1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
|
#!/usr/bin/env ruby
require_relative '../../lazar/lib/lazar'
include OpenTox
table = {}
table["close"] = { "rmse" => [], "r_squared" => [], "nr_predicted" => [] }
table["distant"] = { "rmse" => [], "r_squared" => [], "nr_predicted" => [] }
table["all"] = { "rmse" => [], "r_squared" => [], "nr_predicted" => [] }
File.open(ARGV[0]).each_line do |id|
cv = Validation::RegressionCrossValidation.find id.chomp
rmse = {"close" => 0, "distant" => 0, "all" => 0}
x = {"close" => [], "distant" => [], "all" => []}
y = {"close" => [], "distant" => [], "all" => []}
cv.predictions.each do |cid,pred|
warnings = false
warnings = true if pred["warnings"] and !pred["warnings"].empty?
if pred[:value] #and pred[:measurements]
if warnings
x["distant"] << pred[:measurements].median
y["distant"] << pred[:value]
else
x["close"] << pred[:measurements].median
y["close"] << pred[:value]
end
x["all"] << pred[:measurements].median
y["all"] << pred[:value]
end
end
["close","distant","all"].each do |cat|
R.assign "measurement", x[cat]
R.assign "prediction", y[cat]
R.eval "r <- cor(measurement,prediction,use='pairwise')"
R.eval "rmse <- sqrt(mean((prediction - measurement)^2))"
table[cat]["r_squared"] << R.eval("r").to_ruby**2
table[cat]["rmse"] << R.eval("rmse").to_ruby
table[cat]["nr_predicted"] << y[cat].size
end
end
File.open("data/50cv.csv","w+") do |f|
f.puts("AD,Param,Mean,SD")
table.each do |dist,data|
data.each do |name,values|
R.assign "x", values
R.eval "sd <- sd(x)"
f.puts "#{dist},#{name},#{values.mean},#{R.eval("sd").to_ruby}"
end
end
end
|
