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module OpenTox
module Algorithm
class Caret
# TODO classification
# model list: https://topepo.github.io/caret/modelList.html
attr_accessor :descriptors, :neighbors, :method, :relevant_features, :data_frame, :feature_names, :weights, :query_features
def initialize descriptors:, neighbors:, method:, relevant_features:
@descriptors = descriptors
@neighbors = neighbors
@method = method
@relevant_features = relevant_features
end
def self.regression descriptors:, neighbors:, method:, relevant_features:nil
caret = new(descriptors:descriptors, neighbors:neighbors, method:method, relevant_features:relevant_features)
# collect training data for R
if descriptors.is_a? Array
caret.fingerprint2R
elsif descriptors.is_a? Hash
caret.properties2R
else
bad_request_error "Descriptors should be a fingerprint (Array) or properties (Hash). Cannot handle '#{descriptors.class}'."
end
if caret.feature_names.empty? or caret.data_frame.flatten.uniq == ["NA"]
prediction = Algorithm::Regression::weighted_average(descriptors: @descriptors, neighbors: neighbors)
prediction[:warning] = "No variables for regression model. Using weighted average of similar substances."
else
prediction = caret.r_model_prediction
if prediction.nil? or prediction[:value].nil?
prediction = Algorithm::Regression::weighted_average(descriptors: @descriptors, neighbors: neighbors)
prediction[:warning] = "Could not create local caret model. Using weighted average of similar substances."
end
end
prediction
end
def fingerprint2R
values = []
features = {}
@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|
features[id] ||= []
features[id] << n["descriptors"].include?(id)
end
end if activities
end
@feature_names = []
@data_frame = [values]
features.each do |k,v|
unless v.uniq.size == 1
@data_frame << v.collect{|m| m ? "T" : "F"}
@feature_names << k
end
end
@query_features = @feature_names.collect{|f| descriptors.include?(f) ? "T" : "F"}
end
def properties2R
@weights = []
@feature_names = []
@query_features = []
# keep only descriptors with values
@relevant_features.keys.each_with_index do |f,i|
if @descriptors[f]
@feature_names << f
@query_features << @descriptors[f].median
else
neighbors.each do |n|
n["descriptors"].delete_at i
end
end
end
measurements = neighbors.collect{|n| n["measurements"]}.flatten
# initialize data frame with 'NA' defaults
@data_frame = Array.new(@feature_names.size+1){Array.new(measurements.size,"NA") }
i = 0
# parse neighbor activities and descriptors
neighbors.each do |n|
activities = n["measurements"]
activities.each do |act| # multiple measurements are treated as separate instances
unless n["descriptors"].include?(nil)
data_frame[0][i] = act
@weights << n["similarity"]
n["descriptors"].each_with_index do |d,j|
@data_frame[j+1][i] = d
end
i += 1
end
end if activities # ignore neighbors without measurements
end
end
def r_model_prediction
begin
R.assign "weights", @weights
r_data_frame = "data.frame(#{@data_frame.collect{|r| "c(#{r.join(',')})"}.join(', ')})"
R.eval "data <- #{r_data_frame}"
R.assign "features", @feature_names
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:"
$logger.debug JSON.pretty_generate(self.inspect)
return nil
end
begin
R.eval "query <- data.frame(rbind(c(#{@query_features.join ','})))"
R.eval "names(query) <- features"
R.eval "prediction <- predict(model,query)"
value = R.eval("prediction").to_f
rmse = R.eval("getTrainPerf(model)$TrainRMSE").to_f
r_squared = R.eval("getTrainPerf(model)$TrainRsquared").to_f
prediction_interval = value-1.96*rmse, value+1.96*rmse
{
: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
return nil
end
end
end
end
end
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