%global __brp_check_rpaths %{nil}
%global packname  triplot
%global packver   1.3.0
%global rlibdir   /usr/local/lib/R/library

Name:             R-CRAN-%{packname}
Version:          1.3.0
Release:          1%{?dist}%{?buildtag}
Summary:          Explaining Correlated Features in Machine Learning Models

License:          GPL-3
URL:              https://cran.r-project.org/package=%{packname}
Source0:          %{url}&version=%{packver}#/%{packname}_%{packver}.tar.gz


BuildRequires:    R-devel >= 3.6
Requires:         R-core >= 3.6
BuildArch:        noarch
BuildRequires:    R-CRAN-DALEX >= 1.3
BuildRequires:    R-CRAN-ggplot2 
BuildRequires:    R-CRAN-glmnet 
BuildRequires:    R-CRAN-ggdendro 
BuildRequires:    R-CRAN-patchwork 
Requires:         R-CRAN-DALEX >= 1.3
Requires:         R-CRAN-ggplot2 
Requires:         R-CRAN-glmnet 
Requires:         R-CRAN-ggdendro 
Requires:         R-CRAN-patchwork 

%description
Tools for exploring effects of correlated features in predictive models.
The predict_triplot() function delivers instance-level explanations that
calculate the importance of the groups of explanatory variables. The
model_triplot() function delivers data-level explanations. The generic
plot function visualises in a concise way importance of hierarchical
groups of predictors. All of the the tools are model agnostic, therefore
works for any predictive machine learning models. Find more details in
Biecek (2018) <arXiv:1806.08915>.

%prep
%setup -q -c -n %{packname}

find -type f -executable -exec grep -Iq . {} \; -exec sed -i -e '$a\' {} \;
[ -d %{packname}/src ] && find %{packname}/src -type f -exec \
  sed -i 's@/usr/bin/strip@/usr/bin/true@g' {} \; || true

%build

%install

mkdir -p %{buildroot}%{rlibdir}
%{_bindir}/R CMD INSTALL -l %{buildroot}%{rlibdir} %{packname}
test -d %{packname}/src && (cd %{packname}/src; rm -f *.o *.so)
rm -f %{buildroot}%{rlibdir}/R.css
find %{buildroot}%{rlibdir} -type f -exec sed -i "s@%{buildroot}@@g" {} \;

%files
%{rlibdir}/%{packname}