Implemented Sample_Importer

main_package/classes/utility/abstract_importer.py

@@ -5,14 +5,18 @@ from abc import ABC, abstractmethod
 import numpy as np
 import pandas as pd
 
+import copy
+
+from sklearn.utils import resample
+
 
 class AbstractImporter(ABC):
     """Abstract class that exposes all the necessary methods to process the trajectories and the net structure.
 
     :param file_path: the file path, or dataset name if you import already processed data
     :type file_path: str
-    :param concatenated_samples: Dataframe or numpy array containing the concatenation of all the processed trajectories
+    :param trajectory_list: Dataframe or numpy array containing the concatenation of all the processed trajectories
-    :type concatenated_samples: typing.Union[pandas.DataFrame, numpy.ndarray]
+    :type trajectory_list: typing.Union[pandas.DataFrame, numpy.ndarray]
     :param variables: Dataframe containing the nodes labels and cardinalities
     :type variables: pandas.DataFrame
     :prior_net_structure: Dataframe containing the structure of the network (edges)
@@ -31,24 +35,25 @@ class AbstractImporter(ABC):
 
     """
 
-    def __init__(self, file_path: str = None, concatenated_samples: typing.Union[pd.DataFrame, np.ndarray] = None,
+    def __init__(self, file_path: str = None, trajectory_list: typing.Union[pd.DataFrame, np.ndarray] = None,
                  variables: pd.DataFrame = None, prior_net_structure: pd.DataFrame = None):
         """Constructor
         """
         self._file_path = file_path
-        self._concatenated_samples = concatenated_samples
+        self._df_samples_list = trajectory_list
+        self._concatenated_samples = []
         self._df_variables = variables
         self._df_structure = prior_net_structure
         self._sorter = None
         super().__init__()
 
     @abstractmethod
-    def build_sorter(self, sample_frame: pd.DataFrame) -> typing.List:
+    def build_sorter(self, trajecory_header: object) -> typing.List:
         """Initializes the ``_sorter`` class member from a trajectory dataframe, exctracting the header of the frame
         and keeping ONLY the variables symbolic labels, cutting out the time label in the header.
 
-        :param sample_frame: The dataframe from which extract the header
+        :param trajecory_header: an object that will be used to define the header
-        :type sample_frame: pandas.DataFrame
+        :type trajecory_header: object
         :return: A list containing the processed header.
         :rtype: List
         """
@@ -73,6 +78,7 @@ class AbstractImporter(ABC):
             the Dataframe ``sample_frame`` has to follow the column structure of this header:
             Header of sample_frame = [Time | Variable values]
         """
+        sample_frame = copy.deepcopy(sample_frame)
         sample_frame.iloc[:, 0] = sample_frame.iloc[:, 0].diff().shift(-1)
         shifted_cols = sample_frame[columns_header].shift(-1).fillna(0).astype('int32')
         shifted_cols.columns = shifted_cols_header
@@ -103,25 +109,25 @@ class AbstractImporter(ABC):
         proc_samples_list = [compute_row_delta(sample, self._sorter, shifted_cols_header)
                                 for sample in df_samples_list]
         self._concatenated_samples = pd.concat(proc_samples_list)
+
         complete_header = self._sorter[:]
         complete_header.insert(0,'Time')
         complete_header.extend(shifted_cols_header)
         self._concatenated_samples = self._concatenated_samples[complete_header]
 
-    def build_list_of_samples_array(self, concatenated_sample: typing.Union[pd.DataFrame, np.ndarray]) -> typing.List:
+    def build_list_of_samples_array(self, concatenated_sample: pd.DataFrame) -> typing.List:
         """Builds a List containing the the delta times numpy array, and the complete transitions matrix
 
         :param concatenated_sample: the dataframe/array from which the time, and transitions matrix have to be extracted
             and converted
-        :type concatenated_sample: typing.Union[pandas.Dataframe, numpy.ndarray]
+        :type concatenated_sample: pandas.Dataframe
         :return: the resulting list of numpy arrays
         :rtype: List
         """
-        if isinstance(concatenated_sample, pd.DataFrame):
+        
-            concatenated_array = concatenated_sample.to_numpy()
+        concatenated_array = concatenated_sample.to_numpy()
-            columns_list = [concatenated_array[:, 0], concatenated_array[:, 1:].astype(int)]
+        columns_list = [concatenated_array[:, 0], concatenated_array[:, 1:].astype(int)]
-        else:
+
-            columns_list = [concatenated_sample[:, 0], concatenated_sample[:, 1:].astype(int)]
         return columns_list
 
     def clear_concatenated_frame(self) -> None:

main_package/classes/utility/sample_importer.py

@@ -0,0 +1,66 @@
+import json
+import typing
+
+import pandas as pd
+import numpy as np
+import sys
+sys.path.append('../')
+
+import utility.abstract_importer as ai
+
+
+
+class SampleImporter(ai.AbstractImporter):
+    #TODO: Scrivere documentazione
+    """Implements the abstracts methods of AbstractImporter and adds all the necessary methods to process and prepare
+    the data in json extension.
+
+    :param file_path: the path of the file that contains tha data to be imported
+    :type file_path: string
+    :param samples_label: the reference key for the samples in the trajectories
+    :type samples_label: string
+    :param structure_label: the reference key for the structure of the network data
+    :type structure_label: string
+    :param variables_label: the reference key for the cardinalites of the nodes data
+    :type variables_label: string
+    :param time_key: the key used to identify the timestamps in each trajectory
+    :type time_key: string
+    :param variables_key: the key used to identify the names of the variables in the net
+    :type variables_key: string
+    :_array_indx: the index of the outer JsonArray to extract the data from
+    :type _array_indx: int
+    :_df_samples_list: a Dataframe list in which every dataframe contains a trajectory
+    :_raw_data: The raw contents of the json file to import
+    :type _raw_data: List
+    """
+
+    def __init__(self, trajectory_list: typing.Union[pd.DataFrame, np.ndarray] = None,
+                 variables: pd.DataFrame = None, prior_net_structure: pd.DataFrame = None):
+        super(SampleImporter, self).__init__(trajectory_list =trajectory_list,
+                                        variables= variables,
+                                        prior_net_structure=prior_net_structure)
+
+    def import_data(self, header_column = None):
+
+        if header_column is None:
+            self._sorter = header_column
+        else:    
+            self._sorter = self.build_sorter(self._df_samples_list[0])
+
+        samples_list= self._df_samples_list
+
+        if isinstance(samples_list, np.ndarray):
+            samples_list = samples_list.tolist()
+
+        self.compute_row_delta_in_all_samples_frames(samples_list)
+
+    def build_sorter(self, sample_frame: pd.DataFrame) -> typing.List:
+        """Implements the abstract method build_sorter of the :class:`AbstractImporter` for this dataset.
+        """
+        columns_header = list(sample_frame.columns.values)
+        del columns_header[0]
+        return columns_header
+
+
+    def dataset_id(self) -> object:
+        pass

main_package/tests/optimizers/test_tabu_search.py

@@ -7,14 +7,17 @@ import unittest
 
 import networkx as nx
 import numpy as np
+import pandas as pd
 import psutil
 from line_profiler import LineProfiler
 import copy
+import json
 
 import utility.cache as ch
 import structure_graph.sample_path as sp
 import estimators.structure_score_based_estimator as se
 import utility.json_importer as ji
+import utility.sample_importer as si
 
 
 
@@ -25,7 +28,24 @@ class TestTabuSearch(unittest.TestCase):
     @classmethod
     def setUpClass(cls):
         #cls.read_files = glob.glob(os.path.join('../../data', "*.json"))
-        cls.importer = ji.JsonImporter("../../data/networks_and_trajectories_ternary_data_5.json", 'samples', 'dyn.str', 'variables', 'Time', 'Name')
+
+        with open("../../data/networks_and_trajectories_binary_data_01_3.json") as f:
+            raw_data = json.load(f)
+
+            trajectory_list_raw= raw_data[0]["samples"]
+
+            trajectory_list = [pd.DataFrame(sample) for sample in trajectory_list_raw]
+
+            variables= pd.DataFrame(raw_data[0]["variables"])
+            prior_net_structure = pd.DataFrame(raw_data[0]["dyn.str"])
+
+
+        cls.importer = si.SampleImporter(
+                                        trajectory_list=trajectory_list,
+                                        variables=variables,
+                                        prior_net_structure=prior_net_structure
+                                    )
+        
         cls.importer.import_data(0)
         cls.s1 = sp.SamplePath(cls.importer)