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3987f13165
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import pandas as pd |
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import numpy as np |
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import math |
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import itertools |
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import networkx as nx |
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from scipy.stats import f as f_dist |
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from scipy.stats import chi2 as chi2_dist |
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import sample_path as sp |
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import structure as st |
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import network_graph as ng |
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import parameters_estimator as pe |
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class StructureEstimator: |
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def __init__(self, sample_path, exp_test_alfa, chi_test_alfa): |
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self.sample_path = sample_path |
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self.complete_graph_frame = self.build_complete_graph_frame(self.sample_path.structure.list_of_nodes_labels()) |
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self.complete_graph = self.build_complete_graph(self.sample_path.structure.list_of_nodes_labels()) |
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self.exp_test_sign = exp_test_alfa |
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self.chi_test_alfa = chi_test_alfa |
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def build_complete_graph_frame(self, node_ids): |
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complete_frame = pd.DataFrame(itertools.permutations(node_ids, 2)) |
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complete_frame.columns = ['From', 'To'] |
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return complete_frame |
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def build_complete_graph(self, node_ids): |
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complete_graph = nx.DiGraph() |
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complete_graph.add_nodes_from(node_ids) |
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complete_graph.add_edges_from(itertools.permutations(node_ids, 2)) |
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return complete_graph |
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def complete_test(self, test_parent, test_child, parent_set): |
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tmp_df = self.complete_graph_frame.loc[self.complete_graph_frame['To'].isin([test_child])] |
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#print(tmp_df) |
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d1 = tmp_df.loc[tmp_df['From'].isin(parent_set)] |
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parent_set.append(test_child) |
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#print(parent_set) |
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v1 = self.sample_path.structure.variables_frame.loc[self.sample_path.structure.variables_frame['Name'].isin(parent_set)] |
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s1 = st.Structure(d1, v1, self.sample_path.total_variables_count) |
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g1 = ng.NetworkGraph(s1) |
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g1.init_graph() |
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parent_set.append(test_parent) |
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d2 = tmp_df.loc[tmp_df['From'].isin(parent_set)] |
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v2 = self.sample_path.structure.variables_frame.loc[self.sample_path.structure.variables_frame['Name'].isin(parent_set)] |
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#print(d2) |
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#print(v2) |
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s2 = st.Structure(d2, v2, self.sample_path.total_variables_count) |
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g2 = ng.NetworkGraph(s2) |
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g2.init_graph() |
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p1 = pe.ParametersEstimator(self.sample_path, g1) |
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p1.init_sets_cims_container() |
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p1.compute_parameters() |
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p2 = pe.ParametersEstimator(self.sample_path, g2) |
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p2.init_sets_cims_container() |
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p2.compute_parameters() |
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#for cim in p1.sets_of_cims_struct.sets_of_cims[s1.get_positional_node_indx(test_child)].actual_cims: |
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#print(cim) |
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#print(cim.state_transition_matrix) |
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#print("C_1", p1.sets_of_cims_struct.sets_of_cims[s1.get_positional_node_indx(test_child)].transition_matrices) |
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indx = 0 |
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for i, cim1 in enumerate( |
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p1.sets_of_cims_struct.sets_of_cims[s1.get_positional_node_indx(test_child)].actual_cims): |
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#for j, cim2 in enumerate( |
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#p2.sets_of_cims_struct.sets_of_cims[s2.get_positional_node_indx(test_child)].actual_cims): |
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for j in range(indx, self.sample_path.structure.get_states_number(test_parent) + indx): |
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print("J", j) |
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cim2 = p2.sets_of_cims_struct.sets_of_cims[s2.get_positional_node_indx(test_child)].actual_cims[j] |
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indx += 1 |
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print(indx) |
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print("Run Test", i, j) |
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if not self.independence_test(test_child, cim1, cim2): |
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return False |
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return True |
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def independence_test(self, tested_child, cim1, cim2): |
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# Fake exp test |
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for val in range(0, self.sample_path.structure.get_states_number(tested_child)): # i possibili valori di tested child TODO QUESTO CONTO DEVE ESSERE VETTORIZZATO |
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r1 = cim1.state_transition_matrix[val][val] |
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r2 = cim2.state_transition_matrix[val][val] |
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print("No Test Parent:",cim1.cim[val][val],"With Test Parent", cim2.cim[val][val]) |
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F = cim2.cim[val][val] / cim1.cim[val][val] |
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print("Exponential test", F, r1, r2) |
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#print(f_dist.ppf(1 - self.exp_test_sign / 2, r1, r2)) |
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#print(f_dist.ppf(self.exp_test_sign / 2, r1, r2)) |
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if F < f_dist.ppf(self.exp_test_sign / 2, r1, r2) or \ |
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F > f_dist.ppf(1 - self.exp_test_sign / 2, r1, r2): |
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print("CONDITIONALLY DEPENDENT EXP") |
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return False |
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# fake chi test |
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M1_no_diag = self.remove_diagonal_elements(cim1.state_transition_matrix) |
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M2_no_diag = self.remove_diagonal_elements(cim2.state_transition_matrix) |
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print("M1 no diag", M1_no_diag) |
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print("M2 no diag", M2_no_diag) |
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chi_2_quantile = chi2_dist.ppf(1 - self.chi_test_alfa, self.sample_path.structure.get_states_number(tested_child) - 1) |
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for val in range(0, self.sample_path.structure.get_states_number(tested_child)): |
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K = math.sqrt(cim1.state_transition_matrix[val][val] / cim2.state_transition_matrix[val][val]) |
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L = 1 / K |
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Chi = np.sum(np.power(K * M2_no_diag[val] - L *M1_no_diag[val], 2) / |
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(M1_no_diag[val] + M2_no_diag[val])) |
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print("Chi Stats", Chi) |
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print("Chi Quantile", chi_2_quantile) |
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if Chi > chi_2_quantile: |
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print("CONDITIONALLY DEPENDENT CHI") |
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return False |
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#print("Chi test", Chi) |
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return True |
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def one_iteration_of_CTPC_algorithm(self, var_id): |
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u = list(self.complete_graph.predecessors(var_id)) |
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tests_parents_numb = len(u) |
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#print(u) |
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b = 0 |
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parent_indx = 0 |
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while b < len(u): |
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#for parent_id in u: |
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parent_indx = 0 |
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while u and parent_indx < tests_parents_numb and b < len(u): |
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# list_without_test_parent = u.remove(parent_id) |
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removed = False |
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print("b", b) |
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print("Parent Indx", parent_indx) |
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#if not list(self.generate_possible_sub_sets_of_size(u, b, u[parent_indx])): |
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#break |
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S = self.generate_possible_sub_sets_of_size(u, b, u[parent_indx]) |
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print("U Set", u) |
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print("S", S) |
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for parents_set in S: |
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print("Parent Set", parents_set) |
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print("Test Parent", u[parent_indx]) |
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if self.complete_test(u[parent_indx], var_id, parents_set): |
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print("Removing EDGE:", u[parent_indx], var_id) |
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self.complete_graph.remove_edge(u[parent_indx], var_id) |
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#self.complete_graph_frame = \ |
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#self.complete_graph_frame[(self.complete_graph_frame.From != |
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# u[parent_indx]) & (self.complete_graph_frame.To != var_id)] |
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u.remove(u[parent_indx]) |
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removed = True |
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#else: |
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#parent_indx += 1 |
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if not removed: |
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parent_indx += 1 |
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b += 1 |
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def generate_possible_sub_sets_of_size(self, u, size, parent_id): |
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print("Inside Generate subsets", u) |
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print("InsideGenerate Subsets", parent_id) |
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list_without_test_parent = u[:] |
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list_without_test_parent.remove(parent_id) |
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# u.remove(parent_id) |
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#print(list(map(list, itertools.combinations(list_without_test_parent, size)))) |
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return map(list, itertools.combinations(list_without_test_parent, size)) |
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def remove_diagonal_elements(self, matrix): |
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m = matrix.shape[0] |
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strided = np.lib.stride_tricks.as_strided |
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s0, s1 = matrix.strides |
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return strided(matrix.ravel()[1:], shape=(m - 1, m), strides=(s0 + s1, s1)).reshape(m, -1) |
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@ -0,0 +1,31 @@ |
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import unittest |
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import sample_path as sp |
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import structure_estimator as se |
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class TestStructureEstimator(unittest.TestCase): |
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@classmethod |
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def setUpClass(cls) -> None: |
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cls.s1 = sp.SamplePath('../data', 'samples', 'dyn.str', 'variables', 'Time', 'Name') |
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cls.s1.build_trajectories() |
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cls.s1.build_structure() |
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def test_init(self): |
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se1 = se.StructureEstimator(self.s1) |
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self.assertEqual(self.s1, se1.sample_path) |
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self.assertEqual(se1.complete_graph_frame.shape[0], |
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self.s1.total_variables_count *(self.s1.total_variables_count - 1)) |
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def test_one_iteration(self): |
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se1 = se.StructureEstimator(self.s1, 0.1, 0.1) |
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se1.one_iteration_of_CTPC_algorithm('X') |
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#self.aux_test_complete_test(se1, 'X', 'Y', ['Z']) |
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print(se1.complete_graph.edges) |
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def aux_test_complete_test(self, estimator, test_par, test_child, p_set): |
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estimator.complete_test(test_par, test_child, p_set) |
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if __name__ == '__main__': |
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unittest.main() |
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