Refactoring classes Structure_estimator and organized files in folders

main_package/basic_main.py

@@ -4,11 +4,11 @@ import os
 import sys
 sys.path.append("./classes/")
 
-import network_graph as ng
+import structure_graph.network_graph as ng
-import sample_path as sp
+import structure_graph.sample_path as sp
-import set_of_cims as sofc
+import structure_graph.set_of_cims as sofc
-import parameters_estimator as pe
+import estimators.parameters_estimator as pe
-import json_importer as ji
+import utility.json_importer as ji
 
 
 def main():

main_package/classes/estimators/fam_score_calculator.py

@@ -1,3 +1,6 @@
+import sys
+sys.path.append('../')
+
 
 import itertools
 import json
@@ -12,9 +15,9 @@ from math import log
 from scipy.special import loggamma
 from random import choice
 
-import set_of_cims as soCims
+import structure_graph.set_of_cims as soCims
-import network_graph as net_graph
+import structure_graph.network_graph as net_graph
-import conditional_intensity_matrix as cim_class
+import structure_graph.conditional_intensity_matrix as cim_class
 
 
 '''

main_package/classes/estimators/parameters_estimator.py

@@ -1,10 +1,11 @@
-
+import sys
+sys.path.append('../')
 import numpy as np
 
-import network_graph as ng
+import structure_graph.network_graph as ng
-import sample_path as sp
+import structure_graph.sample_path as sp
-import set_of_cims as sofc
+import structure_graph.set_of_cims as sofc
-import sets_of_cims_container as acims
+import structure_graph.sets_of_cims_container as acims
 
 
 class ParametersEstimator:

main_package/classes/estimators/structure_constraint_based_estimator.py

@@ -1,4 +1,5 @@
-
+import sys
+sys.path.append('../')
 import itertools
 import json
 import typing
@@ -9,52 +10,28 @@ from networkx.readwrite import json_graph
 from scipy.stats import chi2 as chi2_dist
 from scipy.stats import f as f_dist
 
-import cache as ch
+import utility.cache as ch
-import conditional_intensity_matrix as condim
+import structure_graph.conditional_intensity_matrix as condim
-import network_graph as ng
+import structure_graph.network_graph as ng
-import parameters_estimator as pe
+import estimators.parameters_estimator as pe
-import sample_path as sp
+import estimators.structure_estimator as se
-import structure as st
+import structure_graph.sample_path as sp
+import structure_graph.structure as st
 
 
-class StructureEstimator:
+class StructureConstraintBasedEstimator(se.StructureEstimator):
     """
     Has the task of estimating the network structure given the trajectories in samplepath.
 
-    :sample_path: the sample_path object containing the trajectories and the real structure
     :exp_test_sign: the significance level for the exponential Hp test
     :chi_test_alfa: the significance level for the chi Hp test
-
-    :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: sp.SamplePath, exp_test_alfa: float, chi_test_alfa: float):
-        self.sample_path = sample_path
+        super().__init__(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.complete_graph = self.build_complete_graph(self.sample_path.structure.nodes_labels)
         self.exp_test_sign = exp_test_alfa
         self.chi_test_alfa = chi_test_alfa
-        self.cache = ch.Cache()
-
-    def build_complete_graph(self, node_ids: typing.List):
-        """
-        Builds a complete directed graph (no self loops) given the nodes labels in the list node_ids:
 
-        Parameters:
-            node_ids: the list of nodes labels
-        Returns:
-            a complete Digraph Object
-        """
-        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 complete_test(self, test_parent: str, test_child: str, parent_set: typing.List, child_states_numb: int,
                       tot_vars_count: int):
@@ -260,23 +237,6 @@ class StructureEstimator:
             b += 1
         self.cache.clear()
 
-    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.
-
-        Parameters:
-            u: the list of nodes
-            size: the size of the subsets
-            parent_label: the nodes to exclude in the subsets generation
-        Returns:
-            a Map Object containing a list of lists
-
-        """
-        list_without_test_parent = u[:]
-        list_without_test_parent.remove(parent_label)
-        return map(list, itertools.combinations(list_without_test_parent, size))
-
     def ctpc_algorithm(self):
         """
         Compute the CTPC algorithm.
@@ -289,26 +249,4 @@ 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]
-        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)
 

main_package/classes/estimators/structure_estimator.py

@@ -0,0 +1,96 @@
+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
+
+from abc import ABC
+
+import utility.cache as ch
+import structure_graph.conditional_intensity_matrix as condim
+import structure_graph.network_graph as ng
+import estimators.parameters_estimator as pe
+import structure_graph.sample_path as sp
+import structure_graph.structure as st
+
+
+class StructureEstimator(ABC):
+    """
+    Has the task of estimating the network structure given the trajectories in samplepath.
+
+    :sample_path: the sample_path object containing the trajectories and the real structure
+
+    :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: sp.SamplePath):
+        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.complete_graph = self.build_complete_graph(self.sample_path.structure.nodes_labels)
+        self.cache = ch.Cache()
+
+    def build_complete_graph(self, node_ids: typing.List):
+        """
+        Builds a complete directed graph (no self loops) given the nodes labels in the list node_ids:
+
+        Parameters:
+            node_ids: the list of nodes labels
+        Returns:
+            a complete Digraph Object
+        """
+        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.
+
+        Parameters:
+            u: the list of nodes
+            size: the size of the subsets
+            parent_label: the nodes to exclude in the subsets generation
+        Returns:
+            a Map Object containing a list of lists
+
+        """
+        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):
+        """
+        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]
+        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)
+

main_package/classes/estimators/structure_score_based_estimator.py

@@ -1,4 +1,5 @@
-
+import sys
+sys.path.append('../')
 import itertools
 import json
 import typing
@@ -10,15 +11,18 @@ from networkx.readwrite import json_graph
 from random import choice
 
 import copy
-import cache as ch
+import utility.cache as ch
-import conditional_intensity_matrix as condim
+import structure_graph.conditional_intensity_matrix as condim
-import network_graph as ng
+import structure_graph.network_graph as ng
-import parameters_estimator as pe
+import estimators.parameters_estimator as pe
-import sample_path as sp
+import estimators.structure_estimator as se
-import structure as st
+import structure_graph.sample_path as sp
-import fam_score_calculator as fam_score
+import structure_graph.structure as st
+import estimators.fam_score_calculator as fam_score
+
+from utility.decorators import timing
+
 
-from decorators import timing
 
 #from numba import njit
 
@@ -32,41 +36,15 @@ from multiprocessing import Pool
 #TODO: Evaluate if it could be better to change list_edges to set for improve the performance
 '''
 
-class StructureScoreBasedEstimator:
+class StructureScoreBasedEstimator(se.StructureEstimator):
     """
     Has the task of estimating the network structure given the trajectories in samplepath by
     using a score based approach.
 
-    :sample_path: the sample_path object containing the trajectories and the real structure
-
-    :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: sp.SamplePath):
-        self.sample_path = sample_path
+        super().__init__(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.complete_graph = self.build_complete_graph(self.sample_path.structure.nodes_labels)
-        self.cache = ch.Cache()
-
-    def build_complete_graph(self, node_ids: typing.List):
-        """
-        Builds a complete directed graph (no self loops) given the nodes labels in the list node_ids:
-
-        Parameters:
-            node_ids: the list of nodes labels
-        Returns:
-            a complete Digraph Object
-        """
-        complete_graph = nx.DiGraph()
-        complete_graph.add_nodes_from(node_ids)
-        complete_graph.add_edges_from(itertools.permutations(node_ids, 2))
-        return complete_graph
 
 
     @timing
@@ -241,26 +219,4 @@ class StructureScoreBasedEstimator:
         list_without_test_parent.remove(parent_label)
         return map(list, itertools.combinations(list_without_test_parent, size))
 
-    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]
-        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)
 

main_package/classes/structure_graph/abstract_sample_path.py

@@ -1,6 +1,9 @@
 from abc import ABC, abstractmethod
 
-import abstract_importer as ai
+import sys
+sys.path.append('../')
+
+import utility.abstract_importer as ai
 
 
 class AbstractSamplePath(ABC):

main_package/classes/structure_graph/conditional_intensity_matrix.py

@@ -1,5 +1,7 @@
 import numpy as np
 
+import sys
+sys.path.append('../')
 
 class ConditionalIntensityMatrix:
     """

main_package/classes/structure_graph/network_graph.py

@@ -4,6 +4,8 @@ import typing
 import networkx as nx
 import numpy as np
 
+import sys
+sys.path.append('../')
 
 class NetworkGraph:
     """

main_package/classes/structure_graph/sample_path.py

@@ -1,7 +1,11 @@
-import abstract_sample_path as asam
+import sys
-import json_importer as imp
+sys.path.append('../')
-import structure as st
+
-import trajectory as tr
+import structure_graph.abstract_sample_path as asam
+import utility.json_importer as imp
+import structure_graph.structure as st
+import structure_graph.trajectory as tr
+
 
 
 class SamplePath(asam.AbstractSamplePath):

main_package/classes/structure_graph/set_of_cims.py

@@ -1,8 +1,11 @@
+import sys
+sys.path.append('../')
+
 import typing
 
 import numpy as np
 
-import conditional_intensity_matrix as cim
+import structure_graph.conditional_intensity_matrix as cim
 
 
 class SetOfCims:

main_package/classes/structure_graph/sets_of_cims_container.py

@@ -1,4 +1,7 @@
-import set_of_cims as socim
+import sys
+sys.path.append('../')
+
+import structure_graph.set_of_cims as socim
 
 
 class SetsOfCimsContainer:

main_package/classes/structure_graph/structure.py

@@ -1,3 +1,6 @@
+import sys
+sys.path.append('../')
+
 import typing as ty
 
 import numpy as np

main_package/classes/structure_graph/trajectory.py

@@ -1,3 +1,5 @@
+import sys
+sys.path.append('../')
 
 import numpy as np
 

main_package/classes/utility/cache.py

@@ -1,6 +1,9 @@
+import sys
+sys.path.append('../')
+
 import typing
 
-import set_of_cims as sofc
+import structure_graph.set_of_cims as sofc
 
 
 class Cache:

main_package/classes/utility/json_importer.py

@@ -1,10 +1,12 @@
+import sys
+sys.path.append('../')
 
 import json
 import typing
 
 import pandas as pd
 
-import abstract_importer as ai
+import utility.abstract_importer as ai
 
 
 class JsonImporter(ai.AbstractImporter):

main_package/tests/estimators/test_parameters_estimator.py

@@ -1,22 +1,22 @@
 import sys
-sys.path.append("../classes/")
+sys.path.append("../../classes/")
 import unittest
 import numpy as np
 import glob
 import os
 
-import network_graph as ng
+import structure_graph.network_graph as ng
-import sample_path as sp
+import structure_graph.sample_path as sp
-import set_of_cims as sofc
+import structure_graph.set_of_cims as sofc
-import parameters_estimator as pe
+import estimators.parameters_estimator as pe
-import json_importer as ji
+import utility.json_importer as ji
 
 
 class TestParametersEstimatior(unittest.TestCase):
 
     @classmethod
     def setUpClass(cls) -> None:
-        cls.read_files = glob.glob(os.path.join('../data', "*.json"))
+        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()

main_package/tests/estimators/test_structure.py

@@ -1,8 +1,8 @@
 import sys
-sys.path.append("../classes/")
+sys.path.append("../../classes/")
 import unittest
 import numpy as np
-import structure as st
+import structure_graph.structure as st
 
 
 class TestStructure(unittest.TestCase):

main_package/tests/estimators/test_structure_constraint_based_estimator.py

@@ -1,5 +1,5 @@
 import sys
-sys.path.append("../classes/")
+sys.path.append("../../classes/")
 import glob
 import math
 import os
@@ -10,17 +10,17 @@ import numpy as np
 import psutil
 from line_profiler import LineProfiler
 
-import cache as ch
+import utility.cache as ch
-import sample_path as sp
+import structure_graph.sample_path as sp
-import structure_estimator as se
+import estimators.structure_constraint_based_estimator as se
-import json_importer as ji
+import utility.json_importer as ji
 
 
-class TestStructureEstimator(unittest.TestCase):
+class TestStructureConstraintBasedEstimator(unittest.TestCase):
 
     @classmethod
     def setUpClass(cls):
-        cls.read_files = glob.glob(os.path.join('../data', "*.json"))
+        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()
@@ -29,7 +29,7 @@ class TestStructureEstimator(unittest.TestCase):
     def test_init(self):
         exp_alfa = 0.1
         chi_alfa = 0.1
-        se1 = se.StructureEstimator(self.s1, exp_alfa, chi_alfa)
+        se1 = se.StructureConstraintBasedEstimator(self.s1, exp_alfa, chi_alfa)
         self.assertEqual(self.s1, se1.sample_path)
         self.assertTrue(np.array_equal(se1.nodes, np.array(self.s1.structure.nodes_labels)))
         self.assertTrue(np.array_equal(se1.nodes_indxs, self.s1.structure.nodes_indexes))
@@ -43,7 +43,7 @@ class TestStructureEstimator(unittest.TestCase):
         exp_alfa = 0.1
         chi_alfa = 0.1
         nodes_numb = len(self.s1.structure.nodes_labels)
-        se1 = se.StructureEstimator(self.s1, exp_alfa, chi_alfa)
+        se1 = se.StructureConstraintBasedEstimator(self.s1, exp_alfa, chi_alfa)
         cg = se1.build_complete_graph(self.s1.structure.nodes_labels)
         self.assertEqual(len(cg.edges), nodes_numb*(nodes_numb - 1))
         for node in self.s1.structure.nodes_labels:
@@ -56,7 +56,7 @@ class TestStructureEstimator(unittest.TestCase):
         exp_alfa = 0.1
         chi_alfa = 0.1
         nodes_numb = len(self.s1.structure.nodes_labels)
-        se1 = se.StructureEstimator(self.s1, exp_alfa, chi_alfa)
+        se1 = se.StructureConstraintBasedEstimator(self.s1, exp_alfa, chi_alfa)
 
         for node in self.s1.structure.nodes_labels:
             for b in range(nodes_numb):
@@ -68,7 +68,7 @@ class TestStructureEstimator(unittest.TestCase):
                     self.assertFalse(node in sset)
 
     def test_time(self):
-        se1 = se.StructureEstimator(self.s1, 0.1, 0.1)
+        se1 = se.StructureConstraintBasedEstimator(self.s1, 0.1, 0.1)
         lp = LineProfiler()
         lp.add_function(se1.complete_test)
         lp.add_function(se1.one_iteration_of_CTPC_algorithm)
@@ -89,7 +89,7 @@ class TestStructureEstimator(unittest.TestCase):
         se1.save_results()
 
     def test_memory(self):
-        se1 = se.StructureEstimator(self.s1, 0.1, 0.1)
+        se1 = se.StructureConstraintBasedEstimator(self.s1, 0.1, 0.1)
         se1.ctpc_algorithm()
         current_process = psutil.Process(os.getpid())
         mem = current_process.memory_info().rss

main_package/tests/estimators/test_structure_score_based_estimator.py

@@ -1,5 +1,5 @@
 import sys
-sys.path.append("../classes/")
+sys.path.append("../../classes/")
 import glob
 import math
 import os
@@ -10,10 +10,10 @@ import numpy as np
 import psutil
 from line_profiler import LineProfiler
 
-import cache as ch
+import utility.cache as ch
-import sample_path as sp
+import structure_graph.sample_path as sp
-import structure_score_based_estimator as se
+import estimators.structure_score_based_estimator as se
-import json_importer as ji
+import utility.json_importer as ji
 
 
 
@@ -21,7 +21,7 @@ class TestStructureScoreBasedEstimator(unittest.TestCase):
 
     @classmethod
     def setUpClass(cls):
-        cls.read_files = glob.glob(os.path.join('../data', "*.json"))
+        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()

main_package/tests/structure_graph/test_cim.py

@@ -1,9 +1,9 @@
 import sys
-sys.path.append("../classes/")
+sys.path.append("../../classes/")
 import unittest
 import numpy as np
 
-import conditional_intensity_matrix as cim
+import structure_graph.conditional_intensity_matrix as cim
 
 
 class TestConditionalIntensityMatrix(unittest.TestCase):

main_package/tests/structure_graph/test_networkgraph.py

@@ -1,5 +1,5 @@
 import sys
-sys.path.append("../classes/")
+sys.path.append("../../classes/")
 import unittest
 import glob
 import os
@@ -7,16 +7,16 @@ import networkx as nx
 import numpy as np
 import itertools
 
-import sample_path as sp
+import structure_graph.sample_path as sp
-import network_graph as ng
+import structure_graph.network_graph as ng
-import json_importer as ji
+import utility.json_importer as ji
 
 
 
 class TestNetworkGraph(unittest.TestCase):
     @classmethod
     def setUpClass(cls):
-        cls.read_files = glob.glob(os.path.join('../data', "*.json"))
+        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()

main_package/tests/structure_graph/test_sample_path.py

@@ -1,19 +1,19 @@
 import sys
-sys.path.append("../classes/")
+sys.path.append("../../classes/")
 import unittest
 import glob
 import os
-import json_importer as ji
+import utility.json_importer as ji
-import sample_path as sp
+import structure_graph.sample_path as sp
-import trajectory as tr
+import structure_graph.trajectory as tr
-import structure as st
+import structure_graph.structure as st
 
 
 class TestSamplePath(unittest.TestCase):
 
     @classmethod
     def setUpClass(cls) -> None:
-        cls.read_files = glob.glob(os.path.join('../data', "*.json"))
+        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):

main_package/tests/structure_graph/test_setofcims.py

@@ -1,10 +1,10 @@
 import sys
-sys.path.append("../classes/")
+sys.path.append("../../classes/")
 import unittest
 import numpy as np
 import itertools
 
-import set_of_cims as soci
+import structure_graph.set_of_cims as soci
 
 
 class TestSetOfCims(unittest.TestCase):

main_package/tests/structure_graph/test_sets_of_cims_container.py

@@ -1,8 +1,8 @@
 import sys
-sys.path.append("../classes/")
+sys.path.append("../../classes/")
 import unittest
-import set_of_cims as sc
+import structure_graph.set_of_cims as sc
-import sets_of_cims_container as scc
+import structure_graph.sets_of_cims_container as scc
 
 
 class TestSetsOfCimsContainer(unittest.TestCase):

main_package/tests/structure_graph/test_trajectory.py

@@ -1,9 +1,9 @@
 import sys
-sys.path.append("../classes/")
+sys.path.append("../../classes/")
 import unittest
 import numpy as np
 
-import trajectory as tr
+import structure_graph.trajectory as tr
 
 
 class TestTrajectory(unittest.TestCase):

main_package/tests/utility/test_cache.py

@@ -1,10 +1,10 @@
 import sys
-sys.path.append("../classes/")
+sys.path.append("../../classes/")
 import unittest
 import numpy as np
 
-import cache as ch
+import utility.cache as ch
-import set_of_cims as soci
+import structure_graph.set_of_cims as soci
 
 
 class TestCache(unittest.TestCase):

main_package/tests/utility/test_json_importer.py

@@ -1,11 +1,11 @@
 import sys
-sys.path.append("../classes/")
+sys.path.append("../../classes/")
 import unittest
 import os
 import glob
 import numpy as np
 import pandas as pd
-import json_importer as ji
+import utility.json_importer as ji
 
 import json
 
@@ -15,7 +15,7 @@ class TestJsonImporter(unittest.TestCase):
 
     @classmethod
     def setUpClass(cls) -> None:
-        cls.read_files = glob.glob(os.path.join('../data', "*.json"))
+        cls.read_files = glob.glob(os.path.join('../../data', "*.json"))
 
     def test_init(self):
         j1 = ji.JsonImporter(self.read_files[0], 'samples', 'dyn.str', 'variables', 'Time', 'Name')