mltools

Exploratory and diagnostic machine learning tools for R

About

The goal of this package is multifold:

• Speed up data preparation for feeding machine-learning models
• Identify structure and patterns in a dataset
• Evaluate the results of a machine-learning model

Installation

CRAN

install.packages("mltools")

or Github (development version)

install.packages("devtools")
devtools::install_github("ben519/mltools")

Demonstration

Predict whether or not someone is an alien.

library(data.table)
library(mltools)

# Copy the toy datasets since they are locked from being modified
train <- copy(alientrain)
test <- copy(alientest)

train
   SkinColor IQScore  Cat1  Cat2   Cat3 IsAlien
1:     green     300 type1 type1  type4    TRUE
2:     white      95 type1 type2  type4   FALSE
3:     brown     105 type2 type6 type11   FALSE
4:     white     250 type4 type5  type2    TRUE
5:      blue     115 type2 type7 type11    TRUE
6:     white      85 type4 type5  type2   FALSE
7:     green     130 type1 type2  type4    TRUE
8:     white     115 type1 type1  type4   FALSE

test
   SkinColor IQScore  Cat1  Cat2  Cat3
1:     white      79 type4 type5 type2
2:     green     100 type4 type5 type2
3:     brown     125 type3 type9 type7
4:     white      90 type1 type8 type4
5:       red     115 type1 type2 type4

Questions about the data:

• Are there any pairs of categorical fields which are highly/perfectly correlated?
• Are there any parent-child related categorical fields?
• How does the target variable change with IQScore?
• What’s the cardinality and skewness of each feature?

# Combine train (excluding IsAlien) and test
alien.all <- rbind(train[, !"IsAlien", with=FALSE], test)

#--------------------------------------------------
## Check for correlated and hierarchical fields

gini_impurities(alien.all, wide=TRUE)  #  weighted conditional gini impurities
        Var1      Cat1      Cat2      Cat3 SkinColor
1:      Cat1 0.0000000 0.3589744 0.0000000 0.4743590
2:      Cat2 0.0000000 0.0000000 0.0000000 0.3461538
3:      Cat3 0.0000000 0.3589744 0.0000000 0.4743590
4: SkinColor 0.4102564 0.5384615 0.4102564 0.0000000

# (Cat1, Cat3) = (Cat3, Cat1) = 0 => Cat1 and Cat3 perfectly correspond to each other
# (Cat1, Cat2) > 0 and (Cat2, Cat1) = 0 => Cat1-Cat2 exhibit a parent-child relationship.
# You can guess Cat1 by knowing Cat2, but not vice-versa.

#--------------------------------------------------
## Check relationship between IQScore and IsAlien by binning IQScore into groups

train[, BinIQScore := bin_data(IQScore, bins=seq(0, 300, by=50))]
   IQScore BinIQScore
1:     300 [250, 300]
2:      95  [50, 100)
3:     105 [100, 150)
4:     250 [250, 300]
5:     115 [100, 150)
6:      85  [50, 100)
7:     130 [100, 150)
8:     115 [100, 150)

train[, list(Samples=.N, IQScore=mean(IQScore)), keyby=BinIQScore]
   BinIQScore Samples IQScore
1:  [50, 100)       2   90.00
2: [100, 150)       4  116.25
3: [250, 300]       2  275.00

# Remove column BinIQScore
train[, BinIQScore := NULL]

#--------------------------------------------------
## Check skewness of fields

skewness(alien.all)
$SkinColor
   SkinColor Count       Pcnt
1:     white     6 0.46153846
2:     green     3 0.23076923
3:     brown     2 0.15384615
4:      blue     1 0.07692308
5:       red     1 0.07692308

$Cat1
    Cat1 Count       Pcnt
1: type1     6 0.46153846
2: type4     4 0.30769231
3: type2     2 0.15384615
4: type3     1 0.07692308
...

Preparing for ML model

• Cateogrical fields in train and test should be factors with the same levels
• Split the training dataset to do cross validation
• Convert datasets to sparses matrices

set.seed(711)

#--------------------------------------------------
## Set SkinColor as a factor, such that it has the same levels in train and test
## Set low frequency skin colors (1 or fewer occurences) as "_other_"

skincolors <- list(train$SkinColor, test$SkinColor)
skincolors <- set_factor(skincolors, aggregationThreshold=1)
train[, SkinColor := skincolors[[1]] ]  # update train with the new values
test[, SkinColor := skincolors[[2]] ]  # update test with the new values

# Repeat the process above for other categorical fields (without setting low freq. values as "_other_")
for(col in c("Cat1", "Cat2", "Cat3")){
  vals <- list(train[[col]], test[[col]])
  vals <- set_factor(vals)
  set(train, j=col, value=vals[[1]])
  set(test, j=col, value=vals[[2]])
}

#--------------------------------------------------
## Randomly split the training data into 2 equally sized datasets

# Partition train into two folds, stratified by IsAlien
train[, FoldID := folds(IsAlien, nfolds=2, stratified=TRUE, seed=2016)]

cvtrain <- train[FoldID==1, !"FoldID"]
   SkinColor IQScore  Cat1  Cat2   Cat3 IsAlien
1:     green     300 type1 type1  type4    TRUE
2:     brown     105 type2 type6 type11   FALSE
3:     green     130 type1 type2  type4    TRUE
4:     white     115 type1 type1  type4   FALSE

cvtest <- train[FoldID==2, !"FoldID"]
   SkinColor IQScore  Cat1  Cat2   Cat3 IsAlien
1:     white      95 type1 type2  type4   FALSE
2:     white     250 type4 type5  type2    TRUE
3:   _other_     115 type2 type7 type11    TRUE
4:     white      85 type4 type5  type2   FALSE

#--------------------------------------------------
## Convert cvtrain and cvtest to sparse matrices
## Note that unordered factors are one-hot-encoded

library(Matrix)

cvtrain.sparse <- sparsify(cvtrain)
4 x 21 sparse Matrix of class "dgCMatrix"
     SkinColor__other_ SkinColor_brown SkinColor_green SkinColor_white IQScore Cat1_type1 ...
[1,]                 .               .               1               .     300          1
[2,]                 .               1               .               .     105          .
[3,]                 .               .               1               .     130          1
[4,]                 .               .               .               1     115          1

cvtest.sparse <- sparsify(cvtest)
4 x 21 sparse Matrix of class "dgCMatrix"
     SkinColor__other_ SkinColor_brown SkinColor_green SkinColor_white IQScore Cat1_type1 ...
[1,]                 .               .               .               1      95          1
[2,]                 .               .               .               1     250          .
[3,]                 1               .               .               .     115          .
[4,]                 .               .               .               1      85          .

Evaluate model

• What was the model’s AUC ROC score?
• How good was the model’s predictions for each sample?

#--------------------------------------------------
## Naive model that guesses someone is an alien if their IQScore is > 130

cvtest[, Prediction := ifelse(IQScore > 130, TRUE, FALSE)]

#--------------------------------------------------
## Evaluate predictions

# Area Under the ROC Curve (AUC ROC)
auc_roc(preds=cvtest$Prediction, actuals=cvtest$IsAlien)
0.75

# Individual scores to determine which predictions were good/bad (see help(roc_scores) for details)
cvtest[, ROCScore := roc_scores(preds=Prediction, actuals=IsAlien)]
cvtest[order(ROCScore)]
   SkinColor IQScore  Cat1  Cat2   Cat3 IsAlien Prediction  ROCScore
1:     white      95 type1 type2  type4   FALSE      FALSE 0.0000000
2:     white     250 type4 type5  type2    TRUE       TRUE 0.0000000
3:     white      85 type4 type5  type2   FALSE      FALSE 0.0000000
4:   _other_     115 type2 type7 type11    TRUE      FALSE 0.1666667

Contact

If you’d like to contact me regarding bugs, questions, or general consulting, feel free to drop me a line - bgorman519@gmail.com

Support

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