ENH: Single pass, stable roll_corr

Uses a Welford-like method for general computations, and includes
detection of exact zeros in the denominator and of exactly identical
sequences.

Author: jaimefrio

Diff for: pandas/algos.pyx

@@ -1253,6 +1253,166 @@ def nancorr_spearman(ndarray[float64_t, ndim=2] mat, Py_ssize_t minp=1):
     return result
 
 
+#----------------------------------------------------------------------
+# Rolling correlation
+
+def roll_corr(ndarray[double_t] x, ndarray[double_t] y, int win, int minp,
+             int ddof=1):
+    """
+    Numerically stable implementation using a Welford-like method, and
+    detection of exactly matching sequences and exactly zero denominators.
+    """
+    cdef double val_x, val_y, prev_x, prev_y, rep_x = NaN, rep_y = NaN
+    cdef double mean_x = 0, mean_y = 0, ssqdm_x = 0, ssqdm_y = 0
+    cdef double sproddm_xy = 0, delta_x, delta_y
+    cdef Py_ssize_t nobs = 0, nrep_x = 0, nrep_y = 0, ndup = 0, i, N = len(x)
+    cdef bint val_not_nan, prev_not_nan
+
+    cdef ndarray[double_t] output = np.empty(N, dtype=float)
+
+    minp = _check_minp(win, minp, N)
+
+    for i from 0 <= i < N:
+        val_x = x[i]
+        val_y = y[i]
+        val_not_nan = val_x == val_x and val_y == val_y
+        if i < win:
+            prev_x = prev_y = NaN
+            prev_not_nan = 0
+        else:
+            prev_x = x[i - win]
+            prev_y = y[i - win]
+            prev_not_nan = prev_x == prev_x and prev_y == prev_y
+
+        # First, count the number of observations and consecutive repeats
+        if prev_not_nan:
+            # prev is not NaN, removing an observation...
+            if nobs == nrep_x:
+                # ...and removing a repeat from x
+                nrep_x -= 1
+                if nrep_x == 0:
+                    rep_x = NaN
+            if nobs == nrep_y:
+                # ...and removing a repeat from y
+                nrep_y -= 1
+                if nrep_y == 0:
+                    rep_y = NaN
+            if nobs == ndup:
+                # ...and removing a duplicate
+                ndup -= 1
+            nobs -= 1
+        if val_not_nan:
+            # val is not NaN, adding an observation
+            if val_x == rep_x:
+                # ...and adding a repeat to x
+                nrep_x += 1
+            else:
+                # ...and resetting x repeats
+                nrep_x = 1
+                rep_x = val_x
+            if val_y == rep_y:
+                # ...and adding a repeat to y
+                nrep_y += 1
+            else:
+                # ...and resetting y repeats
+                nrep_y = 1
+                rep_y = val_y
+            if val_x == val_y:
+                # ...and adding a duplicate
+                ndup += 1
+            else:
+                # ...and resetting duplicates
+                ndup = 0
+            nobs += 1
+
+        # Then, compute the new mean, sums of squared differences from the
+        # mean and sum of product of differences from the mean
+        if nobs == nrep_x and nobs == nrep_y:
+            if nobs > 0:
+                mean_x = rep_x
+                mean_y = rep_y
+            else:
+                mean_x = mean_y = 0
+            ssqdm_x = ssqdm_y = sproddm_xy = 0
+        elif val_not_nan:
+            # Adding one observation...
+            if prev_not_nan:
+                # ...and removing another
+                delta_x = val_x - prev_x
+                prev_x -= mean_x
+                if nobs == nrep_x:
+                    mean_x = rep_x
+                    val_x = 0
+                    ssqdm_x = 0
+                else:
+                    mean_x += delta_x / nobs
+                    val_x -= mean_x
+                    ssqdm_x += (val_x + prev_x) * delta_x
+                delta_y = val_y - prev_y
+                prev_y -= mean_y
+                if nobs == nrep_y:
+                    mean_y = rep_y
+                    val_y = 0
+                    ssqdm_y = 0
+                else:
+                    mean_y += delta_y / nobs
+                    val_y -= mean_y
+                    ssqdm_y += (val_y + prev_y) * delta_y
+                sproddm_xy += (delta_x * (val_y + prev_y) +
+                               delta_y * (val_x + prev_x)) * 0.5
+            else:
+                # ...and not removing any
+                if nobs == nrep_x:
+                    delta_x = 0
+                    mean_x = rep_x
+                    ssqdm_x = 0
+                else:
+                    delta_x = val_x - mean_x
+                    mean_x += delta_x / nobs
+                    ssqdm_x += delta_x * (val_x - mean_x)
+                if nobs == nrep_y:
+                    delta_y = 0
+                    mean_y = rep_y
+                    ssqdm_y = 0
+                else:
+                    delta_y = val_y - mean_y
+                    mean_y += delta_y / nobs
+                    ssqdm_y += delta_y * (val_y - mean_y)
+                sproddm_xy += ((val_x - mean_x) * delta_y +
+                               (val_y - mean_y) * delta_x) * 0.5
+        elif prev_not_nan:
+            # Adding no new observation, but removing one
+            if nobs == nrep_x:
+                delta_x = 0
+                mean_x = rep_x
+                ssqdm_x = 0
+            else:
+                delta_x = prev_x - mean_x
+                mean_x -= delta_x / nobs
+                ssqdm_x -= delta_x * (prev_x - mean_x)
+            if nobs == nrep_y:
+                delta_y = 0
+                mean_y = rep_y
+                ssqdm_y = 0
+            else:
+                delta_y = prev_y - mean_y
+                mean_y -= delta_y / nobs
+                ssqdm_y -= delta_y * (prev_y - mean_y)
+            sproddm_xy -= ((prev_x - mean_x) * delta_y +
+                           (prev_y - mean_y) * delta_x) * 0.5
+        # Correlation is unchanged if no observation is added or removed
+
+        # Finally, compute and write the rolling correlation to output
+        if nobs < minp or nobs < ddof or ssqdm_x <= 0 or ssqdm_y <= 0:
+            output[i] = NaN
+        elif nobs == ndup:
+            output[i] = 1.0
+        else:
+            output[i] = sproddm_xy / sqrt(ssqdm_x * ssqdm_y)
+
+    return output
+
+
 #----------------------------------------------------------------------
 # Rolling covariance
 
@@ -1261,8 +1421,8 @@ def roll_cov(ndarray[double_t] x, ndarray[double_t] y, int win, int minp,
     """
     Numerically stable implementation using a Welford-like method.
     """
-    cdef double val_x, val_y, prev_x, prev_y, delta_x, delta_y, temp_x, temp_y
-    cdef double mean_x = 0, mean_y = 0, proddm_xy = 0
+    cdef double val_x, val_y, prev_x, prev_y, delta_x, delta_y
+    cdef double mean_x = 0, mean_y = 0, sproddm_xy = 0
     cdef Py_ssize_t nobs = 0, i, N = len(x)
     cdef bint val_not_nan, prev_not_nan
 
@@ -1294,16 +1454,17 @@ def roll_cov(ndarray[double_t] x, ndarray[double_t] y, int win, int minp,
                 prev_y -= mean_y
                 mean_y += delta_y / nobs
                 val_y -= mean_y
-                proddm_xy += (delta_x * (val_y + prev_y) + delta_y * (val_x + prev_x)) / 2
+                sproddm_xy += (delta_x * (val_y + prev_y) +
+                               delta_y * (val_x + prev_x)) * 0.5
             else:
                 # ...and not removing any
                 nobs += 1
                 delta_x = val_x - mean_x
                 mean_x += delta_x / nobs
                 delta_y = val_y - mean_y
                 mean_y += delta_y / nobs
-                proddm_xy += ((val_x - mean_x) * delta_y +
-                              (val_y - mean_y) * delta_x) * 0.5
+                sproddm_xy += ((val_x - mean_x) * delta_y +
+                               (val_y - mean_y) * delta_x) * 0.5
         elif prev_not_nan:
             # Adding no new observation, but removing one
             nobs -= 1
@@ -1312,15 +1473,15 @@ def roll_cov(ndarray[double_t] x, ndarray[double_t] y, int win, int minp,
                 mean_x -= delta_x / nobs
                 delta_y = prev_y - mean_y
                 mean_y -= delta_y / nobs
-                proddm_xy -= ((prev_x - mean_x) * delta_y +
-                              (prev_y - mean_y) * delta_x) * 0.5
+                sproddm_xy -= ((prev_x - mean_x) * delta_y +
+                               (prev_y - mean_y) * delta_x) * 0.5
             else:
-                mean_x = mean_y = proddm_xy = 0
+                mean_x = mean_y = sproddm_xy = 0
         # Covariance is unchanged if no observation is added or removed
 
         # Finally, compute and write the rolling covariance to output
         if nobs >= minp and nobs > ddof:
-            output[i] = proddm_xy / (nobs - ddof)
+            output[i] = sproddm_xy / (nobs - ddof)
         else:
             output[i] = NaN
 
@@ -1334,7 +1495,7 @@ def roll_var(ndarray[double_t] input, int win, int minp, int ddof=1):
     """
     Numerically stable implementation using Welford's method.
     """
-    cdef double val, prev, mean_x = 0, ssqdm_x = 0, nobs = 0, delta, out
+    cdef double val, prev, mean_x = 0, ssqdm_x = 0, nobs = 0, delta
     cdef Py_ssize_t i
     cdef Py_ssize_t N = len(input)
 
@@ -1350,7 +1511,7 @@ def roll_var(ndarray[double_t] input, int win, int minp, int ddof=1):
             # Adding one observation...
             if prev == prev:
                 # ...and removing another
-                delta  val - prev
+                delta = val - prev
                 prev -= mean_x
                 mean_x += delta / nobs
                 val -= mean_x
@@ -1362,7 +1523,7 @@ def roll_var(ndarray[double_t] input, int win, int minp, int ddof=1):
                 mean_x += delta / nobs
                 ssqdm_x += delta * (val - mean_x)
         elif prev == prev:
-            # adding no new observation, but removing one
+            # Adding no new observation, but removing one
             nobs -= 1
             if nobs:
                 delta = prev - mean_x
@@ -1372,13 +1533,14 @@ def roll_var(ndarray[double_t] input, int win, int minp, int ddof=1):
                 mean_x = ssqdm_x = 0
         # Variance is unchanged if no observation is added or removed
 
+        # Sum of squared differences from the mean must be non-negative
+        ssqdm_x = 0 if ssqdm_x < 0 else ssqdm_x
+
+        # Finally, compute and write the rolling variance to output
         if nobs >= minp and nobs > ddof:
-            out = ssqdm_x / (nobs - ddof)
-            out = 0 if out < 0 else out
+            output[i] = ssqdm_x / (nobs - ddof)
         else:
-            out = NaN
-
-        output[i] = out
+            output[i] = NaN
 
     return output