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Error while reading the parameter "funit" from SISSO.in #72

@dywu101

Description

@dywu101

I am trying to run the regression example in folder input_templates, and I get this error message,

Error: Cannot read the parameter "funit"

The version of repository I used is SISSO.3.5, commit bf5d691.

Bug Reproduction

mkdir demo
cp input_templates/SISSO.in ./demo
cp input_templates/train.dat_regression ./demo/train.dat
cd ./demo
SISSO

Then you will get the same error message.
Comment funit sentence in SISSO.in will cancel error output, but I want to control units of features. So, it's not a feasible solution.

! funit=(1:2)(3:3)

Files

SISSO.in

!>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
! Texts after a exclamation mark (!) are comments
! The (R), (C) and (R&C) denotes the keyword used by regression, classification, and both, respectively.
! A complete list and more explanations on these keywords can be found in the SISSO_Guide.pdf
! The setting below is just an example, and user may need to change them for their jobs.
!>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
ptype=1                !Property type 1: regression, 2:classification.
ntask=1                !(R&C) Multi-task learning (MTL) is invoked if >1.
scmt=.false.           !(R) Sign-Constrained MTL is invoked if .true.
desc_dim=2             !(R&C) Dimension of the descriptor/model.
nsample=5              !(R) Number of samples in train.dat. Set nsample=N1,N2,... for MTL.
!nsample=(n1,n2,...)   !(C) Number of samples. Set nsample=(n1,n2,...),(m1,m2,...),... for MTL.
restart=0              !(R&C) 0: starts from scratch, 1: continues the job(progress in the file CONTINUE)

!>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
! Feature construction (FC) and sure independence screening (SIS)
! Implemented operators:(+)(-)(*)(/)(exp)(exp-)(^-1)(^2)(^3)(sqrt)(cbrt)(log)(|-|)(scd)(^6)(sin)(cos)
! scd: standard Cauchy distribution
!>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
fstore=1               !(R&C) Features storage in memory. 1: by data (fast); 2: by S-expression (low-memory)
nsf= 3                 !(R&C) Number of scalar features provided in the file train.dat
ops='(+)(-)(*)(/)'     !(R&C) Operators to be customized by user from the list shown above.
fcomplexity=3          !(R&C) Maximal feature complexity (# of operators in a feature), starting from 0.
funit=(1:2)(3:3)       !(R&C) Feature unit: (n1:n2), features from n1 to n2 in train.dat have the same unit
fmax_min=1e-3          !(R&C) The feature will be discarded if the max. abs. value in it is < fmax_min.
fmax_max=1e5           !(R&C) The feature will be discarded if the max. abs. value in it is > fmax_max.
nf_sis=50000           !(R&C) Number of features in each of the SIS-selected subspace.

!>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
! Descriptor identification (DI) via sparse regression (SO)
!>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
method_so= 'L0'        !(R&C) 'L0' or 'L1L0'(LASSO+L0). 'L0' is always recommended.
fit_intercept=.true.   !(R) Fit to a nonzero (.true.) or zero (.false.) intercept for the linear model.
metric= 'RMSE'         !(R) The metric for model selection in regression: RMSE or MaxAE (max absolute error)
nmodel=100             !(R&C) Number of the top-ranked models to output (see the folder 'Models')
isconvex=(1,1,...)     !(C) Each data group constrained to be convex domain, 1: YES; 0: NO
bwidth=0.001           !(C) Boundary tolerance for classification

train.dat_regression

materials    property  feature1  feature2  feature3
sample1       3.0862    0.8626    0.7043    0.6312
sample2       2.8854    0.7260    0.7818    0.6119
sample3       0.6907    0.4943    0.0044    0.4420
sample4       0.9902    0.0106    0.0399    0.9877
sample5       0.7242    0.0970    0.3199    0.5504

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