsic.py
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# Copyright (c) 2006, National ICT Australia
# All rights reserved.
#
# The contents of this file are subject to the Mozilla Public License Version
# 1.1 (the 'License'); you may not use this file except in compliance with
# the License. You may obtain a copy of the License at
# http://www.mozilla.org/MPL/
#
# Software distributed under the License is distributed on an 'AS IS' basis,
# WITHOUT WARRANTY OF ANY KIND, either express or implied. See the License
# for the specific language governing rights and limitations under the
# License.
#
# Authors: Le Song ([email protected])
# Created: (20/10/2006)
# Last Updated: (dd/mm/yyyy)
#
##\package elefant.fselection.bahsic
# This module perform backward elimination for feature selection
# using HSIC (BAHSIC).
#
# The algorithm proceeds recursively, eliminating the least
# relevant features and adding them to the eliminated list
# in each iteration. For more theoretical underpinning see the
# following reference for more information:
#
# Le Song, Justin Bedo, Karsten M. Borgwardt, Arthur Gretton
# and Alex Smola. The BAHSIC family of gene selection algorithms.
#
__version__ = '$Revision: $'
# $Source$
import numpy
from scipy import optimize
import vector
from hsic import CHSIC
from setdiag0 import setdiag0
## Class that perform backward elimination for feature selection (BAHSIC).
#
# It has two version of BAHSIC: one without optimization over the kernel
# parameters and one with optimization over the kernel parameters.
#
class CBAHSIC(object):
def __init__(self):
pass
## BAHSIC with optimization over the kernel parameters.
# @param x The data.
# @param y The labels.
# @param kernelx The kernel on the data.
# @param kernely The kernel on the labels.
# @param flg3 The number of desired features.
# @param flg4 The proportion of features eleminated in each iteration.
#
def BAHSICOpt(self, x, y, kernelx, kernely, flg3, flg4):
assert len(x.shape) == 2, 'Argument 1 has wrong shape'
assert len(y.shape) == 2, 'Argument 2 has wrong shape'
assert x.shape[0] == y.shape[0], \
'Argument 1 and 2 have different number of data points'
print '--initializing...'
hsic = CHSIC()
L = kernely.Dot(y, y)
setdiag0(L)
sL = numpy.sum(L, axis=1)
ssL = numpy.sum(sL)
n = x.shape
eliminatedI = []
selectedI = set(numpy.arange(n[1]))
kernelx.CreateCacheKernel(x)
sga = kernelx._typicalParam
sgaN = sga.shape
sgaN = sgaN[0]
while True:
selectedI = selectedI - set(eliminatedI)
sI = numpy.array([j for j in selectedI])
m = len(sI)
print m
if (m == 1):
eliminatedI.append(selectedI.pop())
break
sgaMat = []
hsicMat = []
for k in range(sgaN):
## bfgs in scipy is not working here
retval = optimize.fmin_cg(hsic.ObjUnBiasedHSIC, \
sga[[k],].ravel(), \
hsic.GradUnBiasedHSIC,\
args=[x, kernelx, L, sL, ssL], \
gtol=1e-6, maxiter=100, \
full_output=True, disp=False)
sgaMat.append(retval[0])
hsicMat.append(retval[1])
k = numpy.argmin(hsicMat)
sga0 = sgaMat[k]
objj = []
for j in selectedI:
K = kernelx.DecDotCacheKernel(x, x[:,[j]], sga0)
setdiag0(K)
objj.append(hsic.UnBiasedHSICFast(K, L, sL, ssL))
if m > flg3:
maxj = numpy.argsort(objj)
num = int(flg4 * m)+1
if m - num <= flg3:
num = m - flg3
maxj = maxj[m:m-num-1:-1]
else:
maxj = numpy.array([numpy.argmax(objj)])
j = numpy.take(sI,maxj)
eliminatedI.extend(j)
kernelx.DecCacheKernel(x, x[:,j])
kernelx.ClearCacheKernel(x)
return eliminatedI
## BAHSIC without optimization over the kernel parameters.
# @param x The data.
# @param y The labels.
# @param kernelx The kernel on the data.
# @param kernely The kernel on the labels.
# @param flg3 The number of desired features.
# @param flg4 The proportion of features eleminated in each iteration.
#
def BAHSICRaw(self, x, y, kernelx, kernely, flg3, flg4):
assert len(x.shape) == 2, 'Argument 1 has wrong shape'
assert len(y.shape) == 2, 'Argument 2 has wrong shape'
assert x.shape[0] == y.shape[0], \
'Argument 1 and 2 have different number of data points'
print '--initializing...'
hsic = CHSIC()
L = kernely.Dot(y, y)
setdiag0(L)
sL = numpy.sum(L, axis=1)
ssL = numpy.sum(sL)
n = x.shape
eliminatedI = []
selectedI = set(numpy.arange(n[1]))
kernelx.CreateCacheKernel(x)
while True:
selectedI = selectedI - set(eliminatedI)
sI = numpy.array([j for j in selectedI])
m = len(sI)
print m
if (m == 1):
eliminatedI.append(selectedI.pop())
break
objj = []
for j in selectedI:
K = kernelx.DecDotCacheKernel(x, x[:,[j]])
setdiag0(K)
objj.append(hsic.UnBiasedHSICFast(K, L, sL, ssL))
if m > flg3:
maxj = numpy.argsort(objj)
num = int(flg4 * m)+1
if m-num <= flg3:
num = m - flg3
maxj = maxj[m:m-num-1:-1]
else:
maxj = numpy.array([numpy.argmax(objj)])
j = numpy.take(sI,maxj)
eliminatedI.extend(j)
kernelx.DecCacheKernel(x, x[:,j])
kernelx.ClearCacheKernel(x)
return eliminatedI