import sys
sys.path.append("../../classes/")
import glob
import math
import os
import unittest

import networkx as nx
import numpy as np
import psutil
from line_profiler import LineProfiler
import copy

import utility.cache as ch
import structure_graph.sample_path as sp
import estimators.structure_constraint_based_estimator as se
import utility.json_importer as ji



class TestTabuSearch(unittest.TestCase):

    @classmethod
    def setUpClass(cls):
        pass



    def test_constr(self):

        list_vals= [3,4,5,6,10,15]
        
        list_dens = [["0.1","_01"],["0.2","_02"], ["0.3",""], ["0.4","_04"] ]
        
        for dens in list_dens:
            for var_n in list_vals:

                var_number= var_n

                cardinality = 4
                cardinality_string = "quaternary"

                density= dens[0]
                density_string = dens[1]



                constraint = 1



                index = 0 
                num_networks=10

                if var_number > 9:
                    num_networks=3

                while index < num_networks:
                    #cls.read_files = glob.glob(os.path.join('../../data', "*.json"))
                    self.importer = ji.JsonImporter(f"../../data/networks_and_trajectories_{cardinality_string}_data{density_string}_{var_number}.json", 
                                                'samples', 'dyn.str', 'variables', 'Time', 'Name', index )
                    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))

                    se1 = se.StructureConstraintBasedEstimator(self.s1, 0.1, 0.1)
                    se1.ctpc_algorithm()

                                    
                    set_list_edges = set(se1.complete_graph.edges)

                    
                    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(f"n archi reali non trovati: {n_missing_edges}")
                    # print(f"n archi non reali aggiunti: {n_added_fake_edges}")
                    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()