How to classify breast cancer as benign or malignant using RTextTools.

cancer.R

# FILE: Classifying Breast Cancer as Benign or Malignant
# AUTHOR: Timothy P. Jurka

library(RTextTools);

# GET THE BREAST CANCER DATA FROM http://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/wdbc.names
data <- read.csv("http://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data",header=FALSE)
data <- data[-1]

# ADD TEXTUAL DESCRIPTORS FOR EACH MASS CHARACTERISTIC FOR THE DOCUMENT-TERM MATRIX
thick <- as.vector(apply(as.matrix(data[1], mode="character"),1,paste,"clump",sep="",collapse=""))
size <- as.vector(apply(as.matrix(data[2], mode="character"),1,paste,"size",sep="",collapse=""))
shape <- as.vector(apply(as.matrix(data[3], mode="character"),1,paste,"shape",sep="",collapse=""))
adhesion <- as.vector(apply(as.matrix(data[4], mode="character"),1,paste,"adhesion",sep="",collapse=""))
single <- as.vector(apply(as.matrix(data[5], mode="character"),1,paste,"single",sep="",collapse=""))
nuclei <- as.vector(apply(as.matrix(data[6], mode="character"),1,paste,"nuclei",sep="",collapse=""))
chromatin <- as.vector(apply(as.matrix(data[7], mode="character"),1,paste,"chromatin",sep="",collapse=""))
nucleoli <- as.vector(apply(as.matrix(data[8], mode="character"),1,paste,"nucleoli",sep="",collapse=""))
mitoses <- as.vector(apply(as.matrix(data[9], mode="character"),1,paste,"mitoses",sep="",collapse=""))

training_data <- cbind(data[10],thick,size,shape,adhesion,single,nuclei,chromatin,nucleoli,mitoses)

# [OPTIONAL] SUBSET YOUR DATA TO GET A RANDOM SAMPLE
training_data <- training_data[sample(1:699,size=600,replace=FALSE),]
training_codes <- training_data[1]
training_data <- training_data[-1]

# CREATE A TERM-DOCUMENT MATRIX THAT REPRESENTS WORD FREQUENCIES IN EACH DOCUMENT
# WE WILL TRAIN ON THE Title and Subject COLUMNS
matrix <- create_matrix(training_data, language="english", removeNumbers=FALSE, stemWords=FALSE, removePunctuation=FALSE, weighting=weightTfIdf)

# CREATE A container THAT IS SPLIT INTO A TRAINING SET AND A TESTING SET
# WE WILL BE USING t(training_codes) AS THE CODE COLUMN. WE DEFINE A 200 
# ARTICLE TRAINING SET AND A 400 ARTICLE TESTING SET.
container <- create_container(matrix,t(training_codes),trainSize=1:200, testSize=201:600,virgin=FALSE)


# THERE ARE TWO METHODS OF TRAINING AND CLASSIFYING DATA.
# ONE WAY IS TO DO THEM AS A BATCH (SEVERAL ALGORITHMS AT ONCE)
models <- train_models(container, algorithms=c("MAXENT","SVM","GLMNET","SLDA","TREE","BAGGING","BOOSTING","RF"))
results <- classify_models(container, models)


# VIEW THE RESULTS BY CREATING ANALYTICS
analytics <- create_analytics(container, results)

# RESULTS WILL BE REPORTED BACK IN THE analytics VARIABLE.
# analytics@algorithm_summary: SUMMARY OF PRECISION, RECALL, F-SCORES, AND ACCURACY SORTED BY TOPIC CODE FOR EACH ALGORITHM
# analytics@label_summary: SUMMARY OF LABEL (e.g. TOPIC) ACCURACY
# analytics@document_summary: RAW SUMMARY OF ALL DATA AND SCORING
# analytics@ensemble_summary: SUMMARY OF ENSEMBLE PRECISION/COVERAGE. USES THE n VARIABLE PASSED INTO create_analytics()

analytics@ensemble_summary