Installation

Python 3.10 or later is required. Install the numerical core with:

pip install pytorch-bsf

The core depends on PyTorch, NumPy and PyYAML (used by internal geometry tools). Conda is an optional environment manager. The repository’s environment.yml and Docker image remain available for reproducibility.

Optional extras:

pytorch-bsf[sparse]: SciPy LSMR least squares.
pytorch-bsf[sklearn]: scikit-learn estimator.
pytorch-bsf[plot]: Matplotlib plotting.
pytorch-bsf[lightning]: explicit Lightning execution adapter.
pytorch-bsf[mlflow]: explicit experiment recording.
pytorch-bsf[all]: every integration.

Random, Sobol and grid sampling use PyTorch and need no extra. Importing the package loads neither Lightning nor MLflow nor SciPy.

This v0 redesign intentionally removes old names and import paths. See Migrating to the unified v0 API for replacement patterns.

Choose dependencies for your workflow

The minimal installation is enough for dense regression, random and Sobol sampling, grids, fixed controls, degree selection, objective minimization and model archives. Add extras only for the integrations your application calls. This makes a small CPU fitting environment easier to reproduce and avoids importing experiment services during numerical computation.

Table 1 Optional dependencies and their entry points
Extra	Adds	Needed when
`sparse`	SciPy	Executing `fit(..., solver=SparseLeastSquares(...))`.
`plot`	Matplotlib and SciPy	Drawing fitted images and running the guide figure generator.
`sklearn`	scikit-learn	Using `BezierRegressor` or scikit-learn searches.
`lightning`	Lightning	Creating the explicit `regression_module` execution adapter.
`mlflow`	MLflow	Calling `log_result` or opting into CLI tracking with `--track`.
`develop`	Ruff, mypy, pytest and development tools	Editing and verifying this repository; see `CONTRIBUTING.md`.

Combine extras in one quoted requirement:

pip install "pytorch-bsf[plot,sparse]"
pip install "pytorch-bsf[sklearn,mlflow]"

Quoting prevents shells from treating square brackets as filename patterns. all includes every integration, but documentation-build extensions are listed separately in docs/requirements.txt. In a development checkout, install the package editable before executing example scripts so imports resolve to the checked-out implementation rather than an older wheel.

Check the environment you actually run

Use the same interpreter for installation and execution. This is particularly important when a notebook, Conda environment and system Python coexist.

python -m pip install pytorch-bsf
python -c "import torch_bsf; print(torch_bsf.__version__); print(torch_bsf.__file__)"
python -m torch_bsf --help

If an optional operation reports a missing dependency, install its extra into that interpreter. Constructing a sparse solver configuration does not execute a sparse solve, so import success alone does not verify that SciPy is available. Similarly, importing the numerical core does not prove that Lightning or MLflow is installed or usable. Test the particular integration your application needs.

The repository’s Conda environment and Docker image provide alternative packaging workflows, while the public numerical operations stay the same. Inside Docker, mount input files and an output directory, then use the current fit or predict subcommand. Model paths inside the container must point to that mount if outputs should survive container removal. See Quickstart for data files, shell commands and saved-model prediction.

Upgrading from the earlier API

Installing an extra does not restore removed public names or CLI flags. solver="trainer" is replaced by an explicit Lightning adapter, legacy model constructors by BezierSpace plus BezierModel, and automatic tracking by explicit recording. Migrate calls using Migrating to the unified v0 API before assuming an installation failure explains an import error for an old symbol.

Core least-squares models now use CPU float64 and normalized data loss with explicit quadratic penalties. Existing penalty values may therefore require validation under the new objective. Keep the training observations and old evaluation results so the upgrade can be compared on the same data. Model archives have a versioned format; an arbitrary old pickle or coefficient CSV is not a geometry-preserving archive for the redesigned API.