ups
This commit is contained in:
parent
c58f0ef453
commit
7e61e585fc
@ -6,10 +6,10 @@ __all__ = ['pca_val', 'pls_val', 'lpls_val', 'pls_jk', 'lpls_jk']
|
||||
__docformat__ = "restructuredtext en"
|
||||
|
||||
from numpy import dot,empty,zeros,sqrt,atleast_2d,argmax,asarray,median,\
|
||||
array_split,arange, isnan, any,newaxis
|
||||
array_split,arange, isnan, any,newaxis,eye,diag,tile,ones
|
||||
from numpy.random import shuffle
|
||||
|
||||
from engines import pls, pca
|
||||
from engines import pls, pca, center
|
||||
from engines import nipals_lpls as lpls
|
||||
|
||||
def pca_val(a, a_max, nsets=None, center_axis=[0], method='cv'):
|
||||
@ -33,8 +33,7 @@ def pca_val(a, a_max, nsets=None, center_axis=[0], method='cv'):
|
||||
Squared error of prediction for each component and xvar (a_max, m)
|
||||
xhat : {array}
|
||||
Crossvalidated predicted a (a_max, m, n)
|
||||
aopt : {integer}
|
||||
Estimate of optimal number of components
|
||||
aopt : {integer} Estimate of optimal number of components
|
||||
|
||||
*Notes*:
|
||||
|
||||
@ -50,59 +49,63 @@ def pca_val(a, a_max, nsets=None, center_axis=[0], method='cv'):
|
||||
n, m = a.shape
|
||||
if nsets == None:
|
||||
nsets = n
|
||||
|
||||
err = zeros((a_max, n, m), dtype=a.dtype)
|
||||
err_mn = zeros((a_max, n, m), dtype=a.dtype)
|
||||
xhat = zeros((a_max, n, m), dtype=a.dtype)
|
||||
|
||||
err = zeros((a_max + 1, n, m), dtype=a.dtype)
|
||||
xhat = zeros((a_max + 1, n, m), dtype=a.dtype)
|
||||
if center_axis[0] == 1:
|
||||
a = a - a.mean(1)[:,newaxis]
|
||||
center_axis = [-1]
|
||||
elif center_axis[0] == 2:
|
||||
a = a - a.mean(1)[:,newaxis]
|
||||
center_axis = [0]
|
||||
if method == 'diag':
|
||||
mn_a = .5*(a.mean(0) + a.mean(1)[:,newaxis])
|
||||
mn_a = dot(ones((n,1)), a.mean(0)[newaxis])
|
||||
for i, val in enumerate(diag_cv(a.shape, nsets)):
|
||||
old_values = a.take(val)
|
||||
true_values = a.take(val)
|
||||
new_values = mn_a.take(val)
|
||||
# impute with mean values
|
||||
b = a.copy()
|
||||
a.put(val, new_values)
|
||||
dat = pca(a, a_max, mode='fast', center_axis=center_axis)
|
||||
Ti, Pi = dat['T'], dat['P']
|
||||
bc = b - dat['mnx']
|
||||
bc2 = b - b.mean(0)
|
||||
Ti, Pi, mnx = dat['T'], dat['P'], dat['mnx']
|
||||
# expand mean values
|
||||
if center_axis[0] == 1:
|
||||
mnx = tile(mnx, (1, m))
|
||||
else:
|
||||
mnx = tile(mnx, (n, 1))
|
||||
err[0,:,:].put(val, (true_values - mnx.take(val))**2)
|
||||
|
||||
for j in xrange(a_max):
|
||||
# estimate the imputed values
|
||||
a_pred = dot(Ti[:,:j+1], Pi[:,:j+1].T).take(val)
|
||||
a_true = bc2.take(val)
|
||||
err[j,:,:].put(val, (a_true - a_pred)**2)
|
||||
err_mn[j,:,:].put(val, (bc.take(val) - a_pred)**2)
|
||||
a_pred = (mnx + dot(Ti[:,:j+1], Pi[:,:j+1].T)).take(val)
|
||||
err[j+1,:,:].put(val, (true_values - a_pred)**2)
|
||||
xhat[j,:,:].put(val, a_pred)
|
||||
# put original values back
|
||||
a.put(val, old_values)
|
||||
a.put(val, true_values)
|
||||
|
||||
elif method == 'cv':
|
||||
for i, (cal, val) in enumerate(cv(n, nsets)):
|
||||
xval = atleast_2d(x[val,:])
|
||||
xcal = x[cal, :]
|
||||
P = pca(xcal, aopt, mode='fast', scale='scores')['P']
|
||||
xcal = a[cal, :]
|
||||
dat = pca(xcal, a_max, mode='fast', scale='scores', center_axis=center_axis)
|
||||
P, mnx = dat['P'], dat['mnx']
|
||||
xval = atleast_2d(a[val,:]) - mnx
|
||||
err[0,val,:] = xval**2 #pc0
|
||||
e = eye(m)
|
||||
rmat = zeros((m, m))
|
||||
for j, p in enumerate(P.T):
|
||||
#d2 = diag(e) - (p**2).ravel()
|
||||
p = p[:,newaxis]
|
||||
e = e - dot(p, p.T)
|
||||
d = diag(e)
|
||||
es = e/atleast_2d(d)
|
||||
xhat[j,cal,:] = dot(xval, es)
|
||||
err[j,cal,:] = (xhat - xval)**2
|
||||
xhat[j+1,val,:] = dot(xval, es)
|
||||
err[j+1,val,:] = (dot(xval, es)**2)
|
||||
|
||||
rmsep = sqrt(err).mean(1) # take mean over samples
|
||||
if method == ''
|
||||
rmsep2 = sqrt(err_mn).mean(1)
|
||||
aopt = rmsep.mean(-1).argmin()
|
||||
return rmsep, xhat, aopt, err, rmsep2
|
||||
return rmsep, xhat, aopt, err
|
||||
|
||||
def pls_val(X, Y, a_max=2, nsets=None, center_axis=[0,0], verbose=False):
|
||||
"""Performs crossvalidation for generalisation error in pls.
|
||||
|
||||
|
||||
|
||||
*Parameters*:
|
||||
|
||||
X : {array}
|
||||
@ -137,7 +140,6 @@ def pls_val(X, Y, a_max=2, nsets=None, center_axis=[0,0], verbose=False):
|
||||
m, n = X.shape
|
||||
k, l = Y.shape
|
||||
assert m == k, "X (%d,%d) - Y (%d,%d) dim mismatch" %(m, n, k, l)
|
||||
assert n == p, "X (%d,%d) - Z (%d,%d) dim mismatch" %(m, n, o, p)
|
||||
if nsets == None:
|
||||
nsets = m
|
||||
if nsets > X.shape[0]:
|
||||
@ -150,7 +152,7 @@ def pls_val(X, Y, a_max=2, nsets=None, center_axis=[0,0], verbose=False):
|
||||
Yhat = empty((a_max, k, l), dtype=dt)
|
||||
for cal, val in cv(k, nsets):
|
||||
# do the training model
|
||||
dat = pls(X[cal], Y[cal], a_max=a_max,center_axis=center_axis)
|
||||
dat = pls(X[cal], Y[cal], aopt=a_max,center_axis=center_axis)
|
||||
|
||||
# center test data
|
||||
xi = X[val,:] - dat['mnx']
|
||||
@ -165,7 +167,7 @@ def pls_val(X, Y, a_max=2, nsets=None, center_axis=[0,0], verbose=False):
|
||||
#aopt = find_aopt_from_sep(rmsep)
|
||||
|
||||
# todo: need a better support for classification error
|
||||
error = prediction_error(Yhat, Y, method='1/2')
|
||||
error = prediction_error(Yhat, Y, method='squared')
|
||||
|
||||
return rmsep, Yhat, error
|
||||
|
||||
@ -282,8 +284,7 @@ def pca_jk(a, aopt, nsets=None, center_axis=[0], method='cv'):
|
||||
|
||||
*Notes*:
|
||||
|
||||
- .
|
||||
|
||||
Nope
|
||||
"""
|
||||
m, n = a.shape
|
||||
if nsets == None:
|
||||
@ -302,7 +303,6 @@ def pca_jk(a, aopt, nsets=None, center_axis=[0], method='cv'):
|
||||
# put original values back
|
||||
a.put(val, old_values)
|
||||
elif method == 'cv':
|
||||
print "using ....cv "
|
||||
for i, (cal, val) in enumerate(cv(m, nsets)):
|
||||
Pcv[i,:,:] = pca(a[cal,:], aopt, mode='fast', scale='loads', center_axis = center_axis)['P']
|
||||
else:
|
||||
@ -624,7 +624,7 @@ def prediction_error(y_hat, y, method='squared'):
|
||||
return error
|
||||
|
||||
def _wkernel_pls_val(X, Y, a_max, n_blocks=None):
|
||||
"""Returns rmsep and aopt for pls tailored for wide X.
|
||||
"""Returns pls crossvalidated predictions tailored for wide X.
|
||||
|
||||
The error of cross validation is calculated
|
||||
based on random block cross-validation. With number of blocks equal to
|
||||
@ -672,19 +672,21 @@ def _wkernel_pls_val(X, Y, a_max, n_blocks=None):
|
||||
[4,5,6], 1
|
||||
"""
|
||||
dt = X.dtype
|
||||
k, l = m_shape(Y)
|
||||
PRESS = zeros((l, a_max+1), dtype=dt)
|
||||
k, l = atleast_2d(Y).shape
|
||||
if k == 1:
|
||||
Y = Y.T
|
||||
k, l = Y.shape
|
||||
PRESS = zeros((l, a_max + 1), dtype=dt)
|
||||
if n_blocks==None:
|
||||
n_blocks = Y.shape[0]
|
||||
XXt = dot(X, X.T)
|
||||
V = w_pls_gen(XXt, Y, n_blocks=n_blocks, center=True)
|
||||
for Din, Doi, Yin, Yout in V:
|
||||
ym = -sum(Yout, 0)[newaxis]/(1.0*Yin.shape[0])
|
||||
PRESS[:,0] = PRESS[:,0] + ((Yout - ym)**2).sum(0)
|
||||
for cal, val in cv(k, n_blocks):
|
||||
ym = -sum(Y[val,:], 0)[newaxis]/(1.0*Y[cal,:].shape[0])
|
||||
PRESS[:,0] = PRESS[:,0] + ((Y[val,:] - ym)**2).sum(0)
|
||||
|
||||
dat = w_simpls(Din, Yin, a_max)
|
||||
dat = w_simpls(XX[cal,:][:,cal], Y[cal,:], a_max)
|
||||
Q, U, H = dat['Q'], dat['U'], dat['H']
|
||||
That = dot(Doi, dot(U, inv(triu(dot(H.T, U))) ))
|
||||
That = dot(XX[val,:][:,cal], dot(U, inv(triu(dot(H.T, U))) ))
|
||||
|
||||
Yhat = zeros((a_max, k, l), dtype=dt)
|
||||
for j in range(l):
|
||||
|
||||
@ -10,8 +10,10 @@ from math import sqrt as msqrt
|
||||
from numpy import dot,empty,zeros,apply_along_axis,newaxis,finfo,sqrt,r_,expand_dims,\
|
||||
minimum,any,isnan,ones,tile
|
||||
from numpy.linalg import inv,svd
|
||||
from scipy.sandbox import arpack
|
||||
|
||||
try:
|
||||
from scipy.sandbox import arpack
|
||||
except:
|
||||
import arpack
|
||||
|
||||
def pca(X, aopt, scale='scores', mode='normal', center_axis=[0]):
|
||||
""" Principal Component Analysis.
|
||||
@ -718,9 +720,10 @@ def center(a, axis):
|
||||
mn = a.mean(1)[:,newaxis]
|
||||
#mn = tile(mn, (1, a.shape[1]))
|
||||
elif axis == 2:
|
||||
#fixme: double centering returns column mean as loc-vector, ok?
|
||||
mn = a.mean(0)[newaxis] + a.mean(1)[:,newaxis] - a.mean()
|
||||
return a - mn , mn
|
||||
# double centering returns row vec
|
||||
# to get correct broadcasting in cv
|
||||
mn = mn[0][newaxis]
|
||||
else:
|
||||
raise IOError("input error: axis must be in [-1,0,1,2]")
|
||||
|
||||
|
||||
Reference in New Issue
Block a user