%global __brp_check_rpaths %{nil}
%global __requires_exclude ^libmpi
%global packname  quickSentiment
%global packver   0.3.4
%global rlibdir   /usr/local/lib/R/library

Name:             R-CRAN-%{packname}
Version:          0.3.4
Release:          1%{?dist}%{?buildtag}
Summary:          A Fast and Flexible Pipeline for Text Classification

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


BuildRequires:    R-devel
Requires:         R-core
BuildArch:        noarch
BuildRequires:    R-CRAN-doParallel 
BuildRequires:    R-CRAN-foreach 
BuildRequires:    R-CRAN-glmnet 
BuildRequires:    R-CRAN-magrittr 
BuildRequires:    R-CRAN-Matrix 
BuildRequires:    R-methods 
BuildRequires:    R-CRAN-naivebayes 
BuildRequires:    R-CRAN-quanteda 
BuildRequires:    R-CRAN-ranger 
BuildRequires:    R-CRAN-stopwords 
BuildRequires:    R-CRAN-stringr 
BuildRequires:    R-CRAN-textstem 
BuildRequires:    R-CRAN-xgboost 
Requires:         R-CRAN-doParallel 
Requires:         R-CRAN-foreach 
Requires:         R-CRAN-glmnet 
Requires:         R-CRAN-magrittr 
Requires:         R-CRAN-Matrix 
Requires:         R-methods 
Requires:         R-CRAN-naivebayes 
Requires:         R-CRAN-quanteda 
Requires:         R-CRAN-ranger 
Requires:         R-CRAN-stopwords 
Requires:         R-CRAN-stringr 
Requires:         R-CRAN-textstem 
Requires:         R-CRAN-xgboost 

%description
A high-level pipeline that simplifies text classification into three
streamlined steps: preprocessing, model training, and standardized
prediction. It unifies the interface for multiple algorithms (including
'glmnet', 'ranger', 'xgboost', and 'naivebayes') and memory-efficient
sparse matrix vectorization methods (Bag-of-Words, Term Frequency, TF-IDF,
and Binary). Users can go from raw text to a fully evaluated sentiment
model, complete with ROC-optimized thresholds, in just a few function
calls. The resulting model artifact automatically aligns the vocabulary of
new datasets during the prediction phase, safely appending predicted
classes and probability matrices directly to the user's original dataframe
to preserve metadata.

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

# fix end of executable files
find -type f -executable -exec grep -Iq . {} \; -exec sed -i -e '$a\' {} \;
# prevent binary stripping
[ -d %{packname}/src ] && find %{packname}/src -type f -exec \
  sed -i 's@/usr/bin/strip@/usr/bin/true@g' {} \; || true
[ -d %{packname}/src ] && find %{packname}/src/Make* -type f -exec \
  sed -i 's@-g0@@g' {} \; || true
# don't allow local prefix in executable scripts
find -type f -executable -exec sed -Ei 's@#!( )*/usr/local/bin@#!/usr/bin@g' {} \;

%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
# remove buildroot from installed files
find %{buildroot}%{rlibdir} -type f -exec sed -i "s@%{buildroot}@@g" {} \;

%files
%{rlibdir}/%{packname}