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module OpenTox
module Algorithm
# Ruby interface for the R caret package
# Caret model list: https://topepo.github.io/caret/modelList.html
class Caret
# Create a local R caret model and make a prediction
# @param [Array<Float,Bool>] dependent_variables
# @param [Array<Array<Float,Bool>>] independent_variables
# @param [Array<Float>] weights
# @param [String] Caret method
# @param [Array<Float,Bool>] query_variables
# @return [Hash]
def self.create_model_and_predict dependent_variables:, independent_variables:, weights:, method:, query_variables:
remove = []
# remove independent_variables with single values
independent_variables.each_with_index { |values,i| remove << i if values.uniq.size == 1}
remove.sort.reverse.each do |i|
independent_variables.delete_at i
query_variables.delete_at i
end
if independent_variables.flatten.uniq == ["NA"] or independent_variables.flatten.uniq == []
prediction = Algorithm::Regression::weighted_average dependent_variables:dependent_variables, weights:weights
prediction[:warning] = "No variables for regression model. Using weighted average of similar substances."
elsif
dependent_variables.size < 3
prediction = Algorithm::Regression::weighted_average dependent_variables:dependent_variables, weights:weights
prediction[:warning] = "Insufficient number of neighbors (#{dependent_variables.size}) for regression model. Using weighted average of similar substances."
else
dependent_variables.each_with_index do |v,i|
dependent_variables[i] = to_r(v)
end
independent_variables.each_with_index do |c,i|
c.each_with_index do |v,j|
independent_variables[i][j] = to_r(v)
end
end
query_variables.each_with_index do |v,i|
query_variables[i] = to_r(v)
end
begin
R.assign "weights", weights
r_data_frame = "data.frame(#{([dependent_variables]+independent_variables).collect{|r| "c(#{r.join(',')})"}.join(', ')})"
R.eval "data <- #{r_data_frame}"
R.assign "features", (0..independent_variables.size-1).to_a
R.eval "names(data) <- append(c('activities'),features)" #
R.eval "model <- train(activities ~ ., data = data, method = '#{method}', na.action = na.pass, allowParallel=TRUE)"
rescue => e
$logger.debug "R caret model creation error for:"
$logger.debug dependent_variables
$logger.debug independent_variables
prediction = Algorithm::Regression::weighted_average dependent_variables:dependent_variables, weights:weights
prediction[:warning] = "R caret model creation error. Using weighted average of similar substances."
return prediction
end
begin
R.eval "query <- data.frame(rbind(c(#{query_variables.join ','})))"
R.eval "names(query) <- features"
R.eval "prediction <- predict(model,query)"
value = R.eval("prediction").to_f
rmse = R.eval("getTrainPerf(model)$TrainRMSE").to_f
r_squared = R.eval("getTrainPerf(model)$TrainRsquared").to_f
prediction_interval = value-1.96*rmse, value+1.96*rmse
prediction = {
:value => value,
:rmse => rmse,
:r_squared => r_squared,
:prediction_interval => prediction_interval
}
rescue => e
$logger.debug "R caret prediction error for:"
$logger.debug self.inspect
prediction = Algorithm::Regression::weighted_average dependent_variables:dependent_variables, weights:weights
prediction[:warning] = "R caret prediction error. Using weighted average of similar substances"
return prediction
end
if prediction.nil? or prediction[:value].nil?
prediction = Algorithm::Regression::weighted_average dependent_variables:dependent_variables, weights:weights
prediction[:warning] = "Could not create local caret model. Using weighted average of similar substances."
end
end
prediction
end
# Call caret methods dynamically, e.g. Caret.pls
def self.method_missing(sym, *args, &block)
args.first[:method] = sym.to_s
self.create_model_and_predict args.first
end
# Convert Ruby values to R values
def self.to_r v
return "F" if v == false
return "T" if v == true
return nil if v.is_a? Float and v.nan?
v
end
end
end
end
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