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module OpenTox
module Algorithm
class Regression
def self.weighted_average descriptors:nil, neighbors:, parameters:nil
# TODO: prediction_interval
weighted_sum = 0.0
sim_sum = 0.0
neighbors.each do |neighbor|
sim = neighbor["similarity"]
activities = neighbor["measurements"]
activities.each do |act|
weighted_sum += sim*act
sim_sum += sim
end if activities
end
sim_sum == 0 ? prediction = nil : prediction = weighted_sum/sim_sum
{:value => prediction}
end
def self.caret descriptors:, neighbors:, method: "pls", parameters:nil
values = []
descriptors = {}
weights = []
descriptor_ids = neighbors.collect{|n| n["descriptors"]}.flatten.uniq.sort
neighbors.each do |n|
activities = n["measurements"]
activities.each do |act|
values << act
weights << n["similarity"]
descriptor_ids.each do |id|
descriptors[id] ||= []
descriptors[id] << n["descriptors"].include?(id)
end
end if activities
end
variables = []
data_frame = [values]
descriptors.each do |k,v|
unless v.uniq.size == 1
data_frame << v.collect{|m| m ? "T" : "F"}
variables << k
end
end
if variables.empty?
prediction = weighted_average(descriptors: descriptors, neighbors: neighbors)
prediction[:warning] = "No variables for regression model. Using weighted average of similar substances."
prediction
else
substance_features = variables.collect{|f| descriptors.include?(f) ? "T" : "F"}
#puts data_frame.to_yaml
prediction = r_model_prediction method, data_frame, variables, weights, substance_features
if prediction.nil? or prediction[:value].nil?
prediction = weighted_average(descriptors: descriptors, neighbors: neighbors)
prediction[:warning] = "Could not create local caret model. Using weighted average of similar substances."
prediction
else
prediction[:prediction_interval] = [prediction[:value]-1.96*prediction[:rmse], prediction[:value]+1.96*prediction[:rmse]]
prediction[:value] = prediction[:value]
prediction[:rmse] = prediction[:rmse]
prediction
end
end
end
def self.fingerprint_regression substance:, neighbors:, method: "pls" #, method_params="sigma=0.05"
values = []
fingerprints = {}
weights = []
fingerprint_ids = neighbors.collect{|n| Compound.find(n["_id"]).fingerprint}.flatten.uniq.sort
neighbors.each do |n|
fingerprint = Substance.find(n["_id"]).fingerprint
activities = n["measurements"]
activities.each do |act|
values << act
weights << n["similarity"]
fingerprint_ids.each do |id|
fingerprints[id] ||= []
fingerprints[id] << fingerprint.include?(id)
end
end if activities
end
variables = []
data_frame = [values]
fingerprints.each do |k,v|
unless v.uniq.size == 1
data_frame << v.collect{|m| m ? "T" : "F"}
variables << k
end
end
if variables.empty?
prediction = weighted_average(substance: substance, neighbors: neighbors)
prediction[:warning] = "No variables for regression model. Using weighted average of similar substances."
prediction
else
substance_features = variables.collect{|f| substance.fingerprint.include?(f) ? "T" : "F"}
prediction = r_model_prediction method, data_frame, variables, weights, substance_features
if prediction.nil? or prediction[:value].nil?
prediction = weighted_average(substance: substance, neighbors: neighbors)
prediction[:warning] = "Could not create local PLS model. Using weighted average of similar substances."
prediction
else
prediction[:prediction_interval] = [prediction[:value]-1.96*prediction[:rmse], prediction[:value]+1.96*prediction[:rmse]]
prediction[:value] = prediction[:value]
prediction[:rmse] = prediction[:rmse]
prediction
end
end
end
=begin
def self.physchem_regression substance:, neighbors:, method: "pls"
activities = []
weights = []
pc_ids = neighbors.collect{|n| n["common_descriptors"].collect{|d| d[:id]}}.flatten.uniq.sort
data_frame = []
data_frame[0] = []
neighbors.each_with_index do |n,i|
activities = n["measurements"]
activities.each do |act|
data_frame[0][i] = act
weights << n["similarity"]
n["common_descriptors"].each do |d|
j = pc_ids.index(d[:id])+1
data_frame[j] ||= []
data_frame[j][i] = d[:scaled_value]
end
end if activities
(0..pc_ids.size).each do |j| # for R: fill empty values with NA
data_frame[j] ||= []
data_frame[j][i] ||= "NA"
end
end
data_frame = data_frame.each_with_index.collect do |r,i|
if r.uniq.size == 1 # remove properties with a single value
r = nil
pc_ids[i-1] = nil # data_frame frame has additional activity entry
end
r
end
data_frame.compact!
pc_ids.compact!
if pc_ids.empty?
prediction = weighted_average(substance: substance, neighbors: neighbors)
prediction[:warning] = "No relevant variables for regression model. Using weighted average of similar substances."
prediction
else
query_descriptors = pc_ids.collect { |i| substance.scaled_values[i] }
query_descriptors = query_descriptors.each_with_index.collect do |v,i|
unless v
v = nil
data_frame[i] = nil
pc_ids[i] = nil
end
v
end
query_descriptors.compact!
data_frame.compact!
pc_ids.compact!
prediction = r_model_prediction method, data_frame, pc_ids.collect{|i| "\"#{i}\""}, weights, query_descriptors
if prediction.nil?
prediction = weighted_average(substance: substance, neighbors: neighbors)
prediction[:warning] = "Could not create local PLS model. Using weighted average of similar substances."
end
p prediction
prediction
end
end
=end
def self.r_model_prediction method, training_data, training_features, training_weights, query_feature_values
R.assign "weights", training_weights
r_data_frame = "data.frame(#{training_data.collect{|r| "c(#{r.join(',')})"}.join(', ')})"
=begin
rlib = File.expand_path(File.join(File.dirname(__FILE__),"..","R"))
File.open("tmp.R","w+"){|f|
f.puts "suppressPackageStartupMessages({
library(iterators,lib=\"#{rlib}\")
library(foreach,lib=\"#{rlib}\")
library(ggplot2,lib=\"#{rlib}\")
library(grid,lib=\"#{rlib}\")
library(gridExtra,lib=\"#{rlib}\")
library(pls,lib=\"#{rlib}\")
library(caret,lib=\"#{rlib}\")
library(doMC,lib=\"#{rlib}\")
registerDoMC(#{NR_CORES})
})"
f.puts "data <- #{r_data_frame}\n"
f.puts "weights <- c(#{training_weights.join(', ')})"
f.puts "features <- c(#{training_features.join(', ')})"
f.puts "names(data) <- append(c('activities'),features)" #
f.puts "ctrl <- rfeControl(functions = #{method}, method = 'repeatedcv', repeats = 5, verbose = T)"
f.puts "lmProfile <- rfe(activities ~ ., data = data, rfeControl = ctrl)"
f.puts "model <- train(activities ~ ., data = data, method = '#{method}')"
f.puts "fingerprint <- data.frame(rbind(c(#{query_feature_values.join ','})))"
f.puts "names(fingerprint) <- features"
f.puts "prediction <- predict(model,fingerprint)"
}
=end
R.eval "data <- #{r_data_frame}"
R.assign "features", training_features
begin
R.eval "names(data) <- append(c('activities'),features)" #
R.eval "model <- train(activities ~ ., data = data, method = '#{method}', na.action = na.pass, allowParallel=TRUE)"
R.eval "fingerprint <- data.frame(rbind(c(#{query_feature_values.join ','})))"
R.eval "names(fingerprint) <- features"
R.eval "prediction <- predict(model,fingerprint)"
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
{
:value => value,
:rmse => rmse,
:r_squared => r_squared,
:prediction_interval => prediction_interval
}
rescue
return nil
end
end
end
end
end
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