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datafold-1.1.6
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مشاهده بیشترتوضیحات
Operator-theoretic models to identify dynamical systems and parametrize point cloud geometry
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | datafold-1.1.6 |
| نام | datafold |
| نسخه کتابخانه | 1.1.6 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | datafold development team |
| ایمیل نویسنده | daniel.lehmberg@hm.edu |
| آدرس صفحه اصلی | https://datafold-dev.gitlab.io/datafold |
| آدرس اینترنتی | https://pypi.org/project/datafold/ |
| مجوز | MIT |
Quick links
^^^^^^^^^^^
`Source repository <https://gitlab.com/datafold-dev/datafold>`__ |
`Contributing and feedback <https://datafold-dev.gitlab.io/datafold/contributing.html>`__ |
`PyPI <https://pypi.org/project/datafold/>`__ |
`Documentation <https://datafold-dev.gitlab.io/datafold/>`__ |
`Tutorials <https://datafold-dev.gitlab.io/datafold/tutorial_index.html>`__ |
`Scientific literature <https://datafold-dev.gitlab.io/datafold/references.html>`__
What is *datafold*?
====================
*datafold* is a `MIT-licensed <https://gitlab.com/datafold-dev/datafold/-/blob/master/LICENSE>`__
Python package containing operator-theoretic, data-driven models to identify dynamical
systems from time series data and to infer geometrical structures in point clouds.
The package includes:
* Data structures to handle point clouds on manifolds (``PCManifold``) and time series
collections (``TSCDataFrame``). The data structures are used both internally and for
model input/outputs. In contrast to solutions found in other projects, such as
lists of Numpy arrays, ``TSCDataFrame`` makes it much easier to describe many forms of
time series data in a single object.
* An efficient implementation of the ``DiffusionMaps`` model to infer geometric
meaningful structures from data, such as the eigenfunctions of the
Laplace-Beltrami operator. As a distinguishing factor to other implementations, the
model can handle a sparse kernel matrix and allows setting an arbitrary kernel,
including the standard Gaussian kernel,
`continuous k-nearest neighbor kernel <https://arxiv.org/abs/1606.02353>`__, or
`dynamics-adapted cone kernel <https://cims.nyu.edu/~dimitris/files/Giannakis15_cone_kernels.pdf>`__.
* Out-of-sample extensions for the Diffusion Maps model, such as the (auto-tuned)
Laplacian Pyramids or Geometric Harmonics to interpolate general function values on a
point cloud manifold.
* An implementation of the (Extended-) Dynamic Mode Decomposition (e.g. model ``DMDFull``
or ``EDMD``) as data-driven methods to identify dynamical systems from time series
collection data. ``EDMD`` subclasses from the flexible scikit-learn
`Pipeline <https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html>`__,
which allows setting up and transforming time series collection data to a more suitable
feature state (cf. Koopman operator theory).
* ``EDMDCV`` allows model parameters to be optimized with cross-validation splittings that
account for the temporal order in time series collections.
See also `this introduction page <https://datafold-dev.gitlab.io/datafold/intro.html>`__.
For a mathematical thorough introduction, we refer to the `scientific literature
<https://datafold-dev.gitlab.io/datafold/references.html>`__.
.. note::
The project is under active development in a research-driven environment.
* Code quality varies from "experimental/early stage" to "well-tested". Well tested
code is listed in the
`software documentation <https://datafold-dev.gitlab.io/datafold/api.html>`__
and are directly accessible through the package levels ``pcfold``, ``dynfold`` or
``appfold`` (e.g. :code:`from datafold.dynfold import ...`). Experimental code is
only accessible via "deep imports"
(e.g. :code:`from datafol.dynfold.outofsample import ...`) and may raise a warning
when using it.
* There is no deprecation cycle. Backwards compatibility is indicated by the
package version, where we use a `semantic versioning <https://semver.org/>`__
policy `[major].[minor].[patch]`, i.e.
* `major` - making incompatible changes in the (documented) API
* `minor` - adding functionality in a backwards-compatible manner
* `patch` - backwards-compatible bug fixes
We do not intend to indicate a feature complete milestone with version `1.0`.
Cite
====
If you use *datafold* in your research, please cite
`this paper <https://joss.theoj.org/papers/10.21105/joss.02283>`__ published in the
*Journal of Open Source Software* (`JOSS <https://joss.theoj.org/>`__).
*Lehmberg et al., (2020). datafold: data-driven models for point clouds and time series on
manifolds. Journal of Open Source Software, 5(51), 2283,* https://doi.org/10.21105/joss.02283
BibTeX:
.. code-block:: latex
@article{Lehmberg2020,
doi = {10.21105/joss.02283},
url = {https://doi.org/10.21105/joss.02283},
year = {2020},
publisher = {The Open Journal},
volume = {5},
number = {51},
pages = {2283},
author = {Daniel Lehmberg and Felix Dietrich and Gerta K{\"o}ster and Hans-Joachim Bungartz},
title = {datafold: data-driven models for point clouds and time series on manifolds},
journal = {Journal of Open Source Software}}
How to get it?
==============
Installation requires `Python>=3.7 <https://www.python.org/>`__ with
`pip <https://pip.pypa.io/en/stable/>`__ and
`setuptools <https://setuptools.readthedocs.io/en/latest/>`__ installed. Both
packages usually ship with a standard Python installation. The package dependencies
install automatically. The main dependencies and their role in *datafold* are listed below
in "Dependencies".
There are two ways to install *datafold*:
1. From PyPI
------------
This is the standard way for users. The package is hosted on the official Python package
index (PyPI) and installs the core package (excluding tutorials and tests). The tutorial
files can be downloaded separately
`here <https://datafold-dev.gitlab.io/datafold/tutorial_index.html>`__.
To install the package and its dependencies with :code:`pip`, run
.. code-block:: bash
python -m pip install datafold
.. note::
If you run Python in an Anaconda environment you can use pip from within ``conda``.
See also
`official instructions <https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-pkgs.html#installing-non-conda-packages>`__.
.. code-block:: bash
conda activate venv
conda install pip
pip install datafold
2. From source
--------------
This way is recommended if you want to access the latest (but potentially unstable)
development, run tests or wish to contribute (see section "Contributing" for details).
Download or git-clone the source code repository.
1. Download the repository
a. If you wish to contribute code, it is required to have
`git <https://git-scm.com/>`__ installed. Clone the repository with
.. code-block:: bash
git clone https://gitlab.com/datafold-dev/datafold.git
b. If you only want access to the source code (current ``master`` branch), download one
of the compressed files
(`zip <https://gitlab.com/datafold-dev/datafold/-/archive/master/datafold-master.zip>`__,
`tar.gz <https://gitlab.com/datafold-dev/datafold/-/archive/master/datafold-master.tar.gz>`__,
`tar.bz2 <https://gitlab.com/datafold-dev/datafold/-/archive/master/datafold-master.tar.bz2>`__,
`tar <https://gitlab.com/datafold-dev/datafold/-/archive/master/datafold-master.tar>`__)
2. Install the package from the downloaded repository
.. code-block:: bash
python -m pip install .
Contributing
============
Any contribution (code/tutorials/documentation improvements), question or feedback is
very welcome. Either use the
`issue tracker <https://gitlab.com/datafold-dev/datafold/-/issues>`__ or
`Email <incoming+datafold-dev-datafold-14878376-issue-@incoming.gitlab.com>`__.
Instructions to set up *datafold* for development can be found
`here <https://datafold-dev.gitlab.io/datafold/contributing.html>`__.
Dependencies
============
The dependencies of the core package are managed in the file
`requirements.txt <https://gitlab.com/datafold-dev/datafold/-/blob/master/requirements.txt>`__
and install with *datafold*. The tests, tutorials, documentation and code analysis
require additional dependencies which are managed in
`requirements-dev.txt <https://gitlab.com/datafold-dev/datafold/-/blob/master/requirements-dev.txt>`__.
*datafold* integrates with common packages from the
`Python scientific computing stack <https://www.scipy.org/about.html>`__:
* `NumPy <https://numpy.org/>`__
The data structure ``PCManifold`` subclasses from NumPy's
`ndarray <https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html>`__. The
class attaches an kernel object to the data to describe point similarity.
NumPy is used throughout *datafold* and is the default package for numerical
data and algorithms.
* `pandas <https://pandas.pydata.org/pandas-docs/stable/index.html>`__
*datafold* uses pandas'
`DataFrame <https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html>`__
as a base class for ``TSCDataFrame``, which captures time series data and
collections thereof. The data structure indexes time, time series ID and
one-or-many spatial features. It includes specific time series collection functionality
and is compatible with pandas rich functionality.
* `scikit-learn <https://scikit-learn.org/stable/>`__
All *datafold* algorithms that are part of the "machine learning pipeline" align
to the scikit-learn `API <https://scikit-learn.org/stable/developers/develop.html>`__.
This is done by deriving the models from
`BaseEstimator <https://scikit-learn.org/stable/modules/generated/sklearn.base.BaseEstimator.html>`__.
and appropriate `MixIns`. *datafold* defines own `MixIns` that align with the
API in a duck-typing fashion to allow identifying dynamical systems from temporal data
in ``TSCDataFrame``.
* `SciPy <https://docs.scipy.org/doc/scipy/reference/index.html>`__
The package is used for elementary numerical algorithms and data structures in
conjunction with NumPy. This includes (sparse) linear least
square regression, (sparse) eigenpairs solver and sparse matrices as
optional data structure for kernel matrices.
How does it compare to other software?
======================================
*The selection only includes other Python packages.*
* `scikit-learn <https://scikit-learn.org/stable/>`__
provides algorithms and models along the entire machine learning pipeline, with a
strong focus on static data (i.e. without temporal context). *datafold* integrates
into scikit-learn' API and all data-driven models are subclasses of
`BaseEstimator <https://scikit-learn.org/stable/modules/generated/sklearn.base.BaseEstimator.html>`__.
An important contribution of *datafold* is the ``DiffusionMaps`` model as popular
framework for manifold learning, which is not contained in scikit-learn's `set of
algorithms <https://scikit-learn.org/stable/auto_examples/manifold/plot_compare_methods
.html#sphx-glr-auto-examples-manifold-plot-compare-methods-py>`__.
Furthermore, *datafold* includes dynamical systems as a new model class that is
operable with scikit-learn - the attributes align to supervised learning tasks.
The key differences are that a model processes data of type ``TSCDataFrame``
and instead of a one-to-one relation in the model's input/output, the model can return
arbitrary many output samples (a time series) for a single input
(an initial condition).
* `PyDMD <https://mathlab.github.io/PyDMD/build/html/index.html>`__
provides many variants of the `Dynamic Mode Decomposition (DMD) <https://en.wikipedia.org/wiki/Dynamic_mode_decomposition>`__.
*datafold* provides a wrapper to make models of ``PyDMD`` accessible. However, a
limitation of ``PyDMD`` is that it only processes single coherent time series, see
`PyDMD issue 86 <https://github.com/mathLab/PyDMD/issues/86>`__. The DMD models that
are directly included in *datafold* utilize the functionality of the data
structure ``TSCDataFrame`` and can therefore process time
series collections - in an extreme case only containing snapshot pairs.
* `PySINDy <https://pysindy.readthedocs.io/en/latest/>`__
specializes on a *sparse* system identification of nonlinear dynamical systems to
infer governing equations.
نیازمندی
| مقدار | نام |
|---|---|
| ~=0.8.9 | findiff |
| >=3.2.0 | matplotlib |
| <3.0.0,>=2.7.1 | numexpr |
| <1.22.0,>=1.18.0 | numpy |
| ~=1.2.0 | pandas |
| ~=0.24.1 | scikit-learn |
| <1.8,>=1.5.0 | scipy |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.7 | Python |
نحوه نصب
نصب پکیج whl datafold-1.1.6:
pip install datafold-1.1.6.whl
نصب پکیج tar.gz datafold-1.1.6:
pip install datafold-1.1.6.tar.gz