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本系列笔记拟采纳golang练习之
网页排名(PageRank/佩奇排名), 随机游走
网页排名（PageRank，也叫作佩奇排名）是一种在搜寻网页时对搜寻后果进行排序的算法。网页排名就是利用网页之间的链接构造计算出网页价值的算法。在网页排名中，链入页面越多的网页，它的重要性也就越高。假如没有链入页面的网页权重为1。有链入页面的网页权重是其链入页面的权重之和。如果一个网页链向多个页面，那么其链向的所有页面将平分它的权重。在网页排名中，链入的页面越多，该网页所收回的链接的价值就越高。能够应用“随机游走模型”（random walk model）来解决网页互链的问题.用户浏览网页的操作就能够这样来定义：用户等概率跳转到以后网页所链向的一个网页的概率为1-；等概率近程跳转到其余网页中的一个网页的概率为。模仿用户随机拜访页面的过程, 每拜访一个页面, 其权重加1,直到拜访的总次数达到N次为止,每个页面的权重值代表的是“某一刻正在浏览这个网页的概率”，可间接将其作为网页的权重来应用。摘自 <<我的第一本算法书>> 【日】石田保辉；宫崎修一指标
- 实现基于随机游走模型的PageRank算法, 并验证其有效性和稳定性(网页权重在模仿若干次后, 保持稳定)
设计
- IPage: 网页模型接口
- IPageRanking: 网页排名算法接口
- tPage: 网页模型的实现
- tRandomWalkPageRanking: 基于随机游走模型的PageRank算法, 实现IPageRanking接口
单元测试
- page_rank_test.go, 验证网页排名算法的有效性和稳定性
- 首先通过简略case验证其有效性
- 而后随机生成大批量随机互链的网页, 验证在多轮随机游走后, 网页权重的稳定性
package othersimport ( "fmt" pr "learning/gooop/others/page_rank" "math/rand" "sort" "testing" "time")func Test_PageRank(t *testing.T) { fnAssertTrue := func(b bool, msg string) { if !b { t.Fatal(msg) } } t.Log("testing simple case") p11 := pr.NewPage("p11") p12 := pr.NewPage("p12") p13 := pr.NewPage("p13") p21 := pr.NewPage("p21") p22 := pr.NewPage("p22") p31 := pr.NewPage("p31") p32 := pr.NewPage("p32") p11.AddLink(p21) p11.AddLink(p22) p12.AddLink(p21) p12.AddLink(p22) p13.AddLink(p21) p13.AddLink(p22) p21.AddLink(p31) p22.AddLink(p31) p31.AddLink(p32) p32.AddLink(p31) samples := []pr.IPage{ p11,p12,p13, p21, p22, p31, p32, } pr.RandomWalkPageRanking.RankAll(samples, 1000) sort.Sort(sort.Reverse(pr.IPageSlice(samples))) for _,p := range samples { t.Log(p) } fnAssertTrue(samples[0].ID() == "p31", "expecting top.1 = p31") fnAssertTrue(samples[1].ID() == "p32", "expecting top.2 = p32") fnAssertTrue(samples[2].ID() == "p21" || samples[2].ID() == "p22", "expecting top.3 in (p21,p22)") fnAssertTrue(samples[3].ID() == "p21" || samples[3].ID() == "p22", "expecting top.4 in (p21,p22)") // generate 1000 random pages iPageCount := 1000 pages := make([]pr.IPage, iPageCount) for i,_ := range pages { pages[i] = pr.NewPage(fmt.Sprintf("p%02d.com", i)) } r := rand.New(rand.NewSource(time.Now().UnixNano())) for i,p := range pages { // add random page links for j,iPageLinks := 0, r.Intn(10);j < iPageLinks; { n := r.Intn(iPageCount) if n != i { j++ p.AddLink(pages[n]) } } } // rank pages and get top 100 mapTop100 := make(map[string]bool, 0) fnTestRanking := func(rounds int) { t.Logf("testing page rank with %v rounds", rounds) bFirstRound := len(mapTop100) == 0 // page ranking pr.RandomWalkPageRanking.RankAll(pages, rounds) // sort pages by ranking sort.Sort(sort.Reverse(pr.IPageSlice(pages))) hits := 0 for i,p := range pages { if i < 10 { t.Log(p) } if i < 100 { if bFirstRound { mapTop100[p.ID()] = true } else if _,ok := mapTop100[p.ID()];ok { hits++ } } else { break } } if !bFirstRound { t.Logf("hit rate: %s%v", "%", hits) } } // test stability under different rounds fnTestRanking(3000) fnTestRanking(10000) fnTestRanking(30000) fnTestRanking(90000)}测试输入
- 测试表明, 当随机游走的总次数 >= 网页数量 * 每个网页的均匀收回链接数时, 所得排名是比较稳定的
$ go test -v page_rank_test.go === RUN Test_PageRank page_rank_test.go:19: testing simple case page_rank_test.go:47: p(p31, 0.4206, [p32]) page_rank_test.go:47: p(p32, 0.3673, [p31]) page_rank_test.go:47: p(p21, 0.0639, [p31]) page_rank_test.go:47: p(p22, 0.0604, [p31]) page_rank_test.go:47: p(p11, 0.0300, [p21 p22]) page_rank_test.go:47: p(p12, 0.0291, [p21 p22]) page_rank_test.go:47: p(p13, 0.0287, [p21 p22]) page_rank_test.go:77: testing page rank with 3000 rounds page_rank_test.go:89: p(p604.com, 0.0039, []) page_rank_test.go:89: p(p807.com, 0.0035, [p709.com p328.com p303.com p110.com p858.com p394.com p231.com p731.com p83.com]) page_rank_test.go:89: p(p72.com, 0.0034, [p249.com p347.com p604.com p533.com p416.com p958.com p966.com p385.com]) page_rank_test.go:89: p(p712.com, 0.0033, [p485.com p451.com p236.com p141.com p168.com p693.com]) page_rank_test.go:89: p(p452.com, 0.0032, [p588.com p344.com p618.com p258.com p394.com p285.com p780.com p606.com p89.com]) page_rank_test.go:89: p(p709.com, 0.0031, [p666.com p554.com p103.com p202.com p230.com]) page_rank_test.go:89: p(p975.com, 0.0029, []) page_rank_test.go:89: p(p840.com, 0.0029, [p974.com p698.com p49.com p851.com p73.com]) page_rank_test.go:89: p(p867.com, 0.0028, [p588.com p196.com p931.com p381.com p621.com p848.com]) page_rank_test.go:89: p(p633.com, 0.0028, [p840.com]) page_rank_test.go:77: testing page rank with 10000 rounds page_rank_test.go:89: p(p604.com, 0.0039, []) page_rank_test.go:89: p(p807.com, 0.0034, [p709.com p328.com p303.com p110.com p858.com p394.com p231.com p731.com p83.com]) page_rank_test.go:89: p(p72.com, 0.0034, [p249.com p347.com p604.com p533.com p416.com p958.com p966.com p385.com]) page_rank_test.go:89: p(p452.com, 0.0033, [p588.com p344.com p618.com p258.com p394.com p285.com p780.com p606.com p89.com]) page_rank_test.go:89: p(p712.com, 0.0033, [p485.com p451.com p236.com p141.com p168.com p693.com]) page_rank_test.go:89: p(p709.com, 0.0031, [p666.com p554.com p103.com p202.com p230.com]) page_rank_test.go:89: p(p975.com, 0.0029, []) page_rank_test.go:89: p(p840.com, 0.0029, [p974.com p698.com p49.com p851.com p73.com]) page_rank_test.go:89: p(p612.com, 0.0028, [p116.com p562.com p179.com p37.com p761.com]) page_rank_test.go:89: p(p319.com, 0.0028, [p726.com p63.com p558.com p301.com p185.com p944.com]) page_rank_test.go:104: hit rate: %98 page_rank_test.go:77: testing page rank with 30000 rounds page_rank_test.go:89: p(p604.com, 0.0039, []) page_rank_test.go:89: p(p807.com, 0.0034, [p709.com p328.com p303.com p110.com p858.com p394.com p231.com p731.com p83.com]) page_rank_test.go:89: p(p72.com, 0.0034, [p249.com p347.com p604.com p533.com p416.com p958.com p966.com p385.com]) page_rank_test.go:89: p(p452.com, 0.0033, [p588.com p344.com p618.com p258.com p394.com p285.com p780.com p606.com p89.com]) page_rank_test.go:89: p(p712.com, 0.0032, [p485.com p451.com p236.com p141.com p168.com p693.com]) page_rank_test.go:89: p(p709.com, 0.0031, [p666.com p554.com p103.com p202.com p230.com]) page_rank_test.go:89: p(p975.com, 0.0029, []) page_rank_test.go:89: p(p840.com, 0.0029, [p974.com p698.com p49.com p851.com p73.com]) page_rank_test.go:89: p(p319.com, 0.0028, [p726.com p63.com p558.com p301.com p185.com p944.com]) page_rank_test.go:89: p(p612.com, 0.0028, [p116.com p562.com p179.com p37.com p761.com]) page_rank_test.go:104: hit rate: %98 page_rank_test.go:77: testing page rank with 90000 rounds page_rank_test.go:89: p(p604.com, 0.0039, []) page_rank_test.go:89: p(p807.com, 0.0034, [p709.com p328.com p303.com p110.com p858.com p394.com p231.com p731.com p83.com]) page_rank_test.go:89: p(p72.com, 0.0034, [p249.com p347.com p604.com p533.com p416.com p958.com p966.com p385.com]) page_rank_test.go:89: p(p452.com, 0.0033, [p588.com p344.com p618.com p258.com p394.com p285.com p780.com p606.com p89.com]) page_rank_test.go:89: p(p712.com, 0.0032, [p485.com p451.com p236.com p141.com p168.com p693.com]) page_rank_test.go:89: p(p709.com, 0.0031, [p666.com p554.com p103.com p202.com p230.com]) page_rank_test.go:89: p(p975.com, 0.0029, []) page_rank_test.go:89: p(p840.com, 0.0029, [p974.com p698.com p49.com p851.com p73.com]) page_rank_test.go:89: p(p612.com, 0.0028, [p116.com p562.com p179.com p37.com p761.com]) page_rank_test.go:89: p(p319.com, 0.0028, [p726.com p63.com p558.com p301.com p185.com p944.com]) page_rank_test.go:104: hit rate: %98--- PASS: Test_PageRank (13.93s)PASSok command-line-arguments 13.936sIPage.go
网页模型接口
package page_rankimport "fmt"type IPage interface { fmt.Stringer ID() string GetWeight() float64 SetWeight(float64) GetLinks() []IPage AddLink(IPage)}type IPageSlice []IPagefunc (me IPageSlice) Len() int { return len(me)}func (me IPageSlice) Less(i,j int) bool { return me[i].GetWeight() < me[j].GetWeight()}func (me IPageSlice) Swap(i,j int) { me[i], me[j] = me[j], me[i]}IPageRanking.go
网页排名算法接口
package page_ranktype IPageRanking interface { RankAll(pages []IPage, rounds int)}tPage.go
网页模型的实现
package page_rankimport ( "fmt" "strings")type tPage struct { id string weight float64 links []IPage}func NewPage(id string) IPage { return &tPage{ id: id, weight: 0, links: []IPage{}, }}func (me *tPage) ID() string { return me.id}func (me *tPage) GetWeight() float64 { return me.weight}func (me *tPage) SetWeight(w float64) { me.weight = w}func (me *tPage) GetLinks() []IPage { return me.links}func (me *tPage) AddLink(p IPage) { me.links = append(me.links, p)}func (me *tPage) String() string { linkStrings := make([]string, len(me.links)) for i,p := range me.links { linkStrings[i] = p.ID() } return fmt.Sprintf("p(%v, %8.4f, [%v])", me.id, me.weight, strings.Join(linkStrings, " "))}tRandomWalkPageRanking.go
基于随机游走模型的PageRank算法, 实现IPageRanking接口
package page_rankimport ( "math/rand" "time")type tRandomWalkPageRanking struct {}var gPossiblityToLinkedPage = 85func newRandomWalkPageRanking() IPageRanking { return &tRandomWalkPageRanking{}}func (me *tRandomWalkPageRanking) RankAll(pages []IPage, rounds int) { iPageCount := len(pages) if iPageCount <= 0 { return } r := rand.New(rand.NewSource(time.Now().UnixNano())) current := pages[0] iVisitCount := iPageCount * rounds for i := 0;i < iVisitCount;i++ { // visit current page current.SetWeight(current.GetWeight() + 1) possibility := r.Intn(100) if possibility < gPossiblityToLinkedPage && len(current.GetLinks())>0 { // goto linked page current = me.VisitLinkedPage(current, r) } else { // goto unlinked page current = me.VisitUnlinkedPage(current, pages, r) } } fVisitCount := float64(iVisitCount) for _,p := range pages { p.SetWeight(p.GetWeight() / fVisitCount) }}func (me *tRandomWalkPageRanking) VisitLinkedPage(current IPage, r *rand.Rand) IPage { links := current.GetLinks() next := links[r.Intn(len(links))] return next}func (me *tRandomWalkPageRanking) VisitUnlinkedPage(current IPage, pages []IPage, r *rand.Rand) IPage { mapLinks := make(map[string]bool, 0) mapLinks[current.ID()] = true for _,p := range current.GetLinks() { mapLinks[p.ID()] = true } n := len(pages) for { next := pages[r.Intn(n)] if _,ok := mapLinks[next.ID()];!ok { return next } }}var RandomWalkPageRanking = newRandomWalkPageRanking()(end)