PyCTBN
======

A Continuous Time Bayesian Networks Library

Installation/Usage
*******************
Download the release in .tar.gz or .whl format and simply use pip install to install it::

    $ pip install PyCTBN-1.0.tar.gz

Documentation
*************
Please refer to https://philipmartini.github.io/PyCTBN/ for the full project documentation.

Implementing your own data importer
***********************************
.. code-block:: python

    """This example demonstrates the implementation of a simple data importer the extends the class abstract importer to import data in csv format.
    The net in exam has three ternary nodes and no prior net structure.
    """

    from PyCTBN import AbstractImporter

    class CSVImporter(AbstractImporter):

        def __init__(self, file_path):
            self._df_samples_list = None
            super(CSVImporter, self).__init__(file_path)

        def import_data(self):
            self.read_csv_file()
            self._sorter = self.build_sorter(self._df_samples_list[0])
            self.import_variables()
            self.compute_row_delta_in_all_samples_frames(self._df_samples_list)

        def read_csv_file(self):
            df = pd.read_csv(self._file_path)
            df.drop(df.columns[[0]], axis=1, inplace=True)
            self._df_samples_list = [df]

        def import_variables(self):
            values_list = [3 for var in self._sorter]
            # initialize dict of lists
            data = {'Name':self._sorter, 'Value':values_list}
            # Create the pandas DataFrame
            self._df_variables = pd.DataFrame(data)

        def build_sorter(self, sample_frame: pd.DataFrame) -> typing.List:
            return list(sample_frame.columns)[1:]

        def dataset_id(self) -> object:
            pass

Parameters Estimation Example
*****************************

.. code-block:: python

    from PyCTBN import JsonImporter
    from PyCTBN import SamplePath
    from PyCTBN import NetworkGraph
    from PyCTBN import ParametersEstimator


    def main():
        read_files = glob.glob(os.path.join('./data', "*.json")) #Take all json files in this dir
        #import data
        importer = JsonImporter(read_files[0], 'samples', 'dyn.str', 'variables', 'Time', 'Name')
        importer.import_data(0)
        #Create a SamplePath Obj passing an already filled AbstractImporter object
        s1 = SamplePath(importer)
        #Build The trajectries and the structural infos
        s1.build_trajectories()
        s1.build_structure()
        print(s1.structure.edges)
        print(s1.structure.nodes_values)
        #From The Structure Object build the Graph
        g = NetworkGraph(s1.structure)
        #Select a node you want to estimate the parameters
        node = g.nodes[2]
        print("Node", node)
        #Init the _graph specifically for THIS node
        g.fast_init(node)
        #Use SamplePath and Grpah to create a ParametersEstimator Object
        p1 = ParametersEstimator(s1.trajectories, g)
        #Init the peEst specifically for THIS node
        p1.fast_init(node)
        #Compute the parameters
        sofc1 = p1.compute_parameters_for_node(node)
        #The est CIMS are inside the resultant SetOfCIms Obj
        print(sofc1.actual_cims)

Structure Estimation Examples
****************************
This example shows how to estimate the structure given a series of trajectories using a constraint based approach.
The first three instructions import all the necessary data (trajectories, nodes cardinalities, nodes labels),
and are contextual to the dataset that is been used, in the code comments are marked as optional <>.
If your data has a different structure or format you should implement your own importer (see Implementing your own importer example).
The other instructions are not optional and should follow the same order.
A SamplePath object is been created, passing an AbstractImporter object that contains the  correct class members filled
with the data that are necessary to estimate the structure.
Next the build_trajectories  and build_structure methods are called to instantiate the objects that will contain
the processed trajectories and all the net infos.
Then an estimator object is created, in this case a constraint based estimator, it necessary to pass a SamplePath object
where build_trajectories and build_structure methods have already been called.
If you have prior knowledge about the net structure pass it to the constructor with the known_edges parameter.
The other three parameters are contextual to the StructureConstraintBasedEstimator, see the documentation for more details.
To estimate the structure simply call the estimate_structure method.
You can obtain the estimated structure as a boolean adjacency matrix with the method adjacency_matrix, or save it as a json file
that contains all the nodes labels, and obviously the estimated edges.
You can also save a graphical model representation of the estimated structure with the save_plot_estimated_structure_graph.

.. code-block:: python

    import glob
    import os

    from PyCTBN import JsonImporter
    from PyCTBN import SamplePath
    from PyCTBN import StructureConstraintBasedEstimator


    def structure_constraint_based_estimation_example():
        # <read the json files in ./data path>
        read_files = glob.glob(os.path.join('./data', "*.json"))
        # <initialize a JsonImporter object for the first file>
        importer = JsonImporter(file_path=read_files[0], samples_label='samples',
                                structure_label='dyn.str', variables_label='variables',
                                time_key='Time', variables_key='Name')
        # <import the data at index 0 of the outer json array>
        importer.import_data(0)
        # construct a SamplePath Object passing a filled AbstractImporter object
        s1 = SamplePath(importer=importer)
        # build the trajectories
        s1.build_trajectories()
        # build the information about the net
        s1.build_structure()
        # construct a StructureEstimator object passing a correctly build SamplePath object and the
        # independence tests significance, if you have prior knowledge about the net structure create a list of tuples
        # that contains them and pass it as known_edges parameter
        se1 = StructureConstraintBasedEstimator(sample_path=s1, exp_test_alfa=0.1, chi_test_alfa=0.1,
                                                known_edges=[], thumb_threshold=25)
        # call the algorithm to estimate the structure
        se1.estimate_structure()
        # obtain the adjacency matrix of the estimated structure
        print(se1.adjacency_matrix())
        # save the estimated structure  to a json file (remember to specify the path AND the .json extension)....
        se1.save_results('./results0.json')
        # ...or save it also in a graphical model fashion (remember to specify the path AND the .png extension)
        se1.save_plot_estimated_structure_graph('./result0.png')