Metadata-Version: 1.1
Name: pytreecat
Version: 0.1.9
Summary: A Bayesian latent tree model of multivariate multinomial data
Home-page: https://github.com/posterior/treecat
Author: Fritz Obermeyer
Author-email: fritz.obermeyer@gmail.com
License: Apache License 2.0
Description: .. figure:: doc/cartoon.png
           :alt: A Bayesian latent tree model
        
           A Bayesian latent tree model
        
        TreeCat
        =======
        
        |Build Status| |Latest Version| |DOI|
        
        Intended Use
        ------------
        
        TreeCat is an inference engine for machine learning and Bayesian
        inference. TreeCat is appropriate for analyzing medium-sized tabular
        data with categorical and ordinal values, possibly with missing
        observations.
        
        +--------------------------+--------------------------+
        |                          | TreeCat supports         |
        +==========================+==========================+
        | Feature Types            | categorical, ordinal     |
        +--------------------------+--------------------------+
        | # Rows (n)               | 1000-100K                |
        +--------------------------+--------------------------+
        | # Features (p)           | 10-1000                  |
        +--------------------------+--------------------------+
        | # Cells (n × p)          | 10K-10M                  |
        +--------------------------+--------------------------+
        | # Categories             | 2-10ish                  |
        +--------------------------+--------------------------+
        | Max Ordinal              | 10ish                    |
        +--------------------------+--------------------------+
        | Missing obervations?     | yes                      |
        +--------------------------+--------------------------+
        | Repeated observations?   | yes                      |
        +--------------------------+--------------------------+
        | Sparse data?             | no, use something else   |
        +--------------------------+--------------------------+
        | Unsupervised             | yes                      |
        +--------------------------+--------------------------+
        | Semisupervised           | yes                      |
        +--------------------------+--------------------------+
        | Supervised               | no, use something else   |
        +--------------------------+--------------------------+
        
        Installing
        ----------
        
        If you already have `Numba <http://numba.pydata.org>`__ installed, you
        should be able to simply
        
        .. code:: sh
        
            pip install pytreecat
        
        If you're new to Numba, we recommend installing it using
        `miniconda <https://conda.io/miniconda.html>`__ or
        `Anaconda <https://www.continuum.io/downloads>`__.
        
        If you want to install TreeCat for development, then clone the source
        code and create a new conda env
        
        .. code:: sh
        
            git clone git@github.com:posterior/treecat
            cd treecat
            conda env create -f environment.3.yml
            source activate treecat3
            pip install -e .
        
        Quick Start
        -----------
        
        1. Format your data as a
           ```data.csv`` <treecat/testdata/tiny_data.csv>`__ file with a header
           row. It's fine to include extra columns that won't be used.
        
           Contents of ```data.csv`` <treecat/testdata/tiny_data.csv>`__:
        
           +-------------+------------+----------+----------+
           | title       | genre      | decade   | rating   |
           +=============+============+==========+==========+
           | vertigo     | thriller   | 1950s    | 5        |
           +-------------+------------+----------+----------+
           | up          | family     | 2000s    | 3        |
           +-------------+------------+----------+----------+
           | desk set    | comedy     | 1950s    | 4        |
           +-------------+------------+----------+----------+
           | santapaws   | family     | 2010s    |          |
           +-------------+------------+----------+----------+
           | ...         | ...        | ...      | ...      |
           +-------------+------------+----------+----------+
        
        2. Generate two schema files
           ```types.csv`` <treecat/testdata/tiny_types.csv>`__ and
           ```values.csv`` <treecat/testdata/tiny_values.csv>`__ using TreeCat's
           ``guess-schema`` command:
        
           .. code:: sh
        
               $ treecat guess-schema data.csv types.csv values.csv
        
           Contents of ```types.csv`` <treecat/testdata/tiny_types.csv>`__:
        
           +----------+---------------+---------+----------+--------------+
           | name     | type          | total   | unique   | singletons   |
           +==========+===============+=========+==========+==============+
           | title    |               | 11      | 11       | 11           |
           +----------+---------------+---------+----------+--------------+
           | genre    | categorical   | 11      | 7        | 4            |
           +----------+---------------+---------+----------+--------------+
           | decade   | categorical   | 11      | 6        | 3            |
           +----------+---------------+---------+----------+--------------+
           | rating   | ordinal       | 10      | 5        | 2            |
           +----------+---------------+---------+----------+--------------+
        
           Contents of ```values.csv`` <treecat/testdata/tiny_values.csv>`__:
        
           +----------+-----------+---------+
           | name     | value     | count   |
           +==========+===========+=========+
           | genre    | drama     | 3       |
           +----------+-----------+---------+
           | genre    | family    | 2       |
           +----------+-----------+---------+
           | genre    | fantasy   | 2       |
           +----------+-----------+---------+
           | decade   | 1950s     | 3       |
           +----------+-----------+---------+
           | ...      | ...       | ...     |
           +----------+-----------+---------+
        
           You can manually fix any incorrectly guessed feature types, or
           add/remove feature values. TreeCat ignores any feature with an empty
           type field.
        
        3. Import your csv files into treecat's internal format. We'll call our
           dataset ``dataset.pkz`` (a gzipped pickle file).
        
           .. code:: sh
        
               $ treecat import-data data.csv types.csv values.csv '' dataset.pkz
        
           (the empty argument '' is an optional structural prior that we
           ignore).
        
        4. Train an ensemble model on your dataset. This typically takes
           ~15minutes for a 1M cell dataset.
        
           .. code:: sh
        
               $ treecat train dataset.pkz model.pkz
        
        5. Load your trained model into a server
        
           .. code:: python
        
               from treecat.serving import serve_model
               server = serve_model('dataset.pkz', 'model.pkz')
        
        6. Run queries against the server. For example we can compute
           expecations
        
           .. code:: python
        
               samples = server.sample(100, evidence={'genre': 'drama'})
               print(np.mean([s['rating'] for s in samples]))
        
           or explore feature structure through the latent correlation matrix
        
           .. code:: python
        
               print(server.latent_correlation())
        
        Tuning Hyperparameters
        ----------------------
        
        TreeCat requires tuning of two parameters: ``learning_init_epochs``
        (like the number of iterations) and ``model_num_clusters`` (the number
        of latent classes above each feature). The easiest way to tune these is
        to do grid search using the ``treecat.validate`` module with a csv file
        of example parameters.
        
        Contents of ```tuning.csv`` <treecat/testdata/tuning.csv>`__:
        
        +------------------------+--------------------------+
        | model\_num\_clusters   | learning\_init\_epochs   |
        +========================+==========================+
        | 2                      | 2                        |
        +------------------------+--------------------------+
        | 2                      | 3                        |
        +------------------------+--------------------------+
        | 4                      | 2                        |
        +------------------------+--------------------------+
        | ...                    | ...                      |
        +------------------------+--------------------------+
        
        .. code:: sh
        
            # This reads parameters from tuning.csv and dumps results to tuning.pkz
            $ treecat.validate tune-csv dataset.pkz tuning.csv tuning.pkz
        
        The ``tune-csv`` command prints its results, but if you want to seem
        them later, you can
        
        .. code:: sh
        
            $ treecat.format cat tuning.pkz
        
        The Server Interface
        --------------------
        
        TreeCat's
        `server <https://github.com/posterior/treecat/blob/master/treecat/serving.py>`__
        interface supports primitives for Bayesian inference and tools to
        inspect latent structure:
        
        -  ``server.sample(N, evidence=None)`` draws ``N`` samples from the
           joint posterior distribution over observable data, optionally
           conditioned on ``evidence``.
        
        -  ``server.logprob(rows, evidence=None)`` computes posterior log
           probability of ``data``, optionally conditioned on ``evidence``.
        
        -  ``server.median(evidence)`` computes L1-loss-minimizing estimates,
           conditioned on ``evidence``.
        
        -  ``server.observed_perplexity()`` computes the
           `perplexity <https://en.wikipedia.org/wiki/Perplexity>`__ (a soft
           measure of cardinality) of each observed feature.
        
        -  ``server.latent_perplexity()`` computes the perplexity of the latent
           class behind each observed feature.
        
        -  ``server.latent_correlation()`` computes the latent-latent
           correlation between each pair of latent variables.
        
        -  ``server.estimate_tree()`` computes a maximum a posteriori estimate
           of the latent tree structure.
        
        -  ``server.sample_tree(N)`` draws ``N`` samples from posterior
           distribution over the latent tree structures.
        
        The Model
        ---------
        
        TreeCat's generative model is closest to Zhang and Poon's Latent Tree
        Analysis [1], with the notable difference that TreeCat fixes exactly one
        latent node per observed node. TreeCat is historically a descendent of
        Mansinghka et al.'s CrossCat, a model in which latent nodes ("views" or
        "kinds") are completely independent. TreeCat addresses the same kind of
        high-dimensional categorical distribution that Dunson and Xing's
        mixture-of-product-multinomial models [3] addresses.
        
        Let ``V`` be a set of vertices (one vertex per feature). Let ``C[v]`` be
        the dimension of the ``v``\ th feature. Let ``N`` be the number of
        datapoints. Let ``K[n,v]`` be the number of observations of feature
        ``v`` in row ``n`` (e.g. 1 for a categorical variable, 0 for missing
        data, or ``k`` for an ordinal value with minimum 0 and maximum ``k``).
        
        TreeCat is the following generative model:
        
        .. code:: python
        
            E ~ UniformSpanningTree(V)    # An undirected tree.
            for v in V:
                Pv[v] ~ Dirichlet(size = [M], alpha = 1/2)
            for (u,v) in E:
                Pe[u,v] ~ Dirichlet(size = [M,M], alpha = 1/(2*M))
                assume(Pv[u] == sum(Pe[u,v], axis = 1))
                assume(Pv[v] == sum(Pe[u,v], axis = 0))
            for v in V:
                for i in 1:M:
                    Q[v,i] ~ Dirichlet(size = [C[v]])
            for n in 1:N:
                for v in V:
                    X[n,v] ~ Categorical(Pv[v])
                for (u,v) in E:
                    (X[n,u],X[n,v]) ~ Categorical(Pe[u,v])
                for v in V:
                    Z[n,v] ~ Multinomial(Q[v,X[n,v]], count = K[n,v])
        
        where we've avoided adding an arbitrary root to the tree, and instead
        presented the model as a manifold with overlapping variables and
        constraints.
        
        The Inference Algorithm
        -----------------------
        
        This package implements fully Bayesian MCMC inference using
        subsample-annealed collapsed Gibbs sampling. There are two pieces of
        latent state that are sampled:
        
        -  Latent class assignments for each row for each vertex (feature).
           These are sampled by single-site collapsed Gibbs sampler with a
           linear subsample-annealing schedule.
        
        -  The latent tree structure is sampled by randomly removing an edge and
           replacing it. Since removing an edge splits the graph into two
           connected components, the only replacement locations that are
           feasible are those that re-connect the graph.
        
        The single-site Gibbs sampler uses dynamic programming to simultaneously
        sample the complete latent assignment vector for each row. A dynamic
        programming program is created each time the tree structure changes.
        This program is interpreted by various virtual machines for different
        purposes (training the model, sampling from the posterior, computing log
        probability of the posterior). The virtual machine for training is
        jit-compiled using numba.
        
        References
        ----------
        
        1. Nevin L. Zhang, Leonard K. M. Poon (2016) `Latent Tree
           Analysis <https://arxiv.org/pdf/1610.00085.pdf>`__
        2. Vikash Mansinghka, Patrick Shafto, Eric Jonas, Cap Petschulat, Max
           Gasner, Joshua B. Tenenbaum (2015) `CrossCat: A Fully Bayesian
           Nonparametric Method for Analyzing Heterogeneous, High Dimensional
           Data <https://arxiv.org/pdf/1512.01272>`__
        3. David B. Dunson, Chuanhua Xing (2012) `Nonparametric Bayes Modeling
           of Multivariate Categorical
           Data <https://dx.doi.org/10.1198%2Fjasa.2009.tm08439>`__
        
        License
        -------
        
        Copyright (c) 2017 Fritz Obermeyer. TreeCat is licensed under the
        `Apache 2.0 License </LICENSE>`__.
        
        .. |Build Status| image:: https://travis-ci.org/posterior/treecat.svg?branch=master
           :target: https://travis-ci.org/posterior/treecat
        .. |Latest Version| image:: https://badge.fury.io/py/pytreecat.svg
           :target: https://pypi.python.org/pypi/pytreecat
        .. |DOI| image:: https://zenodo.org/badge/93913649.svg
           :target: https://zenodo.org/badge/latestdoi/93913649
        
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX :: Linux
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.5
Classifier: Topic :: Scientific/Engineering :: Information Analysis
