EnsembleML is an R package for performing feature creation in time series and frequency series, building multiple regression and classification models and combining those models to be an ensemble. You can save and read models created using this package and also deploy them as API within the same model.
The pacakge is currently only available in Github and won't be seeing anytime in CRAN. Use devtools to install from github as follows.
devtools::install_github("nagdevAmruthnath/EnsembleML")
library(EnsembleML)
Features can be created both in time series and frequency series using this package. Use featureCreationTS() for time series and featureCreationF for frequency domain. The standard features include mean, sd, median, trimmed, mad, min, max, range, skew, kurtosis, se, iqr, nZero, nUnique, lowerBound, upperBound, and quantiles.
data = rnorm(50)
featureCreationTS(data)
# TS_mean TS_sd TS_median TS_trimmed TS_mad TS_min TS_max TS_range TS_skew TS_kurtosis
#X1 0.2107398 1.025315 0.1822303 0.2097342 0.8026293 -2.194434 3.161181 5.355616 0.1251634 0.6376311
# TS_se TS_iqr TS_nZero TS_nUnique TS_lowerBound TS_upperBound TS_X1. TS_X5. TS_X25.
#X1 0.1450015 1.041068 0 50 -1.850106 2.314167 -2.116863 -1.388573 -0.288504
# TS_X50. TS_X75. TS_X95. TS_X99.
#X1 0.1822303 0.7525642 1.661972 2.814687
numSummary() function can be used to generate the numerical summary of the entire data set. The example for iris data set is shown below. Rest of the documentation will include using iris data set.
data(iris)
numSummary(iris)
# n mean sd max min range nunique nzeros iqr lowerbound upperbound noutlier kurtosis
# Sepal.Length 150 5.84 0.828 7.9 4.3 3.6 35 0 1.30 3.15 8.35 0 -0.606
# Sepal.Width 150 3.06 0.436 4.4 2.0 2.4 23 0 0.50 2.05 4.05 4 0.139
# Petal.Length 150 3.76 1.765 6.9 1.0 5.9 43 0 3.55 -3.72 10.42 0 -1.417
# Petal.Width 150 1.20 0.762 2.5 0.1 2.4 22 0 1.50 -1.95 4.05 0 -1.358
# skewness mode miss miss% 1% 5% 25% 50% 75% 95% 99%
# Sepal.Length 0.309 5.0 0 0 4.40 4.60 5.1 5.80 6.4 7.25 7.70
# Sepal.Width 0.313 3.0 0 0 2.20 2.34 2.8 3.00 3.3 3.80 4.15
# Petal.Length -0.269 1.4 0 0 1.15 1.30 1.6 4.35 5.1 6.10 6.70
# Petal.Width -0.101 0.2 0 0 0.10 0.20 0.3 1.30 1.8 2.30 2.50
For most prototyping we end up training multiple models manually. This is not only time consuming but also not very efficient. multipleModels() function can be used to train multiple models at once as shown below. All the models uses caret function models. You an read more about it here https://topepo.github.io/caret/available-models.html
mm = multipleModels(train = iris, test = iris, y = "Species", models = c("C5.0", "parRF"))
# $summary
# Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull AccuracyPValue McnemarPValue
# C5.0 0.960 0.94 0.915 0.985 0.333 2.53e-60 NaN
# parRF 0.973 0.96 0.933 0.993 0.333 8.88e-64 NaN
The bench mark for training multiple models for iris data set is as follows
microbenchmark::microbenchmark(multipleModels(train = iris, test = iris, y = "Species", models = c("C5.0", "parRF")), times = 5)
# Unit: seconds
# expr min lq mean
# multipleModels(train = iris, test = iris, y = "Species", models = c("C5.0", "parRF")) 22.6 22.6 22.9
# median uq max neval
# 22.7 22.7 23.8 5
Ensemble training is a concept of joining results from multiple models and feeding it to a different model. You can use ensembleTrain() function to achieve this. We use the results from multiple models mm and then feed it to this function as follows
em = ensembleTrain(mm, train = iris, test = iris, y = "Species", emsembleModelTrain = "C5.0")
# $summary
# Confusion Matrix and Statistics
#
# Reference
# Prediction setosa versicolor virginica
# setosa 50 0 0
# versicolor 0 47 1
# virginica 0 3 49
#
# Overall Statistics
#
# Accuracy : 0.973
# 95% CI : (0.933, 0.993)
# No Information Rate : 0.333
# P-Value [Acc > NIR] : <2e-16
#
# Kappa : 0.96
#
# Mcnemar's Test P-Value : NA
#
# Statistics by Class:
#
# Class: setosa Class: versicolor Class: virginica
# Sensitivity 1.000 0.940 0.980
# Specificity 1.000 0.990 0.970
# Pos Pred Value 1.000 0.979 0.942
# Neg Pred Value 1.000 0.971 0.990
# Prevalence 0.333 0.333 0.333
# Detection Rate 0.333 0.313 0.327
# Detection Prevalence 0.333 0.320 0.347
# Balanced Accuracy 1.000 0.965 0.975
predictEnsemble() function is used to predict from ensemble model
predictEnsemble(em, iris)
# prediction
# 1 setosa
# 2 setosa
# 3 setosa
# 4 setosa
# 5 setosa
# 6 setosa
# 7 setosa
# 8 setosa
# .
# .
# .
Ensemble models can be saved and read back to the memory as follows
saveRDS(ensembleModel, "/home/savedEnsembleModel.RDS")
readRDS("/home/savedEnsembleModel.RDS")
The trained models could be deployed as API using the same package as follows. First we need to save the models and then call them as follows
library(dplyr)
createAPI(host = '192.168.1.1', port = 8890)
# Serving the jug at http://192.168.1.1:8890
# [1] "Model was successfully loaded"
# HTTP | /predict - POST - 200
Lets curl and see what we get
curl -X POST \
http://192.168.1.1:8890/predict \
-H 'Host: http://192.168.1.1:8890' \
-H 'content-type: multipart/form-data' \
-F 'jsondata={"model":["/home/savedEnsembleModel.RDS"],"test":[{"Sepal.Length":5.1,"Sepal.Width":3.5,"Petal.Length":1.4,"Petal.Width":0.2,"Species":"setosa"}]}'
If you have any issues related to the project, please post an issue and I will try to address it.