Example

.gitignore

@@ -14,3 +14,4 @@ test2.json
 test3.json
 result0.png
 example.json
+test_time.py

PyCTBN/PyCTBN/structure_graph/network_generator.py

@@ -4,8 +4,7 @@ from .conditional_intensity_matrix import ConditionalIntensityMatrix
 from .set_of_cims import SetOfCims
 import numpy as np
 import pandas as pd
-import os
-import json
+import random
 
 class NetworkGenerator(object):
     """Provides the methods to generate a network graph and the CIMs related to it
@@ -30,15 +29,28 @@ class NetworkGenerator(object):
         self._graph = None
         self._cims = None
 
-    def generate_graph(self, density):
+    def generate_graph(self, density, fixed: bool = False):
         """Generate the edges according to specified density, and then instantiate the NetworkGraph object
             to represent the network
 
         :param density: Probability of an edge between two nodes to exist
         :type density: float
+        :param fixed: Specifies whether the required density is mandatory or it's just a probability
+        :type fixed: bool
         """
+        
+        if fixed:
+            n_edges = density * len(self._labels) * (len(self._labels) - 1)
+            if not float.is_integer(n_edges):
+                raise RuntimeError("Invalid Density")
+            else:
+                n_edges = int(n_edges)
+            edges = [(i, j) for i in self._labels for j in self._labels if i != j]
+            random.shuffle(edges)
+            edges = edges[:n_edges]
+        else:
+            edges = [(i, j) for i in self._labels for j in self._labels if np.random.binomial(1, density) == 1 and i != j]
 
-        edges = [(i, j) for i in self._labels for j in self._labels if np.random.binomial(1, density) == 1 and i != j]
         s = Structure(self._labels, self._indxs, self._vals, edges, len(self._labels))
         self._graph = NetworkGraph(s)
         self._graph.add_nodes(s.nodes_labels)

PyCTBN/PyCTBN/structure_graph/trajectory_generator.py

@@ -25,7 +25,7 @@ class TrajectoryGenerator(object):
     :type _generated_trajectory: pandas.DataFrame
     """
 
-    def __init__(self, importer: AbstractImporter = None, variables: list = None, dyn_str: list = None, dyn_cims: dict = None):
+    def __init__(self, importer: AbstractImporter = None, variables: pd.DataFrame = None, dyn_str: pd.DataFrame = None, dyn_cims: dict = None):
         """Constructor Method
             It parses and elaborates the data fetched from importer (if defined, otherwise variables, dyn_str and dyn_cims are used) 
             in order to make the objects structure more suitable for the forthcoming trajectory generation
@@ -34,31 +34,35 @@ class TrajectoryGenerator(object):
         self._importer = importer
 
         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] = 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
-        for v in sampled_cims.keys():
-            p_combs = []
-            v_cims = []
-            for comb in sampled_cims[v].keys():
-                p_combs.append(np.array(re.findall(r"=(\d)", comb)).astype("int"))
-                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 = np.array(p_combs), cims = v_cims)
-            else:
-                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
+        if importer is not None:
+            sampled_cims = self._importer._cims
+        
+            for v in sampled_cims.keys():
+                p_combs = []
+                v_cims = []
+                for comb in sampled_cims[v].keys():
+                    p_combs.append(np.array(re.findall(r"=(\d)", comb)).astype("int"))
+                    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 = np.array(p_combs), cims = v_cims)
+                else:
+                    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
+        else:
+            self._cims = dyn_cims
 
     def CTBN_Sample(self, t_end = -1, max_tr = -1):
         """This method implements the generation of a trajectory, basing on the network structure and
@@ -97,7 +101,6 @@ class TrajectoryGenerator(object):
 
                     time[i] = t + random.exponential(scale = param)
         
-            # next = index of the variable that will transition first
             next = time.argmin()
             t = time[next]
 
@@ -173,11 +176,4 @@ class TrajectoryGenerator(object):
         for p in processes:
             p.join()
 
-        return trajectories
-
-    def to_json(self):
-        """Convert the last generated trajectory from pandas.DataFrame object type to JSON format
-            (suitable to do input/output of the trajectory with file)
-        """
-
-        return json.loads(self._generated_trajectory.to_json(orient="records"))
+        return trajectories

PyCTBN/PyCTBN/utility/abstract_exporter.py

@@ -4,7 +4,7 @@ import os
 from abc import ABC, abstractmethod
 
 class AbstractExporter(ABC):
-    """Provides the methods to save in json format a network information
+    """Abstract class that exposes 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

PyCTBN/PyCTBN/utility/json_importer.py

@@ -30,8 +30,8 @@ class JsonImporter(AbstractImporter):
     :type _raw_data: List
     """
 
-    def __init__(self, file_path: str, samples_label: str, structure_label: str, variables_label: str, time_key: str,
-                 variables_key: str):
+    def __init__(self, file_path: str, samples_label: str, structure_label: str, variables_label: str, time_key: str, 
+                    variables_key: str, cims_label: str = None):
         """Constructor method
 
         .. note::
@@ -42,6 +42,7 @@ class JsonImporter(AbstractImporter):
         self._samples_label = samples_label
         self._structure_label = structure_label
         self._variables_label = variables_label
+        self._cims_label = cims_label
         self._time_key = time_key
         self._variables_key = variables_key
         self._df_samples_list = None
@@ -63,6 +64,9 @@ class JsonImporter(AbstractImporter):
         self._df_structure = self.import_structure(self._raw_data)
         self._df_variables = self.import_variables(self._raw_data)
 
+        if self._cims_label != None:
+            self._cims = self._raw_data[indx][self._cims_label]
+
     def import_trajectories(self, raw_data: typing.List) -> typing.List:
         """Imports the trajectories from the list of dicts ``raw_data``.
 

example.py

@@ -6,40 +6,36 @@ 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
 
-""" 
-    if __name__ == "__main__":
-        trajectories = tg.multi_trajectory(t_ends = [100, 100, 100])
-"""
+def main():
+    # 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)
 
-# 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
+    e1 = JsonExporter(ng.variables, ng.dyn_str, ng.cims)
+    tg = TrajectoryGenerator(variables = ng.variables, dyn_str = ng.dyn_str, dyn_cims = ng.cims)
+    sigma = tg.CTBN_Sample(max_tr = 30000)
+    e1.add_trajectory(sigma)
+    e1.out_file("example.json")
 
-# Trajectory Generation
-print(ng.dyn_str)
-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",
+                            structure_label = "dyn.str", variables_label = "variables",
+                            cims_label = "dyn.cims", 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)
 
-# 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('./result1.png')
-print(se1.adjacency_matrix())
-print(edges)
+if __name__ == "__main__":
+    main()