:og:description: Adapting NOTEARS for large problems with low-rank causal graphs. .. rubric:: Example Config file: `gcastle.json `_ Command: .. code:: bash snakemake --cores all --use-apptainer --configfile config/gcastle.json :numref:`gcastleplot` shows the pattern graph's FP/P vs. TP/P benchmark results for 12 gCastle algorithms (and comparison with :ref:`tetrad_boss` and :ref:`bidag_itsearch`). The benchmark is based on 5 datasets corresponding to 5 realisations of a 20-variable random Gaussian SEM with Erdős-Rényi structure (expected degree 4, max parents 5). Each dataset contains 300 standardized samples. The SEM parameters are uniformly sampled from [0.25, 1]. .. _gcastleplot: .. figure:: https://raw.githubusercontent.com/felixleopoldo/benchpress/master/docs/source/_static/gcastle_benchmarks.png :width: 640 :alt: FP/P vs. TP/P for gCastle algorithms :align: center FP/P vs. TP/P for gCastle algorithms. :og:image:alt: Benchpress logo :og:sitename: Benchpress causal discovery platform :og:title: NO TEARS low rank (gCastle) .. meta:: :title: NO TEARS low rank (gCastle) :description: Adapting NOTEARS for large problems with low-rank causal graphs. .. rubric:: Example Config file: `gcastle.json `_ Command: .. code:: bash snakemake --cores all --use-apptainer --configfile config/gcastle.json :numref:`gcastleplot` shows the pattern graph's FP/P vs. TP/P benchmark results for 12 gCastle algorithms (and comparison with :ref:`tetrad_boss` and :ref:`bidag_itsearch`). The benchmark is based on 5 datasets corresponding to 5 realisations of a 20-variable random Gaussian SEM with Erdős-Rényi structure (expected degree 4, max parents 5). Each dataset contains 300 standardized samples. The SEM parameters are uniformly sampled from [0.25, 1]. .. _gcastleplot: .. figure:: https://raw.githubusercontent.com/felixleopoldo/benchpress/master/docs/source/_static/gcastle_benchmarks.png :width: 640 :alt: FP/P vs. TP/P for gCastle algorithms :align: center FP/P vs. TP/P for gCastle algorithms. .. _gcastle_notears_low_rank: NO TEARS low rank (gCastle) **************************** .. list-table:: * - Module name - `gcastle_notears_low_rank `__ * - Package - `gCastle `__ * - Version - 1.0.3 * - Language - `Python `__ * - Docs - `here `__ * - Paper - :footcite:t:`https://doi.org/10.48550/arxiv.2006.05691` * - Graph type - `DAG `__ * - MCMC - No * - Edge constraints - No * - Data type - C * - Data missingness - * - Intervention type - * - Docker - `bpimages/gcastle:1.0.3 `__ NO TEARS low rank --------------------- Adapting NOTEARS for large problems with low-rank causal graphs. .. rubric:: Example Config file: `gcastle.json `_ Command: .. code:: bash snakemake --cores all --use-apptainer --configfile config/gcastle.json :numref:`gcastleplot` shows the pattern graph's FP/P vs. TP/P benchmark results for 12 gCastle algorithms (and comparison with :ref:`tetrad_boss` and :ref:`bidag_itsearch`). The benchmark is based on 5 datasets corresponding to 5 realisations of a 20-variable random Gaussian SEM with Erdős-Rényi structure (expected degree 4, max parents 5). Each dataset contains 300 standardized samples. The SEM parameters are uniformly sampled from [0.25, 1]. .. _gcastleplot: .. figure:: https://raw.githubusercontent.com/felixleopoldo/benchpress/master/docs/source/_static/gcastle_benchmarks.png :width: 640 :alt: FP/P vs. TP/P for gCastle algorithms :align: center FP/P vs. TP/P for gCastle algorithms. .. rubric:: Example JSON .. code-block:: json [ { "id": "gcastle_notears_low_rank", "rank": 15, "w_init": null, "max_iter": 15, "h_tol": "1e-6", "rho_max": "1e+20", "w_threshold": 0.3, "timeout": null } ] .. footbibliography::