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PyCTBN |
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====== |
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A Continuous Time Bayesian Networks Library |
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Installation/Usage: |
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******************* |
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Download the release in .tar.gz or .whl format and simply use pip install to install it:: |
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$ pip install PyCTBN-1.0.tar.gz |
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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 and no prior net structure. |
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""" |
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from PyCTBN 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.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 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 import JsonImporter |
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from PyCTBN import SamplePath |
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from PyCTBN import NetworkGraph |
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from PyCTBN 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 import JsonImporter |
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from PyCTBN import SamplePath |
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from PyCTBNimport 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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