Refactor SamplePath class

main_package/classes/abstract_importer.py

@@ -3,34 +3,21 @@ from abc import ABC, abstractmethod
 
 class AbstractImporter(ABC):
     """
-    Interfaccia che espone i metodi necessari all'importing delle trajectories e della struttura della CTBN
+    Interface that exposes all the necessary methods to import the trajectories and the net structure.
 
-    :files_path: il path in cui sono presenti i/il file da importare
+    :file_path: the file path
 
     """
 
-    def __init__(self, files_path):
+    def __init__(self, file_path: str):
-        self.files_path = files_path
+        self.file_path = file_path
         super().__init__()
 
     @abstractmethod
     def import_trajectories(self, raw_data):
-        """
-        Costruisce le traj partendo dal dataset raw_data
-        Parameters:
-            raw_data: il dataset da cui estrarre le traj
-        Returns:
-            void
-        """
         pass
 
     @abstractmethod
     def import_structure(self, raw_data):
-        """
-        Costruisce la struttura della rete partendo dal dataset raw_data
-        Parameters:
-            raw_data: il dataset da cui estrarre la struttura
-        Returns:
-            void
-        """
         pass
+

main_package/classes/abstract_sample_path.py

@@ -0,0 +1,37 @@
+from abc import ABC, abstractmethod
+import 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

main_package/classes/json_importer.py

@@ -15,7 +15,7 @@ class JsonImporter(AbstractImporter):
         |_ dyn.str
         |_ samples
         |_ variabels
-    :files_path: the path that contains tha data to be imported
+    :file_path: the path of the file that contains tha data to be imported
     :samples_label: the reference key for the samples in the trajectories
     :structure_label: the reference key for the structure of the network data
     :variables_label: the reference key for the cardinalites of the nodes data
@@ -28,7 +28,7 @@ class JsonImporter(AbstractImporter):
     :sorter: the columns header(excluding the time column) of the Dataframe concatenated_samples
     """
 
-    def __init__(self, files_path: str, samples_label: str, structure_label: str, variables_label: str, time_key: str,
+    def __init__(self, file_path: str, samples_label: str, structure_label: str, variables_label: str, time_key: str,
                  variables_key: str):
         self.samples_label = samples_label
         self.structure_label = structure_label
@@ -40,7 +40,7 @@ class JsonImporter(AbstractImporter):
         self._df_variables = pd.DataFrame()
         self._concatenated_samples = None
         self.sorter = None
-        super(JsonImporter, self).__init__(files_path)
+        super(JsonImporter, self).__init__(file_path)
 
     def import_data(self):
         """
@@ -110,15 +110,15 @@ class JsonImporter(AbstractImporter):
               data: the contents of the json file
 
         """
-        try:
+        #try:
-            read_files = glob.glob(os.path.join(self.files_path, "*.json"))
+            #read_files = glob.glob(os.path.join(self.files_path, "*.json"))
-            if not read_files:
+            #if not read_files:
-                raise ValueError('No .json file found in the entered path!')
+                #raise ValueError('No .json file found in the entered path!')
-            with open(read_files[0]) as f:
+        with open(self.file_path) as f:
-                data = json.load(f)
+            data = json.load(f)
-                return data
+            return data
-        except ValueError as err:
+        #except ValueError as err:
-            print(err.args)
+            #print(err.args)
 
     def one_level_normalizing(self, raw_data: typing.List, indx: int, key: str) -> pd.DataFrame:
         """
@@ -207,13 +207,11 @@ class JsonImporter(AbstractImporter):
 
     def build_list_of_samples_array(self, data_frame: pd.DataFrame) -> typing.List:
         """
-        Costruisce una lista contenente le colonne presenti nel dataframe data_frame convertendole in numpy_array
         Builds a List containing the columns of dataframe and converts them to a numpy array.
         Parameters:
             :data_frame: the dataframe from which the columns have to be extracted and converted
         Returns:
             :columns_list: the resulting list of numpy arrays
-
         """
         columns_list = [data_frame[column].to_numpy() for column in data_frame]
         #for column in data_frame:

main_package/classes/network_graph.py

@@ -136,7 +136,7 @@ class NetworkGraph:
             fancy_indx = [i[1] for i in self.aggregated_info_about_nodes_parents]
             return fancy_indx
 
-    def build_time_scalar_indexing_structure_for_a_node(self, node_id: str, parents_vals: typing.List):
+    def build_time_scalar_indexing_structure_for_a_node(self, node_id: str, parents_vals: typing.List) -> np.ndarray:
         """
         Builds an indexing structure for the computation of state residence times values.
 
@@ -153,7 +153,7 @@ class NetworkGraph:
         return T_vector
 
 
-    def build_transition_scalar_indexing_structure_for_a_node(self, node_id: str, parents_vals: typing.List):
+    def build_transition_scalar_indexing_structure_for_a_node(self, node_id: str, parents_vals: typing.List) -> np.ndarray:
         """
         Builds an indexing structure for the computation of state transitions values.
 
@@ -171,7 +171,7 @@ class NetworkGraph:
         M_vector = M_vector.cumprod().astype(np.int)
         return M_vector
 
-    def build_time_columns_filtering_for_a_node(self, node_indx: int, p_indxs: typing.List):
+    def build_time_columns_filtering_for_a_node(self, 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.
@@ -183,7 +183,7 @@ class NetworkGraph:
         """
         return np.append(np.array([node_indx], dtype=np.int), p_indxs).astype(np.int)
 
-    def build_transition_filtering_for_a_node(self, node_indx, p_indxs):
+    def build_transition_filtering_for_a_node(self, node_indx, p_indxs) -> 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 transitions values.
@@ -196,7 +196,7 @@ class NetworkGraph:
         nodes_number = self.graph_struct.total_variables_number
         return np.array([node_indx + nodes_number, node_indx, *p_indxs], dtype=np.int)
 
-    def build_p_comb_structure_for_a_node(self, parents_values: typing.List):
+    def build_p_comb_structure_for_a_node(self, parents_values: typing.List) -> np.ndarray:
         """
         Builds the combinatory structure that contains the combinations of all the values contained in parents_values.
 
@@ -252,12 +252,6 @@ class NetworkGraph:
 
     def get_states_number(self, node_id):
         return self.graph.nodes[node_id]['val']
-    """
-    def get_states_number_by_indx(self, node_indx):
-        return self.graph_struct.get_states_number_by_indx(node_indx)
-    
-    def get_node_by_index(self, node_indx):
-        return self.graph_struct.get_node_id(node_indx)"""
 
     def get_node_indx(self, node_id):
         return nx.get_node_attributes(self.graph, 'indx')[node_id]

main_package/classes/parameters_estimator.py

@@ -62,30 +62,8 @@ class ParametersEstimator:
                                                       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)
-    """
+
-    def compute_parameters_for_node(self, node_id):
+    def compute_parameters_for_node(self, node_id: str) -> sofc.SetOfCims:
-        pos_index = self.net_graph.get_positional_node_indx(node_id)
-        node_indx = self.net_graph.get_node_indx(node_id)
-        state_res_times = self.sets_of_cims_struct.sets_of_cims[pos_index].state_residence_times
-        transition_matrices = self.sets_of_cims_struct.sets_of_cims[pos_index].transition_matrices
-        #print("Nodes", self.net_graph.get_nodes())
-        self.compute_state_res_time_for_node(node_indx, self.sample_path.trajectories.times,
-                                             self.sample_path.trajectories.trajectory,
-                                             self.net_graph.time_filtering[pos_index],
-                                             self.net_graph.time_scalar_indexing_strucure[pos_index],
-                                             state_res_times)
-        # print(self.net_graph.transition_filtering[indx])
-        # print(self.net_graph.transition_scalar_indexing_structure[indx])
-        self.compute_state_transitions_for_a_node(node_indx,
-                                                  self.sample_path.trajectories.complete_trajectory,
-                                                  self.net_graph.transition_filtering[pos_index],
-                                                  self.net_graph.transition_scalar_indexing_structure[pos_index],
-                                                  transition_matrices)
-        self.sets_of_cims_struct.sets_of_cims[pos_index].build_cims(
-            state_res_times,
-            transition_matrices) #TODO potrebbe restituire direttamente l'oggetto setof cims
-    """
-    def compute_parameters_for_node(self, node_id: str):
         """
         Compute the CIMS of the node identified by the label node_id
 

main_package/classes/sample_path.py

@@ -1,41 +1,35 @@
-
+import abstract_sample_path as asam
 import json_importer as imp
 import trajectory as tr
 import structure as st
 
 
-class SamplePath:
+class SamplePath(asam.AbstractSamplePath):
     """
     Aggregates all the informations about the trajectories, the real structure of the sampled net and variables
     cardinalites.
     Has the task of creating the objects that will contain the mentioned data.
-
-    :files_path: the path that contains tha data to be imported
-    :samples_label: the reference key for the samples in the trajectories
-    :structure_label: the reference key for the structure of the network data
-    :variables_label: the reference key for the cardinalites of the nodes data
-    :time_key: the key used to identify the timestamps in each trajectory
-    :variables_key: the key used to identify the names of the variables in the net
-
     :importer: the Importer objects that will import ad process data
+
     :trajectories: the Trajectory object that will contain all the concatenated trajectories
     :structure: the Structure Object that will contain all the structurral infos about the net
     :total_variables_count: the number of variables in the net
 
     """
 
-    def __init__(self, files_path: str, samples_label: str, structure_label: str, variables_label: str, time_key: str,
+    #def __init__(self, files_path: str, samples_label: str, structure_label: str, variables_label: str, time_key: str,
-                 variables_key: str):
+                 #variables_key: str):
-        self.importer = imp.JsonImporter(files_path, samples_label, structure_label,
+    def __init__(self, importer: imp.JsonImporter):
-                                         variables_label, time_key, variables_key)
+        #self.importer =importer
-        self._trajectories = None
+        super().__init__(importer)
-        self._structure = None
+        #self._trajectories = None
+        #self._structure = None
         self.total_variables_count = None
 
     def build_trajectories(self):
         """
         Builds the Trajectory object that will contain all the trajectories.
-        Clears all the unsed dataframes in Importer Object
+        Clears all the unused dataframes in Importer Object
 
         Parameters:
             void

main_package/classes/set_of_cims.py

@@ -46,6 +46,16 @@ class SetOfCims:
                                                  self.node_states_number], dtype=np.int)
 
     def build_cims(self, state_res_times: typing.List, transition_matrices: typing.List):
+        """
+        Build the ConditionalIntensityMatrix object given the state residence times and transitions matrices.
+        Compute the cim coefficients.
+
+        Parameters:
+            state_res_times: the state residence times matrix
+            transition_matrices: the transition matrices
+        Returns:
+            void
+        """
         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()
@@ -54,7 +64,16 @@ class SetOfCims:
         self.transition_matrices = None
         self.state_residence_times = None
 
-    def filter_cims_with_mask(self, mask_arr: np.ndarray, comb: typing.List):
+    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.
+        Parameters:
+            mask_arr: the boolean mask
+            comb: the indexes of the selected cims
+
+        Returns:
+            Array of ConditionalIntensityMatrix
+        """
         if mask_arr.size <= 1:
             return self.actual_cims
         else:

main_package/classes/structure.py

@@ -13,7 +13,7 @@ class Structure:
     :total_variables_number: the total number of variables in the net
     """
 
-    def __init__(self, nodes_label_list: ty.List, node_indexes_arr: np.array, nodes_vals_arr: np.array,
+    def __init__(self, nodes_label_list: ty.List, node_indexes_arr: np.ndarray, nodes_vals_arr: np.ndarray,
                  edges_list: ty.List, total_variables_number: int):
         self._nodes_labels_list = nodes_label_list
         self._nodes_indexes_arr = node_indexes_arr
@@ -32,36 +32,31 @@ class Structure:
         return self._nodes_labels_list
 
     @property
-    def nodes_indexes(self):
+    def nodes_indexes(self) -> np.ndarray:
         return self._nodes_indexes_arr
 
     @property
-    def nodes_values(self):
+    def nodes_values(self) -> np.ndarray:
         return self._nodes_vals_arr
 
     @property
     def total_variables_number(self):
         return self._total_variables_number
 
-    def get_node_id(self, node_indx: int):
+    def get_node_id(self, node_indx: int) -> str:
         return self._nodes_labels_list[node_indx]
 
-    def get_node_indx(self, node_id: str):
+    def get_node_indx(self, node_id: str) -> 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):
+    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):
+    def get_states_number(self, node: str) -> int:
         pos_indx = self._nodes_labels_list.index(node)
         return self._nodes_vals_arr[pos_indx]
 
-    def get_states_number_by_indx(self, node_indx: int):
-        #print(self.value_label)
-        #print("Node indx", node_indx)
-        return self._nodes_vals_arr[node_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)

main_package/classes/structure_estimator.py

@@ -2,10 +2,13 @@
 import numpy as np
 import itertools
 import networkx as nx
+from networkx.readwrite import json_graph
+import json
 import typing
 from scipy.stats import f as f_dist
 from scipy.stats import chi2 as chi2_dist
 
+
 import sample_path as sp
 import structure as st
 import network_graph as ng
@@ -289,6 +292,22 @@ class StructureEstimator:
         total_vars_numb = self.sample_path.total_variables_count
         [ctpc_algo(n, total_vars_numb) for n in self.nodes]
 
+    def save_results(self):
+        """
+        Save the estimated Structure to a .json file
+
+        Parameters:
+            void
+        Returns:
+            void
+        """
+        res = json_graph.node_link_data(self.complete_graph)
+        name = self.sample_path.importer.file_path.rsplit('/',1)[-1]
+        #print(name)
+        name = 'results_' + name
+        with open(name, 'w') as f:
+            json.dump(res, f)
+
 
     def remove_diagonal_elements(self, matrix):
         m = matrix.shape[0]

main_package/classes/trajectory.py

@@ -22,7 +22,7 @@ class Trajectory:
         self._times = np.array(list_of_columns[0], dtype=np.float)
 
     @property
-    def trajectory(self):
+    def trajectory(self) -> np.ndarray:
         """
         Parameters:
             void
@@ -32,7 +32,7 @@ class Trajectory:
         return self._actual_trajectory[:, :self.original_cols_number]
 
     @property
-    def complete_trajectory(self):
+    def complete_trajectory(self) -> np.ndarray:
         """
                 Parameters:
                     void

main_package/tests/test_json_importer.py

@@ -1,6 +1,8 @@
 import sys
 sys.path.append("/Users/Zalum/Desktop/Tesi/CTBN_Project/main_package/classes/")
 import unittest
+import os
+import glob
 import numpy as np
 import pandas as pd
 import json_importer as ji
@@ -13,15 +15,18 @@ import json
 
 class TestJsonImporter(unittest.TestCase):
 
+    @classmethod
+    def setUpClass(cls) -> None:
+        cls.read_files = glob.glob(os.path.join('../data', "*.json"))
+
     def test_init(self):
-        path = os.getcwd()
+        j1 = ji.JsonImporter(self.read_files[0], 'samples', 'dyn.str', 'variables', 'Time', 'Name')
-        j1 = ji.JsonImporter(path, 'samples', 'dyn.str', 'variables', 'Time', 'Name')
         self.assertEqual(j1.samples_label, 'samples')
         self.assertEqual(j1.structure_label, 'dyn.str')
         self.assertEqual(j1.variables_label, 'variables')
         self.assertEqual(j1.time_key, 'Time')
         self.assertEqual(j1.variables_key, 'Name')
-        self.assertEqual(j1.files_path, path)
+        self.assertEqual(j1.file_path, self.read_files[0])
         self.assertFalse(j1.df_samples_list)
         self.assertTrue(j1.variables.empty)
         self.assertTrue(j1.structure.empty)
@@ -33,6 +38,7 @@ class TestJsonImporter(unittest.TestCase):
         with open('data.json', 'w') as f:
             json.dump(data_set, f)
         path = os.getcwd()
+        path = path + '/data.json'
         j1 = ji.JsonImporter(path, '', '', '', '', '')
         imported_data = j1.read_json_file()
         self.assertTrue(self.ordered(data_set) == self.ordered(imported_data))
@@ -40,11 +46,12 @@ class TestJsonImporter(unittest.TestCase):
 
     def test_read_json_file_not_found(self):
         path = os.getcwd()
+        path = path + '/data.json'
         j1 = ji.JsonImporter(path, '', '', '', '', '')
-        self.assertIsNone(j1.read_json_file())
+        self.assertRaises(FileNotFoundError, j1.read_json_file)
 
     def test_normalize_trajectories(self):
-        j1 = ji.JsonImporter('../data', 'samples', 'dyn.str', 'variables', 'Time', 'Name')
+        j1 = ji.JsonImporter(self.read_files[0], 'samples', 'dyn.str', 'variables', 'Time', 'Name')
         raw_data = j1.read_json_file()
         #print(raw_data)
         j1.normalize_trajectories(raw_data, 0, j1.samples_label)
@@ -52,17 +59,17 @@ class TestJsonImporter(unittest.TestCase):
         self.assertEqual(list(j1.df_samples_list[0].columns.values)[1:], j1.sorter)
 
     def test_normalize_trajectories_wrong_indx(self):
-        j1 = ji.JsonImporter('../data', 'samples', 'dyn.str', 'variables', 'Time', 'Name')
+        j1 = ji.JsonImporter(self.read_files[0], 'samples', 'dyn.str', 'variables', 'Time', 'Name')
         raw_data = j1.read_json_file()
         self.assertRaises(IndexError, j1.normalize_trajectories, raw_data, 474, j1.samples_label)
 
     def test_normalize_trajectories_wrong_key(self):
-        j1 = ji.JsonImporter('../data', 'sample', 'dyn.str', 'variables', 'Time', 'Name')
+        j1 = ji.JsonImporter(self.read_files[0], 'sample', 'dyn.str', 'variables', 'Time', 'Name')
         raw_data = j1.read_json_file()
         self.assertRaises(KeyError, j1.normalize_trajectories, raw_data, 0, j1.samples_label)
 
     def test_compute_row_delta_single_samples_frame(self):
-        j1 = ji.JsonImporter('../data', 'samples', 'dyn.str', 'variables', 'Time', 'Name')
+        j1 = ji.JsonImporter(self.read_files[0], 'samples', 'dyn.str', 'variables', 'Time', 'Name')
         raw_data = j1.read_json_file()
         j1.normalize_trajectories(raw_data, 0, j1.samples_label)
         sample_frame = j1.df_samples_list[0]
@@ -75,7 +82,7 @@ class TestJsonImporter(unittest.TestCase):
         self.assertEqual(sample_frame.shape[0] - 1, new_sample_frame.shape[0])
 
     def test_compute_row_delta_in_all_frames(self):
-        j1 = ji.JsonImporter('../data', 'samples', 'dyn.str', 'variables', 'Time', 'Name')
+        j1 = ji.JsonImporter(self.read_files[0], 'samples', 'dyn.str', 'variables', 'Time', 'Name')
         raw_data = j1.read_json_file()
         j1.import_trajectories(raw_data)
         j1.compute_row_delta_in_all_samples_frames(j1.time_key)
@@ -83,7 +90,7 @@ class TestJsonImporter(unittest.TestCase):
         self.assertEqual(list(j1.concatenated_samples.columns.values)[0], j1.time_key)
 
     def test_clear_data_frame_list(self):
-        j1 = ji.JsonImporter('../data', 'samples', 'dyn.str', 'variables', 'Time', 'Name')
+        j1 = ji.JsonImporter(self.read_files[0], 'samples', 'dyn.str', 'variables', 'Time', 'Name')
         raw_data = j1.read_json_file()
         j1.import_trajectories(raw_data)
         j1.compute_row_delta_in_all_samples_frames(j1.time_key)
@@ -96,6 +103,7 @@ class TestJsonImporter(unittest.TestCase):
         with open('data.json', 'w') as f:
             json.dump(data_set, f)
         path = os.getcwd()
+        path = path + '/data.json'
         j1 = ji.JsonImporter(path, '', '', '', '', '')
         raw_data = j1.read_json_file()
         frame = pd.DataFrame(raw_data)
@@ -108,14 +116,14 @@ class TestJsonImporter(unittest.TestCase):
         os.remove('data.json')
 
     def test_import_variables(self):
-        j1 = ji.JsonImporter('../data', 'samples', 'dyn.str', 'variables', 'Time', 'Name')
+        j1 = ji.JsonImporter(self.read_files[0], 'samples', 'dyn.str', 'variables', 'Time', 'Name')
         sorter = ['X', 'Y', 'Z']
         raw_data = [{'variables':{"Name": ['Z', 'Y', 'X'], "value": [3, 3, 3]}}]
         j1.import_variables(raw_data, sorter)
         self.assertEqual(list(j1.variables[j1.variables_key]), sorter)
 
     def test_import_data(self):
-        j1 = ji.JsonImporter('../data', 'samples', 'dyn.str', 'variables', 'Time', 'Name')
+        j1 = ji.JsonImporter(self.read_files[0], 'samples', 'dyn.str', 'variables', 'Time', 'Name')
         lp = LineProfiler()
 
         lp_wrapper = lp(j1.import_data)

main_package/tests/test_networkgraph.py

@@ -1,4 +1,6 @@
 import unittest
+import glob
+import os
 import networkx as nx
 import numpy as np
 import itertools
@@ -6,13 +8,16 @@ from line_profiler import LineProfiler
 
 import sample_path as sp
 import network_graph as ng
+import json_importer as ji
 
 
 
 class TestNetworkGraph(unittest.TestCase):
     @classmethod
     def setUpClass(cls):
-        cls.s1 = sp.SamplePath('../data', 'samples', 'dyn.str', 'variables', 'Time', 'Name')
+        cls.read_files = glob.glob(os.path.join('../data', "*.json"))
+        cls.importer = ji.JsonImporter(cls.read_files[0], 'samples', 'dyn.str', 'variables', 'Time', 'Name')
+        cls.s1 = sp.SamplePath(cls.importer)
         cls.s1.build_trajectories()
         cls.s1.build_structure()
 

main_package/tests/test_parameters_estimator.py

@@ -1,20 +1,23 @@
 import unittest
 import numpy as np
+import glob
+import os
 from line_profiler import LineProfiler
 
 import network_graph as ng
 import sample_path as sp
 import set_of_cims as sofc
-import sets_of_cims_container as scc
 import parameters_estimator as pe
 import json_importer as ji
 
-#TODO bisogna trovare un modo per testare i metodi che stimano i tempi e le transizioni per i singoli nodi
+
 class TestParametersEstimatior(unittest.TestCase):
 
     @classmethod
     def setUpClass(cls) -> None:
-        cls.s1 = sp.SamplePath('../data', 'samples', 'dyn.str', 'variables', 'Time', 'Name')
+        cls.read_files = glob.glob(os.path.join('../data', "*.json"))
+        cls.importer = ji.JsonImporter(cls.read_files[0], 'samples', 'dyn.str', 'variables', 'Time', 'Name')
+        cls.s1 = sp.SamplePath(cls.importer)
         cls.s1.build_trajectories()
         cls.s1.build_structure()
         cls.g1 = ng.NetworkGraph(cls.s1.structure)
@@ -41,12 +44,12 @@ class TestParametersEstimatior(unittest.TestCase):
             sofc1 = p1.compute_parameters_for_node(node)
             sampled_cims = self.aux_import_sampled_cims('dyn.cims')
             sc = list(sampled_cims.values())
-            print(sc[indx])
+            #print(sc[indx])
             self.equality_of_cims_of_node(sc[indx], sofc1.actual_cims)
 
     def equality_of_cims_of_node(self, sampled_cims, estimated_cims):
         #print(sampled_cims)
-        print(estimated_cims)
+        #print(estimated_cims)
         self.assertEqual(len(sampled_cims), len(estimated_cims))
         for c1, c2 in zip(sampled_cims, estimated_cims):
             self.cim_equality_test(c1, c2.cim)
@@ -56,7 +59,7 @@ class TestParametersEstimatior(unittest.TestCase):
             self.assertTrue(np.all(np.isclose(r1, r2, 1e-01, 1e-01) == True))
 
     def aux_import_sampled_cims(self, cims_label):
-        i1 = ji.JsonImporter('../data', '', '', '', '', '')
+        i1 = ji.JsonImporter(self.read_files[0], '', '', '', '', '')
         raw_data = i1.read_json_file()
         return i1.import_sampled_cims(raw_data, 0, cims_label)
 

main_package/tests/test_sample_path.py

@@ -1,4 +1,7 @@
 import unittest
+import glob
+import os
+import json_importer as ji
 import sample_path as sp
 import trajectory as tr
 import structure as st
@@ -6,8 +9,13 @@ import structure as st
 
 class TestSamplePath(unittest.TestCase):
 
+    @classmethod
+    def setUpClass(cls) -> None:
+        cls.read_files = glob.glob(os.path.join('../data', "*.json"))
+        cls.importer = ji.JsonImporter(cls.read_files[0], 'samples', 'dyn.str', 'variables', 'Time', 'Name')
+
     def test_init(self):
-        s1 = sp.SamplePath('../data', 'samples', 'dyn.str', 'variables', 'Time', 'Name')
+        s1 = sp.SamplePath(self.importer)
         s1.build_trajectories()
         self.assertIsNotNone(s1.trajectories)
         self.assertIsInstance(s1.trajectories, tr.Trajectory)

main_package/tests/test_structure.py

@@ -1,13 +1,8 @@
 import sys
 sys.path.append("/Users/Zalum/Desktop/Tesi/CTBN_Project/main_package/classes/")
 import unittest
-import pandas as pd
 import numpy as np
-
-import sample_path as sp
 import structure as st
-import network_graph as ng
-import parameters_estimator as pe
 
 
 class TestStructure(unittest.TestCase):
@@ -22,7 +17,9 @@ class TestStructure(unittest.TestCase):
     def test_init(self):
         s1 = st.Structure(self.labels, self.indxs, self.vals, self.edges, self.vars_numb)
         self.assertListEqual(self.labels,s1.nodes_labels)
+        self.assertIsInstance(s1.nodes_indexes, np.ndarray)
         self.assertTrue(np.array_equal(self.indxs, s1.nodes_indexes))
+        self.assertIsInstance(s1.nodes_values, np.ndarray)
         self.assertTrue(np.array_equal(self.vals, s1.nodes_values))
         self.assertListEqual(self.edges, s1.edges)
         self.assertEqual(self.vars_numb, s1.total_variables_number)
@@ -70,62 +67,6 @@ class TestStructure(unittest.TestCase):
         s1 = st.Structure(l2, i2, v2, e2, n2)
         for val, node in zip(v2, l2):
             self.assertEqual(val, s1.get_states_number(node))
-#TODO FORSE QUESTO TEST NON serve verificare se questo metodo sia davvero utile
-    """def test_get_states_numeber_by_indx(self):
-        s1 = st.Structure(self.structure_frame, self.variables_frame, len(self.variables_frame.index))
-        for indx, row in self.variables_frame.iterrows():
-            self.assertEqual(row[1], s1.get_states_number_by_indx(indx))
-
-    def test_new_init(self):
-        #self.variables_frame.drop(self.variables_frame[(self.variables_frame['Name'] == 'Y')].index, inplace=True)
-        labels = self.variables_frame['Name'].to_list()
-        indxs = self.variables_frame.index.to_numpy()
-        vals = self.variables_frame['Value'].to_numpy()
-        edges = list(self.structure_frame.to_records(index=False))
-        print(labels)
-        print(indxs)
-        print(vals)
-        print(edges)
-        s1 = st.Structure(labels, indxs, vals, edges, len(self.variables_frame.index))
-        #print(s1.get_node_id(2))
-        print(s1.get_node_indx('Z'))
-        print(s1.get_positional_node_indx('Z'))
-        print(s1.get_states_number('Z'))
-        print(s1.get_states_number_by_indx(1))
-        [CIM:
-[[-4.82318981  1.18421625  3.63997346]
- [ 4.44726473 -9.20141291  4.755239  ]
- [ 2.93950444  4.36292948 -7.30152554]], CIM:
-[[-6.0336893   1.69212904  4.34235011]
- [ 3.32692085 -5.03977237  1.7137923 ]
- [ 3.65519241  3.81402509 -7.46819716]], CIM:
-[[-6.78778897  1.98559721  4.80306557]
- [ 1.23811008 -6.26366842  5.0265376 ]
- [ 3.02720526  4.0256821  -7.05222539]]]
- array([ 3,  9, 27])
- array([3, 9])
- array([1, 2])
- array([4, 1, 2])
-
-        sp1 = sp.SamplePath('../data', 'samples', 'dyn.str', 'variables', 'Time', 'Name')
-        sp1.build_trajectories()
-        sp1.build_structure()
-        st1 = st.Structure(['X','Y','Z'], np.array([0,1,2]), np.array([3,3,3]), [('Z','X'),('Y', 'X')], sp1.total_variables_count)
-        g1 = ng.NetworkGraph(st1)
-        g1.init_graph()
-        print("M Vector",g1.transition_scalar_indexing_structure)
-        print("Time Vecotr",g1.time_scalar_indexing_strucure)
-        print("Time Filter",g1.time_filtering)
-        print("M Filter",g1.transition_filtering)
-        print(g1.p_combs)
-        print("AGG STR", g1.aggregated_info_about_nodes_parents)
-        p1 = pe.ParametersEstimator(sp1,g1)
-        p1.init_sets_cims_container()
-        p1.compute_parameters_for_node('X')
-        #print(p1.sets_of_cims_struct.get_cims_of_node(0,[1,0]))
-        print(p1.sets_of_cims_struct.sets_of_cims[1].actual_cims)
-        #print(p1.sets_of_cims_struct.sets_of_cims[2].get_cims_where_parents_except_last_are_in_state(np.array([0])))
-        #print(p1.sets_of_cims_struct.sets_of_cims[0].p_combs)"""
 
 
 if __name__ == '__main__':

main_package/tests/test_structure_estimator.py

@@ -1,9 +1,13 @@
 import unittest
 import numpy as np
 import networkx as nx
+import glob
+import os
 import math
 from line_profiler import LineProfiler
+import psutil
 
+import json_importer as ji
 import sample_path as sp
 import structure_estimator as se
 import cache as ch
@@ -13,7 +17,9 @@ class TestStructureEstimator(unittest.TestCase):
 
     @classmethod
     def setUpClass(cls) -> None:
-        cls.s1 = sp.SamplePath('../data', 'samples', 'dyn.str', 'variables', 'Time', 'Name')
+        cls.read_files = glob.glob(os.path.join('../data', "*.json"))
+        cls.importer = ji.JsonImporter(cls.read_files[0], 'samples', 'dyn.str', 'variables', 'Time', 'Name')
+        cls.s1 = sp.SamplePath(cls.importer)
         cls.s1.build_trajectories()
         cls.s1.build_structure()
 
@@ -58,7 +64,7 @@ class TestStructureEstimator(unittest.TestCase):
                 for sset in sets2:
                     self.assertFalse(node in sset)
 
-    def test_one_iteration(self):
+    def test_time(self):
         se1 = se.StructureEstimator(self.s1, 0.1, 0.1)
         lp = LineProfiler()
         lp.add_function(se1.complete_test)
@@ -76,8 +82,16 @@ class TestStructureEstimator(unittest.TestCase):
         for ed in se1.complete_graph.edges:
             if not(ed in tuples_edges):
                 spurious_edges.append(ed)
+
         print("Spurious Edges:",spurious_edges)
+        se1.save_results()
 
+    def test_memory(self):
+        se1 = se.StructureEstimator(self.s1, 0.1, 0.1)
+        se1.ctpc_algorithm()
+        current_process = psutil.Process(os.getpid())
+        mem = current_process.memory_info().rss
+        print("Average Memory Usage in MB:", mem / 10**6)
 
 if __name__ == '__main__':
     unittest.main()