Use Heap to query multiples neighbours

kdtree/kdtree.pyx

@@ -9,6 +9,8 @@ from runtime.runtime cimport BatchMailBox, NullResult, Scheduler, WaitResult
 from libc.stdio cimport printf
 from libc.stdlib cimport malloc, free
 
+from cython.parallel import prange
+
 ## Types declaration
 ctypedef int I_t
 ctypedef double D_t
@@ -132,7 +134,7 @@ cdef cypclass Node activable:
     void query(self,
                 D_t * query_points,
                 I_t i,
-                active NeighborsHeap heap.
+                active NeighborsHeap heaps,
     ):
         cdef:
             I_t j, k, closest = -1
@@ -150,19 +152,17 @@ cdef cypclass Node activable:
                         )
                     dist += tmp * tmp
                 
-                heap.push
+                heaps.push(NULL, i, dist, j)
 
                 if dist < min_distance:
                     closest = j
                     min_distance = dist            
-            
-            container.update(NULL, i, closest)            
             return
 
-        if query_points[ i * self._n_dims +  self._dim] < self._point[self._dim]:
-            self._left.query(NULL, query_points, i, container)
-        else:
-            self._right.query(NULL, query_points, i, container)
+        # if query_points[ i * self._n_dims +  self._dim] < self._point[self._dim]:
+        self._left.query(NULL, query_points, i, heaps)
+        # else:
+        self._right.query(NULL, query_points, i, heaps)
 
 
 cdef cypclass Counter activable:
@@ -224,6 +224,88 @@ cdef inline void dual_swap(D_t* darr, I_t* iarr, I_t i1, I_t i2) nogil:
     iarr[i2] = itmp
 
 
+cdef void _simultaneous_sort(
+    D_t* dist,
+    I_t* idx,
+    I_t size
+) nogil:
+    """
+    Perform a recursive quicksort on the dist array, simultaneously
+    performing the same swaps on the idx array.  The equivalent in
+    numpy (though quite a bit slower) is
+
+    def simultaneous_sort(dist, idx):
+        i = np.argsort(dist)
+        return dist[i], idx[i]
+    """
+    cdef I_t pivot_idx, i, store_idx
+    cdef D_t pivot_val
+
+    # in the small-array case, do things efficiently
+    if size <= 1:
+        pass
+    elif size == 2:
+        if dist[0] > dist[1]:
+            dual_swap(dist, idx, 0, 1)
+    elif size == 3:
+        if dist[0] > dist[1]:
+            dual_swap(dist, idx, 0, 1)
+        if dist[1] > dist[2]:
+            dual_swap(dist, idx, 1, 2)
+            if dist[0] > dist[1]:
+                dual_swap(dist, idx, 0, 1)
+    else:
+        # Determine the pivot using the median-of-three rule.
+        # The smallest of the three is moved to the beginning of the array,
+        # the middle (the pivot value) is moved to the end, and the largest
+        # is moved to the pivot index.
+        pivot_idx = size // 2
+        if dist[0] > dist[size - 1]:
+            dual_swap(dist, idx, 0, size - 1)
+        if dist[size - 1] > dist[pivot_idx]:
+            dual_swap(dist, idx, size - 1, pivot_idx)
+            if dist[0] > dist[size - 1]:
+                dual_swap(dist, idx, 0, size - 1)
+        pivot_val = dist[size - 1]
+
+        # partition indices about pivot.  At the end of this operation,
+        # pivot_idx will contain the pivot value, everything to the left
+        # will be smaller, and everything to the right will be larger.
+        store_idx = 0
+        for i in range(size - 1):
+            if dist[i] < pivot_val:
+                dual_swap(dist, idx, i, store_idx)
+                store_idx += 1
+        dual_swap(dist, idx, store_idx, size - 1)
+        pivot_idx = store_idx
+
+        # recursively sort each side of the pivot
+        if pivot_idx > 1:
+            _simultaneous_sort(dist, idx, pivot_idx)
+        if pivot_idx + 2 < size:
+            _simultaneous_sort(dist + pivot_idx + 1,
+                                    idx + pivot_idx + 1,
+                                    size - pivot_idx - 1)
+
+
+cdef void _sort(
+    D_t* dist,
+    I_t* idx,
+    I_t n_rows,
+    I_t size,
+) nogil:
+    """simultaneously sort the distances and indices"""
+    cdef I_t row
+    for row in prange(n_rows,
+                      nogil=True,
+                      schedule="static",
+                      num_threads=4):
+        _simultaneous_sort(
+            dist + row * size,
+            idx + row * size,
+            size)
+
+
 cdef cypclass NeighborsHeap activable:
     """A max-heap structure to keep track of distances/indices of neighbors
 
@@ -247,25 +329,28 @@ cdef cypclass NeighborsHeap activable:
 
     I_t _n_pts
     I_t _n_nbrs
-
+    I_t _n_pushes
     bint _sorted
 
-    __init__(self, I_t n_pts, I_t n_nbrs):
+    __init__(self, I_t * indices, I_t n_pts, I_t n_nbrs):
         self._n_pts = n_pts
         self._n_nbrs = n_nbrs
         self._active_result_class = WaitResult.construct
         self._active_queue_class = consume BatchMailBox(scheduler)
         self._distances = <D_t *> malloc(n_pts * n_nbrs * sizeof(D_t))
-        self._indices = <I_t *> malloc(n_pts * n_nbrs * sizeof(I_t))
+        self._indices = indices
+        self._n_pushes = 0
+        self._sorted = False
 
     void __dealloc__(self):
-        free(self._indices)
         free(self._distances)
 
     void push(self, I_t row, D_t val, I_t i_val):
         """push (val, i_val) into the given row"""
         cdef I_t i, left_child_idx, right_child_idx, swap_idx
 
+        self._n_pushes += 1
+
         # check if val should be in heap
         if val > self._distances[0]:
             return
@@ -307,78 +392,17 @@ cdef cypclass NeighborsHeap activable:
         self._indices[i] = i_val
 
 
-    void _sort(self):
-        """simultaneously sort the distances and indices"""
-        cdef I_t row
-        for row in range(self._n_pts):
-            self._simultaneous_sort(
-                self._distances + row * self._n_nbrs,
-                self._indices + row * self._n_nbrs,
-                self._n_nbrs)
-
+    void  sort(self):
+        _sort(self._distances, self._indices,
+              self._n_pts, self._n_nbrs)
+        self._sorted = False
 
-    void _simultaneous_sort(
-        self,
-        D_t* dist,
-        I_t* idx,
-        I_t size):
-        """
-        Perform a recursive quicksort on the dist array, simultaneously
-        performing the same swaps on the idx array.  The equivalent in
-        numpy (though quite a bit slower) is
-
-        def simultaneous_sort(dist, idx):
-            i = np.argsort(dist)
-            return dist[i], idx[i]
-        """
-        cdef I_t pivot_idx, i, store_idx
-        cdef D_t pivot_val
-
-        # in the small-array case, do things efficiently
-        if size <= 1:
-            pass
-        elif size == 2:
-            if dist[0] > dist[1]:
-                dual_swap(dist, idx, 0, 1)
-        elif size == 3:
-            if dist[0] > dist[1]:
-                dual_swap(dist, idx, 0, 1)
-            if dist[1] > dist[2]:
-                dual_swap(dist, idx, 1, 2)
-                if dist[0] > dist[1]:
-                    dual_swap(dist, idx, 0, 1)
-        else:
-            # Determine the pivot using the median-of-three rule.
-            # The smallest of the three is moved to the beginning of the array,
-            # the middle (the pivot value) is moved to the end, and the largest
-            # is moved to the pivot index.
-            pivot_idx = size // 2
-            if dist[0] > dist[size - 1]:
-                dual_swap(dist, idx, 0, size - 1)
-            if dist[size - 1] > dist[pivot_idx]:
-                dual_swap(dist, idx, size - 1, pivot_idx)
-                if dist[0] > dist[size - 1]:
-                    dual_swap(dist, idx, 0, size - 1)
-            pivot_val = dist[size - 1]
+    int n_pushes(self):
+        return self._n_pushes
 
-            # partition indices about pivot.  At the end of this operation,
-            # pivot_idx will contain the pivot value, everything to the left
-            # will be smaller, and everything to the right will be larger.
-            store_idx = 0
-            for i in range(size - 1):
-                if dist[i] < pivot_val:
-                    dual_swap(dist, idx, i, store_idx)
-                    store_idx += 1
-            dual_swap(dist, idx, store_idx, size - 1)
-            pivot_idx = store_idx
+    int is_sorted(self):
+        return 1 if self._sorted else 0
 
-            # recursively sort each side of the pivot
-            if pivot_idx > 1:
-                self._simultaneous_sort(dist, idx, pivot_idx)
-            if pivot_idx + 2 < size:
-                self._simultaneous_sort(dist + pivot_idx + 1,
-                                        idx + pivot_idx + 1,
-                                        size - pivot_idx - 1)
 
 
 cdef cypclass KDTree:
@@ -466,18 +490,25 @@ cdef cypclass KDTree:
             I_t completed_queries = 0
             I_t i
             I_t n_query = query_points.shape[0]
-            I_t n_neighbors = query_points.shape[1]
-            active Container closests_container
+            I_t n_neighbors = closests.shape[1]
+            I_t total_n_pushes = n_query * self._n
+
+            active NeighborsHeap heaps
 
-        closests_container = consume Container(<I_t *> closests.data, n_query)
+        heaps = consume NeighborsHeap(<I_t *> closests.data,
+                                      n_query,
+                                      n_neighbors)
 
         for i in range(n_query):
-            self._root.query(NULL, 
-                                   <D_t *> query_points.data,
-                                   i, closests_container)
+            self._root.query(NULL, <D_t *> query_points.data, i, heaps)
+
+        while(completed_queries < total_n_pushes):
+            completed_queries = heaps.n_pushes(NULL).getIntResult()
+
+        # heaps.sort(NULL)
         
-        while(completed_queries < n_query):
-            completed_queries = closests_container.get_n_updates(NULL).getIntResult()
+        # while not(heaps.is_sorted(NULL).getIntResult()):
+        #     pass
 
 
 cdef public int main() nogil:

kdtree/query_poc.py

+import numpy as np
+import pytest
+import kdtree
+from sklearn.neighbors import KDTree
+
+if __name__ == '__main__':
+    
+    n = 1000
+    n_query = 100
+    d = 10
+    k = 10
+     
+    np.random.seed(1)
+    X = np.random.rand(n, d)
+    query_points = np.random.rand(n_query, d)
+
+    tree = kdtree.KDTree(X, leaf_size=256)
+    closests = np.zeros((n_query, k), dtype=np.int32)
+    
+    # There's currently a deadlock here
+    tree.query(query_points, closests)
\ No newline at end of file

kdtree/tests/test_conf.py

@@ -14,8 +14,8 @@ def test_against_sklearn(n, d, leaf_size):
     tree = kdtree.KDTree(X, leaf_size=256)
     skl_tree = KDTree(X, leaf_size=256)
     
-    closests = np.zeros((n), dtype=np.int32)
-    tree.get_closest(query_points, closests)
+    closests = np.zeros((n, 2), dtype=np.int32)
+    tree.query(query_points, closests)
     skl_closests = skl_tree.query(query_points, return_distance=False)
     
     # The back tracking part of the algorithm is not yet implemented