杭州网站建站公司,成都建设局官网,怎么能够找到免费的网址,小当网 绵阳网站建设本篇主要介绍mlr3包的基本使用。
一个简单的机器学习流程在mlr3中可被分解为以下几个部分#xff1a;
创建任务 比如回归、分裂、生存分析、降维、密度任务等等挑选学习器#xff08;算法/模型#xff09; 比如随机森林、决策树、SVM、KNN等等训练和预测
创建任务
本次示…本篇主要介绍mlr3包的基本使用。
一个简单的机器学习流程在mlr3中可被分解为以下几个部分
创建任务 比如回归、分裂、生存分析、降维、密度任务等等挑选学习器算法/模型 比如随机森林、决策树、SVM、KNN等等训练和预测
创建任务
本次示例将使用mtcars数据集创建一个回归任务结果变量或者叫因变量等等是mpg。
# 首先加载数据
data(mtcars,package datasets)
data - mtcars[,1:3]
str(data)
## data.frame: 32 obs. of 3 variables:
## $ mpg : num 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
## $ cyl : num 6 6 4 6 8 6 8 4 4 6 ...
## $ disp: num 160 160 108 258 360 ...使用as_task_regr()创建回归任务as_task_classif()可创建分类任务。
library(mlr3)task_mtcars - as_task_regr(data,target mpg,idcars) # id是随便起一个名字
print(task_mtcars)
## TaskRegr:cars (32 x 3)
## * Target: mpg
## * Properties: -
## * Features (2):
## - dbl (2): cyl, disp可以看到数据以供32行3列target是mpgfeature是cyl和disp都是bdl类型。
在创建模型前先探索数据
library(mlr3viz) # 使用此包可视化数据
autoplot(task_mtcars, type pairs) # 基于GGally我之前介绍过
## Registered S3 method overwritten by GGally:
## method from
## .gg ggplot2如果你觉得每次加载1个R包很烦可以直接使用library(mlr3verse)加载所有基础包
如果你想使用自带数据集进行学习此包也自带了很多流行的机器学习数据集。
查看内置数据集
as.data.table(mlr_tasks)
## key task_type nrow ncol properties lgl int dbl chr fct ord pxc
## 1: boston_housing regr 506 19 0 3 13 0 2 0 0
## 2: breast_cancer classif 683 10 twoclass 0 0 0 0 0 9 0
## 3: german_credit classif 1000 21 twoclass 0 3 0 0 14 3 0
## 4: iris classif 150 5 multiclass 0 0 4 0 0 0 0
## 5: mtcars regr 32 11 0 0 10 0 0 0 0
## 6: penguins classif 344 8 multiclass 0 3 2 0 2 0 0
## 7: pima classif 768 9 twoclass 0 0 8 0 0 0 0
## 8: sonar classif 208 61 twoclass 0 0 60 0 0 0 0
## 9: spam classif 4601 58 twoclass 0 0 57 0 0 0 0
## 10: wine classif 178 14 multiclass 0 2 11 0 0 0 0
## 11: zoo classif 101 17 multiclass 15 1 0 0 0 0 0结果很详细给出了任务类型行列数变量类型等。
如果想要使用内置数据集可使用以下代码
task_penguin - tsk(penguins)
print(task_penguin)
## TaskClassif:penguins (344 x 8)
## * Target: species
## * Properties: multiclass
## * Features (7):
## - int (3): body_mass, flipper_length, year
## - dbl (2): bill_depth, bill_length
## - fct (2): island, sex可以非常方便的取子集查看
library(mlr3verse)
as.data.table(mlr_tasks)[, 1:4]
## key task_type nrow ncol
## 1: actg surv 1151 13
## 2: bike_sharing regr 17379 14
## 3: boston_housing regr 506 19
## 4: breast_cancer classif 683 10
## 5: faithful dens 272 1
## 6: gbcs surv 686 10
## 7: german_credit classif 1000 21
## 8: grace surv 1000 8
## 9: ilpd classif 583 11
## 10: iris classif 150 5
## 11: kc_housing regr 21613 20
## 12: lung surv 228 10
## 13: moneyball regr 1232 15
## 14: mtcars regr 32 11
## 15: optdigits classif 5620 65
## 16: penguins classif 344 8
## 17: pima classif 768 9
## 18: precip dens 70 1
## 19: rats surv 300 5
## 20: sonar classif 208 61
## 21: spam classif 4601 58
## 22: titanic classif 1309 11
## 23: unemployment surv 3343 6
## 24: usarrests clust 50 4
## 25: whas surv 481 11
## 26: wine classif 178 14
## 27: zoo classif 101 17
## key task_type nrow ncol支持非常多探索数据的操作
task_penguin$ncol
## [1] 8
task_penguin$nrow
## [1] 344
task_penguin$feature_names
## [1] bill_depth bill_length body_mass flipper_length
## [5] island sex year
task_penguin$feature_types
## id type
## 1: bill_depth numeric
## 2: bill_length numeric
## 3: body_mass integer
## 4: flipper_length integer
## 5: island factor
## 6: sex factor在这里插入代码片
## 7: year integer
task_penguin$target_names
## [1] species
task_penguin$task_type
## [1] classif
task_penguin$data()
## species bill_depth bill_length body_mass flipper_length island sex
## 1: Adelie 18.7 39.1 3750 181 Torgersen male
## 2: Adelie 17.4 39.5 3800 186 Torgersen female
## 3: Adelie 18.0 40.3 3250 195 Torgersen female
## 4: Adelie NA NA NA NA Torgersen NA
## 5: Adelie 19.3 36.7 3450 193 Torgersen female
## ---
## 340: Chinstrap 19.8 55.8 4000 207 Dream male
## 341: Chinstrap 18.1 43.5 3400 202 Dream female
## 342: Chinstrap 18.2 49.6 3775 193 Dream male
## 343: Chinstrap 19.0 50.8 4100 210 Dream male
## 344: Chinstrap 18.7 50.2 3775 198 Dream female
## year
## 1: 2007
## 2: 2007
## 3: 2007
## 4: 2007
## 5: 2007
## ---
## 340: 2009
## 341: 2009
## 342: 2009
## 343: 2009
## 344: 2009
task_penguin$head(3)
## species bill_depth bill_length body_mass flipper_length island sex
## 1: Adelie 18.7 39.1 3750 181 Torgersen male
## 2: Adelie 17.4 39.5 3800 186 Torgersen female
## 3: Adelie 18.0 40.3 3250 195 Torgersen female
## year
## 1: 2007
## 2: 2007
## 3: 2007
# 还有很多行列选择操作、改变变量的id比如某个变量不参与模型训练等多种操作可视化数据很多都是基于GGally包可以看我之前的介绍
autoplot(task_penguin)1 autoplot(task_penguin, type pairs)
## stat_bin() using bins 30. Pick better value with binwidth.
## stat_bin() using bins 30. Pick better value with binwidth.
## stat_bin() using bins 30. Pick better value with binwidth.
## stat_bin() using bins 30. Pick better value with binwidth.
## stat_bin() using bins 30. Pick better value with binwidth.
## stat_bin() using bins 30. Pick better value with binwidth.
## stat_bin() using bins 30. Pick better value with binwidth.
## stat_bin() using bins 30. Pick better value with binwidth.
## stat_bin() using bins 30. Pick better value with binwidth.
## stat_bin() using bins 30. Pick better value with binwidth.
## stat_bin() using bins 30. Pick better value with binwidth.
## stat_bin() using bins 30. Pick better value with binwidth.
## stat_bin() using bins 30. Pick better value with binwidth.
## stat_bin() using bins 30. Pick better value with binwidth.
## stat_bin() using bins 30. Pick better value with binwidth.autoplot(task_penguin, type duo)1 创建learner
所有的学习器都通过以下2个步骤工作
mlr3verse只支持常见的学习器比如随机森林、决策树、SVM、KNN等如果想要查看所有的学习器可以安装mlr3extralearners。
查看所有的支持的learner:All learners
# 加载R包常见的算法
library(mlr3verse)
mlr_learners
## DictionaryLearner with 53 stored values
## Keys: classif.cv_glmnet, classif.debug, classif.featureless,
## classif.glmnet, classif.kknn, classif.lda, classif.log_reg,
## classif.multinom, classif.naive_bayes, classif.nnet, classif.qda,
## classif.ranger, classif.rpart, classif.svm, classif.xgboost,
## clust.agnes, clust.ap, clust.cmeans, clust.cobweb, clust.dbscan,
## clust.diana, clust.em, clust.fanny, clust.featureless, clust.ff,
## clust.hclust, clust.kkmeans, clust.kmeans, clust.MBatchKMeans,
## clust.meanshift, clust.pam, clust.SimpleKMeans, clust.xmeans,
## dens.hist, dens.kde, regr.cv_glmnet, regr.debug, regr.featureless,
## regr.glmnet, regr.kknn, regr.km, regr.lm, regr.ranger, regr.rpart,
## regr.svm, regr.xgboost, surv.coxph, surv.cv_glmnet, surv.glmnet,
## surv.kaplan, surv.ranger, surv.rpart, surv.xgboost创建learner
# 决策树
learner lrn(classif.rpart)
print(learner)
## LearnerClassifRpart:classif.rpart
## * Model: -
## * Parameters: xval0
## * Packages: mlr3, rpart
## * Predict Type: response
## * Feature types: logical, integer, numeric, factor, ordered
## * Properties: importance, missings, multiclass, selected_features,
## twoclass, weights查看支持的超参数
learner$param_set
## ParamSet
## id class lower upper nlevels default value
## 1: cp ParamDbl 0 1 Inf 0.01
## 2: keep_model ParamLgl NA NA 2 FALSE
## 3: maxcompete ParamInt 0 Inf Inf 4
## 4: maxdepth ParamInt 1 30 30 30
## 5: maxsurrogate ParamInt 0 Inf Inf 5
## 6: minbucket ParamInt 1 Inf Inf NoDefault[3]
## 7: minsplit ParamInt 1 Inf Inf 20
## 8: surrogatestyle ParamInt 0 1 2 0
## 9: usesurrogate ParamInt 0 2 3 2
## 10: xval ParamInt 0 Inf Inf 10 0一目了然方便使用记不住了可以看看毕竟太多了这一点比tidymodels贴心。
设定超参数的值
learner$param_set$values list(cp 0.01, xval 0)
learner
## LearnerClassifRpart:classif.rpart
## * Model: -
## * Parameters: cp0.01, xval0
## * Packages: mlr3, rpart
## * Predict Type: response
## * Feature types: logical, integer, numeric, factor, ordered
## * Properties: importance, missings, multiclass, selected_features,
## twoclass, weights也可以在指定learner时设定
learner lrn(classif.rpart, xval0, cp 0.001)
learner$param_set$values
## $xval
## [1] 0
##
## $cp
## [1] 0.001训练、预测和性能评价
创建任务选择模型
library(mlr3verse)task tsk(penguins) # 使用内置数据集
learner lrn(classif.rpart) #决策树分类划分训练集和测试集
spilt - partition(task,ratio 0.6, stratify T)
spilt$train
## [1] 2 3 4 5 7 8 10 11 12 14 15 16 17 19 23 25 26 27
## [19] 28 30 31 33 34 36 37 40 42 45 46 48 50 51 53 56 59 60
## [37] 61 62 64 66 67 68 69 71 73 75 78 82 83 84 88 89 91 94
## [55] 96 97 99 100 101 102 104 107 108 113 114 115 118 120 121 123 125 126
## [73] 127 128 129 130 131 132 133 135 136 137 138 139 142 143 145 149 150 151
## [91] 152 154 156 157 159 160 163 169 170 171 172 173 175 176 179 180 181 182
## [109] 183 186 187 188 189 193 194 197 199 200 201 203 206 208 210 211 212 213
## [127] 214 215 216 218 219 220 222 223 224 225 226 228 229 230 233 236 237 239
## [145] 240 241 242 243 247 248 249 252 253 254 255 256 257 259 260 262 266 271
## [163] 272 273 274 277 279 280 285 288 290 291 293 294 295 296 297 299 300 301
## [181] 302 304 305 306 309 310 312 313 317 319 321 322 323 324 325 328 330 331
## [199] 332 334 337 338 339 340 341 342训练模型
learner$train(task, row_ids spilt$train)
print(learner$model)
## n 206
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 206 115 Adelie (0.44174757 0.19902913 0.35922330)
## 2) flipper_length 207.5 128 39 Adelie (0.69531250 0.30468750 0.00000000)
## 4) bill_length 42.35 86 0 Adelie (1.00000000 0.00000000 0.00000000) *
## 5) bill_length42.35 42 3 Chinstrap (0.07142857 0.92857143 0.00000000) *
## 3) flipper_length207.5 78 4 Gentoo (0.02564103 0.02564103 0.94871795) *预测
prediction - learner$predict(task, row_ids spilt$test)
print(prediction)
## PredictionClassif for 138 observations:
## row_ids truth response
## 1 Adelie Adelie
## 6 Adelie Adelie
## 9 Adelie Adelie
## ---
## 336 Chinstrap Chinstrap
## 343 Chinstrap Gentoo
## 344 Chinstrap Chinstrap混淆矩阵
prediction$confusion
## truth
## response Adelie Chinstrap Gentoo
## Adelie 53 1 0
## Chinstrap 8 24 2
## Gentoo 0 2 48可视化
autoplot(prediction)1 模型评价
先查看下支持的评价指标
mlr_measures
## DictionaryMeasure with 87 stored values
## Keys: aic, bic, classif.acc, classif.auc, classif.bacc, classif.bbrier,
## classif.ce, classif.costs, classif.dor, classif.fbeta, classif.fdr,
## classif.fn, classif.fnr, classif.fomr, classif.fp, classif.fpr,
## classif.logloss, classif.mbrier, classif.mcc, classif.npv,
## classif.ppv, classif.prauc, classif.precision, classif.recall,
## classif.sensitivity, classif.specificity, classif.tn, classif.tnr,
## classif.tp, classif.tpr, clust.ch, clust.db, clust.dunn,
## clust.silhouette, clust.wss, debug, dens.logloss, oob_error,
## regr.bias, regr.ktau, regr.mae, regr.mape, regr.maxae, regr.medae,
## regr.medse, regr.mse, regr.msle, regr.pbias, regr.rae, regr.rmse,
## regr.rmsle, regr.rrse, regr.rse, regr.rsq, regr.sae, regr.smape,
## regr.srho, regr.sse, selected_features, sim.jaccard, sim.phi,
## surv.brier, surv.calib_alpha, surv.calib_beta, surv.chambless_auc,
## surv.cindex, surv.dcalib, surv.graf, surv.hung_auc, surv.intlogloss,
## surv.logloss, surv.mae, surv.mse, surv.nagelk_r2, surv.oquigley_r2,
## surv.rmse, surv.schmid, surv.song_auc, surv.song_tnr, surv.song_tpr,
## surv.uno_auc, surv.uno_tnr, surv.uno_tpr, surv.xu_r2, time_both,
## time_predict, time_train这里我们选择accuracy
measure - msr(classif.acc)
prediction$score(measure)
## classif.acc
## 0.9057971选择多个指标
measures - msrs(c(classif.acc,classif.auc,classif.ce))
prediction$score(measures)
## classif.acc classif.auc classif.ce
## 0.9057971 NaN 0.0942029
