Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
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60 lines
1.6 KiB
60 lines
1.6 KiB
import pandas as pd
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import glob
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import os
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import typing
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from PyCTBN.classes.abstract_importer import AbstractImporter
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from PyCTBN.classes.sample_path import SamplePath
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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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def main():
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read_files = glob.glob(os.path.join('../../data', "*.csv"))
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print(read_files[0])
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csvimp = CSVImporter(read_files[0])
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csvimp.import_data()
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s1 = SamplePath(csvimp)
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s1.build_trajectories()
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s1.build_structure()
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print(s1.structure)
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print(s1.trajectories)
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if __name__ == "__main__":
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main()
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