Updated all dependencies to the latest version (except for `networkx` updated to `2.8.8`), updated code to reflect the changes

doc-requirements.txt

@@ -1,2 +1,2 @@
-sphinx==3.3.1
+sphinx==6.1.3
 sphinx-pdj-theme==0.2.1

lint-requirements.txt

@@ -1,2 +1,2 @@
-pylint==2.7.2
-flake8==3.9.0
+pylint==2.16.0
+flake8==6.0.0

setup-requirements.txt

@@ -1,6 +1,6 @@
-matplotlib==3.3.3
-networkx==2.5
-numpy==1.19.4
-pandas==1.1.5
-scipy==1.5.4
-tqdm==4.54.1
+matplotlib==3.6.3
+networkx==2.8.8
+numpy==1.24.1
+pandas==1.5.3
+scipy==1.10.0
+tqdm==4.64.1

src/pyctbn/legacy/estimators/parameters_estimator.py

@@ -5,6 +5,7 @@
 import sys
 sys.path.append('../')
 import numpy as np
+from numpy import int64
 
 from ..structure_graph.network_graph import NetworkGraph
 from ..structure_graph.set_of_cims import SetOfCims
@@ -83,7 +84,7 @@ class ParametersEstimator(object):
         :type T: numpy.ndArray
         """
         T[:] = np.bincount(np.sum(trajectory[:, cols_filter] * scalar_indexes_struct / scalar_indexes_struct[0], axis=1)
-                           .astype(np.int), \
+                           .astype(int), \
                            times,
                            minlength=scalar_indexes_struct[-1]).reshape(-1, T.shape[1])
 
@@ -104,9 +105,9 @@ class ParametersEstimator(object):
         :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)
+                                dtype=int64)
+        trj_tmp = trajectory[trajectory[:, int(trajectory.shape[1] / 2) + node_indx].astype(int) >= 0]
+        M[:] = np.bincount(np.sum(trj_tmp[:, cols_filter] * scalar_indexing / scalar_indexing[0], axis=1).astype(int)
                            , minlength=scalar_indexing[-1]).reshape(-1, M.shape[1], M.shape[2])
         M_raveled = M.ravel()
         M_raveled[diag_indices] = 0

src/pyctbn/legacy/optimizers/tabu_search.py

@@ -122,7 +122,7 @@ class TabuSearch(Optimizer):
 
             'choose a new random edge according to tabu restiction'
             if(len(current_possible_nodes) > 0):
-                current_new_parent = sample(current_possible_nodes,k=1)[0]
+                current_new_parent = sample(list(current_possible_nodes),k=1)[0]
             else:
                 current_new_parent = tabu_queue.get()
                 tabu_set.remove(current_new_parent)

src/pyctbn/legacy/structure_graph/conditional_intensity_matrix.py

@@ -3,6 +3,7 @@
 # License: MIT License
 
 import numpy as np
+from numpy import float64
 
 
 class ConditionalIntensityMatrix(object):
@@ -24,7 +25,7 @@ class ConditionalIntensityMatrix(object):
         if cim is not None:
             self._cim = cim
         else:
-            self._cim = self.state_transition_matrix.astype(np.float64)
+            self._cim = self.state_transition_matrix.astype(float64)
 
     def compute_cim_coefficients(self) -> None:
         """Compute the coefficients of the matrix _cim by using the following equality q_xx' = M[x, x'] / T[x].

src/pyctbn/legacy/structure_graph/network_graph.py

@@ -164,7 +164,7 @@ class NetworkGraph(object):
         """
         T_vector = np.array([node_states])
         T_vector = np.append(T_vector, parents_vals)
-        T_vector = T_vector.cumprod().astype(np.int)
+        T_vector = T_vector.cumprod().astype(int)
         return T_vector
 
     @staticmethod
@@ -182,7 +182,7 @@ class NetworkGraph(object):
         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)
+        M_vector = M_vector.cumprod().astype(int)
         return M_vector
 
     @staticmethod
@@ -198,7 +198,7 @@ class NetworkGraph(object):
         :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)
+        return np.append(np.array([node_indx], dtype=int), p_indxs).astype(int)
 
     @staticmethod
     def build_transition_filtering_for_a_node(node_indx: int, p_indxs: typing.List, nodes_number: int) \
@@ -215,7 +215,7 @@ class NetworkGraph(object):
         :return: The filtering structure for transitions estimation
         :rtype: numpy.ndArray
         """
-        return np.array([node_indx + nodes_number, node_indx, *p_indxs], dtype=np.int)
+        return np.array([node_indx + nodes_number, node_indx, *p_indxs], dtype=int)
 
     @staticmethod
     def build_p_comb_structure_for_a_node(parents_values: typing.List) -> np.ndarray:
@@ -238,7 +238,7 @@ class NetworkGraph(object):
                 parents_comb[:, 1] = parents_comb[:, 0].copy()
                 parents_comb[:, 0] = tmp_comb
         else:
-            parents_comb = np.array([[]], dtype=np.int)
+            parents_comb = np.array([[]], dtype=int)
         return parents_comb
 
     def get_parents_by_id(self, node_id) -> typing.List:

src/pyctbn/legacy/structure_graph/set_of_cims.py

@@ -48,13 +48,13 @@ class SetOfCims(object):
         """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)
+            self._state_residence_times = np.zeros((1, self._node_states_number), dtype=float)
+            self._transition_matrices = np.zeros((1, self._node_states_number, self._node_states_number), dtype=int)
         else:
             self._state_residence_times = \
-                np.zeros((np.prod(self._parents_states_number), self._node_states_number), dtype=np.float)
+                np.zeros((np.prod(self._parents_states_number), self._node_states_number), dtype=float)
             self._transition_matrices = np.zeros([np.prod(self._parents_states_number), self._node_states_number,
-                                                  self._node_states_number], dtype=np.int)
+                                                  self._node_states_number], dtype=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.

src/pyctbn/legacy/utility/json_importer.py

@@ -10,20 +10,6 @@ import pandas as pd
 
 from .abstract_importer import AbstractImporter
 
-def read_json_file(file_path) -> typing.List:
-    """Reads the JSON file in the path self.filePath.
-
-    :return: The contents of the json file
-    :rtype: List
-    """
-    with open(file_path) as f:
-        data = json.load(f)
-
-        if (isinstance(data,list)):
-            return data
-        else:
-            return [data]
-
 class JsonImporter(AbstractImporter):
     """Implements the abstracts methods of AbstractImporter and adds all the necessary methods to process and prepare
     the data in json extension.
@@ -65,7 +51,7 @@ class JsonImporter(AbstractImporter):
         self._df_samples_list = None
         self._array_indx = None
         super(JsonImporter, self).__init__(file_path)
-        self._raw_data = read_json_file(file_path)
+        self._raw_data = self.read_json_file()
 
     def import_data(self, indx: int = 0) -> None:
         """Implements the abstract method of :class:`AbstractImporter`.
@@ -114,6 +100,20 @@ class JsonImporter(AbstractImporter):
         """
         return self.one_level_normalizing(raw_data, self._array_indx, self._variables_label)
 
+    def read_json_file(self) -> typing.List:
+        """Reads the JSON file in the path self.filePath.
+
+        :return: The contents of the json file
+        :rtype: List
+        """
+        with open(self._file_path) as f:
+            data = json.load(f)
+
+            if (isinstance(data,list)):
+                return data
+            else:
+                return [data]
+
     def one_level_normalizing(self, raw_data: typing.List, indx: int, key: str) -> pd.DataFrame:
         """Extracts the one-level nested data in the list ``raw_data`` at the index ``indx`` at the key ``key``.
 

test-requirements.txt

@@ -1,5 +1,5 @@
-pytest-xdist==2.2.1
-setuptools==54.1.2
-pytest==6.2.2
-pytest-cov==2.11.1
-psutil==5.9.0
+pytest-xdist==3.1.0
+setuptools==67.1.0
+pytest==7.2.1
+pytest-cov==4.0.0
+psutil==5.9.4

tests/estimators/test_structure_estimator.py

@@ -92,7 +92,7 @@ class TestStructureEstimator(unittest.TestCase):
     def test_save_results(self):
         se1 = StructureConstraintBasedEstimator(self.s1, 0.1, 0.1)
         se1.ctpc_algorithm()
-        file_name = './PyCTBN/tests/estimators/test_save.json'
+        file_name = './tests/estimators/test_save.json'
         se1.save_results(file_name)
         with open(file_name) as f:
             js_graph = json.load(f)
@@ -109,7 +109,7 @@ class TestStructureEstimator(unittest.TestCase):
     def test_save_plot_estimated_graph(self):
         se1 = StructureConstraintBasedEstimator(self.s1, 0.1, 0.1)
         edges = se1.estimate_structure(disable_multiprocessing=True)
-        file_name = './PyCTBN/tests/estimators/test_plot.png'
+        file_name = './tests/estimators/test_plot.png'
         se1.save_plot_estimated_structure_graph(file_name)
         os.remove(file_name)
 

tests/structure_graph/test_cim.py

@@ -25,13 +25,13 @@ class TestConditionalIntensityMatrix(unittest.TestCase):
             state_transition_matrix = self.state_transition_matrix)
         self.assertTrue(np.array_equal(self.state_res_times, c1.state_residence_times))
         self.assertTrue(np.array_equal(self.state_transition_matrix, c1.state_transition_matrix))
-        self.assertEqual(c1.cim.dtype, np.float)
+        self.assertEqual(c1.cim.dtype, float)
         self.assertEqual(self.state_transition_matrix.shape, c1.cim.shape)
 
     def test_compute_cim_coefficients(self):
         c1 = ConditionalIntensityMatrix(state_residence_times = self.state_res_times, 
             state_transition_matrix = self.state_transition_matrix)
-        c2 = self.state_transition_matrix.astype(np.float)
+        c2 = self.state_transition_matrix.astype(float)
         np.fill_diagonal(c2, c2.diagonal() * -1)
         for i in range(0, len(self.state_res_times)):
             for j in range(0, len(self.state_res_times)):

tests/structure_graph/test_setofcims.py

@@ -124,7 +124,7 @@ class TestSetOfCims(unittest.TestCase):
                 parents_comb[:, 1] = parents_comb[:, 0].copy()
                 parents_comb[:, 0] = tmp_comb
         else:
-            parents_comb = np.array([[]], dtype=np.int)
+            parents_comb = np.array([[]], dtype=int)
         return parents_comb
 
     def another_filtering_method(self,p_combs, mask, parent_value):