GCC Code Coverage Report

Directory:	src/lib/
File:	src/lib/mod_accuracy.f90
Date:	2024-06-28 12:51:18

	Exec	Total	Coverage
Lines:	27	28	96.4%
Functions:	0	0	-%
Branches:	208	392	53.1%

  
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      !!!#############################################################################
    
      !!! Code written by Ned Thaddeus Taylor 
    
      !!! Code part of the ATHENA library - a feedforward neural network library
    
      !!!#############################################################################
    
      !!! module contains functions to compute the accuracy of a model
    
      !!! module includes the following procedures:
    
      !!! - categorical_score - computes the accuracy of a categorisation model
    
      !!! - mae_score         - computes the mean absolute error of a continuous model
    
      !!! - mse_score         - computes the mean squared error of a continuous model
    
      !!! - r2_score          - computes the R^2 score of a continuous model
    
      !!!#############################################################################
    
      module accuracy
    
        use constants, only: real12
    
        implicit none
    
        private
    
        public :: categorical_score
    
        public :: mae_score, mse_score
    
        public :: r2_score
    
      contains
    
      !!!#############################################################################
    
      !!! compute accuracy
    
      !!! this only works (and is only valid for?) categorisation problems
    
      !!!#############################################################################
    
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      2
        function categorical_score(output, expected) result(accuracy)
    
          implicit none
    
          real(real12), dimension(:,:), intent(in) :: output
    
          integer, dimension(:,:) :: expected
    
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          real(real12), dimension(size(expected,2)) :: accuracy
    
          integer :: s
    
          !! Compute the accuracy
    
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          do concurrent(s=1:size(expected,2))
    
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             if (maxloc(expected(:,s),dim=1).eq.maxloc(output(:,s),dim=1)) then
    
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                accuracy(s) = 1._real12
    
             else
    
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                accuracy(s) = 0._real12
    
             end if
    
          end do
    
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        end function categorical_score
    
      !!!-----------------------------------------------------------------------------
    
      !!!-----------------------------------------------------------------------------
    
      !!! works for continuous datasets
    
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      503
        function mae_score(output, expected) result(accuracy)
    
          implicit none
    
          real(real12), dimension(:,:), intent(in) :: output, expected
    
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      503
          real(real12), dimension(size(expected,2)) :: accuracy
    
          !! Compute the accuracy
    
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      2030
          accuracy = 1._real12 - sum(abs(expected - output),dim=1)/size(expected(:,1))
    
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      503
        end function mae_score
    
      !!!-----------------------------------------------------------------------------
    
      !!!-----------------------------------------------------------------------------
    
      !!! works for continuous datasets
    
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      2
        function mse_score(output, expected) result(accuracy)
    
          implicit none
    
          real(real12), dimension(:,:), intent(in) :: output, expected
    
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          real(real12), dimension(size(expected,2)) :: accuracy
    
          !! Compute the accuracy
    
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          accuracy = 1._real12 - sum((expected - output)**2._real12,dim=1)/size(expected(:,1))
    
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        end function mse_score
    
      !!!-----------------------------------------------------------------------------
    
      !!!-----------------------------------------------------------------------------
    
      !!! works for continuous datasets (CURRENTLY UNAVAILABLE)
    
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        function r2_score(output, expected) result(accuracy)
    
          implicit none
    
          real(real12), dimension(:,:), intent(in) :: output, expected
    
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          real(real12), dimension(size(expected,2)) :: y_mean, rss, tss
    
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          real(real12), dimension(size(expected,2)) :: accuracy
    
          real(real12), parameter :: epsilon = 1.E-8_real12
    
          integer :: s
    
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          do s = 1, size(expected,2)
    
             !! compute mean of true/expected
    
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             y_mean(s) = sum(expected(:,s),dim=1) / size(expected(:,s),dim=1)
    
             !! compute total sum of squares
    
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             tss(s) = sum( ( expected(:,s) - y_mean(s) ) ** 2._real12, dim=1 )
    
             !! compute residual sum of squares
    
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             rss(s) = sum( ( expected(:,s) - output(:,s) ) ** 2._real12, dim=1 )
    
             !! compute accuracy (R^2 score)
    
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             if(abs(rss(s)).lt.epsilon)then
    
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               accuracy(s) = 1._real12
    
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             elseif(abs(tss(s)).lt.epsilon.or.rss(s)/tss(s).gt.1._real12)then
    
      ✗
               accuracy(s) = 0._real12
    
             else
    
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               accuracy(s) = 1._real12 - rss(s)/tss(s)
    
             end if
    
          end do
    
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        end function r2_score
    
      !!!#############################################################################
    
      end module accuracy

Line	Branch	Exec	Source
1			!!!#############################################################################
2			!!! Code written by Ned Thaddeus Taylor
3			!!! Code part of the ATHENA library - a feedforward neural network library
4			!!!#############################################################################
5			!!! module contains functions to compute the accuracy of a model
6			!!! module includes the following procedures:
7			!!! - categorical_score - computes the accuracy of a categorisation model
8			!!! - mae_score - computes the mean absolute error of a continuous model
9			!!! - mse_score - computes the mean squared error of a continuous model
10			!!! - r2_score - computes the R^2 score of a continuous model
11			!!!#############################################################################
12			module accuracy
13			use constants, only: real12
14			implicit none
15
16
17			private
18
19			public :: categorical_score
20			public :: mae_score, mse_score
21			public :: r2_score
22
23
24			contains
25
26			!!!#############################################################################
27			!!! compute accuracy
28			!!! this only works (and is only valid for?) categorisation problems
29			!!!#############################################################################
30	3/6 ✓ Branch 0 taken 2 times. ✗ Branch 1 not taken. ✓ Branch 2 taken 2 times. ✗ Branch 3 not taken. ✓ Branch 4 taken 2 times. ✗ Branch 5 not taken.	2	function categorical_score(output, expected) result(accuracy)
31			implicit none
32			real(real12), dimension(:,:), intent(in) :: output
33			integer, dimension(:,:) :: expected
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35
36			integer :: s
37
38			!! Compute the accuracy
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42			else
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44			end if
45			end do
46
47	1/2 ✗ Branch 0 not taken. ✓ Branch 1 taken 2 times.	2	end function categorical_score
48			!!!-----------------------------------------------------------------------------
49			!!!-----------------------------------------------------------------------------
50			!!! works for continuous datasets
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52			implicit none
53			real(real12), dimension(:,:), intent(in) :: output, expected
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55
56			!! Compute the accuracy
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58
59	1/2 ✗ Branch 0 not taken. ✓ Branch 1 taken 503 times.	503	end function mae_score
60			!!!-----------------------------------------------------------------------------
61			!!!-----------------------------------------------------------------------------
62			!!! works for continuous datasets
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64			implicit none
65			real(real12), dimension(:,:), intent(in) :: output, expected
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67
68			!! Compute the accuracy
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70
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72			!!!-----------------------------------------------------------------------------
73			!!!-----------------------------------------------------------------------------
74			!!! works for continuous datasets (CURRENTLY UNAVAILABLE)
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76			implicit none
77			real(real12), dimension(:,:), intent(in) :: output, expected
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80
81			real(real12), parameter :: epsilon = 1.E-8_real12
82
83			integer :: s
84
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86			!! compute mean of true/expected
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88
89			!! compute total sum of squares
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91
92			!! compute residual sum of squares
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94
95			!! compute accuracy (R^2 score)
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99		✗	accuracy(s) = 0._real12
100			else
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102			end if
103			end do
104
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106			!!!#############################################################################
107
108			end module accuracy
109