{ "info": { "author": "Pymc-Learn Team", "author_email": "daniel.emaasit@gmail.com", "bugtrack_url": null, "classifiers": [], "description": "All rights reserved.\n\nRedistribution and use in source and binary forms, with or without modification,\nare permitted provided that the following conditions are met:\n\n1. Redistributions of source code must retain the above copyright notice,\nthis list of conditions and the following disclaimer.\n\n2. Redistributions in binary form must reproduce the above copyright notice,\nthis list of conditions and the following disclaimer in the documentation\nand/or other materials provided with the distribution.\n\n3. Neither the name of Pymc-learn nor the names of any contributors may be used to\nendorse or promote products derived from this software without specific prior\nwritten permission.\n\nTHIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"\nAND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\nIMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE\nARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE\nLIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR\nCONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF\nSUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS\nINTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN\nCONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)\nARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF\nTHE POSSIBILITY OF SUCH DAMAGE.\n\nDescription: pymc-learn: Practical Probabilistic Machine Learning in Python\n ===============================================================\n \n .. image:: https://github.com/pymc-learn/pymc-learn/blob/master/docs/logos/pymc-learn-logo.jpg?raw=true\n :width: 350px\n :alt: Pymc-Learn logo\n :align: center\n \n |status| |Travis| |Coverage| |Docs| |License| |Pypi| |Binder|\n \n **Contents:**\n \n #. `Github repo`_\n #. `What is pymc-learn?`_\n #. `Quick Install`_\n #. `Quick Start`_\n #. `Index`_\n \n \n .. _Github repo: https://github.com/pymc-learn/pymc-learn\n \n ----\n \n What is pymc-learn?\n ------------------------\n \n *pymc-learn is a library for practical probabilistic\n machine learning in Python*.\n \n It provides a variety of state-of-the art probabilistic models for supervised\n and unsupervised machine learning. **It is inspired by**\n `scikit-learn `_ **and focuses on bringing probabilistic\n machine learning to non-specialists**. It uses a syntax that mimics scikit-learn.\n Emphasis is put on ease of use, productivity, flexibility, performance,\n documentation, and an API consistent with scikit-learn. It depends on scikit-learn\n and `PyMC3 `_ and is distributed under the new BSD-3 license,\n encouraging its use in both academia and industry.\n \n Users can now have calibrated quantities of uncertainty in their models\n using powerful inference algorithms -- such as MCMC or Variational inference --\n provided by `PyMC3 `_.\n See :doc:`why` for a more detailed description of why ``pymc-learn`` was\n created.\n \n .. NOTE::\n ``pymc-learn`` leverages and extends the Base template provided by the\n PyMC3 Models project: https://github.com/parsing-science/pymc3_models\n \n \n Transitioning from PyMC3 to PyMC4\n ..................................\n \n .. raw:: html\n \n \n

.@pymc_learn has been following closely the development of #PyMC4 with the aim of switching its backend from #PyMC3 to PyMC4 as the latter grows to maturity. Core devs are invited. Here's the tentative roadmap for PyMC4: https://t.co/Kwjkykqzup cc @pymc_devs https://t.co/Ze0tyPsIGH

— pymc-learn (@pymc_learn) November 5, 2018
\n \n \n ----\n \n Familiar user interface\n -----------------------\n ``pymc-learn`` mimics scikit-learn. You don't have to completely rewrite\n your scikit-learn ML code.\n \n .. code-block:: python\n \n from sklearn.linear_model \\ from pmlearn.linear_model \\\n import LinearRegression import LinearRegression\n lr = LinearRegression() lr = LinearRegression()\n lr.fit(X, y) lr.fit(X, y)\n \n The difference between the two models is that ``pymc-learn`` estimates model\n parameters using Bayesian inference algorithms such as MCMC or variational\n inference. This produces calibrated quantities of uncertainty for model\n parameters and predictions.\n \n ----\n \n Quick Install\n -----------------\n \n ``pymc-learn`` requires a working Python interpreter (2.7 or 3.5+).\n It is recommend installing Python and key numerical libraries using the `Anaconda Distribution `_,\n which has one-click installers available on all major platforms.\n \n Assuming a standard Python environment is installed on your machine\n (including pip), ``pymc-learn`` itself can be installed in one line using pip:\n \n You can install ``pymc-learn`` from PyPi using pip as follows:\n \n .. code-block:: bash\n \n pip install pymc-learn\n \n \n Or from source as follows:\n \n .. code-block:: bash\n \n pip install git+https://github.com/pymc-learn/pymc-learn\n \n \n .. CAUTION::\n ``pymc-learn`` is under heavy development.\n \n It is recommended installing ``pymc-learn`` in a Conda environment because it\n provides `Math Kernel Library `_ (MKL)\n routines to accelerate math functions. If you are having trouble, try using\n a distribution of Python that includes these packages like\n `Anaconda `_.\n \n \n \n Dependencies\n ................\n \n ``pymc-learn`` is tested on Python 2.7, 3.5 & 3.6 and depends on Theano,\n PyMC3, Scikit-learn, NumPy, SciPy, and Matplotlib (see ``requirements.txt``\n for version information).\n \n ----\n \n \n Quick Start\n ------------------\n \n .. code-block:: python\n \n # For regression using Bayesian Nonparametrics\n >>> from sklearn.datasets import make_friedman2\n >>> from pmlearn.gaussian_process import GaussianProcessRegressor\n >>> from pmlearn.gaussian_process.kernels import DotProduct, WhiteKernel\n >>> X, y = make_friedman2(n_samples=500, noise=0, random_state=0)\n >>> kernel = DotProduct() + WhiteKernel()\n >>> gpr = GaussianProcessRegressor(kernel=kernel).fit(X, y)\n >>> gpr.score(X, y)\n 0.3680...\n >>> gpr.predict(X[:2,:], return_std=True)\n (array([653.0..., 592.1...]), array([316.6..., 316.6...]))\n \n ----\n \n Scales to Big Data & Complex Models\n -----------------------------------\n \n Recent research has led to the development of variational inference algorithms\n that are fast and almost as flexible as MCMC. For instance Automatic\n Differentation Variational Inference (ADVI) is illustrated in the code below.\n \n .. code-block:: python\n \n from pmlearn.neural_network import MLPClassifier\n model = MLPClassifier()\n model.fit(X_train, y_train, inference_type=\"advi\")\n \n \n Instead of drawing samples from the posterior, these algorithms fit\n a distribution (e.g. normal) to the posterior turning a sampling problem into\n an optimization problem. ADVI is provided PyMC3.\n \n ----\n \n Citing pymc-learn\n ------------------\n \n To cite ``pymc-learn`` in publications, please use the following::\n \n Emaasit, Daniel (2018). Pymc-learn: Practical probabilistic machine\n learning in Python. arXiv preprint arXiv:1811.00542.\n \n Or using BibTex as follows:\n \n .. code-block:: latex\n \n @article{emaasit2018pymc,\n title={Pymc-learn: Practical probabilistic machine learning in {P}ython},\n author={Emaasit, Daniel and others},\n journal={arXiv preprint arXiv:1811.00542},\n year={2018}\n }\n \n If you want to cite ``pymc-learn`` for its API, you may also want to consider\n this reference::\n \n Carlson, Nicole (2018). Custom PyMC3 models built on top of the scikit-learn\n API. https://github.com/parsing-science/pymc3_models\n \n Or using BibTex as follows:\n \n .. code-block:: latex\n \n @article{Pymc3_models,\n title={pymc3_models: Custom PyMC3 models built on top of the scikit-learn API,\n author={Carlson, Nicole},\n journal={},\n url={https://github.com/parsing-science/pymc3_models}\n year={2018}\n }\n \n License\n ..............\n \n `New BSD-3 license `__\n \n ----\n \n Index\n -----\n \n **Getting Started**\n \n * :doc:`install`\n * :doc:`support`\n * :doc:`why`\n \n .. toctree::\n :maxdepth: 1\n :hidden:\n :caption: Getting Started\n \n install.rst\n support.rst\n why.rst\n \n ----\n \n **User Guide**\n \n The main documentation. This contains an in-depth description of all models\n and how to apply them.\n \n * :doc:`user_guide`\n \n .. toctree::\n :maxdepth: 1\n :hidden:\n :caption: User Guide\n \n user_guide.rst\n \n ----\n \n **Examples**\n \n Pymc-learn provides probabilistic models for machine learning,\n in a familiar scikit-learn syntax.\n \n * :doc:`regression`\n * :doc:`classification`\n * :doc:`mixture`\n * :doc:`neural_networks`\n * :doc:`api`\n \n .. toctree::\n :maxdepth: 1\n :hidden:\n :caption: Examples\n \n regression.rst\n classification.rst\n mixture.rst\n neural_networks.rst\n \n ----\n \n **API Reference**\n \n ``pymc-learn`` leverages and extends the Base template provided by the PyMC3\n Models project: https://github.com/parsing-science/pymc3_models.\n \n * :doc:`api`\n \n .. toctree::\n :maxdepth: 1\n :hidden:\n :caption: API Reference\n \n api.rst\n \n ----\n \n **Help & reference**\n \n * :doc:`develop`\n * :doc:`support`\n * :doc:`changelog`\n * :doc:`cite`\n \n .. toctree::\n 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