GCC Code Coverage Report

Directory:	src/athena/
File:	src/athena/athena_accuracy.f90
Date:	2026-04-15 16:08:59

	Exec	Total	Coverage
Lines:	27	34	79.4%
Functions:	0	0	-%
Branches:	222	494	44.9%

  
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      module athena__accuracy
    
        !! Module containing functions to compute the accuracy of a model
    
        use coreutils, only: real32
    
        implicit none
    
        private
    
        public :: compute_accuracy_function
    
        public :: categorical_score
    
        public :: mae_score, mse_score, rmse_score
    
        public :: r2_score
    
        abstract interface
    
           !! Interface for the accuracy function
    
           pure function compute_accuracy_function(predicted, expected) result(output)
    
             !! Compute the accuracy of a model
    
             import real32
    
             real(real32), dimension(:,:), intent(in) :: predicted, expected
    
             !! Predicted and expected values
    
             real(real32), dimension(size(expected,2)) :: output
    
             !! Accuracy of the model
    
           end function compute_accuracy_function
    
        end interface
    
      contains
    
      !###############################################################################
    
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      2
        pure function categorical_score(predicted, expected) result(output)
    
          !! Compute the categorical accuracy of a model
    
          !!
    
          !! This function is only valid for categorical/classification datasets
    
          implicit none
    
          !! Arguments
    
          real(real32), dimension(:,:), intent(in) :: predicted, expected
    
          !! Predicted and expected values
    
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          real(real32), dimension(size(expected,2)) :: output
    
          !! Categorical accuracy
    
          ! Local variables
    
          integer :: s
    
          !! Loop index
    
          !! 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(predicted(:,s),dim=1))then
    
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                output(s) = 1._real32
    
             else
    
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                output(s) = 0._real32
    
             end if
    
          end do
    
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        end function categorical_score
    
      !###############################################################################
    
      !###############################################################################
    
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        pure function mae_score(predicted, expected) result(output)
    
          !! Compute the mean absolute error of a model
    
          !!
    
          !! This function is only valid for continuous datasets
    
          implicit none
    
          ! Arguments
    
          real(real32), dimension(:,:), intent(in) :: predicted, expected
    
          !! Predicted and expected values
    
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          real(real32), dimension(size(expected,2)) :: output
    
          !! Mean absolute error
    
          ! Compute the accuracy
    
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          output = 1._real32 - sum(abs(expected - predicted),dim=1)/size(expected,1)
    
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        end function mae_score
    
      !###############################################################################
    
      !###############################################################################
    
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      1718
        pure function mse_score(predicted, expected) result(output)
    
          !! Compute the mean squared error of a model
    
          !!
    
          !! This function is only valid for continuous datasets
    
          implicit none
    
          ! Arguments
    
          real(real32), dimension(:,:), intent(in) :: predicted, expected
    
          !! Predicted and expected values
    
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          real(real32), dimension(size(expected,2)) :: output
    
          !! Mean squared error
    
          ! Compute the accuracy
    
      ✗
          output = 1._real32 - &
    
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      7151
               sum((expected - predicted)**2._real32,dim=1)/size(expected,1)
    
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        end function mse_score
    
      !###############################################################################
    
      !###############################################################################
    
      ✗
        pure function rmse_score(predicted, expected) result(output)
    
          !! Compute the root mean squared error of a model
    
          !!
    
          !! This function is only valid for continuous datasets
    
          implicit none
    
          ! Arguments
    
          real(real32), dimension(:,:), intent(in) :: predicted, expected
    
          !! Predicted and expected values
    
      ✗
          real(real32), dimension(size(expected,2)) :: output
    
          !! Root mean squared error
    
          ! Compute the accuracy
    
      ✗
          output = 1._real32 - &
    
      ✗
               sqrt(sum((expected - predicted)**2._real32,dim=1)/size(expected,1))
    
      ✗
        end function rmse_score
    
      !###############################################################################
    
      !###############################################################################
    
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        pure function r2_score(predicted, expected) result(output)
    
          !! Compute the R^2 score of a model
    
          !!
    
          !! This function is only valid for continuous datasets
    
          implicit none
    
          ! Arguments
    
          real(real32), dimension(:,:), intent(in) :: predicted, expected
    
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          real(real32), dimension(size(expected,2)) :: y_mean, rss, tss
    
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          real(real32), dimension(size(expected,2)) :: output
    
          ! Local variables
    
          real(real32), parameter :: epsilon = 1.E-8_real32
    
          !! Small value to avoid division by zero
    
          integer :: s
    
          !! Loop index
    
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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,dim=1)
    
             ! compute total sum of squares
    
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             tss(s) = sum( ( expected(:,s) - y_mean(s) ) ** 2._real32, dim=1 )
    
             ! compute residual sum of squares
    
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             rss(s) = sum( ( expected(:,s) - predicted(:,s) ) ** 2._real32, dim=1 )
    
             ! compute accuracy (R^2 score)
    
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             if(abs(rss(s)).lt.epsilon)then
    
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                output(s) = 1._real32
    
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             elseif(abs(tss(s)).lt.epsilon.or.rss(s)/tss(s).gt.1._real32)then
    
      ✗
                output(s) = 0._real32
    
             else
    
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                output(s) = 1._real32 - rss(s)/tss(s)
    
             end if
    
          end do
    
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        end function r2_score
    
      !###############################################################################
    
      end module athena__accuracy

Line	Branch	Exec	Source
1			module athena__accuracy
2			!! Module containing functions to compute the accuracy of a model
3			use coreutils, only: real32
4			implicit none
5
6
7			private
8
9			public :: compute_accuracy_function
10			public :: categorical_score
11			public :: mae_score, mse_score, rmse_score
12			public :: r2_score
13
14
15			abstract interface
16			!! Interface for the accuracy function
17			pure function compute_accuracy_function(predicted, expected) result(output)
18			!! Compute the accuracy of a model
19			import real32
20			real(real32), dimension(:,:), intent(in) :: predicted, expected
21			!! Predicted and expected values
22			real(real32), dimension(size(expected,2)) :: output
23			!! Accuracy of the model
24			end function compute_accuracy_function
25			end interface
26
27			contains
28
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	pure function categorical_score(predicted, expected) result(output)
31			!! Compute the categorical accuracy of a model
32			!!
33			!! This function is only valid for categorical/classification datasets
34			implicit none
35
36			!! Arguments
37			real(real32), dimension(:,:), intent(in) :: predicted, expected
38			!! Predicted and expected values
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40			!! Categorical accuracy
41
42			! Local variables
43			integer :: s
44			!! Loop index
45
46			!! Compute the accuracy
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50			else
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52			end if
53			end do
54
55	1/2 ✗ Branch 0 not taken. ✓ Branch 1 taken 2 times.	2	end function categorical_score
56			!###############################################################################
57
58
59			!###############################################################################
60	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	pure function mae_score(predicted, expected) result(output)
61			!! Compute the mean absolute error of a model
62			!!
63			!! This function is only valid for continuous datasets
64			implicit none
65
66			! Arguments
67			real(real32), dimension(:,:), intent(in) :: predicted, expected
68			!! Predicted and expected values
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70			!! Mean absolute error
71
72			! Compute the accuracy
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74
75	1/2 ✗ Branch 0 not taken. ✓ Branch 1 taken 2 times.	2	end function mae_score
76			!###############################################################################
77
78
79			!###############################################################################
80	3/6 ✓ Branch 0 taken 1718 times. ✗ Branch 1 not taken. ✓ Branch 2 taken 1718 times. ✗ Branch 3 not taken. ✓ Branch 4 taken 1718 times. ✗ Branch 5 not taken.	1718	pure function mse_score(predicted, expected) result(output)
81			!! Compute the mean squared error of a model
82			!!
83			!! This function is only valid for continuous datasets
84			implicit none
85
86			! Arguments
87			real(real32), dimension(:,:), intent(in) :: predicted, expected
88			!! Predicted and expected values
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90			!! Mean squared error
91
92			! Compute the accuracy
93		✗	output = 1._real32 - &
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95
96	1/2 ✗ Branch 0 not taken. ✓ Branch 1 taken 1718 times.	1718	end function mse_score
97			!###############################################################################
98
99
100			!###############################################################################
101		✗	pure function rmse_score(predicted, expected) result(output)
102			!! Compute the root mean squared error of a model
103			!!
104			!! This function is only valid for continuous datasets
105			implicit none
106
107			! Arguments
108			real(real32), dimension(:,:), intent(in) :: predicted, expected
109			!! Predicted and expected values
110		✗	real(real32), dimension(size(expected,2)) :: output
111			!! Root mean squared error
112
113			! Compute the accuracy
114		✗	output = 1._real32 - &
115		✗	sqrt(sum((expected - predicted)**2._real32,dim=1)/size(expected,1))
116
117		✗	end function rmse_score
118			!###############################################################################
119
120
121			!###############################################################################
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123			!! Compute the R^2 score of a model
124			!!
125			!! This function is only valid for continuous datasets
126			implicit none
127
128			! Arguments
129			real(real32), dimension(:,:), intent(in) :: predicted, expected
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132
133			! Local variables
134			real(real32), parameter :: epsilon = 1.E-8_real32
135			!! Small value to avoid division by zero
136			integer :: s
137			!! Loop index
138
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140			! compute mean of true/expected
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142
143			! compute total sum of squares
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145
146			! compute residual sum of squares
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148
149			! compute accuracy (R^2 score)
150	4/6 ✗ Branch 0 not taken. ✓ Branch 1 taken 6 times. ✗ Branch 3 not taken. ✓ Branch 4 taken 6 times. ✓ Branch 6 taken 5 times. ✓ Branch 7 taken 1 times.	8	if(abs(rss(s)).lt.epsilon)then
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153		✗	output(s) = 0._real32
154			else
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156			end if
157			end do
158
159	1/2 ✗ Branch 0 not taken. ✓ Branch 1 taken 2 times.	2	end function r2_score
160			!###############################################################################
161
162			end module athena__accuracy
163