From 81d32c80c96f2fd20bef1828a5980568ec77c947 Mon Sep 17 00:00:00 2001 From: lbluque Date: Sat, 14 Oct 2023 00:16:13 +0000 Subject: [PATCH 1/3] auto-update pre-commit hooks --- .pre-commit-config.yaml | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 6af47c2..d1679fd 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -6,7 +6,7 @@ ci: repos: - repo: https://github.com/pre-commit/pre-commit-hooks - rev: v4.4.0 + rev: v4.5.0 hooks: - id: check-yaml - id: fix-encoding-pragma @@ -38,7 +38,7 @@ repos: - --profile=black - repo: https://github.com/asottile/pyupgrade - rev: v3.13.0 + rev: v3.15.0 hooks: - id: pyupgrade args: [--py38-plus] @@ -71,6 +71,6 @@ repos: - id: rst-inline-touching-normal - repo: https://github.com/pre-commit/mirrors-mypy - rev: 'v1.5.1' # Use the sha / tag you want to point at + rev: 'v1.6.0' # Use the sha / tag you want to point at hooks: - id: mypy From 56c7bdd99927f78f9e578126c51f3caf77bcca27 Mon Sep 17 00:00:00 2001 From: lbluque Date: Tue, 24 Oct 2023 12:28:22 -0700 Subject: [PATCH 2/3] add requirements.txt --- requirements.txt | 5 +++++ 1 file changed, 5 insertions(+) create mode 100644 requirements.txt diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..8a30e10 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,5 @@ +numpy >=1.23 +cvxpy >=1.2 +scikit-learn > 1.2 +scipy >=1.9 +joblib \ No newline at end of file From d7fba0ea32ce1099d120aa74ee8040b8e6ecab47 Mon Sep 17 00:00:00 2001 From: lbluque Date: Tue, 24 Oct 2023 12:45:18 -0700 Subject: [PATCH 3/3] move applications to statement of need --- paper/paper.md | 23 +++++++++++------------ 1 file changed, 11 insertions(+), 12 deletions(-) diff --git a/paper/paper.md b/paper/paper.md index 84e7e98..3f6be27 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -73,6 +73,17 @@ available solvers. `sparse-lm` satisfies the need for a flexible and comprehensi library that enables easy experimentation and comparisons of different sparse linear regression algorithms within a single package. +Statistical regression models with structured sparsity (involving grouped covariates, +sparse grouped covariates, and hierarchical relationships between covariates terms) +parametrized via Group Lasso or Best Subset Selection based objetives have been used in a +wide range of scientific disciplines, including genomics [@Chen:2021], bioinformatics [@Ma:2007], +medicine [@Kim:2012], econometrics [@Athey:2017], chemistry [@Gu:2018], and materials science +[@Leong:2019]. The flexible implementation of sparse linear regression models in `sparse-lm` +allows researchers to easily experiment and choose the best regression model for their +specific problem. `sparse-lm` has already been used to build linear models with +structured sparsity in a handful of material science studies +[@Barroso-Luque:2022; @Zhong:2022; @Xie:2023, @Zhong:2023]. + # Background ![Schematic of a linear model with grouped covariates with hierarchical relations. @@ -137,18 +148,6 @@ introduce hierarchical structure into the model. Finally, we have also included $\ell_2$ regularization term controlled by the hyperparameter $\lambda$, which is useful when dealing with poorly conditioned design matrices. -Statistical regression models with structured sparsity (involving grouped covariates, -sparse grouped covariates, and hierarchical relationships between covariates terms) -parametrized via Group Lasso or Best Subset Selection based objetives have been used in a -wide range of scientific disciplines, including genomics [@Chen:2021], bioinformatics [@Ma:2007], -medicine [@Kim:2012], econometrics [@Athey:2017], chemistry [@Gu:2018], and materials science -[@Leong:2019]. The flexible implementation of sparse linear regression models in `sparse-lm` -allows researchers to easily experiment and choose the best regression model for their -specific problem. `sparse-lm` has already been used to build linear models with -structured sparsity in a handful of material science studies -[@Barroso-Luque:2022; @Zhong:2022; @Xie:2023, @Zhong:2023]. - - # Usage Since the linear regression models in `sparse-lm` are implemented to be compatible with