Metadata-Version: 2.1
Name: bnlearn
Version: 0.3.14
Summary: Python package for learning the graphical structure of Bayesian networks, parameter learning, inference and sampling methods.
Home-page: https://github.com/erdogant/bnlearn
Author: Erdogan Taskesen
Author-email: erdogant@gmail.com
License: UNKNOWN
Download-URL: https://github.com/erdogant/bnlearn/archive/0.3.14.tar.gz
Description: # bnlearn - Graphical structure of Bayesian networks
        
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            Star it if you like it!
        
        ``bnlearn`` is Python package for learning the graphical structure of Bayesian networks, parameter learning, inference and sampling methods. This work is inspired by the R package (bnlearn.com) that has been very usefull to me for many years. Although there are very good Python packages for probabilistic graphical models, it still can remain difficult (and somethimes unnecessarily) to (re)build certain pipelines. Bnlearn for python (this package) is build on the <a href="https://github.com/pgmpy/pgmpy">pgmpy</a> package and contains the most-wanted pipelines. Navigate to [API documentations](https://erdogant.github.io/bnlearn/) for more detailed information.
        
        ### Method overview
        Learning a Bayesian network can be split into two problems which are both implemented in this package:
        * Structure learning: Given a set of data samples, estimate a DAG that captures the dependencies between the variables.
        * Parameter learning: Given a set of data samples and a DAG that captures the dependencies between the variables, estimate the (conditional) probability distributions of the individual variables.
        
        #### The following functions are available after importing bnlearn.
        
        ```python
        # Import library
        import bnlearn as bn
        
        # Structure learning
        bn.structure_learning.fit()
        
        # Parameter learning
        bn.parameter_learning.fit()
        
        # Inference
        bn.inference.fit()
        
        # Based on a DAG, you can sample the number of samples you want.
        bn.sampling()
        
        # Load well known examples to play arround with or load your own .bif file.
        bn.import_DAG()
        
        # Load simple dataframe of sprinkler dataset.
        bn.import_example()
        
        # Compare 2 graphs
        bn.compare_networks()
        
        # Plot graph
        bn.plot()
        
        # To make the directed grapyh undirected
        bn.to_undirected()
        
        # Convert to one-hot datamatrix
        bn.df2onehot()
         
        # See below for the exact working of the functions
        ```
        
        #### The following methods are also included:
        * inference
        * sampling
        * comparing two networks
        * loading bif files
        * conversion of directed to undirected graphs
        
        ## Conda installation
        It is advisable to create a new environment. 
        ```bash
        conda create -n env_bnlearn python=3.8
        conda activate env_bnlearn
        ```
        
        ## Conda installation
        ```python
        conda install -c ankurankan pgmpy
        pip install -U bnlearn # -U is to force download latest version
        ```
        
        ## Pip installation
        ```python
        pip install -U pgmpy=>0.1.13
        pip install -U bnlearn # -U is to force to overwrite current version
        ```
        
        * Alternatively, install bnlearn from the GitHub source:
        ```bash
        git clone https://github.com/erdogant/bnlearn.git
        cd bnlearn
        pip install -U .
        ```  
        
        ## Import bnlearn package
        ```python
        import bnlearn as bn
        ```
        
        ## Example: Structure Learning
        ```python
        # Example dataframe sprinkler_data.csv can be loaded with: 
        df = bn.import_example()
        # df = pd.read_csv('sprinkler_data.csv')
        model = bn.structure_learning.fit(df)
        G = bn.plot(model)
        ```
        
        #### df looks like this
        ```
             Cloudy  Sprinkler  Rain  Wet_Grass
        0         0          1     0          1
        1         1          1     1          1
        2         1          0     1          1
        3         0          0     1          1
        4         1          0     1          1
        ..      ...        ...   ...        ...
        995       0          0     0          0
        996       1          0     0          0
        997       0          0     1          0
        998       1          1     0          1
        999       1          0     1          1
        ```
        
        <p align="center">
          <img src="https://github.com/erdogant/bnlearn/blob/master/docs/figs/fig_sprinkler_sl.png" width="600" />
        </p>
        
        * Choosing various methodtypes and scoringtypes:
        ```python
        model_hc_bic  = bn.structure_learning.fit(df, methodtype='hc', scoretype='bic')
        model_hc_k2   = bn.structure_learning.fit(df, methodtype='hc', scoretype='k2')
        model_hc_bdeu = bn.structure_learning.fit(df, methodtype='hc', scoretype='bdeu')
        model_ex_bic  = bn.structure_learning.fit(df, methodtype='ex', scoretype='bic')
        model_ex_k2   = bn.structure_learning.fit(df, methodtype='ex', scoretype='k2')
        model_ex_bdeu = bn.structure_learning.fit(df, methodtype='ex', scoretype='bdeu')
        model_cl      = bn.structure_learning.fit(df, methodtype='cl', root_node='Wet_Grass')
        ```
        
        ## Example: Parameter Learning
        ```python
        # Import dataframe
        df = bn.import_example()
        # As an example we set the CPD at False which returns an "empty" DAG
        model = bn.import_DAG('sprinkler', CPD=False)
        # Now we learn the parameters of the DAG using the df
        model_update = bn.parameter_learning.fit(model, df)
        # Make plot
        G = bn.plot(model_update)
        ```
        
        ## Example: Inference
        ```python
        model = bn.import_DAG('sprinkler')
        q_1 = bn.inference.fit(model, variables=['Rain'], evidence={'Cloudy':1,'Sprinkler':0, 'Wet_Grass':1})
        q_2 = bn.inference.fit(model, variables=['Rain'], evidence={'Cloudy':1})
        ```
        
        ## Example: Sampling to create dataframe
        ```python
        model = bn.import_DAG('sprinkler')
        df = bn.sampling(model, n=1000)
        ```
        
        * Output of the model:
        ```
        [bnlearn] Model correct: True
        CPD of Cloudy:
        +-----------+-----+
        | Cloudy(0) | 0.5 |
        +-----------+-----+
        | Cloudy(1) | 0.5 |
        +-----------+-----+
        CPD of Sprinkler:
        +--------------+-----------+-----------+
        | Cloudy       | Cloudy(0) | Cloudy(1) |
        +--------------+-----------+-----------+
        | Sprinkler(0) | 0.5       | 0.9       |
        +--------------+-----------+-----------+
        | Sprinkler(1) | 0.5       | 0.1       |
        +--------------+-----------+-----------+
        CPD of Rain:
        +---------+-----------+-----------+
        | Cloudy  | Cloudy(0) | Cloudy(1) |
        +---------+-----------+-----------+
        | Rain(0) | 0.8       | 0.2       |
        +---------+-----------+-----------+
        | Rain(1) | 0.2       | 0.8       |
        +---------+-----------+-----------+
        CPD of Wet_Grass:
        +--------------+--------------+--------------+--------------+--------------+
        | Sprinkler    | Sprinkler(0) | Sprinkler(0) | Sprinkler(1) | Sprinkler(1) |
        +--------------+--------------+--------------+--------------+--------------+
        | Rain         | Rain(0)      | Rain(1)      | Rain(0)      | Rain(1)      |
        +--------------+--------------+--------------+--------------+--------------+
        | Wet_Grass(0) | 1.0          | 0.1          | 0.1          | 0.01         |
        +--------------+--------------+--------------+--------------+--------------+
        | Wet_Grass(1) | 0.0          | 0.9          | 0.9          | 0.99         |
        +--------------+--------------+--------------+--------------+--------------+
        [bnlearn] Nodes: ['Cloudy', 'Sprinkler', 'Rain', 'Wet_Grass']
        [bnlearn] Edges: [('Cloudy', 'Sprinkler'), ('Cloudy', 'Rain'), ('Sprinkler', 'Wet_Grass'), ('Rain', 'Wet_Grass')]
        [bnlearn] Independencies:
        (Cloudy _|_ Wet_Grass | Rain, Sprinkler)
        (Sprinkler _|_ Rain | Cloudy)
        (Rain _|_ Sprinkler | Cloudy)
        (Wet_Grass _|_ Cloudy | Rain, Sprinkler)
        ```
        
        ## Example: Loading DAG from bif files
        ```python
        bif_file= 'sprinkler'
        bif_file= 'alarm'
        bif_file= 'andes'
        bif_file= 'asia'
        bif_file= 'pathfinder'
        bif_file= 'sachs'
        bif_file= 'miserables'
        bif_file= 'filepath/to/model.bif'
        
        # Loading example dataset
        model = bn.import_DAG(bif_file)
        ```
        
        ## Example: Comparing networks
        ```python
        # Load asia DAG
        model = bn.import_DAG('asia')
        # plot ground truth
        G = bn.plot(model)
        # Sampling
        df = bn.sampling(model, n=10000)
        # Structure learning of sampled dataset
        model_sl = bn.structure_learning.fit(df, methodtype='hc', scoretype='bic')
        # Plot based on structure learning of sampled data
        bn.plot(model_sl, pos=G['pos'])
        # Compare networks and make plot
        bn.compare_networks(model, model_sl, pos=G['pos'])
        ```
        
        #### Graph of ground truth
        <p align="center">
          <img src="https://github.com/erdogant/bnlearn/blob/master/docs/figs/fig2a_asia_groundtruth.png" width="600" />
        </p>
        
        #### Graph based on Structure learning
        <p align="center">
          <img src="https://github.com/erdogant/bnlearn/blob/master/docs/figs/fig2b_asia_structurelearning.png" width="600" />
        </p>
        
        #### Graph comparison ground truth vs. structure learning
        <p align="center">
          <img src="https://github.com/erdogant/bnlearn/blob/master/docs/figs/fig2c_asia_comparion.png" width="600" />
          <img src="https://github.com/erdogant/bnlearn/blob/master/docs/figs/fig2d_confmatrix.png" width="400" />
        </p>
        
        
        ## Citation
        Please cite bnlearn in your publications if this is useful for your research. Here is an example BibTeX entry:
        ```BibTeX
        @misc{erdogant2019bnlearn,
          title={bnlearn},
          author={Erdogan Taskesen},
          year={2019},
          howpublished={\url{https://github.com/erdogant/bnlearn}},
        }
        ```
        
        ## References
        * https://erdogant.github.io/bnlearn/
        * http://pgmpy.org
        * https://programtalk.com/python-examples/pgmpy.factors.discrete.TabularCPD/
        * http://www.bnlearn.com/
        * http://www.bnlearn.com/bnrepository/
        
        
        ### Maintainer
        * Erdogan Taskesen, github: [erdogant](https://github.com/erdogant)
        * Contributions are welcome.
        * If you wish to buy me a <a href="https://erdogant.github.io/donate/?currency=USD&amount=5">Coffee</a> for this work, it is very appreciated :)
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3
Description-Content-Type: text/markdown
