Metadata-Version: 2.1
Name: pybbn
Version: 3.1.1
Summary: Learning and Inference in Bayesian Belief Networks
Home-page: https://github.com/vangj/py-bbn
Author: Jee Vang
Author-email: vangjee@gmail.com
License: UNKNOWN
Description: ![pybbn logo](https://py-bbn.readthedocs.io/_images/logo-250.png)
        
        # PyBBN
        
        PyBBN is Python library for Bayesian Belief Networks (BBNs) exact inference using the [junction tree algorithm](https://en.wikipedia.org/wiki/Junction_tree_algorithm) or Probability Propagation in Trees of Clusters (PPTC). The implementation is taken directly from [C. Huang and A. Darwiche, "Inference in
        Belief Networks: A Procedural Guide," in International Journal of Approximate Reasoning, vol. 15,
        pp. 225--263, 1999](http://pages.cs.wisc.edu/~dpage/ijar95.pdf). In this API, PPTC is applied to BBNs with all discrete variables. When dealing with a BBN with all Gaussian variables (or a Gaussian Belief Network, GBN), exact inference is conducted through an incremental algorithm manipulating the means and covariance matrix. Additionally, there is the ability to generate singly- and multi-connected graphs, which is taken from [JS Ide and FG Cozman, 
        "Random Generation of Bayesian Network," in Advances in Artificial Intelligence, Lecture Notes in Computer Science, vol 2507](https://pdfs.semanticscholar.org/5273/2fb57129443592024b0e7e46c2a1ec36639c.pdf). There is also the option to generate sample data from your BBN. This synthetic data may be summarized to generate your posterior marginal probabilities and work as a form of approximate inference. Lastly, we have added Pearl's `do-operator` for causal inference.
        
        # Power Up, Next Level
        
        turing_bbn                                                                            |  pyspark-bbn
        :------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------:
        ![turing_bbn logo](https://turing-bbn.oneoffcoder.com/_images/turing-bbn-150x150.png) |![pyspark-bbn logo](https://pyspark-bbn.oneoffcoder.com/_images/pyspark-bbn-150x150.png)
        
        If you like py-bbn, please inquire about our next-generation products below! info@oneoffcoder.com
        
        * [turing_bbn](https://turing-bbn.oneoffcoder.com/) is a C++17 implementation of py-bbn; take your causal and probabilistic inferences to the next computing level!
        * [pyspark-bbn](https://pyspark-bbn.oneoffcoder.com/) is a is a scalable, massively parallel processing MPP framework for learning structures and parameters of Bayesian Belief Networks BBNs using [Apache Spark](https://spark.apache.org/).
        
        # Exact Inference, Discrete Variables
        
        Below is an example code to create a Bayesian Belief Network, transform it into a join tree, and then set observation evidence. The last line prints the marginal probabilities for each node.
        
        ```python
        from pybbn.graph.dag import Bbn
        from pybbn.graph.edge import Edge, EdgeType
        from pybbn.graph.jointree import EvidenceBuilder
        from pybbn.graph.node import BbnNode
        from pybbn.graph.variable import Variable
        from pybbn.pptc.inferencecontroller import InferenceController
        
        # create the nodes
        a = BbnNode(Variable(0, 'a', ['on', 'off']), [0.5, 0.5])
        b = BbnNode(Variable(1, 'b', ['on', 'off']), [0.5, 0.5, 0.4, 0.6])
        c = BbnNode(Variable(2, 'c', ['on', 'off']), [0.7, 0.3, 0.2, 0.8])
        d = BbnNode(Variable(3, 'd', ['on', 'off']), [0.9, 0.1, 0.5, 0.5])
        e = BbnNode(Variable(4, 'e', ['on', 'off']), [0.3, 0.7, 0.6, 0.4])
        f = BbnNode(Variable(5, 'f', ['on', 'off']), [0.01, 0.99, 0.01, 0.99, 0.01, 0.99, 0.99, 0.01])
        g = BbnNode(Variable(6, 'g', ['on', 'off']), [0.8, 0.2, 0.1, 0.9])
        h = BbnNode(Variable(7, 'h', ['on', 'off']), [0.05, 0.95, 0.95, 0.05, 0.95, 0.05, 0.95, 0.05])
        
        # create the network structure
        bbn = Bbn() \
            .add_node(a) \
            .add_node(b) \
            .add_node(c) \
            .add_node(d) \
            .add_node(e) \
            .add_node(f) \
            .add_node(g) \
            .add_node(h) \
            .add_edge(Edge(a, b, EdgeType.DIRECTED)) \
            .add_edge(Edge(a, c, EdgeType.DIRECTED)) \
            .add_edge(Edge(b, d, EdgeType.DIRECTED)) \
            .add_edge(Edge(c, e, EdgeType.DIRECTED)) \
            .add_edge(Edge(d, f, EdgeType.DIRECTED)) \
            .add_edge(Edge(e, f, EdgeType.DIRECTED)) \
            .add_edge(Edge(c, g, EdgeType.DIRECTED)) \
            .add_edge(Edge(e, h, EdgeType.DIRECTED)) \
            .add_edge(Edge(g, h, EdgeType.DIRECTED))
        
        # convert the BBN to a join tree
        join_tree = InferenceController.apply(bbn)
        
        # insert an observation evidence
        ev = EvidenceBuilder() \
            .with_node(join_tree.get_bbn_node_by_name('a')) \
            .with_evidence('on', 1.0) \
            .build()
        join_tree.set_observation(ev)
        
        # print the marginal probabilities
        for node in join_tree.get_bbn_nodes():
            potential = join_tree.get_bbn_potential(node)
            print(node)
            print(potential)
        ```
        
        # Exact Inference, Gaussian Variables
        
        The example belows shows how to perform inference on multivariate Gaussian variables.
        
        ```python
        import numpy as np
        
        from pybbn.gaussian.inference import GaussianInference
        
        
        def get_cowell_data():
            """
            Gets Cowell data.
        
            :return: Data and headers.
            """
            n = 10000
            Y = np.random.normal(0, 1, n)
            X = np.random.normal(Y, 1, n)
            Z = np.random.normal(X, 1, n)
        
            D = np.vstack([Y, X, Z]).T
            return D, ['Y', 'X', 'Z']
        
        
        # assume we have data and headers (variable names per column)
        # X is the data (rows are observations, columns are variables)
        # H is just a list of variable names
        X, H = get_cowell_data()
        
        # then we can compute the means and covariance matrix easily
        M = X.mean(axis=0)
        E = np.cov(X.T)
        
        # the means and covariance matrix are all we need for gaussian inference
        # notice how we keep `g` around?
        # we'll use `g` over and over to do inference with evidence/observations
        g = GaussianInference(H, M, E)
        # {'Y': (0.00967, 0.98414), 'X': (0.01836, 2.02482), 'Z': (0.02373, 3.00646)}
        print(g.P)
        
        # we can make a single observation with do_inference()
        g1 = g.do_inference('X', 1.5)
        # {'X': (1.5, 0), 'Y': (0.76331, 0.49519), 'Z': (1.51893, 1.00406)}
        print(g1.P)
        
        # we can make multiple observations with get_inference()
        g2 = g.get_inference([('Z', 1.5), ('X', 2.0)])
        # {'Z': (1.5, 0), 'X': (2.0, 0), 'Y': (1.97926, 0.49509)}
        print(g2.P)
        ```
        
        # Building
        
        To build, you will need 3.7. Managing environments through [Anaconda](https://www.anaconda.com/download/#linux) is highly recommended to be able to build this project (though not absolutely required if you know what you are doing). Assuming you have installed Anaconda, you may create an environment as follows (make sure you `cd` into the root of this project's location).
        
        To create the environment, use the following commands.
        
        ```bash
        conda env create -f environment.yml
        ```
        
        If you want to use the environments with Jupyter, install the kernel.
        
        ```bash
        conda activate pybbn37
        python -m ipykernel install --user --name pybbn37 --display-name "pybbn37"
        ```
        
        Then you may build the project as follows. (Note that in Python 3.6 you will get some warnings).
        
        ```bash
        make build
        ```
        
        To build the documents, go into the docs sub-directory and type in the following.
        
        ```bash
        make html
        ```
        
        # Installing
        
        ## From PyPi
        Use pip to install the package as it has been published to [PyPi](https://pypi.python.org/pypi/pybbn).
        
        ```bash
        pip install pybbn
        ```
        
        ## From Source
        
        If you check out the source do the following.
        
        ```bash
        pip list | grep pybbn
        pip uninstall pybbn
        python setup.py install
        pip list | grep pybbn
        ```
        
        ## GraphViz issue
        
        Make sure you [install GraphViz](https://graphviz.gitlab.io/download/) on your system.
        
        * CentOS: `yum install graphviz*`
        * Ubuntu: `sudo apt-get install graphviz libgraphviz-dev`
        * Mac OSX: `brew install graphviz` and when you install pygraphviz `pip install pygraphviz --install-option="--include-path=/usr/local/lib/graphviz/" --install-option="--library-path=/usr/local/lib/graphviz/"`
        * Windows: use the [msi installer](https://graphviz.gitlab.io/_pages/Download/windows/graphviz-2.38.msi)
          * For Anaconda + Windows, install pygraphviz from this [channel](https://anaconda.org/alubbock/pygraphviz) `conda install -c alubbock pygraphviz`
        
        ## testpypi issue
        
        You should **NOT** be doing this operation, but if you do want to install from `testpypi`, then add the `--extra-index-url` as follows.
        
        ```bash
        pip install -i https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple/ pybbn
        ```
        
        # Other Python Bayesian Belief Network Inference Libraries
        
        Here is a list of other Python libraries for inference in Bayesian Belief Networks. 
        
        | Library | Algorithm | Algorithm Type | License |
        | -------:| ---------:| -------------: | -------:|
        | [BayesPy](https://github.com/bayespy/bayespy)| variational message passing | approximate | MIT |
        | [pomegranate](https://github.com/jmschrei/pomegranate) | loopy belief | approximate | MIT |
        | [pgmpy](https://github.com/pgmpy/pgmpy) | multiple | approximate/exact | MIT |
        | [libpgm](https://github.com/CyberPoint/libpgm) | likelihood sampling | approximate | Proprietary |
        | [bayesnetinference](https://github.com/sonph/bayesnetinference) | variable elimination | exact | None |
        
        I found other [packages](https://pypi.python.org/pypi?%3Aaction=search&term=bayesian+network&submit=search) in PyPI too.
        
        # Java
        
        But I am coming from the Java mothership and I want to use Bayesian Belief Networks in Java. How do I perform probabilistic inference in Java?
        
        This Python code base is a port of the [original Java code](https://github.com/vangj/jbayes).
        
        # Help
        
        - [Documentation](https://py-bbn.readthedocs.io/)
        - [Chat Room](https://gitter.im/dataflava/py-bbn)
        
        # Citation
        
        ```
        @misc{vang_2017, 
        title={PyBBN}, 
        url={https://github.com/vangj/py-bbn/}, 
        journal={GitHub},
        author={Vang, Jee}, 
        year={2017}, 
        month={Jan}}
        ```
        
        # Copyright Stuff
        
        ## Software
        
        ```
        Copyright 2017 Jee Vang
        
        Licensed under the Apache License, Version 2.0 (the "License");
        you may not use this file except in compliance with the License.
        You may obtain a copy of the License at
        
            http://www.apache.org/licenses/LICENSE-2.0
        
        Unless required by applicable law or agreed to in writing, software
        distributed under the License is distributed on an "AS IS" BASIS,
        WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
        See the License for the specific language governing permissions and
        limitations under the License.
        ```
        
        ## Art Copyright
        
        Copyright 2020 Daytchia Vang
        
        # Sponsor, Love
        
        - [Patreon](https://www.patreon.com/vangj)
        - [GitHub](https://github.com/sponsors/vangj)
Keywords: bayesian belief network exact approximate inference junction tree algorithm pptc dag gibbs sampling multivariate conditional gaussian linear causal causality structure parameter causal causality
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Development Status :: 5 - Production/Stable
Description-Content-Type: text/markdown
