Full Execution Example

.gitignore

@@ -13,3 +13,4 @@ test1.json
 test2.json
 test3.json
 result0.png
+example.json

PyCTBN/PyCTBN/structure_graph/network_generator.py

@@ -3,6 +3,8 @@ from .network_graph import NetworkGraph
 from .conditional_intensity_matrix import ConditionalIntensityMatrix
 from .set_of_cims import SetOfCims
 import numpy as np
+import os
+import json
 
 class NetworkGenerator(object):
     """Provides the methods to generate a network graph and the CIMs related to it
@@ -94,20 +96,30 @@ class NetworkGenerator(object):
             row /= (sum(row) - row[i])
             row *= diag
             row[i] = -1 * diag
-            cim[i] = row
+            cim[i] = np.around(row, 4)
 
         return cim
 
-    def out_json(self, filename):
-        """Create a file in current directory and write on it the generated network and the CIMs, 
-            after having restructured the objects in order to respect the standard JSON file structure.
+    @property
+    def graph(self) -> NetworkGraph:
+        return self._graph
 
-        :param filename: Name of the output file (it must include json extension)
-        :type filename: string
-        """
+    @property
+    def cims(self) -> NetworkGraph:
+        return self._cims   
+
+    @property
+    def dyn_str(self) -> list:
+        return [{"From": edge[0], "To": edge[1]} for edge in self._graph.edges]
 
-        dyn_str = [{"From": edge[0], "To": edge[1]} for edge in self._graph.edges]
-        variables = [{"Name": l, "Value": self._vals[i]} for i, l in enumerate(self._labels)]
+    @property
+    def variables(self) -> list:
+        return [{"Name": l, "Value": self._vals[i]} for i, l in enumerate(self._labels)]
+
+    """Restructure the CIMs object in order to fit the standard JSON file structure 
+    """
+    @property
+    def dyn_cims(self) -> dict:
         dyn_cims = {}
 
         for i, l in enumerate(self._labels):
@@ -115,10 +127,13 @@ class NetworkGenerator(object):
             parents = self._graph.get_ordered_by_indx_set_of_parents(l)[0]
             for j, comb in enumerate(self._cims[l].p_combs):
                 comb_key = ""
+                if len(parents) != 0:
                     for k, val in enumerate(comb):
                         comb_key += parents[k] + "=" + str(val)
                         if k < len(comb) - 1:
                             comb_key += ","
+                else:
+                    comb_key = l
 
                 cim = self._cims[l].filter_cims_with_mask(np.array([True for p in parents]), comb)
                 if len(parents) == 1:
@@ -126,25 +141,8 @@ class NetworkGenerator(object):
                 elif len(parents) == 0:
                     cim = cim[0].cim
                 else:
-                    cim = cim.cim
+                    cim = cim[0].cim
                 
                 dyn_cims[l][comb_key] = [dict([(str(i), val) for i, val in enumerate(row)]) for row in cim]
 
-        data = {
-            "dyn.str": dyn_str,
-            "variables": variables,
-            "dyn.cims": dyn_cims,
-            "samples": []
-        }
-
-        path = os.getcwd()
-        with open(path + "/" + filename, "w") as json_file:
-            json.dump(data, json_file)
-
-    @property
-    def graph(self) -> NetworkGraph:
-        return self._graph
-
-    @property
-    def cims(self) -> NetworkGraph:
-        return self._cims   
+        return dyn_cims

PyCTBN/PyCTBN/structure_graph/set_of_cims.py

@@ -84,7 +84,7 @@ class SetOfCims(object):
             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]
+            return np.array(self._actual_cims)[flat_indxs.astype(int)]
 
     @property
     def actual_cims(self) -> np.ndarray:

PyCTBN/PyCTBN/structure_graph/trajectory_generator.py

@@ -24,7 +24,7 @@ class TrajectoryGenerator(object):
     :type _generated_trajectory: pandas.DataFrame
     """
 
-    def __init__(self, importer: AbstractImporter):
+    def __init__(self, importer: AbstractImporter = None, variables: list = None, dyn_str: list = None, dyn_cims: dict = None):
         """Constructor Method
             It parses and elaborates the data fetched from importer in order to make the objects structure
             more suitable for the forthcoming trajectory generation
@@ -32,14 +32,17 @@ class TrajectoryGenerator(object):
         
         self._importer = importer
 
-        self._vnames = self._importer._df_variables.iloc[:, 0].to_list()
+        self._vnames = self._importer._df_variables.iloc[:, 0].to_list() if importer is not None else [v["Name"] for v in variables]
         
         self._parents = {}
         for v in self._vnames:
+            if importer is not None:
                 self._parents[v] = self._importer._df_structure.where(self._importer._df_structure["To"] == v).dropna()["From"].tolist()
+            else:
+                self._parents[v] = [edge["From"] for edge in dyn_str if edge["To"] == v]
 
         self._cims = {}
-        sampled_cims = self._importer._raw_data[0]["dyn.cims"]
+        sampled_cims = self._importer._raw_data[0]["dyn.cims"] if importer is not None else dyn_cims
         for v in sampled_cims.keys():
             p_combs = []
             v_cims = []
@@ -48,8 +51,12 @@ class TrajectoryGenerator(object):
                 cim = pd.DataFrame(sampled_cims[v][comb]).to_numpy()    
                 v_cims.append(ConditionalIntensityMatrix(cim = cim))
 
+            if importer is not None:
                 sof = SetOfCims(node_id = v, parents_states_number = [self._importer._df_variables.where(self._importer._df_variables["Name"] == p)["Value"] for p in self._parents[v]], 
-                node_states_number = self._importer._df_variables.where(self._importer._df_variables["Name"] == v)["Value"], p_combs = p_combs, cims = v_cims)
+                    node_states_number = self._importer._df_variables.where(self._importer._df_variables["Name"] == v)["Value"], p_combs = np.array(p_combs), cims = v_cims)
+            else:
+                sof = SetOfCims(node_id = v, parents_states_number = [[variable["Value"] for variable in variables if variable["Name"] == p][0] for p in self._parents[v]], 
+                    node_states_number = [variable for variable in variables if variable["Name"] == v][0]["Value"], p_combs = np.array(p_combs), cims = v_cims)
             self._cims[v] = sof
 
     def CTBN_Sample(self, t_end = -1, max_tr = -1):

example.py

@@ -0,0 +1,41 @@
+from PyCTBN.PyCTBN.structure_graph.trajectory_generator import TrajectoryGenerator
+from PyCTBN.PyCTBN.structure_graph.network_generator import NetworkGenerator
+from PyCTBN.PyCTBN.utility.json_importer import JsonImporter
+from PyCTBN.PyCTBN.utility.json_exporter import JsonExporter
+from PyCTBN.PyCTBN.structure_graph.structure import Structure
+from PyCTBN.PyCTBN.structure_graph.sample_path import SamplePath
+from PyCTBN.PyCTBN.estimators.structure_constraint_based_estimator import StructureConstraintBasedEstimator
+
+# Network Generation
+labels = ["X", "Y", "Z"]
+card = 3
+vals = [card for l in labels]
+cim_min = 1
+cim_max = 3
+
+ng = NetworkGenerator(labels, vals)
+ng.generate_graph(0.3)
+ng.generate_cims(cim_min, cim_max)
+
+# Trajectory Generation
+print(ng.dyn_str)
+e1 = JsonExporter(ng.variables, ng.dyn_str, ng.dyn_cims)
+tg = TrajectoryGenerator(variables = ng.variables, dyn_str = ng.dyn_str, dyn_cims = ng.dyn_cims)
+sigma = tg.CTBN_Sample(max_tr = 10)
+e1.add_trajectory(tg.to_json())
+e1.out_json("example.json")
+
+# Network Estimation (Constraint Based)
+importer = JsonImporter(file_path="example.json", samples_label='samples',
+                        structure_label='dyn.str', variables_label='variables',
+                        time_key='Time', variables_key='Name')
+importer.import_data(0)
+s1 = SamplePath(importer=importer)
+s1.build_trajectories()
+s1.build_structure()
+se1 = StructureConstraintBasedEstimator(sample_path=s1, exp_test_alfa=0.1, chi_test_alfa=0.1,
+                                        known_edges=[], thumb_threshold=25)
+edges = se1.estimate_structure(True)
+se1.save_plot_estimated_structure_graph('./result.png')
+print(se1.adjacency_matrix())
+print(edges)