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authorChristoph Helma <helma@in-silico.ch>2017-02-14 15:02:17 +0100
committerChristoph Helma <helma@in-silico.ch>2017-02-14 15:02:17 +0100
commitbb8797e0047f02768033cf6839dc926d30c016d2 (patch)
tree740c4765fe887315bb8b29c31a767afd227facc0 /scripts
parent127380ef9189b58b1541b2a35989900003c26510 (diff)
rf models, sim 0.5, no weighted average
Diffstat (limited to 'scripts')
-rwxr-xr-xscripts/crossvalidation.rb6
-rwxr-xr-xscripts/test-validation.rb2
2 files changed, 2 insertions, 6 deletions
diff --git a/scripts/crossvalidation.rb b/scripts/crossvalidation.rb
index 32b9950..e02c5ca 100755
--- a/scripts/crossvalidation.rb
+++ b/scripts/crossvalidation.rb
@@ -5,13 +5,11 @@ require 'yaml'
name = File.basename ARGV[0], ".csv"
file = File.join "data",ARGV[0]
dataset = Dataset.from_csv_file file
-#model = Model::LazarRegression.create(training_dataset: dataset)#, :prediction_algorithm => "OpenTox::Algorithm::Regression.local_fingerprint_regression")
-model = Model::LazarRegression.create(training_dataset: dataset, algorithms: { :similarity => { :min => 0.5 }})
+model = Model::LazarRegression.create(training_dataset: dataset, algorithms: { :prediction => {:method => "Algorithm::Caret.rf"}, :similarity => { :min => 0.5 }})
csv_file = File.join("data",ARGV[0].sub(/.csv/,"-cv-#{ARGV[1]}.csv"))
id_file = File.join("data",ARGV[0].sub(/.csv/,"-cv-#{ARGV[1]}.id"))
cv = Validation::RegressionCrossValidation.create model
File.open(id_file,"w+"){|f| f.puts cv.id}
-#cv = Validation::RegressionCrossValidation.first
p cv.id
data = []
cv.predictions.each do |cid,p|
@@ -29,5 +27,3 @@ CSV.open(csv_file,"w+") do |csv|
csv << ["SMILES","LOAEL_measured_median","LOAEL_predicted","Prediction_interval_low","Prediction_interval_high"]
data.each{|r| csv << r}
end
-=begin
-=end
diff --git a/scripts/test-validation.rb b/scripts/test-validation.rb
index b64edd6..0b8c0a7 100755
--- a/scripts/test-validation.rb
+++ b/scripts/test-validation.rb
@@ -5,6 +5,6 @@ include OpenTox
test = Dataset.from_csv_file(File.join("data","test_log10.csv"))
train = Dataset.from_csv_file(File.join("data","training_log10.csv"))
-model = Model::LazarRegression.create(training_dataset: train, algorithms: { :similarity => { :min => 0.5 }})
+model = Model::LazarRegression.create(training_dataset: train, algorithms: { :prediction => {:method => "Algorithm::Caret.rf"}, :similarity => { :min => 0.5 }})
validation = Validation::TrainTest.create model, train, test
File.open(File.join("data","training-test-predictions.id"),"w+") { |f| f.puts validation.id }