ii

pyblm/crossvalidation.py

@@ -12,7 +12,7 @@ from numpy.random import shuffle
 from engines import pls, pca
 from engines import nipals_lpls as lpls
 
-def pca_val(a, a_max, nsets=None, center_axis=[0]):
+def pca_val(a, a_max, nsets=None, center_axis=[0], method='cv'):
     """Returns error estimate of crossvalidated PCA.
     
     *Parameters*:
@@ -25,6 +25,7 @@ def pca_val(a, a_max, nsets=None, center_axis=[0]):
             Centering
         nsets : {integer}
             number of segments
+        method : {['cv', 'diag']}
     
     *Returns*:
 
@@ -37,8 +38,14 @@ def pca_val(a, a_max, nsets=None, center_axis=[0]):
     
     *Notes*:
 
+        - Crossvalidation of PCA is somewhat artificial as we do not have
+        any external information to predict. There are essentially two approaches
+        to this, one is to use a projection error, the other is to leave out
+        elements in the data matrix then record a missing-value estimator error.
+
         - Crossvalidation method is currently set to random blocks of diagonals.
 
+        
     """
     n, m = a.shape
     if nsets == None:
@@ -47,12 +54,13 @@ def pca_val(a, a_max, nsets=None, center_axis=[0]):
     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)
-    mn_a = .5*(a.mean(0) + a.mean(1)[:,newaxis])
 
+    if method == 'diag':
+        mn_a = .5*(a.mean(0) + a.mean(1)[:,newaxis])
         for i, val in enumerate(diag_cv(a.shape, nsets)):
             old_values = a.take(val)
             new_values = mn_a.take(val)
-        # impute mean values
+            # impute with mean values
             b = a.copy()
             a.put(val, new_values)
             dat = pca(a, a_max, mode='normal', center_axis=center_axis)
@@ -60,16 +68,30 @@ def pca_val(a, a_max, nsets=None, center_axis=[0]):
             bc = b - dat['mnx']
             bc2 = b - b.mean(0)
             for j in xrange(a_max):
-            # predict the imputed values    
+                # 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)
                 xhat[j,:,:].put(val, a_pred)
-        
             # put original values back
             a.put(val, old_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']
+            e = eye(m)
+            rmat = zeros((m, m))
+            for j, p in enumerate(P.T):
+                d2 = diag(e) - (p**2).ravel()
+                e = e - dot(p, p.T)
+                d = diag(e)
+                es = e/atleast_2d(d)
+                xhat[j,:,:] = dot(xval, es)
+                err[i, a] = (dot(xval, es)**2).sum()
+                
     rmsep = sqrt(err).mean(1) # take mean over samples
     rmsep2 = sqrt(err_mn).mean(1)
     aopt = rmsep.mean(-1).argmin()
@@ -120,8 +142,8 @@ def pls_val(X, Y, a_max=2, nsets=None, center_axis=[0,0], verbose=False):
     if nsets > X.shape[0]:
         print "nsets (%d) is larger than number of variables (%d).\nnsets:  %d -> %d" %(nsets, m, nsets, m)
         nsets = m
-    if m > n:
-        # kernel boosting
+    if n > 5*m:
+        # boosting (wide x)
         Yhat = _w_pls_predict(X, Y, a_max)
         
     Yhat = empty((a_max, k, l), dtype=dt)
@@ -259,7 +281,7 @@ def pca_jk(a, aopt, nsets=None, center_axis=[0], method='cv'):
 
     *Notes*:
 
-        - Crossvalidation method is currently set to random blocks of samples.
+        - .
 
     """
     m, n = a.shape
@@ -518,7 +540,7 @@ def diag_cv(shape, nsets=9, randomise=True):
     if nsets>m or nsets>n:
         msg = "You may not use more subsets than max(n_rows, n_cols)"
         raise ValueError, msg
-         msg = "You may not use more subsets than max(n_rows, n_cols)"
+         
     nsets = min(m, n)
     nm = n*m
     index = arange(nm)
@@ -533,20 +555,6 @@ def diag_cv(shape, nsets=9, randomise=True):
         #training = [j for j in index if j not in validation]
         yield list(validation)
 
-def class_error(y_hat, y, method='vanilla'):
-    """ Not used.
-    """
-    a_opt, k, l = y_hat.shape
-    y_hat_c = zeros((k, l), dtype='d')
-    if method == vanilla:
-        pass
-    for a in range(a_opt):
-        for i in range(k):
-            y_hat_c[a, val, argmax(y_hat[a,val,:])] = 1.0
-    err = 100*((y_hat_c + y) == 2).sum(1)/y.sum(0).astype('d')
-
-    return y_hat_c, err
-
 def prediction_error(y_hat, y, method='squared'):
     """Loss function on multiclass Y.
 

pyblm/statistics.py

@@ -84,7 +84,8 @@ def hotelling(Pcv, P, p_center='median', cov_center='median',
 
     if cov_center == 'median':
         Cov_p = median(Cov_i)
-    else cov_center == 'mean':
+    else:
+        # cov_center == 'mean'
         Cov_p = Cov.mean(0)
     reg_cov = (1. - alpha)*Cov_i + alpha*Cov_p
     for i in xrange(n):