221 lines
6.7 KiB
Python
221 lines
6.7 KiB
Python
"""Matrix cross validation selection generators
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"""
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from scipy import take,arange,ceil,repeat,newaxis,mean,asarray,dot,ones,\
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random,array_split,floor,vstack,asarray,minimum
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from cx_utils import randperm
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def w_pls_gen(aat,b,n_blocks=None,center=True,index_out=False):
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"""Random block crossvalidation for wide (XX.T) trick in PLS.
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Leave-one-out is a subset, with n_blocks equals nSamples
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aat -- outerproduct of X
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b -- Y
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n_blocks =
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center -- use centering of calibration ,sets (aat_in,b_in) are centered
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Returns:
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-- aat_in,aat_out,b_in,b_out,[out]
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"""
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m,n = aat.shape
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index = randperm(m)
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nValuesInBlock = m/n_blocks
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if n_blocks==m:
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index = arange(m)
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out_ind = [index[i*nValuesInBlock:(i+1)*nValuesInBlock] for i in range(n_blocks)]
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for out in out_ind:
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inn = [i for i in index if i not in out]
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aat_in = aat[inn,:][:,inn]
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aat_out = aat[out,:][:,inn]
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b_in = b[inn,:]
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b_out = b[out,:]
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if center:
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aat_in, mn = outerprod_centering(aat_in)
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aat_out = aat_out - mn
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if index_out:
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yield aat_in,aat_out,b_in,b_out,out
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else:
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yield aat_in,aat_out,b_in,b_out
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def pls_gen(a, b, n_blocks=None, center=False, index_out=False,axis=0, metric=None):
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"""Random block crossvalidation
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Leave-one-out is a subset, with n_blocks equals a.shape[-1]
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"""
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index = randperm(a.shape[axis])
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if n_blocks==None:
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n_blocks = a.shape[axis]
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n_in_set = ceil(float(a.shape[axis])/n_blocks)
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out_ind_sets = [index[i*n_in_set:(i+1)*n_in_set] for i in range(n_blocks)]
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for out in out_ind_sets:
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inn = [i for i in index if i not in out]
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acal = a.take(inn, 0)
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atrue = a.take(out, 0)
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bcal = b.take(inn, 0)
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btrue = b.take(out, 0)
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if center:
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mn_a = acal.mean(0)[newaxis]
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acal = acal - mn_a
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atrue = atrue - mn_a
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mn_b = bcal.mean(0)[newaxis]
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bcal = bcal - mn_b
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btrue = btrue - mn_b
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if metric!=None:
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acal = dot(acal, metric)
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if index_out:
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yield acal, atrue, bcal, btrue, out
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else:
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yield acal, atrue, bcal, btrue
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def pca_gen(a, n_sets=None, center=False, index_out=False, axis=0):
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"""Returns a generator of crossvalidation sample segments.
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input:
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-- a, data matrix (m x n)
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-- n_sets, number of segments/subsets to generate.
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-- center, bool, choice of centering each subset
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-- index_out, bool, return subset index
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-- axis, int, which axis to get subset from
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ouput:
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-- V, generator with (n_sets) memebers (subsets)
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"""
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m = a.shape[axis]
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index = randperm(m)
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if n_sets==None:
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n_sets = m
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n_in_set = ceil(float(m)/n_sets)
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out_ind_sets = [index[i*n_in_set:(i+1)*n_in_set] for i in range(n_sets)]
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for out in out_ind_sets:
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inn = [i for i in index if i not in out]
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acal = a.take(inn, 0)
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atrue = a.take(out, 0)
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if center:
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mn_a = acal.mean(0)[newaxis]
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acal = acal - mn_a
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atrue = atrue - mn_a
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if index_out:
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yield acal, atrue, out
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else:
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yield acal, atrue
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def w_pls_gen_jk(a, b, n_sets=None, center=True,
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index_out=False, axis=0):
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"""Random block crossvalidation for wide X (m>>n)
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Leave-one-out is a subset, with n_sets equals a.shape[-1]
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Returns : X_m and X_m'Y_m
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"""
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m = a.shape[axis]
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ab = dot(a.T, b)
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index = randperm(m)
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if n_sets==None:
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n_sets = m
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n_in_set = ceil(float(m)/n_sets)
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out_ind_sets = [index[i*n_in_set:(i+1)*n_in_set] for i in range(n_sets)]
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for out in out_ind_sets:
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inn = [i for i in index if i not in out]
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nin = len(inn)
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nout = len(out)
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a_in = a[inn,:]
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mn_a = 0
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mAB = 0
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if center:
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mn_a = a_in.mean(0)[newaxis]
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mAin = dot(-ones((1,nout)), a[out,:])/nin
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mBin = dot(-ones((1,nout)), b[out,:])/nin
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mAB = dot(mAin.T, (mBin*nin))
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ab_in = ab - dot(a[out,].T, b[out,:]) - mAB
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a_in = a_in - mn_a
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if index_out:
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yield a_in, ab_in, out
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else:
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yield a_in, ab_in
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def shuffle_1d_block(a, n_sets=None, blocks=None, index_out=False, axis=0):
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"""Random block shuffling along 1d axis
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Returns : Shuffled a by axis
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"""
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m = a.shape[axis]
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if blocks==None:
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blocks = m
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for ii in xrange(n_sets):
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index = randperm(m)
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if blocks==m:
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a_out = a.take(index, axis)
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else:
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index = arange(m)
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dummy = map(random.shuffle, array_split(index, blocks))
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a_out = a.take(index, axis)
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if index_out:
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yield a_out, index
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else:
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yield a_out
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def shuffle_1d(a, n_sets, axis=0):
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"""Random shuffling along 1d axis.
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Returns : Shuffled a by axis
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"""
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m = a.shape[axis]
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for ii in xrange(n_sets):
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index = randperm(m)
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yield a.take(index, axis)
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def diag_pert(a, n_sets=10, center=True, index_out=False):
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"""Alter generator returning sets perturbed with means at diagonals.
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input:
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X -- matrix, data
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alpha -- scalar, approx. portion of data perturbed
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"""
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m, n = a.shape
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tr=False
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if m>n:
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a = a.T
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m, n = a.shape
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tr = True
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if n_sets>m or n_sets>n:
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msg = "You may not use more subsets than max(n_rows, n_cols)"
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raise ValueError, msg
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nm=n*m
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start_inds = array_split(randperm(m),n_sets) # we use random start diags
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if center:
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a = a - mean(a, 0)[newaxis]
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for v in range(n_sets):
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a_out = a.copy()
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out = []
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for start in start_inds[v]:
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ind = arange(start+v, nm, n+1)
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[out.append(i) for i in ind]
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if center:
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a_out.put(a.mean(),ind)
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else:
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a_out.put(0, ind)
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if tr:
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a_out = a_out.T
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if index_out:
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yield a_out, asarray(out)
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else:
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yield a_out
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def outerprod_centering(aat, ret_mn=True):
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"""Returns mean centered symmetric outerproduct matrix.
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"""
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n = aat.shape[0]
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h = aat.sum(0)[:,newaxis]
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h = (h - mean(h)/2)/n
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mn_a = h + h.T
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aatc = aat - mn_a
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if ret_mn:
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return aatc, aat.mean(0)
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return aat - mn_a
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