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module OpenTox
module Algorithm
class Classification
def self.weighted_majority_vote compound, neighbors
return {:value => nil,:confidence => nil,:warning => "Cound not find similar compounds."} if neighbors.empty?
weighted_sum = {}
sim_sum = 0.0
neighbors.each do |row|
n,sim,acts = row
acts.each do |act|
weighted_sum[act] ||= 0
weighted_sum[act] += sim
end
end
case weighted_sum.size
when 1
return {:value => weighted_sum.keys.first, :confidence => weighted_sum.values.first/neighbors.size.abs}
when 2
sim_sum = weighted_sum[weighted_sum.keys[0]]
sim_sum -= weighted_sum[weighted_sum.keys[1]]
sim_sum > 0 ? prediction = weighted_sum.keys[0] : prediction = weighted_sum.keys[1]
confidence = (sim_sum/neighbors.size).abs
return {:value => prediction,:confidence => confidence}
else
bad_request_error "Cannot predict more than 2 classes, multinomial classifications is not yet implemented. Received classes were: '#{weighted.sum.keys}'"
end
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.fminer_weighted_majority_vote neighbors, training_dataset
neighbor_contribution = 0.0
confidence_sum = 0.0
$logger.debug "Weighted Majority Vote Classification."
values = neighbors.collect{|n| n[2]}.uniq
neighbors.each do |neighbor|
i = training_dataset.compound_ids.index n.id
neighbor_weight = neighbor[1]
activity = values.index(neighbor[2]) + 1 # map values to integers > 1
neighbor_contribution += activity * neighbor_weight
if values.size == 2 # AM: provide compat to binary classification: 1=>false 2=>true
case activity
when 1
confidence_sum -= neighbor_weight
when 2
confidence_sum += neighbor_weight
end
else
confidence_sum += neighbor_weight
end
end
if values.size == 2
if confidence_sum >= 0.0
prediction = values[1]
elsif confidence_sum < 0.0
prediction = values[0]
end
elsif values.size == 1 # all neighbors have the same value
prediction = values[0]
else
prediction = (neighbor_contribution/confidence_sum).round # AM: new multinomial prediction
end
confidence = (confidence_sum/neighbors.size).abs
{:value => 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_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
{:value => prediction, :confidence => confidence}
end
end
end
end
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