Imbalanced datasets help

Machine Learning and Modeling
1

this is my code with which i tried to attack my imbalanced dataset from here https://www.kaggle.com/mlg-ulb/creditcardfraud.

Now i have 2 questions. 1) When i try to split my data into train/test data and try to run the table() function, i always get the error : Error in table(train) : attempt to make a table with >= 2^31 elements How do i fix that and the other errors? 2) Now i try to compare accuary between those techniques. I would love to use the ConfusionMatrix on it? How do i implement it into my code?

Thats my code:

library(ROSE)
library(rpart)
library(rpart.plot)
library(ggplot2)
library(UBL)
library(caret)
library(SMOTE)
library(tidyverse)
library(DMwR)
install.packages("partykit")
library(partykit)

setwd("C:\\Users\\loren\\Dropbox\\Uni\\Präsentation\\Datensätze")
daten <- read.csv("creditcard.csv")
head(data1)
prop.table(table(daten$Class))
table(daten$Class)

index1 <- sample(2, nrow(daten), replace = TRUE, prob = c(0.8, 0.2))
train <- daten[index1==1,]
test <- daten[index1==2,]
table(train)

index <- createDataPartition(daten$Class, p = 0.8, list = FALSE)
train_data <- daten[index, ]
test_data  <- daten[-index, ]

table(train_data)
table(test_data)

# Down-Sample
down_train <- downSample(x = train_data[, -ncol(train_data)],
                     y = train_data$Class)
table(down_train$Class)
fit_down <- rpart(Class~., data = down_train)
pred_down <- predict(fit_down, newdata = test)

# Up-Sample
up_train <- upSample(x = uniaug_train[, -ncol(uniaug_train)],
                 y = uniaug_train$)
table(up_train$Class)
fit_up <- rpart(Class~., data = up_train)
pred_up <- predict(fit_up, newdata = test)

# SMOTE
library(DMwR)
smote_train <- SMOTE(Class~., data = train_data)
table(smote_train$Class)
fit_smote <- rpart(Class~., data = smote_train)
pred_smote <- predict(fit_smote, newdata = test)

# ROSE
rose_train <- ROSE(Class~., data = train_data)$data
table(rose_train$Class)
fit_rose <- rpart(Class~., data = rose_train)
pred_rose <- predict(fit_rose, newdata = test)


# Confusion-Matrix

Thx in advance

2

Hi @Mingabua: It may be that someone might be able to diagnose the issue from looking at your code, but if you could a sample of your data, that help folks be able to help you more easily. Could you run dput(head(daten, 50)) and paste the output here, like this?

```
<--- paste output here
```
1 Like
3

First of all, thanks for your answer. I got rid of the "to many elements" error by adding $Class to table(train_data$Class)

Thats the output. Now that i got the first part fixed, my over/under/Smote wont work.
These are the errors.

  1. In downSample(x = train_data[, -ncol(train_data)], y = train_data$Class) : Down-sampling requires a factor variable as the response. The original data was returned. For Downsampling

  2. Error: unexpected ')' in: "up_train <- upSample(x = uniaug_train[, -ncol(uniaug_train)], y = uniaug_train$)" For Upsampling

  3. Error in T[i, ] : subscript out of bounds In addition: There were 50 or more warnings (use warnings() to see the first 50) Not sure if this is an error

4

It would be helpful for you to post the modified code you used, too, otherwise it's hard to know what triggered the error.

5

Sure, sorry.

setwd("C:\\Users\\loren\\Dropbox\\Uni\\Präsentation\\Datensätze")
daten <- read.csv("creditcard.csv")
head(daten)
prop.table(table(daten$Class))
table(daten$Class)

index <- createDataPartition(daten$Class, p = 0.8, list = FALSE)
train_data <- daten[index, ]
test_data  <- daten[-index, ]

table(train_data$Class)
prop.table(table(train_data$Class))
table(test_data$Class)
prop.table(table(test_data$Class))

# Down-Sample
down_train <- downSample(x = train_data[, -ncol(train_data)],
                         y = train_data$Class)
table(down_train$Class)
fit_down <- rpart(Class~., data = down_train)
pred_down <- predict(fit_down, newdata = test_data)

# Up-Sample
up_train <- upSample(x = train_data[, -ncol(train_data)],
                     y = train_data$Class)
table(up_train$Class)
fit_up <- rpart(Class~., data = up_train)
pred_up <- predict(fit_up, newdata = test_data)

# SMOTE
library(DMwR)
smote_train <- SMOTE(Class~., data = train_data)
table(smote_train$Class)
fit_smote <- rpart(Class~., data = smote_train)
pred_smote <- predict(fit_smote, newdata = test_data)

# ROSE
rose_train <- ROSE(Class~., data = train_data)$data
table(rose_train$Class)
fit_rose <- rpart(Class~., data = rose_train)
pred_rose <- predict(fit_rose, newdata = test_data)
6

Hi @Mingabua: I'm finding it difficult to follow since your code is changing and doesn't match the errors you asked about. I would suggest trying to deal with each error you encounter, one at a time, so maybe next it would be helpful to first post the data and code needed to create train_data, and then post just the 'Down-Sample' code. If you do this as a reprex, the errors will show up for everyone to see. Does this make sense?

7 

setwd("C:\\Users\\loren\\Dropbox\\Uni\\Präsentation\\Datensätze")
daten <- read.csv("creditcard.csv")
head(daten)
prop.table(table(daten$Class))
table(daten$Class)

index <- createDataPartition(daten$Class, p = 0.8, list = FALSE)
train_data <- daten[index, ]
test_data  <- daten[-index, ]

table(train_data$Class)
prop.table(table(train_data$Class))
table(test_data$Class)
prop.table(table(test_data$Class))

down_train <- downSample(x = train_data[, -ncol(train_data)],
                         y = train_data$Class)

This is my code which i use to create train_data and test_data. I got rid of the first error. The problem now is, i cant run the downSample command on it. I always get this error.

"Warning message:
In downSample(x = train_data[, -ncol(train_data)], y = train_data$Class) :
Down-sampling requires a factor variable as the response. The original data was returned." How do i fix that?

8

Thanks, @Mingabua: Could you say what packages contain the functions prop.table(), createDataPartition, and downSample? That would help me selectively install what's needed since I have limited space on my machine.

9

prop.table should be a standard function. downSample is from ‚caret‘ and createDataPartition is from ,caret, aswell

10

Great -- thanks, @Mingabua. I'll see if I can reproduce the error from the data you posted.

11

OK, @Mingabua: Here's a self-contained reprex, which is what I would ask you do to, too, as folks try to help with these issues. I've separated data you supplied from the code, and I get an error on the first command:

load your data 
daten <-
structure(list(Time = c(0, 0, 1, 1, 2, 2, 4, 7, 7, 9, 10, 10, 
10, 11, 12, 12, 12, 13, 14, 15, 16, 17, 18, 18, 22, 22, 23, 23, 
23, 23, 24, 25, 26, 26, 26, 26, 27, 27, 29, 29, 32, 32, 33, 33, 
34, 34, 34, 34, 35, 35), V1 = c(-1.359807134, 1.191857111, -1.358354062, 
-0.966271712, -1.158233093, -0.425965884, 1.229657635, -0.644269442, 
-0.894286082, -0.338261752, 1.449043781, 0.384978215, 1.249998742, 
1.069373588, -2.791854766, -0.752417043, 1.103215435, -0.436905071, 
-5.401257663, 1.492935977, 0.694884776, 0.96249607, 1.166616382, 
0.247491128, -1.946525131, -2.074294672, 1.17328461, 1.322707269, 
-0.41428881, 1.059387115, 1.23742903, 1.114008595, -0.529912284, 
-0.529912284, -0.535387763, -0.535387763, -0.246045949, -1.452187279, 
0.996369532, 1.110880342, 1.24905472, -2.008871795, -0.607877143, 
-0.935731509, -0.762255666, 1.138315566, -0.291540245, 0.201685891, 
1.386396974, -1.063235679), V2 = c(-0.072781173, 0.266150712, 
-1.340163075, -0.185226008, 0.877736755, 0.960523045, 0.141003507, 
1.417963545, 0.286157196, 1.119593376, -1.176338825, 0.616109459, 
-1.221636809, 0.287722129, -0.327770757, 0.345485415, -0.040296215, 
0.918966213, -5.450147834, -1.029345732, -1.361819103, 0.328461026, 
0.502120088, 0.277665627, -0.044900505, -0.121481799, 0.353497877, 
-0.174040833, 0.905437323, -0.175319187, 0.061042584, 0.08554609, 
0.873891581, 0.873891581, 0.865267808, 0.865267808, 0.473266903, 
1.765123739, -0.122588787, 0.168716771, -0.624727077, 2.198526503, 
1.031345078, 0.170415774, 0.127767752, 0.05695597, 0.445575314, 
0.497483215, -0.794209465, 1.418190626), V3 = c(2.536346738, 
0.166480113, 1.773209343, 1.79299334, 1.548717847, 1.141109342, 
0.045370774, 1.074380376, -0.113192213, 1.044366552, 0.913859833, 
-0.874299703, 0.383930151, 0.828612727, 1.641750161, 2.057322913, 
1.267332089, 0.924590774, 1.186304631, 0.454794734, 1.02922104, 
-0.171479054, -0.067300314, 1.185470842, -0.405570068, 1.32202063, 
0.283905065, 0.434555031, 1.727452944, 1.266129643, 0.38052588, 
0.493702487, 1.347247329, 1.347247329, 1.351076288, 1.351076288, 
1.695737554, 0.611668541, 0.546819473, 0.51714396, -0.710588904, 
0.14424174, 1.740449737, 2.746261269, 2.650056386, 0.649418965, 
1.249752116, 1.373912574, 0.778224191, 1.086672863), V4 = c(1.378155224, 
0.448154078, 0.379779593, -0.863291275, 0.403033934, -0.16825208, 
1.202612737, -0.492199018, -0.27152613, -0.222187277, -1.375666655, 
-0.094018626, -1.234898688, 2.71252043, 1.767472744, -1.468643298, 
1.28909147, -0.727219054, 1.7362388, -1.43802588, 0.834159299, 
2.109204068, 2.261569239, -0.09260255, -1.013057337, 0.410007514, 
1.133563318, 0.576037652, 1.473471267, 1.186109955, 0.761564111, 
1.335759985, 0.145456677, 0.145456677, 0.147575474, 0.147575474, 
0.262411488, 1.176824984, 0.706579541, 1.32540692, -0.991600361, 
1.159432262, 1.23210555, -1.077964912, -1.875662771, 0.873062041, 
-1.73573589, 0.571405318, -0.864708434, 1.2414396), V5 = c(-0.33832077, 
0.060017649, -0.503198133, -0.01030888, -0.407193377, 0.420986881, 
0.191880989, 0.948934095, 2.66959866, 0.499360806, -1.971383165, 
2.924584378, -1.485419474, -0.178398016, -0.136588446, -1.15839368, 
-0.735997164, 0.915678718, 3.049105878, -1.555434101, -1.191208794, 
1.129565571, 0.428804195, -1.314393979, 2.9419677, 0.295197546, 
-0.172577182, -0.836758046, 0.007442741, -0.786001753, -0.35977071, 
-0.300188551, 0.414208858, 0.414208858, 0.433680212, 0.433680212, 
-0.010866414, -0.445979892, 0.134559557, -0.191573354, 1.429973192, 
-0.815174288, 0.41859226, -0.305594036, -0.893356398, -0.468466331, 
0.085755559, -0.63065775, -1.06413228, 0.002306126), V6 = c(0.462387778, 
-0.082360809, 1.800499381, 1.247203168, 0.095921462, -0.029727552, 
0.272708123, 0.428118463, 3.721818061, -0.246761101, -0.629152139, 
3.317027168, -0.753230165, 0.33754373, 0.807596468, -0.077849829, 
0.288069163, -0.127867352, -1.763405574, -0.720961147, 1.309108819, 
1.696037686, 0.089473517, -0.150115998, 2.955053397, -0.95953723, 
-0.916053707, -0.831083411, -0.200330677, 0.578435276, -0.49408415, 
-0.010753783, 0.100223094, 0.100223094, 0.086982938, 0.086982938, 
-0.610835935, 0.246826452, 1.156995112, 0.019503723, 3.692977019, 
0.182288271, 0.119168119, 0.011577039, -0.268714889, -0.410194552, 
-0.121924299, -0.539659124, 0.351296174, 0.045901581), V7 = c(0.239598554, 
-0.078802983, 0.791460956, 0.23760894, 0.592940745, 0.476200949, 
-0.005159003, 1.120631358, 0.370145128, 0.651583206, -1.423235601, 
0.470454672, -0.689404975, -0.096716862, -0.42291139, -0.608581418, 
-0.586056786, 0.707641607, -1.559737699, -1.08066413, -0.878585911, 
0.107711607, 0.24114658, -0.94636495, -0.063063147, 0.543985491, 
0.369024845, -0.264904961, 0.740228319, -0.767084276, 0.006494218, 
-0.118760015, 0.711206083, 0.711206083, 0.693039311, 0.693039311, 
0.793936546, -0.257566156, -0.294561331, -0.031849108, -1.090208641, 
-0.617108302, 0.850892666, -0.296178451, -0.419863597, -0.013897554, 
0.407715857, -0.076270291, -1.191454583, 0.514120897), V8 = c(0.098697901, 
0.085101655, 0.247675787, 0.377435875, -0.270532677, 0.260314333, 
0.08121294, -3.807864239, 0.851084443, 0.069538587, 0.048455888, 
0.538247228, -0.227487228, 0.115981736, -1.907107476, 0.003603484, 
0.189379714, 0.087962355, 0.160841747, -0.053127118, 0.445290128, 
0.521502164, 0.138081705, -1.617935051, 0.855546309, -0.104626728, 
-0.327260242, -0.220981943, -0.0292474, 0.401046149, -0.13386238, 
0.188616696, 0.176065957, 0.176065957, 0.179742261, 0.179742261, 
-0.247252831, 1.092472497, 0.407429099, 0.11761992, 0.967290815, 
1.530816728, -0.176267419, 0.40277557, 0.146233407, -0.072439609, 
0.095309378, -0.91710575, 0.052685634, 0.241252403), V9 = c(0.36378697, 
-0.255425128, -1.514654323, -1.387024063, 0.817739308, -0.568671376, 
0.464959995, 0.615374731, -0.392047587, -0.736727316, -1.720408393, 
-0.558894612, -2.094010573, -0.221082566, 0.755712908, -0.436166984, 
0.782332892, -0.665271354, 1.23308974, -1.978681595, -0.446195832, 
-1.191311102, -0.989162395, 1.544071402, 0.0499669, 0.475664018, 
-0.246651028, -1.071424618, -0.593392019, 0.699499676, 0.438809737, 
0.205686849, -0.286716935, -0.286716935, -0.285641861, -0.285641861, 
0.138879114, -0.607524461, 0.337862664, 0.017664721, 0.850148519, 
-0.586832286, -0.243501348, -0.040471746, -0.86963529, 0.306787909, 
0.815902287, 0.270008406, -0.304404039, -0.154499951), V10 = c(0.090794172, 
-0.166974414, 0.207642865, -0.054951922, 0.753074432, -0.371407197, 
-0.099254321, 1.249376178, -0.410430433, -0.366845639, 1.626659058, 
0.309755394, 1.323729274, 0.460230444, 1.151086988, 0.747730827, 
-0.267975067, -0.737979824, 0.345172827, 1.638076037, 0.568520735, 
0.724396315, 0.922174967, -0.829880601, 0.573742508, 0.149450615, 
-0.046139302, 0.868558548, -0.346188231, -0.064737556, -0.207358046, 
0.082262259, -0.484687683, -0.484687683, -0.482474471, -0.482474471, 
-0.401007068, 0.047155553, -0.408150493, 0.044864791, -0.307081112, 
0.129875751, 0.14845549, -0.852045976, -0.038651669, -0.269952637, 
-1.491188012, -0.480280682, 0.576516809, 0.280672622), V11 = c(-0.551599533, 
1.612726661, 0.624501459, -0.226487264, -0.822842878, 1.34126198, 
-1.416907243, -0.619467796, -0.705116587, 1.017614468, 1.19964395, 
-0.259115564, 0.227666231, -0.773656931, 0.844555471, -0.793980603, 
-0.45031128, 0.324097813, 0.917229868, 1.077542412, 1.019150613, 
1.690329921, 0.744785789, -0.583199527, -0.081256515, -0.856566364, 
-0.143418527, -0.64150629, -0.012142188, 1.048292488, -0.929182115, 
1.133555671, 0.87248959, 0.87248959, 0.87179958, 0.87179958, 
-0.812050382, 0.783726853, 0.682646124, 1.345074799, -0.456245308, 
0.658479448, -0.387003094, 1.552109306, -0.686124387, -0.002601848, 
-0.84619138, -0.51253274, -1.631111762, -0.531948028), V12 = c(-0.617800856, 
1.065235311, 0.066083685, 0.178228226, 0.53819555, 0.359893837, 
-0.153825826, 0.291474353, -0.110452262, 0.83638957, -0.671439778, 
-0.326143234, -0.242681999, 0.323387245, 0.792943952, -0.770406729, 
0.936707715, 0.277192107, 0.970116716, -0.632046515, 1.298328701, 
0.406773576, -0.53137725, 0.524933232, -0.215745003, -0.180523156, 
0.979350376, -0.111315775, 0.786796316, 1.005618365, 0.527106061, 
0.626699002, 0.85163586, 0.85163586, 0.853447435, 0.853447435, 
-0.183524462, 1.096385674, 1.253719685, 1.286339621, 0.22998135, 
1.265049218, 0.398299277, 0.95356103, -0.502910879, 1.124304216, 
0.05653255, 0.680906259, 0.042559512, 0.589857529), V13 = c(-0.991389847, 
0.489095016, 0.717292731, 0.50775687, 1.345851593, -0.358090653, 
-0.751062716, 1.757964214, -0.286253632, 1.006843514, -0.513947153, 
-0.090046723, 1.205416808, -0.011075887, 0.370448093, 1.047626997, 
0.708380406, 0.252624256, -0.266567765, -0.416957167, 0.420480265, 
-0.936421296, -2.10534645, -0.453375297, 0.044160628, -0.65523293, 
1.492285435, 0.36148541, 0.635953883, -0.542001579, 0.348675901, 
-1.492780392, -0.571745303, -0.571745303, -0.571821891, -0.571821891, 
-0.630112887, -0.268094206, 0.391917044, -0.252267066, -0.0169131, 
-0.206518166, 0.481916748, 0.415429902, 1.380826322, 0.744206684, 
-0.05895353, 0.089923037, 2.047898316, 0.555086956), V14 = c(-0.311169354, 
-0.143772296, -0.165945923, -0.287923745, -1.119669835, -0.1371337, 
0.167371963, -1.32386522, 0.07435536, -0.443522817, -0.095045045, 
0.362832369, -0.317630527, -0.178485175, -0.734975106, -1.066603681, 
-0.468647288, -0.29189646, -0.479129929, 0.052010515, -0.372650997, 
0.983739419, 1.126870105, 0.081393088, 0.033897757, -0.279796856, 
0.101417526, 0.171945122, -0.086324472, -0.039914502, -0.152535139, 
0.520787894, 0.100974273, 0.100974273, 0.102252103, 0.102252103, 
-0.286217451, 0.76864804, -0.037510858, 0.274457682, -0.220846086, 
1.013603212, -0.365439093, -0.233899925, -1.039124606, -0.188353421, 
0.151922602, -0.157405057, -0.739338429, -0.116357329), V15 = c(1.468176972, 
0.635558093, 2.345864949, -0.631418118, 0.17512113, 0.517616807, 
0.050143594, 0.686132504, -0.32878305, 0.150219101, 0.230930409, 
0.928903661, 0.72567499, -0.655564278, 0.40679571, 1.106953457, 
0.354574063, -0.184520169, -0.526608503, -0.042978923, -0.807979513, 
0.710910766, 0.003075323, 1.555204196, 1.190717675, -0.211667955, 
0.761477545, 0.782166532, 0.076803687, -0.218683248, -0.21838563, 
-0.674592597, -1.519771833, -1.519771833, -1.519991203, -1.519991203, 
-0.337046324, -0.524367354, 0.795301519, -0.810394372, 0.362417699, 
-0.54941351, 0.235544572, 0.452335832, 0.652842089, -0.075661963, 
1.982594882, -0.588819332, 1.456222044, 0.165886171), V16 = c(-0.470400525, 
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0.128290673, 0.094311913, 0.04666377, 0.319222973, -0.002498362, 
0.131001421, 0.142757247, 0.000531332, 0.11875364), V28 = c(-0.021053053, 
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0L)), row.names = c(NA, 50L), class = "data.frame")
### end of structure 

library(caret)
#> Loading required package: lattice
#> Loading required package: ggplot2
index <- createDataPartition(daten$Class, p = 0.8, list = FALSE)
#> Error in cut.default(y, unique(quantile(y, probs = seq(0, 1, length = groups))), : invalid number of intervals

Created on 2020-04-01 by the reprex package (v0.3.0)

12

@dromano Thats weird. Did you use the dataset i referenced to? I ran the same code on my machine and the createDataPartition command worked

13

Could you try it with the data I posted and see what happens?

14

I did, i got the exact same error. Why dont you try to use the csv i shared the link to? I works with that csv. I fixed it thanks

15

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