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 *********************************** | This example demonstrates the implementation of a simple data importer the extends the class AbstractImporter | to import data in csv format. The net in exam has three ternary nodes and no prior net structure. | Suppose the trajectories that have to be inported have this structure: .. image:: docs-out/esempio_dataset.png :width: 600 :alt: An example trajectory to be imported. | In the read_csv_file method the data are imported in memory, put in a list and assigned to the _df_samples_list class | member, so that it contains all the trajectories to be processed. | In the import_variables method the dataframe containing the nodes labels and the cardinalities of the nodes | is assigned to the _df_variables class member. | The class member _sorter has to contain the nodes labels in the same order of the trajectory columns, | just override the build_sorter method to do that. | If your datasets names have particular id, you can keep it using the dataset_id method to assign the id to a new class member. | Finally the import_data method call all the previously implemented methods and calls the compute_row_delta_in_all_samples_frames | to process all the trajectories in _df_samples_list. | For more information about the class memebers and methods of AbstractImporter please refer to the documentation. .. code-block:: python import pandas as pd import typing from PyCTBN import AbstractImporter from PyCTBN import SamplePath 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 def main(): # create the importer object csvimp = CSVImporter('/dataset_example.csv') # call the wrapping method that wil import and process the data csvimp.import_data() # pass the AbstractImporter object to the SamplePath constructor s1 = SamplePath(csvimp) # SamplePath will contain the Trajecotry object... s1.build_trajectories() #...and the Structure object with all the process data s1.build_structure() 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 information. | 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_files = glob.glob(os.path.join('./data', "*.json")) # importer = JsonImporter(file_path=read_files[0], samples_label='samples', structure_label='dyn.str', variables_label='variables', time_key='Time', variables_key='Name') # 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')