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Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍 https://github.com/madlabunimib/PyCTBN
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README.rst

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 Example
****************************

.. code-block:: python

from PyCTBN import JsonImporter
from PyCTBN import SamplePath
from PyCTBN import StructureEstimator

def structure_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(read_files[0], 'samples', 'dyn.str', 'variables', 'Time', 'Name')
# import the data at index 0 of the outer json array
importer.import_data(0)
# construct a SamplePath Object passing a filled AbstractImporter
s1 = SamplePath(importer)
# build the trajectories
s1.build_trajectories()
# build the real structure
s1.build_structure()
# construct a StructureEstimator object
se1 = StructureEstimator(s1, 0.1, 0.1)
# call the ctpc algorithm
se1.ctpc_algorithm()
# the adjacency matrix of the estimated structure
print(se1.adjacency_matrix())
# save results to a json file
se1.save_results()