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|
=begin
* Name: neighbors.rb
* Description: Prediction algorithms library
* Author: Andreas Maunz <andreas@maunz.de>
* Date: 10/2012
=end
require 'rinruby'
module OpenTox
module Algorithm
class Neighbors
# Get confidence.
# @param[Hash] Required keys: :sims, :activities
# @return[Float] Confidence
def self.get_confidence(params)
conf = params[:sims].inject{|sum,x| sum + x }
confidence = conf/params[:sims].size
#$logger.debug "Confidence: '" + confidence.to_s + "'."
return confidence
end
# Classification with majority vote from neighbors weighted by similarity
# @param [Hash] params Keys `:activities, :sims, :value_map` are required
# @return [Numeric] A prediction value.
def self.weighted_majority_vote(params)
neighbor_contribution = 0.0
confidence_sum = 0.0
confidence = 0.0
prediction = nil
$logger.debug "Weighted Majority Vote Classification."
params[:activities].each_index do |idx|
neighbor_weight = params[:sims][1][idx]
neighbor_contribution += params[:activities][idx] * neighbor_weight
if params[:value_map].size == 2 # AM: provide compat to binary classification: 1=>false 2=>true
case params[:activities][idx]
when 1
confidence_sum -= neighbor_weight
when 2
confidence_sum += neighbor_weight
end
else
confidence_sum += neighbor_weight
end
end
if params[:value_map].size == 2
if confidence_sum >= 0.0
prediction = 2 unless params[:activities].size==0
elsif confidence_sum < 0.0
prediction = 1 unless params[:activities].size==0
end
else
prediction = (neighbor_contribution/confidence_sum).round unless params[:activities].size==0 # AM: new multinomial prediction
end
#$logger.debug "Prediction: '" + prediction.to_s + "'." unless prediction.nil?
confidence = (confidence_sum/params[:activities].size).abs if params[:activities].size > 0
#$logger.debug "Confidence: '" + confidence.to_s + "'." unless prediction.nil?
return {:prediction => prediction, :confidence => confidence.abs}
end
# Local support vector regression from neighbors
# @param [Hash] params Keys `:props, :activities, :sims, :min_train_performance` are required
# @return [Numeric] A prediction value.
def self.local_svm_regression(params)
confidence = 0.0
prediction = nil
$logger.debug "Local SVM."
if params[:activities].size>0
if params[:props]
n_prop = params[:props][0].collect.to_a
q_prop = params[:props][1].collect.to_a
props = [ n_prop, q_prop ]
end
activities = params[:activities].collect.to_a
prediction = local_svm_prop( props, activities, params[:min_train_performance]) # params[:props].nil? signals non-prop setting
prediction = nil if (!prediction.nil? && prediction.infinite?)
#$logger.debug "Prediction: '" + prediction.to_s + "' ('#{prediction.class}')."
confidence = get_confidence({:sims => params[:sims][1], :activities => params[:activities]})
confidence = 0.0 if prediction.nil?
end
{:prediction => prediction, :confidence => confidence}
end
# Local support vector regression from neighbors
# @param [Hash] params Keys `:props, :activities, :sims, :min_train_performance` are required
# @return [Numeric] A prediction value.
def self.local_svm_classification(params)
confidence = 0.0
prediction = nil
$logger.debug "Local SVM."
if params[:activities].size>0
if params[:props]
n_prop = params[:props][0].collect.to_a
q_prop = params[:props][1].collect.to_a
props = [ n_prop, q_prop ]
end
activities = params[:activities].collect.to_a
activities = activities.collect{|v| "Val" + v.to_s} # Convert to string for R to recognize classification
prediction = local_svm_prop( props, activities, params[:min_train_performance]) # params[:props].nil? signals non-prop setting
prediction = prediction.sub(/Val/,"") if prediction # Convert back
confidence = 0.0 if prediction.nil?
#$logger.debug "Prediction: '" + prediction.to_s + "' ('#{prediction.class}')."
confidence = get_confidence({:sims => params[:sims][1], :activities => params[:activities]})
end
{:prediction => prediction, :confidence => confidence}
end
# Local support vector prediction from neighbors.
# Uses propositionalized setting.
# Not to be called directly (use local_svm_regression or local_svm_classification).
# @param [Array] props, propositionalization of neighbors and query structure e.g. [ Array_for_q, two-nested-Arrays_for_n ]
# @param [Array] activities, activities for neighbors.
# @param [Float] min_train_performance, parameter to control censoring
# @return [Numeric] A prediction value.
def self.local_svm_prop(props, activities, min_train_performance)
$logger.debug "Local SVM (Propositionalization / Kernlab Kernel)."
n_prop = props[0] # is a matrix, i.e. two nested Arrays.
q_prop = props[1] # is an Array.
prediction = nil
if activities.uniq.size == 1
prediction = activities[0]
else
#$logger.debug gram_matrix.to_yaml
@r = RinRuby.new(true,false) # global R instance leads to Socket errors after a large number of requests
@r.eval "suppressPackageStartupMessages(library('caret'))" # requires R packages "caret" and "kernlab"
@r.eval "suppressPackageStartupMessages(library('doMC'))" # requires R packages "multicore"
@r.eval "registerDoMC()" # switch on parallel processing
@r.eval "set.seed(1)"
begin
# set data
$logger.debug "Setting R data ..."
@r.n_prop = n_prop.flatten
@r.n_prop_x_size = n_prop.size
@r.n_prop_y_size = n_prop[0].size
@r.y = activities
@r.q_prop = q_prop
#@r.eval "y = matrix(y)"
@r.eval "prop_matrix = matrix(n_prop, n_prop_x_size, n_prop_y_size, byrow=T)"
@r.eval "q_prop = matrix(q_prop, 1, n_prop_y_size, byrow=T)"
# prepare data
$logger.debug "Preparing R data ..."
@r.eval <<-EOR
weights=NULL
if (!(class(y) == 'numeric')) {
y = factor(y)
suppressPackageStartupMessages(library('class'))
weights=unlist(as.list(prop.table(table(y))))
weights=(weights-1)^2
}
EOR
@r.eval <<-EOR
rem = nearZeroVar(prop_matrix)
if (length(rem) > 0) {
prop_matrix = prop_matrix[,-rem,drop=F]
q_prop = q_prop[,-rem,drop=F]
}
rem = findCorrelation(cor(prop_matrix))
if (length(rem) > 0) {
prop_matrix = prop_matrix[,-rem,drop=F]
q_prop = q_prop[,-rem,drop=F]
}
EOR
# model + support vectors
$logger.debug "Creating R SVM model ..."
train_success = @r.eval <<-EOR
model = train(prop_matrix,y,
method="svmradial",
preProcess=c("center", "scale"),
class.weights=weights,
trControl=trainControl(method="LGOCV",number=10),
tuneLength=8
)
perf = ifelse ( class(y)!='numeric', max(model$results$Accuracy), model$results[which.min(model$results$RMSE),]$Rsquared )
EOR
if train_success
# prediction
$logger.debug "Predicting ..."
@r.eval "predict(model,q_prop); p = predict(model,q_prop)" # kernlab bug: predict twice
#@r.eval "p = predict(model,q_prop)" # kernlab bug: predict twice
@r.eval "if (class(y)!='numeric') p = as.character(p)"
prediction = @r.p
# censoring
prediction = nil if ( @r.perf.nan? || @r.perf < min_train_performance.to_f )
prediction = nil if prediction =~ /NA/
$logger.debug "Performance: '#{sprintf("%.2f", @r.perf)}'"
else
$logger.debug "Model creation failed."
prediction = nil
end
rescue Exception => e
$logger.debug "#{e.class}: #{e.message}"
$logger.debug "Backtrace:\n\t#{e.backtrace.join("\n\t")}"
ensure
#puts @r.inspect
#TODO: broken pipe
#@r.quit # free R
end
end
prediction
end
end
end
end
|
