:og:description: Notears Nonlinear. Include notears-mlp and notears-sob. A gradient-based algorithm using neural network or Sobolev space modeling for non-linear causal relationships. .. rubric:: Example Config file: `gcastle_nonlinear.json `_ Command: .. code:: bash snakemake --cores all --use-apptainer --configfile config/gcastle_nonlinear.json :numref:`gcastlenonlinearplot` and :numref:`gcastlenonlinearquadplot` show the pattern graph's FP/P vs. TP/P benchmark results for gCastle algorithms, and comparison with :ref:`tetrad_boss` and :ref:`bidag_itsearch`, tested on nonlinear data. The benchmark is based on 5 datasets corresponding to 5 realisations of a 20-variable random nonlinear Gaussian SEM with Erdős-Rényi structure (expected degree 4, max parents 5). The nonlinear relationships are modeled using multi-layer perceptrons (MLP) and quadratic functions, see :ref:`gcastle_iidsim`. Each dataset contains 300 standardized samples. The SEM parameters are uniformly sampled from [0.25, 1]. .. _gcastlenonlinearplot: .. figure:: https://raw.githubusercontent.com/felixleopoldo/benchpress/master/docs/source/_static/gcastle_benchmarks_nonlinear_anm.png :width: 640 :alt: FP/P vs. TP/P for gCastle algorithms on nonlinear MLP data :align: left FP/P vs. TP/P for gCastle algorithms on nonlinear MLP data. .. _gcastlenonlinearquadplot: .. figure:: https://raw.githubusercontent.com/felixleopoldo/benchpress/master/docs/source/_static/gcastle_benchmarks_nonlinear_quadratic.png :width: 640 :alt: FP/P vs. TP/P for gCastle algorithms on nonlinear quadratic data :align: right FP/P vs. TP/P for gCastle algorithms on nonlinear quadratic data. :og:image:alt: Benchpress logo :og:sitename: Benchpress causal discovery platform :og:title: NO TEARS non-linear (gCastle) .. meta:: :title: NO TEARS non-linear (gCastle) :description: Notears Nonlinear. Include notears-mlp and notears-sob. A gradient-based algorithm using neural network or Sobolev space modeling for non-linear causal relationships. .. rubric:: Example Config file: `gcastle_nonlinear.json `_ Command: .. code:: bash snakemake --cores all --use-apptainer --configfile config/gcastle_nonlinear.json :numref:`gcastlenonlinearplot` and :numref:`gcastlenonlinearquadplot` show the pattern graph's FP/P vs. TP/P benchmark results for gCastle algorithms, and comparison with :ref:`tetrad_boss` and :ref:`bidag_itsearch`, tested on nonlinear data. The benchmark is based on 5 datasets corresponding to 5 realisations of a 20-variable random nonlinear Gaussian SEM with Erdős-Rényi structure (expected degree 4, max parents 5). The nonlinear relationships are modeled using multi-layer perceptrons (MLP) and quadratic functions, see :ref:`gcastle_iidsim`. Each dataset contains 300 standardized samples. The SEM parameters are uniformly sampled from [0.25, 1]. .. _gcastlenonlinearplot: .. figure:: https://raw.githubusercontent.com/felixleopoldo/benchpress/master/docs/source/_static/gcastle_benchmarks_nonlinear_anm.png :width: 640 :alt: FP/P vs. TP/P for gCastle algorithms on nonlinear MLP data :align: left FP/P vs. TP/P for gCastle algorithms on nonlinear MLP data. .. _gcastlenonlinearquadplot: .. figure:: https://raw.githubusercontent.com/felixleopoldo/benchpress/master/docs/source/_static/gcastle_benchmarks_nonlinear_quadratic.png :width: 640 :alt: FP/P vs. TP/P for gCastle algorithms on nonlinear quadratic data :align: right FP/P vs. TP/P for gCastle algorithms on nonlinear quadratic data. .. _gcastle_notears_nonlinear: NO TEARS non-linear (gCastle) ****************************** .. list-table:: * - Module name - `gcastle_notears_nonlinear `__ * - Package - `gCastle `__ * - Version - 1.0.3 * - Language - `Python `__ * - Docs - `here `__ * - Paper - :footcite:t:`pmlr-v108-zheng20a` * - Graph type - `DAG `__ * - MCMC - No * - Edge constraints - No * - Data type - C * - Data missingness - * - Intervention type - * - Docker - `bpimages/gcastle:1.0.3 `__ NO TEARS non-linear ----------------------- Notears Nonlinear. Include notears-mlp and notears-sob. A gradient-based algorithm using neural network or Sobolev space modeling for non-linear causal relationships. .. rubric:: Example Config file: `gcastle_nonlinear.json `_ Command: .. code:: bash snakemake --cores all --use-apptainer --configfile config/gcastle_nonlinear.json :numref:`gcastlenonlinearplot` and :numref:`gcastlenonlinearquadplot` show the pattern graph's FP/P vs. TP/P benchmark results for gCastle algorithms, and comparison with :ref:`tetrad_boss` and :ref:`bidag_itsearch`, tested on nonlinear data. The benchmark is based on 5 datasets corresponding to 5 realisations of a 20-variable random nonlinear Gaussian SEM with Erdős-Rényi structure (expected degree 4, max parents 5). The nonlinear relationships are modeled using multi-layer perceptrons (MLP) and quadratic functions, see :ref:`gcastle_iidsim`. Each dataset contains 300 standardized samples. The SEM parameters are uniformly sampled from [0.25, 1]. .. _gcastlenonlinearplot: .. figure:: https://raw.githubusercontent.com/felixleopoldo/benchpress/master/docs/source/_static/gcastle_benchmarks_nonlinear_anm.png :width: 640 :alt: FP/P vs. TP/P for gCastle algorithms on nonlinear MLP data :align: left FP/P vs. TP/P for gCastle algorithms on nonlinear MLP data. .. _gcastlenonlinearquadplot: .. figure:: https://raw.githubusercontent.com/felixleopoldo/benchpress/master/docs/source/_static/gcastle_benchmarks_nonlinear_quadratic.png :width: 640 :alt: FP/P vs. TP/P for gCastle algorithms on nonlinear quadratic data :align: right FP/P vs. TP/P for gCastle algorithms on nonlinear quadratic data. .. rubric:: Example JSON .. code-block:: json [ { "id": "gcastle_notears_mlp", "lambda1": 0.01, "lambda2": 0.01, "max_iter": 100, "h_tol": "1e-8", "rho_max": "1e+16", "w_threshold": 0.3, "bias": true, "model_type": "mlp", "device_type": "cpu", "device_ids": null, "timeout": null }, { "id": "gcastle_notears_sob", "lambda1": 0.01, "lambda2": 0.01, "max_iter": 100, "h_tol": "1e-8", "rho_max": "1e+16", "w_threshold": 0.3, "bias": true, "model_type": "sob", "device_type": "cpu", "device_ids": null, "timeout": null } ] .. footbibliography::