Metadata-Version: 2.1
Name: lassobbn
Version: 1.0.0
Summary: Learning Bayesian Belief Networks with LASSO
Home-page: https://github.com/oneoffcoder/lassobbn
Author: Jee Vang
Author-email: g@oneoffcoder.com
License: UNKNOWN
Description: ![pybbn logo](https://lasso-bbn.readthedocs.io/en/latest/_images/logo-250x250.png)
        
        # LASSO BBN
        
        Learning Bayesian Belief Networks (BBNs) with LASSO. Example code is as below. 
        
        ```python
        from lassobbn.learn import learn_parameters, learn_structure, to_bbn, to_join_tree, posteriors_to_df
        
        # Step 1. Learn the structure
        df_path = './data/data-binary.csv'
        meta_path = './data/data-binary-complete.json'
        
        parents = learn_structure(df_path, meta_path, n_way=2, ignore_neg_gt=-0.01, ignore_pos_lt=0.05)
        
        # Step 2. Learn the parameters
        d, g, p = learn_parameters(df_path, parents)
        
        # Step 3. Get the BBN
        bbn = to_bbn(d, g, p)
        
        # Step 4. Get the Join Tree
        jt = to_join_tree(bbn)
        
        ```
        
        You can then use [Py-BBN](https://py-bbn.readthedocs.io/) to create a BBN and join tree (JT) instance and perform exact inference.
        
        # Installation
        
        ```bash
        pip install lassobbn
        ```
        
        # Links
        
        - [Code](https://github.com/oneoffcoder/lasso-bbn)
        - [Documentation](https://lasso-bbn.readthedocs.io/en/latest/index.html)
        - [PyPi](https://pypi.org/project/lassobbn/)
        
        # Additional APIs
        
        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)
        
        * [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/).
        
        # Citation
        
        ```
        @misc{alemi_2021,
        title={lasso-bbn},
        url={https://lasso-bbn.readthedocs.io/},
        author={F. Alemi, J. Vang},
        year={2021},
        month={Aug}}
        ```
        
        # Copyright Stuff
        
        ## Software
        
        ```
        Copyright 2021 Farrokh Alemi and 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 2021 Daytchia Vang
        
        # Sponsor, Love
        
        - [Patreon](https://www.patreon.com/vangj)
        - [GitHub](https://github.com/sponsors/vangj)
Keywords: bayesian belief network,bayesian network,causal learning,causal discovery,lasso regression,structure learning
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
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
