From 83591831c6e36c36d87159acba6afdfedab95522 Mon Sep 17 00:00:00 2001
From: Christoph Helma <helma@in-silico.ch>
Date: Thu, 18 Mar 2021 16:48:36 +0100
Subject: fingerprint predictions added

---
 bin/classification-summary.rb | 92 -------------------------------------------
 1 file changed, 92 deletions(-)
 delete mode 100755 bin/classification-summary.rb

(limited to 'bin/classification-summary.rb')

diff --git a/bin/classification-summary.rb b/bin/classification-summary.rb
deleted file mode 100755
index 45ffb29..0000000
--- a/bin/classification-summary.rb
+++ /dev/null
@@ -1,92 +0,0 @@
-#!/usr/bin/env ruby
-require_relative "../lib/lazar"
-#stat = ClassificationStatistics.new ARGV[0]
-#stat.summary
-dir = File.join(File.dirname(ARGV[0]),"crossvalidation")
-thresh = ARGV[1].to_f
-folds = Dir[File.join(dir,"[0-9]*")]
-
-predictions = []
-tp=0
-tn=0
-fp=0
-fn=0
-hc_tp=0
-hc_tn=0
-hc_fp=0
-hc_fn=0
-#n=0
-experimental = {}
-
-lines = File.readlines(File.join(ARGV[0])) 
-lines.shift
-lines.each do |line|
-  items = line.chomp.split(',')
-  experimental[items[0]] ||= []
-  experimental[items[0]] << items[1].to_i
-end
-
-File.open(File.join(dir,"predictions.csv"),"w+") do |f|
-  folds.each do |fold|
-    pred = File.readlines(File.join(fold,"test-prediction.csv")).collect{|row| row.chomp.split(",")}
-    pred.shift
-    pred.each do |prediction|
-      smi = prediction[0]
-      exp = experimental[smi]
-      maxsim = prediction[5].to_f
-      v = "NA"
-      unless exp.nil? or prediction[2].empty? or exp.empty?
-        p = prediction[2].to_i
-        #n+=1
-        exp.each do |e|
-          if p and e
-            if p == 1 and e == 1
-              v = "TP"
-              tp+=1
-              hc_tp+=1 if maxsim > thresh
-            elsif p == 0 and e == 0
-              v = "TN"
-              tn+=1
-              hc_tn+=1 if maxsim > thresh
-            elsif p == 1 and e == 0
-              v = "FP"
-              fp+=1
-              hc_fp+=1 if maxsim > thresh
-            elsif p == 0 and e == 1
-              v = "FN"
-              fn+=1
-              hc_fn+=1 if maxsim > thresh
-            end
-          end
-          predictions << v
-        end
-      end
-      f.puts([smi,v,maxsim].join(","))
-    end
-  end
-end
-
-File.open(File.join(dir,"confusion-matrix-all.csv"),"w+") do |f|
-  f.puts "#{tp},#{fp}\n#{fn},#{tn}"
-end
-
-File.open(File.join(dir,"confusion-matrix-high-confidence.csv"),"w+") do |f|
-  f.puts "#{hc_tp},#{hc_fp}\n#{hc_fn},#{hc_tn}"
-end
-
-File.open(File.join(dir,"summary-all.csv"),"w+") do |f|
-  f.puts "accuracy,#{(tp+tn)/(tp+fp+tn+fn).to_f}"
-  f.puts "true_positive_rate,#{tp/(tp+fn).to_f}"
-  f.puts "true_negative_rate,#{tn/(tn+fp).to_f}"
-  f.puts "positive_predictive_value,#{tp/(tp+fp).to_f}"
-  f.puts "negative_predictive_value,#{tn/(tn+fn).to_f}"
-end
-
-File.open(File.join(dir,"summary-high-confidence.csv"),"w+") do |f|
-  f.puts "accuracy,#{(hc_tp+hc_tn)/(hc_tp+hc_fp+hc_tn+hc_fn).to_f}"
-  f.puts "true_positive_rate,#{hc_tp/(hc_tp+hc_fn).to_f}"
-  f.puts "true_negative_rate,#{hc_tn/(hc_tn+hc_fp).to_f}"
-  f.puts "positive_predictive_value,#{hc_tp/(hc_tp+hc_fp).to_f}"
-  f.puts "negative_predictive_value,#{hc_tn/(hc_tn+hc_fn).to_f}"
-end
-
-- 
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