#+ knitr_setup, include = FALSE

# Whether to cache the intensive code sections. Set to FALSE to recalculate

# everything afresh.

cacheoption <- TRUE

# Disable lazy caching globally, because it fails for large objects, and all the

# objects we wish to cache are large...

opts_chunk$set(cache.lazy = FALSE)

#' # Variable selection in data-driven health records

#' 

#' Having extracted around 600 variables which occur most frequently in patient

#' records, let's try to narrow these down using the methodology of varSelRf.

#' 

#' ## User variables

#' 

#+ user_variables

output.filename.base <- '../../output/rf-bigdata-try11-varselmiss'

nsplit <- 20

n.trees.cv <- 500

n.trees.final <- 500

split.rule <- 'logrank'

n.imputations <- 3

cv.n.folds <- 3

vars.drop.frac <- 0.2 # Fraction of variables to drop at each iteration

bootstraps <- 200

n.data <- NA # This is after any variables being excluded in prep

n.threads <- 40

#' ## Data set-up

#' 

#+ data_setup

data.filename.big <- '../../data/cohort-datadriven-02.csv'

surv.predict.old <- c('age', 'smokstatus', 'imd_score', 'gender')

untransformed.vars <- c('time_death', 'endpoint_death', 'exclude')

source('../lib/shared.R')

require(xtable)

# Define these after shared.R or they will be overwritten!

exclude.vars <-

  c(

    # Entity type 4 is smoking status, which we already have

    "clinical.values.4_data1", "clinical.values.4_data5",

    "clinical.values.4_data6",

    # Entity 13 data2 is the patient's weight centile, and not a single one is

    # entered, but they come out as 0 so the algorithm, looking for NAs, thinks

    # it's a useful column

    "clinical.values.13_data2",

    # Entities 148 and 149 are to do with death certification. I'm not sure how 

    # it made it into the dataset, but since all the datapoints in this are

    # looking back in time, they're all NA. This causes rfsrc to fail.

    "clinical.values.148_data1", "clinical.values.148_data2",

    "clinical.values.148_data3", "clinical.values.148_data4",

    "clinical.values.148_data5",

    "clinical.values.149_data1", "clinical.values.149_data2"

  )

COHORT <- fread(data.filename.big)

bigdata.prefixes <-

  c(

    'hes.icd.',

    'hes.opcs.',

    'tests.enttype.',

    'clinical.history.',

    'clinical.values.',

    'bnf.'

  )

bigdata.columns <-

  colnames(COHORT)[

    which(

      # Does is start with one of the data column names?

      startsWithAny(names(COHORT), bigdata.prefixes) &

        # And it's not one of the columns we want to exclude?

        !(colnames(COHORT) %in% exclude.vars)

    )

    ]

COHORT.bigdata <-

  COHORT[, c(

    untransformed.vars, surv.predict.old, bigdata.columns

  ),

  with = FALSE

  ]

# Get the missingness before we start removing missing values

missingness <- sort(sapply(COHORT.bigdata, percentMissing))

# Remove values for the 'untransformed.vars' above, which are the survival

# values plus exclude column

missingness <- missingness[!(names(missingness) %in% untransformed.vars)]

# Deal appropriately with missing data

# Most of the variables are number of days since the first record of that type

time.based.vars <-

  names(COHORT.bigdata)[

    startsWithAny(

      names(COHORT.bigdata),

      c('hes.icd.', 'hes.opcs.', 'clinical.history.')

    )

    ]

# We're dealing with this as a logical, so we want non-NA values to be TRUE,

# is there is something in the history

for (j in time.based.vars) {

  set(COHORT.bigdata, j = j, value = !is.na(COHORT.bigdata[[j]]))

}

# Again, taking this as a logical, set any non-NA value to TRUE.

prescriptions.vars <- names(COHORT.bigdata)[startsWith(names(COHORT.bigdata), 'bnf.')]

for (j in prescriptions.vars) {

  set(COHORT.bigdata, j = j, value = !is.na(COHORT.bigdata[[j]]))

}

# This leaves tests and clinical.values, which are test results and should be

# imputed.

# Manually fix clinical values items...

#

# "clinical.values.1_data1"  "clinical.values.1_data2"

# These are just blood pressure values...fine to impute

#

# "clinical.values.13_data1" "clinical.values.13_data3"

# These are weight and BMI...also fine to impute

#

# Entity 5 is alcohol consumption status, 1 = Yes, 2 = No, 3 = Ex, so should be

# a factor, and NA can be a factor level

COHORT.bigdata$clinical.values.5_data1 <-

  factorNAfix(factor(COHORT.bigdata$clinical.values.5_data1), NAval = 'missing')

# Both gender and smokstatus are factors...fix that

COHORT.bigdata$gender <- factor(COHORT.bigdata$gender)

COHORT.bigdata$smokstatus <-

  factorNAfix(factor(COHORT.bigdata$smokstatus), NAval = 'missing')

# Exclude invalid patients

COHORT.bigdata <- COHORT.bigdata[!COHORT.bigdata$exclude]

COHORT.bigdata$exclude <- NULL

COHORT.bigdata <-

  prepSurvCol(data.frame(COHORT.bigdata), 'time_death', 'endpoint_death', 'Death')

# If n.data was specified, trim the data table down to size

if(!is.na(n.data)) {

  COHORT.bigdata <- sample.df(COHORT.bigdata, n.data)

}

# Define test set

test.set <- testSetIndices(COHORT.bigdata, random.seed = 78361)

# Start by predicting survival with all the variables provided

surv.predict <- c(surv.predict.old, bigdata.columns)

# Set up a csv file to store calibration data, or retrieve previous data

calibration.filename <- paste0(output.filename.base, '-varselcalibration.csv')

#' ## Run random forest calibration

#' 

#' If there's not already a calibration file, we run the rfVarSel methodology:

#'   1. Fit a big forest to the whole dataset to obtain variable importances.

#'   2. Cross-validate as number of most important variables kept is reduced.

#' 

#' (If there is already a calibration file, just load the previous work.)

#' 

#+ rf_var_sel_calibration

# If we've not already done a calibration, then do one

if(!file.exists(calibration.filename)) {

  # Create an empty data frame to aggregate stats per fold

  cv.performance <- data.frame()

  # Cross-validate over number of variables to try

  cv.vars <- getVarNums(length(missingness))

  COHORT.cv <- COHORT.bigdata[-test.set, ]

  # Run crossvalidations. No need to parallelise because rfsrc is parallelised

  for(i in 1:length(cv.vars)) {

    # Get the subset of most important variables to use

    surv.predict.partial <- names(missingness)[1:cv.vars[i]]

    # Get folds for cross-validation

    cv.folds <- cvFolds(nrow(COHORT.cv), cv.n.folds)

    cv.fold.performance <- data.frame()

    for(j in 1:cv.n.folds) {

      time.start <- handyTimer()

      # Fit model to the training set

      surv.model.fit <-

        survivalFit(

          surv.predict.partial,

          COHORT.cv[-cv.folds[[j]],],

          model.type = 'rfsrc',

          n.trees = n.trees.cv,

          split.rule = split.rule,

          n.threads = n.threads,

          nsplit = nsplit,

          nimpute = n.imputations,

          na.action = 'na.impute'

        )

      time.learn <- handyTimer(time.start)

      time.start <- handyTimer()

      # Get C-index on validation set

      c.index.val <-

        cIndex(

          surv.model.fit, COHORT.cv[cv.folds[[j]],],

          na.action = 'na.impute'

        )

      time.c.index <- handyTimer(time.start)

      time.start <- handyTimer()

      # Get calibration score validation set

      calibration.score <-

        calibrationScore(

          calibrationTable(

            surv.model.fit, COHORT.cv[cv.folds[[j]],], na.action = 'na.impute'

          )

        )

      time.calibration <- handyTimer(time.start)

      # Append the stats we've obtained from this fold

      cv.fold.performance <-

        rbind(

          cv.fold.performance,

          data.frame(

            calibration = i,

            cv.fold = j,

            n.vars = cv.vars[i],

            c.index.val,

            calibration.score,

            time.learn,

            time.c.index,

            time.calibration

          )

        )

    } # End cross-validation loop (j)

    # rbind the performance by fold

    cv.performance <-

      rbind(

        cv.performance,

        cv.fold.performance

      )

    # Save output at the end of each loop

    write.csv(cv.performance, calibration.filename)

  } # End calibration loop (i)

} else {

  cv.performance <- read.csv(calibration.filename)

}

#' ## Find the best model from the calibrations

#' 

#' ### Plot model performance

#' 

#+ model_performance

# Find the best calibration...

# First, average performance across cross-validation folds

cv.performance.average <-

  aggregate(

    c.index.val ~ n.vars,

    data = cv.performance,

    mean

  )

cv.calibration.average <-

  aggregate(

    area ~ n.vars,

    data = cv.performance,

    mean

  )

ggplot(cv.performance.average, aes(x = n.vars, y = c.index.val)) +

  geom_line() +

  geom_point(data = cv.performance) +

  ggtitle(label = 'C-index by n.vars')

ggplot(cv.calibration.average, aes(x = n.vars, y = area)) +

  geom_line() +

  geom_point(data = cv.performance) +

  ggtitle(label = 'Calibration performance by n.vars')

# Find the highest value

n.vars <-

  cv.performance.average$n.vars[

    which.max(cv.performance.average$c.index.val)

    ]

# Fit a full model with the variables provided

surv.predict.partial <- names(missingness)[1:n.vars]

#' ## Best model

#' 

#' The best model contained `r n.vars` variables. Let's see what those were...

#' 

#+ variables_used

vars.df <-

  data.frame(

    vars = surv.predict.partial

  )

vars.df$descriptions <- lookUpDescriptions(surv.predict.partial)

vars.df$missingness <- missingness[1:n.vars]

#+ variables_table, results='asis'

print(

  xtable(vars.df),

  type = 'html',

  include.rownames = FALSE

)

#' ## Perform the final fit

#' 

#' Having found the best number of variables by cross-validation, let's perform

#' the final fit with the full training set and `r n.trees.final` trees.

#' 

#+ final_fit

time.start <- handyTimer()

surv.model.fit.final <-

  survivalFit(

    surv.predict.partial,

    COHORT.bigdata[-test.set,],

    model.type = 'rfsrc',

    n.trees = n.trees.final,

    split.rule = split.rule,

    n.threads = n.threads,

    nimpute = 3,

    nsplit = nsplit,

    na.action = 'na.impute'

  )

time.fit.final <- handyTimer(time.start)

saveRDS(surv.model.fit.final, paste0(output.filename.base, '-finalmodel.rds'))

#' Final model of `r n.trees.final` trees fitted in `r round(time.fit.final)`

#' seconds! 

#' 

#' Also bootstrap this final fitting stage. A fully proper bootstrap would

#' iterate over the whole model-building process including variable selection,

#' but that would be prohibitive in terms of computational time.

#' 

#+ bootstrap_final

surv.model.fit.boot <-

  survivalBootstrap(

    surv.predict.partial,

    COHORT.bigdata[-test.set,], # Training set

    COHORT.bigdata[test.set,],  # Test set

    model.type = 'rfsrc',

    n.trees = n.trees.final,

    split.rule = split.rule,

    n.threads = n.threads,

    nimpute = 3,

    nsplit = nsplit,

    na.action = 'na.impute',

    bootstraps = bootstraps

  )

# Get coefficients and variable importances from bootstrap fits

surv.model.fit.coeffs <- bootStats(surv.model.fit.boot, uncertainty = '95ci')

#' ## Performance

#' 

#' ### C-index

#' 

#' C-indices are **`r round(surv.model.fit.coeffs['c.train', 'val'], 3)`

#' (`r round(surv.model.fit.coeffs['c.train', 'lower'], 3)` - 

#' `r round(surv.model.fit.coeffs['c.train', 'upper'], 3)`)**

#' on the training set and

#' **`r round(surv.model.fit.coeffs['c.test', 'val'], 3)`

#' (`r round(surv.model.fit.coeffs['c.test', 'lower'], 3)` - 

#' `r round(surv.model.fit.coeffs['c.test', 'upper'], 3)`)** on the test set.

#' 

#'

#' ### Calibration

#' 

#' The bootstrapped calibration score is

#' **`r round(surv.model.fit.coeffs['calibration.score', 'val'], 3)`

#' (`r round(surv.model.fit.coeffs['calibration.score', 'lower'], 3)` - 

#' `r round(surv.model.fit.coeffs['calibration.score', 'upper'], 3)`)**.

#' 

#' Let's draw a representative curve from the unbootstrapped fit... (It would be

#' better to draw all the curves from the bootstrap fit to get an idea of

#' variability, but I've not implemented this yet.)

#' 

#+ calibration_plot

calibration.table <-

  calibrationTable(

    # Standard calibration options

    surv.model.fit.final, COHORT.bigdata[test.set,],

    # Always need to specify NA imputation for rfsrc

    na.action = 'na.impute'

  )

calibration.score <- calibrationScore(calibration.table)

calibrationPlot(calibration.table)

#' The area between the calibration curve and the diagonal is 

#' **`r round(calibration.score[['area']], 3)`** +/-

#' **`r round(calibration.score[['se']], 3)`**.

#'

#+ save_results

# Save performance results

varsToTable(

  data.frame(

    model = 'rf-varselmiss',

    imputation = FALSE,

    discretised = FALSE,

    c.index = surv.model.fit.coeffs['c.train', 'val'],

    c.index.lower = surv.model.fit.coeffs['c.train', 'lower'],

    c.index.upper = surv.model.fit.coeffs['c.train', 'upper'],

    calibration.score = surv.model.fit.coeffs['calibration.score', 'val'],

    calibration.score.lower =

      surv.model.fit.coeffs['calibration.score', 'lower'],

    calibration.score.upper =

      surv.model.fit.coeffs['calibration.score', 'upper']

  ),

  performance.file,

  index.cols = c('model', 'imputation', 'discretised')

)