Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
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123 lines
4.4 KiB
123 lines
4.4 KiB
Examples
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=============
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Installation/Usage:
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*******************
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Implementing your own data importer
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***********************************
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.. code-block:: python
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"""This example demonstrates the implementation of a simple data importer the extends the class abstract importer to import data in csv format.
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The net in exam has three ternary nodes.
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"""
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from .abstract_importer import AbstractImporter
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class CSVImporter(AbstractImporter):
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def __init__(self, file_path):
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self._df_samples_list = None
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super(CSVImporter, self).__init__(file_path)
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def import_data(self):
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self.read_csv_file()
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self._sorter = self.build_sorter(self._df_samples_list[0])
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self.import_variables()
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self.import_structure()
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self.compute_row_delta_in_all_samples_frames(self._df_samples_list)
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def read_csv_file(self):
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df = pd.read_csv(self._file_path)
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df.drop(df.columns[[0]], axis=1, inplace=True)
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self._df_samples_list = [df]
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def import_variables(self):
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values_list = [3 for var in self._sorter]
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# initialize dict of lists
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data = {'Name':self._sorter, 'Value':values_list}
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# Create the pandas DataFrame
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self._df_variables = pd.DataFrame(data)
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def build_sorter(self, sample_frame: pd.DataFrame) -> typing.List:
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return list(sample_frame.columns)[1:]
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def import_structure(self):
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data = {'From':['X','Y','Z'], 'To':['Z','Z','Y']}
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self._df_structure = pd.DataFrame(data)
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def dataset_id(self) -> object:
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pass
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Parameters Estimation Example
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*****************************
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.. code-block:: python
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from PyCTBN.PyCTBN.json_importer import JsonImporter
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from PyCTBN.PyCTBN.sample_path import SamplePath
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from PyCTBN.PyCTBN.network_graph import NetworkGraph
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from PyCTBN.PyCTBN.parameters_estimator import ParametersEstimator
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def main():
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read_files = glob.glob(os.path.join('./data', "*.json")) #Take all json files in this dir
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#import data
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importer = JsonImporter(read_files[0], 'samples', 'dyn.str', 'variables', 'Time', 'Name')
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importer.import_data(0)
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#Create a SamplePath Obj passing an already filled AbstractImporter object
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s1 = SamplePath(importer)
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#Build The trajectries and the structural infos
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s1.build_trajectories()
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s1.build_structure()
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print(s1.structure.edges)
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print(s1.structure.nodes_values)
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#From The Structure Object build the Graph
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g = NetworkGraph(s1.structure)
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#Select a node you want to estimate the parameters
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node = g.nodes[2]
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print("Node", node)
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#Init the _graph specifically for THIS node
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g.fast_init(node)
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#Use SamplePath and Grpah to create a ParametersEstimator Object
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p1 = ParametersEstimator(s1.trajectories, g)
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#Init the peEst specifically for THIS node
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p1.fast_init(node)
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#Compute the parameters
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sofc1 = p1.compute_parameters_for_node(node)
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#The est CIMS are inside the resultant SetOfCIms Obj
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print(sofc1.actual_cims)
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Structure Estimation Example
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****************************
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.. code-block:: python
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from PyCTBN.PyCTBN.json_importer import JsonImporter
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from PyCTBN.PyCTBN.sample_path import SamplePath
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from PyCTBN.PyCTBN.structure_estimator import StructureEstimator
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def structure_estimation_example():
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# read the json files in ./data path
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read_files = glob.glob(os.path.join('./data', "*.json"))
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# initialize a JsonImporter object for the first file
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importer = JsonImporter(read_files[0], 'samples', 'dyn.str', 'variables', 'Time', 'Name')
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# import the data at index 0 of the outer json array
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importer.import_data(0)
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# construct a SamplePath Object passing a filled AbstractImporter
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s1 = SamplePath(importer)
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# build the trajectories
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s1.build_trajectories()
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# build the real structure
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s1.build_structure()
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# construct a StructureEstimator object
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se1 = StructureEstimator(s1, 0.1, 0.1)
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# call the ctpc algorithm
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se1.ctpc_algorithm()
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# the adjacency matrix of the estimated structure
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print(se1.adjacency_matrix())
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# save results to a json file
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se1.save_results()
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