R语言机器学习mlr3:数据预处理和pipelines

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目录

    • pipeops
    • `mlr3pipelines`中的管道符: %>>%
    • 建立模型
    • 非线性graph
      • branching & copying
      • bagging
      • stacking
      • 一个超级复杂的例子
    • 一些特殊预处理步骤
      • 缺失值处理:`PipeOpImpute`
      • 创建新的变量:`PipeOpMutate`
      • 使用子集训练:`PipeOpChunk`
      • 特征选择:`PipeOpFilter`和`PipeOpSelect`

mlr3pipelines是一种数据流编程套件,完整的机器学习工作流可被称为Graph/Pipelines,包含数据预处理、建模、多个模型比较等,不同的模型需要不同的数据预处理方法,另外还有集成学习、各种非线性模型等,这些都可以通过mlr3pipelines解决。

数据预处理的R包有很多,比如caretrecipes等,mlr3pipelines创造性的使用了图流的方式。
R语言机器学习mlr3:数据预处理和pipelines_第1张图片

pipeops

进行数据预处理的各种方法在mlr3pipelines中被称为pipeops,目前基本涵盖常见的数据预处理方法,比如独热编码、稀疏矩阵、缺失值处理、降维、数据标准化、因子分组等等。

可以用来连接预处理和模型,或者构建复杂的统计建模步骤,例如多种不同的预处理连接多种不同的模型等

查看所有的pipeops

library(mlr3pipelines)
as.data.table(mlr_pipeops) # 目前共有64种
##                       key                         packages
##  1:                boxcox      mlr3pipelines,bestNormalize
##  2:                branch                    mlr3pipelines
##  3:                 chunk                    mlr3pipelines
##  4:        classbalancing                    mlr3pipelines
##  5:            classifavg              mlr3pipelines,stats
##  6:          classweights                    mlr3pipelines
##  7:              colapply                    mlr3pipelines
##  8:       collapsefactors                    mlr3pipelines
##  9:              colroles                    mlr3pipelines
## 10:                  copy                    mlr3pipelines
## 11:          datefeatures                    mlr3pipelines
## 12:                encode              mlr3pipelines,stats
## 13:          encodeimpact                    mlr3pipelines
## 14:            encodelmer        mlr3pipelines,lme4,nloptr
## 15:          featureunion                    mlr3pipelines
## 16:                filter                    mlr3pipelines
## 17:            fixfactors                    mlr3pipelines
## 18:               histbin           mlr3pipelines,graphics
## 19:                   ica            mlr3pipelines,fastICA
## 20:        imputeconstant                    mlr3pipelines
## 21:            imputehist           mlr3pipelines,graphics
## 22:         imputelearner                    mlr3pipelines
## 23:            imputemean                    mlr3pipelines
## 24:          imputemedian              mlr3pipelines,stats
## 25:            imputemode                    mlr3pipelines
## 26:             imputeoor                    mlr3pipelines
## 27:          imputesample                    mlr3pipelines
## 28:             kernelpca            mlr3pipelines,kernlab
## 29:               learner                    mlr3pipelines
## 30:            learner_cv                    mlr3pipelines
## 31:               missind                    mlr3pipelines
## 32:           modelmatrix              mlr3pipelines,stats
## 33:     multiplicityexply                    mlr3pipelines
## 34:     multiplicityimply                    mlr3pipelines
## 35:                mutate                    mlr3pipelines
## 36:                   nmf           mlr3pipelines,MASS,NMF
## 37:                   nop                    mlr3pipelines
## 38:              ovrsplit                    mlr3pipelines
## 39:              ovrunite                    mlr3pipelines
## 40:                   pca                    mlr3pipelines
## 41:                 proxy                    mlr3pipelines
## 42:           quantilebin              mlr3pipelines,stats
## 43:      randomprojection                    mlr3pipelines
## 44:        randomresponse                    mlr3pipelines
## 45:               regravg                    mlr3pipelines
## 46:       removeconstants                    mlr3pipelines
## 47:         renamecolumns                    mlr3pipelines
## 48:             replicate                    mlr3pipelines
## 49:                 scale                    mlr3pipelines
## 50:           scalemaxabs                    mlr3pipelines
## 51:            scalerange                    mlr3pipelines
## 52:                select                    mlr3pipelines
## 53:                 smote        mlr3pipelines,smotefamily
## 54:           spatialsign                    mlr3pipelines
## 55:             subsample                    mlr3pipelines
## 56:          targetinvert                    mlr3pipelines
## 57:          targetmutate                    mlr3pipelines
## 58: targettrafoscalerange                    mlr3pipelines
## 59:        textvectorizer mlr3pipelines,quanteda,stopwords
## 60:             threshold                    mlr3pipelines
## 61:         tunethreshold              mlr3pipelines,bbotk
## 62:              unbranch                    mlr3pipelines
## 63:                vtreat             mlr3pipelines,vtreat
## 64:            yeojohnson      mlr3pipelines,bestNormalize
##                       key                         packages
##                                 tags
##  1:                   data transform
##  2:                             meta
##  3:                             meta
##  4:   imbalanced data,data transform
##  5:                         ensemble
##  6:   imbalanced data,data transform
##  7:                   data transform
##  8:                   data transform
##  9:                   data transform
## 10:                             meta
## 11:                   data transform
## 12:            encode,data transform
## 13:            encode,data transform
## 14:            encode,data transform
## 15:                         ensemble
## 16: feature selection,data transform
## 17:         robustify,data transform
## 18:                   data transform
## 19:                   data transform
## 20:                         missings
## 21:                         missings
## 22:                         missings
## 23:                         missings
## 24:                         missings
## 25:                         missings
## 26:                         missings
## 27:                         missings
## 28:                   data transform
## 29:                          learner
## 30:  learner,ensemble,data transform
## 31:          missings,data transform
## 32:                   data transform
## 33:                     multiplicity
## 34:                     multiplicity
## 35:                   data transform
## 36:                   data transform
## 37:                             meta
## 38:    target transform,multiplicity
## 39:            multiplicity,ensemble
## 40:                   data transform
## 41:                             meta
## 42:                   data transform
## 43:                   data transform
## 44:                         abstract
## 45:                         ensemble
## 46:         robustify,data transform
## 47:                   data transform
## 48:                     multiplicity
## 49:                   data transform
## 50:                   data transform
## 51:                   data transform
## 52: feature selection,data transform
## 53:   imbalanced data,data transform
## 54:                   data transform
## 55:                   data transform
## 56:                         abstract
## 57:                 target transform
## 58:                 target transform
## 59:                   data transform
## 60:                 target transform
## 61:                 target transform
## 62:                             meta
## 63:   encode,missings,data transform
## 64:                   data transform
##                                 tags
##                                            feature_types input.num output.num
##  1:                                      numeric,integer         1          1
##  2:                                                   NA         1         NA
##  3:                                                   NA         1         NA
##  4: logical,integer,numeric,character,factor,ordered,...         1          1
##  5:                                                   NA        NA          1
##  6: logical,integer,numeric,character,factor,ordered,...         1          1
##  7: logical,integer,numeric,character,factor,ordered,...         1          1
##  8:                                       factor,ordered         1          1
##  9: logical,integer,numeric,character,factor,ordered,...         1          1
## 10:                                                   NA         1         NA
## 11:                                              POSIXct         1          1
## 12:                                       factor,ordered         1          1
## 13:                                       factor,ordered         1          1
## 14:                                       factor,ordered         1          1
## 15:                                                   NA        NA          1
## 16: logical,integer,numeric,character,factor,ordered,...         1          1
## 17:                                       factor,ordered         1          1
## 18:                                      numeric,integer         1          1
## 19:                                      numeric,integer         1          1
## 20: logical,integer,numeric,character,factor,ordered,...         1          1
## 21:                                      integer,numeric         1          1
## 22:                               logical,factor,ordered         1          1
## 23:                                      numeric,integer         1          1
## 24:                                      numeric,integer         1          1
## 25:               factor,integer,logical,numeric,ordered         1          1
## 26:             character,factor,integer,numeric,ordered         1          1
## 27:               factor,integer,logical,numeric,ordered         1          1
## 28:                                      numeric,integer         1          1
## 29:                                                   NA         1          1
## 30: logical,integer,numeric,character,factor,ordered,...         1          1
## 31: logical,integer,numeric,character,factor,ordered,...         1          1
## 32: logical,integer,numeric,character,factor,ordered,...         1          1
## 33:                                                   NA         1         NA
## 34:                                                   NA        NA          1
## 35: logical,integer,numeric,character,factor,ordered,...         1          1
## 36:                                      numeric,integer         1          1
## 37:                                                   NA         1          1
## 38:                                                   NA         1          1
## 39:                                                   NA         1          1
## 40:                                      numeric,integer         1          1
## 41:                                                   NA        NA          1
## 42:                                      numeric,integer         1          1
## 43:                                      numeric,integer         1          1
## 44:                                                   NA         1          1
## 45:                                                   NA        NA          1
## 46: logical,integer,numeric,character,factor,ordered,...         1          1
## 47: logical,integer,numeric,character,factor,ordered,...         1          1
## 48:                                                   NA         1          1
## 49:                                      numeric,integer         1          1
## 50:                                      numeric,integer         1          1
## 51:                                      numeric,integer         1          1
## 52: logical,integer,numeric,character,factor,ordered,...         1          1
## 53: logical,integer,numeric,character,factor,ordered,...         1          1
## 54:                                      numeric,integer         1          1
## 55: logical,integer,numeric,character,factor,ordered,...         1          1
## 56:                                                   NA         2          1
## 57:                                                   NA         1          2
## 58:                                                   NA         1          2
## 59:                                            character         1          1
## 60:                                                   NA         1          1
## 61:                                                   NA         1          1
## 62:                                                   NA        NA          1
## 63: logical,integer,numeric,character,factor,ordered,...         1          1
## 64:                                      numeric,integer         1          1
##                                            feature_types input.num output.num
##     input.type.train  input.type.predict output.type.train output.type.predict
##  1:             Task                Task              Task                Task
##  2:                *                   *                 *                   *
##  3:             Task                Task              Task                Task
##  4:      TaskClassif         TaskClassif       TaskClassif         TaskClassif
##  5:             NULL   PredictionClassif              NULL   PredictionClassif
##  6:      TaskClassif         TaskClassif       TaskClassif         TaskClassif
##  7:             Task                Task              Task                Task
##  8:             Task                Task              Task                Task
##  9:             Task                Task              Task                Task
## 10:                *                   *                 *                   *
## 11:             Task                Task              Task                Task
## 12:             Task                Task              Task                Task
## 13:             Task                Task              Task                Task
## 14:             Task                Task              Task                Task
## 15:             Task                Task              Task                Task
## 16:             Task                Task              Task                Task
## 17:             Task                Task              Task                Task
## 18:             Task                Task              Task                Task
## 19:             Task                Task              Task                Task
## 20:             Task                Task              Task                Task
## 21:             Task                Task              Task                Task
## 22:             Task                Task              Task                Task
## 23:             Task                Task              Task                Task
## 24:             Task                Task              Task                Task
## 25:             Task                Task              Task                Task
## 26:             Task                Task              Task                Task
## 27:             Task                Task              Task                Task
## 28:             Task                Task              Task                Task
## 29:      TaskClassif         TaskClassif              NULL   PredictionClassif
## 30:      TaskClassif         TaskClassif       TaskClassif         TaskClassif
## 31:             Task                Task              Task                Task
## 32:             Task                Task              Task                Task
## 33:              [*]                 [*]                 *                   *
## 34:                *                   *               [*]                 [*]
## 35:             Task                Task              Task                Task
## 36:             Task                Task              Task                Task
## 37:                *                   *                 *                   *
## 38:      TaskClassif         TaskClassif     [TaskClassif]       [TaskClassif]
## 39:           [NULL] [PredictionClassif]              NULL   PredictionClassif
## 40:             Task                Task              Task                Task
## 41:                *                   *                 *                   *
## 42:             Task                Task              Task                Task
## 43:             Task                Task              Task                Task
## 44:             NULL          Prediction              NULL          Prediction
## 45:             NULL      PredictionRegr              NULL      PredictionRegr
## 46:             Task                Task              Task                Task
## 47:             Task                Task              Task                Task
## 48:                *                   *               [*]                 [*]
## 49:             Task                Task              Task                Task
## 50:             Task                Task              Task                Task
## 51:             Task                Task              Task                Task
## 52:             Task                Task              Task                Task
## 53:             Task                Task              Task                Task
## 54:             Task                Task              Task                Task
## 55:             Task                Task              Task                Task
## 56:        NULL,NULL function,Prediction              NULL          Prediction
## 57:             Task                Task         NULL,Task       function,Task
## 58:         TaskRegr            TaskRegr     NULL,TaskRegr   function,TaskRegr
## 59:             Task                Task              Task                Task
## 60:             NULL   PredictionClassif              NULL   PredictionClassif
## 61:             Task                Task              NULL          Prediction
## 62:                *                   *                 *                   *
## 63:             Task                Task              Task                Task
## 64:             Task                Task              Task                Task
##     input.type.train  input.type.predict output.type.train output.type.predict

看到有很多数据预处理方法了,但其实常用的也就10来种左右。

创建预处理步骤可通过以下方法:

pca <- mlr_pipeops$get("pca")

# 或者用简便写法
pca <- po("pca")

非常重要的一点是,不仅能创建预处理步骤,也可以用这种方法选择算法,选择特征选择方法等:

# 选择学习器/算法
library(mlr3)
learner <- po("learner" ,lrn("classif.rpart"))

# 选择特征选择的方法并设置参数
filter <- po("filter",
             filter = mlr3filters::flt("variance"),
             filter.frac = 0.5
             )

mlr3pipelines中的管道符: %>>%

这是mlr3团队发明的专用管道符,可用于连接不同的预处理步骤、预处理和模型等操作:

gr <- po("scale") %>>% po("pca")
gr$plot(html = F)

R语言机器学习mlr3:数据预处理和pipelines_第2张图片

很多强大的操作都是基于此管道符运行的。

建立模型

一个简单的例子,先预处理数据,再训练

# 连接预处理和模型,有点类似tidymodels的workflow
mutate <- po("mutate")
filter <- po("filter",
             filter = mlr3filters::flt("variance"),
             param_vals = list(filter.frac = 0.5))

graph <- mutate %>>%
  filter %>>%
  po("learner", learner = lrn("classif.rpart"))

现在这个graph就变成了一个含有预处理步骤的学习器(learner),可以像前面介绍的那样直接用于训练、预测:

task <- tsk("iris")
graph$train(task)
## $classif.rpart.output
## NULL

预测

graph$predict(task)
## $classif.rpart.output
##  for 150 observations:
##     row_ids     truth  response
##           1    setosa    setosa
##           2    setosa    setosa
##           3    setosa    setosa
## ---                            
##         148 virginica virginica
##         149 virginica virginica
##         150 virginica virginica

除此之外,还可以把graph变成一个GraphLearner对象,用于resamplebenchmark

glrn <- as_learner(graph) # 变成graphlearner

cv3 <- rsmp("cv", folds = 5)
resample(task, glrn, cv3)
## INFO  [21:01:53.145] [mlr3] Applying learner 'mutate.variance.classif.rpart' on task 'iris' (iter 2/5) 
## INFO  [21:01:53.230] [mlr3] Applying learner 'mutate.variance.classif.rpart' on task 'iris' (iter 5/5) 
## INFO  [21:01:53.291] [mlr3] Applying learner 'mutate.variance.classif.rpart' on task 'iris' (iter 1/5) 
## INFO  [21:01:53.350] [mlr3] Applying learner 'mutate.variance.classif.rpart' on task 'iris' (iter 4/5) 
## INFO  [21:01:53.407] [mlr3] Applying learner 'mutate.variance.classif.rpart' on task 'iris' (iter 3/5)
##  of 5 iterations
## * Task: iris
## * Learner: mutate.variance.classif.rpart
## * Warnings: 0 in 0 iterations
## * Errors: 0 in 0 iterations

在很多数据预处理步骤中也是有参数需要调整的,mlr3pipelines不仅可以用于调整算法的超参数,还可以调整预处理中的参数。

library(paradox)

ps <- ps(
  classif.rpart.cp = p_dbl(0,0.05), # 算法中的参数
  variance.filter.frac = p_dbl(0.25,1) # 特征选择方法中的参数
)

library(mlr3tuning)
instance <- TuningInstanceSingleCrit$new(
  task = task,
  learner = glrn,
  resampling = rsmp("holdout", ratio = 0.7),
  measure = msr("classif.acc"),
  search_space = ps,
  terminator = trm("evals", n_evals = 20)
)

tuner <- tnr("random_search")

lgr::get_logger("mlr3")$set_threshold("warn")
lgr::get_logger("bbotk")$set_threshold("warn")

tuner$optimize(instance)
##    classif.rpart.cp variance.filter.frac learner_param_vals  x_domain
## 1:       0.02162802            0.3852356           
##    classif.acc
## 1:   0.9777778

instance$result_y
## classif.acc 
##   0.9777778
instance$result_learner_param_vals
## $mutate.mutation
## list()
## 
## $mutate.delete_originals
## [1] FALSE
## 
## $variance.filter.frac
## [1] 0.3852356
## 
## $classif.rpart.xval
## [1] 0
## 
## $classif.rpart.cp
## [1] 0.02162802

可以看到结果直接给出了算法的超参数和特征选择中的参数。

非线性graph

  • Branching: 一个点通往多个分支,例如在比较多个特征选择方法时很有用。只有一条路会被执行。
  • Copying: 一个点通往多个分支,所有的分支都会被执行,但是只能1次执行1个分支,并行计算目前还不支持。
  • Stacking: 单个图被彼此堆叠,一个图的输出是另一个图的输入。

branching & copying

使用PipeOpBranchPipeOpUnbranch实现分支操作,分支操作的概念如下图所示:
R语言机器学习mlr3:数据预处理和pipelines_第3张图片

下面一个例子演示了分支操作,分支之后一定要解除分支:

graph <- po("branch", c("nop","pca","scale")) %>>% # 开始分支
  gunion(list(
    po("nop", id = "null1"), # 分支1,并且取了个名字null1
    po("pca"),               # 分支2
    po("scale")              # 分支3
  )) %>>%
  po("unbranch",c("nop","pca","scale")) # 结束分支

graph$plot(html = F)

R语言机器学习mlr3:数据预处理和pipelines_第4张图片

bagging

属于集成学习的一种,概念不做介绍,感兴趣的可自行学习,其概念可查看下图:
R语言机器学习mlr3:数据预处理和pipelines_第5张图片

下面演示基本使用方法。

single_pred <- po("subsample", frac = 0.7) %>>%
  po("learner", lrn("classif.rpart")) # 建立一个模型

pred_set <- ppl("greplicate", single_pred, 10L) # 复制10次

bagging <- pred_set %>>%
  po("classifavg", innum = 10)

bagging$plot(html = FALSE)

R语言机器学习mlr3:数据预处理和pipelines_第6张图片

把上面的对象变成一个GraphLearner,然后就可以进行训练和预测了:

task <- tsk("iris")
split <- partition(task, ratio = 0.7, stratify = T)


baglrn <- as_learner(bagging)
baglrn$train(task, row_ids = split$train)
baglrn$predict(task, row_ids = split$test)
##  for 45 observations:
##     row_ids     truth  response prob.setosa prob.versicolor prob.virginica
##           4    setosa    setosa           1               0              0
##           6    setosa    setosa           1               0              0
##           8    setosa    setosa           1               0              0
## ---                                                                       
##         141 virginica virginica           0               0              1
##         147 virginica virginica           0               0              1
##         150 virginica virginica           0               0              1

stacking

另一种提高模型性能的方法,概念可看下图:
R语言机器学习mlr3:数据预处理和pipelines_第7张图片

这里为了防止过拟合,使用PipeOpLearnerCV预测袋外数据,它可以在数据内部自动执行嵌套重抽样。

首先创建level 0学习器,然后复制一份,并取一个名字:

lrn <- lrn("classif.rpart")
lrn_0 <- po("learner_cv", lrn$clone())
lrn_0$id<- "rpart_cv"

然后联合使用gunionPipeOpNOP,把没动过的task传到下一个level,这样经过决策树的task和没处理过的task就能一起传到下一个level了。

level_0 <- gunion(list(lrn_0, po("nop")))

把上面传下来的东西联合到一起:

combined <- level_0 %>>% po("featureunion", 2)
stack <- combined %>>% po("learner", lrn$clone())
stack$plot(html = FALSE)

R语言机器学习mlr3:数据预处理和pipelines_第8张图片

然后就可以进行训练、预测了:

stacklrn <- as_learner(stack)
stacklrn$train(task, split$train)
stacklrn$predict(task, split$test)
##  for 45 observations:
##     row_ids     truth  response
##           4    setosa    setosa
##           6    setosa    setosa
##           8    setosa    setosa
## ---                            
##         141 virginica virginica
##         147 virginica virginica
##         150 virginica virginica

一个超级复杂的例子

这个例子有多个不同的预处理步骤,使用多个不同的算法。

library("magrittr")
library("mlr3learners") 

rprt = lrn("classif.rpart", predict_type = "prob")
glmn = lrn("classif.glmnet", predict_type = "prob")

# 创建学习器
lrn_0 = po("learner_cv", rprt, id = "rpart_cv_1")
lrn_0$param_set$values$maxdepth = 5L
lrn_1 = po("pca", id = "pca1") %>>% po("learner_cv", rprt, id = "rpart_cv_2")
lrn_1$param_set$values$rpart_cv_2.maxdepth = 1L
lrn_2 = po("pca", id = "pca2") %>>% po("learner_cv", glmn)

# 第0层
level_0 = gunion(list(lrn_0, lrn_1, lrn_2, po("nop", id = "NOP1")))

# 第1层
level_1 = level_0 %>>%
  po("featureunion", 4) %>>%
  po("copy", 3) %>>%
  gunion(list(
    po("learner_cv", rprt, id = "rpart_cv_l1"),
    po("learner_cv", glmn, id = "glmnt_cv_l1"),
    po("nop", id = "NOP_l1")
  ))

# 第2层
level_2 = level_1 %>>%
  po("featureunion", 3, id = "u2") %>>%
  po("learner", rprt, id = "rpart_l2")


level_2$plot(html = FALSE)

R语言机器学习mlr3:数据预处理和pipelines_第9张图片

下面就可以进行训练、预测:

task = tsk("iris")
lrn = as_learner(level_2)

lrn$
  train(task, split$train)$
  predict(task, split$test)$
  score()
## classif.ce 
## 0.08888889

一些特殊预处理步骤

其实是一些很常用的步骤…

缺失值处理:PipeOpImpute

缺失值处理实在是太常见了,mlr3pipelines对于数值型和因子型都能处理。

pom <- po("missind")
pon <- po("imputehist", # 条形图插补数值型 
          id  = "impute_num", # 取个名字
          affect_columns  = is.numeric # 设置处理哪些列
          )

pof = po("imputeoor", id = "imputer_fct", affect_columns = is.factor) # 处理因子

imputer = pom %>>% pon %>>% pof

连接学习器:

polrn <- po("learner", lrn("classif.rpart"))
lrn <- as_learner(imputer %>>% polrn)

创建新的变量:PipeOpMutate

pom <- po("mutate",
          mutation = list(
            Sepal.Sum = ~ Sepal.Length + Sepal.Width,
            Petal.Sum = ~ Petal.Length + Petal.Width,
            Sepal.Petal.Ratio = ~ (Sepal.Length / Petal.Length)
            )
          )

使用子集训练:PipeOpChunk

有时候数据集太大,把数据分割成小块进行分块训练是很好的办法。

chks = po("chunk", 4)
lrns = ppl("greplicate", po("learner", lrn("classif.rpart")), 4)

mjv = po("classifavg", 4)

pipeline = chks %>>% lrns %>>% mjv
pipeline$plot(html = FALSE)

R语言机器学习mlr3:数据预处理和pipelines_第10张图片

task = tsk("iris")
train.idx = sample(seq_len(task$nrow), 120)
test.idx = setdiff(seq_len(task$nrow), train.idx)

pipelrn = as_learner(pipeline)
pipelrn$train(task, train.idx)$
  predict(task, train.idx)$
  score()
## classif.ce 
##  0.3333333

特征选择:PipeOpFilterPipeOpSelect

可以使用PipeOpFilter对象把mlr3filters里面的变量选择方法放进mlr3pipelines中。

po("filter", mlr3filters::flt("information_gain"))
## PipeOp:  (not trained)
## values: 
## Input channels :
##   input [Task,Task]
## Output channels :
##   output [Task,Task]

可使用filter_nfeat/filter_frac/filter_cutoff决定保留哪些变量/特征。

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