AbstractExporter

PyCTBN/PyCTBN/structure_graph/network_generator.py

@@ -3,6 +3,7 @@ from .network_graph import NetworkGraph
 from .conditional_intensity_matrix import ConditionalIntensityMatrix
 from .set_of_cims import SetOfCims
 import numpy as np
+import pandas as pd
 import os
 import json
 
@@ -105,44 +106,13 @@ class NetworkGenerator(object):
         return self._graph
 
     @property
-    def cims(self) -> NetworkGraph:
+    def cims(self) -> dict:
         return self._cims   
 
     @property
     def dyn_str(self) -> list:
-        return [{"From": edge[0], "To": edge[1]} for edge in self._graph.edges]
+        return pd.DataFrame([[edge[0], edge[1]] for edge in self._graph.edges], columns = ["From", "To"])
 
     @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):
-            dyn_cims[l] = {}
-            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:
-                    cim = cim[comb[0]].cim
-                elif len(parents) == 0:
-                    cim = cim[0].cim
-                else:
-                    cim = cim[0].cim
-                
-                dyn_cims[l][comb_key] = [dict([(str(i), val) for i, val in enumerate(row)]) for row in cim]
-
-        return dyn_cims
+        return pd.DataFrame([[l, self._vals[i]] for i, l in enumerate(self._labels)], columns = ["Name", "Value"])

PyCTBN/PyCTBN/structure_graph/trajectory_generator.py

@@ -33,14 +33,14 @@ class TrajectoryGenerator(object):
         
         self._importer = importer
 
-        self._vnames = self._importer._df_variables.iloc[:, 0].to_list() if importer is not None else [v["Name"] for v in variables]
+        self._vnames = self._importer._df_variables.iloc[:, 0].to_list() if importer is not None else variables.iloc[:, 0].to_list()
         
         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._parents[v] = dyn_str.where(dyn_str["To"] == v).dropna()["From"].tolist()
 
         self._cims = {}
         sampled_cims = self._importer._raw_data[0]["dyn.cims"] if importer is not None else dyn_cims
@@ -56,8 +56,8 @@ class TrajectoryGenerator(object):
                 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 = 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)
+                sof = SetOfCims(node_id = v, parents_states_number = [variables.where(variables["Name"] == p)["Value"] for p in self._parents[v]], 
+                    node_states_number = variables.where(variables["Name"] == v)["Value"], p_combs = np.array(p_combs), cims = v_cims)
             self._cims[v] = sof
 
     def CTBN_Sample(self, t_end = -1, max_tr = -1):

PyCTBN/PyCTBN/utility/abstract_exporter.py

@@ -0,0 +1,42 @@
+import json
+import pandas as pd
+import os
+from abc import ABC, abstractmethod
+
+class AbstractExporter(ABC):
+    """Provides the methods to save in json format a network information
+    along with one or more trajectories generated basing on it
+    
+    :param _variables: Dataframe containing the nodes labels and cardinalities
+    :type _variables: pandas.DataFrame
+    :param _dyn_str: Dataframe containing the structure of the network (edges)
+    :type _dyn_str: pandas.DataFrame
+    :param _dyn_cims: It contains, for every variable (label is the key), the SetOfCims object related to it
+    :type _dyn_cims: dict
+    :param _trajectories: List of trajectories, that can be added subsequently
+    :type _trajectories: List
+    """
+
+    def __init__(self, variables: pd.DataFrame = None, dyn_str: pd.DataFrame = None, dyn_cims: dict = None):
+        self._variables = variables
+        self._dyn_str = dyn_str
+        self._dyn_cims = dyn_cims
+        self._trajectories = []
+
+    def add_trajectory(self, trajectory: list):
+        """Add a new trajectory to the current list
+
+        :param trajectory: The trajectory to add
+        :type trajectory: pandas.DataFrame
+        """
+
+        self._trajectories.append(trajectory)
+
+    @abstractmethod
+    def out_file(self, filename):
+        """Create a file in current directory and write on it the previously added data 
+            (variables, dyn_str, dyn_cims and trajectories)
+
+        :param filename: Name of the output file (it must include json extension)
+        :type filename: string
+        """

PyCTBN/PyCTBN/utility/json_exporter.py

@@ -1,37 +1,31 @@
 import json
 import pandas as pd
+import numpy as np
 import os
 
-class JsonExporter(object):
+from .abstract_exporter import AbstractExporter
+
+class JsonExporter(AbstractExporter):
     """Provides the methods to save in json format a network information
     along with one or more trajectories generated basing on it
     
-    :param _variables: List of dictionaries, representing the variables in the network and their cardinality
-    :type _variables: List
-    :param _dyn_str: List of dictionaries, each of which represents an edge ({"From": "", "To": ""})
-    :type _dyn_str: List
-    :param _dyn_cims: It contains, for every variable (label is the key), the CIM values related to it
-    :type _dyn_cims: Dict
+    :param _variables: Dataframe containing the nodes labels and cardinalities
+    :type _variables: pandas.DataFrame
+    :param _dyn_str: Dataframe containing the structure of the network (edges)
+    :type _dyn_str: pandas.DataFrame
+    :param _dyn_cims: It contains, for every variable (label is the key), the SetOfCims object related to it
+    :type _dyn_cims: dict
     :param _trajectories: List of trajectories, that can be added subsequently
     :type _trajectories: List
     """
 
-    def __init__(self, variables, dyn_str, dyn_cims):
+    def __init__(self, variables: pd.DataFrame = None, dyn_str: pd.DataFrame = None, dyn_cims: dict = None):
         self._variables = variables
         self._dyn_str = dyn_str
         self._dyn_cims = dyn_cims
         self._trajectories = []
 
-    def add_trajectory(self, trajectory: list):
-        """Add a new trajectory to the current list
-
-        :param trajectory: The trajectory to add. It must already be in json form, and not as pandas.DataFrame
-        :type trajectory: List
-        """
-
-        self._trajectories.append(trajectory)
-
-    def out_json(self, filename):
+    def out_file(self, filename):
         """Create a file in current directory and write on it the previously added data 
             (variables, dyn_str, dyn_cims and trajectories)
 
@@ -40,12 +34,42 @@ class JsonExporter(object):
         """
 
         data = [{
-            "dyn.str": self._dyn_str,
-            "variables": self._variables,
-            "dyn.cims": self._dyn_cims,
-            "samples": self._trajectories
+            "dyn.str": json.loads(self._dyn_str.to_json(orient="records")),
+            "variables": json.loads(self._variables.to_json(orient="records")),
+            "dyn.cims": self.cims_to_json(),
+            "samples": [json.loads(trajectory.to_json(orient="records")) for trajectory in self._trajectories]
         }]
 
         path = os.getcwd()
         with open(path + "/" + filename, "w") as json_file:
-            json.dump(data, json_file)
+            json.dump(data, json_file)
+
+    """Restructure the CIMs object in order to fit the standard JSON file structure 
+    """
+    def cims_to_json(self) -> dict:
+        json_cims = {}
+
+        for i, l in enumerate(self._variables.iloc[:, 0].to_list()):
+            json_cims[l] = {}
+            parents = self._dyn_str.where(self._dyn_str["To"] == l).dropna()["From"].tolist()
+            for j, comb in enumerate(self._dyn_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._dyn_cims[l].filter_cims_with_mask(np.array([True for p in parents]), comb)
+                if len(parents) == 1:
+                    cim = cim[comb[0]].cim
+                elif len(parents) == 0:
+                    cim = cim[0].cim
+                else:
+                    cim = cim[0].cim
+                
+                json_cims[l][comb_key] = [dict([(str(i), val) for i, val in enumerate(row)]) for row in cim]
+
+        return json_cims

example.py

@@ -23,11 +23,11 @@ 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 = 100)
-e1.add_trajectory(tg.to_json())
-e1.out_json("example.json")
+e1 = JsonExporter(ng.variables, ng.dyn_str, ng.cims)
+tg = TrajectoryGenerator(variables = ng.variables, dyn_str = ng.dyn_str, dyn_cims = e1.cims_to_json())
+sigma = tg.CTBN_Sample(max_tr = 30000)
+e1.add_trajectory(sigma)
+e1.out_file("example.json")
 
 # Network Estimation (Constraint Based)
 importer = JsonImporter(file_path="example.json", samples_label='samples',