Class: OpenTox::Algorithm::Caret
- Defined in:
- lib/caret.rb,
lib/caret-classification.rb
Overview
Ruby interface for the R caret package Caret model list: topepo.github.io/caret/modelList.html
Class Method Summary collapse
-
.create_model_and_predict(dependent_variables:, independent_variables:, weights:, method:, query_variables:) ⇒ Hash
Create a local R caret model and make a prediction.
-
.method_missing(sym, *args, &block) ⇒ Object
Call caret methods dynamically, e.g.
-
.to_r(v) ⇒ Object
Convert Ruby values to R values.
Class Method Details
.create_model_and_predict(dependent_variables:, independent_variables:, weights:, method:, query_variables:) ⇒ Hash
Create a local R caret model and make a prediction
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# File 'lib/caret.rb', line 15 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[:warnings] = ["No variables for regression model, using weighted average of similar substances (no prediction interval available)."] elsif dependent_variables.size < 3 prediction = Algorithm::Regression::weighted_average dependent_variables:dependent_variables, weights:weights prediction[:warnings] = ["Insufficient number of neighbors (#{dependent_variables.size}) for regression model, using weighted average of similar substances (no prediction interval available)."] 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 $logger.debug independent_variables prediction = Algorithm::Regression::weighted_average dependent_variables:dependent_variables, weights:weights prediction[:warnings] ||= [] prediction[:warnings] << "R caret model creation error, using weighted average of similar substances (no prediction interval available)." return prediction end begin R.eval "query <- data.frame(rbind(c(#{query_variables.join ','})))" R.eval "names(query) <- features" R.eval "prediction <- predict(model,query)" value = R.eval("prediction").to_f rmse = R.eval("getTrainPerf(model)$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[:warnings] << "R caret prediction error, using weighted average of similar substances (no prediction interval available)." return prediction end if prediction.nil? or prediction[:value].nil? prediction = Algorithm::Regression::weighted_average dependent_variables:dependent_variables, weights:weights prediction[:warnings] << "Empty R caret prediction, using weighted average of similar substances (no prediction interval available)." end end prediction end |
.method_missing(sym, *args, &block) ⇒ Object
Call caret methods dynamically, e.g. Caret.pls
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# File 'lib/caret.rb', line 89 def self.method_missing(sym, *args, &block) args.first[:method] = sym.to_s self.create_model_and_predict args.first end |