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import unittest |
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import os |
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import glob |
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import numpy as np |
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import pandas as pd |
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import timeit |
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|
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from ..PyCTBN.sample_path import SamplePath |
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from ..PyCTBN.structure_estimator import StructureEstimator |
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from ..PyCTBN.json_importer import JsonImporter |
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from PyCTBN.tests.original_ctpc_algorithm import OriginalCTPCAlgorithm |
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|
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|
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class PerformanceComparisons(unittest.TestCase): |
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|
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@classmethod |
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def setUpClass(cls): |
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cls.read_files = glob.glob(os.path.join('./data', "*.json")) |
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cls.datasets_numb = None |
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cls.s1 = None |
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cls.importer = None |
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cls.original_algo = None |
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cls.original_times = [] |
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cls.optimized_times = [] |
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cls.results = [] |
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|
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def test_time_comparisons(self): |
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for file_path in self.read_files: |
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self.importer = JsonImporter(file_path, 'samples', 'dyn.str', 'variables', 'Time', 'Name') |
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self.original_algo = OriginalCTPCAlgorithm(file_path, 'samples', 'dyn.str', 'variables', |
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'Time', 'Name', self.importer._raw_data) |
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self.datasets_numb = self.original_algo.datasets_numb() |
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print("Testing on file: ", file_path, " with ", self.datasets_numb, " Datasets") |
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for indx in range(self.datasets_numb): |
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print("Running Test: ", indx) |
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self.aux_build_importer(indx) |
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self.aux_build_original_algo(indx) |
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se1 = StructureEstimator(self.s1, 0.1, 0.1) |
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opt_time = timeit.timeit(se1.ctpc_algorithm, number=1) |
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original_time = timeit.timeit(self.original_algo.cb_structure_algo, number=1) |
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opt_res = se1.adjacency_matrix() |
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original_res = self.original_algo.matrix |
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self.original_times.append(original_time) |
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self.optimized_times.append(opt_time) |
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self.results.append((original_res, opt_res)) |
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self.assertTrue(np.array_equal(original_res, opt_res)) |
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self.save_datas(self.original_times, self.optimized_times) |
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self.original_times[:] = [] |
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self.optimized_times[:] = [] |
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|
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def aux_build_importer(self, indx): |
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self.importer.import_data(indx) |
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self.s1 = SamplePath(self.importer) |
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self.s1.build_trajectories() |
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self.s1.build_structure() |
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|
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def aux_build_original_algo(self, indx): |
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self.original_algo.import_data(indx) |
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self.original_algo.prepare_trajectories(self.original_algo.df_samples_list, self.original_algo.variables) |
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|
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def save_datas(self, original_list, opt_list): |
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df_results = pd.DataFrame({'orginal_execution_time': original_list, 'optimized_execution_time': opt_list}) |
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name = self.importer.file_path.rsplit('/', 1)[-1] |
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name = name.split('.', 1)[0] |
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name = 'execution_times_' + name + '.csv' |
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df_results.to_csv(name, index=False) |
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|
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|
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if __name__ == '__main__': |
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unittest.main() |
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PyCTBN package |
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============== |
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|
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Submodules |
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---------- |
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|
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PyCTBN.abstract\_importer module |
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-------------------------------- |
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|
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.. automodule:: PyCTBN.abstract_importer |
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:members: |
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:undoc-members: |
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:show-inheritance: |
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|
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PyCTBN.cache module |
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------------------- |
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|
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.. automodule:: PyCTBN.cache |
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:members: |
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:undoc-members: |
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:show-inheritance: |
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|
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PyCTBN.conditional\_intensity\_matrix module |
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-------------------------------------------- |
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|
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.. automodule:: PyCTBN.conditional_intensity_matrix |
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:members: |
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:undoc-members: |
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:show-inheritance: |
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|
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PyCTBN.json\_importer module |
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---------------------------- |
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|
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.. automodule:: PyCTBN.json_importer |
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:members: |
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:undoc-members: |
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:show-inheritance: |
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|
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PyCTBN.network\_graph module |
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---------------------------- |
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|
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.. automodule:: PyCTBN.network_graph |
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:members: |
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:undoc-members: |
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:show-inheritance: |
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|
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PyCTBN.parameters\_estimator module |
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----------------------------------- |
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|
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.. automodule:: PyCTBN.parameters_estimator |
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:members: |
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:undoc-members: |
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:show-inheritance: |
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|
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PyCTBN.sample\_path module |
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-------------------------- |
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.. automodule:: PyCTBN.sample_path |
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:members: |
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:undoc-members: |
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:show-inheritance: |
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|
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PyCTBN.set\_of\_cims module |
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--------------------------- |
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.. automodule:: PyCTBN.set_of_cims |
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:members: |
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:undoc-members: |
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:show-inheritance: |
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|
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PyCTBN.structure module |
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----------------------- |
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.. automodule:: PyCTBN.structure |
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:members: |
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:undoc-members: |
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:show-inheritance: |
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|
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PyCTBN.structure\_estimator module |
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---------------------------------- |
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.. automodule:: PyCTBN.structure_estimator |
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:members: |
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:undoc-members: |
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:show-inheritance: |
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|
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PyCTBN.trajectory module |
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------------------------ |
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.. automodule:: PyCTBN.trajectory |
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:members: |
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:undoc-members: |
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:show-inheritance: |
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|
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Module contents |
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--------------- |
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.. automodule:: PyCTBN |
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:members: |
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:undoc-members: |
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:show-inheritance: |
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# Sphinx build info version 1 |
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# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done. |
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config: 0c64bed84aa4ce76aedaca18241d82e7 |
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config: 5ff9615d2690725079f899e2857b650e |
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tags: 645f666f9bcd5a90fca523b33c5a78b7 |
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|
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PyCTBN package |
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============== |
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|
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Submodules |
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---------- |
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|
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PyCTBN.abstract\_importer module |
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-------------------------------- |
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.. automodule:: PyCTBN.abstract_importer |
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:members: |
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:undoc-members: |
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:show-inheritance: |
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|
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PyCTBN.cache module |
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------------------- |
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.. automodule:: PyCTBN.cache |
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:members: |
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:undoc-members: |
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:show-inheritance: |
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|
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PyCTBN.conditional\_intensity\_matrix module |
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-------------------------------------------- |
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.. automodule:: PyCTBN.conditional_intensity_matrix |
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:members: |
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:undoc-members: |
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:show-inheritance: |
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|
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PyCTBN.json\_importer module |
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---------------------------- |
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.. automodule:: PyCTBN.json_importer |
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:members: |
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:undoc-members: |
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:show-inheritance: |
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|
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PyCTBN.network\_graph module |
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---------------------------- |
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.. automodule:: PyCTBN.network_graph |
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:members: |
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:undoc-members: |
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:show-inheritance: |
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|
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PyCTBN.parameters\_estimator module |
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----------------------------------- |
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.. automodule:: PyCTBN.parameters_estimator |
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:members: |
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:undoc-members: |
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:show-inheritance: |
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|
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PyCTBN.sample\_path module |
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-------------------------- |
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.. automodule:: PyCTBN.sample_path |
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:members: |
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:undoc-members: |
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:show-inheritance: |
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|
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PyCTBN.set\_of\_cims module |
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--------------------------- |
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|
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.. automodule:: PyCTBN.set_of_cims |
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:members: |
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:undoc-members: |
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:show-inheritance: |
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|
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PyCTBN.structure module |
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----------------------- |
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|
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.. automodule:: PyCTBN.structure |
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:members: |
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:undoc-members: |
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:show-inheritance: |
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|
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PyCTBN.structure\_estimator module |
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---------------------------------- |
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|
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.. automodule:: PyCTBN.structure_estimator |
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:members: |
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:undoc-members: |
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:show-inheritance: |
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|
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PyCTBN.trajectory module |
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------------------------ |
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.. automodule:: PyCTBN.trajectory |
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:members: |
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:undoc-members: |
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:show-inheritance: |
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|
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Module contents |
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--------------- |
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|
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.. automodule:: PyCTBN |
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:members: |
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:undoc-members: |
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:show-inheritance: |
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Examples |
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============= |
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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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|
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Structure Estimation Example |
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**************************** |
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|
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.. code-block:: python |
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|
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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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|
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def structure_estimation_example(): |
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|
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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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|
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PyCTBN |
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====== |
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|
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.. toctree:: |
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:maxdepth: 4 |
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|
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PyCTBN |
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abstract\_importer module |
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========================= |
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|
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.. automodule:: abstract_importer |
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:members: |
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:undoc-members: |
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:show-inheritance: |
@ -1,7 +0,0 @@ |
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cache module |
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============ |
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|
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.. automodule:: cache |
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:members: |
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:undoc-members: |
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:show-inheritance: |
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conditional\_intensity\_matrix module |
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===================================== |
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|
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.. automodule:: conditional_intensity_matrix |
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:members: |
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:undoc-members: |
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:show-inheritance: |
@ -1,7 +0,0 @@ |
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json\_importer module |
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===================== |
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|
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.. automodule:: json_importer |
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:members: |
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:undoc-members: |
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:show-inheritance: |
@ -1,17 +0,0 @@ |
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scripts |
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======= |
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|
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.. toctree:: |
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:maxdepth: 4 |
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|
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abstract_importer |
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cache |
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conditional_intensity_matrix |
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json_importer |
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network_graph |
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parameters_estimator |
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sample_path |
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set_of_cims |
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structure |
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structure_estimator |
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trajectory |
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network\_graph module |
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===================== |
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|
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.. automodule:: network_graph |
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:members: |
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:undoc-members: |
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:show-inheritance: |
@ -1,7 +0,0 @@ |
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parameters\_estimator module |
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============================ |
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|
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.. automodule:: parameters_estimator |
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:members: |
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:undoc-members: |
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:show-inheritance: |
@ -1,7 +0,0 @@ |
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sample\_path module |
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=================== |
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|
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.. automodule:: sample_path |
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:members: |
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:undoc-members: |
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:show-inheritance: |
@ -1,7 +0,0 @@ |
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set\_of\_cims module |
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==================== |
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|
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.. automodule:: set_of_cims |
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:members: |
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:undoc-members: |
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:show-inheritance: |
@ -1,7 +0,0 @@ |
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structure module |
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================ |
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|
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.. automodule:: structure |
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:members: |
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:undoc-members: |
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:show-inheritance: |
@ -1,7 +0,0 @@ |
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structure\_estimator module |
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=========================== |
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|
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.. automodule:: structure_estimator |
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:members: |
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:undoc-members: |
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:show-inheritance: |
@ -1,7 +0,0 @@ |
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trajectory module |
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================= |
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|
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.. automodule:: trajectory |
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:members: |
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:undoc-members: |
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:show-inheritance: |
@ -1,2 +1,7 @@ |
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pre { line-height: 125%; } |
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td.linenos pre { color: #000000; background-color: #f0f0f0; padding-left: 5px; padding-right: 5px; } |
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span.linenos { color: #000000; background-color: #f0f0f0; padding-left: 5px; padding-right: 5px; } |
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td.linenos pre.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; } |
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span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; } |
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.highlight .hll { background-color: #ffffcc } |
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.highlight { background: #ffffff; } |
@ -0,0 +1,315 @@ |
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|
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|
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|
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<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.1//EN" "http://www.w3.org/TR/xhtml11/DTD/xhtml11.dtd"> |
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<html xml:lang="" lang="" version="-//W3C//DTD XHTML 1.1//EN" xmlns="http://www.w3.org/1999/xhtml"> |
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<head> |
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<meta charset="utf-8"> |
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<meta name="viewport" content="width=device-width, initial-scale=1.0"> |
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|
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|
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<title>Examples — PyCTBN 1.0 documentation</title> |
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|
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|
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|
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|
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|
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|
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<link href='https://fonts.googleapis.com/css?family=Lato:400,700,400italic,700italic|Roboto+Slab:400,700|Inconsolata:400,700' rel='stylesheet' type='text/css'/> |
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<link rel="stylesheet" href="_static/css/pdj.css" type="text/css" /> |
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|
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|
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|
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|
||||
|
||||
|
||||
<link rel="index" title="Index" |
||||
href="genindex.html"/> |
||||
<link rel="search" title="Search" href="search.html"/> |
||||
<link rel="top" title="PyCTBN 1.0 documentation" href="index.html"/> |
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<link rel="prev" title="PyCTBN package" href="PyCTBN.html"/> |
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|
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<meta http-equiv="content-type" content="text/html; charset=utf-8" /> |
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<meta http-equiv="cache-control" content="public" /> |
||||
<meta name="robots" content="follow, all" /> |
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0" /> |
||||
<!-- Add jQuery library --> |
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<script type="text/javascript" src="http://code.jquery.com/jquery-latest.min.js"></script> |
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<script src="https://cdnjs.cloudflare.com/ajax/libs/modernizr/2.6.2/modernizr.min.js"></script> |
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|
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</head> |
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|
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<body class="wy-body-for-nav" role="document"> |
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|
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<div class="wy-grid-for-nav"> |
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<nav data-toggle="wy-nav-shift" class="wy-nav-side"> |
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<p class="caption"><span class="caption-text">Contents:</span></p> |
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<ul class="current"> |
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<li class="toctree-l1"><a class="reference internal" href="PyCTBN.html">PyCTBN package</a><ul> |
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<li class="toctree-l2"><a class="reference internal" href="PyCTBN.html#submodules">Submodules</a></li> |
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<li class="toctree-l2"><a class="reference internal" href="PyCTBN.html#module-PyCTBN.abstract_importer">PyCTBN.abstract_importer module</a></li> |
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<li class="toctree-l2"><a class="reference internal" href="PyCTBN.html#module-PyCTBN.cache">PyCTBN.cache module</a></li> |
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<li class="toctree-l2"><a class="reference internal" href="PyCTBN.html#module-PyCTBN.conditional_intensity_matrix">PyCTBN.conditional_intensity_matrix module</a></li> |
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<li class="toctree-l2"><a class="reference internal" href="PyCTBN.html#module-PyCTBN.json_importer">PyCTBN.json_importer module</a></li> |
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<li class="toctree-l2"><a class="reference internal" href="PyCTBN.html#module-PyCTBN.network_graph">PyCTBN.network_graph module</a></li> |
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<li class="toctree-l2"><a class="reference internal" href="PyCTBN.html#module-PyCTBN.parameters_estimator">PyCTBN.parameters_estimator module</a></li> |
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<li class="toctree-l2"><a class="reference internal" href="PyCTBN.html#module-PyCTBN.sample_path">PyCTBN.sample_path module</a></li> |
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<li class="toctree-l2"><a class="reference internal" href="PyCTBN.html#module-PyCTBN.set_of_cims">PyCTBN.set_of_cims module</a></li> |
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<li class="toctree-l2"><a class="reference internal" href="PyCTBN.html#module-PyCTBN.structure">PyCTBN.structure module</a></li> |
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<li class="toctree-l2"><a class="reference internal" href="PyCTBN.html#module-PyCTBN.structure_estimator">PyCTBN.structure_estimator module</a></li> |
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<li class="toctree-l2"><a class="reference internal" href="PyCTBN.html#module-PyCTBN.trajectory">PyCTBN.trajectory module</a></li> |
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<li class="toctree-l2"><a class="reference internal" href="PyCTBN.html#module-PyCTBN">Module contents</a></li> |
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</ul> |
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<li class="toctree-l1 current"><a class="current reference internal" href="#">Examples</a><ul> |
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<li class="toctree-l2"><a class="reference internal" href="#installation-usage">Installation/Usage:</a></li> |
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<li class="toctree-l2"><a class="reference internal" href="#implementing-your-own-data-importer">Implementing your own data importer</a></li> |
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<li class="toctree-l2"><a class="reference internal" href="#parameters-estimation-example">Parameters Estimation Example</a></li> |
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<li class="toctree-l2"><a class="reference internal" href="#structure-estimation-example">Structure Estimation Example</a></li> |
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</ul> |
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</li> |
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<div id="content" class="hfeed entry-container hentry"> |
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<div class="section" id="examples"> |
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<h1>Examples<a class="headerlink" href="#examples" title="Permalink to this headline">¶</a></h1> |
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<div class="section" id="installation-usage"> |
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<h2>Installation/Usage:<a class="headerlink" href="#installation-usage" title="Permalink to this headline">¶</a></h2> |
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</div> |
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<div class="section" id="implementing-your-own-data-importer"> |
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<h2>Implementing your own data importer<a class="headerlink" href="#implementing-your-own-data-importer" title="Permalink to this headline">¶</a></h2> |
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<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="sd">"""This example demonstrates the implementation of a simple data importer the extends the class abstract importer to import data in csv format.</span> |
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<span class="sd">The net in exam has three ternary nodes.</span> |
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<span class="sd">"""</span> |
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|
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<span class="kn">from</span> <span class="nn">.abstract_importer</span> <span class="kn">import</span> <span class="n">AbstractImporter</span> |
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|
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<span class="k">class</span> <span class="nc">CSVImporter</span><span class="p">(</span><span class="n">AbstractImporter</span><span class="p">):</span> |
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|
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<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">file_path</span><span class="p">):</span> |
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<span class="bp">self</span><span class="o">.</span><span class="n">_df_samples_list</span> <span class="o">=</span> <span class="kc">None</span> |
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<span class="nb">super</span><span class="p">(</span><span class="n">CSVImporter</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">file_path</span><span class="p">)</span> |
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|
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<span class="k">def</span> <span class="nf">import_data</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
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<span class="bp">self</span><span class="o">.</span><span class="n">read_csv_file</span><span class="p">()</span> |
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<span class="bp">self</span><span class="o">.</span><span class="n">_sorter</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">build_sorter</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_df_samples_list</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> |
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<span class="bp">self</span><span class="o">.</span><span class="n">import_variables</span><span class="p">()</span> |
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<span class="bp">self</span><span class="o">.</span><span class="n">import_structure</span><span class="p">()</span> |
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<span class="bp">self</span><span class="o">.</span><span class="n">compute_row_delta_in_all_samples_frames</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_df_samples_list</span><span class="p">)</span> |
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|
||||
<span class="k">def</span> <span class="nf">read_csv_file</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
||||
<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_file_path</span><span class="p">)</span> |
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<span class="n">df</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">columns</span><span class="p">[[</span><span class="mi">0</span><span class="p">]],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> |
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<span class="bp">self</span><span class="o">.</span><span class="n">_df_samples_list</span> <span class="o">=</span> <span class="p">[</span><span class="n">df</span><span class="p">]</span> |
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|
||||
<span class="k">def</span> <span class="nf">import_variables</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
||||
<span class="n">values_list</span> <span class="o">=</span> <span class="p">[</span><span class="mi">3</span> <span class="k">for</span> <span class="n">var</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_sorter</span><span class="p">]</span> |
||||
<span class="c1"># initialize dict of lists</span> |
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<span class="n">data</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'Name'</span><span class="p">:</span><span class="bp">self</span><span class="o">.</span><span class="n">_sorter</span><span class="p">,</span> <span class="s1">'Value'</span><span class="p">:</span><span class="n">values_list</span><span class="p">}</span> |
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<span class="c1"># Create the pandas DataFrame</span> |
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_df_variables</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> |
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|
||||
<span class="k">def</span> <span class="nf">build_sorter</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sample_frame</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">)</span> <span class="o">-></span> <span class="n">typing</span><span class="o">.</span><span class="n">List</span><span class="p">:</span> |
||||
<span class="k">return</span> <span class="nb">list</span><span class="p">(</span><span class="n">sample_frame</span><span class="o">.</span><span class="n">columns</span><span class="p">)[</span><span class="mi">1</span><span class="p">:]</span> |
||||
|
||||
<span class="k">def</span> <span class="nf">import_structure</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span> |
||||
<span class="n">data</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'From'</span><span class="p">:[</span><span class="s1">'X'</span><span class="p">,</span><span class="s1">'Y'</span><span class="p">,</span><span class="s1">'Z'</span><span class="p">],</span> <span class="s1">'To'</span><span class="p">:[</span><span class="s1">'Z'</span><span class="p">,</span><span class="s1">'Z'</span><span class="p">,</span><span class="s1">'Y'</span><span class="p">]}</span> |
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_df_structure</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> |
||||
|
||||
<span class="k">def</span> <span class="nf">dataset_id</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-></span> <span class="nb">object</span><span class="p">:</span> |
||||
<span class="k">pass</span> |
||||
</pre></div> |
||||
</div> |
||||
</div> |
||||
<div class="section" id="parameters-estimation-example"> |
||||
<h2>Parameters Estimation Example<a class="headerlink" href="#parameters-estimation-example" title="Permalink to this headline">¶</a></h2> |
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<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">PyCTBN.PyCTBN.json_importer</span> <span class="kn">import</span> <span class="n">JsonImporter</span> |
||||
<span class="kn">from</span> <span class="nn">PyCTBN.PyCTBN.sample_path</span> <span class="kn">import</span> <span class="n">SamplePath</span> |
||||
<span class="kn">from</span> <span class="nn">PyCTBN.PyCTBN.network_graph</span> <span class="kn">import</span> <span class="n">NetworkGraph</span> |
||||
<span class="kn">from</span> <span class="nn">PyCTBN.PyCTBN.parameters_estimator</span> <span class="kn">import</span> <span class="n">ParametersEstimator</span> |
||||
|
||||
|
||||
<span class="k">def</span> <span class="nf">main</span><span class="p">():</span> |
||||
<span class="n">read_files</span> <span class="o">=</span> <span class="n">glob</span><span class="o">.</span><span class="n">glob</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="s1">'./data'</span><span class="p">,</span> <span class="s2">"*.json"</span><span class="p">))</span> <span class="c1">#Take all json files in this dir</span> |
||||
<span class="c1">#import data</span> |
||||
<span class="n">importer</span> <span class="o">=</span> <span class="n">JsonImporter</span><span class="p">(</span><span class="n">read_files</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="s1">'samples'</span><span class="p">,</span> <span class="s1">'dyn.str'</span><span class="p">,</span> <span class="s1">'variables'</span><span class="p">,</span> <span class="s1">'Time'</span><span class="p">,</span> <span class="s1">'Name'</span><span class="p">)</span> |
||||
<span class="n">importer</span><span class="o">.</span><span class="n">import_data</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> |
||||
<span class="c1">#Create a SamplePath Obj passing an already filled AbstractImporter object</span> |
||||
<span class="n">s1</span> <span class="o">=</span> <span class="n">SamplePath</span><span class="p">(</span><span class="n">importer</span><span class="p">)</span> |
||||
<span class="c1">#Build The trajectries and the structural infos</span> |
||||
<span class="n">s1</span><span class="o">.</span><span class="n">build_trajectories</span><span class="p">()</span> |
||||
<span class="n">s1</span><span class="o">.</span><span class="n">build_structure</span><span class="p">()</span> |
||||
<span class="nb">print</span><span class="p">(</span><span class="n">s1</span><span class="o">.</span><span class="n">structure</span><span class="o">.</span><span class="n">edges</span><span class="p">)</span> |
||||
<span class="nb">print</span><span class="p">(</span><span class="n">s1</span><span class="o">.</span><span class="n">structure</span><span class="o">.</span><span class="n">nodes_values</span><span class="p">)</span> |
||||
<span class="c1">#From The Structure Object build the Graph</span> |
||||
<span class="n">g</span> <span class="o">=</span> <span class="n">NetworkGraph</span><span class="p">(</span><span class="n">s1</span><span class="o">.</span><span class="n">structure</span><span class="p">)</span> |
||||
<span class="c1">#Select a node you want to estimate the parameters</span> |
||||
<span class="n">node</span> <span class="o">=</span> <span class="n">g</span><span class="o">.</span><span class="n">nodes</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> |
||||
<span class="nb">print</span><span class="p">(</span><span class="s2">"Node"</span><span class="p">,</span> <span class="n">node</span><span class="p">)</span> |
||||
<span class="c1">#Init the _graph specifically for THIS node</span> |
||||
<span class="n">g</span><span class="o">.</span><span class="n">fast_init</span><span class="p">(</span><span class="n">node</span><span class="p">)</span> |
||||
<span class="c1">#Use SamplePath and Grpah to create a ParametersEstimator Object</span> |
||||
<span class="n">p1</span> <span class="o">=</span> <span class="n">ParametersEstimator</span><span class="p">(</span><span class="n">s1</span><span class="o">.</span><span class="n">trajectories</span><span class="p">,</span> <span class="n">g</span><span class="p">)</span> |
||||
<span class="c1">#Init the peEst specifically for THIS node</span> |
||||
<span class="n">p1</span><span class="o">.</span><span class="n">fast_init</span><span class="p">(</span><span class="n">node</span><span class="p">)</span> |
||||
<span class="c1">#Compute the parameters</span> |
||||
<span class="n">sofc1</span> <span class="o">=</span> <span class="n">p1</span><span class="o">.</span><span class="n">compute_parameters_for_node</span><span class="p">(</span><span class="n">node</span><span class="p">)</span> |
||||
<span class="c1">#The est CIMS are inside the resultant SetOfCIms Obj</span> |
||||
<span class="nb">print</span><span class="p">(</span><span class="n">sofc1</span><span class="o">.</span><span class="n">actual_cims</span><span class="p">)</span> |
||||
</pre></div> |
||||
</div> |
||||
</div> |
||||
<div class="section" id="structure-estimation-example"> |
||||
<h2>Structure Estimation Example<a class="headerlink" href="#structure-estimation-example" title="Permalink to this headline">¶</a></h2> |
||||
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">PyCTBN.PyCTBN.json_importer</span> <span class="kn">import</span> <span class="n">JsonImporter</span> |
||||
<span class="kn">from</span> <span class="nn">PyCTBN.PyCTBN.sample_path</span> <span class="kn">import</span> <span class="n">SamplePath</span> |
||||
<span class="kn">from</span> <span class="nn">PyCTBN.PyCTBN.structure_estimator</span> <span class="kn">import</span> <span class="n">StructureEstimator</span> |
||||
|
||||
<span class="k">def</span> <span class="nf">structure_estimation_example</span><span class="p">():</span> |
||||
|
||||
<span class="c1"># read the json files in ./data path</span> |
||||
<span class="n">read_files</span> <span class="o">=</span> <span class="n">glob</span><span class="o">.</span><span class="n">glob</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="s1">'./data'</span><span class="p">,</span> <span class="s2">"*.json"</span><span class="p">))</span> |
||||
<span class="c1"># initialize a JsonImporter object for the first file</span> |
||||
<span class="n">importer</span> <span class="o">=</span> <span class="n">JsonImporter</span><span class="p">(</span><span class="n">read_files</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="s1">'samples'</span><span class="p">,</span> <span class="s1">'dyn.str'</span><span class="p">,</span> <span class="s1">'variables'</span><span class="p">,</span> <span class="s1">'Time'</span><span class="p">,</span> <span class="s1">'Name'</span><span class="p">)</span> |
||||
<span class="c1"># import the data at index 0 of the outer json array</span> |
||||
<span class="n">importer</span><span class="o">.</span><span class="n">import_data</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> |
||||
<span class="c1"># construct a SamplePath Object passing a filled AbstractImporter</span> |
||||
<span class="n">s1</span> <span class="o">=</span> <span class="n">SamplePath</span><span class="p">(</span><span class="n">importer</span><span class="p">)</span> |
||||
<span class="c1"># build the trajectories</span> |
||||
<span class="n">s1</span><span class="o">.</span><span class="n">build_trajectories</span><span class="p">()</span> |
||||
<span class="c1"># build the real structure</span> |
||||
<span class="n">s1</span><span class="o">.</span><span class="n">build_structure</span><span class="p">()</span> |
||||
<span class="c1"># construct a StructureEstimator object</span> |
||||
<span class="n">se1</span> <span class="o">=</span> <span class="n">StructureEstimator</span><span class="p">(</span><span class="n">s1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">)</span> |
||||
<span class="c1"># call the ctpc algorithm</span> |
||||
<span class="n">se1</span><span class="o">.</span><span class="n">ctpc_algorithm</span><span class="p">()</span> |
||||
<span class="c1"># the adjacency matrix of the estimated structure</span> |
||||
<span class="nb">print</span><span class="p">(</span><span class="n">se1</span><span class="o">.</span><span class="n">adjacency_matrix</span><span class="p">())</span> |
||||
<span class="c1"># save results to a json file</span> |
||||
<span class="n">se1</span><span class="o">.</span><span class="n">save_results</span><span class="p">()</span> |
||||
</pre></div> |
||||
</div> |
||||
</div> |
||||
</div> |
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</div> |
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<footer> |
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<li class="toctree-l1"><a class="reference internal" href="PyCTBN.html">PyCTBN package</a><ul> |
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<li class="toctree-l2"><a class="reference internal" href="PyCTBN.html#module-PyCTBN.network_graph">PyCTBN.network_graph module</a></li> |
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<li class="toctree-l2"><a class="reference internal" href="PyCTBN.html#module-PyCTBN.parameters_estimator">PyCTBN.parameters_estimator module</a></li> |
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<li class="toctree-l2"><a class="reference internal" href="PyCTBN.html#module-PyCTBN.structure">PyCTBN.structure module</a></li> |
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<h1>PyCTBN<a class="headerlink" href="#pyctbn" title="Permalink to this headline">¶</a></h1> |
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<span id="abstract-importer-module"></span><h1>abstract_importer module<a class="headerlink" href="#module-abstract_importer" title="Permalink to this headline">¶</a></h1> |
||||
<dl class="py class"> |
||||
<dt id="abstract_importer.AbstractImporter"> |
||||
<em class="property">class </em><code class="sig-prename descclassname">abstract_importer.</code><code class="sig-name descname">AbstractImporter</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">file_path</span><span class="p">:</span> <span class="n">str</span></em><span class="sig-paren">)</span><a class="headerlink" href="#abstract_importer.AbstractImporter" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">abc.ABC</span></code></p> |
||||
<p>Abstract class that exposes all the necessary methods to process the trajectories and the net structure.</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><p><strong>file_path</strong> (<em>str</em>) – the file path</p> |
||||
</dd> |
||||
<dt class="field-even">_concatenated_samples</dt> |
||||
<dd class="field-even"><p>Dataframe containing the concatenation of all the processed trajectories</p> |
||||
</dd> |
||||
<dt class="field-odd">_df_structure</dt> |
||||
<dd class="field-odd"><p>Dataframe containing the structure of the network (edges)</p> |
||||
</dd> |
||||
<dt class="field-even">_df_variables</dt> |
||||
<dd class="field-even"><p>Dataframe containing the nodes cardinalities</p> |
||||
</dd> |
||||
<dt class="field-odd">_sorter</dt> |
||||
<dd class="field-odd"><p>A list containing the columns header (excluding the time column) of the <cite>_concatenated_samples</cite></p> |
||||
</dd> |
||||
</dl> |
||||
<dl class="py method"> |
||||
<dt id="abstract_importer.AbstractImporter.build_list_of_samples_array"> |
||||
<code class="sig-name descname">build_list_of_samples_array</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data_frame</span><span class="p">:</span> <span class="n">pandas.core.frame.DataFrame</span></em><span class="sig-paren">)</span> → List<a class="headerlink" href="#abstract_importer.AbstractImporter.build_list_of_samples_array" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Builds a List containing the columns of data_frame and converts them to a numpy array.</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><p><strong>data_frame</strong> (<em>pandas.Dataframe</em>) – the dataframe from which the columns have to be extracted and converted</p> |
||||
</dd> |
||||
<dt class="field-even">Returns</dt> |
||||
<dd class="field-even"><p>the resulting list of numpy arrays</p> |
||||
</dd> |
||||
<dt class="field-odd">Return type</dt> |
||||
<dd class="field-odd"><p>List</p> |
||||
</dd> |
||||
</dl> |
||||
</dd></dl> |
||||
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||||
<dl class="py method"> |
||||
<dt id="abstract_importer.AbstractImporter.build_sorter"> |
||||
<em class="property">abstract </em><code class="sig-name descname">build_sorter</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">sample_frame</span><span class="p">:</span> <span class="n">pandas.core.frame.DataFrame</span></em><span class="sig-paren">)</span> → List<a class="headerlink" href="#abstract_importer.AbstractImporter.build_sorter" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Initializes the <code class="docutils literal notranslate"><span class="pre">_sorter</span></code> class member from a trajectory dataframe, exctracting the header of the frame |
||||
and keeping ONLY the variables symbolic labels, cutting out the time label in the header.</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><p><strong>sample_frame</strong> (<em>pandas.DataFrame</em>) – The dataframe from which extract the header</p> |
||||
</dd> |
||||
<dt class="field-even">Returns</dt> |
||||
<dd class="field-even"><p>A list containing the processed header.</p> |
||||
</dd> |
||||
<dt class="field-odd">Return type</dt> |
||||
<dd class="field-odd"><p>List</p> |
||||
</dd> |
||||
</dl> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="abstract_importer.AbstractImporter.clear_concatenated_frame"> |
||||
<code class="sig-name descname">clear_concatenated_frame</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → None<a class="headerlink" href="#abstract_importer.AbstractImporter.clear_concatenated_frame" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Removes all values in the dataframe concatenated_samples.</p> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="abstract_importer.AbstractImporter.compute_row_delta_in_all_samples_frames"> |
||||
<code class="sig-name descname">compute_row_delta_in_all_samples_frames</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">df_samples_list</span><span class="p">:</span> <span class="n">List</span></em><span class="sig-paren">)</span> → None<a class="headerlink" href="#abstract_importer.AbstractImporter.compute_row_delta_in_all_samples_frames" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Calls the method <code class="docutils literal notranslate"><span class="pre">compute_row_delta_sigle_samples_frame</span></code> on every dataframe present in the list |
||||
<code class="docutils literal notranslate"><span class="pre">df_samples_list</span></code>. |
||||
Concatenates the result in the dataframe <code class="docutils literal notranslate"><span class="pre">concatanated_samples</span></code></p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><p><strong>df_samples_list</strong> (<em>List</em>) – the datframe’s list to be processed and concatenated</p> |
||||
</dd> |
||||
</dl> |
||||
<div class="admonition warning"> |
||||
<p class="admonition-title">Warning</p> |
||||
<p>The Dataframe sample_frame has to follow the column structure of this header: |
||||
Header of sample_frame = [Time | Variable values] |
||||
The class member self._sorter HAS to be properly INITIALIZED (See class members definition doc)</p> |
||||
</div> |
||||
<div class="admonition note"> |
||||
<p class="admonition-title">Note</p> |
||||
<p>After the call of this method the class member <code class="docutils literal notranslate"><span class="pre">concatanated_samples</span></code> will contain all processed |
||||
and merged trajectories</p> |
||||
</div> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="abstract_importer.AbstractImporter.compute_row_delta_sigle_samples_frame"> |
||||
<code class="sig-name descname">compute_row_delta_sigle_samples_frame</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">sample_frame</span><span class="p">:</span> <span class="n">pandas.core.frame.DataFrame</span></em>, <em class="sig-param"><span class="n">columns_header</span><span class="p">:</span> <span class="n">List</span></em>, <em class="sig-param"><span class="n">shifted_cols_header</span><span class="p">:</span> <span class="n">List</span></em><span class="sig-paren">)</span> → pandas.core.frame.DataFrame<a class="headerlink" href="#abstract_importer.AbstractImporter.compute_row_delta_sigle_samples_frame" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Computes the difference between each value present in th time column. |
||||
Copies and shift by one position up all the values present in the remaining columns.</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><ul class="simple"> |
||||
<li><p><strong>sample_frame</strong> (<em>pandas.Dataframe</em>) – the traj to be processed</p></li> |
||||
<li><p><strong>columns_header</strong> (<em>List</em>) – the original header of sample_frame</p></li> |
||||
<li><p><strong>shifted_cols_header</strong> (<em>List</em>) – a copy of columns_header with changed names of the contents</p></li> |
||||
</ul> |
||||
</dd> |
||||
<dt class="field-even">Returns</dt> |
||||
<dd class="field-even"><p>The processed dataframe</p> |
||||
</dd> |
||||
<dt class="field-odd">Return type</dt> |
||||
<dd class="field-odd"><p>pandas.Dataframe</p> |
||||
</dd> |
||||
</dl> |
||||
<div class="admonition warning"> |
||||
<p class="admonition-title">Warning</p> |
||||
<p>the Dataframe <code class="docutils literal notranslate"><span class="pre">sample_frame</span></code> has to follow the column structure of this header: |
||||
Header of sample_frame = [Time | Variable values]</p> |
||||
</div> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="abstract_importer.AbstractImporter.concatenated_samples"> |
||||
<em class="property">property </em><code class="sig-name descname">concatenated_samples</code><a class="headerlink" href="#abstract_importer.AbstractImporter.concatenated_samples" title="Permalink to this definition">¶</a></dt> |
||||
<dd></dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="abstract_importer.AbstractImporter.dataset_id"> |
||||
<em class="property">abstract </em><code class="sig-name descname">dataset_id</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → object<a class="headerlink" href="#abstract_importer.AbstractImporter.dataset_id" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>If the original dataset contains multiple dataset, this method returns a unique id to identify the current |
||||
dataset</p> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="abstract_importer.AbstractImporter.file_path"> |
||||
<em class="property">property </em><code class="sig-name descname">file_path</code><a class="headerlink" href="#abstract_importer.AbstractImporter.file_path" title="Permalink to this definition">¶</a></dt> |
||||
<dd></dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="abstract_importer.AbstractImporter.import_data"> |
||||
<em class="property">abstract </em><code class="sig-name descname">import_data</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → None<a class="headerlink" href="#abstract_importer.AbstractImporter.import_data" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Imports all the trajectories, variables cardinalities, and net edges.</p> |
||||
<div class="admonition warning"> |
||||
<p class="admonition-title">Warning</p> |
||||
<p>The class members <code class="docutils literal notranslate"><span class="pre">_df_variables</span></code> and <code class="docutils literal notranslate"><span class="pre">_df_structure</span></code> HAVE to be properly constructed |
||||
as Pandas Dataframes with the following structure: |
||||
Header of _df_structure = [From_Node | To_Node] |
||||
Header of _df_variables = [Variable_Label | Variable_Cardinality]</p> |
||||
</div> |
||||
<div class="admonition note"> |
||||
<p class="admonition-title">Note</p> |
||||
<p>See :class:<code class="docutils literal notranslate"><span class="pre">JsonImporter</span></code> for an example of implementation of this method.</p> |
||||
</div> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="abstract_importer.AbstractImporter.sorter"> |
||||
<em class="property">property </em><code class="sig-name descname">sorter</code><a class="headerlink" href="#abstract_importer.AbstractImporter.sorter" title="Permalink to this definition">¶</a></dt> |
||||
<dd></dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="abstract_importer.AbstractImporter.structure"> |
||||
<em class="property">property </em><code class="sig-name descname">structure</code><a class="headerlink" href="#abstract_importer.AbstractImporter.structure" title="Permalink to this definition">¶</a></dt> |
||||
<dd></dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="abstract_importer.AbstractImporter.variables"> |
||||
<em class="property">property </em><code class="sig-name descname">variables</code><a class="headerlink" href="#abstract_importer.AbstractImporter.variables" title="Permalink to this definition">¶</a></dt> |
||||
<dd></dd></dl> |
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<li><a class="reference internal" href="#">cache module</a></li> |
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<div class="section" id="module-cache"> |
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<span id="cache-module"></span><h1>cache module<a class="headerlink" href="#module-cache" title="Permalink to this headline">¶</a></h1> |
||||
<dl class="py class"> |
||||
<dt id="cache.Cache"> |
||||
<em class="property">class </em><code class="sig-prename descclassname">cache.</code><code class="sig-name descname">Cache</code><a class="headerlink" href="#cache.Cache" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p> |
||||
<p>This class acts as a cache of <code class="docutils literal notranslate"><span class="pre">SetOfCims</span></code> objects for a node.</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">_list_of_sets_of_parents</dt> |
||||
<dd class="field-odd"><p>a list of <code class="docutils literal notranslate"><span class="pre">Sets</span></code> objects of the parents to which the cim in cache at SAME |
||||
index is related</p> |
||||
</dd> |
||||
<dt class="field-even">_actual_cache</dt> |
||||
<dd class="field-even"><p>a list of setOfCims objects</p> |
||||
</dd> |
||||
</dl> |
||||
<dl class="py method"> |
||||
<dt id="cache.Cache.clear"> |
||||
<code class="sig-name descname">clear</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → None<a class="headerlink" href="#cache.Cache.clear" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Clear the contents both of <code class="docutils literal notranslate"><span class="pre">_actual_cache</span></code> and <code class="docutils literal notranslate"><span class="pre">_list_of_sets_of_parents</span></code>.</p> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="cache.Cache.find"> |
||||
<code class="sig-name descname">find</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">parents_comb</span><span class="p">:</span> <span class="n">Set</span></em><span class="sig-paren">)</span> → <a class="reference internal" href="set_of_cims.html#set_of_cims.SetOfCims" title="set_of_cims.SetOfCims">set_of_cims.SetOfCims</a><a class="headerlink" href="#cache.Cache.find" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Tries to find in cache given the symbolic parents combination <code class="docutils literal notranslate"><span class="pre">parents_comb</span></code> the <code class="docutils literal notranslate"><span class="pre">SetOfCims</span></code> |
||||
related to that <code class="docutils literal notranslate"><span class="pre">parents_comb</span></code>.</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><p><strong>parents_comb</strong> (<em>Set</em>) – the parents related to that <code class="docutils literal notranslate"><span class="pre">SetOfCims</span></code></p> |
||||
</dd> |
||||
<dt class="field-even">Returns</dt> |
||||
<dd class="field-even"><p>A <code class="docutils literal notranslate"><span class="pre">SetOfCims</span></code> object if the <code class="docutils literal notranslate"><span class="pre">parents_comb</span></code> index is found in <code class="docutils literal notranslate"><span class="pre">_list_of_sets_of_parents</span></code>. |
||||
None otherwise.</p> |
||||
</dd> |
||||
<dt class="field-odd">Return type</dt> |
||||
<dd class="field-odd"><p><a class="reference internal" href="set_of_cims.html#set_of_cims.SetOfCims" title="set_of_cims.SetOfCims">SetOfCims</a></p> |
||||
</dd> |
||||
</dl> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="cache.Cache.put"> |
||||
<code class="sig-name descname">put</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">parents_comb</span><span class="p">:</span> <span class="n">Set</span></em>, <em class="sig-param"><span class="n">socim</span><span class="p">:</span> <span class="n"><a class="reference internal" href="set_of_cims.html#set_of_cims.SetOfCims" title="set_of_cims.SetOfCims">set_of_cims.SetOfCims</a></span></em><span class="sig-paren">)</span> → None<a class="headerlink" href="#cache.Cache.put" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Place in cache the <code class="docutils literal notranslate"><span class="pre">SetOfCims</span></code> object, and the related symbolic index <code class="docutils literal notranslate"><span class="pre">parents_comb</span></code> in |
||||
<code class="docutils literal notranslate"><span class="pre">_list_of_sets_of_parents</span></code>.</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><ul class="simple"> |
||||
<li><p><strong>parents_comb</strong> (<em>Set</em>) – the symbolic set index</p></li> |
||||
<li><p><strong>socim</strong> (<a class="reference internal" href="set_of_cims.html#set_of_cims.SetOfCims" title="set_of_cims.SetOfCims"><em>SetOfCims</em></a>) – the related SetOfCims object</p></li> |
||||
</ul> |
||||
</dd> |
||||
</dl> |
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</dd></dl> |
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<div class="section" id="module-conditional_intensity_matrix"> |
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<span id="conditional-intensity-matrix-module"></span><h1>conditional_intensity_matrix module<a class="headerlink" href="#module-conditional_intensity_matrix" title="Permalink to this headline">¶</a></h1> |
||||
<dl class="py class"> |
||||
<dt id="conditional_intensity_matrix.ConditionalIntensityMatrix"> |
||||
<em class="property">class </em><code class="sig-prename descclassname">conditional_intensity_matrix.</code><code class="sig-name descname">ConditionalIntensityMatrix</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">state_residence_times</span><span class="p">:</span> <span class="n">numpy.array</span></em>, <em class="sig-param"><span class="n">state_transition_matrix</span><span class="p">:</span> <span class="n">numpy.array</span></em><span class="sig-paren">)</span><a class="headerlink" href="#conditional_intensity_matrix.ConditionalIntensityMatrix" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p> |
||||
<p>Abstracts the Conditional Intesity matrix of a node as aggregation of the state residence times vector |
||||
and state transition matrix and the actual CIM matrix.</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><ul class="simple"> |
||||
<li><p><strong>state_residence_times</strong> (<em>numpy.array</em>) – state residence times vector</p></li> |
||||
<li><p><strong>state_transition_matrix</strong> (<em>numpy.ndArray</em>) – the transitions count matrix</p></li> |
||||
</ul> |
||||
</dd> |
||||
<dt class="field-even">_cim</dt> |
||||
<dd class="field-even"><p>the actual cim of the node</p> |
||||
</dd> |
||||
</dl> |
||||
<dl class="py method"> |
||||
<dt id="conditional_intensity_matrix.ConditionalIntensityMatrix.cim"> |
||||
<em class="property">property </em><code class="sig-name descname">cim</code><a class="headerlink" href="#conditional_intensity_matrix.ConditionalIntensityMatrix.cim" title="Permalink to this definition">¶</a></dt> |
||||
<dd></dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="conditional_intensity_matrix.ConditionalIntensityMatrix.compute_cim_coefficients"> |
||||
<code class="sig-name descname">compute_cim_coefficients</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → None<a class="headerlink" href="#conditional_intensity_matrix.ConditionalIntensityMatrix.compute_cim_coefficients" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Compute the coefficients of the matrix _cim by using the following equality q_xx’ = M[x, x’] / T[x]. |
||||
The class member <code class="docutils literal notranslate"><span class="pre">_cim</span></code> will contain the computed cim</p> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="conditional_intensity_matrix.ConditionalIntensityMatrix.state_residence_times"> |
||||
<em class="property">property </em><code class="sig-name descname">state_residence_times</code><a class="headerlink" href="#conditional_intensity_matrix.ConditionalIntensityMatrix.state_residence_times" title="Permalink to this definition">¶</a></dt> |
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<dd></dd></dl> |
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|
||||
<dl class="py method"> |
||||
<dt id="conditional_intensity_matrix.ConditionalIntensityMatrix.state_transition_matrix"> |
||||
<em class="property">property </em><code class="sig-name descname">state_transition_matrix</code><a class="headerlink" href="#conditional_intensity_matrix.ConditionalIntensityMatrix.state_transition_matrix" title="Permalink to this definition">¶</a></dt> |
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<div class="section" id="module-json_importer"> |
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<span id="json-importer-module"></span><h1>json_importer module<a class="headerlink" href="#module-json_importer" title="Permalink to this headline">¶</a></h1> |
||||
<dl class="py class"> |
||||
<dt id="json_importer.JsonImporter"> |
||||
<em class="property">class </em><code class="sig-prename descclassname">json_importer.</code><code class="sig-name descname">JsonImporter</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">file_path</span><span class="p">:</span> <span class="n">str</span></em>, <em class="sig-param"><span class="n">samples_label</span><span class="p">:</span> <span class="n">str</span></em>, <em class="sig-param"><span class="n">structure_label</span><span class="p">:</span> <span class="n">str</span></em>, <em class="sig-param"><span class="n">variables_label</span><span class="p">:</span> <span class="n">str</span></em>, <em class="sig-param"><span class="n">time_key</span><span class="p">:</span> <span class="n">str</span></em>, <em class="sig-param"><span class="n">variables_key</span><span class="p">:</span> <span class="n">str</span></em>, <em class="sig-param"><span class="n">array_indx</span><span class="p">:</span> <span class="n">int</span></em><span class="sig-paren">)</span><a class="headerlink" href="#json_importer.JsonImporter" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Bases: <a class="reference internal" href="abstract_importer.html#abstract_importer.AbstractImporter" title="abstract_importer.AbstractImporter"><code class="xref py py-class docutils literal notranslate"><span class="pre">abstract_importer.AbstractImporter</span></code></a></p> |
||||
<p>Implements the abstracts methods of AbstractImporter and adds all the necessary methods to process and prepare the data in json ext. |
||||
with the following structure: |
||||
[0]</p> |
||||
<blockquote> |
||||
<div><p><a href="#id1"><span class="problematic" id="id2">|</span></a>_ dyn.cims |
||||
<a href="#id3"><span class="problematic" id="id4">|</span></a>_ dyn.str |
||||
<a href="#id5"><span class="problematic" id="id6">|</span></a>_ samples |
||||
<a href="#id7"><span class="problematic" id="id8">|</span></a>_ variabels</p> |
||||
</div></blockquote> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><ul class="simple"> |
||||
<li><p><strong>file_path</strong> (<em>string</em>) – the path of the file that contains tha data to be imported</p></li> |
||||
<li><p><strong>samples_label</strong> (<em>string</em>) – the reference key for the samples in the trajectories</p></li> |
||||
<li><p><strong>structure_label</strong> (<em>string</em>) – the reference key for the structure of the network data</p></li> |
||||
<li><p><strong>variables_label</strong> (<em>string</em>) – the reference key for the cardinalites of the nodes data</p></li> |
||||
<li><p><strong>time_key</strong> (<em>string</em>) – the key used to identify the timestamps in each trajectory</p></li> |
||||
<li><p><strong>variables_key</strong> (<em>string</em>) – the key used to identify the names of the variables in the net</p></li> |
||||
<li><p><strong>array_indx</strong> (<em>int</em>) – the index of the outer JsonArray to exctract the data from</p></li> |
||||
</ul> |
||||
</dd> |
||||
<dt class="field-even">_df_samples_list</dt> |
||||
<dd class="field-even"><p>a Dataframe list in which every dataframe contains a trajectory</p> |
||||
</dd> |
||||
</dl> |
||||
<dl class="py method"> |
||||
<dt id="json_importer.JsonImporter.build_sorter"> |
||||
<code class="sig-name descname">build_sorter</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">sample_frame</span><span class="p">:</span> <span class="n">pandas.core.frame.DataFrame</span></em><span class="sig-paren">)</span> → List<a class="headerlink" href="#json_importer.JsonImporter.build_sorter" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Implements the abstract method build_sorter of the <code class="xref py py-class docutils literal notranslate"><span class="pre">AbstractImporter</span></code> for this dataset</p> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="json_importer.JsonImporter.clear_data_frame_list"> |
||||
<code class="sig-name descname">clear_data_frame_list</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → None<a class="headerlink" href="#json_importer.JsonImporter.clear_data_frame_list" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Removes all values present in the dataframes in the list <code class="docutils literal notranslate"><span class="pre">_df_samples_list</span></code>.</p> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="json_importer.JsonImporter.dataset_id"> |
||||
<code class="sig-name descname">dataset_id</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → object<a class="headerlink" href="#json_importer.JsonImporter.dataset_id" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>If the original dataset contains multiple dataset, this method returns a unique id to identify the current |
||||
dataset</p> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="json_importer.JsonImporter.import_data"> |
||||
<code class="sig-name descname">import_data</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → None<a class="headerlink" href="#json_importer.JsonImporter.import_data" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Implements the abstract method of <code class="xref py py-class docutils literal notranslate"><span class="pre">AbstractImporter</span></code></p> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="json_importer.JsonImporter.import_sampled_cims"> |
||||
<code class="sig-name descname">import_sampled_cims</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">raw_data</span><span class="p">:</span> <span class="n">List</span></em>, <em class="sig-param"><span class="n">indx</span><span class="p">:</span> <span class="n">int</span></em>, <em class="sig-param"><span class="n">cims_key</span><span class="p">:</span> <span class="n">str</span></em><span class="sig-paren">)</span> → Dict<a class="headerlink" href="#json_importer.JsonImporter.import_sampled_cims" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Imports the synthetic CIMS in the dataset in a dictionary, using variables labels |
||||
as keys for the set of CIMS of a particular node.</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><ul class="simple"> |
||||
<li><p><strong>raw_data</strong> (<em>List</em>) – List of Dicts</p></li> |
||||
<li><p><strong>indx</strong> (<em>int</em>) – The index of the array from which the data have to be extracted</p></li> |
||||
<li><p><strong>cims_key</strong> (<em>string</em>) – the key where the json object cims are placed</p></li> |
||||
</ul> |
||||
</dd> |
||||
<dt class="field-even">Returns</dt> |
||||
<dd class="field-even"><p>a dictionary containing the sampled CIMS for all the variables in the net</p> |
||||
</dd> |
||||
<dt class="field-odd">Return type</dt> |
||||
<dd class="field-odd"><p>Dictionary</p> |
||||
</dd> |
||||
</dl> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="json_importer.JsonImporter.import_structure"> |
||||
<code class="sig-name descname">import_structure</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">raw_data</span><span class="p">:</span> <span class="n">List</span></em><span class="sig-paren">)</span> → pandas.core.frame.DataFrame<a class="headerlink" href="#json_importer.JsonImporter.import_structure" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Imports in a dataframe the data in the list raw_data at the key <code class="docutils literal notranslate"><span class="pre">_structure_label</span></code></p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><p><strong>raw_data</strong> (<em>List</em>) – List of Dicts</p> |
||||
</dd> |
||||
<dt class="field-even">Returns</dt> |
||||
<dd class="field-even"><p>Dataframe containg the starting node a ending node of every arc of the network</p> |
||||
</dd> |
||||
<dt class="field-odd">Return type</dt> |
||||
<dd class="field-odd"><p>pandas.Dataframe</p> |
||||
</dd> |
||||
</dl> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="json_importer.JsonImporter.import_trajectories"> |
||||
<code class="sig-name descname">import_trajectories</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">raw_data</span><span class="p">:</span> <span class="n">List</span></em><span class="sig-paren">)</span> → List<a class="headerlink" href="#json_importer.JsonImporter.import_trajectories" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Imports the trajectories from the list of dicts <code class="docutils literal notranslate"><span class="pre">raw_data</span></code>.</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><p><strong>raw_data</strong> (<em>List</em>) – List of Dicts</p> |
||||
</dd> |
||||
<dt class="field-even">Returns</dt> |
||||
<dd class="field-even"><p>List of dataframes containing all the trajectories</p> |
||||
</dd> |
||||
<dt class="field-odd">Return type</dt> |
||||
<dd class="field-odd"><p>List</p> |
||||
</dd> |
||||
</dl> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="json_importer.JsonImporter.import_variables"> |
||||
<code class="sig-name descname">import_variables</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">raw_data</span><span class="p">:</span> <span class="n">List</span></em><span class="sig-paren">)</span> → pandas.core.frame.DataFrame<a class="headerlink" href="#json_importer.JsonImporter.import_variables" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Imports the data in <code class="docutils literal notranslate"><span class="pre">raw_data</span></code> at the key <code class="docutils literal notranslate"><span class="pre">_variables_label</span></code>.</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><p><strong>raw_data</strong> (<em>List</em>) – List of Dicts</p> |
||||
</dd> |
||||
<dt class="field-even">Returns</dt> |
||||
<dd class="field-even"><p>Datframe containg the variables simbolic labels and their cardinalities</p> |
||||
</dd> |
||||
<dt class="field-odd">Return type</dt> |
||||
<dd class="field-odd"><p>pandas.Dataframe</p> |
||||
</dd> |
||||
</dl> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="json_importer.JsonImporter.normalize_trajectories"> |
||||
<code class="sig-name descname">normalize_trajectories</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">raw_data</span><span class="p">:</span> <span class="n">List</span></em>, <em class="sig-param"><span class="n">indx</span><span class="p">:</span> <span class="n">int</span></em>, <em class="sig-param"><span class="n">trajectories_key</span><span class="p">:</span> <span class="n">str</span></em><span class="sig-paren">)</span> → List<a class="headerlink" href="#json_importer.JsonImporter.normalize_trajectories" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Extracts the trajectories in <code class="docutils literal notranslate"><span class="pre">raw_data</span></code> at the index <code class="docutils literal notranslate"><span class="pre">index</span></code> at the key <code class="docutils literal notranslate"><span class="pre">trajectories</span> <span class="pre">key</span></code>.</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><ul class="simple"> |
||||
<li><p><strong>raw_data</strong> (<em>List</em>) – List of Dicts</p></li> |
||||
<li><p><strong>indx</strong> (<em>int</em>) – The index of the array from which the data have to be extracted</p></li> |
||||
<li><p><strong>trajectories_key</strong> (<em>string</em>) – the key of the trajectories objects</p></li> |
||||
</ul> |
||||
</dd> |
||||
<dt class="field-even">Returns</dt> |
||||
<dd class="field-even"><p>A list of daframes containg the trajectories</p> |
||||
</dd> |
||||
<dt class="field-odd">Return type</dt> |
||||
<dd class="field-odd"><p>List</p> |
||||
</dd> |
||||
</dl> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="json_importer.JsonImporter.one_level_normalizing"> |
||||
<code class="sig-name descname">one_level_normalizing</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">raw_data</span><span class="p">:</span> <span class="n">List</span></em>, <em class="sig-param"><span class="n">indx</span><span class="p">:</span> <span class="n">int</span></em>, <em class="sig-param"><span class="n">key</span><span class="p">:</span> <span class="n">str</span></em><span class="sig-paren">)</span> → pandas.core.frame.DataFrame<a class="headerlink" href="#json_importer.JsonImporter.one_level_normalizing" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Extracts the one-level nested data in the list <code class="docutils literal notranslate"><span class="pre">raw_data</span></code> at the index <code class="docutils literal notranslate"><span class="pre">indx</span></code> at the key <code class="docutils literal notranslate"><span class="pre">key</span></code>.</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><ul class="simple"> |
||||
<li><p><strong>raw_data</strong> (<em>List</em>) – List of Dicts</p></li> |
||||
<li><p><strong>indx</strong> (<em>int</em>) – The index of the array from which the data have to be extracted</p></li> |
||||
<li><p><strong>key</strong> (<em>string</em>) – the key for the Dicts from which exctract data</p></li> |
||||
</ul> |
||||
</dd> |
||||
<dt class="field-even">Returns</dt> |
||||
<dd class="field-even"><p>A normalized dataframe</p> |
||||
</dd> |
||||
<dt class="field-odd">Return type</dt> |
||||
<dd class="field-odd"><p>pandas.Datframe</p> |
||||
</dd> |
||||
</dl> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="json_importer.JsonImporter.read_json_file"> |
||||
<code class="sig-name descname">read_json_file</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → List<a class="headerlink" href="#json_importer.JsonImporter.read_json_file" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Reads the JSON file in the path self.filePath</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Returns</dt> |
||||
<dd class="field-odd"><p>The contents of the json file</p> |
||||
</dd> |
||||
<dt class="field-even">Return type</dt> |
||||
<dd class="field-even"><p>List</p> |
||||
</dd> |
||||
</dl> |
||||
</dd></dl> |
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<div class="section" id="scripts"> |
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<h1>scripts<a class="headerlink" href="#scripts" title="Permalink to this headline">¶</a></h1> |
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<div class="toctree-wrapper compound"> |
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<ul> |
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<li class="toctree-l1"><a class="reference internal" href="abstract_importer.html">abstract_importer module</a></li> |
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<li class="toctree-l1"><a class="reference internal" href="cache.html">cache module</a></li> |
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<li class="toctree-l1"><a class="reference internal" href="conditional_intensity_matrix.html">conditional_intensity_matrix module</a></li> |
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<li class="toctree-l1"><a class="reference internal" href="json_importer.html">json_importer module</a></li> |
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<li class="toctree-l1"><a class="reference internal" href="network_graph.html">network_graph module</a></li> |
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<li class="toctree-l1"><a class="reference internal" href="parameters_estimator.html">parameters_estimator module</a></li> |
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<li class="toctree-l1"><a class="reference internal" href="trajectory.html">trajectory module</a></li> |
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<div class="section" id="module-network_graph"> |
||||
<span id="network-graph-module"></span><h1>network_graph module<a class="headerlink" href="#module-network_graph" title="Permalink to this headline">¶</a></h1> |
||||
<dl class="py class"> |
||||
<dt id="network_graph.NetworkGraph"> |
||||
<em class="property">class </em><code class="sig-prename descclassname">network_graph.</code><code class="sig-name descname">NetworkGraph</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">graph_struct</span><span class="p">:</span> <span class="n"><a class="reference internal" href="structure.html#structure.Structure" title="structure.Structure">structure.Structure</a></span></em><span class="sig-paren">)</span><a class="headerlink" href="#network_graph.NetworkGraph" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p> |
||||
<p>Abstracts the infos contained in the Structure class in the form of a directed graph. |
||||
Has the task of creating all the necessary filtering and indexing structures for parameters estimation</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><p><strong>graph_struct</strong> (<a class="reference internal" href="structure.html#structure.Structure" title="structure.Structure"><em>Structure</em></a>) – the <code class="docutils literal notranslate"><span class="pre">Structure</span></code> object from which infos about the net will be extracted</p> |
||||
</dd> |
||||
<dt class="field-even">_graph</dt> |
||||
<dd class="field-even"><p>directed graph</p> |
||||
</dd> |
||||
<dt class="field-odd">_aggregated_info_about_nodes_parents</dt> |
||||
<dd class="field-odd"><p>a structure that contains all the necessary infos |
||||
about every parents of the node of which all the indexing and filtering structures will be constructed.</p> |
||||
</dd> |
||||
<dt class="field-even">_time_scalar_indexing_structure</dt> |
||||
<dd class="field-even"><p>the indexing structure for state res time estimation</p> |
||||
</dd> |
||||
<dt class="field-odd">_transition_scalar_indexing_structure</dt> |
||||
<dd class="field-odd"><p>the indexing structure for transition computation</p> |
||||
</dd> |
||||
<dt class="field-even">_time_filtering</dt> |
||||
<dd class="field-even"><p>the columns filtering structure used in the computation of the state res times</p> |
||||
</dd> |
||||
<dt class="field-odd">_transition_filtering</dt> |
||||
<dd class="field-odd"><p>the columns filtering structure used in the computation of the transition |
||||
from one state to another</p> |
||||
</dd> |
||||
<dt class="field-even">_p_combs_structure</dt> |
||||
<dd class="field-even"><p>all the possible parents states combination for the node of interest</p> |
||||
</dd> |
||||
</dl> |
||||
<dl class="py method"> |
||||
<dt id="network_graph.NetworkGraph.add_edges"> |
||||
<code class="sig-name descname">add_edges</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">list_of_edges</span><span class="p">:</span> <span class="n">List</span></em><span class="sig-paren">)</span> → None<a class="headerlink" href="#network_graph.NetworkGraph.add_edges" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Add the edges to the <code class="docutils literal notranslate"><span class="pre">_graph</span></code> contained in the list <code class="docutils literal notranslate"><span class="pre">list_of_edges</span></code>.</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><p><strong>list_of_edges</strong> (<em>List</em>) – the list containing of tuples containing the edges</p> |
||||
</dd> |
||||
</dl> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="network_graph.NetworkGraph.add_nodes"> |
||||
<code class="sig-name descname">add_nodes</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">list_of_nodes</span><span class="p">:</span> <span class="n">List</span></em><span class="sig-paren">)</span> → None<a class="headerlink" href="#network_graph.NetworkGraph.add_nodes" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Adds the nodes to the <code class="docutils literal notranslate"><span class="pre">_graph</span></code> contained in the list of nodes <code class="docutils literal notranslate"><span class="pre">list_of_nodes</span></code>. |
||||
Sets all the properties that identify a nodes (index, positional index, cardinality)</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><p><strong>list_of_nodes</strong> (<em>List</em>) – the nodes to add to <code class="docutils literal notranslate"><span class="pre">_graph</span></code></p> |
||||
</dd> |
||||
</dl> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="network_graph.NetworkGraph.build_p_comb_structure_for_a_node"> |
||||
<code class="sig-name descname">build_p_comb_structure_for_a_node</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">parents_values</span><span class="p">:</span> <span class="n">List</span></em><span class="sig-paren">)</span> → numpy.ndarray<a class="headerlink" href="#network_graph.NetworkGraph.build_p_comb_structure_for_a_node" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Builds the combinatorial structure that contains the combinations of all the values contained in |
||||
<code class="docutils literal notranslate"><span class="pre">parents_values</span></code>.</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><p><strong>parents_values</strong> (<em>List</em>) – the cardinalities of the nodes</p> |
||||
</dd> |
||||
<dt class="field-even">Returns</dt> |
||||
<dd class="field-even"><p>A numpy matrix containing a grid of the combinations</p> |
||||
</dd> |
||||
<dt class="field-odd">Return type</dt> |
||||
<dd class="field-odd"><p>numpy.ndArray</p> |
||||
</dd> |
||||
</dl> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="network_graph.NetworkGraph.build_time_columns_filtering_for_a_node"> |
||||
<code class="sig-name descname">build_time_columns_filtering_for_a_node</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">node_indx</span><span class="p">:</span> <span class="n">int</span></em>, <em class="sig-param"><span class="n">p_indxs</span><span class="p">:</span> <span class="n">List</span></em><span class="sig-paren">)</span> → numpy.ndarray<a class="headerlink" href="#network_graph.NetworkGraph.build_time_columns_filtering_for_a_node" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Builds the necessary structure to filter the desired columns indicated by <code class="docutils literal notranslate"><span class="pre">node_indx</span></code> and <code class="docutils literal notranslate"><span class="pre">p_indxs</span></code> |
||||
in the dataset. |
||||
This structute will be used in the computation of the state res times. |
||||
:param node_indx: the index of the node |
||||
:type node_indx: int |
||||
:param p_indxs: the indexes of the node’s parents |
||||
:type p_indxs: List |
||||
:return: The filtering structure for times estimation |
||||
:rtype: numpy.ndArray</p> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="network_graph.NetworkGraph.build_time_scalar_indexing_structure_for_a_node"> |
||||
<code class="sig-name descname">build_time_scalar_indexing_structure_for_a_node</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">node_id</span><span class="p">:</span> <span class="n">str</span></em>, <em class="sig-param"><span class="n">parents_vals</span><span class="p">:</span> <span class="n">List</span></em><span class="sig-paren">)</span> → numpy.ndarray<a class="headerlink" href="#network_graph.NetworkGraph.build_time_scalar_indexing_structure_for_a_node" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Builds an indexing structure for the computation of state residence times values.</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><ul class="simple"> |
||||
<li><p><strong>node_id</strong> (<em>string</em>) – the node label</p></li> |
||||
<li><p><strong>parents_vals</strong> (<em>List</em>) – the caridinalites of the node’s parents</p></li> |
||||
</ul> |
||||
</dd> |
||||
<dt class="field-even">Returns</dt> |
||||
<dd class="field-even"><p>The time indexing structure</p> |
||||
</dd> |
||||
<dt class="field-odd">Return type</dt> |
||||
<dd class="field-odd"><p>numpy.ndArray</p> |
||||
</dd> |
||||
</dl> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="network_graph.NetworkGraph.build_transition_filtering_for_a_node"> |
||||
<code class="sig-name descname">build_transition_filtering_for_a_node</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">node_indx</span><span class="p">:</span> <span class="n">int</span></em>, <em class="sig-param"><span class="n">p_indxs</span><span class="p">:</span> <span class="n">List</span></em><span class="sig-paren">)</span> → numpy.ndarray<a class="headerlink" href="#network_graph.NetworkGraph.build_transition_filtering_for_a_node" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Builds the necessary structure to filter the desired columns indicated by <code class="docutils literal notranslate"><span class="pre">node_indx</span></code> and <code class="docutils literal notranslate"><span class="pre">p_indxs</span></code> |
||||
in the dataset. |
||||
This structure will be used in the computation of the state transitions values. |
||||
:param node_indx: the index of the node |
||||
:type node_indx: int |
||||
:param p_indxs: the indexes of the node’s parents |
||||
:type p_indxs: List |
||||
:return: The filtering structure for transitions estimation |
||||
:rtype: numpy.ndArray</p> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="network_graph.NetworkGraph.build_transition_scalar_indexing_structure_for_a_node"> |
||||
<code class="sig-name descname">build_transition_scalar_indexing_structure_for_a_node</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">node_id</span><span class="p">:</span> <span class="n">str</span></em>, <em class="sig-param"><span class="n">parents_vals</span><span class="p">:</span> <span class="n">List</span></em><span class="sig-paren">)</span> → numpy.ndarray<a class="headerlink" href="#network_graph.NetworkGraph.build_transition_scalar_indexing_structure_for_a_node" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Builds an indexing structure for the computation of state transitions values.</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><ul class="simple"> |
||||
<li><p><strong>node_id</strong> (<em>string</em>) – the node label</p></li> |
||||
<li><p><strong>parents_vals</strong> (<em>List</em>) – the caridinalites of the node’s parents</p></li> |
||||
</ul> |
||||
</dd> |
||||
<dt class="field-even">Returns</dt> |
||||
<dd class="field-even"><p>The transition indexing structure</p> |
||||
</dd> |
||||
<dt class="field-odd">Return type</dt> |
||||
<dd class="field-odd"><p>numpy.ndArray</p> |
||||
</dd> |
||||
</dl> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="network_graph.NetworkGraph.edges"> |
||||
<em class="property">property </em><code class="sig-name descname">edges</code><a class="headerlink" href="#network_graph.NetworkGraph.edges" title="Permalink to this definition">¶</a></dt> |
||||
<dd></dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="network_graph.NetworkGraph.fast_init"> |
||||
<code class="sig-name descname">fast_init</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">node_id</span><span class="p">:</span> <span class="n">str</span></em><span class="sig-paren">)</span> → None<a class="headerlink" href="#network_graph.NetworkGraph.fast_init" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Initializes all the necessary structures for parameters estimation of the node identified by the label |
||||
node_id</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><p><strong>node_id</strong> (<em>string</em>) – the label of the node</p> |
||||
</dd> |
||||
</dl> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="network_graph.NetworkGraph.get_node_indx"> |
||||
<code class="sig-name descname">get_node_indx</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">node_id</span></em><span class="sig-paren">)</span> → int<a class="headerlink" href="#network_graph.NetworkGraph.get_node_indx" title="Permalink to this definition">¶</a></dt> |
||||
<dd></dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="network_graph.NetworkGraph.get_ordered_by_indx_set_of_parents"> |
||||
<code class="sig-name descname">get_ordered_by_indx_set_of_parents</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">node</span><span class="p">:</span> <span class="n">str</span></em><span class="sig-paren">)</span> → Tuple<a class="headerlink" href="#network_graph.NetworkGraph.get_ordered_by_indx_set_of_parents" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Builds the aggregated structure that holds all the infos relative to the parent set of the node, namely |
||||
(parents_labels, parents_indexes, parents_cardinalities).</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><p><strong>node</strong> (<em>string</em>) – the label of the node</p> |
||||
</dd> |
||||
<dt class="field-even">Returns</dt> |
||||
<dd class="field-even"><p>a tuple containing all the parent set infos</p> |
||||
</dd> |
||||
<dt class="field-odd">Return type</dt> |
||||
<dd class="field-odd"><p>Tuple</p> |
||||
</dd> |
||||
</dl> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="network_graph.NetworkGraph.get_parents_by_id"> |
||||
<code class="sig-name descname">get_parents_by_id</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">node_id</span></em><span class="sig-paren">)</span> → List<a class="headerlink" href="#network_graph.NetworkGraph.get_parents_by_id" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Returns a list of labels of the parents of the node <code class="docutils literal notranslate"><span class="pre">node_id</span></code></p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><p><strong>node_id</strong> (<em>string</em>) – the node label</p> |
||||
</dd> |
||||
<dt class="field-even">Returns</dt> |
||||
<dd class="field-even"><p>a List of labels of the parents</p> |
||||
</dd> |
||||
<dt class="field-odd">Return type</dt> |
||||
<dd class="field-odd"><p>List</p> |
||||
</dd> |
||||
</dl> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="network_graph.NetworkGraph.get_positional_node_indx"> |
||||
<code class="sig-name descname">get_positional_node_indx</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">node_id</span></em><span class="sig-paren">)</span> → int<a class="headerlink" href="#network_graph.NetworkGraph.get_positional_node_indx" title="Permalink to this definition">¶</a></dt> |
||||
<dd></dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="network_graph.NetworkGraph.get_states_number"> |
||||
<code class="sig-name descname">get_states_number</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">node_id</span></em><span class="sig-paren">)</span> → int<a class="headerlink" href="#network_graph.NetworkGraph.get_states_number" title="Permalink to this definition">¶</a></dt> |
||||
<dd></dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="network_graph.NetworkGraph.nodes"> |
||||
<em class="property">property </em><code class="sig-name descname">nodes</code><a class="headerlink" href="#network_graph.NetworkGraph.nodes" title="Permalink to this definition">¶</a></dt> |
||||
<dd></dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="network_graph.NetworkGraph.nodes_indexes"> |
||||
<em class="property">property </em><code class="sig-name descname">nodes_indexes</code><a class="headerlink" href="#network_graph.NetworkGraph.nodes_indexes" title="Permalink to this definition">¶</a></dt> |
||||
<dd></dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="network_graph.NetworkGraph.nodes_values"> |
||||
<em class="property">property </em><code class="sig-name descname">nodes_values</code><a class="headerlink" href="#network_graph.NetworkGraph.nodes_values" title="Permalink to this definition">¶</a></dt> |
||||
<dd></dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="network_graph.NetworkGraph.p_combs"> |
||||
<em class="property">property </em><code class="sig-name descname">p_combs</code><a class="headerlink" href="#network_graph.NetworkGraph.p_combs" title="Permalink to this definition">¶</a></dt> |
||||
<dd></dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="network_graph.NetworkGraph.time_filtering"> |
||||
<em class="property">property </em><code class="sig-name descname">time_filtering</code><a class="headerlink" href="#network_graph.NetworkGraph.time_filtering" title="Permalink to this definition">¶</a></dt> |
||||
<dd></dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="network_graph.NetworkGraph.time_scalar_indexing_strucure"> |
||||
<em class="property">property </em><code class="sig-name descname">time_scalar_indexing_strucure</code><a class="headerlink" href="#network_graph.NetworkGraph.time_scalar_indexing_strucure" title="Permalink to this definition">¶</a></dt> |
||||
<dd></dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="network_graph.NetworkGraph.transition_filtering"> |
||||
<em class="property">property </em><code class="sig-name descname">transition_filtering</code><a class="headerlink" href="#network_graph.NetworkGraph.transition_filtering" title="Permalink to this definition">¶</a></dt> |
||||
<dd></dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="network_graph.NetworkGraph.transition_scalar_indexing_structure"> |
||||
<em class="property">property </em><code class="sig-name descname">transition_scalar_indexing_structure</code><a class="headerlink" href="#network_graph.NetworkGraph.transition_scalar_indexing_structure" title="Permalink to this definition">¶</a></dt> |
||||
<dd></dd></dl> |
||||
|
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<div class="section" id="module-parameters_estimator"> |
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<span id="parameters-estimator-module"></span><h1>parameters_estimator module<a class="headerlink" href="#module-parameters_estimator" title="Permalink to this headline">¶</a></h1> |
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<dl class="py class"> |
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<dt id="parameters_estimator.ParametersEstimator"> |
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<em class="property">class </em><code class="sig-prename descclassname">parameters_estimator.</code><code class="sig-name descname">ParametersEstimator</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">trajectories</span><span class="p">:</span> <span class="n"><a class="reference internal" href="trajectory.html#trajectory.Trajectory" title="trajectory.Trajectory">trajectory.Trajectory</a></span></em>, <em class="sig-param"><span class="n">net_graph</span><span class="p">:</span> <span class="n"><a class="reference internal" href="network_graph.html#network_graph.NetworkGraph" title="network_graph.NetworkGraph">network_graph.NetworkGraph</a></span></em><span class="sig-paren">)</span><a class="headerlink" href="#parameters_estimator.ParametersEstimator" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p> |
||||
<p>Has the task of computing the cims of particular node given the trajectories and the net structure |
||||
in the graph <code class="docutils literal notranslate"><span class="pre">_net_graph</span></code>.</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><ul class="simple"> |
||||
<li><p><strong>trajectories</strong> (<a class="reference internal" href="trajectory.html#trajectory.Trajectory" title="trajectory.Trajectory"><em>Trajectory</em></a>) – the trajectories</p></li> |
||||
<li><p><strong>net_graph</strong> (<a class="reference internal" href="network_graph.html#network_graph.NetworkGraph" title="network_graph.NetworkGraph"><em>NetworkGraph</em></a>) – the net structure</p></li> |
||||
</ul> |
||||
</dd> |
||||
<dt class="field-even">_single_set_of_cims</dt> |
||||
<dd class="field-even"><p>the set of cims object that will hold the cims of the node</p> |
||||
</dd> |
||||
</dl> |
||||
<dl class="py method"> |
||||
<dt id="parameters_estimator.ParametersEstimator.compute_parameters_for_node"> |
||||
<code class="sig-name descname">compute_parameters_for_node</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">node_id</span><span class="p">:</span> <span class="n">str</span></em><span class="sig-paren">)</span> → <a class="reference internal" href="set_of_cims.html#set_of_cims.SetOfCims" title="set_of_cims.SetOfCims">set_of_cims.SetOfCims</a><a class="headerlink" href="#parameters_estimator.ParametersEstimator.compute_parameters_for_node" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Compute the CIMS of the node identified by the label <code class="docutils literal notranslate"><span class="pre">node_id</span></code>.</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><p><strong>node_id</strong> (<em>string</em>) – the node label</p> |
||||
</dd> |
||||
<dt class="field-even">Returns</dt> |
||||
<dd class="field-even"><p>A SetOfCims object filled with the computed CIMS</p> |
||||
</dd> |
||||
<dt class="field-odd">Return type</dt> |
||||
<dd class="field-odd"><p><a class="reference internal" href="set_of_cims.html#set_of_cims.SetOfCims" title="set_of_cims.SetOfCims">SetOfCims</a></p> |
||||
</dd> |
||||
</dl> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="parameters_estimator.ParametersEstimator.compute_state_res_time_for_node"> |
||||
<code class="sig-name descname">compute_state_res_time_for_node</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">node_indx</span><span class="p">:</span> <span class="n">int</span></em>, <em class="sig-param"><span class="n">times</span><span class="p">:</span> <span class="n">numpy.ndarray</span></em>, <em class="sig-param"><span class="n">trajectory</span><span class="p">:</span> <span class="n">numpy.ndarray</span></em>, <em class="sig-param"><span class="n">cols_filter</span><span class="p">:</span> <span class="n">numpy.ndarray</span></em>, <em class="sig-param"><span class="n">scalar_indexes_struct</span><span class="p">:</span> <span class="n">numpy.ndarray</span></em>, <em class="sig-param"><span class="n">T</span><span class="p">:</span> <span class="n">numpy.ndarray</span></em><span class="sig-paren">)</span> → None<a class="headerlink" href="#parameters_estimator.ParametersEstimator.compute_state_res_time_for_node" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Compute the state residence times for a node and fill the matrix <code class="docutils literal notranslate"><span class="pre">T</span></code> with the results</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><ul class="simple"> |
||||
<li><p><strong>node_indx</strong> (<em>int</em>) – the index of the node</p></li> |
||||
<li><p><strong>times</strong> (<em>numpy.array</em>) – the times deltas vector</p></li> |
||||
<li><p><strong>trajectory</strong> (<em>numpy.ndArray</em>) – the trajectory</p></li> |
||||
<li><p><strong>cols_filter</strong> (<em>numpy.array</em>) – the columns filtering structure</p></li> |
||||
<li><p><strong>scalar_indexes_struct</strong> (<em>numpy.array</em>) – the indexing structure</p></li> |
||||
<li><p><strong>T</strong> (<em>numpy.ndArray</em>) – the state residence times vectors</p></li> |
||||
</ul> |
||||
</dd> |
||||
</dl> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="parameters_estimator.ParametersEstimator.compute_state_transitions_for_a_node"> |
||||
<code class="sig-name descname">compute_state_transitions_for_a_node</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">node_indx</span><span class="p">:</span> <span class="n">int</span></em>, <em class="sig-param"><span class="n">trajectory</span><span class="p">:</span> <span class="n">numpy.ndarray</span></em>, <em class="sig-param"><span class="n">cols_filter</span><span class="p">:</span> <span class="n">numpy.ndarray</span></em>, <em class="sig-param"><span class="n">scalar_indexing</span><span class="p">:</span> <span class="n">numpy.ndarray</span></em>, <em class="sig-param"><span class="n">M</span><span class="p">:</span> <span class="n">numpy.ndarray</span></em><span class="sig-paren">)</span><a class="headerlink" href="#parameters_estimator.ParametersEstimator.compute_state_transitions_for_a_node" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Compute the state residence times for a node and fill the matrices <code class="docutils literal notranslate"><span class="pre">M</span></code> with the results.</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><ul class="simple"> |
||||
<li><p><strong>node_indx</strong> (<em>int</em>) – the index of the node</p></li> |
||||
<li><p><strong>trajectory</strong> (<em>numpy.ndArray</em>) – the trajectory</p></li> |
||||
<li><p><strong>cols_filter</strong> (<em>numpy.array</em>) – the columns filtering structure</p></li> |
||||
<li><p><strong>scalar_indexing</strong> (<em>numpy.array</em>) – the indexing structure</p></li> |
||||
<li><p><strong>M</strong> (<em>numpy.ndArray</em>) – the state transitions matrices</p></li> |
||||
</ul> |
||||
</dd> |
||||
</dl> |
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</dd></dl> |
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|
||||
<dl class="py method"> |
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<dt id="parameters_estimator.ParametersEstimator.fast_init"> |
||||
<code class="sig-name descname">fast_init</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">node_id</span><span class="p">:</span> <span class="n">str</span></em><span class="sig-paren">)</span> → None<a class="headerlink" href="#parameters_estimator.ParametersEstimator.fast_init" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Initializes all the necessary structures for the parameters estimation for the node <code class="docutils literal notranslate"><span class="pre">node_id</span></code>.</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><p><strong>node_id</strong> (<em>string</em>) – the node label</p> |
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</dd> |
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<span id="sample-path-module"></span><h1>sample_path module<a class="headerlink" href="#module-sample_path" title="Permalink to this headline">¶</a></h1> |
||||
<dl class="py class"> |
||||
<dt id="sample_path.SamplePath"> |
||||
<em class="property">class </em><code class="sig-prename descclassname">sample_path.</code><code class="sig-name descname">SamplePath</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">importer</span><span class="p">:</span> <span class="n"><a class="reference internal" href="abstract_importer.html#abstract_importer.AbstractImporter" title="abstract_importer.AbstractImporter">abstract_importer.AbstractImporter</a></span></em><span class="sig-paren">)</span><a class="headerlink" href="#sample_path.SamplePath" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p> |
||||
<p>Aggregates all the informations about the trajectories, the real structure of the sampled net and variables |
||||
cardinalites. Has the task of creating the objects <code class="docutils literal notranslate"><span class="pre">Trajectory</span></code> and <code class="docutils literal notranslate"><span class="pre">Structure</span></code> that will |
||||
contain the mentioned data.</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><p><strong>importer</strong> (<a class="reference internal" href="abstract_importer.html#abstract_importer.AbstractImporter" title="abstract_importer.AbstractImporter"><em>AbstractImporter</em></a>) – the Importer objects that will import ad process data</p> |
||||
</dd> |
||||
<dt class="field-even">_trajectories</dt> |
||||
<dd class="field-even"><p>the <code class="docutils literal notranslate"><span class="pre">Trajectory</span></code> object that will contain all the concatenated trajectories</p> |
||||
</dd> |
||||
<dt class="field-odd">_structure</dt> |
||||
<dd class="field-odd"><p>the <code class="docutils literal notranslate"><span class="pre">Structure</span></code> Object that will contain all the structurral infos about the net</p> |
||||
</dd> |
||||
<dt class="field-even">_total_variables_count</dt> |
||||
<dd class="field-even"><p>the number of variables in the net</p> |
||||
</dd> |
||||
</dl> |
||||
<dl class="py method"> |
||||
<dt id="sample_path.SamplePath.build_structure"> |
||||
<code class="sig-name descname">build_structure</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → None<a class="headerlink" href="#sample_path.SamplePath.build_structure" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Builds the <code class="docutils literal notranslate"><span class="pre">Structure</span></code> object that aggregates all the infos about the net.</p> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="sample_path.SamplePath.build_trajectories"> |
||||
<code class="sig-name descname">build_trajectories</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → None<a class="headerlink" href="#sample_path.SamplePath.build_trajectories" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Builds the Trajectory object that will contain all the trajectories. |
||||
Clears all the unused dataframes in <code class="docutils literal notranslate"><span class="pre">_importer</span></code> Object</p> |
||||
</dd></dl> |
||||
|
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<dl class="py method"> |
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<dt id="sample_path.SamplePath.structure"> |
||||
<em class="property">property </em><code class="sig-name descname">structure</code><a class="headerlink" href="#sample_path.SamplePath.structure" title="Permalink to this definition">¶</a></dt> |
||||
<dd></dd></dl> |
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<dt id="sample_path.SamplePath.total_variables_count"> |
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|
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<dt id="sample_path.SamplePath.trajectories"> |
||||
<em class="property">property </em><code class="sig-name descname">trajectories</code><a class="headerlink" href="#sample_path.SamplePath.trajectories" title="Permalink to this definition">¶</a></dt> |
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<div class="section" id="module-set_of_cims"> |
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<span id="set-of-cims-module"></span><h1>set_of_cims module<a class="headerlink" href="#module-set_of_cims" title="Permalink to this headline">¶</a></h1> |
||||
<dl class="py class"> |
||||
<dt id="set_of_cims.SetOfCims"> |
||||
<em class="property">class </em><code class="sig-prename descclassname">set_of_cims.</code><code class="sig-name descname">SetOfCims</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">node_id</span><span class="p">:</span> <span class="n">str</span></em>, <em class="sig-param"><span class="n">parents_states_number</span><span class="p">:</span> <span class="n">List</span></em>, <em class="sig-param"><span class="n">node_states_number</span><span class="p">:</span> <span class="n">int</span></em>, <em class="sig-param"><span class="n">p_combs</span><span class="p">:</span> <span class="n">numpy.ndarray</span></em><span class="sig-paren">)</span><a class="headerlink" href="#set_of_cims.SetOfCims" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p> |
||||
<p>Aggregates all the CIMS of the node identified by the label _node_id.</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><ul class="simple"> |
||||
<li><p><strong>node_id</strong> – the node label</p></li> |
||||
<li><p><strong>parents_states_number</strong> (<em>List</em>) – the cardinalities of the parents</p></li> |
||||
<li><p><strong>node_states_number</strong> (<em>int</em>) – the caridinality of the node</p></li> |
||||
<li><p><strong>p_combs</strong> (<em>numpy.ndArray</em>) – the p_comb structure bound to this node</p></li> |
||||
</ul> |
||||
</dd> |
||||
<dt class="field-even">_state_residence_time</dt> |
||||
<dd class="field-even"><p>matrix containing all the state residence time vectors for the node</p> |
||||
</dd> |
||||
<dt class="field-odd">_transition_matrices</dt> |
||||
<dd class="field-odd"><p>matrix containing all the transition matrices for the node</p> |
||||
</dd> |
||||
<dt class="field-even">_actual_cims</dt> |
||||
<dd class="field-even"><p>the cims of the node</p> |
||||
</dd> |
||||
</dl> |
||||
<dl class="py method"> |
||||
<dt id="set_of_cims.SetOfCims.actual_cims"> |
||||
<em class="property">property </em><code class="sig-name descname">actual_cims</code><a class="headerlink" href="#set_of_cims.SetOfCims.actual_cims" title="Permalink to this definition">¶</a></dt> |
||||
<dd></dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="set_of_cims.SetOfCims.build_cims"> |
||||
<code class="sig-name descname">build_cims</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">state_res_times</span><span class="p">:</span> <span class="n">numpy.ndarray</span></em>, <em class="sig-param"><span class="n">transition_matrices</span><span class="p">:</span> <span class="n">numpy.ndarray</span></em><span class="sig-paren">)</span> → None<a class="headerlink" href="#set_of_cims.SetOfCims.build_cims" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Build the <code class="docutils literal notranslate"><span class="pre">ConditionalIntensityMatrix</span></code> objects given the state residence times and transitions matrices. |
||||
Compute the cim coefficients.The class member <code class="docutils literal notranslate"><span class="pre">_actual_cims</span></code> will contain the computed cims.</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><ul class="simple"> |
||||
<li><p><strong>state_res_times</strong> (<em>numpy.ndArray</em>) – the state residence times matrix</p></li> |
||||
<li><p><strong>transition_matrices</strong> (<em>numpy.ndArray</em>) – the transition matrices</p></li> |
||||
</ul> |
||||
</dd> |
||||
</dl> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="set_of_cims.SetOfCims.build_times_and_transitions_structures"> |
||||
<code class="sig-name descname">build_times_and_transitions_structures</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → None<a class="headerlink" href="#set_of_cims.SetOfCims.build_times_and_transitions_structures" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Initializes at the correct dimensions the state residence times matrix and the state transition matrices.</p> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="set_of_cims.SetOfCims.filter_cims_with_mask"> |
||||
<code class="sig-name descname">filter_cims_with_mask</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">mask_arr</span><span class="p">:</span> <span class="n">numpy.ndarray</span></em>, <em class="sig-param"><span class="n">comb</span><span class="p">:</span> <span class="n">List</span></em><span class="sig-paren">)</span> → numpy.ndarray<a class="headerlink" href="#set_of_cims.SetOfCims.filter_cims_with_mask" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Filter the cims contained in the array <code class="docutils literal notranslate"><span class="pre">_actual_cims</span></code> given the boolean mask <code class="docutils literal notranslate"><span class="pre">mask_arr</span></code> and the index |
||||
<code class="docutils literal notranslate"><span class="pre">comb</span></code>.</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><ul class="simple"> |
||||
<li><p><strong>mask_arr</strong> (<em>numpy.array</em>) – the boolean mask that indicates which parent to consider</p></li> |
||||
<li><p><strong>comb</strong> (<em>numpy.array</em>) – the state/s of the filtered parents</p></li> |
||||
</ul> |
||||
</dd> |
||||
<dt class="field-even">Returns</dt> |
||||
<dd class="field-even"><p>Array of <code class="docutils literal notranslate"><span class="pre">ConditionalIntensityMatrix</span></code> objects</p> |
||||
</dd> |
||||
<dt class="field-odd">Return type</dt> |
||||
<dd class="field-odd"><p>numpy.array</p> |
||||
</dd> |
||||
</dl> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="set_of_cims.SetOfCims.get_cims_number"> |
||||
<code class="sig-name descname">get_cims_number</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#set_of_cims.SetOfCims.get_cims_number" title="Permalink to this definition">¶</a></dt> |
||||
<dd></dd></dl> |
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|
||||
<dl class="py method"> |
||||
<dt id="set_of_cims.SetOfCims.p_combs"> |
||||
<em class="property">property </em><code class="sig-name descname">p_combs</code><a class="headerlink" href="#set_of_cims.SetOfCims.p_combs" title="Permalink to this definition">¶</a></dt> |
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<dd></dd></dl> |
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<li><a class="reference internal" href="#">structure module</a></li> |
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<div class="section" id="module-structure"> |
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<span id="structure-module"></span><h1>structure module<a class="headerlink" href="#module-structure" title="Permalink to this headline">¶</a></h1> |
||||
<dl class="py class"> |
||||
<dt id="structure.Structure"> |
||||
<em class="property">class </em><code class="sig-prename descclassname">structure.</code><code class="sig-name descname">Structure</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">nodes_labels_list</span><span class="p">:</span> <span class="n">List</span></em>, <em class="sig-param"><span class="n">nodes_indexes_arr</span><span class="p">:</span> <span class="n">numpy.ndarray</span></em>, <em class="sig-param"><span class="n">nodes_vals_arr</span><span class="p">:</span> <span class="n">numpy.ndarray</span></em>, <em class="sig-param"><span class="n">edges_list</span><span class="p">:</span> <span class="n">List</span></em>, <em class="sig-param"><span class="n">total_variables_number</span><span class="p">:</span> <span class="n">int</span></em><span class="sig-paren">)</span><a class="headerlink" href="#structure.Structure" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p> |
||||
<p>Contains all the infos about the network structure(nodes labels, nodes caridinalites, edges, indexes)</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><ul class="simple"> |
||||
<li><p><strong>nodes_labels_list</strong> (<em>List</em>) – the symbolic names of the variables</p></li> |
||||
<li><p><strong>nodes_indexes_arr</strong> (<em>numpy.ndArray</em>) – the indexes of the nodes</p></li> |
||||
<li><p><strong>nodes_vals_arr</strong> (<em>numpy.ndArray</em>) – the cardinalites of the nodes</p></li> |
||||
<li><p><strong>edges_list</strong> (<em>List</em>) – the edges of the network</p></li> |
||||
<li><p><strong>total_variables_number</strong> (<em>int</em>) – the total number of variables in the net</p></li> |
||||
</ul> |
||||
</dd> |
||||
</dl> |
||||
<dl class="py method"> |
||||
<dt id="structure.Structure.edges"> |
||||
<em class="property">property </em><code class="sig-name descname">edges</code><a class="headerlink" href="#structure.Structure.edges" title="Permalink to this definition">¶</a></dt> |
||||
<dd></dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="structure.Structure.get_node_id"> |
||||
<code class="sig-name descname">get_node_id</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">node_indx</span><span class="p">:</span> <span class="n">int</span></em><span class="sig-paren">)</span> → str<a class="headerlink" href="#structure.Structure.get_node_id" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Given the <code class="docutils literal notranslate"><span class="pre">node_index</span></code> returns the node label.</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><p><strong>node_indx</strong> (<em>int</em>) – the node index</p> |
||||
</dd> |
||||
<dt class="field-even">Returns</dt> |
||||
<dd class="field-even"><p>the node label</p> |
||||
</dd> |
||||
<dt class="field-odd">Return type</dt> |
||||
<dd class="field-odd"><p>string</p> |
||||
</dd> |
||||
</dl> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="structure.Structure.get_node_indx"> |
||||
<code class="sig-name descname">get_node_indx</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">node_id</span><span class="p">:</span> <span class="n">str</span></em><span class="sig-paren">)</span> → int<a class="headerlink" href="#structure.Structure.get_node_indx" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Given the <code class="docutils literal notranslate"><span class="pre">node_index</span></code> returns the node label.</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><p><strong>node_id</strong> (<em>string</em>) – the node label</p> |
||||
</dd> |
||||
<dt class="field-even">Returns</dt> |
||||
<dd class="field-even"><p>the node index</p> |
||||
</dd> |
||||
<dt class="field-odd">Return type</dt> |
||||
<dd class="field-odd"><p>int</p> |
||||
</dd> |
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</dl> |
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</dd></dl> |
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|
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<dl class="py method"> |
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<dt id="structure.Structure.get_positional_node_indx"> |
||||
<code class="sig-name descname">get_positional_node_indx</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">node_id</span><span class="p">:</span> <span class="n">str</span></em><span class="sig-paren">)</span> → int<a class="headerlink" href="#structure.Structure.get_positional_node_indx" title="Permalink to this definition">¶</a></dt> |
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<dd></dd></dl> |
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|
||||
<dl class="py method"> |
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<dt id="structure.Structure.get_states_number"> |
||||
<code class="sig-name descname">get_states_number</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">node</span><span class="p">:</span> <span class="n">str</span></em><span class="sig-paren">)</span> → int<a class="headerlink" href="#structure.Structure.get_states_number" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Given the node label <code class="docutils literal notranslate"><span class="pre">node</span></code> returns the cardinality of the node.</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><p><strong>node</strong> (<em>string</em>) – the node label</p> |
||||
</dd> |
||||
<dt class="field-even">Returns</dt> |
||||
<dd class="field-even"><p>the node cardinality</p> |
||||
</dd> |
||||
<dt class="field-odd">Return type</dt> |
||||
<dd class="field-odd"><p>int</p> |
||||
</dd> |
||||
</dl> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="structure.Structure.nodes_indexes"> |
||||
<em class="property">property </em><code class="sig-name descname">nodes_indexes</code><a class="headerlink" href="#structure.Structure.nodes_indexes" title="Permalink to this definition">¶</a></dt> |
||||
<dd></dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="structure.Structure.nodes_labels"> |
||||
<em class="property">property </em><code class="sig-name descname">nodes_labels</code><a class="headerlink" href="#structure.Structure.nodes_labels" title="Permalink to this definition">¶</a></dt> |
||||
<dd></dd></dl> |
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|
||||
<dl class="py method"> |
||||
<dt id="structure.Structure.nodes_values"> |
||||
<em class="property">property </em><code class="sig-name descname">nodes_values</code><a class="headerlink" href="#structure.Structure.nodes_values" title="Permalink to this definition">¶</a></dt> |
||||
<dd></dd></dl> |
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<dl class="py method"> |
||||
<dt id="structure.Structure.total_variables_number"> |
||||
<em class="property">property </em><code class="sig-name descname">total_variables_number</code><a class="headerlink" href="#structure.Structure.total_variables_number" title="Permalink to this definition">¶</a></dt> |
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<div class="section" id="module-structure_estimator"> |
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<span id="structure-estimator-module"></span><h1>structure_estimator module<a class="headerlink" href="#module-structure_estimator" title="Permalink to this headline">¶</a></h1> |
||||
<dl class="py class"> |
||||
<dt id="structure_estimator.StructureEstimator"> |
||||
<em class="property">class </em><code class="sig-prename descclassname">structure_estimator.</code><code class="sig-name descname">StructureEstimator</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">sample_path</span><span class="p">:</span> <span class="n"><a class="reference internal" href="sample_path.html#sample_path.SamplePath" title="sample_path.SamplePath">sample_path.SamplePath</a></span></em>, <em class="sig-param"><span class="n">exp_test_alfa</span><span class="p">:</span> <span class="n">float</span></em>, <em class="sig-param"><span class="n">chi_test_alfa</span><span class="p">:</span> <span class="n">float</span></em><span class="sig-paren">)</span><a class="headerlink" href="#structure_estimator.StructureEstimator" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p> |
||||
<p>Has the task of estimating the network structure given the trajectories in <code class="docutils literal notranslate"><span class="pre">samplepath</span></code>.</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><ul class="simple"> |
||||
<li><p><strong>sample_path</strong> (<a class="reference internal" href="sample_path.html#sample_path.SamplePath" title="sample_path.SamplePath"><em>SamplePath</em></a>) – the _sample_path object containing the trajectories and the real structure</p></li> |
||||
<li><p><strong>exp_test_alfa</strong> (<em>float</em>) – the significance level for the exponential Hp test</p></li> |
||||
<li><p><strong>chi_test_alfa</strong> (<em>float</em>) – the significance level for the chi Hp test</p></li> |
||||
</ul> |
||||
</dd> |
||||
<dt class="field-even">_nodes</dt> |
||||
<dd class="field-even"><p>the nodes labels</p> |
||||
</dd> |
||||
<dt class="field-odd">_nodes_vals</dt> |
||||
<dd class="field-odd"><p>the nodes cardinalities</p> |
||||
</dd> |
||||
<dt class="field-even">_nodes_indxs</dt> |
||||
<dd class="field-even"><p>the nodes indexes</p> |
||||
</dd> |
||||
<dt class="field-odd">_complete_graph</dt> |
||||
<dd class="field-odd"><p>the complete directed graph built using the nodes labels in <code class="docutils literal notranslate"><span class="pre">_nodes</span></code></p> |
||||
</dd> |
||||
<dt class="field-even">_cache</dt> |
||||
<dd class="field-even"><p>the Cache object</p> |
||||
</dd> |
||||
</dl> |
||||
<dl class="py method"> |
||||
<dt id="structure_estimator.StructureEstimator.build_complete_graph"> |
||||
<code class="sig-name descname">build_complete_graph</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">node_ids</span><span class="p">:</span> <span class="n">List</span></em><span class="sig-paren">)</span> → networkx.classes.digraph.DiGraph<a class="headerlink" href="#structure_estimator.StructureEstimator.build_complete_graph" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Builds a complete directed graph (no self loops) given the nodes labels in the list <code class="docutils literal notranslate"><span class="pre">node_ids</span></code>:</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><p><strong>node_ids</strong> (<em>List</em>) – the list of nodes labels</p> |
||||
</dd> |
||||
<dt class="field-even">Returns</dt> |
||||
<dd class="field-even"><p>a complete Digraph Object</p> |
||||
</dd> |
||||
<dt class="field-odd">Return type</dt> |
||||
<dd class="field-odd"><p>networkx.DiGraph</p> |
||||
</dd> |
||||
</dl> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="structure_estimator.StructureEstimator.complete_test"> |
||||
<code class="sig-name descname">complete_test</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">test_parent</span><span class="p">:</span> <span class="n">str</span></em>, <em class="sig-param"><span class="n">test_child</span><span class="p">:</span> <span class="n">str</span></em>, <em class="sig-param"><span class="n">parent_set</span><span class="p">:</span> <span class="n">List</span></em>, <em class="sig-param"><span class="n">child_states_numb</span><span class="p">:</span> <span class="n">int</span></em>, <em class="sig-param"><span class="n">tot_vars_count</span><span class="p">:</span> <span class="n">int</span></em><span class="sig-paren">)</span> → bool<a class="headerlink" href="#structure_estimator.StructureEstimator.complete_test" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Performs a complete independence test on the directed graphs G1 = {test_child U parent_set} |
||||
G2 = {G1 U test_parent} (added as an additional parent of the test_child). |
||||
Generates all the necessary structures and datas to perform the tests.</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><ul class="simple"> |
||||
<li><p><strong>test_parent</strong> (<em>string</em>) – the node label of the test parent</p></li> |
||||
<li><p><strong>test_child</strong> (<em>string</em>) – the node label of the child</p></li> |
||||
<li><p><strong>parent_set</strong> (<em>List</em>) – the common parent set</p></li> |
||||
<li><p><strong>child_states_numb</strong> (<em>int</em>) – the cardinality of the <code class="docutils literal notranslate"><span class="pre">test_child</span></code></p></li> |
||||
<li><p><strong>tot_vars_count</strong> (<em>int</em>) – the total number of variables in the net</p></li> |
||||
</ul> |
||||
</dd> |
||||
<dt class="field-even">Returns</dt> |
||||
<dd class="field-even"><p>True iff test_child and test_parent are independent given the sep_set parent_set. False otherwise</p> |
||||
</dd> |
||||
<dt class="field-odd">Return type</dt> |
||||
<dd class="field-odd"><p>bool</p> |
||||
</dd> |
||||
</dl> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="structure_estimator.StructureEstimator.ctpc_algorithm"> |
||||
<code class="sig-name descname">ctpc_algorithm</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → None<a class="headerlink" href="#structure_estimator.StructureEstimator.ctpc_algorithm" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Compute the CTPC algorithm over the entire net.</p> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="structure_estimator.StructureEstimator.generate_possible_sub_sets_of_size"> |
||||
<code class="sig-name descname">generate_possible_sub_sets_of_size</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">u</span><span class="p">:</span> <span class="n">List</span></em>, <em class="sig-param"><span class="n">size</span><span class="p">:</span> <span class="n">int</span></em>, <em class="sig-param"><span class="n">parent_label</span><span class="p">:</span> <span class="n">str</span></em><span class="sig-paren">)</span> → Iterator<a class="headerlink" href="#structure_estimator.StructureEstimator.generate_possible_sub_sets_of_size" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Creates a list containing all possible subsets of the list <code class="docutils literal notranslate"><span class="pre">u</span></code> of size <code class="docutils literal notranslate"><span class="pre">size</span></code>, |
||||
that do not contains a the node identified by <code class="docutils literal notranslate"><span class="pre">parent_label</span></code>.</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><ul class="simple"> |
||||
<li><p><strong>u</strong> (<em>List</em>) – the list of nodes</p></li> |
||||
<li><p><strong>size</strong> (<em>int</em>) – the size of the subsets</p></li> |
||||
<li><p><strong>parent_label</strong> (<em>string</em>) – the node to exclude in the subsets generation</p></li> |
||||
</ul> |
||||
</dd> |
||||
<dt class="field-even">Returns</dt> |
||||
<dd class="field-even"><p>an Iterator Object containing a list of lists</p> |
||||
</dd> |
||||
<dt class="field-odd">Return type</dt> |
||||
<dd class="field-odd"><p>Iterator</p> |
||||
</dd> |
||||
</dl> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="structure_estimator.StructureEstimator.independence_test"> |
||||
<code class="sig-name descname">independence_test</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">child_states_numb</span><span class="p">:</span> <span class="n">int</span></em>, <em class="sig-param"><span class="n">cim1</span><span class="p">:</span> <span class="n"><a class="reference internal" href="conditional_intensity_matrix.html#conditional_intensity_matrix.ConditionalIntensityMatrix" title="conditional_intensity_matrix.ConditionalIntensityMatrix">conditional_intensity_matrix.ConditionalIntensityMatrix</a></span></em>, <em class="sig-param"><span class="n">cim2</span><span class="p">:</span> <span class="n"><a class="reference internal" href="conditional_intensity_matrix.html#conditional_intensity_matrix.ConditionalIntensityMatrix" title="conditional_intensity_matrix.ConditionalIntensityMatrix">conditional_intensity_matrix.ConditionalIntensityMatrix</a></span></em><span class="sig-paren">)</span> → bool<a class="headerlink" href="#structure_estimator.StructureEstimator.independence_test" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Compute the actual independence test using two cims. |
||||
It is performed first the exponential test and if the null hypothesis is not rejected, |
||||
it is performed also the chi_test.</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><ul class="simple"> |
||||
<li><p><strong>child_states_numb</strong> (<em>int</em>) – the cardinality of the test child</p></li> |
||||
<li><p><strong>cim1</strong> (<a class="reference internal" href="conditional_intensity_matrix.html#conditional_intensity_matrix.ConditionalIntensityMatrix" title="conditional_intensity_matrix.ConditionalIntensityMatrix"><em>ConditionalIntensityMatrix</em></a>) – a cim belonging to the graph without test parent</p></li> |
||||
<li><p><strong>cim2</strong> (<a class="reference internal" href="conditional_intensity_matrix.html#conditional_intensity_matrix.ConditionalIntensityMatrix" title="conditional_intensity_matrix.ConditionalIntensityMatrix"><em>ConditionalIntensityMatrix</em></a>) – a cim belonging to the graph with test parent</p></li> |
||||
</ul> |
||||
</dd> |
||||
</dl> |
||||
<p>:return:True iff both tests do NOT reject the null hypothesis of indipendence. False otherwise. |
||||
:rtype: bool</p> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="structure_estimator.StructureEstimator.one_iteration_of_CTPC_algorithm"> |
||||
<code class="sig-name descname">one_iteration_of_CTPC_algorithm</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">var_id</span><span class="p">:</span> <span class="n">str</span></em>, <em class="sig-param"><span class="n">tot_vars_count</span><span class="p">:</span> <span class="n">int</span></em><span class="sig-paren">)</span> → None<a class="headerlink" href="#structure_estimator.StructureEstimator.one_iteration_of_CTPC_algorithm" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Performs an iteration of the CTPC algorithm using the node <code class="docutils literal notranslate"><span class="pre">var_id</span></code> as <code class="docutils literal notranslate"><span class="pre">test_child</span></code>.</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><ul class="simple"> |
||||
<li><p><strong>var_id</strong> (<em>string</em>) – the node label of the test child</p></li> |
||||
<li><p><strong>tot_vars_count</strong> (<em>int</em>) – the number of _nodes in the net</p></li> |
||||
</ul> |
||||
</dd> |
||||
</dl> |
||||
</dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="structure_estimator.StructureEstimator.save_results"> |
||||
<code class="sig-name descname">save_results</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → None<a class="headerlink" href="#structure_estimator.StructureEstimator.save_results" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Save the estimated Structure to a .json file in the path where the data are loaded from. |
||||
The file is named as the input dataset but the <a href="#id1"><span class="problematic" id="id2">results_</span></a> word is appendend to the results file.</p> |
||||
</dd></dl> |
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<div class="section" id="module-trajectory"> |
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<span id="trajectory-module"></span><h1>trajectory module<a class="headerlink" href="#module-trajectory" title="Permalink to this headline">¶</a></h1> |
||||
<dl class="py class"> |
||||
<dt id="trajectory.Trajectory"> |
||||
<em class="property">class </em><code class="sig-prename descclassname">trajectory.</code><code class="sig-name descname">Trajectory</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">list_of_columns</span><span class="p">:</span> <span class="n">List</span></em>, <em class="sig-param"><span class="n">original_cols_number</span><span class="p">:</span> <span class="n">int</span></em><span class="sig-paren">)</span><a class="headerlink" href="#trajectory.Trajectory" title="Permalink to this definition">¶</a></dt> |
||||
<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p> |
||||
<p>Abstracts the infos about a complete set of trajectories, represented as a numpy array of doubles (the time deltas) |
||||
and a numpy matrix of ints (the changes of states).</p> |
||||
<dl class="field-list simple"> |
||||
<dt class="field-odd">Parameters</dt> |
||||
<dd class="field-odd"><ul class="simple"> |
||||
<li><p><strong>list_of_columns</strong> (<em>List</em>) – the list containing the times array and values matrix</p></li> |
||||
<li><p><strong>original_cols_number</strong> (<em>int</em>) – total number of cols in the data</p></li> |
||||
</ul> |
||||
</dd> |
||||
<dt class="field-even">_actual_trajectory</dt> |
||||
<dd class="field-even"><p>the trajectory containing also the duplicated/shifted values</p> |
||||
</dd> |
||||
<dt class="field-odd">_times</dt> |
||||
<dd class="field-odd"><p>the array containing the time deltas</p> |
||||
</dd> |
||||
</dl> |
||||
<dl class="py method"> |
||||
<dt id="trajectory.Trajectory.complete_trajectory"> |
||||
<em class="property">property </em><code class="sig-name descname">complete_trajectory</code><a class="headerlink" href="#trajectory.Trajectory.complete_trajectory" title="Permalink to this definition">¶</a></dt> |
||||
<dd></dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="trajectory.Trajectory.size"> |
||||
<code class="sig-name descname">size</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#trajectory.Trajectory.size" title="Permalink to this definition">¶</a></dt> |
||||
<dd></dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="trajectory.Trajectory.times"> |
||||
<em class="property">property </em><code class="sig-name descname">times</code><a class="headerlink" href="#trajectory.Trajectory.times" title="Permalink to this definition">¶</a></dt> |
||||
<dd></dd></dl> |
||||
|
||||
<dl class="py method"> |
||||
<dt id="trajectory.Trajectory.trajectory"> |
||||
<em class="property">property </em><code class="sig-name descname">trajectory</code><a class="headerlink" href="#trajectory.Trajectory.trajectory" title="Permalink to this definition">¶</a></dt> |
||||
<dd></dd></dl> |
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Examples |
||||
============= |
||||
|
||||
Installation/Usage: |
||||
******************* |
||||
|
||||
|
||||
Implementing your own data importer |
||||
*********************************** |
||||
.. code-block:: python |
||||
|
||||
"""This example demonstrates the implementation of a simple data importer the extends the class abstract importer to import data in csv format. |
||||
The net in exam has three ternary nodes. |
||||
""" |
||||
|
||||
from .abstract_importer import AbstractImporter |
||||
|
||||
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.import_structure() |
||||
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 import_structure(self): |
||||
data = {'From':['X','Y','Z'], 'To':['Z','Z','Y']} |
||||
self._df_structure = pd.DataFrame(data) |
||||
|
||||
def dataset_id(self) -> object: |
||||
pass |
||||
|
||||
Parameters Estimation Example |
||||
***************************** |
||||
|
||||
.. code-block:: python |
||||
|
||||
from PyCTBN.PyCTBN.json_importer import JsonImporter |
||||
from PyCTBN.PyCTBN.sample_path import SamplePath |
||||
from PyCTBN.PyCTBN.network_graph import NetworkGraph |
||||
from PyCTBN.PyCTBN.parameters_estimator import ParametersEstimator |
||||
|
||||
|
||||
def main(): |
||||
read_files = glob.glob(os.path.join('./data', "*.json")) #Take all json files in this dir |
||||
#import data |
||||
importer = JsonImporter(read_files[0], 'samples', 'dyn.str', 'variables', 'Time', 'Name') |
||||
importer.import_data(0) |
||||
#Create a SamplePath Obj passing an already filled AbstractImporter object |
||||
s1 = SamplePath(importer) |
||||
#Build The trajectries and the structural infos |
||||
s1.build_trajectories() |
||||
s1.build_structure() |
||||
print(s1.structure.edges) |
||||
print(s1.structure.nodes_values) |
||||
#From The Structure Object build the Graph |
||||
g = NetworkGraph(s1.structure) |
||||
#Select a node you want to estimate the parameters |
||||
node = g.nodes[2] |
||||
print("Node", node) |
||||
#Init the _graph specifically for THIS node |
||||
g.fast_init(node) |
||||
#Use SamplePath and Grpah to create a ParametersEstimator Object |
||||
p1 = ParametersEstimator(s1.trajectories, g) |
||||
#Init the peEst specifically for THIS node |
||||
p1.fast_init(node) |
||||
#Compute the parameters |
||||
sofc1 = p1.compute_parameters_for_node(node) |
||||
#The est CIMS are inside the resultant SetOfCIms Obj |
||||
print(sofc1.actual_cims) |
||||
|
||||
Structure Estimation Example |
||||
**************************** |
||||
|
||||
.. code-block:: python |
||||
|
||||
from PyCTBN.PyCTBN.json_importer import JsonImporter |
||||
from PyCTBN.PyCTBN.sample_path import SamplePath |
||||
from PyCTBN.PyCTBN.structure_estimator import StructureEstimator |
||||
|
||||
def structure_estimation_example(): |
||||
|
||||
# read the json files in ./data path |
||||
read_files = glob.glob(os.path.join('./data', "*.json")) |
||||
# initialize a JsonImporter object for the first file |
||||
importer = JsonImporter(read_files[0], 'samples', 'dyn.str', 'variables', 'Time', 'Name') |
||||
# import the data at index 0 of the outer json array |
||||
importer.import_data(0) |
||||
# construct a SamplePath Object passing a filled AbstractImporter |
||||
s1 = SamplePath(importer) |
||||
# build the trajectories |
||||
s1.build_trajectories() |
||||
# build the real structure |
||||
s1.build_structure() |
||||
# construct a StructureEstimator object |
||||
se1 = StructureEstimator(s1, 0.1, 0.1) |
||||
# call the ctpc algorithm |
||||
se1.ctpc_algorithm() |
||||
# the adjacency matrix of the estimated structure |
||||
print(se1.adjacency_matrix()) |
||||
# save results to a json file |
||||
se1.save_results() |
||||
|
@ -0,0 +1,7 @@ |
||||
PyCTBN |
||||
====== |
||||
|
||||
.. toctree:: |
||||
:maxdepth: 4 |
||||
|
||||
PyCTBN |
@ -1,48 +0,0 @@ |
||||
abstract\_importer module |
||||
========================= |
||||
|
||||
.. automodule:: abstract_importer |
||||
:members: |
||||
:undoc-members: |
||||
:show-inheritance: |
||||
|
||||
An example of a simple CSV Importer |
||||
=================================== |
||||
Suppose you have a csv dataset containing only the trajectories, three variables labels and cardinalites. |
||||
Then the resulting importer that inherit and extends AbstractImpoter would be: |
||||
|
||||
.. code_block:: python |
||||
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.import_structure() |
||||
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 import_structure(self): |
||||
data = {'From':['X','Y','Z'], 'To':['Z','Z','Y']} |
||||
self._df_structure = pd.DataFrame(data) |
||||
|
||||
def dataset_id(self) -> object: |
||||
pass |
@ -1,7 +0,0 @@ |
||||
cache module |
||||
============ |
||||
|
||||
.. automodule:: cache |
||||
:members: |
||||
:undoc-members: |
||||
:show-inheritance: |
@ -1,7 +0,0 @@ |
||||
conditional\_intensity\_matrix module |
||||
===================================== |
||||
|
||||
.. automodule:: conditional_intensity_matrix |
||||
:members: |
||||
:undoc-members: |
||||
:show-inheritance: |
@ -1,7 +0,0 @@ |
||||
json\_importer module |
||||
===================== |
||||
|
||||
.. automodule:: json_importer |
||||
:members: |
||||
:undoc-members: |
||||
:show-inheritance: |
@ -1,17 +0,0 @@ |
||||
scripts |
||||
======= |
||||
|
||||
.. toctree:: |
||||
:maxdepth: 4 |
||||
|
||||
abstract_importer |
||||
cache |
||||
conditional_intensity_matrix |
||||
json_importer |
||||
network_graph |
||||
parameters_estimator |
||||
sample_path |
||||
set_of_cims |
||||
structure |
||||
structure_estimator |
||||
trajectory |
@ -1,7 +0,0 @@ |
||||
network\_graph module |
||||
===================== |
||||
|
||||
.. automodule:: network_graph |
||||
:members: |
||||
:undoc-members: |
||||
:show-inheritance: |
@ -1,7 +0,0 @@ |
||||
parameters\_estimator module |
||||
============================ |
||||
|
||||
.. automodule:: parameters_estimator |
||||
:members: |
||||
:undoc-members: |
||||
:show-inheritance: |
@ -1,7 +0,0 @@ |
||||
sample\_path module |
||||
=================== |
||||
|
||||
.. automodule:: sample_path |
||||
:members: |
||||
:undoc-members: |
||||
:show-inheritance: |
@ -1,7 +0,0 @@ |
||||
set\_of\_cims module |
||||
==================== |
||||
|
||||
.. automodule:: set_of_cims |
||||
:members: |
||||
:undoc-members: |
||||
:show-inheritance: |
@ -1,7 +0,0 @@ |
||||
structure module |
||||
================ |
||||
|
||||
.. automodule:: structure |
||||
:members: |
||||
:undoc-members: |
||||
:show-inheritance: |
@ -1,7 +0,0 @@ |
||||
structure\_estimator module |
||||
=========================== |
||||
|
||||
.. automodule:: structure_estimator |
||||
:members: |
||||
:undoc-members: |
||||
:show-inheritance: |
@ -1,7 +0,0 @@ |
||||
trajectory module |
||||
================= |
||||
|
||||
.. automodule:: trajectory |
||||
:members: |
||||
:undoc-members: |
||||
:show-inheritance: |
@ -0,0 +1,42 @@ |
||||
|
||||
import os |
||||
import glob |
||||
|
||||
|
||||
from PyCTBN.PyCTBN.json_importer import JsonImporter |
||||
from PyCTBN.PyCTBN.sample_path import SamplePath |
||||
from PyCTBN.PyCTBN.network_graph import NetworkGraph |
||||
from PyCTBN.PyCTBN.parameters_estimator import ParametersEstimator |
||||
|
||||
|
||||
def main(): |
||||
read_files = glob.glob(os.path.join('./data', "*.json")) #Take all json files in this dir |
||||
#import data |
||||
importer = JsonImporter(read_files[0], 'samples', 'dyn.str', 'variables', 'Time', 'Name') |
||||
importer.import_data(0) |
||||
#Create a SamplePath Obj |
||||
s1 = SamplePath(importer) |
||||
#Build The trajectries and the structural infos |
||||
s1.build_trajectories() |
||||
s1.build_structure() |
||||
print(s1.structure.edges) |
||||
print(s1.structure.nodes_values) |
||||
#From The Structure Object build the Graph |
||||
g = NetworkGraph(s1.structure) |
||||
#Select a node you want to estimate the parameters |
||||
node = g.nodes[2] |
||||
print("Node", node) |
||||
#Init the _graph specifically for THIS node |
||||
g.fast_init(node) |
||||
#Use SamplePath and Grpah to create a ParametersEstimator Object |
||||
p1 = ParametersEstimator(s1.trajectories, g) |
||||
#Init the peEst specifically for THIS node |
||||
p1.fast_init(node) |
||||
#Compute the parameters |
||||
sofc1 = p1.compute_parameters_for_node(node) |
||||
#The est CIMS are inside the resultant SetOfCIms Obj |
||||
print(sofc1.actual_cims) |
||||
|
||||
|
||||
if __name__ == "__main__": |
||||
main() |
@ -0,0 +1,30 @@ |
||||
import os |
||||
import glob |
||||
|
||||
from PyCTBN.PyCTBN.json_importer import JsonImporter |
||||
from PyCTBN.PyCTBN.sample_path import SamplePath |
||||
from PyCTBN.PyCTBN.structure_estimator import StructureEstimator |
||||
|
||||
|
||||
def structure_estimation_example(): |
||||
|
||||
# read the json files in ./data path |
||||
read_files = glob.glob(os.path.join('./data', "*.json")) |
||||
# initialize a JsonImporter object for the first file |
||||
importer = JsonImporter(read_files[0], 'samples', 'dyn.str', 'variables', 'Time', 'Name') |
||||
# import the data at index 0 of the outer json array |
||||
importer.import_data(0) |
||||
# construct a SamplePath Object passing a filled AbstractImporter |
||||
s1 = SamplePath(importer) |
||||
# build the trajectories |
||||
s1.build_trajectories() |
||||
# build the real structure |
||||
s1.build_structure() |
||||
# construct a StructureEstimator object |
||||
se1 = StructureEstimator(s1, 0.1, 0.1) |
||||
# call the ctpc algorithm |
||||
se1.ctpc_algorithm() |
||||
# the adjacency matrix of the estimated structure |
||||
print(se1.adjacency_matrix()) |
||||
# save results to a json file |
||||
se1.save_results() |
@ -0,0 +1,241 @@ |
||||
<# |
||||
.Synopsis |
||||
Activate a Python virtual environment for the current PowerShell session. |
||||
|
||||
.Description |
||||
Pushes the python executable for a virtual environment to the front of the |
||||
$Env:PATH environment variable and sets the prompt to signify that you are |
||||
in a Python virtual environment. Makes use of the command line switches as |
||||
well as the `pyvenv.cfg` file values present in the virtual environment. |
||||
|
||||
.Parameter VenvDir |
||||
Path to the directory that contains the virtual environment to activate. The |
||||
default value for this is the parent of the directory that the Activate.ps1 |
||||
script is located within. |
||||
|
||||
.Parameter Prompt |
||||
The prompt prefix to display when this virtual environment is activated. By |
||||
default, this prompt is the name of the virtual environment folder (VenvDir) |
||||
surrounded by parentheses and followed by a single space (ie. '(.venv) '). |
||||
|
||||
.Example |
||||
Activate.ps1 |
||||
Activates the Python virtual environment that contains the Activate.ps1 script. |
||||
|
||||
.Example |
||||
Activate.ps1 -Verbose |
||||
Activates the Python virtual environment that contains the Activate.ps1 script, |
||||
and shows extra information about the activation as it executes. |
||||
|
||||
.Example |
||||
Activate.ps1 -VenvDir C:\Users\MyUser\Common\.venv |
||||
Activates the Python virtual environment located in the specified location. |
||||
|
||||
.Example |
||||
Activate.ps1 -Prompt "MyPython" |
||||
Activates the Python virtual environment that contains the Activate.ps1 script, |
||||
and prefixes the current prompt with the specified string (surrounded in |
||||
parentheses) while the virtual environment is active. |
||||
|
||||
.Notes |
||||
On Windows, it may be required to enable this Activate.ps1 script by setting the |
||||
execution policy for the user. You can do this by issuing the following PowerShell |
||||
command: |
||||
|
||||
PS C:\> Set-ExecutionPolicy -ExecutionPolicy RemoteSigned -Scope CurrentUser |
||||
|
||||
For more information on Execution Policies: |
||||
https://go.microsoft.com/fwlink/?LinkID=135170 |
||||
|
||||
#> |
||||
Param( |
||||
[Parameter(Mandatory = $false)] |
||||
[String] |
||||
$VenvDir, |
||||
[Parameter(Mandatory = $false)] |
||||
[String] |
||||
$Prompt |
||||
) |
||||
|
||||
<# Function declarations --------------------------------------------------- #> |
||||
|
||||
<# |
||||
.Synopsis |
||||
Remove all shell session elements added by the Activate script, including the |
||||
addition of the virtual environment's Python executable from the beginning of |
||||
the PATH variable. |
||||
|
||||
.Parameter NonDestructive |
||||
If present, do not remove this function from the global namespace for the |
||||
session. |
||||
|
||||
#> |
||||
function global:deactivate ([switch]$NonDestructive) { |
||||
# Revert to original values |
||||
|
||||
# The prior prompt: |
||||
if (Test-Path -Path Function:_OLD_VIRTUAL_PROMPT) { |
||||
Copy-Item -Path Function:_OLD_VIRTUAL_PROMPT -Destination Function:prompt |
||||
Remove-Item -Path Function:_OLD_VIRTUAL_PROMPT |
||||
} |
||||
|
||||
# The prior PYTHONHOME: |
||||
if (Test-Path -Path Env:_OLD_VIRTUAL_PYTHONHOME) { |
||||
Copy-Item -Path Env:_OLD_VIRTUAL_PYTHONHOME -Destination Env:PYTHONHOME |
||||
Remove-Item -Path Env:_OLD_VIRTUAL_PYTHONHOME |
||||
} |
||||
|
||||
# The prior PATH: |
||||
if (Test-Path -Path Env:_OLD_VIRTUAL_PATH) { |
||||
Copy-Item -Path Env:_OLD_VIRTUAL_PATH -Destination Env:PATH |
||||
Remove-Item -Path Env:_OLD_VIRTUAL_PATH |
||||
} |
||||
|
||||
# Just remove the VIRTUAL_ENV altogether: |
||||
if (Test-Path -Path Env:VIRTUAL_ENV) { |
||||
Remove-Item -Path env:VIRTUAL_ENV |
||||
} |
||||
|
||||
# Just remove the _PYTHON_VENV_PROMPT_PREFIX altogether: |
||||
if (Get-Variable -Name "_PYTHON_VENV_PROMPT_PREFIX" -ErrorAction SilentlyContinue) { |
||||
Remove-Variable -Name _PYTHON_VENV_PROMPT_PREFIX -Scope Global -Force |
||||
} |
||||
|
||||
# Leave deactivate function in the global namespace if requested: |
||||
if (-not $NonDestructive) { |
||||
Remove-Item -Path function:deactivate |
||||
} |
||||
} |
||||
|
||||
<# |
||||
.Description |
||||
Get-PyVenvConfig parses the values from the pyvenv.cfg file located in the |
||||
given folder, and returns them in a map. |
||||
|
||||
For each line in the pyvenv.cfg file, if that line can be parsed into exactly |
||||
two strings separated by `=` (with any amount of whitespace surrounding the =) |
||||
then it is considered a `key = value` line. The left hand string is the key, |
||||
the right hand is the value. |
||||
|
||||
If the value starts with a `'` or a `"` then the first and last character is |
||||
stripped from the value before being captured. |
||||
|
||||
.Parameter ConfigDir |
||||
Path to the directory that contains the `pyvenv.cfg` file. |
||||
#> |
||||
function Get-PyVenvConfig( |
||||
[String] |
||||
$ConfigDir |
||||
) { |
||||
Write-Verbose "Given ConfigDir=$ConfigDir, obtain values in pyvenv.cfg" |
||||
|
||||
# Ensure the file exists, and issue a warning if it doesn't (but still allow the function to continue). |
||||
$pyvenvConfigPath = Join-Path -Resolve -Path $ConfigDir -ChildPath 'pyvenv.cfg' -ErrorAction Continue |
||||
|
||||
# An empty map will be returned if no config file is found. |
||||
$pyvenvConfig = @{ } |
||||
|
||||
if ($pyvenvConfigPath) { |
||||
|
||||
Write-Verbose "File exists, parse `key = value` lines" |
||||
$pyvenvConfigContent = Get-Content -Path $pyvenvConfigPath |
||||
|
||||
$pyvenvConfigContent | ForEach-Object { |
||||
$keyval = $PSItem -split "\s*=\s*", 2 |
||||
if ($keyval[0] -and $keyval[1]) { |
||||
$val = $keyval[1] |
||||
|
||||
# Remove extraneous quotations around a string value. |
||||
if ("'""".Contains($val.Substring(0, 1))) { |
||||
$val = $val.Substring(1, $val.Length - 2) |
||||
} |
||||
|
||||
$pyvenvConfig[$keyval[0]] = $val |
||||
Write-Verbose "Adding Key: '$($keyval[0])'='$val'" |
||||
} |
||||
} |
||||
} |
||||
return $pyvenvConfig |
||||
} |
||||
|
||||
|
||||
<# Begin Activate script --------------------------------------------------- #> |
||||
|
||||
# Determine the containing directory of this script |
||||
$VenvExecPath = Split-Path -Parent $MyInvocation.MyCommand.Definition |
||||
$VenvExecDir = Get-Item -Path $VenvExecPath |
||||
|
||||
Write-Verbose "Activation script is located in path: '$VenvExecPath'" |
||||
Write-Verbose "VenvExecDir Fullname: '$($VenvExecDir.FullName)" |
||||
Write-Verbose "VenvExecDir Name: '$($VenvExecDir.Name)" |
||||
|
||||
# Set values required in priority: CmdLine, ConfigFile, Default |
||||
# First, get the location of the virtual environment, it might not be |
||||
# VenvExecDir if specified on the command line. |
||||
if ($VenvDir) { |
||||
Write-Verbose "VenvDir given as parameter, using '$VenvDir' to determine values" |
||||
} |
||||
else { |
||||
Write-Verbose "VenvDir not given as a parameter, using parent directory name as VenvDir." |
||||
$VenvDir = $VenvExecDir.Parent.FullName.TrimEnd("\\/") |
||||
Write-Verbose "VenvDir=$VenvDir" |
||||
} |
||||
|
||||
# Next, read the `pyvenv.cfg` file to determine any required value such |
||||
# as `prompt`. |
||||
$pyvenvCfg = Get-PyVenvConfig -ConfigDir $VenvDir |
||||
|
||||
# Next, set the prompt from the command line, or the config file, or |
||||
# just use the name of the virtual environment folder. |
||||
if ($Prompt) { |
||||
Write-Verbose "Prompt specified as argument, using '$Prompt'" |
||||
} |
||||
else { |
||||
Write-Verbose "Prompt not specified as argument to script, checking pyvenv.cfg value" |
||||
if ($pyvenvCfg -and $pyvenvCfg['prompt']) { |
||||
Write-Verbose " Setting based on value in pyvenv.cfg='$($pyvenvCfg['prompt'])'" |
||||
$Prompt = $pyvenvCfg['prompt']; |
||||
} |
||||
else { |
||||
Write-Verbose " Setting prompt based on parent's directory's name. (Is the directory name passed to venv module when creating the virutal environment)" |
||||
Write-Verbose " Got leaf-name of $VenvDir='$(Split-Path -Path $venvDir -Leaf)'" |
||||
$Prompt = Split-Path -Path $venvDir -Leaf |
||||
} |
||||
} |
||||
|
||||
Write-Verbose "Prompt = '$Prompt'" |
||||
Write-Verbose "VenvDir='$VenvDir'" |
||||
|
||||
# Deactivate any currently active virtual environment, but leave the |
||||
# deactivate function in place. |
||||
deactivate -nondestructive |
||||
|
||||
# Now set the environment variable VIRTUAL_ENV, used by many tools to determine |
||||
# that there is an activated venv. |
||||
$env:VIRTUAL_ENV = $VenvDir |
||||
|
||||
if (-not $Env:VIRTUAL_ENV_DISABLE_PROMPT) { |
||||
|
||||
Write-Verbose "Setting prompt to '$Prompt'" |
||||
|
||||
# Set the prompt to include the env name |
||||
# Make sure _OLD_VIRTUAL_PROMPT is global |
||||
function global:_OLD_VIRTUAL_PROMPT { "" } |
||||
Copy-Item -Path function:prompt -Destination function:_OLD_VIRTUAL_PROMPT |
||||
New-Variable -Name _PYTHON_VENV_PROMPT_PREFIX -Description "Python virtual environment prompt prefix" -Scope Global -Option ReadOnly -Visibility Public -Value $Prompt |
||||
|
||||
function global:prompt { |
||||
Write-Host -NoNewline -ForegroundColor Green "($_PYTHON_VENV_PROMPT_PREFIX) " |
||||
_OLD_VIRTUAL_PROMPT |
||||
} |
||||
} |
||||
|
||||
# Clear PYTHONHOME |
||||
if (Test-Path -Path Env:PYTHONHOME) { |
||||
Copy-Item -Path Env:PYTHONHOME -Destination Env:_OLD_VIRTUAL_PYTHONHOME |
||||
Remove-Item -Path Env:PYTHONHOME |
||||
} |
||||
|
||||
# Add the venv to the PATH |
||||
Copy-Item -Path Env:PATH -Destination Env:_OLD_VIRTUAL_PATH |
||||
$Env:PATH = "$VenvExecDir$([System.IO.Path]::PathSeparator)$Env:PATH" |
@ -0,0 +1,66 @@ |
||||
# This file must be used with "source bin/activate" *from bash* |
||||
# you cannot run it directly |
||||
|
||||
deactivate () { |
||||
# reset old environment variables |
||||
if [ -n "${_OLD_VIRTUAL_PATH:-}" ] ; then |
||||
PATH="${_OLD_VIRTUAL_PATH:-}" |
||||
export PATH |
||||
unset _OLD_VIRTUAL_PATH |
||||
fi |
||||
if [ -n "${_OLD_VIRTUAL_PYTHONHOME:-}" ] ; then |
||||
PYTHONHOME="${_OLD_VIRTUAL_PYTHONHOME:-}" |
||||
export PYTHONHOME |
||||
unset _OLD_VIRTUAL_PYTHONHOME |
||||
fi |
||||
|
||||
# This should detect bash and zsh, which have a hash command that must |
||||
# be called to get it to forget past commands. Without forgetting |
||||
# past commands the $PATH changes we made may not be respected |
||||
if [ -n "${BASH:-}" -o -n "${ZSH_VERSION:-}" ] ; then |
||||
hash -r 2> /dev/null |
||||
fi |
||||
|
||||
if [ -n "${_OLD_VIRTUAL_PS1:-}" ] ; then |
||||
PS1="${_OLD_VIRTUAL_PS1:-}" |
||||
export PS1 |
||||
unset _OLD_VIRTUAL_PS1 |
||||
fi |
||||
|
||||
unset VIRTUAL_ENV |
||||
if [ ! "${1:-}" = "nondestructive" ] ; then |
||||
# Self destruct! |
||||
unset -f deactivate |
||||
fi |
||||
} |
||||
|
||||
# unset irrelevant variables |
||||
deactivate nondestructive |
||||
|
||||
VIRTUAL_ENV="/Users/Zalum/Desktop/Tesi/PyCTBN/venv" |
||||
export VIRTUAL_ENV |
||||
|
||||
_OLD_VIRTUAL_PATH="$PATH" |
||||
PATH="$VIRTUAL_ENV/bin:$PATH" |
||||
export PATH |
||||
|
||||
# unset PYTHONHOME if set |
||||
# this will fail if PYTHONHOME is set to the empty string (which is bad anyway) |
||||
# could use `if (set -u; : $PYTHONHOME) ;` in bash |
||||
if [ -n "${PYTHONHOME:-}" ] ; then |
||||
_OLD_VIRTUAL_PYTHONHOME="${PYTHONHOME:-}" |
||||
unset PYTHONHOME |
||||
fi |
||||
|
||||
if [ -z "${VIRTUAL_ENV_DISABLE_PROMPT:-}" ] ; then |
||||
_OLD_VIRTUAL_PS1="${PS1:-}" |
||||
PS1="(venv) ${PS1:-}" |
||||
export PS1 |
||||
fi |
||||
|
||||
# This should detect bash and zsh, which have a hash command that must |
||||
# be called to get it to forget past commands. Without forgetting |
||||
# past commands the $PATH changes we made may not be respected |
||||
if [ -n "${BASH:-}" -o -n "${ZSH_VERSION:-}" ] ; then |
||||
hash -r 2> /dev/null |
||||
fi |
@ -0,0 +1,25 @@ |
||||
# This file must be used with "source bin/activate.csh" *from csh*. |
||||
# You cannot run it directly. |
||||
# Created by Davide Di Blasi <davidedb@gmail.com>. |
||||
# Ported to Python 3.3 venv by Andrew Svetlov <andrew.svetlov@gmail.com> |
||||
|
||||
alias deactivate 'test $?_OLD_VIRTUAL_PATH != 0 && setenv PATH "$_OLD_VIRTUAL_PATH" && unset _OLD_VIRTUAL_PATH; rehash; test $?_OLD_VIRTUAL_PROMPT != 0 && set prompt="$_OLD_VIRTUAL_PROMPT" && unset _OLD_VIRTUAL_PROMPT; unsetenv VIRTUAL_ENV; test "\!:*" != "nondestructive" && unalias deactivate' |
||||
|
||||
# Unset irrelevant variables. |
||||
deactivate nondestructive |
||||
|
||||
setenv VIRTUAL_ENV "/Users/Zalum/Desktop/Tesi/PyCTBN/venv" |
||||
|
||||
set _OLD_VIRTUAL_PATH="$PATH" |
||||
setenv PATH "$VIRTUAL_ENV/bin:$PATH" |
||||
|
||||
|
||||
set _OLD_VIRTUAL_PROMPT="$prompt" |
||||
|
||||
if (! "$?VIRTUAL_ENV_DISABLE_PROMPT") then |
||||
set prompt = "(venv) $prompt" |
||||
endif |
||||
|
||||
alias pydoc python -m pydoc |
||||
|
||||
rehash |
@ -0,0 +1,64 @@ |
||||
# This file must be used with "source <venv>/bin/activate.fish" *from fish* |
||||
# (https://fishshell.com/); you cannot run it directly. |
||||
|
||||
function deactivate -d "Exit virtual environment and return to normal shell environment" |
||||
# reset old environment variables |
||||
if test -n "$_OLD_VIRTUAL_PATH" |
||||
set -gx PATH $_OLD_VIRTUAL_PATH |
||||
set -e _OLD_VIRTUAL_PATH |
||||
end |
||||
if test -n "$_OLD_VIRTUAL_PYTHONHOME" |
||||
set -gx PYTHONHOME $_OLD_VIRTUAL_PYTHONHOME |
||||
set -e _OLD_VIRTUAL_PYTHONHOME |
||||
end |
||||
|
||||
if test -n "$_OLD_FISH_PROMPT_OVERRIDE" |
||||
functions -e fish_prompt |
||||
set -e _OLD_FISH_PROMPT_OVERRIDE |
||||
functions -c _old_fish_prompt fish_prompt |
||||
functions -e _old_fish_prompt |
||||
end |
||||
|
||||
set -e VIRTUAL_ENV |
||||
if test "$argv[1]" != "nondestructive" |
||||
# Self-destruct! |
||||
functions -e deactivate |
||||
end |
||||
end |
||||
|
||||
# Unset irrelevant variables. |
||||
deactivate nondestructive |
||||
|
||||
set -gx VIRTUAL_ENV "/Users/Zalum/Desktop/Tesi/PyCTBN/venv" |
||||
|
||||
set -gx _OLD_VIRTUAL_PATH $PATH |
||||
set -gx PATH "$VIRTUAL_ENV/bin" $PATH |
||||
|
||||
# Unset PYTHONHOME if set. |
||||
if set -q PYTHONHOME |
||||
set -gx _OLD_VIRTUAL_PYTHONHOME $PYTHONHOME |
||||
set -e PYTHONHOME |
||||
end |
||||
|
||||
if test -z "$VIRTUAL_ENV_DISABLE_PROMPT" |
||||
# fish uses a function instead of an env var to generate the prompt. |
||||
|
||||
# Save the current fish_prompt function as the function _old_fish_prompt. |
||||
functions -c fish_prompt _old_fish_prompt |
||||
|
||||
# With the original prompt function renamed, we can override with our own. |
||||
function fish_prompt |
||||
# Save the return status of the last command. |
||||
set -l old_status $status |
||||
|
||||
# Output the venv prompt; color taken from the blue of the Python logo. |
||||
printf "%s%s%s" (set_color 4B8BBE) "(venv) " (set_color normal) |
||||
|
||||
# Restore the return status of the previous command. |
||||
echo "exit $old_status" | . |
||||
# Output the original/"old" prompt. |
||||
_old_fish_prompt |
||||
end |
||||
|
||||
set -gx _OLD_FISH_PROMPT_OVERRIDE "$VIRTUAL_ENV" |
||||
end |
@ -0,0 +1,8 @@ |
||||
#!/Users/Zalum/Desktop/Tesi/PyCTBN/venv/bin/python |
||||
# -*- coding: utf-8 -*- |
||||
import re |
||||
import sys |
||||
from setuptools.command.easy_install import main |
||||
if __name__ == '__main__': |
||||
sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) |
||||
sys.exit(main()) |
@ -0,0 +1,8 @@ |
||||
#!/Users/Zalum/Desktop/Tesi/PyCTBN/venv/bin/python |
||||
# -*- coding: utf-8 -*- |
||||
import re |
||||
import sys |
||||
from setuptools.command.easy_install import main |
||||
if __name__ == '__main__': |
||||
sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) |
||||
sys.exit(main()) |
@ -0,0 +1,8 @@ |
||||
#!/Users/Zalum/Desktop/Tesi/PyCTBN/venv/bin/python |
||||
# -*- coding: utf-8 -*- |
||||
import re |
||||
import sys |
||||
from numpy.f2py.f2py2e import main |
||||
if __name__ == '__main__': |
||||
sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) |
||||
sys.exit(main()) |
@ -0,0 +1,8 @@ |
||||
#!/Users/Zalum/Desktop/Tesi/PyCTBN/venv/bin/python |
||||
# -*- coding: utf-8 -*- |
||||
import re |
||||
import sys |
||||
from numpy.f2py.f2py2e import main |
||||
if __name__ == '__main__': |
||||
sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) |
||||
sys.exit(main()) |
@ -0,0 +1,8 @@ |
||||
#!/Users/Zalum/Desktop/Tesi/PyCTBN/venv/bin/python |
||||
# -*- coding: utf-8 -*- |
||||
import re |
||||
import sys |
||||
from numpy.f2py.f2py2e import main |
||||
if __name__ == '__main__': |
||||
sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) |
||||
sys.exit(main()) |
@ -0,0 +1,8 @@ |
||||
#!/Users/Zalum/Desktop/Tesi/PyCTBN/venv/bin/python |
||||
# -*- coding: utf-8 -*- |
||||
import re |
||||
import sys |
||||
from pip._internal.cli.main import main |
||||
if __name__ == '__main__': |
||||
sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) |
||||
sys.exit(main()) |
@ -0,0 +1,8 @@ |
||||
#!/Users/Zalum/Desktop/Tesi/PyCTBN/venv/bin/python |
||||
# -*- coding: utf-8 -*- |
||||
import re |
||||
import sys |
||||
from pip._internal.cli.main import main |
||||
if __name__ == '__main__': |
||||
sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) |
||||
sys.exit(main()) |
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Reference in new issue