Add SampleImporter class; Refactor AbstractImporter method

PyCTBN/classes/abstract_importer.py

@@ -31,14 +31,15 @@ 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, trajectories_list: typing.Union[typing.List, 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 = trajectories_list
         self._df_variables = variables
         self._df_structure = prior_net_structure
+        self._concatenated_samples = None
         self._sorter = None
         super().__init__()
 
@@ -117,17 +118,15 @@ class AbstractImporter(ABC):
         :return: the resulting list of numpy arrays
         :rtype: List
         """
-        if isinstance(concatenated_sample, pd.DataFrame):
+
         concatenated_array = concatenated_sample.to_numpy()
         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:
         """Removes all values in the dataframe concatenated_samples.
          """
-        if isinstance(self._concatenated_samples, pd.DataFrame):
         self._concatenated_samples = self._concatenated_samples.iloc[0:0]
 
     @abstractmethod

PyCTBN/classes/sample_importer.py

@@ -0,0 +1,61 @@
+
+
+import numpy as np
+import pandas as pd
+import typing
+
+from .abstract_importer import AbstractImporter
+
+
+class SampleImporter(AbstractImporter):
+    """Implements the abstracts methods of AbstractImporter and adds all the necessary methods to process and prepare
+    the data loaded directly by using DataFrame
+    :param trajectory_list: the data that describes the trajectories
+    :type trajectory_list: typing.Union[pd.DataFrame, np.ndarray, typing.List]
+    :param variables: the data that describes the variables with name and cardinality
+    :type variables: typing.Union[pd.DataFrame, np.ndarray, typing.List]
+    :param prior_net_structure: the data of the real structure, if it exists
+    :type prior_net_structure: typing.Union[pd.DataFrame, np.ndarray, typing.List]
+    :_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[typing.List, np.ndarray] = None,
+                variables: typing.Union[pd.DataFrame, np.ndarray, typing.List] = None,
+                prior_net_structure: typing.Union[pd.DataFrame, np.ndarray,typing.List] = None):
+
+        'If the data are not DataFrame, it will be converted'
+        if isinstance(variables,list) or isinstance(variables,np.ndarray):
+            variables = pd.DataFrame(variables)
+        if isinstance(variables,list) or isinstance(variables,np.ndarray):
+            prior_net_structure=pd.DataFrame(prior_net_structure)
+
+        super(SampleImporter, self).__init__(file_path=None, trajectories_list =trajectory_list,
+                                        variables= variables,
+                                        prior_net_structure=prior_net_structure)
+
+    def import_data(self, header_column = None):
+
+        if header_column is not 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` in order to get the ordered variables list.
+        """
+        columns_header = list(sample_frame.columns.values)
+        del columns_header[0]
+        return columns_header
+
+    def dataset_id(self) -> object:
+        pass

PyCTBN/tests/test_json_importer.py

@@ -121,6 +121,7 @@ class TestJsonImporter(unittest.TestCase):
         self.assertTrue(j1.concatenated_samples.empty)
 
     def test_build_list_of_samples_array(self):
+        """
         data_set = {"key1": [1, 2, 3], "key2": [4.1, 5.2, 6.3]}
         with open('data.json', 'w') as f:
             json.dump(data_set, f)
@@ -136,6 +137,15 @@ class TestJsonImporter(unittest.TestCase):
         for a1, a2 in zip(col_list, forced_list):
             self.assertTrue(np.array_equal(a1, a2))
         os.remove('data.json')
+        """
+        j1 = JsonImporter(self.read_files[0], 'samples', 'dyn.str', 'variables', 'Time', 'Name')
+        j1.import_data(0)
+        times_state_changes_list = j1.build_list_of_samples_array(j1._concatenated_samples)
+        self.assertEqual(len(times_state_changes_list), 2)
+        self.assertIsInstance(times_state_changes_list[0], np.ndarray)
+        self.assertIsInstance(times_state_changes_list[1], np.ndarray)
+        self.assertIsInstance(times_state_changes_list[0][0], float)
+        self.assertIsInstance(times_state_changes_list[1][0][0], np.int64)
 
     def test_import_variables(self):
         j1 = JsonImporter(self.read_files[0], 'samples', 'dyn.str', 'variables', 'Time', 'Name')