I am applying the kNN algorithm to a data set with 6 predictors and 1 class variable with 1599 rows, I have reviewed my syntax many times and gone back over other examples to try and find my error. I am totally bamboozled at present. I have broken the data set up into test_set, test_set_class, training_set, training_set_class. Any assistance would be fantastic, see below for the code and error.
num_obs <- nrow(wine_preds3)
# set the sample size to be 80%
sample_size <- as.integer(num_obs*0.8)
# set the seed for the sample split
set.seed(0)
# randomly split 80% the data indexes in reduced wine
split_index <- sample(num_obs, size = sample_size, replace = FALSE)
# subset the reduced wine into a testing subset of 20%
test_wine_preds <- wine_preds3[-split_index, 1:6]
test_wine_class <- wine_preds3[-split_index, 7]
# subset the reduced wine into a training subset of 80%
train_wine_preds <- wine_preds3[split_index, 1:6]
train_wine_class <- wine_preds3[split_index, 7]
Pred_class <- kNN(train = train_wine_preds, test = test_wine_preds, cl = train_wine_class, k = 15)
Error in kNN(train = train_wine_preds, test = test_wine_preds, cl = train_wine_class, :
unused arguments (train = train_wine_preds, test = test_wine_preds, cl = train_wine_class)
I have a dataset of 25 variables and 248 rows.
There are 8-factor variables and the rest are integers and numbers.
I am trying to run XGBoost.
I have done the following code: -
# Partition Data
set.seed(1234)
ind <- sample(2, nrow(mission), replace = T, prob = c(0.7,0.3))
train <- mission[ind == 1,]
test <- mission[ind == 2,]
# Create matrix - One-Hot Encoding for Factor variables
trainm <- sparse.model.matrix(GRL ~ .-1, data = train)
head(trainm)
train_label <- train[,"GRL"]
train_matrix <- xgb.DMatrix(data = as.matrix(trainm), label = train_label)
testm <- sparse.model.matrix(GRL~.-1, data = test)
test_label <- test[,"GRL"]
test_matrix <- xgb.DMatrix(data = as.matrix(testm),label = test_label)
The response variable here is "GRL" and I am running the test_label <- test[,"GRL"]
The above code is getting executed but when I am trying to use it in xgb.DMatrix, I am encountering the following error:
Error in setinfo.xgb.DMatrix(dmat, names(p), p[[1]]) :
The length of labels must equal to the number of rows in the input data
I have partitioned the data into 70:30.
test[,"GRL"] returns a data.frame, and XGBoost needs the label to be a vector.
Just use teste$GRL or test[["GRL"]] instead. You also need to do the same for the training dataset
I use PCA on my divided train dataset and project the test dataset to the results after removing irrelevant columns.
data <- read.csv('bottom10.csv')
set.seed(1)
inTrain <- createDataPartition(data$cuisine, p = .8)[[1]]
dataTrain <- data[,-1][inTrain,][,-1]
dataTest <- data[,-1][-inTrain,][,-1]
cuisine.pca <- prcomp(dataTrain[,-1])
Then I extract the first 500 components and project the test dataset.
traincom <- cuisine.pca$x[,1:500]
testcom <- scale(dataTest[,-1], cuisine.pca$center) %*% cuisine.pca$rotation
Then I transfer the labels into integer, and combine components and labels into xgbDMatrix form.
label_train <- as.integer(dataTrain$cuisine) - 1
label_test <- as.integer(dataTest$cuisine) - 1
xgb_train <- xgb.DMatrix(data = traincom, label = label_train)
xgb_test <- xgb.DMatrix(data = testcom, label = label_test)
Then I build the xgboost model as
xgb.fit <- xgboost(cuisine~., data = xgb_train, nrounds = 40, num_class = 10, early_stopping_rounds = 5)
And after I run this, there is a warning but the training can still run.
xgboost: label will be ignored
I can predict the train dataset using the model but when I try to predict test dataset there will be an error.
xgb_pred <- predict(xgb.fit, newdata = xgb_train)
sum(label_train == xgb_pred)/length(label_train)
xgb_pred <- predict(xgb.fit, newdata = xgb_test, rescale = T)
Error in predict.xgb.Booster(xgb.fit, newdata = xgb_test, rescale = T) :
Feature names stored in `object` and `newdata` are different!
Please let me know what am I doing wrong?
Regards
I am currently using genomic expression levels, age, and smoking intensity levels to predict the number of days Lung Cancer Patients have to live. I have a small amount of data; 173 patients and 20,438 variables, including gene expression levels (which make up for 20,436). I have split up my data into test and training, utilizing an 80:20 ratio. There are no missing values in the data.
I am using knn() to train the model. Here is what the code looks like:
prediction <- knn(train = trainData, test = testData, cl = trainAnswers, k=1)
Nothing seems out of the ordinary until you notice that k=1. "Why is k=1?" you may ask. The reason k=1 is because when k=1, the model is the most accurate. This makes no sense to me. There are quite a few concerns:
I am using knn() to predict a continuous variable. I should be using something along the lines of, cox maybe.
The model is waaaaaaay too accurate. Here are a few examples of the test answer and the model's predictions. For the first patient, the number of days to death is 274. The model predicts 268. For the second patient, test: 1147, prediction: 1135. 3rd, test: 354, prediction: 370. 4th, test: 995, prediction 995. How is this possible? Out of the entire test data, the model was only off by and average of 9.0625 days! The median difference was 7 days, and the mode was 6 days. Here is a graph of the results:
Bar Graph.
So I guess my main question is what does knn() do, what does k represent, and how is the model so accurate when k=1? Here is my entire code (I am unable to attach the actual data):
# install.packages(c('caret', 'skimr', 'RANN', 'randomForest', 'fastAdaboost', 'gbm', 'xgboost', 'caretEnsemble', 'C50', 'earth'))
library(caret)
# Gather the data and store it in variables
LUAD <- read.csv('/Users/username/Documents/ClinicalData.csv')
geneData <- read.csv('/Users/username/Documents/GenomicExpressionLevelData.csv')
geneData <- data.frame(geneData)
row.names(geneData) = geneData$X
geneData <- geneData[2:514]
colNamesGeneData <- gsub(".","-",colnames(geneData),fixed = TRUE)
colnames(geneData) = colNamesGeneData
# Organize the data
# Important columns are 148 (smoking), 123 (OS Month, basically how many days old), and the gene data. And column 2 (barcode).
LUAD = data.frame(LUAD$patient, LUAD$TOBACCO_SMOKING_HISTORY_INDICATOR, LUAD$OS_MONTHS, LUAD$days_to_death)[complete.cases(data.frame(LUAD$patient, LUAD$TOBACCO_SMOKING_HISTORY_INDICATOR, LUAD$OS_MONTHS, LUAD$days_to_death)), ]
rownames(LUAD)=LUAD$LUAD.patient
LUAD <- LUAD[2:4]
# intersect(rownames(LUAD),colnames(geneData))
# ind=which(colnames(geneData)=="TCGA-778-7167-01A-11R-2066-07")
gene_expression=geneData[, rownames(LUAD)]
# Merge the two datasets to use the geneomic expression levels in your model
LUAD <- data.frame(LUAD,t(gene_expression))
LUAD.days_to_death <- LUAD[,3]
LUAD <- LUAD[,c(1:2,4:20438)]
LUAD <- data.frame(LUAD.days_to_death,LUAD)
set.seed(401)
# Number of Rows in the training data (createDataPartition(dataSet, percentForTraining, boolReturnAsList))
trainRowNum <- createDataPartition(LUAD$LUAD.days_to_death, p=0.8, list=FALSE)
# Training/Test Dataset
trainData <- LUAD[trainRowNum, ]
testData <- LUAD[-trainRowNum, ]
x = trainData[, c(2:20438)]
y = trainData$LUAD.days_to_death
v = testData[, c(2:20438)]
w = testData$LUAD.days_to_death
# Imputing missing values into the data
preProcess_missingdata_model <- preProcess(trainData, method='knnImpute')
library(RANN)
if (anyNA(trainData)) {
trainData <- predict(preProcess_missingdata_model, newdata = trainData)
}
anyNA(trainData)
# Normalizing the data
preProcess_range_model <- preProcess(trainData, method='range')
trainData <- predict(preProcess_range_model, newdata = trainData)
trainData$LUAD.days_to_death <- y
apply(trainData[,1:20438], 2, FUN=function(x){c('min'=min(x), 'max'=max(x))})
preProcess_range_model_Test <- preProcess(testData, method='range')
testData <- predict(preProcess_range_model_Test, newdata = testData)
testData$LUAD.days_to_death <- w
apply(testData[,1:20438], 2, FUN=function(v){c('min'=min(v), 'max'=max(v))})
# To uncomment, select the text and press 'command' + 'shift' + 'c'
# set.seed(401)
# options(warn=-1)
# subsets <- c(1:10)
# ctrl <- rfeControl(functions = rfFuncs,
# method = "repeatedcv",
# repeats = 5,
# verbose = TRUE)
# lmProfile <- rfe(x=trainData[1:20437], y=trainAnswers,
# sizes = subsets,
# rfeControl = ctrl)
# lmProfile
trainAnswers <- trainData[,1]
testAnswers <- testData[,1]
library(class)
prediction <- knn(train = trainData, test = testData, cl = trainAnswers, k=1)
#install.packages("plotly")
library(plotly)
Test_Question_Number <- c(1:32)
prediction2 <- data.frame(prediction[1:32])
prediction2 <- as.numeric(as.vector(prediction2[c(1:32),]))
data <- data.frame(Test_Question_Number, prediction2, testAnswers)
names(data) <- c("Test Question Number","Prediction","Answer")
p <- plot_ly(data, x = ~Test_Question_Number, y = ~prediction2, type = 'bar', name = 'Prediction') %>%
add_trace(y = ~testAnswers, name = 'Answer') %>%
layout(yaxis = list(title = 'Days to Death'), barmode = 'group')
p
merge <- data.frame(prediction2,testAnswers)
difference <- abs((merge[,1])-(merge[,2]))
difference <- sort(difference)
meanDifference <- mean(difference)
medianDifference <- median(difference)
modeDifference <- names(table(difference))[table(difference)==max(table(difference))]
cat("Mean difference:", meanDifference, "\n")
cat("Median difference:", medianDifference, "\n")
cat("Mode difference:", modeDifference,"\n")
Lastly, for clarification purposes, ClinicalData.csv is the age, days to death, and smoking intensity data. The other .csv is the genomic expression data. The data above line 29 doesn't really matter, so you can just skip to the part of the code where it says "set.seed(401)".
Edit: Some samples of the data:
days_to_death OS_MONTHS
121 3.98
NACC1 2001.5708 2363.8063 1419.879
NACC2 58.2948 61.8157 43.4386
NADK 706.868 1053.4424 732.1562
NADSYN1 1628.7634 912.1034 638.6471
NAE1 832.8825 793.3014 689.7123
NAF1 140.3264 165.4858 186.355
NAGA 1523.3441 1524.4619 1858.9074
NAGK 983.6809 899.869 1168.2003
NAGLU 621.3457 510.9453 1172.511
NAGPA 346.9762 257.5654 275.5533
NAGS 460.7732 107.2116 321.9763
NAIF1 217.1219 202.5108 132.3054
NAIP 101.2305 87.8942 77.261
NALCN 13.9628 36.7031 48.0809
NAMPT 3245.6584 1257.8849 5465.6387
Because K = 1 is the most complex knn model. It has the most flexible decision boundary. It creates an overfit. It will perform well within the training data by poorly on a holdout set (but not always).
I'm new to R and its the first time i'm using SOM.
I want to predict survival using Self Organizing Map.
The following is the code i used to ingest data:
load raw data
train <- read.csv("train.csv", header = TRUE)
test <- read.csv("test.csv", header = TRUE)
Add a "Survived" variable to the test set to allow for combining data sets
test.survived <- data.frame(survived = rep("None", nrow(test)), test[,])
Combine data sets
data.combined <- rbind(train, test.survived)
Changed the variable to factors
data.combined$Survived <- as.factor(data.combined$survived)
data.combined$Pclass <- as.factor(data.combined$pclass)
Fitting the data to the SOM model
library(kohonen)
Train SOM
som.train.1 <- data.combined[1:891, c("pclass", "title")]
som.label <- as.factor(train$survived)
table(som.train.1)
table(som.label)
som.train.1.grid <- somgrid(xdim = 20, ydim=20, topo="hexagonal")
set.seed(1234)
som.model <- som(som.label,
grid=som.train.1.grid,
rlen = 100,
alpha = c(0.05, 0.01),
keep.data = TRUE,
normalizeDataLayers = TRUE)
plot(som.model)
I get an error that says: sort.list(y): 'x' must be atomic for 'sort.list'