KDD2009vtreat

John Mount

Practical data science with R built chapter 6 built a number of single variable models. In Listing 6.11 it used an ad-hoc entropy based out of sample effect size estimate for variable selection. This likely (though it isn’t completely rigorous) picked variables conservatively.

We show here how to repeat this work on the KDD2009 dataset using more standard techniques more quickly. For vtreat details see: http://www.win-vector.com/blog/2014/08/vtreat-designing-a-package-for-variable-treatment/ and Chapter 6 of Practical Data Science with R: http://www.amazon.com/Practical-Data-Science/dp/1617291560 For details on data see: https://github.com/WinVector/zmPDSwR/tree/master/KDD2009 There is an issue that any data row used to build the single variable models isn’t exchangable with future unseen rows for the purposes of scoring and training. So the most hygienic way to work is to use one subset of data to build the single variable models, and then another to built the composite model, and a third for scoring. In particular models trained using rows used to build sub-models think the sub-models have large effects that the sub-models will in the future, and under-estimate degrees of freedom of complicated sub-models.

date()

## [1] "Sat Jul  6 18:02:57 2019"

#load some libraries
library('vtreat')
packageVersion("vtreat")

## [1] '1.4.3'

library('WVPlots') 

library('parallel')
library('xgboost')



# load the data as in the book
# change this path to match your directory structure
#dir = '~/Documents/work/PracticalDataScienceWithR/zmPDSwR/KDD2009/' 
#dir = '~/Documents/work/zmPDSwR/KDD2009/' 
dir = "./"


d = read.table(paste(dir,'orange_small_train.data.gz',sep=''),
                header=T,sep='\t',na.strings=c('NA',''), 
               stringsAsFactors=FALSE)
churn = read.table(paste(dir,'orange_small_train_churn.labels.txt',sep=''),
                    header=F,sep='\t')
d$churn = churn$V1
appetency = read.table(paste(dir,'orange_small_train_appetency.labels.txt',sep=''),
                        header=F,sep='\t')
d$appetency = appetency$V1
upselling = read.table(paste(dir,'orange_small_train_upselling.labels.txt',sep=''),
                        header=F,sep='\t')
d$upselling = upselling$V1
set.seed(729375)
d$rgroup = runif(dim(d)[[1]])
dTrain = subset(d,rgroup<=0.9)  # set for building models and impact coding
dTest = subset(d,rgroup>0.9) # set for evaluation
rm(list=c('d','churn','appetency','upselling','dir'))

dim(dTrain)

## [1] 45028   234

dim(dTest)

## [1] 4972  234

outcomes = c('churn','appetency','upselling')
vars = setdiff(colnames(dTrain),
                c(outcomes,'rgroup'))
yName = 'churn'
yTarget = 1

set.seed(239525)

ncore <- parallel::detectCores()
cl = parallel::makeCluster(ncore)
date()

## [1] "Sat Jul  6 18:03:05 2019"

date()

## [1] "Sat Jul  6 18:03:05 2019"

var_values <- vtreat::value_variables_C(dTrain,
    vars,yName,yTarget,
    smFactor=2.0, 
    parallelCluster=cl
    )

knitr::kable(var_values)

	rsq	count	sig	var
Var1	2.376463e-05	2	0.9059967	Var1
Var10	9.813199e-04	2	0.0000028	Var10
Var100	3.502919e-05	2	0.7245050	Var100
Var101	6.009046e-04	2	0.0003219	Var101
Var102	1.742866e-04	2	0.0842595	Var102
Var103	9.813199e-04	2	0.0000028	Var103
Var104	2.278508e-04	2	0.0402904	Var104
Var105	2.278508e-04	2	0.0402904	Var105
Var106	9.593148e-04	2	0.0000037	Var106
Var107	9.813199e-04	2	0.0000028	Var107
Var108	2.411380e-05	2	0.8993961	Var108
Var109	1.922021e-04	2	0.0656641	Var109
Var11	8.812072e-04	2	0.0000098	Var11
Var110	1.029613e-04	2	0.2365791	Var110
Var111	8.364368e-04	2	0.0000170	Var111
Var112	2.752908e-03	2	0.0000000	Var112
Var113	6.466721e-03	3	0.0000000	Var113
Var114	8.842007e-04	2	0.0000094	Var114
Var115	2.278508e-04	2	0.0402904	Var115
Var116	2.376463e-05	2	0.9059967	Var116
Var117	9.593148e-04	2	0.0000037	Var117
Var118	5.017689e-07	1	0.9131691	Var118
Var119	2.669092e-03	2	0.0000000	Var119
Var12	3.286502e-05	2	0.7550253	Var12
Var120	9.813199e-04	2	0.0000028	Var120
Var121	2.376463e-05	2	0.9059967	Var121
Var122	8.842007e-04	2	0.0000094	Var122
Var123	2.752908e-03	2	0.0000000	Var123
Var124	9.593148e-04	2	0.0000037	Var124
Var125	2.013427e-03	2	0.0000000	Var125
Var126	1.382784e-02	2	0.0000000	Var126
Var127	5.822901e-04	2	0.0004071	Var127
Var128	5.822901e-04	2	0.0004071	Var128
Var129	2.376463e-05	2	0.9059967	Var129
Var13	5.312771e-03	2	0.0000000	Var13
Var130	8.812072e-04	2	0.0000098	Var130
Var131	8.857586e-05	2	0.2948029	Var131
Var132	2.752908e-03	2	0.0000000	Var132
Var133	2.752908e-03	2	0.0000000	Var133
Var134	2.752908e-03	2	0.0000000	Var134
Var135	9.593148e-04	2	0.0000037	Var135
Var136	6.724954e-05	2	0.4136311	Var136
Var137	2.376463e-05	2	0.9059967	Var137
Var138	9.593148e-04	2	0.0000037	Var138
Var139	9.813199e-04	2	0.0000028	Var139
Var14	8.812072e-04	2	0.0000098	Var14
Var140	2.972593e-03	2	0.0000000	Var140
Var142	8.540294e-05	2	0.3097124	Var142
Var143	2.752908e-03	2	0.0000000	Var143
Var144	5.421069e-03	2	0.0000000	Var144
Var145	9.593148e-04	2	0.0000037	Var145
Var146	9.813199e-04	2	0.0000028	Var146
Var147	9.813199e-04	2	0.0000028	Var147
Var148	9.813199e-04	2	0.0000028	Var148
Var149	3.035956e-04	2	0.0146283	Var149
Var150	9.593148e-04	2	0.0000037	Var150
Var151	6.334417e-04	2	0.0002138	Var151
Var152	9.593148e-04	2	0.0000037	Var152
Var153	2.752908e-03	2	0.0000000	Var153
Var154	2.376463e-05	2	0.9059967	Var154
Var155	9.593148e-04	2	0.0000037	Var155
Var156	1.905424e-05	2	1.0000000	Var156
Var157	8.364368e-04	2	0.0000170	Var157
Var158	6.009046e-04	2	0.0003219	Var158
Var159	8.842007e-04	2	0.0000094	Var159
Var16	9.813199e-04	2	0.0000028	Var16
Var160	2.752908e-03	2	0.0000000	Var160
Var161	9.593148e-04	2	0.0000037	Var161
Var162	8.842007e-04	2	0.0000094	Var162
Var163	2.752908e-03	2	0.0000000	Var163
Var164	9.593148e-04	2	0.0000037	Var164
Var165	6.009046e-04	2	0.0003219	Var165
Var166	9.813199e-04	2	0.0000028	Var166
Var168	2.632379e-04	2	0.0250101	Var168
Var17	9.593148e-04	2	0.0000037	Var17
Var170	8.842007e-04	2	0.0000094	Var170
Var171	5.822901e-04	2	0.0004071	Var171
Var172	9.813199e-04	2	0.0000028	Var172
Var173	2.752908e-03	2	0.0000000	Var173
Var174	9.593148e-04	2	0.0000037	Var174
Var176	8.812072e-04	2	0.0000098	Var176
Var177	8.842007e-04	2	0.0000094	Var177
Var178	1.729184e-04	2	0.0858903	Var178
Var179	9.593148e-04	2	0.0000037	Var179
Var18	9.593148e-04	2	0.0000037	Var18
Var180	2.376463e-05	2	0.9059967	Var180
Var181	2.752908e-03	2	0.0000000	Var181
Var182	9.593148e-04	2	0.0000037	Var182
Var183	8.842007e-04	2	0.0000094	Var183
Var184	8.842007e-04	2	0.0000094	Var184
Var186	2.376463e-05	2	0.9059967	Var186
Var187	2.376463e-05	2	0.9059967	Var187
Var188	8.842007e-04	2	0.0000094	Var188
Var189	1.215778e-02	2	0.0000000	Var189
Var19	9.593148e-04	2	0.0000037	Var19
Var190	7.148827e-05	2	0.3861432	Var190
Var191	5.822881e-04	2	0.0004071	Var191
Var192	5.621971e-03	2	0.0000000	Var192
Var193	7.309619e-03	2	0.0000000	Var193
Var194	6.818729e-04	2	0.0001165	Var194
Var195	8.627090e-04	2	0.0000123	Var195
Var196	1.182542e-04	2	0.1882689	Var196
Var197	9.702002e-04	2	0.0000033	Var197
Var198	4.062008e-03	2	0.0000000	Var198
Var199	8.519979e-03	2	0.0000000	Var199
Var2	8.842007e-04	2	0.0000094	Var2
Var200	5.225536e-03	2	0.0000000	Var200
Var201	6.739672e-04	2	0.0001287	Var201
Var202	3.203968e-03	2	0.0000000	Var202
Var203	2.129657e-04	2	0.0493503	Var203
Var204	1.715598e-03	2	0.0000000	Var204
Var205	7.834535e-03	2	0.0000000	Var205
Var206	1.260427e-02	2	0.0000000	Var206
Var207	5.925474e-03	2	0.0000000	Var207
Var208	8.690949e-05	2	0.3025293	Var208
Var21	2.669092e-03	2	0.0000000	Var21
Var210	4.061590e-03	2	0.0000000	Var210
Var211	1.982178e-03	2	0.0000000	Var211
Var212	9.900362e-03	2	0.0000000	Var212
Var213	8.362860e-04	2	0.0000170	Var213
Var214	5.225536e-03	2	0.0000000	Var214
Var215	1.905564e-05	2	1.0000000	Var215
Var216	4.452981e-03	2	0.0000000	Var216
Var217	1.197942e-02	2	0.0000000	Var217
Var218	1.218248e-02	2	0.0000000	Var218
Var219	2.882058e-04	2	0.0179332	Var219
Var22	2.752908e-03	2	0.0000000	Var22
Var220	4.062008e-03	2	0.0000000	Var220
Var221	3.638509e-03	2	0.0000000	Var221
Var222	4.062008e-03	2	0.0000000	Var222
Var223	1.066714e-04	2	0.2237208	Var223
Var224	2.279107e-04	2	0.0402577	Var224
Var225	5.896221e-03	2	0.0000000	Var225
Var226	2.463084e-03	2	0.0000000	Var226
Var227	5.584397e-03	2	0.0000000	Var227
Var228	9.281733e-03	2	0.0000000	Var228
Var229	7.058449e-03	2	0.0000000	Var229
Var23	9.813199e-04	2	0.0000028	Var23
Var24	2.381645e-04	2	0.0350371	Var24
Var25	2.752908e-03	2	0.0000000	Var25
Var26	9.813199e-04	2	0.0000028	Var26
Var27	9.813199e-04	2	0.0000028	Var27
Var28	2.758522e-03	2	0.0000000	Var28
Var29	2.992132e-05	2	0.7995316	Var29
Var3	8.812072e-04	2	0.0000098	Var3
Var30	2.376463e-05	2	0.9059967	Var30
Var33	6.334417e-04	2	0.0002138	Var33
Var34	8.842007e-04	2	0.0000094	Var34
Var35	2.752908e-03	2	0.0000000	Var35
Var36	8.842007e-04	2	0.0000094	Var36
Var37	9.593148e-04	2	0.0000037	Var37
Var38	2.752908e-03	2	0.0000000	Var38
Var4	9.593148e-04	2	0.0000037	Var4
Var40	8.842007e-04	2	0.0000094	Var40
Var41	2.376463e-05	2	0.9059967	Var41
Var43	8.842007e-04	2	0.0000094	Var43
Var44	2.752908e-03	2	0.0000000	Var44
Var45	6.815674e-05	2	0.4075594	Var45
Var46	8.842007e-04	2	0.0000094	Var46
Var47	2.376463e-05	2	0.9059967	Var47
Var49	8.842007e-04	2	0.0000094	Var49
Var5	9.813199e-04	2	0.0000028	Var5
Var50	2.376463e-05	2	0.9059967	Var50
Var51	6.809351e-04	2	0.0001179	Var51
Var53	9.100583e-05	2	0.2839318	Var53
Var54	8.842007e-04	2	0.0000094	Var54
Var56	1.729184e-04	2	0.0858903	Var56
Var57	2.044753e-04	3	0.0831623	Var57
Var58	2.376463e-05	2	0.9059967	Var58
Var59	2.278508e-04	2	0.0402904	Var59
Var6	2.669092e-03	2	0.0000000	Var6
Var60	9.813199e-04	2	0.0000028	Var60
Var61	6.334417e-04	2	0.0002138	Var61
Var62	1.707981e-05	2	1.0000000	Var62
Var63	1.905424e-05	2	1.0000000	Var63
Var64	7.138463e-05	2	0.3867893	Var64
Var65	2.974265e-03	2	0.0000000	Var65
Var66	1.905424e-05	2	1.0000000	Var66
Var67	9.813199e-04	2	0.0000028	Var67
Var68	8.842007e-04	2	0.0000094	Var68
Var69	9.813199e-04	2	0.0000028	Var69
Var7	9.401229e-03	2	0.0000000	Var7
Var70	9.813199e-04	2	0.0000028	Var70
Var71	8.364368e-04	2	0.0000170	Var71
Var72	2.007878e-03	2	0.0000000	Var72
Var73	2.011271e-02	3	0.0000000	Var73
Var74	6.365026e-03	2	0.0000000	Var74
Var75	8.842007e-04	2	0.0000094	Var75
Var76	2.752908e-03	2	0.0000000	Var76
Var77	3.158199e-05	2	0.7739775	Var77
Var78	2.752908e-03	2	0.0000000	Var78
Var80	9.813199e-04	2	0.0000028	Var80
Var81	2.669092e-03	2	0.0000000	Var81
Var82	9.593148e-04	2	0.0000037	Var82
Var83	2.752908e-03	2	0.0000000	Var83
Var84	8.812072e-04	2	0.0000098	Var84
Var85	2.752908e-03	2	0.0000000	Var85
Var86	2.376463e-05	2	0.9059967	Var86
Var87	2.376463e-05	2	0.9059967	Var87
Var88	5.822901e-04	2	0.0004071	Var88
Var89	1.729184e-04	2	0.0858903	Var89
Var9	2.376463e-05	2	0.9059967	Var9
Var90	2.376463e-05	2	0.9059967	Var90
Var91	8.364368e-04	2	0.0000170	Var91
Var92	1.829928e-05	2	1.0000000	Var92
Var93	9.813199e-04	2	0.0000028	Var93
Var94	3.706455e-04	2	0.0060810	Var94
Var95	8.842007e-04	2	0.0000094	Var95
Var96	8.842007e-04	2	0.0000094	Var96
Var97	9.813199e-04	2	0.0000028	Var97
Var98	1.749498e-05	2	1.0000000	Var98
Var99	9.593148e-04	2	0.0000037	Var99

summary(var_values$sig < 1/nrow(var_values))

##    Mode   FALSE    TRUE 
## logical      59     153

vars <- var_values$var[var_values$sig < 1/nrow(var_values)]


date()

## [1] "Sat Jul  6 18:08:22 2019"

date()

## [1] "Sat Jul  6 18:08:22 2019"

# Run other models (with proper coding/training separation).
#
# This gets us back to AUC 0.74 range

customCoders = list('c.PiecewiseV.num' = vtreat::solve_piecewise,
                    'n.PiecewiseV.num' = vtreat::solve_piecewise,
                    'c.knearest.num' = vtreat::square_window,
                    'n.knearest.num' = vtreat::square_window,
                    'c.spline.num' = vtreat::spline_variable,
                    'n.spline.num' = vtreat::spline_variable)
                   # 'n.poolN.center' = vtreat::ppCoderN, 
                   # 'c.poolC.center' = vtreat::ppCoderC)
                   # 'n.NonDecreasingV.num' = vtreat::solveNonDecreasing,
                   # 'n.NonIncreasingV.num' = vtreat::solveNonIncreasing, 
                   # 'c.NonDecreasingV.num' = vtreat::solveNonDecreasing,
                   # 'c.NonIncreasingV.num' = vtreat::solveNonIncreasing)
cfe = mkCrossFrameCExperiment(dTrain,
                              vars,yName,yTarget,
                              customCoders=customCoders,
                              smFactor=2.0, 
                              parallelCluster=cl)

## [1] "vtreat 1.4.3 start initial treatment design Sat Jul  6 18:08:22 2019"
## [1] " start cross frame work Sat Jul  6 18:15:08 2019"
## [1] " vtreat::mkCrossFrameCExperiment done Sat Jul  6 18:17:50 2019"

treatmentsC = cfe$treatments
scoreFrame = treatmentsC$scoreFrame
table(scoreFrame$code)

## 
##       catB       catP      clean      isBAD   knearest        lev 
##         28         28        122        120          2        121 
## PiecewiseV     spline 
##        118         83

selvars <- scoreFrame$varName
treatedTrainM <- cfe$crossFrame[,c(yName,selvars),drop=FALSE]
treatedTrainM[[yName]] = treatedTrainM[[yName]]==yTarget

treatedTest = prepare(treatmentsC,
                      dTest,
                      pruneSig=NULL, 
                      varRestriction = selvars,
                      parallelCluster=cl)
treatedTest[[yName]] = treatedTest[[yName]]==yTarget

# prepare plotting frames
treatedTrainP = treatedTrainM[, yName, drop=FALSE]
treatedTestP = treatedTest[, yName, drop=FALSE]
date()

## [1] "Sat Jul  6 18:17:51 2019"

date()

## [1] "Sat Jul  6 18:17:51 2019"

mname = 'xgbPred'
print(paste(mname,length(selvars)))

## [1] "xgbPred 622"

params <- list(max_depth = 5, 
              objective = "binary:logistic",
              nthread = ncore)
model <- xgb.cv(data = as.matrix(treatedTrainM[, selvars, drop = FALSE]),
                label = treatedTrainM[[yName]],
                nrounds = 400,
                params = params,
                nfold = 5,
                early_stopping_rounds = 10,
                eval_metric = "logloss")

## [1]  train-logloss:0.503118+0.000619 test-logloss:0.504132+0.001167 
## Multiple eval metrics are present. Will use test_logloss for early stopping.
## Will train until test_logloss hasn't improved in 10 rounds.
## 
## [2]  train-logloss:0.400317+0.001025 test-logloss:0.402179+0.002146 
## [3]  train-logloss:0.338216+0.001284 test-logloss:0.341292+0.002991 
## [4]  train-logloss:0.299239+0.001445 test-logloss:0.303358+0.003886 
## [5]  train-logloss:0.274050+0.001584 test-logloss:0.279312+0.004833 
## [6]  train-logloss:0.257435+0.001738 test-logloss:0.263867+0.005338 
## [7]  train-logloss:0.246297+0.001758 test-logloss:0.254254+0.005848 
## [8]  train-logloss:0.238483+0.001759 test-logloss:0.247876+0.006400 
## [9]  train-logloss:0.232978+0.001624 test-logloss:0.243947+0.006830 
## [10] train-logloss:0.228714+0.001697 test-logloss:0.241427+0.006993 
## [11] train-logloss:0.225146+0.001731 test-logloss:0.239994+0.007271 
## [12] train-logloss:0.222296+0.001564 test-logloss:0.238938+0.007755 
## [13] train-logloss:0.219822+0.001521 test-logloss:0.238190+0.007910 
## [14] train-logloss:0.217801+0.001439 test-logloss:0.237995+0.007935 
## [15] train-logloss:0.215918+0.001480 test-logloss:0.237707+0.007992 
## [16] train-logloss:0.213984+0.001646 test-logloss:0.237540+0.008098 
## [17] train-logloss:0.212287+0.001626 test-logloss:0.237529+0.008226 
## [18] train-logloss:0.210559+0.001695 test-logloss:0.237715+0.008316 
## [19] train-logloss:0.209040+0.001769 test-logloss:0.237638+0.008203 
## [20] train-logloss:0.207600+0.001686 test-logloss:0.237583+0.008416 
## [21] train-logloss:0.206182+0.001846 test-logloss:0.237564+0.008456 
## [22] train-logloss:0.204802+0.001708 test-logloss:0.237705+0.008422 
## [23] train-logloss:0.203413+0.001602 test-logloss:0.237697+0.008590 
## [24] train-logloss:0.202214+0.001850 test-logloss:0.237877+0.008622 
## [25] train-logloss:0.201347+0.001721 test-logloss:0.237981+0.008500 
## [26] train-logloss:0.199966+0.001484 test-logloss:0.238009+0.008654 
## [27] train-logloss:0.198762+0.001469 test-logloss:0.238275+0.008497 
## Stopping. Best iteration:
## [17] train-logloss:0.212287+0.001626 test-logloss:0.237529+0.008226

nrounds <- model$best_iteration
print(paste("nrounds", nrounds))

## [1] "nrounds 17"

model <- xgboost(data = as.matrix(treatedTrainM[, selvars, drop = FALSE]),
                 label = treatedTrainM[[yName]],
                 nrounds = nrounds,
                 params = params)

## [1]  train-error:0.071733 
## [2]  train-error:0.071800 
## [3]  train-error:0.071644 
## [4]  train-error:0.071800 
## [5]  train-error:0.071711 
## [6]  train-error:0.071578 
## [7]  train-error:0.071733 
## [8]  train-error:0.071644 
## [9]  train-error:0.071689 
## [10] train-error:0.071667 
## [11] train-error:0.071600 
## [12] train-error:0.070956 
## [13] train-error:0.070845 
## [14] train-error:0.070734 
## [15] train-error:0.070245 
## [16] train-error:0.070134 
## [17] train-error:0.070023

treatedTrainP[[mname]] = predict(model, 
                                 newdata=as.matrix(treatedTrainM[, selvars, drop = FALSE]), 
                                 type='response')
treatedTestP[[mname]] = predict(model,
                                newdata=as.matrix(treatedTest[, selvars, drop = FALSE]), 
                                n.trees=nTrees)
date()

## [1] "Sat Jul  6 18:22:34 2019"

date()

## [1] "Sat Jul  6 18:22:34 2019"

t1 = paste(mname,'trainingM data')
print(DoubleDensityPlot(treatedTrainP, mname, yName, 
                        title=t1))

print(ROCPlot(treatedTrainP, mname, yName, yTarget,
              title=t1))

print(WVPlots::PRPlot(treatedTrainP, mname, yName, yTarget,
              title=t1))

t2 = paste(mname,'test data')
print(DoubleDensityPlot(treatedTestP, mname, yName, 
                        title=t2))

print(ROCPlot(treatedTestP, mname, yName, yTarget,
              title=t2))

print(WVPlots::PRPlot(treatedTestP, mname, yName, yTarget,
              title=t2))

print(date())

## [1] "Sat Jul  6 18:22:42 2019"

print("*****************************")

## [1] "*****************************"

date()

## [1] "Sat Jul  6 18:22:42 2019"

if(!is.null(cl)) {
    parallel::stopCluster(cl)
    cl = NULL
}

WinVector / zmPDSwR

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zmPDSwR/KDD2009/KDD2009vtreat.md

KDD2009vtreat

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