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Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍 https://github.com/madlabunimib/PyCTBN
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PyCTBN/main_package/classes/utility/json_importer.py

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import json
import typing
import pandas as pd
from .abstract_importer import AbstractImporter
class JsonImporter(AbstractImporter):
"""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, file_path: str, samples_label: str, structure_label: str, variables_label: str, time_key: str,
variables_key: str):
"""Constructor method
.. note::
This constructor calls also the method ``read_json_file()``, so after the construction of the object
the class member ``_raw_data`` will contain the raw imported json data.
"""
self._samples_label = samples_label
self._structure_label = structure_label
self._variables_label = variables_label
self._time_key = time_key
self._variables_key = variables_key
self._df_samples_list = None
self._array_indx = None
super(JsonImporter, self).__init__(file_path)
self._raw_data = self.read_json_file()
def import_data(self, indx: int) -> None:
"""Implements the abstract method of :class:`AbstractImporter`.
:param indx: the index of the outer JsonArray to extract the data from
:type indx: int
"""
self._array_indx = indx
self._df_samples_list = self.import_trajectories(self._raw_data)
self._sorter = self.build_sorter(self._df_samples_list[0])
self.compute_row_delta_in_all_samples_frames(self._df_samples_list)
self.clear_data_frame_list()
self._df_structure = self.import_structure(self._raw_data)
self._df_variables = self.import_variables(self._raw_data)
def import_trajectories(self, raw_data: typing.List) -> typing.List:
"""Imports the trajectories from the list of dicts ``raw_data``.
:param raw_data: List of Dicts
:type raw_data: List
:return: List of dataframes containing all the trajectories
:rtype: List
"""
return self.normalize_trajectories(raw_data, self._array_indx, self._samples_label)
def import_structure(self, raw_data: typing.List) -> pd.DataFrame:
"""Imports in a dataframe the data in the list raw_data at the key ``_structure_label``
:param raw_data: List of Dicts
:type raw_data: List
:return: Dataframe containg the starting node a ending node of every arc of the network
:rtype: pandas.Dataframe
"""
return self.one_level_normalizing(raw_data, self._array_indx, self._structure_label)
def import_variables(self, raw_data: typing.List) -> pd.DataFrame:
"""Imports the data in ``raw_data`` at the key ``_variables_label``.
:param raw_data: List of Dicts
:type raw_data: List
:return: Datframe containg the variables simbolic labels and their cardinalities
:rtype: pandas.Dataframe
"""
return self.one_level_normalizing(raw_data, self._array_indx, self._variables_label)
def read_json_file(self) -> typing.List:
"""Reads the JSON file in the path self.filePath.
:return: The contents of the json file
:rtype: List
"""
with open(self._file_path) as f:
data = json.load(f)
return data
def one_level_normalizing(self, raw_data: typing.List, indx: int, key: str) -> pd.DataFrame:
"""Extracts the one-level nested data in the list ``raw_data`` at the index ``indx`` at the key ``key``.
:param raw_data: List of Dicts
:type raw_data: List
:param indx: The index of the array from which the data have to be extracted
:type indx: int
:param key: the key for the Dicts from which exctract data
:type key: string
:return: A normalized dataframe
:rtype: pandas.Datframe
"""
return pd.DataFrame(raw_data[indx][key])
def normalize_trajectories(self, raw_data: typing.List, indx: int, trajectories_key: str) -> typing.List:
"""
Extracts the trajectories in ``raw_data`` at the index ``index`` at the key ``trajectories key``.
:param raw_data: List of Dicts
:type raw_data: List
:param indx: The index of the array from which the data have to be extracted
:type indx: int
:param trajectories_key: the key of the trajectories objects
:type trajectories_key: string
:return: A list of daframes containg the trajectories
:rtype: List
"""
dataframe = pd.DataFrame
smps = raw_data[indx][trajectories_key]
df_samples_list = [dataframe(sample) for sample in smps]
return df_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)
columns_header.remove(self._time_key)
return columns_header
def clear_data_frame_list(self) -> None:
"""Removes all values present in the dataframes in the list ``_df_samples_list``.
"""
for indx in range(len(self._df_samples_list)):
self._df_samples_list[indx] = self._df_samples_list[indx].iloc[0:0]
def dataset_id(self) -> object:
return self._array_indx
def import_sampled_cims(self, raw_data: typing.List, indx: int, cims_key: str) -> typing.Dict:
"""Imports the synthetic CIMS in the dataset in a dictionary, using variables labels
as keys for the set of CIMS of a particular node.
:param raw_data: List of Dicts
:type raw_data: List
:param indx: The index of the array from which the data have to be extracted
:type indx: int
:param cims_key: the key where the json object cims are placed
:type cims_key: string
:return: a dictionary containing the sampled CIMS for all the variables in the net
:rtype: Dictionary
"""
cims_for_all_vars = {}
for var in raw_data[indx][cims_key]:
sampled_cims_list = []
cims_for_all_vars[var] = sampled_cims_list
for p_comb in raw_data[indx][cims_key][var]:
cims_for_all_vars[var].append(pd.DataFrame(raw_data[indx][cims_key][var][p_comb]).to_numpy())
return cims_for_all_vars