ENH: use bottleneck moving max/min impl for perf improvement close #1504

, #50

RELEASE.rst

@@ -38,6 +38,8 @@ pandas 0.8.1
 
 **Improvements to existing features**
 
+  - Use moving min/max algorithms from Bottleneck in rolling_min/rolling_max
+    for > 100x speedup. (#1504, #50)
   - Drastically improve ``to_datetime`` performance on ISO8601 datetime strings
     (with no time zones) (#1571)
   - Add ability to append hierarchical index levels with ``set_index`` and to

pandas/src/moments.pyx

@@ -613,12 +613,100 @@ cdef double_t _get_median(object sl, int nobs, int minp):
     else:
         return NaN
 
+#----------------------------------------------------------------------
+
+# Moving maximum / minimum code taken from Bottleneck under the terms
+# of its Simplified BSD license
+# https://github.com/kwgoodman/bottleneck
+
+cdef struct pairs:
+    double value
+    int death
+
+from libc cimport stdlib
+
+@cython.boundscheck(False)
+@cython.wraparound(False)
+def roll_max2(ndarray[float64_t] a, int window, int minp):
+    "Moving max of 1d array of dtype=float64 along axis=0 ignoring NaNs."
+    cdef np.float64_t ai, aold
+    cdef Py_ssize_t count
+    cdef pairs* ring
+    cdef pairs* minpair
+    cdef pairs* end
+    cdef pairs* last
+    cdef Py_ssize_t i0
+    cdef np.npy_intp *dim
+    dim = PyArray_DIMS(a)
+    cdef Py_ssize_t n0 = dim[0]
+    cdef np.npy_intp *dims = [n0]
+    cdef np.ndarray[np.float64_t, ndim=1] y = PyArray_EMPTY(1, dims,
+		NPY_float64, 0)
+
+    minp = _check_minp(minp, n0)
+
+    if (window < 1) or (window > n0):
+        raise ValueError('Invalid window size %d for len %d array'
+                         % (window, n0))
+
+    ring = <pairs*>stdlib.malloc(window * sizeof(pairs))
+    end = ring + window
+    last = ring
+
+    minpair = ring
+    ai = a[0]
+    if ai == ai:
+        minpair.value = ai
+    else:
+        minpair.value = MINfloat64
+    minpair.death = window
+
+    count = 0
+    for i0 in range(n0):
+        ai = a[i0]
+        if ai == ai:
+            count += 1
+        else:
+            ai = MINfloat64
+        if i0 >= window:
+            aold = a[i0 - window]
+            if aold == aold:
+                count -= 1
+        if minpair.death == i0:
+            minpair += 1
+            if minpair >= end:
+                minpair = ring
+        if ai >= minpair.value:
+            minpair.value = ai
+            minpair.death = i0 + window
+            last = minpair
+        else:
+            while last.value <= ai:
+                if last == ring:
+                    last = end
+                last -= 1
+            last += 1
+            if last == end:
+                last = ring
+            last.value = ai
+            last.death = i0 + window
+        if count >= minp:
+            y[i0] = minpair.value
+        else:
+            y[i0] = NaN
+    for i0 in range(window - 1):
+        y[i0] = NaN
+
+    stdlib.free(ring)
+    return y
+
 def roll_max(ndarray input, int win, int minp):
     '''
     O(N log(window)) implementation using skip list
     '''
     return _roll_skiplist_op(input, win, minp, _get_max)
 
+
 cdef double_t _get_max(object skiplist, int nobs, int minp):
     if nobs >= minp:
         return <IndexableSkiplist> skiplist.get(nobs - 1)
@@ -631,6 +719,80 @@ def roll_min(ndarray input, int win, int minp):
     '''
     return _roll_skiplist_op(input, win, minp, _get_min)
 
+@cython.boundscheck(False)
+@cython.wraparound(False)
+def roll_min2(np.ndarray[np.float64_t, ndim=1] a, int window, int minp):
+    "Moving min of 1d array of dtype=float64 along axis=0 ignoring NaNs."
+    cdef np.float64_t ai, aold
+    cdef Py_ssize_t count
+    cdef pairs* ring
+    cdef pairs* minpair
+    cdef pairs* end
+    cdef pairs* last
+    cdef Py_ssize_t i0
+    cdef np.npy_intp *dim
+    dim = PyArray_DIMS(a)
+    cdef Py_ssize_t n0 = dim[0]
+    cdef np.npy_intp *dims = [n0]
+    cdef np.ndarray[np.float64_t, ndim=1] y = PyArray_EMPTY(1, dims,
+		NPY_float64, 0)
+    if (window < 1) or (window > n0):
+        raise ValueError('Invalid window size %d for len %d array'
+                         % (window, n0))
+
+    minp = _check_minp(minp, n0)
+
+    ring = <pairs*>stdlib.malloc(window * sizeof(pairs))
+    end = ring + window
+    last = ring
+
+    minpair = ring
+    ai = a[0]
+    if ai == ai:
+        minpair.value = ai
+    else:
+        minpair.value = MAXfloat64
+    minpair.death = window
+
+    count = 0
+    for i0 in range(n0):
+        ai = a[i0]
+        if ai == ai:
+            count += 1
+        else:
+            ai = MAXfloat64
+        if i0 >= window:
+            aold = a[i0 - window]
+            if aold == aold:
+                count -= 1
+        if minpair.death == i0:
+            minpair += 1
+            if minpair >= end:
+                minpair = ring
+        if ai <= minpair.value:
+            minpair.value = ai
+            minpair.death = i0 + window
+            last = minpair
+        else:
+            while last.value >= ai:
+                if last == ring:
+                    last = end
+                last -= 1
+            last += 1
+            if last == end:
+                last = ring
+            last.value = ai
+            last.death = i0 + window
+        if count >= minp:
+            y[i0] = minpair.value
+        else:
+            y[i0] = NaN
+    for i0 in range(window - 1):
+        y[i0] = NaN
+
+    stdlib.free(ring)
+    return y
+
 cdef double_t _get_min(object skiplist, int nobs, int minp):
     if nobs >= minp:
         return <IndexableSkiplist> skiplist.get(0)

pandas/stats/moments.py

@@ -395,8 +395,8 @@ def call_cython(arg, window, minp, **kwds):
 
     return f
 
-rolling_max = _rolling_func(_tseries.roll_max, 'Moving maximum')
-rolling_min = _rolling_func(_tseries.roll_min, 'Moving minimum')
+rolling_max = _rolling_func(_tseries.roll_max2, 'Moving maximum')
+rolling_min = _rolling_func(_tseries.roll_min2, 'Moving minimum')
 rolling_sum = _rolling_func(_tseries.roll_sum, 'Moving sum')
 rolling_mean = _rolling_func(_tseries.roll_mean, 'Moving mean')
 rolling_median = _rolling_func(_tseries.roll_median_cython, 'Moving median')