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44 lines
1.9 KiB
44 lines
1.9 KiB
2 years ago
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#!/usr/bin/env python3
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import glob
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import os
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from pyctbn.legacy import JsonImporter
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from pyctbn.legacy import SamplePath
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from pyctbn.legacy import StructureConstraintBasedEstimator
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def structure_constraint_based_estimation_example():
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# <read the json files in ./data path>
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read_files = glob.glob(os.path.join('./', "*.json"))
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# <initialize a JsonImporter object for the first file>
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importer = JsonImporter(file_path=read_files[0], samples_label='samples',
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structure_label='dyn.str', variables_label='variables',
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time_key='Time', variables_key='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 object
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s1 = SamplePath(importer=importer)
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# build the trajectories
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s1.build_trajectories()
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# build the information about the net
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s1.build_structure()
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# construct a StructureEstimator object passing a correctly build SamplePath object
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# and the independence tests significance, if you have prior knowledge about
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# the net structure create a list of tuples
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# that contains them and pass it as known_edges parameter
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se1 = StructureConstraintBasedEstimator(sample_path=s1, exp_test_alfa=0.1, chi_test_alfa=0.1,
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known_edges=[], thumb_threshold=25)
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# call the algorithm to estimate the structure
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se1.estimate_structure()
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# obtain the adjacency matrix of the estimated structure
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print(se1.adjacency_matrix())
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# save the estimated structure to a json file
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# (remember to specify the path AND the .json extension)....
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se1.save_results('./results0.json')
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# ...or save it also in a graphical model fashion
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# (remember to specify the path AND the .png extension)
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se1.save_plot_estimated_structure_graph('./result0.png')
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structure_constraint_based_estimation_example()
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