RF: Optimize ICC_rep_anova using a global cache

nipype/algorithms/icc.py

@@ -1,7 +1,7 @@
 # -*- coding: utf-8 -*-
 import os
 import numpy as np
-from numpy import ones, kron, mean, eye, hstack, dot, tile
+from numpy import ones, kron, mean, eye, hstack, dot, tile, nan_to_num
 from numpy.linalg import pinv
 import nibabel as nb
 from ..interfaces.base import (
@@ -86,20 +86,41 @@ def _list_outputs(self):
         return outputs
 
 
-def ICC_rep_anova(Y):
+def ICC_rep_anova(Y, nocache=False):
     """
     the data Y are entered as a 'table' ie subjects are in rows and repeated
     measures in columns
-
     One Sample Repeated measure ANOVA
-
     Y = XB + E with X = [FaTor / Subjects]
-    """
 
-    [nb_subjects, nb_conditions] = Y.shape
-    dfc = nb_conditions - 1
-    dfe = (nb_subjects - 1) * dfc
-    dfr = nb_subjects - 1
+    This is a hacked up (but fully compatible) version of ICC_rep_anova
+    from nipype that caches some very expensive operations that depend
+    only on the input array shape - if you're going to run the routine
+    multiple times (like, on every voxel of an image), this gives you a
+    HUGE speed boost for large input arrays.  If you change the dimensions
+    of Y, it will reinitialize automatially.  Set nocache to True to get
+    the original, much slower behavior.  No, actually, don't do that.
+    """
+    global icc_inited
+    global current_Y_shape
+    global dfc, dfe, dfr
+    global nb_subjects, nb_conditions
+    global x, x0, X
+    global centerbit
+
+    try:
+        current_Y_shape
+        if nocache or (current_Y_shape != Y.shape):
+            icc_inited = False
+    except NameError:
+        icc_inited = False
+
+    if not icc_inited:
+        [nb_subjects, nb_conditions] = Y.shape
+        current_Y_shape = Y.shape
+        dfc = nb_conditions - 1
+        dfe = (nb_subjects - 1) * dfc
+        dfr = nb_subjects - 1
 
     # Compute the repeated measure effect
     # ------------------------------------
@@ -109,20 +130,22 @@ def ICC_rep_anova(Y):
     SST = ((Y - mean_Y) ** 2).sum()
 
     # create the design matrix for the different levels
-    x = kron(eye(nb_conditions), ones((nb_subjects, 1)))  # sessions
-    x0 = tile(eye(nb_subjects), (nb_conditions, 1))  # subjects
-    X = hstack([x, x0])
+    if not icc_inited:
+        x = kron(eye(nb_conditions), ones((nb_subjects, 1)))  # sessions
+        x0 = tile(eye(nb_subjects), (nb_conditions, 1))  # subjects
+        X = hstack([x, x0])
+        centerbit = dot(dot(X, pinv(dot(X.T, X))), X.T)
 
     # Sum Square Error
-    predicted_Y = dot(dot(dot(X, pinv(dot(X.T, X))), X.T), Y.flatten("F"))
+    predicted_Y = dot(centerbit, Y.flatten("F"))
     residuals = Y.flatten("F") - predicted_Y
     SSE = (residuals ** 2).sum()
 
     residuals.shape = Y.shape
 
     MSE = SSE / dfe
 
-    # Sum square session effect - between colums/sessions
+    # Sum square session effect - between columns/sessions
     SSC = ((mean(Y, 0) - mean_Y) ** 2).sum() * nb_subjects
     MSC = SSC / dfc / nb_subjects
 
@@ -134,9 +157,11 @@ def ICC_rep_anova(Y):
 
     # ICC(3,1) = (mean square subjeT - mean square error) /
     #            (mean square subjeT + (k-1)*-mean square error)
-    ICC = (MSR - MSE) / (MSR + dfc * MSE)
+    ICC = nan_to_num((MSR - MSE) / (MSR + dfc * MSE))
 
     e_var = MSE  # variance of error
     r_var = (MSR - MSE) / nb_conditions  # variance between subjects
 
+    icc_inited = True
+
     return ICC, r_var, e_var, session_effect_F, dfc, dfe