diff --git a/main_package/classes/estimators/parameters_estimator.py.bak b/main_package/classes/estimators/parameters_estimator.py.bak
deleted file mode 100644
index 2805819..0000000
--- a/main_package/classes/estimators/parameters_estimator.py.bak
+++ /dev/null
@@ -1,143 +0,0 @@
-import sys
-sys.path.append('../')
-import numpy as np
-
-from ..structure_graph.network_graph import NetworkGraph
-from ..structure_graph.sample_path import SetOfCims
-from ..structure_graph.trajectory import Trajectory
-
-
-class ParametersEstimator(object):
-    """Has the task of computing the cims of particular node given the trajectories and the net structure
-    in the graph ``_net_graph``.
-
-    :param trajectories:  the trajectories
-    :type trajectories: Trajectory
-    :param net_graph: the net structure
-    :type net_graph: NetworkGraph
-    :_single_set_of_cims: the set of cims object that will hold the cims of the node
-    """
-
-    def __init__(self, trajectories: Trajectory, net_graph: NetworkGraph):
-        """Constructor Method
-        """
-        self._trajectories = trajectories
-        self._net_graph = net_graph
-        self._single_set_of_cims = None
-
-    def fast_init(self, node_id: str) -> None:
-        """Initializes all the necessary structures for the parameters estimation for the node ``node_id``.
-
-        :param node_id: the node label
-        :type node_id: string
-        """
-        p_vals = self._net_graph._aggregated_info_about_nodes_parents[2]
-        node_states_number = self._net_graph.get_states_number(node_id)
-        self._single_set_of_cims = SetOfCims(node_id, p_vals, node_states_number, self._net_graph.p_combs)
-
-    def compute_parameters_for_node(self, node_id: str) -> SetOfCims:
-        """Compute the CIMS of the node identified by the label ``node_id``.
-
-        :param node_id: the node label
-        :type node_id: string
-        :return: A SetOfCims object filled with the computed CIMS
-        :rtype: SetOfCims
-        """
-        node_indx = self._net_graph.get_node_indx(node_id)
-        state_res_times = self._single_set_of_cims._state_residence_times
-        transition_matrices = self._single_set_of_cims._transition_matrices
-        ParametersEstimator.compute_state_res_time_for_node(self._trajectories.times,
-                                             self._trajectories.trajectory,
-                                             self._net_graph.time_filtering,
-                                             self._net_graph.time_scalar_indexing_strucure,
-                                             state_res_times)
-        ParametersEstimator.compute_state_transitions_for_a_node(node_indx, self._trajectories.complete_trajectory,
-                                                                 self._net_graph.transition_filtering,
-                                                                 self._net_graph.transition_scalar_indexing_structure,
-                                                                 transition_matrices)
-        self._single_set_of_cims.build_cims(state_res_times, transition_matrices)
-        return self._single_set_of_cims
-
-    @staticmethod
-    def compute_state_res_time_for_node(times: np.ndarray, trajectory: np.ndarray,
-                                        cols_filter: np.ndarray, scalar_indexes_struct: np.ndarray,
-                                        T: np.ndarray) -> None:
-        """Compute the state residence times for a node and fill the matrix ``T`` with the results
-
-        :param node_indx: the index of the node
-        :type node_indx: int
-        :param times: the times deltas vector
-        :type times: numpy.array
-        :param trajectory: the trajectory
-        :type trajectory: numpy.ndArray
-        :param cols_filter: the columns filtering structure
-        :type cols_filter: numpy.array
-        :param scalar_indexes_struct: the indexing structure
-        :type scalar_indexes_struct: numpy.array
-        :param T: the state residence times vectors
-        :type T: numpy.ndArray
-        """
-        T[:] = np.bincount(np.sum(trajectory[:, cols_filter] * scalar_indexes_struct / scalar_indexes_struct[0], axis=1)
-                           .astype(np.int), \
-                           times,
-                           minlength=scalar_indexes_struct[-1]).reshape(-1, T.shape[1])
-
-    @staticmethod
-    def compute_state_transitions_for_a_node(node_indx: int, trajectory: np.ndarray, cols_filter: np.ndarray,
-                                             scalar_indexing: np.ndarray, M: np.ndarray) -> None:
-        """Compute the state residence times for a node and fill the matrices ``M`` with the results.
-
-        :param node_indx: the index of the node
-        :type node_indx: int
-        :param trajectory: the trajectory
-        :type trajectory: numpy.ndArray
-        :param cols_filter: the columns filtering structure
-        :type cols_filter: numpy.array
-        :param scalar_indexing: the indexing structure
-        :type scalar_indexing: numpy.array
-        :param M: the state transitions matrices
-        :type M: numpy.ndArray
-        """
-        diag_indices = np.array([x * M.shape[1] + x % M.shape[1] for x in range(M.shape[0] * M.shape[1])],
-                                dtype=np.int64)
-        trj_tmp = trajectory[trajectory[:, int(trajectory.shape[1] / 2) + node_indx].astype(np.int) >= 0]
-        M[:] = np.bincount(np.sum(trj_tmp[:, cols_filter] * scalar_indexing / scalar_indexing[0], axis=1).astype(np.int)
-                           , minlength=scalar_indexing[-1]).reshape(-1, M.shape[1], M.shape[2])
-        M_raveled = M.ravel()
-        M_raveled[diag_indices] = 0
-        M_raveled[diag_indices] = np.sum(M, axis=2).ravel()
-
-    def init_sets_cims_container(self):
-        self.sets_of_cims_struct = acims.SetsOfCimsContainer(self.net_graph.nodes,
-                                                             self.net_graph.nodes_values,
-                                                             self.net_graph.get_ordered_by_indx_parents_values_for_all_nodes(),
-                                                             self.net_graph.p_combs)
-
-    def compute_parameters(self):
-        #print(self.net_graph.get_nodes())
-        #print(self.amalgamated_cims_struct.sets_of_cims)
-        #enumerate(zip(self.net_graph.get_nodes(), self.amalgamated_cims_struct.sets_of_cims))
-        for indx, aggr in enumerate(zip(self.net_graph.nodes, self.sets_of_cims_struct.sets_of_cims)):
-            #print(self.net_graph.time_filtering[indx])
-            #print(self.net_graph.time_scalar_indexing_strucure[indx])
-            self.compute_state_res_time_for_node(self.net_graph.get_node_indx(aggr[0]), self.sample_path.trajectories.times,
-                                                 self.sample_path.trajectories.trajectory,
-                                                 self.net_graph.time_filtering[indx],
-                                                 self.net_graph.time_scalar_indexing_strucure[indx],
-                                                 aggr[1]._state_residence_times)
-            #print(self.net_graph.transition_filtering[indx])
-            #print(self.net_graph.transition_scalar_indexing_structure[indx])
-            self.compute_state_transitions_for_a_node(self.net_graph.get_node_indx(aggr[0]),
-                                                      self.sample_path.trajectories.complete_trajectory,
-                                                      self.net_graph.transition_filtering[indx],
-                                                      self.net_graph.transition_scalar_indexing_structure[indx],
-                                                      aggr[1]._transition_matrices)
-            aggr[1].build_cims(aggr[1]._state_residence_times, aggr[1]._transition_matrices)
-
-
-
-
-
-
-
-
diff --git a/main_package/classes/estimators/structure_constraint_based_estimator.py.bak b/main_package/classes/estimators/structure_constraint_based_estimator.py.bak
deleted file mode 100644
index 558a625..0000000
--- a/main_package/classes/estimators/structure_constraint_based_estimator.py.bak
+++ /dev/null
@@ -1,245 +0,0 @@
-import sys
-sys.path.append('../')
-import itertools
-import json
-import typing
-
-import networkx as nx
-import numpy as np
-from networkx.readwrite import json_graph
-import os
-from scipy.stats import chi2 as chi2_dist
-from scipy.stats import f as f_dist
-from tqdm import tqdm
-
-from ..utility.cache as ch
-from ..structure_graph.conditional_intensity_matrix import ConditionalIntensityMatrix
-from ..structure_graph.network_graph import NetworkGraph
-from .parameters_estimator import ParametersEstimator
-from .structure_estimator import StructureEstimator
-from ..structure_graph.sample_path import SamplePath
-from ..structure_graph.structure import Structure
-from ..optimizers.constraint_based_optimizer import ConstraintBasedOptimizer
-
-import concurrent.futures
-
-from utility.decorators import timing,timing_write
-
-import multiprocessing
-from multiprocessing import Pool
-
-
-class StructureConstraintBasedEstimator(se.StructureEstimator):
-    """
-    Has the task of estimating the network structure given the trajectories in samplepath by using a constraint-based approach.
-
-    :param sample_path: the _sample_path object containing the trajectories and the real structure
-    :type sample_path: SamplePath
-    :param exp_test_alfa: the significance level for the exponential Hp test
-    :type exp_test_alfa: float
-    :param chi_test_alfa: the significance level for the chi Hp test
-    :type chi_test_alfa: float
-    :_nodes: the nodes labels
-    :_nodes_vals: the nodes cardinalities
-    :_nodes_indxs: the nodes indexes
-    :_complete_graph: the complete directed graph built using the nodes labels in ``_nodes``
-    :_cache: the Cache object
-    """
-
-    def __init__(self, sample_path: SamplePath, exp_test_alfa: float, chi_test_alfa: float,known_edges: typing.List= [],thumb_threshold:int = 25):
-        super().__init__(sample_path,known_edges)
-        self._exp_test_sign = exp_test_alfa
-        self._chi_test_alfa = chi_test_alfa
-		self._thumb_threshold = thumb_threshold
-                      tot_vars_count: int, parent_indx, child_indx) -> bool:
-
-    def complete_test(self, test_parent: str, test_child: str, parent_set: typing.List, child_states_numb: int,
-                      tot_vars_count: int, parent_indx, child_indx) -> bool:
-        """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.
-
-        :param test_parent: the node label of the test parent
-        :type test_parent: string
-        :param test_child: the node label of the child
-        :type test_child: string
-        :param parent_set: the common parent set
-        :type parent_set: List
-        :param child_states_numb: the cardinality of the ``test_child``
-        :type child_states_numb: int
-        :param tot_vars_count: the total number of variables in the net
-        :type tot_vars_count: int
-        :return: True iff test_child and test_parent are independent given the sep_set parent_set. False otherwise
-        :rtype: bool
-        """
-        p_set = parent_set[:]
-        complete_info = parent_set[:]
-        complete_info.append(test_child)
-
-        parents = np.array(parent_set)
-        parents = np.append(parents, test_parent)
-        sorted_parents = self._nodes[np.isin(self._nodes, parents)]
-        cims_filter = sorted_parents != test_parent
-
-        p_set.insert(0, test_parent)
-        sofc2 = self._cache.find(set(p_set))
-
-        if not sofc2:
-            complete_info.append(test_parent)
-            bool_mask2 = np.isin(self._nodes, complete_info)
-            l2 = list(self._nodes[bool_mask2])
-            indxs2 = self._nodes_indxs[bool_mask2]
-            vals2 = self._nodes_vals[bool_mask2]
-            eds2 = list(itertools.product(p_set, test_child))
-            s2 = Structure(l2, indxs2, vals2, eds2, tot_vars_count)
-            g2 = NetworkGraph(s2)
-            g2.fast_init(test_child)
-            p2 = ParametersEstimator(self._sample_path.trajectories, g2)
-            p2.fast_init(test_child)
-            sofc2 = p2.compute_parameters_for_node(test_child)
-            self._cache.put(set(p_set), sofc2)
-
-        del p_set[0]
-        sofc1 = self._cache.find(set(p_set))
-        if not sofc1:
-            g2.remove_node(test_parent)
-            g2.fast_init(test_child)
-            p2 = ParametersEstimator(self._sample_path.trajectories, g2)
-            p2.fast_init(test_child)
-            sofc1 = p2.compute_parameters_for_node(test_child)
-            self._cache.put(set(p_set), sofc1)
-        thumb_value = 0.0
-        if child_states_numb > 2:
-            parent_val = self._sample_path.structure.get_states_number(test_parent)
-            bool_mask_vals = np.isin(self._nodes, parent_set)
-            parents_vals = self._nodes_vals[bool_mask_vals]
-            thumb_value = self.compute_thumb_value(parent_val, child_states_numb, parents_vals)
-        for cim1, p_comb in zip(sofc1.actual_cims, sofc1.p_combs):
-            cond_cims = sofc2.filter_cims_with_mask(cims_filter, p_comb)
-            for cim2 in cond_cims:
-                if not self.independence_test(child_states_numb, cim1, cim2, thumb_value, parent_indx, child_indx):
-                    return False
-        return True
-
-    def independence_test(self, child_states_numb: int, cim1: ConditionalIntensityMatrix,
-                          cim2: ConditionalIntensityMatrix, thumb_value: float, parent_indx, child_indx) -> bool:
-        """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.
-
-        :param child_states_numb: the cardinality of the test child
-        :type child_states_numb: int
-        :param cim1: a cim belonging to the graph without test parent
-        :type cim1: ConditionalIntensityMatrix
-        :param cim2: a cim belonging to the graph with test parent
-        :type cim2: ConditionalIntensityMatrix
-        :return: True iff both tests do NOT reject the null hypothesis of independence. False otherwise.
-        :rtype: bool
-        """
-        M1 = cim1.state_transition_matrix
-        M2 = cim2.state_transition_matrix
-        r1s = M1.diagonal()
-        r2s = M2.diagonal()
-        C1 = cim1.cim
-        C2 = cim2.cim
-        if child_states_numb > 2:
-            if (np.sum(np.diagonal(M1)) / thumb_value) < self._thumb_threshold:
-                self._removable_edges_matrix[parent_indx][child_indx] = False
-                return False
-        F_stats = C2.diagonal() / C1.diagonal()
-        exp_alfa = self._exp_test_sign
-        for val in range(0, child_states_numb):
-            if F_stats[val] < f_dist.ppf(exp_alfa / 2, r1s[val], r2s[val]) or \
-                    F_stats[val] > f_dist.ppf(1 - exp_alfa / 2, r1s[val], r2s[val]):
-                return False
-        M1_no_diag = M1[~np.eye(M1.shape[0], dtype=bool)].reshape(M1.shape[0], -1)
-        M2_no_diag = M2[~np.eye(M2.shape[0], dtype=bool)].reshape(
-            M2.shape[0], -1)
-        chi_2_quantile = chi2_dist.ppf(1 - self._chi_test_alfa, child_states_numb - 1)
-        Ks = np.sqrt(r1s / r2s)
-        Ls = np.sqrt(r2s / r1s)
-        for val in range(0, child_states_numb):
-            Chi = np.sum(np.power(Ks[val] * M2_no_diag[val] - Ls[val] *M1_no_diag[val], 2) /
-                         (M1_no_diag[val] + M2_no_diag[val]))
-            if Chi > chi_2_quantile:
-                return False
-        return True
-        
-    def compute_thumb_value(self, parent_val, child_val, parent_set_vals):
-        """Compute the value to test against the thumb_threshold.
-        
-        :param parent_val: test parent's variable cardinality
-        :type parent_val: int
-        :param child_val: test child's variable cardinality
-        :type child_val: int
-        :param parent_set_vals: the cardinalities of the nodes in the current sep-set
-        :type parent_set_vals: List
-        :return: the thumb value for the current independence test
-        :rtype: int
-        """
-        df = (child_val - 1) ** 2
-        df = df * parent_val
-        for v in parent_set_vals:
-            df = df * v
-        return df
-        
-    def one_iteration_of_CTPC_algorithm(self, var_id: str, tot_vars_count: int)-> typing.List:
-        """Performs an iteration of the CTPC algorithm using the node ``var_id`` as ``test_child``.
-
-        :param var_id: the node label of the test child
-        :type var_id: string
-        """
-        optimizer_obj = optimizer.ConstraintBasedOptimizer(
-                                                            node_id = var_id,
-                                                            structure_estimator = self,
-                                                            tot_vars_count = tot_vars_count)
-        return optimizer_obj.optimize_structure()
-
-    
-    def ctpc_algorithm(self,disable_multiprocessing:bool= False ):
-        """
-        Compute the CTPC algorithm.
-        Parameters:
-            void
-        Returns:
-            void
-        """
-        ctpc_algo = self.one_iteration_of_CTPC_algorithm
-        total_vars_numb = self._sample_path.total_variables_count
-
-        n_nodes= len(self.nodes)
-
-        total_vars_numb_array =  [total_vars_numb] *  n_nodes
-
-        'get the number of CPU'
-        cpu_count = multiprocessing.cpu_count()
-
-
-
-        'Remove all the edges from the structure'   
-        self._sample_path.structure.clean_structure_edges()
-
-        'Estimate the best parents for each node'
-        #with multiprocessing.Pool(processes=cpu_count) as pool:
-        #with get_context("spawn").Pool(processes=cpu_count) as pool:
-        if disable_multiprocessing:
-            print("DISABILITATO")
-            cpu_count = 1
-            list_edges_partial = [ctpc_algo(n,total_vars_numb) for n in self.nodes]
-        else:
-            with concurrent.futures.ProcessPoolExecutor(max_workers=cpu_count) as executor:
-                list_edges_partial = executor.map(ctpc_algo,
-                                                                 self.nodes,
-                                                                 total_vars_numb_array)
-            #list_edges_partial = [ctpc_algo(n,total_vars_numb) for n in self.nodes]
-
-        return set(itertools.chain.from_iterable(list_edges_partial))
-
-        
-    @timing
-    def estimate_structure(self,disable_multiprocessing:bool=False):
-        return self.ctpc_algorithm(disable_multiprocessing=disable_multiprocessing)
-
-    
-
-
diff --git a/main_package/classes/estimators/structure_estimator.py.bak b/main_package/classes/estimators/structure_estimator.py.bak
deleted file mode 100644
index 36fcb04..0000000
--- a/main_package/classes/estimators/structure_estimator.py.bak
+++ /dev/null
@@ -1,189 +0,0 @@
-import sys
-sys.path.append('../')
-import itertools
-import json
-import typing
-
-import matplotlib.pyplot as plt
-import networkx as nx
-import numpy as np
-from networkx.readwrite import json_graph
-
-from abc import ABC
-
-import abc
-
-import ..utility.cache as ch
-import ..structure_graph.conditional_intensity_matrix import ConditionalIntensityMatrix
-import ..structure_graph.network_graph import NetworkGraph
-import .parameters_estimator import ParametersEstimator
-import ..structure_graph.sample_path import SamplePath
-from ..structure_graph.structure import Structure
-
-
-class StructureEstimator(object):
-    """Has the task of estimating the network structure given the trajectories in ``samplepath``.
-
-    :param sample_path: the _sample_path object containing the trajectories and the real structure
-    :type sample_path: SamplePath
-    :_nodes: the nodes labels
-    :_nodes_vals: the nodes cardinalities
-    :_nodes_indxs: the nodes indexes
-    :_complete_graph: the complete directed graph built using the nodes labels in ``_nodes``
-    """
-
-    def __init__(self, sample_path: SamplePath, known_edges: typing.List = None):
-        self._sample_path = sample_path
-        self.nodes = np.array(self._sample_path.structure.nodes_labels)
-        self.nodes_vals = self._sample_path.structure.nodes_values
-        self.nodes_indxs = self._sample_path.structure.nodes_indexes
-        self._removable_edges_matrix = self.
-        (known_edges)
-        self.complete_graph = self.build_complete_graph(self._sample_path.structure.nodes_labels)
-        self.cache = ch.Cache()
-
-    def build_removable_edges_matrix(self, known_edges: typing.List):
-        """Builds a boolean matrix who shows if a edge could be removed or not, based on prior knowledge given:
-
-        :param known_edges: the list of nodes labels
-        :type known_edges: List
-        :return: a boolean matrix
-        :rtype: np.ndarray
-        """
-        tot_vars_count = self._sample_path.total_variables_count
-        complete_adj_matrix = np.full((tot_vars_count, tot_vars_count), True)
-        if known_edges:
-            for edge in known_edges:
-                i = self._sample_path.structure.get_node_indx(edge[0])
-                j = self._sample_path.structure.get_node_indx(edge[1])
-                complete_adj_matrix[i][j] = False
-        return complete_adj_matrix
-
-    @staticmethod
-    def build_complete_graph(node_ids: typing.List) -> nx.DiGraph:
-        """Builds a complete directed graph (no self loops) given the nodes labels in the list ``node_ids``:
-
-        :param node_ids: the list of nodes labels
-        :type node_ids: List
-        :return: a complete Digraph Object
-        :rtype: networkx.DiGraph
-        """
-        complete_graph = nx.DiGraph()
-        complete_graph.add_nodes_from(node_ids)
-        complete_graph.add_edges_from(itertools.permutations(node_ids, 2))
-        return complete_graph
-
-
-
-    def generate_possible_sub_sets_of_size(self, u: typing.List, size: int, parent_label: str):
-        """Creates a list containing all possible subsets of the list ``u`` of size ``size``,
-        that do not contains a the node identified by ``parent_label``.
-
-        :param u: the list of nodes
-        :type u: List
-        :param size: the size of the subsets
-        :type size: int
-        :param parent_label: the node to exclude in the subsets generation
-        :type parent_label: string
-        :return: an Iterator Object containing a list of lists
-        :rtype: Iterator
-        """
-        list_without_test_parent = u[:]
-        list_without_test_parent.remove(parent_label)
-        return map(list, itertools.combinations(list_without_test_parent, size))
-
- 	def save_results(self) -> None:
-        """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 `results_` word is appended to the results file.
-        """
-        res = json_graph.node_link_data(self._complete_graph)
-        name = self._sample_path._importer.file_path.rsplit('/', 1)[-1]
-        name = name.split('.', 1)[0]
-        name += '_' + str(self._sample_path._importer.dataset_id())
-        name += '.json'
-        file_name = 'results_' + name
-        with open(file_name, 'w') as f:
-            json.dump(res, f)
-
-
-    def remove_diagonal_elements(self, matrix):
-        m = matrix.shape[0]
-        strided = np.lib.stride_tricks.as_strided
-        s0, s1 = matrix.strides
-        return strided(matrix.ravel()[1:], shape=(m - 1, m), strides=(s0 + s1, s1)).reshape(m, -1)
-
-
-    @abc.abstractmethod
-    def estimate_structure(self) -> typing.List:
-        """Abstract method to estimate the structure
-
-        :return: List of estimated edges
-        :rtype: Typing.List
-        """
-        pass
-
-    
-    def adjacency_matrix(self) -> np.ndarray:
-        """Converts the estimated structure ``_complete_graph`` to a boolean adjacency matrix representation.
-
-        :return: The adjacency matrix of the graph ``_complete_graph``
-        :rtype: numpy.ndArray
-        """
-        return nx.adj_matrix(self._complete_graph).toarray().astype(bool)
-
-    def spurious_edges(self) -> typing.List:
-        """Return the spurious edges present in the estimated structure, if a prior net structure is present in
-            ``_sample_path.structure``.
-
-        :return: A list containing the spurious edges
-        :rtype: List
-        """
-        if not self._sample_path.has_prior_net_structure:
-            raise RuntimeError("Can not compute spurious edges with no prior net structure!")
-        real_graph = nx.DiGraph()
-        real_graph.add_nodes_from(self._sample_path.structure.nodes_labels)
-        real_graph.add_edges_from(self._sample_path.structure.edges)
-        return nx.difference(real_graph, self._complete_graph).edges
-
-    def save_plot_estimated_structure_graph(self) -> None:
-        """Plot the estimated structure in a graphical model style.
-        Spurious edges are colored in red.
-        """
-        graph_to_draw = nx.DiGraph()
-        spurious_edges = self.spurious_edges()
-        non_spurious_edges = list(set(self._complete_graph.edges) - set(spurious_edges))
-        print(non_spurious_edges)
-        edges_colors = ['red' if edge in spurious_edges else 'black' for edge in self._complete_graph.edges]
-        graph_to_draw.add_edges_from(spurious_edges)
-        graph_to_draw.add_edges_from(non_spurious_edges)
-        pos = nx.spring_layout(graph_to_draw, k=0.5*1/np.sqrt(len(graph_to_draw.nodes())), iterations=50,scale=10)
-        options = {
-            "node_size": 2000,
-            "node_color": "white",
-            "edgecolors": "black",
-            'linewidths':2,
-            "with_labels":True,
-            "font_size":13,
-            'connectionstyle': 'arc3, rad = 0.1',
-            "arrowsize": 15,
-            "arrowstyle": '<|-',
-            "width": 1,
-            "edge_color":edges_colors,
-        }
-
-        nx.draw(graph_to_draw, pos, **options)
-        ax = plt.gca()
-        ax.margins(0.20)
-        plt.axis("off")
-        name = self._sample_path._importer.file_path.rsplit('/', 1)[-1]
-        name = name.split('.', 1)[0]
-        name += '_' + str(self._sample_path._importer.dataset_id())
-        name += '.png'
-        plt.savefig(name)
-        plt.clf()
-        print("Estimated Structure Plot Saved At: ", os.path.abspath(name))
-
-
-
-
-
diff --git a/main_package/classes/estimators/structure_score_based_estimator.py b/main_package/classes/estimators/structure_score_based_estimator.py
index 19e7fc4..2903db3 100644
--- a/main_package/classes/estimators/structure_score_based_estimator.py
+++ b/main_package/classes/estimators/structure_score_based_estimator.py
@@ -23,7 +23,6 @@ from .fam_score_calculator import FamScoreCalculator
 from ..optimizers.hill_climbing_search import HillClimbing
 from ..optimizers.tabu_search import TabuSearch
 
-from ..utility.decorators import timing,timing_write
 
 import multiprocessing
 from multiprocessing import Pool
@@ -53,7 +52,6 @@ class StructureScoreBasedEstimator(StructureEstimator):
         self.alpha_xu=alpha_xu
 
 
-    @timing
     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 = 'tabu',disable_multiprocessing:bool= False ):
diff --git a/main_package/classes/structure_graph/abstract_sample_path.py b/main_package/classes/structure_graph/abstract_sample_path.py
deleted file mode 100644
index 36965b0..0000000
--- a/main_package/classes/structure_graph/abstract_sample_path.py
+++ /dev/null
@@ -1,41 +0,0 @@
-from abc import ABC, abstractmethod
-
-import sys
-sys.path.append('../')
-
-import utility.abstract_importer as ai
-
-
-class AbstractSamplePath(ABC):
-
-    def __init__(self, importer: ai.AbstractImporter):
-        self.importer = importer
-        self._trajectories = None
-        self._structure = None
-        super().__init__()
-
-    @abstractmethod
-    def build_trajectories(self):
-        """
-        Builds the Trajectory object that will contain all the trajectories.
-        Assigns the Trajectoriy object to the instance attribute _trajectories
-        Clears all the unused dataframes in Importer Object
-
-        Parameters:
-            void
-        Returns:
-            void
-        """
-        pass
-
-    @abstractmethod
-    def build_structure(self):
-        """
-        Builds the Structure object that aggregates all the infos about the net.
-        Assigns the Structure object to the instance attribuite _structure
-        Parameters:
-            void
-        Returns:
-            void
-        """
-        pass
diff --git a/main_package/classes/structure_graph/conditional_intensity_matrix.py.bak b/main_package/classes/structure_graph/conditional_intensity_matrix.py.bak
deleted file mode 100644
index e87b662..0000000
--- a/main_package/classes/structure_graph/conditional_intensity_matrix.py.bak
+++ /dev/null
@@ -1,44 +0,0 @@
-import numpy as np
-
-import sys
-sys.path.append('../')
-
-class ConditionalIntensityMatrix:
-    """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.
-
-    :param state_residence_times: state residence times vector
-    :type state_residence_times: numpy.array
-    :param state_transition_matrix: the transitions count matrix
-    :type state_transition_matrix: numpy.ndArray
-    :_cim: the actual cim of the node
-    """
-    def __init__(self, state_residence_times: np.array, state_transition_matrix: np.array):
-        """Constructor Method
-        """
-        self._state_residence_times = state_residence_times
-        self._state_transition_matrix = state_transition_matrix
-        self._cim = self.state_transition_matrix.astype(np.float64)
-
-    def compute_cim_coefficients(self):
-        """Compute the coefficients of the matrix _cim by using the following equality q_xx' = M[x, x'] / T[x].
-        The class member ``_cim`` will contain the computed cim
-        """
-        np.fill_diagonal(self._cim, self._cim.diagonal() * -1)
-        self._cim = ((self._cim.T + 1) / (self._state_residence_times + 1)).T
-
-    @property
-    def state_residence_times(self):
-        return self._state_residence_times
-
-    @property
-    def state_transition_matrix(self):
-        return self._state_transition_matrix
-
-    @property
-    def cim(self):
-        return self._cim
-
-    def __repr__(self):
-        return 'CIM:\n' + str(self.cim)
-
diff --git a/main_package/classes/structure_graph/network_graph.py.bak b/main_package/classes/structure_graph/network_graph.py.bak
deleted file mode 100644
index b1ced85..0000000
--- a/main_package/classes/structure_graph/network_graph.py.bak
+++ /dev/null
@@ -1,285 +0,0 @@
-
-import typing
-
-import networkx as nx
-import numpy as np
-
-from .structure import Structure
-
-
-class NetworkGraph(object):
-    """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
-
-    :param graph_struct: the ``Structure`` object from which infos about the net will be extracted
-    :type graph_struct: Structure
-    :_graph: directed graph
-    :_aggregated_info_about_nodes_parents: 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.
-    :_time_scalar_indexing_structure: the indexing structure for state res time estimation
-    :_transition_scalar_indexing_structure: the indexing structure for transition computation
-    :_time_filtering: the columns filtering structure used in the computation of the state res times
-    :_transition_filtering: the columns filtering structure used in the computation of the transition
-        from one state to another
-    :_p_combs_structure: all the possible parents states combination for the node of interest
-    """
-
-    def __init__(self, graph_struct: Structure):
-        """Constructor Method
-        """
-        self._graph_struct = graph_struct
-        self._graph = nx.DiGraph()
-        self._aggregated_info_about_nodes_parents = None
-        self._time_scalar_indexing_structure = None
-        self._transition_scalar_indexing_structure = None
-        self._time_filtering = None
-        self._transition_filtering = None
-        self._p_combs_structure = None
-
-    def init_graph(self):
-        self.add_nodes(self._nodes_labels)
-        self.add_edges(self.graph_struct.edges)
-        self.aggregated_info_about_nodes_parents = self.get_ord_set_of_par_of_all_nodes()
-        self._fancy_indexing = self.build_fancy_indexing_structure(0)
-        self.build_scalar_indexing_structures()
-        self.build_time_columns_filtering_structure()
-        self.build_transition_columns_filtering_structure()
-        self._p_combs_structure = self.build_p_combs_structure()
-
-    def fast_init(self, node_id: str) -> None:
-        """Initializes all the necessary structures for parameters estimation of the node identified by the label
-        node_id
-
-        :param node_id: the label of the node
-        :type node_id: string
-        """
-        self.add_nodes(self._graph_struct.nodes_labels)
-        self.add_edges(self._graph_struct.edges)
-        self._aggregated_info_about_nodes_parents = self.get_ordered_by_indx_set_of_parents(node_id)
-        p_indxs = self._aggregated_info_about_nodes_parents[1]
-        p_vals = self._aggregated_info_about_nodes_parents[2]
-        node_states = self.get_states_number(node_id)
-        node_indx = self.get_node_indx(node_id)
-        cols_number = self._graph_struct.total_variables_number
-        self._time_scalar_indexing_structure = NetworkGraph.\
-            build_time_scalar_indexing_structure_for_a_node(node_states, p_vals)
-        self._transition_scalar_indexing_structure = NetworkGraph.\
-            build_transition_scalar_indexing_structure_for_a_node(node_states, p_vals)
-        self._time_filtering = NetworkGraph.build_time_columns_filtering_for_a_node(node_indx, p_indxs)
-        self._transition_filtering = NetworkGraph.build_transition_filtering_for_a_node(node_indx, p_indxs, cols_number)
-        self._p_combs_structure = NetworkGraph.build_p_comb_structure_for_a_node(p_vals)
-
-    def add_nodes(self, list_of_nodes: typing.List) -> None:
-        """Adds the nodes to the ``_graph`` contained in the list of nodes ``list_of_nodes``.
-        Sets all the properties that identify a nodes (index, positional index, cardinality)
-
-        :param list_of_nodes: the nodes to add to ``_graph``
-        :type list_of_nodes: List
-        """
-        nodes_indxs = self._graph_struct.nodes_indexes
-        nodes_vals = self._graph_struct.nodes_values
-        pos = 0
-        for id, node_indx, node_val in zip(list_of_nodes, nodes_indxs, nodes_vals):
-            self._graph.add_node(id, indx=node_indx, val=node_val, pos_indx=pos)
-            pos += 1
-
-    def has_edge(self,edge:tuple)-> bool:
-        """
-        Check if the graph contains a specific edge
-
-        Parameters:
-            edge: a tuple that rappresents the edge
-        Returns:
-            bool
-        """
-        return self.graph.has_edge(edge[0],edge[1])
-
-    def add_edges(self, list_of_edges: typing.List) -> None:
-        """Add the edges to the ``_graph`` contained in the list ``list_of_edges``.
-
-        :param list_of_edges: the list containing of tuples containing the edges
-        :type list_of_edges: List
-        """
-        self._graph.add_edges_from(list_of_edges)
-
-    def remove_node(self, node_id: str) -> None:
-        """Remove the node ``node_id`` from all the class members.
-        Initialize all the filtering/indexing structures.
-        """
-        self._graph.remove_node(node_id)
-        self._graph_struct.remove_node(node_id)
-        self.clear_indexing_filtering_structures()
-
-    def clear_indexing_filtering_structures(self) -> None:
-        """Initialize all the filtering/indexing structures.
-        """
-        self._aggregated_info_about_nodes_parents = None
-        self._time_scalar_indexing_structure = None
-        self._transition_scalar_indexing_structure = None
-        self._time_filtering = None
-        self._transition_filtering = None
-        self._p_combs_structure = None
-
-    def get_ordered_by_indx_set_of_parents(self, node: str) -> typing.Tuple:
-        """Builds the aggregated structure that holds all the infos relative to the parent set of the node, namely
-        (parents_labels, parents_indexes, parents_cardinalities).
-
-        :param node: the label of the node
-        :type node: string
-        :return: a tuple containing all the parent set infos
-        :rtype: Tuple
-        """
-        parents = self.get_parents_by_id(node)
-        nodes = self._graph_struct.nodes_labels
-        d = {v: i for i, v in enumerate(nodes)}
-        sorted_parents = sorted(parents, key=lambda v: d[v])
-        get_node_indx = self.get_node_indx
-        p_indxes = [get_node_indx(node) for node in sorted_parents]
-        p_values = [self.get_states_number(node) for node in sorted_parents]
-        return sorted_parents, p_indxes, p_values
-
-    @staticmethod
-    def build_time_scalar_indexing_structure_for_a_node(node_states: int,
-                                                        parents_vals: typing.List) -> np.ndarray:
-        """Builds an indexing structure for the computation of state residence times values.
-
-        :param node_states: the node cardinality
-        :type node_states: int
-        :param parents_vals: the caridinalites of the node's parents
-        :type parents_vals: List
-        :return: The time indexing structure
-        :rtype: numpy.ndArray
-        """
-        T_vector = np.array([node_states])
-        T_vector = np.append(T_vector, parents_vals)
-        T_vector = T_vector.cumprod().astype(np.int)
-        return T_vector
-
-    @staticmethod
-    def build_transition_scalar_indexing_structure_for_a_node(node_states_number: int, parents_vals: typing.List) \
-            -> np.ndarray:
-        """Builds an indexing structure for the computation of state transitions values.
-
-        :param node_states_number: the node cardinality
-        :type node_states_number: int
-        :param parents_vals: the caridinalites of the node's parents
-        :type parents_vals: List
-        :return: The transition indexing structure
-        :rtype: numpy.ndArray
-        """
-        M_vector = np.array([node_states_number,
-                             node_states_number])
-        M_vector = np.append(M_vector, parents_vals)
-        M_vector = M_vector.cumprod().astype(np.int)
-        return M_vector
-
-    @staticmethod
-    def build_time_columns_filtering_for_a_node(node_indx: int, p_indxs: typing.List) -> np.ndarray:
-        """
-        Builds the necessary structure to filter the desired columns indicated by ``node_indx`` and ``p_indxs``
-        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
-        """
-        return np.append(np.array([node_indx], dtype=np.int), p_indxs).astype(np.int)
-
-    @staticmethod
-    def build_transition_filtering_for_a_node(node_indx: int, p_indxs: typing.List, nodes_number: int) \
-            -> np.ndarray:
-        """Builds the necessary structure to filter the desired columns indicated by ``node_indx`` and ``p_indxs``
-        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
-        :param nodes_number: the total number of nodes in the dataset
-        :type nodes_number: int
-        :return: The filtering structure for transitions estimation
-        :rtype: numpy.ndArray
-        """
-        return np.array([node_indx + nodes_number, node_indx, *p_indxs], dtype=np.int)
-
-    @staticmethod
-    def build_p_comb_structure_for_a_node(parents_values: typing.List) -> np.ndarray:
-        """
-        Builds the combinatorial structure that contains the combinations of all the values contained in
-        ``parents_values``.
-
-        :param parents_values: the cardinalities of the nodes
-        :type parents_values: List
-        :return: A numpy matrix containing a grid of the combinations
-        :rtype: numpy.ndArray
-        """
-        tmp = []
-        for val in parents_values:
-            tmp.append([x for x in range(val)])
-        if len(parents_values) > 0:
-            parents_comb = np.array(np.meshgrid(*tmp)).T.reshape(-1, len(parents_values))
-            if len(parents_values) > 1:
-                tmp_comb = parents_comb[:, 1].copy()
-                parents_comb[:, 1] = parents_comb[:, 0].copy()
-                parents_comb[:, 0] = tmp_comb
-        else:
-            parents_comb = np.array([[]], dtype=np.int)
-        return parents_comb
-
-    def get_parents_by_id(self, node_id) -> typing.List:
-        """Returns a list of labels of the parents of the node ``node_id``
-
-        :param node_id: the node label
-        :type node_id: string
-        :return: a List of labels of the parents
-        :rtype: List
-        """
-        return list(self._graph.predecessors(node_id))
-
-    def get_states_number(self, node_id) -> int:
-        return self._graph.nodes[node_id]['val']
-
-    def get_node_indx(self, node_id) -> int:
-        return nx.get_node_attributes(self._graph, 'indx')[node_id]
-
-    def get_positional_node_indx(self, node_id) -> int:
-        return self._graph.nodes[node_id]['pos_indx']
-
-    @property
-    def nodes(self) -> typing.List:
-        return self._graph_struct.nodes_labels
-
-    @property
-    def edges(self) -> typing.List:
-        return list(self._graph.edges)
-
-    @property
-    def nodes_indexes(self) -> np.ndarray:
-        return self._graph_struct.nodes_indexes
-
-    @property
-    def nodes_values(self) -> np.ndarray:
-        return self._graph_struct.nodes_values
-
-    @property
-    def time_scalar_indexing_strucure(self) -> np.ndarray:
-        return self._time_scalar_indexing_structure
-
-    @property
-    def time_filtering(self) -> np.ndarray:
-        return self._time_filtering
-
-    @property
-    def transition_scalar_indexing_structure(self) -> np.ndarray:
-        return self._transition_scalar_indexing_structure
-
-    @property
-    def transition_filtering(self) -> np.ndarray:
-        return self._transition_filtering
-
-    @property
-    def p_combs(self) -> np.ndarray:
-        return self._p_combs_structure
\ No newline at end of file
diff --git a/main_package/classes/structure_graph/sample_path.py.bak b/main_package/classes/structure_graph/sample_path.py.bak
deleted file mode 100644
index 4162b80..0000000
--- a/main_package/classes/structure_graph/sample_path.py.bak
+++ /dev/null
@@ -1,95 +0,0 @@
-import sys
-sys.path.append('../')
-
-
-import numpy as np
-import pandas as pd
-
-import .abstract_sample_path as asam
-import ..utility.json_importer as imp
-from .structure import Structure
-from .trajectory import Trajectory
-import ..utility.abstract_importer as ai
-
-
-
-class SamplePath(object):
-    """Aggregates all the informations about the trajectories, the real structure of the sampled net and variables
-    cardinalites. Has the task of creating the objects ``Trajectory`` and ``Structure`` that will
-    contain the mentioned data.
-
-    :param importer: the Importer object which contains the imported and processed data
-    :type importer: AbstractImporter
-    :_trajectories: the ``Trajectory`` object that will contain all the concatenated trajectories
-    :_structure: the ``Structure`` Object that will contain all the structural infos about the net
-    :_total_variables_count: the number of variables in the net
-    """
-    def __init__(self, importer: ai.AbstractImporter):
-        """Constructor Method
-        """
-        self._importer = importer
-        if self._importer._df_variables is None or self._importer._concatenated_samples is None:
-            raise RuntimeError('The importer object has to contain the all processed data!')
-        if self._importer._df_variables.empty:
-            raise RuntimeError('The importer object has to contain the all processed data!')
-        if isinstance(self._importer._concatenated_samples, pd.DataFrame):
-            if self._importer._concatenated_samples.empty:
-                raise RuntimeError('The importer object has to contain the all processed data!')
-        if isinstance(self._importer._concatenated_samples, np.ndarray):
-            if self._importer._concatenated_samples.size == 0:
-                raise RuntimeError('The importer object has to contain the all processed data!')
-        self._trajectories = None
-        self._structure = None
-        self._total_variables_count = None
-
-    def build_trajectories(self) -> None:
-        """Builds the Trajectory object that will contain all the trajectories.
-        Clears all the unused dataframes in ``_importer`` Object
-        """
-        self._trajectories = \
-            Trajectory(self._importer.build_list_of_samples_array(self._importer.concatenated_samples),
-                len(self._importer.sorter) + 1)
-        self._importer.clear_concatenated_frame()
-
-    def build_structure(self) -> None:
-        """
-        Builds the ``Structure`` object that aggregates all the infos about the net.
-        """
-        if self._importer.sorter != self._importer.variables.iloc[:, 0].to_list():
-            raise RuntimeError("The Dataset columns order have to match the order of labels in the variables Frame!")
-
-        self._total_variables_count = len(self._importer.sorter)
-        labels = self._importer.variables.iloc[:, 0].to_list()
-        indxs = self._importer.variables.index.to_numpy()
-        vals = self._importer.variables.iloc[:, 1].to_numpy()
-        if self._importer.structure is None or self._importer.structure.empty:
-            edges = []
-        else:
-            edges = list(self._importer.structure.to_records(index=False))
-        self._structure = Structure(labels, indxs, vals, edges,
-                                       self._total_variables_count)
-
-    def clear_memory(self):
-        self._importer._raw_data = [] 
-
-    @property
-    def trajectories(self) -> Trajectory:
-        return self._trajectories
-
-    @property
-    def structure(self) -> Structure:
-        return self._structure
-
-    @property
-    def total_variables_count(self) -> int:
-        return self._total_variables_count
-
-    @property
-    def has_prior_net_structure(self) -> bool:
-        return bool(self._structure.edges)
-
-
-
-
-
-
diff --git a/main_package/classes/structure_graph/set_of_cims.py.bak b/main_package/classes/structure_graph/set_of_cims.py.bak
deleted file mode 100644
index 62d2365..0000000
--- a/main_package/classes/structure_graph/set_of_cims.py.bak
+++ /dev/null
@@ -1,98 +0,0 @@
-import sys
-sys.path.append('../')
-
-import typing
-
-import numpy as np
-
-import structure_graph.conditional_intensity_matrix as cim
-
-
-class SetOfCims(object):
-    """Aggregates all the CIMS of the node identified by the label _node_id.
-
-    :param node_id: the node label
-    :type node_ind: string
-    :param parents_states_number: the cardinalities of the parents
-    :type parents_states_number: List
-    :param node_states_number: the caridinality of the node
-    :type node_states_number: int
-    :param p_combs: the p_comb structure bound to this node
-    :type p_combs: numpy.ndArray
-    :_state_residence_time: matrix containing all the state residence time vectors for the node
-    :_transition_matrices: matrix containing all the transition matrices for the node
-    :_actual_cims: the cims of the node
-    """
-
-    def __init__(self, node_id: str, parents_states_number: typing.List, node_states_number: int, p_combs: np.ndarray):
-        """Constructor Method
-        """
-        self._node_id = node_id
-        self._parents_states_number = parents_states_number
-        self._node_states_number = node_states_number
-        self._actual_cims = []
-        self._state_residence_times = None
-        self._transition_matrices = None
-        self._p_combs = p_combs
-        self.build_times_and_transitions_structures()
-
-    def build_times_and_transitions_structures(self) -> None:
-        """Initializes at the correct dimensions the state residence times matrix and the state transition matrices.
-        """
-        if not self._parents_states_number:
-            self._state_residence_times = np.zeros((1, self._node_states_number), dtype=np.float)
-            self._transition_matrices = np.zeros((1, self._node_states_number, self._node_states_number), dtype=np.int)
-        else:
-            self._state_residence_times = \
-                np.zeros((np.prod(self._parents_states_number), self._node_states_number), dtype=np.float)
-            self._transition_matrices = np.zeros([np.prod(self._parents_states_number), self._node_states_number,
-                                                  self._node_states_number], dtype=np.int)
-
-    def build_cims(self, state_res_times: np.ndarray, transition_matrices: np.ndarray) -> None:
-        """Build the ``ConditionalIntensityMatrix`` objects given the state residence times and transitions matrices.
-        Compute the cim coefficients.The class member ``_actual_cims`` will contain the computed cims.
-
-        :param state_res_times: the state residence times matrix
-        :type state_res_times: numpy.ndArray
-        :param transition_matrices: the transition matrices
-        :type transition_matrices: numpy.ndArray
-        """
-        for state_res_time_vector, transition_matrix in zip(state_res_times, transition_matrices):
-            cim_to_add = cim.ConditionalIntensityMatrix(state_res_time_vector, transition_matrix)
-            cim_to_add.compute_cim_coefficients()
-            self._actual_cims.append(cim_to_add)
-        self._actual_cims = np.array(self._actual_cims)
-        self._transition_matrices = None
-        self._state_residence_times = None
-
-    def filter_cims_with_mask(self, mask_arr: np.ndarray, comb: typing.List) -> np.ndarray:
-        """Filter the cims contained in the array ``_actual_cims`` given the boolean mask ``mask_arr`` and the index
-        ``comb``.
-
-        :param mask_arr: the boolean mask that indicates which parent to consider
-        :type mask_arr: numpy.array
-        :param comb: the state/s of the filtered parents
-        :type comb: numpy.array
-        :return: Array of ``ConditionalIntensityMatrix`` objects
-        :rtype: numpy.array
-        """
-        if mask_arr.size <= 1:
-            return self._actual_cims
-        else:
-            flat_indxs = np.argwhere(np.all(self._p_combs[:, mask_arr] == comb, axis=1)).ravel()
-            return self._actual_cims[flat_indxs]
-
-    @property
-    def actual_cims(self) -> np.ndarray:
-        return self._actual_cims
-
-    @property
-    def p_combs(self) -> np.ndarray:
-        return self._p_combs
-
-    def get_cims_number(self):
-        return len(self._actual_cims)
-
-
-
-
diff --git a/main_package/classes/structure_graph/sets_of_cims_container.py b/main_package/classes/structure_graph/sets_of_cims_container.py
deleted file mode 100644
index 6fa6834..0000000
--- a/main_package/classes/structure_graph/sets_of_cims_container.py
+++ /dev/null
@@ -1,28 +0,0 @@
-import sys
-sys.path.append('../')
-
-import structure_graph.set_of_cims as socim
-
-
-class SetsOfCimsContainer:
-    """
-    Aggrega un insieme di oggetti SetOfCims
-    """
-    def __init__(self, list_of_keys, states_number_per_node, list_of_parents_states_number, p_combs_list):
-        self.sets_of_cims = None
-        self.init_cims_structure(list_of_keys, states_number_per_node, list_of_parents_states_number, p_combs_list)
-        #self.states_per_variable = states_number
-
-    def init_cims_structure(self, keys, states_number_per_node, list_of_parents_states_number, p_combs_list):
-        """for indx, key in enumerate(keys):
-            self.sets_of_cims.append(
-                socim.SetOfCims(key, list_of_parents_states_number[indx], states_number_per_node[indx]))"""
-        self.sets_of_cims = [socim.SetOfCims(pair[1], list_of_parents_states_number[pair[0]], states_number_per_node[pair[0]], p_combs_list[pair[0]])
-                             for pair in enumerate(keys)]
-
-    def get_set_of_cims(self, node_indx):
-        return self.sets_of_cims[node_indx]
-
-    def get_cims_of_node(self, node_indx, cim_indx):
-        return self.sets_of_cims[node_indx].get_cim(cim_indx)
-
diff --git a/main_package/classes/structure_graph/structure.py.bak b/main_package/classes/structure_graph/structure.py.bak
deleted file mode 100644
index 0c74b60..0000000
--- a/main_package/classes/structure_graph/structure.py.bak
+++ /dev/null
@@ -1,128 +0,0 @@
-import sys
-sys.path.append('../')
-
-import typing as ty
-
-import numpy as np
-
-
-class Structure(object):
-    """Contains all the infos about the network structure(nodes labels, nodes caridinalites, edges, indexes)
-
-    :param nodes_labels_list: the symbolic names of the variables
-    :type nodes_labels_list: List
-    :param nodes_indexes_arr: the indexes of the nodes
-    :type nodes_indexes_arr: numpy.ndArray
-    :param nodes_vals_arr: the cardinalites of the nodes
-    :type nodes_vals_arr: numpy.ndArray
-    :param edges_list: the edges of the network
-    :type edges_list: List
-    :param total_variables_number: the total number of variables in the dataset
-    :type total_variables_number: int
-    """
-
-    def __init__(self, nodes_labels_list: ty.List, nodes_indexes_arr: np.ndarray, nodes_vals_arr: np.ndarray,
-                 edges_list: ty.List, total_variables_number: int):
-        """Constructor Method
-        """
-        self._nodes_labels_list = nodes_labels_list
-        self._nodes_indexes_arr = nodes_indexes_arr
-        self._nodes_vals_arr = nodes_vals_arr
-        self._edges_list = edges_list
-        self._total_variables_number = total_variables_number
-
-    def remove_node(self, node_id: str) -> None:
-        """Remove the node ``node_id`` from all the class members.
-        The class member ``_total_variables_number`` since it refers to the total number of variables in the dataset.
-        """
-        node_positional_indx = self._nodes_labels_list.index(node_id)
-        del self._nodes_labels_list[node_positional_indx]
-        self._nodes_indexes_arr = np.delete(self._nodes_indexes_arr, node_positional_indx)
-        self._nodes_vals_arr = np.delete(self._nodes_vals_arr, node_positional_indx)
-        self._edges_list = [(from_node, to_node) for (from_node, to_node) in self._edges_list if (from_node != node_id
-                                                                                        and to_node != node_id)]
-
-    @property
-    def edges(self) -> ty.List:
-        return self._edges_list
-
-    @property
-    def nodes_labels(self) -> ty.List:
-        return self._nodes_labels_list
-
-    @property
-    def nodes_indexes(self) -> np.ndarray:
-        return self._nodes_indexes_arr
-
-    @property
-    def nodes_values(self) -> np.ndarray:
-        return self._nodes_vals_arr
-
-    @property
-    def total_variables_number(self) -> int:
-        return self._total_variables_number
-
-    def get_node_id(self, node_indx: int) -> str:
-        """
-        Given the ``node_index`` returns the node label.
-
-        :param node_indx: the node index
-        :type node_indx: int
-        :return: the node label
-        :rtype: string
-        """
-        return self._nodes_labels_list[node_indx]
-
-    def clean_structure_edges(self):
-        self._edges_list = list()
-
-    def add_edge(self,edge: tuple):
-        self._edges_list.append(tuple)
-        print(self._edges_list)
-    
-    def remove_edge(self,edge: tuple):
-        self._edges_list.remove(tuple)
-
-    def contains_edge(self,edge:tuple) -> bool:
-        return edge in self._edges_list
-
-    def get_node_indx(self, node_id: str) -> int:
-        """
-        Given the ``node_index`` returns the node label.
-
-        :param node_id: the node label
-        :type node_id: string
-        :return: the node index
-        :rtype: int
-        """
-        pos_indx = self._nodes_labels_list.index(node_id)
-        return self._nodes_indexes_arr[pos_indx]
-
-    def get_positional_node_indx(self, node_id: str) -> int:
-        return self._nodes_labels_list.index(node_id)
-
-    def get_states_number(self, node: str) -> int:
-        """Given the node label ``node`` returns the cardinality of the node.
-
-        :param node: the node label
-        :type node: string
-        :return: the node cardinality
-        :rtype: int
-        """
-        pos_indx = self._nodes_labels_list.index(node)
-        return self._nodes_vals_arr[pos_indx]
-
-    def __repr__(self):
-        return "Variables:\n" + str(self._nodes_labels_list) +"\nValues:\n"+ str(self._nodes_vals_arr) +\
-               "\nEdges: \n" + str(self._edges_list)
-
-    def __eq__(self, other):
-        """Overrides the default implementation"""
-        if isinstance(other, Structure):
-            return set(self._nodes_labels_list) == set(other._nodes_labels_list) and \
-                   np.array_equal(self._nodes_vals_arr, other._nodes_vals_arr) and \
-                   np.array_equal(self._nodes_indexes_arr, other._nodes_indexes_arr) and \
-                   self._edges_list == other._edges_list
-
-        return False
-
diff --git a/main_package/classes/utility/decorators.py b/main_package/classes/utility/decorators.py
deleted file mode 100644
index 6868307..0000000
--- a/main_package/classes/utility/decorators.py
+++ /dev/null
@@ -1,28 +0,0 @@
-from functools import wraps
-from time import time
-
-def timing(f):
-    @wraps(f)
-    def wrap(*args, **kw):
-        ts = time()
-        result = f(*args, **kw)
-        te = time()
-        print (f"{f.__name__} args:[{args},{kw}] took: {te-ts} sec") 
-          
-        return result
-    return wrap
-
-
-def timing_write(f):
-    @wraps(f)
-    def wrap(*args, **kw):
-        ts = time()
-        result = f(*args, **kw)
-        te = time()
-        print (f"{f.__name__} args:[{args},{kw}] took: {te-ts} sec") 
-
-        with open("../results/results.csv", 'a+') as fi:
-            fi.write(f"\n{round(te-ts,3)},")
-          
-        return result
-    return wrap
\ No newline at end of file
diff --git a/main_package/tests/results/results copy.csv b/main_package/tests/results/results copy.csv
deleted file mode 100644
index 4a83269..0000000
--- a/main_package/tests/results/results copy.csv	
+++ /dev/null
@@ -1,1729 +0,0 @@
-Time,Type,Variables,Density_Network,Cardinality,Index,F1,Precision,Recall
-89.185,1,15,0.1,3,0,0.88,0.786,1.0
-96.358,1,15,0.1,3,1,0.939,0.885,1.0
-83.178,1,15,0.1,3,2,0.954,0.913,1.0
-70.18,0,15,0.1,3,0,1.0,1.0,1.0
-77.368,0,15,0.1,3,1,1.0,1.0,1.0
-76.239,0,15,0.1,3,2,1.0,1.0,1.0
-286.024,1,20,0.1,3,0,0.967,0.936,1.0
-285.421,1,20,0.1,3,1,0.98,0.961,1.0
-274.174,1,20,0.1,3,2,0.945,0.896,1.0
-232.594,0,20,0.1,3,0,1.0,1.0,1.0
-224.365,0,20,0.1,3,1,1.0,1.0,1.0
-229.797,0,20,0.1,3,2,0.988,1.0,0.977
-18.538,1,10,0.1,3,0,0.941,0.889,1.0
-20.889,1,10,0.1,3,1,1.0,1.0,1.0
-21.828,1,10,0.1,3,2,0.909,0.833,1.0
-24.52,0,10,0.1,3,0,1.0,1.0,1.0
-22.606,0,10,0.1,3,1,1.0,1.0,1.0
-27.887,0,10,0.1,3,2,1.0,1.0,1.0
-4.285,1,6,0.1,3,0,0.923,0.857,1.0
-3.306,1,6,0.1,3,1,1.0,1.0,1.0
-3.262,1,6,0.1,3,2,1.0,1.0,1.0
-4.285,1,6,0.1,3,3,0.857,0.75,1.0
-4.583,1,6,0.1,3,4,1.0,1.0,1.0
-3.21,1,6,0.1,3,5,1.0,1.0,1.0
-3.262,1,6,0.1,3,6,0.8,0.667,1.0
-4.03,1,6,0.1,3,7,0.909,0.833,1.0
-3.195,1,6,0.1,3,8,1.0,1.0,1.0
-3.52,1,6,0.1,3,9,0.889,0.8,1.0
-9.183,0,6,0.1,3,0,1.0,1.0,1.0
-8.894,0,6,0.1,3,1,1.0,1.0,1.0
-8.882,0,6,0.1,3,2,1.0,1.0,1.0
-8.971,0,6,0.1,3,3,1.0,1.0,1.0
-9.391,0,6,0.1,3,4,1.0,1.0,1.0
-9.114,0,6,0.1,3,5,1.0,1.0,1.0
-8.439,0,6,0.1,3,6,1.0,1.0,1.0
-9.54,0,6,0.1,3,7,1.0,1.0,1.0
-9.043,0,6,0.1,3,8,1.0,1.0,1.0
-9.744,0,6,0.1,3,9,1.0,1.0,1.0
-1.921,1,5,0.1,3,0,0.889,0.8,1.0
-1.567,1,5,0.1,3,1,1.0,1.0,1.0
-2.0,1,5,0.1,3,2,1.0,1.0,1.0
-2.105,1,5,0.1,3,3,0.889,0.8,1.0
-2.264,1,5,0.1,3,4,1.0,1.0,1.0
-2.317,1,5,0.1,3,5,0.769,0.625,1.0
-2.244,1,5,0.1,3,6,0.727,0.571,1.0
-1.998,1,5,0.1,3,7,0.889,0.8,1.0
-1.718,1,5,0.1,3,8,1.0,1.0,1.0
-2.387,1,5,0.1,3,9,1.0,1.0,1.0
-7.091,0,5,0.1,3,0,1.0,1.0,1.0
-6.221,0,5,0.1,3,1,1.0,1.0,1.0
-6.945,0,5,0.1,3,2,1.0,1.0,1.0
-7.138,0,5,0.1,3,3,1.0,1.0,1.0
-6.903,0,5,0.1,3,4,1.0,1.0,1.0
-6.972,0,5,0.1,3,5,1.0,1.0,1.0
-7.052,0,5,0.1,3,6,1.0,1.0,1.0
-6.911,0,5,0.1,3,7,1.0,1.0,1.0
-6.843,0,5,0.1,3,8,1.0,1.0,1.0
-8.103,0,5,0.1,3,9,1.0,1.0,1.0
-5.421,0,4,0.1,3,0,1.0,1.0,1.0
-5.777,0,4,0.1,3,1,1.0,1.0,1.0
-5.247,0,4,0.1,3,2,1.0,1.0,1.0
-5.308,0,4,0.1,3,3,1.0,1.0,1.0
-5.678,0,4,0.1,3,4,1.0,1.0,1.0
-5.654,0,4,0.1,3,5,1.0,1.0,1.0
-5.263,0,4,0.1,3,6,1.0,1.0,1.0
-5.632,0,4,0.1,3,7,1.0,1.0,1.0
-5.377,0,4,0.1,3,8,1.0,1.0,1.0
-5.509,0,4,0.1,3,9,1.0,1.0,1.0
-1.269,1,4,0.1,3,0,0.857,0.75,1.0
-0.866,1,4,0.1,3,1,0.667,0.5,1.0
-0.916,1,4,0.1,3,2,0.857,0.75,1.0
-0.96,1,4,0.1,3,3,1.0,1.0,1.0
-0.855,1,4,0.1,3,4,1.0,1.0,1.0
-1.211,1,4,0.1,3,5,0.75,0.6,1.0
-0.89,1,4,0.1,3,6,1.0,1.0,1.0
-0.932,1,4,0.1,3,7,1.0,1.0,1.0
-0.711,1,4,0.1,3,8,0.8,0.667,1.0
-0.841,1,4,0.1,3,9,1.0,1.0,1.0
-0.318,1,3,0.1,3,0,1.0,1.0,1.0
-0.327,1,3,0.1,3,1,1.0,1.0,1.0
-0.426,1,3,0.1,3,2,1.0,1.0,1.0
-0.358,1,3,0.1,3,3,0.8,0.667,1.0
-0.316,1,3,0.1,3,4,1.0,1.0,1.0
-0.366,1,3,0.1,3,5,1.0,1.0,1.0
-0.343,1,3,0.1,3,6,0.667,0.5,1.0
-0.348,1,3,0.1,3,7,0.8,0.667,1.0
-0.353,1,3,0.1,3,8,0.8,0.667,1.0
-0.343,1,3,0.1,3,9,1.0,1.0,1.0
-3.851,0,3,0.1,3,0,1.0,1.0,1.0
-4.047,0,3,0.1,3,1,1.0,1.0,1.0
-4.129,0,3,0.1,3,2,1.0,1.0,1.0
-4.26,0,3,0.1,3,3,1.0,1.0,1.0
-4.134,0,3,0.1,3,4,1.0,1.0,1.0
-4.353,0,3,0.1,3,5,1.0,1.0,1.0
-4.904,0,3,0.1,3,6,1.0,1.0,1.0
-4.892,0,3,0.1,3,7,1.0,1.0,1.0
-4.609,0,3,0.1,3,8,1.0,1.0,1.0
-4.58,0,3,0.1,3,9,1.0,1.0,1.0
-0.318,1,3,0.2,3,0,1.0,1.0,1.0
-0.41,1,3,0.2,3,1,1.0,1.0,1.0
-0.297,1,3,0.2,3,2,0.8,0.667,1.0
-0.399,1,3,0.2,3,3,1.0,1.0,1.0
-0.384,1,3,0.2,3,4,1.0,1.0,1.0
-0.355,1,3,0.2,3,5,1.0,1.0,1.0
-0.333,1,3,0.2,3,6,1.0,1.0,1.0
-0.32,1,3,0.2,3,7,1.0,1.0,1.0
-0.385,1,3,0.2,3,8,1.0,1.0,1.0
-0.394,1,3,0.2,3,9,0.857,0.75,1.0
-0.915,1,4,0.2,3,0,0.857,0.75,1.0
-1.087,1,4,0.2,3,1,1.0,1.0,1.0
-0.884,1,4,0.2,3,2,0.889,0.8,1.0
-0.95,1,4,0.2,3,3,1.0,1.0,1.0
-1.204,1,4,0.2,3,4,0.889,0.8,1.0
-0.689,1,4,0.2,3,5,1.0,1.0,1.0
-0.907,1,4,0.2,3,6,1.0,1.0,1.0
-0.884,1,4,0.2,3,7,1.0,1.0,1.0
-0.809,1,4,0.2,3,8,0.857,0.75,1.0
-0.685,1,4,0.2,3,9,1.0,1.0,1.0
-1.899,1,5,0.2,3,0,1.0,1.0,1.0
-2.177,1,5,0.2,3,1,1.0,1.0,1.0
-2.276,1,5,0.2,3,2,0.909,0.833,1.0
-3.268,1,5,0.2,3,3,0.941,0.889,1.0
-2.412,1,5,0.2,3,4,1.0,1.0,1.0
-2.218,1,5,0.2,3,5,1.0,1.0,1.0
-2.093,1,5,0.2,3,6,1.0,1.0,1.0
-1.542,1,5,0.2,3,7,0.75,0.6,1.0
-2.438,1,5,0.2,3,8,0.8,0.667,1.0
-2.503,1,5,0.2,3,9,0.909,0.833,1.0
-3.469,1,6,0.2,3,0,1.0,1.0,1.0
-3.45,1,6,0.2,3,1,0.923,0.857,1.0
-4.485,1,6,0.2,3,2,1.0,1.0,1.0
-2.936,1,6,0.2,3,3,1.0,1.0,1.0
-3.17,1,6,0.2,3,4,1.0,1.0,1.0
-3.033,1,6,0.2,3,5,0.727,0.571,1.0
-3.689,1,6,0.2,3,6,1.0,1.0,1.0
-5.302,1,6,0.2,3,7,1.0,1.0,1.0
-4.174,1,6,0.2,3,8,0.933,0.875,1.0
-3.694,1,6,0.2,3,9,1.0,1.0,1.0
-26.953,1,10,0.2,3,0,0.979,0.958,1.0
-26.558,1,10,0.2,3,1,1.0,1.0,1.0
-25.296,1,10,0.2,3,2,0.971,0.944,1.0
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\ No newline at end of file
diff --git a/main_package/tests/results/results.csv b/main_package/tests/results/results.csv
deleted file mode 100644
index 24d6fc4..0000000
--- a/main_package/tests/results/results.csv
+++ /dev/null
@@ -1,14 +0,0 @@
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\ No newline at end of file
diff --git a/main_package/tests/results/results16-32RAM.csv b/main_package/tests/results/results16-32RAM.csv
deleted file mode 100644
index e266adb..0000000
--- a/main_package/tests/results/results16-32RAM.csv
+++ /dev/null
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\ No newline at end of file
diff --git a/main_package/tests/results/results_tabu.csv b/main_package/tests/results/results_tabu.csv
deleted file mode 100644
index 62ee335..0000000
--- a/main_package/tests/results/results_tabu.csv
+++ /dev/null
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-121.834,0,15,0.3,4,0,1.0,1.0,1.0
-119.824,0,15,0.3,4,1,1.0,1.0,1.0
-120.162,0,15,0.3,4,2,0.889,1.0,0.8
-4.594,0,3,0.4,4,0,1.0,1.0,1.0
-4.686,0,3,0.4,4,1,1.0,1.0,1.0
-4.707,0,3,0.4,4,2,1.0,1.0,1.0
-5.269,0,3,0.4,4,3,1.0,1.0,1.0
-4.729,0,3,0.4,4,4,1.0,1.0,1.0
-4.6,0,3,0.4,4,5,1.0,1.0,1.0
-4.727,0,3,0.4,4,6,1.0,1.0,1.0
-4.707,0,3,0.4,4,7,1.0,1.0,1.0
-4.69,0,3,0.4,4,8,1.0,1.0,1.0
-4.944,0,3,0.4,4,9,1.0,1.0,1.0
-6.441,0,4,0.4,4,0,1.0,1.0,1.0
-6.75,0,4,0.4,4,1,1.0,1.0,1.0
-6.198,0,4,0.4,4,2,1.0,1.0,1.0
-6.496,0,4,0.4,4,3,1.0,1.0,1.0
-6.631,0,4,0.4,4,4,1.0,1.0,1.0
-6.262,0,4,0.4,4,5,1.0,1.0,1.0
-6.865,0,4,0.4,4,6,1.0,1.0,1.0
-6.739,0,4,0.4,4,7,1.0,1.0,1.0
-6.357,0,4,0.4,4,8,1.0,1.0,1.0
-7.206,0,4,0.4,4,9,1.0,1.0,1.0
-8.677,0,5,0.4,4,0,1.0,1.0,1.0
-9.465,0,5,0.4,4,1,1.0,1.0,1.0
-10.355,0,5,0.4,4,2,1.0,1.0,1.0
-9.04,0,5,0.4,4,3,1.0,1.0,1.0
-10.206,0,5,0.4,4,4,1.0,1.0,1.0
-8.605,0,5,0.4,4,5,1.0,1.0,1.0
-9.591,0,5,0.4,4,6,1.0,1.0,1.0
-9.072,0,5,0.4,4,7,1.0,1.0,1.0
-9.002,0,5,0.4,4,8,1.0,1.0,1.0
-9.264,0,5,0.4,4,9,1.0,1.0,1.0
-13.615,0,6,0.4,4,0,1.0,1.0,1.0
-13.133,0,6,0.4,4,1,1.0,1.0,1.0
-13.687,0,6,0.4,4,2,1.0,1.0,1.0
-13.901,0,6,0.4,4,3,1.0,1.0,1.0
-12.545,0,6,0.4,4,4,1.0,1.0,1.0
-13.513,0,6,0.4,4,5,1.0,1.0,1.0
-14.006,0,6,0.4,4,6,1.0,1.0,1.0
-13.376,0,6,0.4,4,7,1.0,1.0,1.0
-14.285,0,6,0.4,4,8,1.0,1.0,1.0
-14.759,0,6,0.4,4,9,1.0,1.0,1.0
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-119.185,0,15,0.4,4,1,0.46,1.0,0.299
-119.897,0,15,0.4,4,2,0.383,1.0,0.237
\ No newline at end of file
diff --git a/main_package/tests/simulators/test_simulation_all.py b/main_package/tests/simulators/test_simulation_all.py
deleted file mode 100644
index 0ef289e..0000000
--- a/main_package/tests/simulators/test_simulation_all.py
+++ /dev/null
@@ -1,147 +0,0 @@
-import sys
-sys.path.append("../../classes/")
-import glob
-import math
-import os
-import unittest
-
-import networkx as nx
-import numpy as np
-import pandas as pd
-import psutil
-from line_profiler import LineProfiler
-import copy
-import json
-
-import utility.cache as ch
-import 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=10
-
-
-                        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 == 0:
-                                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()
-
diff --git a/main_package/tests/simulators/test_simulation_constraint.py b/main_package/tests/simulators/test_simulation_constraint.py
deleted file mode 100644
index c66f62f..0000000
--- a/main_package/tests/simulators/test_simulation_constraint.py
+++ /dev/null
@@ -1,108 +0,0 @@
-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()
-
diff --git a/main_package/tests/simulators/test_simulation_score.py b/main_package/tests/simulators/test_simulation_score.py
deleted file mode 100644
index 6ff3695..0000000
--- a/main_package/tests/simulators/test_simulation_score.py
+++ /dev/null
@@ -1,122 +0,0 @@
-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_score_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:
-
-                patience = 20
-
-                var_number= var_n
-
-                if var_number > 11:
-                    patience = 25
-
-                if var_number > 16:
-                    patience = 35
-
-
-                cardinality = 4
-                cardinality_string = "quaternary"
-
-                density= dens[0]
-                density_string = dens[1]
-
-                constraint = 0
-
-                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.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 = 'hill'
-                                        )
-
-                                    
-                    
-
-                    
-                    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()
-