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module OpenTox
module Algorithm
class FeatureSelection
def self.correlation_filter dataset:, prediction_feature:, types:nil
# TODO: speedup, single assignment of all features to R+ parallel computation of significance?
relevant_features = {}
measurements = []
substances = []
dataset.substances.each do |s|
dataset.values(s,prediction_feature).each do |act|
measurements << act
substances << s
end
end
R.assign "tox", measurements
feature_ids = dataset.substances.collect{ |s| s["properties"].keys}.flatten.uniq
feature_ids.select!{|fid| types.include? Feature.find(fid).category} if types
feature_ids.each do |feature_id|
feature_values = substances.collect{|s| s["properties"][feature_id].first if s["properties"][feature_id]}
unless feature_values.uniq.size == 1
R.assign "feature", feature_values
begin
R.eval "cor <- cor.test(tox,feature,method = 'pearson',use='pairwise')"
pvalue = R.eval("cor$p.value").to_ruby
if pvalue <= 0.05
r = R.eval("cor$estimate").to_ruby
relevant_features[feature_id] = {}
relevant_features[feature_id]["pvalue"] = pvalue
relevant_features[feature_id]["r"] = r
relevant_features[feature_id]["mean"] = R.eval("mean(feature, na.rm=TRUE)").to_ruby
relevant_features[feature_id]["sd"] = R.eval("sd(feature, na.rm=TRUE)").to_ruby
end
rescue
warn "Correlation of '#{Feature.find(feature_id).name}' (#{feature_values}) with '#{Feature.find(prediction_feature_id).name}' (#{measurements}) failed."
end
end
end
relevant_features.sort{|a,b| a[1]["pvalue"] <=> b[1]["pvalue"]}.to_h
end
end
end
end
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