Use Base.Ordering for heap, and other performance improvements

.travis.yml

@@ -4,7 +4,6 @@ os:
   - linux
   - osx
 julia:
-  - 0.7
   - 1.0
   - nightly
 notifications:

Project.toml

@@ -3,10 +3,12 @@ uuid = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8"
 version = "0.18.0-DEV"
 
 [deps]
+Compat = "34da2185-b29b-5c13-b0c7-acf172513d20"
 InteractiveUtils = "b77e0a4c-d291-57a0-90e8-8db25a27a240"
 OrderedCollections = "bac558e1-5e72-5ebc-8fee-abe8a469f55d"
 
 [compat]
+Compat = "3.0.0"
 OrderedCollections = "1.1.0"
 julia = "1"
 

appveyor.yml

@@ -1,6 +1,6 @@
 environment:
   matrix:
-  - julia_version: 0.7
+  - julia_version: 1.0
   - julia_version: latest
 
 platform:

benchmark/bench_heap.jl

@@ -26,13 +26,12 @@ heaptypes = [BinaryHeap, MutableBinaryHeap]
 aexps = [1,3]
 datatypes = [Int, Float64]
 baseorderings = Dict(
-    "Min" => DataStructures.LessThan,
-    #"Max" => DataStructures.GreaterThan,
+    "Min" => Base.ForwardOrdering,
+    #"Max" => Base.ReverseOrdering,
     )
 fastfloatorderings = Dict(
-    # These will be enabled upon reordering change
-    #"FastMin" => DataStructures.FasterForward(),
-    #"FastMax" => DataStructures.FasterReverse(),
+    "Min" => DataStructures.FasterForward,
+    "Max" => DataStructures.FasterReverse,
     )
 
 for heap in heaptypes
@@ -41,7 +40,8 @@ for heap in heaptypes
             Random.seed!(0)
             a = rand(dt, 10^aexp)
 
-            orderings = baseorderings
+            # Dict types to force use of abstract type if containing single value
+            orderings = Dict{String, DataType}(baseorderings)
             if dt == Float64
                 # swap to faster ordering operation
                 for (k,v) in orderings
@@ -66,38 +66,22 @@ for heap in heaptypes
     end
 end
 
-# Quick check to ensure no Float regressions with Min/Max convenience functions
-# These don't fit in well with the above loop, since ordering is hardcoded.
-heapalias = Dict(
-    "BinaryMinHeap" => BinaryMinHeap,
-    "BinaryMaxHeap" => BinaryMaxHeap,
-    "BinaryMinMaxHeap" => BinaryMinMaxHeap, # <- no alias issue
-)
-for (heapname, heap) in heapalias
-    for aexp in aexps
-        for dt in [Float64]
-            Random.seed!(0)
-            a = rand(dt, 10^aexp)
-            prepath = [heapname]
-            postpath = [string(dt), "10^"*string(aexp)]
-            suite[vcat(prepath, ["make"], postpath)] =
-                @benchmarkable $(heap)($a)
-            suite[vcat(prepath, ["push"], postpath)] =
-                @benchmarkable push_heap(h, $a) setup=(h=$(heap){$dt}())
-            suite[vcat(prepath, ["pop"], postpath)] =
-                @benchmarkable pop_heap(h) setup=(h=$(heap)($a))
-        end
-    end
-end
+fast_extreme_orderings = Dict(
+    nsmallest => DataStructures.FasterForward(),
+    nlargest => DataStructures.FasterReverse(),
+    )
 
 for func in [nlargest, nsmallest]
+    fastord = fast_extreme_orderings[func]
     for aexp in [4]
         Random.seed!(0);
         a = rand(10^aexp);
         for nexp in [2]
             n = 10^nexp
-            suite[[string(func), "a=rand(10^"*string(aexp)*")", "n=10^"*string(nexp)]] =
+            suite[["Slow " * string(func), "a=rand(10^"*string(aexp)*")", "n=10^"*string(nexp)]] =
                 @benchmarkable $(func)($n, $a)
+            suite[[string(func), "a=rand(10^"*string(aexp)*")", "n=10^"*string(nexp)]] =
+                @benchmarkable DataStructures.nextreme($fastord, $n, $a)
         end
     end
 end

docs/src/heaps.md

@@ -7,25 +7,32 @@ All heaps in this package are derived from `AbstractHeap`, and provide
 the following interface:
 
 ```julia
-# Let h be a heap, i be a handle, and v be a value.
+# Let `h` be a heap, `v` be a value, and `n` be an integer size
 
-length(h)         # returns the number of elements
+length(h)            # returns the number of elements
 
-isempty(h)        # returns whether the heap is empty
+isempty(h)           # returns whether the heap is empty
 
-push!(h, v)       # add a value to the heap
+push!(h, v)          # add a value to the heap
 
-first(h)            # return the first (top) value of a heap
+first(h)             # return the first (top) value of a heap
 
-pop!(h)           # removes the first (top) value, and returns it
+pop!(h)              # removes the first (top) value, and returns it
 
+extract_all!(h)      # removes all elements and returns sorted array
+
+extract_all_rev!(h)  # removes all elements and returns reverse sorted array
+
+sizehint!(h, n)      # reserve capacity for at least `n` elements
 ```
 
 Mutable heaps (values can be changed after being pushed to a heap) are
 derived from `AbstractMutableHeap <: AbstractHeap`, and additionally
 provides the following interface:
 
 ```julia
+# Let `h` be a heap, `i` be a handle, and `v` be a value.
+
 i = push!(h, v)              # adds a value to the heap and and returns a handle to v
 
 update!(h, i, v)             # updates the value of an element (referred to by the handle i)
@@ -54,6 +61,21 @@ h = MutableBinaryMinHeap([1,4,3,2])
 h = MutableBinaryMaxHeap([1,4,3,2])    # create a mutable min/max heap from a vector
 ```
 
+Heaps may be constructed with a custom ordering. One use case for custom orderings
+is to achieve faster performance with `Float` elements with the risk of random ordering
+if any elements are `NaN`. The provided `DataStructures.FasterForward` and
+`DataStructures.FasterReverse` orderings are optimized for this purpose.
+Custom orderings may also be used for defining the order of structs as heap elements.
+```julia
+h = BinaryHeap{Float64, DataStructures.FasterForward}() # faster min heap
+h = BinaryHeap{Float64, DataStructures.FasterReverse}() # faster max heap
+
+h = MutableBinaryHeap{Float64, DataStructures.FasterForward}() # faster mutable min heap
+h = MutableBinaryHeap{Float64, DataStructures.FasterReverse}() # faster mutable max heap
+
+h = BinaryHeap{MyStruct, MyStructOrdering}() # heap containing custom struct
+```
+
 ## Min-max heaps
 Min-max heaps maintain the minimum _and_ the maximum of a set,
 allowing both to be retrieved in constant (`O(1)`) time.
@@ -97,5 +119,9 @@ nlargest(3, [0,21,-12,68,-25,14]) # => [68,21,14]
 nsmallest(3, [0,21,-12,68,-25,14]) # => [-25,-12,0]
 ```
 
-`nlargest(n, a)` is equivalent to `sort(a, lt = >)[1:min(n, end)]`, and
-`nsmallest(n, a)` is equivalent to `sort(a, lt = <)[1:min(n, end)]`.
+Note that if the array contains floats and is free of NaN values,
+then the following alternatives may be used to achieve a 2x performance boost.
+```
+DataStructures.nextreme(DataStructures.FasterReverse(), n, a) # faster nlargest(n, a)
+DataStructures.nextreme(DataStructures.FasterForward(), n, a) # faster nsmallest(n, a)
+```

src/DataStructures.jl

@@ -15,6 +15,7 @@ module DataStructures
                  zero, checkbounds
 
 
+    using Compat # Provides Base.Order.ReverseOrdering(). May remove this line with julia 1.4
     using OrderedCollections
     import OrderedCollections: filter, filter!, isordered
     export OrderedDict, OrderedSet, LittleDict
@@ -30,7 +31,7 @@ module DataStructures
     export IntDisjointSets, DisjointSets, num_groups, find_root!, in_same_set, root_union!
     export FenwickTree, length, inc!, dec!, incdec!, prefixsum
 
-    export AbstractHeap, compare, extract_all!
+    export AbstractHeap, compare, extract_all!, extract_all_rev!
     export BinaryHeap, BinaryMinHeap, BinaryMaxHeap, nlargest, nsmallest
     export MutableBinaryHeap, MutableBinaryMinHeap, MutableBinaryMaxHeap
     export heapify!, heapify, heappop!, heappush!, isheap

src/heaps.jl

@@ -24,7 +24,7 @@
 #
 #   - sizehint!(h, s)     set size hint to a heap
 #
-#   - first(h)              return the first (top) value of a heap
+#   - first(h)            return the first (top) value of a heap
 #
 #   - pop!(h)             removes the first (top) value, and
 #                         returns it
@@ -37,7 +37,7 @@
 #   - update!(h, i, v)    updates the value of an element
 #                         (referred to by the handle i)
 #
-#   - delete!(h, i)      deletes the node with
+#   - delete!(h, i)       deletes the node with
 #                         handle i from the heap
 #
 #   - top_with_handle(h)  return the top value of a heap
@@ -55,28 +55,24 @@ abstract type AbstractMutableHeap{VT,HT} <: AbstractHeap{VT} end
 
 abstract type AbstractMinMaxHeap{VT} <: AbstractHeap{VT} end
 
-# comparer
-
-struct LessThan
-end
-
-struct GreaterThan
-end
-
-compare(c::LessThan, x, y) = x < y
-compare(c::GreaterThan, x, y) = x > y
-
 # heap implementations
 
 include("heaps/binary_heap.jl")
 include("heaps/mutable_binary_heap.jl")
-include("heaps/arrays_as_heaps.jl")
 include("heaps/minmax_heap.jl")
 
 # generic functions
 
 Base.eltype(::Type{<:AbstractHeap{T}}) where T = T
 
+"""
+    extract_all!(h)
+
+Return an array of heap elements in sorted order (heap head at first index).
+
+Note that for simple heaps (not mutable or minmax)
+sorting the internal array of elements in-place is faster.
+"""
 function extract_all!(h::AbstractHeap{VT}) where VT
     n = length(h)
     r = Vector{VT}(undef, n)
@@ -86,6 +82,14 @@ function extract_all!(h::AbstractHeap{VT}) where VT
     return r
 end
 
+"""
+    extract_all_rev!(h)
+
+Return an array of heap elements in reverse sorted order (heap head at last index).
+
+Note that for simple heaps (not mutable or minmax)
+sorting the internal array of elements in-place is faster.
+"""
 function extract_all_rev!(h::AbstractHeap{VT}) where VT
     n = length(h)
     r = Vector{VT}(undef, n)
@@ -97,50 +101,65 @@ end
 
 # Array functions using heaps
 
-function nextreme(comp::Comp, n::Int, arr::AbstractVector{T}) where {T, Comp}
+"""
+    nextreme(ord, n, arr)
+
+return an array of the first `n` values of `arr` sorted by `ord`.
+"""
+function nextreme(ord::Base.Ordering, n::Int, arr::AbstractVector{T}) where T
     if n <= 0
         return T[] # sort(arr)[1:n] returns [] for n <= 0
     elseif n >= length(arr)
-        return sort(arr, lt = (x, y) -> compare(comp, y, x))
+        return sort(arr, order = ord)
     end
 
-    buffer = BinaryHeap{T,Comp}()
+    rev = Base.ReverseOrdering(ord)
 
-    for i = 1 : n
-        @inbounds xi = arr[i]
-        push!(buffer, xi)
-    end
+    buffer = heapify(arr[1:n], rev)
 
     for i = n + 1 : length(arr)
         @inbounds xi = arr[i]
-        if compare(comp, first(buffer), xi)
-            # This could use a pushpop method
-            pop!(buffer)
-            push!(buffer, xi)
+        if Base.lt(rev, buffer[1], xi)
+            buffer[1] = xi
+            percolate_down!(buffer, 1, rev)
         end
     end
 
-    return extract_all_rev!(buffer)
+    return sort!(buffer, order = ord)
 end
 
 """
     nlargest(n, arr)
 
 Return the `n` largest elements of the array `arr`.
 
-Equivalent to `sort(arr, lt = >)[1:min(n, end)]`
+Equivalent to:
+    sort(arr, order = Base.Reverse)[1:min(n, end)]
+
+Note that if `arr` contains floats and is free of NaN values,
+then the following alternative may be used to achieve 2x performance.
+    DataStructures.nextreme(DataStructures.FasterReverse(), n, arr)
+This faster version is equivalent to:
+    sort(arr, lt = >)[1:min(n, end)]
 """
-function nlargest(n::Int, arr::AbstractVector{T}) where T
-    return nextreme(LessThan(), n, arr)
+function nlargest(n::Int, arr::AbstractVector)
+    return nextreme(Base.Reverse, n, arr)
 end
 
 """
     nsmallest(n, arr)
 
 Return the `n` smallest elements of the array `arr`.
 
-Equivalent to `sort(arr, lt = <)[1:min(n, end)]`
+Equivalent to:
+    sort(arr, order = Base.Forward)[1:min(n, end)]
+
+Note that if `arr` contains floats and is free of NaN values,
+then the following alternative may be used to achieve 2x performance.
+    DataStructures.nextreme(DataStructures.FasterForward(), n, arr)
+This faster version is equivalent to:
+    sort(arr, lt = <)[1:min(n, end)]
 """
-function nsmallest(n::Int, arr::AbstractVector{T}) where T
-    return nextreme(GreaterThan(), n, arr)
+function nsmallest(n::Int, arr::AbstractVector)
+    return nextreme(Base.Forward, n, arr)
 end

src/heaps/arrays_as_heaps.jl

@@ -44,7 +44,7 @@ function percolate_up!(xs::AbstractArray, i::Integer, x=xs[i], o::Ordering=Forwa
     xs[i] = x
 end
 
-percolate_up!(xs::AbstractArray{T}, i::Integer, o::Ordering) where {T} = percolate_up!(xs, i, xs[i], o)
+percolate_up!(xs::AbstractArray, i::Integer, o::Ordering) = percolate_up!(xs, i, xs[i], o)
 
 """
     heappop!(v, [ord])
@@ -69,12 +69,12 @@ For efficiency, this function does not check that the array is indeed heap-order
 """
 function heappush!(xs::AbstractArray, x, o::Ordering=Forward)
     push!(xs, x)
-    percolate_up!(xs, length(xs), x, o)
+    percolate_up!(xs, length(xs), o)
     return xs
 end
 
 
-# Turn an arbitrary array into a binary min-heap in linear time.
+# Turn an arbitrary array into a binary min-heap (by default) in linear time.
 """
     heapify!(v, ord::Ordering=Forward)
 
@@ -111,6 +111,7 @@ julia> heapify(a, Base.Order.Reverse)
  2
 ```
 """
+# Todo, benchmarking shows copy(xs) outperforms copyto!(similar(xs), xs) for 10^6 Float64
 heapify(xs::AbstractArray, o::Ordering=Forward) = heapify!(copyto!(similar(xs), xs), o)
 
 """