Metadata-Version: 1.2
Name: openTSNE
Version: 0.3.12
Summary: Extensible, parallel implementations of t-SNE
Home-page: https://github.com/pavlin-policar/openTSNE
Author: Pavlin Poličar
Author-email: pavlin.g.p@gmail.com
License: BSD-3-Clause
Project-URL: Documentation, https://opentsne.readthedocs.io/
Project-URL: Source, https://github.com/pavlin-policar/openTSNE
Project-URL: Issue Tracker, https://github.com/pavlin-policar/openTSNE/issues
Description: openTSNE
        ========
        
        |Build Status| |ReadTheDocs Badge| |Codacy Badge| |License Badge|
        
        openTSNE is a modular Python implementation of t-Distributed Stochasitc Neighbor Embedding (t-SNE), a popular dimensionality-reduction algorithm for visualizing high-dimensional data sets. openTSNE incorporates the latest improvements to the t-SNE algorithm, including the ability to add new data points to existing embeddings, massive speed improvements, enabling t-SNE to scale to millions of data points and various tricks to improve global alignment of the resulting visualizations.
        
        .. figure:: docs/source/images/macosko_2015.png
           :alt: Macosko 2015 mouse retina t-SNE embedding
           :align: center
        
           A visualization of 44,808 single cell transcriptomes obtained from the mouse retina [5]_ embedded using the multiscale kernel trick to better preserve the global aligment of the clusters.
        
        - `Documentation <http://opentsne.readthedocs.io>`__
        - `User Guide and Tutorial <https://opentsne.readthedocs.io/en/latest/tsne_algorithm.html>`__
        - Examples: `basic <https://opentsne.readthedocs.io/en/latest/examples/01_simple_usage/01_simple_usage.html>`__, `advanced <https://opentsne.readthedocs.io/en/latest/examples/02_advanced_usage/02_advanced_usage.html>`__, `preserving global alignment <https://opentsne.readthedocs.io/en/latest/examples/03_preserving_global_structure/03_preserving_global_structure.html>`__, `embedding large data sets <https://opentsne.readthedocs.io/en/latest/examples/04_large_data_sets/04_large_data_sets.html>`__
        - `Speed benchmarks <https://opentsne.readthedocs.io/en/latest/benchmarks.html>`__
        
        Installation
        ------------
        
        openTSNE requires Python 3.6 or higher in order to run.
        
        Conda
        ~~~~~
        
        openTSNE can be easily installed from ``conda-forge`` with
        
        ::
        
           conda install --channel conda-forge opentsne
        
        `Conda package <https://anaconda.org/conda-forge/opentsne>`__
        
        PyPi
        ~~~~
        
        openTSNE is also available through ``pip`` and can be installed with
        
        ::
        
           pip install opentsne
        
        `PyPi package <https://pypi.org/project/openTSNE>`__
        
        Note that openTSNE requires a C/C++ compiler. ``numpy`` must also be
        installed.
        
        In order for openTSNE to utilize multiple threads, the C/C++ compiler
        must also implement ``OpenMP``. In practice, almost all compilers
        implement this with the exception of older version of ``clang`` on OSX
        systems.
        
        To squeeze the most out of openTSNE, you may also consider installing
        FFTW3 prior to installation. FFTW3 implements the Fast Fourier
        Transform, which is heavily used in openTSNE. If FFTW3 is not available,
        openTSNE will use numpy’s implementation of the FFT, which is slightly
        slower than FFTW. The difference is only noticeable with large data sets
        containing millions of data points.
        
        A hello world example
        ---------------------
        
        Getting started with openTSNE is very simple. First, we'll load up some data using scikit-learn
        
        .. code:: python
        
           from sklearn import datasets
        
           iris = datasets.load_iris()
           x, y = iris["data"], iris["target"]
        
        then, we'll import and run
        
        .. code:: python
        
           from openTSNE import TSNE
        
           embedding = TSNE().fit(x)
        
        Citation
        --------
        
        If you make use of openTSNE for your work we would appreciate it if you would cite the paper
        
        .. code::
        
            @article {Poli{\v c}ar731877,
                author = {Poli{\v c}ar, Pavlin G. and Stra{\v z}ar, Martin and Zupan, Bla{\v z}},
                title = {openTSNE: a modular Python library for t-SNE dimensionality reduction and embedding},
                year = {2019},
                doi = {10.1101/731877},
                publisher = {Cold Spring Harbor Laboratory},
                URL = {https://www.biorxiv.org/content/early/2019/08/13/731877},
                eprint = {https://www.biorxiv.org/content/early/2019/08/13/731877.full.pdf},
                journal = {bioRxiv}
            }
        
        
        References
        ----------
        
        .. [1] Maaten, Laurens van der, and Geoffrey Hinton. `“Visualizing data using
            t-SNE.” <http://www.jmlr.org/papers/volume9/vandermaaten08a/vandermaaten08a.pdf>`__
            Journal of machine learning research 9.Nov (2008): 2579-2605.
        .. [2] Van Der Maaten, Laurens. `“Accelerating t-SNE using tree-based algorithms.”
            <http://www.jmlr.org/papers/volume15/vandermaaten14a/vandermaaten14a.pdf>`__
            The Journal of Machine Learning Research 15.1 (2014): 3221-3245.
        .. [3] Linderman, George C., et al. `"Fast interpolation-based t-SNE for improved
            visualization of single-cell RNA-seq data." <https://www.nature.com/articles/s41592-018-0308-4>`__ Nature methods 16.3 (2019): 243.
        .. [4] Kobak, Dmitry, and Philipp Berens. `“The art of using t-SNE for single-cell
            transcriptomics.” <https://www.biorxiv.org/content/early/2018/10/25/453449>`__
            bioRxiv (2018): 453449.
        .. [5] Macosko, Evan Z., et al. \ `“Highly parallel genome-wide expression profiling of
            individual cells using nanoliter droplets.”
            <https://www.sciencedirect.com/science/article/pii/S0092867415005498>`__
            Cell 161.5 (2015): 1202-1214.
        
        .. |Build Status| image:: https://dev.azure.com/pavlingp/openTSNE/_apis/build/status/Test?branchName=master
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Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: Topic :: Software Development
Classifier: Topic :: Scientific/Engineering
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS
Classifier: License :: OSI Approved
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Visualization
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.6
