simetrica - ¿Calcular eficientemente la intersección de dos conjuntos en Java?

programa en c++ de operaciones de conjuntos

Question

¿Cuál es la forma más eficiente de encontrar el tamaño de la intersección de dos conjuntos no dispersos en Java? Esta es una operación que realizaré en grandes series una gran cantidad de veces, por lo que la optimización es importante. No puedo modificar los conjuntos originales.

He observado Apache Commons CollectionUtils.intersection, que parece ser bastante lento. Mi enfoque actual es tomar el más pequeño de los dos conjuntos, clonarlo y luego llamar a .retainAll en el mayor de los dos conjuntos.

public static int getIntersection(Set<Long> set1, Set<Long> set2) {
boolean set1IsLarger = set1.size() > set2.size();
Set<Long> cloneSet = new HashSet<Long>(set1IsLarger ? set2 : set1);
cloneSet.retainAll(set1IsLarger ? set1 : set2);
return cloneSet.size();
}

Answer 1

¿Pueden los miembros de los conjuntos mapearse fácilmente en un rango relativamente pequeño de números enteros? Si es así, considere usar BitSets. La intersección es solo bitwise y es: 32 miembros potenciales a la vez.

Answer 2

Ejecute algunas pruebas con el enfoque publicado y contra la construcción de un nuevo HashSet. Es decir, deje que A sea ​​el más pequeño de los conjuntos y B el más grande y luego, para cada ítem en A , si también existe en B, luego agréguelo a C (un nuevo HashSet) - para solo contar, el intermedio El conjunto C se puede omitir.

Al igual que el enfoque publicado, este debería ser un costo O(|A|) ya que la iteración es O(|A|) y la sonda en B es O(1) . No tengo idea de cómo se comparará con el enfoque de clonar y eliminar.

Feliz codificación - y publicar algunos resultados ;-)

En realidad, al seguir pensando, creo que esto tiene límites ligeramente mejores que el método en la publicación: O(|A|) frente a O(|A| + |B|) . No tengo idea si esto hará alguna diferencia (o mejora) en la realidad y solo esperaría que fuera relevante cuando |A| <<< |B| |A| <<< |B| .

De acuerdo, entonces estaba realmente aburrido. Al menos en JDK 7 (Windows 7 x64), parece que el método presentado en la publicación es más lento que el anterior , por un factor bueno (aunque aparentemente constante). Mi cálculo aproximado de eyeball dice que es cuatro veces más lento que la sugerencia anterior que solo usa un contador y el doble de lento que cuando se crea un nuevo HashSet. Esto parece ser "bastante consistente" en los diferentes tamaños de conjuntos iniciales.

( Tenga en cuenta que, como señaló Voo, los números anteriores y este micro-benchmark suponen que se está utilizando un HashSet! Y, como siempre, existen peligros con los micro-puntos de referencia. YMMV.)

Estos son los resultados desagradables (tiempos en milisegundos):

Running tests for 1x1 IntersectTest$PostMethod@6cc2060e took 13.9808544 count=1000000 IntersectTest$MyMethod1@7d38847d took 2.9893732 count=1000000 IntersectTest$MyMethod2@9826ac5 took 7.775945 count=1000000 Running tests for 1x10 IntersectTest$PostMethod@67fc9fee took 12.4647712 count=734000 IntersectTest$MyMethod1@7a67f797 took 3.1567252 count=734000 IntersectTest$MyMethod2@3fb01949 took 6.483941 count=734000 Running tests for 1x100 IntersectTest$PostMethod@16675039 took 11.3069326 count=706000 IntersectTest$MyMethod1@58c3d9ac took 2.3482693 count=706000 IntersectTest$MyMethod2@2207d8bb took 4.8687103 count=706000 Running tests for 1x1000 IntersectTest$PostMethod@33d626a4 took 10.28656 count=729000 IntersectTest$MyMethod1@3082f392 took 2.3478658 count=729000 IntersectTest$MyMethod2@65450f1f took 4.109205 count=729000 Running tests for 10x2 IntersectTest$PostMethod@55c4d594 took 10.4137618 count=736000 IntersectTest$MyMethod1@6da21389 took 2.374206 count=736000 IntersectTest$MyMethod2@2bb0bf9a took 4.9802039 count=736000 Running tests for 10x10 IntersectTest$PostMethod@7930ebb took 25.811083 count=4370000 IntersectTest$MyMethod1@47ac1adf took 6.9409306 count=4370000 IntersectTest$MyMethod2@74184b3b took 14.2603248 count=4370000 Running tests for 10x100 IntersectTest$PostMethod@7f423820 took 25.0577691 count=4251000 IntersectTest$MyMethod1@5472fe25 took 6.1376042 count=4251000 IntersectTest$MyMethod2@498b5a73 took 13.9880385 count=4251000 Running tests for 10x1000 IntersectTest$PostMethod@3033b503 took 25.0312716 count=4138000 IntersectTest$MyMethod1@12b0f0ae took 6.0932898 count=4138000 IntersectTest$MyMethod2@1e893918 took 13.8332505 count=4138000 Running tests for 100x1 IntersectTest$PostMethod@6366de01 took 9.4531628 count=700000 IntersectTest$MyMethod1@767946a2 took 2.4284762 count=700000 IntersectTest$MyMethod2@140c7272 took 4.7580235 count=700000 Running tests for 100x10 IntersectTest$PostMethod@3351e824 took 24.9788668 count=4192000 IntersectTest$MyMethod1@465fadce took 6.1462852 count=4192000 IntersectTest$MyMethod2@338bd37a took 13.1742654 count=4192000 Running tests for 100x100 IntersectTest$PostMethod@297630d5 took 193.0121077 count=41047000 IntersectTest$MyMethod1@e800537 took 45.2652397 count=41047000 IntersectTest$MyMethod2@76d66550 took 120.8494766 count=41047000 Running tests for 100x1000 IntersectTest$PostMethod@33576738 took 199.6269531 count=40966000 IntersectTest$MyMethod1@2f39a7dd took 45.5255814 count=40966000 IntersectTest$MyMethod2@723bb663 took 122.1704975 count=40966000 Running tests for 1x1 IntersectTest$PostMethod@35e3bdb5 took 9.5598373 count=1000000 IntersectTest$MyMethod1@7abbd1b6 took 2.6359174 count=1000000 IntersectTest$MyMethod2@40c542ad took 6.1091794 count=1000000 Running tests for 1x10 IntersectTest$PostMethod@3c33a0c5 took 9.4648528 count=733000 IntersectTest$MyMethod1@61800463 took 2.302116 count=733000 IntersectTest$MyMethod2@1ba03197 took 5.4803628 count=733000 Running tests for 1x100 IntersectTest$PostMethod@71b8da5 took 9.4971057 count=719000 IntersectTest$MyMethod1@21f04f48 took 2.2983538 count=719000 IntersectTest$MyMethod2@27e51160 took 5.3926902 count=719000 Running tests for 1x1000 IntersectTest$PostMethod@2fe7106a took 9.4702331 count=692000 IntersectTest$MyMethod1@6ae6b7b7 took 2.3013066 count=692000 IntersectTest$MyMethod2@51278635 took 5.4488882 count=692000 Running tests for 10x2 IntersectTest$PostMethod@223b2d85 took 9.5660879 count=743000 IntersectTest$MyMethod1@5b298851 took 2.3481445 count=743000 IntersectTest$MyMethod2@3b4ac99 took 4.8268489 count=743000 Running tests for 10x10 IntersectTest$PostMethod@51c700a0 took 23.0709476 count=4326000 IntersectTest$MyMethod1@5ffa3251 took 5.5460785 count=4326000 IntersectTest$MyMethod2@22fd9511 took 13.4853948 count=4326000 Running tests for 10x100 IntersectTest$PostMethod@46b49793 took 25.1295491 count=4256000 IntersectTest$MyMethod1@7a4b5828 took 5.8520418 count=4256000 IntersectTest$MyMethod2@6888e8d1 took 14.0856942 count=4256000 Running tests for 10x1000 IntersectTest$PostMethod@5339af0d took 25.1752685 count=4158000 IntersectTest$MyMethod1@7013a92a took 5.7978328 count=4158000 IntersectTest$MyMethod2@1ac73de2 took 13.8914112 count=4158000 Running tests for 100x1 IntersectTest$PostMethod@3d1344c8 took 9.5123442 count=717000 IntersectTest$MyMethod1@3c08c5cb took 2.34665 count=717000 IntersectTest$MyMethod2@63f1b137 took 4.907277 count=717000 Running tests for 100x10 IntersectTest$PostMethod@71695341 took 24.9830339 count=4180000 IntersectTest$MyMethod1@39d90a92 took 5.8467864 count=4180000 IntersectTest$MyMethod2@584514e9 took 13.2197964 count=4180000 Running tests for 100x100 IntersectTest$PostMethod@21b8dc91 took 195.1796213 count=41060000 IntersectTest$MyMethod1@6f98c4e2 took 44.5775162 count=41060000 IntersectTest$MyMethod2@16a60aab took 121.1754402 count=41060000 Running tests for 100x1000 IntersectTest$PostMethod@20b37d62 took 200.973133 count=40940000 IntersectTest$MyMethod1@67ecbdb3 took 45.4832226 count=40940000 IntersectTest$MyMethod2@679a6812 took 121.791293 count=40940000 Running tests for 1x1 IntersectTest$PostMethod@237aa07b took 9.2210288 count=1000000 IntersectTest$MyMethod1@47bdfd6f took 2.3394042 count=1000000 IntersectTest$MyMethod2@a49a735 took 6.1688936 count=1000000 Running tests for 1x10 IntersectTest$PostMethod@2b25ffba took 9.4103967 count=736000 IntersectTest$MyMethod1@4bb82277 took 2.2976994 count=736000 IntersectTest$MyMethod2@25ded977 took 5.3310813 count=736000 Running tests for 1x100 IntersectTest$PostMethod@7154a6d5 took 9.3818786 count=704000 IntersectTest$MyMethod1@6c952413 took 2.3014931 count=704000 IntersectTest$MyMethod2@33739316 took 5.3307998 count=704000 Running tests for 1x1000 IntersectTest$PostMethod@58334198 took 9.3831841 count=736000 IntersectTest$MyMethod1@d178f65 took 2.3071236 count=736000 IntersectTest$MyMethod2@5c7369a took 5.4062184 count=736000 Running tests for 10x2 IntersectTest$PostMethod@7c4bdf7c took 9.4040537 count=735000 IntersectTest$MyMethod1@593d85a4 took 2.3584088 count=735000 IntersectTest$MyMethod2@5610ffc1 took 4.8318229 count=735000 Running tests for 10x10 IntersectTest$PostMethod@46bd9fb8 took 23.004925 count=4331000 IntersectTest$MyMethod1@4b410d50 took 5.5678172 count=4331000 IntersectTest$MyMethod2@1bd125c9 took 14.6517184 count=4331000 Running tests for 10x100 IntersectTest$PostMethod@75d6ecff took 25.0114913 count=4223000 IntersectTest$MyMethod1@716195c9 took 5.798676 count=4223000 IntersectTest$MyMethod2@3db0f946 took 13.8064737 count=4223000 Running tests for 10x1000 IntersectTest$PostMethod@761d8e2a took 25.1910652 count=4292000 IntersectTest$MyMethod1@e60a3fb took 5.8621189 count=4292000 IntersectTest$MyMethod2@6aadbb1c took 13.8150282 count=4292000 Running tests for 100x1 IntersectTest$PostMethod@48a50a6e took 9.4141906 count=736000 IntersectTest$MyMethod1@4b4fe104 took 2.3507252 count=736000 IntersectTest$MyMethod2@693df43c took 4.7506854 count=736000 Running tests for 100x10 IntersectTest$PostMethod@4f7d29df took 24.9574096 count=4219000 IntersectTest$MyMethod1@2248183e took 5.8628954 count=4219000 IntersectTest$MyMethod2@2b2fa007 took 12.9836817 count=4219000 Running tests for 100x100 IntersectTest$PostMethod@545d7b6a took 193.2436192 count=40987000 IntersectTest$MyMethod1@4551976b took 44.634367 count=40987000 IntersectTest$MyMethod2@6fac155a took 119.2478037 count=40987000 Running tests for 100x1000 IntersectTest$PostMethod@7b6c238d took 200.4385174 count=40817000 IntersectTest$MyMethod1@78923d48 took 45.6225227 count=40817000 IntersectTest$MyMethod2@48f57fcf took 121.0602757 count=40817000 Running tests for 1x1 IntersectTest$PostMethod@102c79f4 took 9.0931408 count=1000000 IntersectTest$MyMethod1@57fa8a77 took 2.3309466 count=1000000 IntersectTest$MyMethod2@198b7c1 took 5.7627226 count=1000000 Running tests for 1x10 IntersectTest$PostMethod@8c646d0 took 9.3208571 count=726000 IntersectTest$MyMethod1@11530630 took 2.3123797 count=726000 IntersectTest$MyMethod2@61bb4232 took 5.405318 count=726000 Running tests for 1x100 IntersectTest$PostMethod@1a137105 took 9.387384 count=710000 IntersectTest$MyMethod1@72610ca2 took 2.2938749 count=710000 IntersectTest$MyMethod2@41849a58 took 5.3865938 count=710000 Running tests for 1x1000 IntersectTest$PostMethod@100001c8 took 9.4289031 count=696000 IntersectTest$MyMethod1@7074f9ac took 2.2977923 count=696000 IntersectTest$MyMethod2@fb3c4e2 took 5.3724119 count=696000 Running tests for 10x2 IntersectTest$PostMethod@52c638d6 took 9.4074124 count=775000 IntersectTest$MyMethod1@53bd940e took 2.3544881 count=775000 IntersectTest$MyMethod2@43434e15 took 4.9228549 count=775000 Running tests for 10x10 IntersectTest$PostMethod@73b7628d took 23.2110252 count=4374000 IntersectTest$MyMethod1@ca75255 took 5.5877838 count=4374000 IntersectTest$MyMethod2@3d0e50f0 took 13.5902641 count=4374000 Running tests for 10x100 IntersectTest$PostMethod@6d6bbba9 took 25.1999918 count=4227000 IntersectTest$MyMethod1@3bed8c5e took 5.7879144 count=4227000 IntersectTest$MyMethod2@689a8e0e took 13.9617882 count=4227000 Running tests for 10x1000 IntersectTest$PostMethod@3da3b0a2 took 25.1627329 count=4222000 IntersectTest$MyMethod1@45a17b4b took 5.8319523 count=4222000 IntersectTest$MyMethod2@6ca59ca3 took 13.8885479 count=4222000 Running tests for 100x1 IntersectTest$PostMethod@360202a5 took 9.5115367 count=705000 IntersectTest$MyMethod1@3dfbba56 took 2.3470254 count=705000 IntersectTest$MyMethod2@598683e4 took 4.8955489 count=705000 Running tests for 100x10 IntersectTest$PostMethod@21426d0d took 25.8234298 count=4231000 IntersectTest$MyMethod1@1005818a took 5.8832067 count=4231000 IntersectTest$MyMethod2@597b933d took 13.3676148 count=4231000 Running tests for 100x100 IntersectTest$PostMethod@6d59b81a took 193.676662 count=41015000 IntersectTest$MyMethod1@1d45eb0c took 44.6519088 count=41015000 IntersectTest$MyMethod2@594a6fd7 took 119.1646115 count=41015000 Running tests for 100x1000 IntersectTest$PostMethod@6d57d9ac took 200.1651432 count=40803000 IntersectTest$MyMethod1@2293e349 took 45.5311168 count=40803000 IntersectTest$MyMethod2@1b2edf5b took 120.1697135 count=40803000

Y aquí está el micro-benchmark feo (y posiblemente defectuoso):

import java.util.*;
public class IntersectTest {
static Random rng = new Random();
static abstract class RunIt {
public long count;
public long nsTime;
abstract int Run (Set<Long> s1, Set<Long> s2);
}
// As presented in the post
static class PostMethod extends RunIt {
public int Run(Set<Long> set1, Set<Long> set2) {
boolean set1IsLarger = set1.size() > set2.size();
Set<Long> cloneSet = new HashSet<Long>(set1IsLarger ? set2 : set1);
cloneSet.retainAll(set1IsLarger ? set1 : set2);
return cloneSet.size();
}
}
// No intermediate HashSet
static class MyMethod1 extends RunIt {
public int Run (Set<Long> set1, Set<Long> set2) {
Set<Long> a;
Set<Long> b;
if (set1.size() <= set2.size()) {
a = set1;
b = set2;
} else {
a = set2;
b = set1;
}
int count = 0;
for (Long e : a) {
if (b.contains(e)) {
count++;
}
}
return count;
}
}
// With intermediate HashSet
static class MyMethod2 extends RunIt {
public int Run (Set<Long> set1, Set<Long> set2) {
Set<Long> a;
Set<Long> b;
Set<Long> res = new HashSet<Long>();
if (set1.size() <= set2.size()) {
a = set1;
b = set2;
} else {
a = set2;
b = set1;
}
for (Long e : a) {
if (b.contains(e)) {
res.add(e);
}
}
return res.size();
}
}
static Set<Long> makeSet (int count, float load) {
Set<Long> s = new HashSet<Long>();
for (int i = 0; i < count; i++) {
s.add((long)rng.nextInt(Math.max(1, (int)(count * load))));
}
return s;
}
// really crummy ubench stuff
public static void main(String[] args) {
int[][] bounds = {
{1, 1},
{1, 10},
{1, 100},
{1, 1000},
{10, 2},
{10, 10},
{10, 100},
{10, 1000},
{100, 1},
{100, 10},
{100, 100},
{100, 1000},
};
int totalReps = 4;
int cycleReps = 1000;
int subReps = 1000;
float load = 0.8f;
for (int tc = 0; tc < totalReps; tc++) {
for (int[] bound : bounds) {
int set1size = bound[0];
int set2size = bound[1];
System.out.println("Running tests for " + set1size + "x" + set2size);
ArrayList<RunIt> allRuns = new ArrayList<RunIt>(
Arrays.asList(
new PostMethod(),
new MyMethod1(),
new MyMethod2()));
for (int r = 0; r < cycleReps; r++) {
ArrayList<RunIt> runs = new ArrayList<RunIt>(allRuns);
Set<Long> set1 = makeSet(set1size, load);
Set<Long> set2 = makeSet(set2size, load);
while (runs.size() > 0) {
int runIdx = rng.nextInt(runs.size());
RunIt run = runs.remove(runIdx);
long start = System.nanoTime();
int count = 0;
for (int s = 0; s < subReps; s++) {
count += run.Run(set1, set2);
}
long time = System.nanoTime() - start;
run.nsTime += time;
run.count += count;
}
}
for (RunIt run : allRuns) {
double sec = run.nsTime / (10e6);
System.out.println(run + " took " + sec + " count=" + run.count);
}
}
}
}
}

Answer 3

Ese es un buen enfoque. Debería obtener el rendimiento O (n) de su solución actual.

Answer 4

Para su información, si cualquier conjunto de conjuntos se clasifica utilizando la misma relación de comparación, entonces puede iterar su intersección en el tiempo N * M, donde N es el tamaño del conjunto más pequeño y M es el número de conjuntos.

Implementación dejada como ejercicio al lector . Aquí hay un ejemplo .

Answer 5

Puede evitar todo el trabajo manual utilizando el método Set retainAll ().

De documentos:

s1.retainAll (s2) - transforma s1 en la intersección de s1 y s2. (La intersección de dos conjuntos es el conjunto que contiene solo los elementos comunes a ambos conjuntos).

Answer 6

Si está calculando la intersección solo por el hecho de contar cuántos elementos hay en el conjunto, le sugiero que solo necesite contar la intersección directamente en lugar de construir el conjunto y luego llamar al size() .

Mi función para contar:

/**
* Computes the size of intersection of two sets
* @param small first set. preferably smaller than the second argument
* @param large second set;
* @param <T> the type
* @return size of intersection of sets
*/
public <T> int countIntersection(Set<T> small, Set<T> large){
//assuming first argument to be smaller than the later;
//however double checking to be sure
if (small.size() > large.size()) {
//swap the references;
Set<T> tmp = small;
small = large;
large = tmp;
}
int result = 0;
for (T item : small) {
if (large.contains(item)){
//item found in both the sets
result++;
}
}
return result;
}

Answer 7

Si se pueden ordenar ambos conjuntos, como TreeSet ejecutar ambos iteradores podría ser una manera más rápida de contar la cantidad de objetos compartidos.

Si realiza esta operación a menudo, puede aportar mucho si puede envolver los conjuntos para que pueda almacenar en caché el resultado de la operación de intersección manteniendo una bandera dirty para rastrear la validez del resultado en caché, calculando nuevamente si es necesario.

Answer 8

Simplemente use el método de Sets#intersection(Set, Set) Google Guava .

Answer 9

Usando la secuencia de Java 8:

set1.stream().filter(s -> set2.contains(s)).collect(Collectors.toList());