From 72b69612b36e5f0449e1930a51b37b7d48524226 Mon Sep 17 00:00:00 2001 From: Luca Moretti Date: Wed, 6 Jan 2021 13:36:00 +0100 Subject: [PATCH] Preparation for test on server --- .../structure_constraint_based_estimator.py | 4 +- .../structure_score_based_estimator.py | 2 +- .../tests/optimizers/test_tabu_search.py | 12 +- main_package/tests/results/results copy.csv | 1729 +++++++++++++++++ main_package/tests/results/results.csv | 1728 ---------------- .../tests/simulators/test_simulation_all.py | 147 ++ 6 files changed, 1885 insertions(+), 1737 deletions(-) create mode 100644 main_package/tests/results/results copy.csv create mode 100644 main_package/tests/simulators/test_simulation_all.py diff --git a/main_package/classes/estimators/structure_constraint_based_estimator.py b/main_package/classes/estimators/structure_constraint_based_estimator.py index 1f4b4e5..0e27ee8 100644 --- a/main_package/classes/estimators/structure_constraint_based_estimator.py +++ b/main_package/classes/estimators/structure_constraint_based_estimator.py @@ -217,7 +217,7 @@ class StructureConstraintBasedEstimator(se.StructureEstimator): tot_vars_count = tot_vars_count) return optimizer_obj.optimize_structure() - @timing + def ctpc_algorithm(self,disable_multiprocessing:bool= False ): """ Compute the CTPC algorithm. @@ -258,7 +258,7 @@ class StructureConstraintBasedEstimator(se.StructureEstimator): return set(itertools.chain.from_iterable(list_edges_partial)) - @timing + @timing_write def estimate_structure(self,disable_multiprocessing:bool=False): return self.ctpc_algorithm(disable_multiprocessing=disable_multiprocessing) diff --git a/main_package/classes/estimators/structure_score_based_estimator.py b/main_package/classes/estimators/structure_score_based_estimator.py index 32de24c..8fe2395 100644 --- a/main_package/classes/estimators/structure_score_based_estimator.py +++ b/main_package/classes/estimators/structure_score_based_estimator.py @@ -50,7 +50,7 @@ class StructureScoreBasedEstimator(se.StructureEstimator): self.alpha_xu=alpha_xu - @timing + @timing_write def estimate_structure(self, max_parents:int = None, iterations_number:int= 40, patience:int = None, tabu_length:int = None, tabu_rules_duration:int = None, optimizer: str = 'hill',disable_multiprocessing:bool= False ): diff --git a/main_package/tests/optimizers/test_tabu_search.py b/main_package/tests/optimizers/test_tabu_search.py index cbde628..f1fd68a 100644 --- a/main_package/tests/optimizers/test_tabu_search.py +++ b/main_package/tests/optimizers/test_tabu_search.py @@ -29,15 +29,15 @@ class TestTabuSearch(unittest.TestCase): def setUpClass(cls): #cls.read_files = glob.glob(os.path.join('../../data', "*.json")) - with open("../../data/networks_and_trajectories_binary_data_01_3.json") as f: + with open("../../data/1.json") as f: raw_data = json.load(f) - trajectory_list_raw= raw_data[0]["samples"] + trajectory_list_raw= raw_data["samples"] trajectory_list = [pd.DataFrame(sample) for sample in trajectory_list_raw] - variables= pd.DataFrame(raw_data[0]["variables"]) - prior_net_structure = pd.DataFrame(raw_data[0]["dyn.str"]) + variables= pd.DataFrame(raw_data["variables"]) + prior_net_structure = pd.DataFrame(raw_data["dyn.str"]) cls.importer = si.SampleImporter( @@ -47,7 +47,7 @@ class TestTabuSearch(unittest.TestCase): ) cls.importer.import_data() - cls.s1 = sp.SamplePath(cls.importer) + #cls.s1 = sp.SamplePath(cls.importer) #cls.traj = cls.s1.concatenated_samples @@ -55,7 +55,7 @@ class TestTabuSearch(unittest.TestCase): cls.s1 = sp.SamplePath(cls.importer) cls.s1.build_trajectories() cls.s1.build_structure() - cls.s1.clear_memory() + #cls.s1.clear_memory() diff --git a/main_package/tests/results/results copy.csv b/main_package/tests/results/results copy.csv new file mode 100644 index 0000000..4a83269 --- /dev/null +++ b/main_package/tests/results/results copy.csv @@ -0,0 +1,1729 @@ 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structure_graph.sample_path as sp +import estimators.structure_score_based_estimator as se_score +import estimators.structure_constraint_based_estimator as se_constr +import utility.sample_importer as si + + + +class TestTabuSearch(unittest.TestCase): + + @classmethod + def setUpClass(cls): + pass + + + + def test_constr(self): + + list_constraint= [0,1] + + list_cardinality= [[2,"binary"],[3,"ternary"], [4,"quaternary"]] + + + + list_dens = [["0.1","_01"],["0.2","_02"], ["0.3",""], ["0.4","_04"] ] + + for constr in list_constraint: + for card in list_cardinality: + for dens in list_dens: + + if card[0] == 4: + list_vals= [3,4,5,6,10,15] + else: + list_vals= [3,4,5,6,10,15,20] + + for var_n in list_vals: + + patience = 25 + + var_number= var_n + + if var_number > 11: + patience = 30 + + if var_number > 16: + patience = 35 + + + cardinality = card[0] + cardinality_string = card[1] + + density= dens[0] + density_string = dens[1] + + constraint = constr + + index = 1 + num_networks=11 + + + while index <= num_networks: + + with open(f"/home/alessandro/Documents/ctbn_cba/data/networks_and_trajectories_{cardinality_string}_data{density_string}_{var_number}/{index}.json") as f: + raw_data = json.load(f) + + trajectory_list_raw= raw_data["samples"] + + trajectory_list = [pd.DataFrame(sample) for sample in trajectory_list_raw] + + variables= pd.DataFrame(raw_data["variables"]) + prior_net_structure = pd.DataFrame(raw_data["dyn.str"]) + + + self.importer = si.SampleImporter( + trajectory_list=trajectory_list, + variables=variables, + prior_net_structure=prior_net_structure + ) + + self.importer.import_data() + self.s1 = sp.SamplePath(self.importer) + self.s1.build_trajectories() + self.s1.build_structure() + + + true_edges = copy.deepcopy(self.s1.structure.edges) + true_edges = set(map(tuple, true_edges)) + + if constr == 1: + se1 = se_score.StructureScoreBasedEstimator(self.s1) + set_list_edges = se1.estimate_structure( + max_parents = None, + iterations_number = 100, + patience = patience, + tabu_length = var_number, + tabu_rules_duration = var_number, + optimizer = 'tabu' + ) + else: + se1 = se_constr.StructureConstraintBasedEstimator(self.s1,0.1,0.1) + set_list_edges = se1.estimate_structure(disable_multiprocessing=False) + + n_added_fake_edges = len(set_list_edges.difference(true_edges)) + + n_missing_edges = len(true_edges.difference(set_list_edges)) + + n_true_positive = len(true_edges) - n_missing_edges + + precision = n_true_positive / (n_true_positive + n_added_fake_edges) + + recall = n_true_positive / (n_true_positive + n_missing_edges) + + f1_measure = round(2* (precision*recall) / (precision+recall),3) + + print(true_edges) + print(set_list_edges) + print(f"precision: {precision} ") + print(f"recall: {recall} ") + + with open("../results/results.csv", 'a+') as fi: + fi.write(f"{constraint},{var_number},{density},{cardinality},{index},{f1_measure},{round(precision,3)},{round(recall,3)}") + + index += 1 + + self.assertEqual(set_list_edges, true_edges) + + + + +if __name__ == '__main__': + unittest.main() +