Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
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177 lines
7.3 KiB
177 lines
7.3 KiB
import json
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import typing
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import pandas as pd
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import sys
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sys.path.append('../')
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import utility.abstract_importer as ai
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class JsonImporter(ai.AbstractImporter):
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"""Implements the abstracts methods of AbstractImporter and adds all the necessary methods to process and prepare
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the data in json extension.
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:param file_path: the path of the file that contains tha data to be imported
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:type file_path: string
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:param samples_label: the reference key for the samples in the trajectories
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:type samples_label: string
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:param structure_label: the reference key for the structure of the network data
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:type structure_label: string
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:param variables_label: the reference key for the cardinalites of the nodes data
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:type variables_label: string
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:param time_key: the key used to identify the timestamps in each trajectory
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:type time_key: string
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:param variables_key: the key used to identify the names of the variables in the net
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:type variables_key: string
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:_array_indx: the index of the outer JsonArray to extract the data from
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:type _array_indx: int
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:_df_samples_list: a Dataframe list in which every dataframe contains a trajectory
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:_raw_data: The raw contents of the json file to import
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:type _raw_data: List
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"""
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def __init__(self, file_path: str, samples_label: str, structure_label: str, variables_label: str, time_key: str,
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variables_key: str):
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"""Constructor method
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.. note::
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This constructor calls also the method ``read_json_file()``, so after the construction of the object
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the class member ``_raw_data`` will contain the raw imported json data.
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"""
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self._samples_label = samples_label
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self._structure_label = structure_label
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self._variables_label = variables_label
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self._time_key = time_key
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self._variables_key = variables_key
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self._df_samples_list = None
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self._array_indx = None
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super(JsonImporter, self).__init__(file_path)
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self._raw_data = self.read_json_file()
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def import_data(self, indx: int) -> None:
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"""Implements the abstract method of :class:`AbstractImporter`.
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:param indx: the index of the outer JsonArray to extract the data from
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:type indx: int
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"""
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self._array_indx = indx
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self._df_samples_list = self.import_trajectories(self._raw_data)
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self._sorter = self.build_sorter(self._df_samples_list[0])
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self.compute_row_delta_in_all_samples_frames(self._df_samples_list)
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self.clear_data_frame_list()
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self._df_structure = self.import_structure(self._raw_data)
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self._df_variables = self.import_variables(self._raw_data)
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def import_trajectories(self, raw_data: typing.List) -> typing.List:
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"""Imports the trajectories from the list of dicts ``raw_data``.
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:param raw_data: List of Dicts
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:type raw_data: List
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:return: List of dataframes containing all the trajectories
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:rtype: List
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"""
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return self.normalize_trajectories(raw_data, self._array_indx, self._samples_label)
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def import_structure(self, raw_data: typing.List) -> pd.DataFrame:
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"""Imports in a dataframe the data in the list raw_data at the key ``_structure_label``
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:param raw_data: List of Dicts
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:type raw_data: List
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:return: Dataframe containg the starting node a ending node of every arc of the network
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:rtype: pandas.Dataframe
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"""
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return self.one_level_normalizing(raw_data, self._array_indx, self._structure_label)
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def import_variables(self, raw_data: typing.List) -> pd.DataFrame:
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"""Imports the data in ``raw_data`` at the key ``_variables_label``.
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:param raw_data: List of Dicts
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:type raw_data: List
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:return: Datframe containg the variables simbolic labels and their cardinalities
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:rtype: pandas.Dataframe
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"""
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return self.one_level_normalizing(raw_data, self._array_indx, self._variables_label)
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def read_json_file(self) -> typing.List:
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"""Reads the JSON file in the path self.filePath.
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:return: The contents of the json file
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:rtype: List
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"""
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with open(self._file_path) as f:
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data = json.load(f)
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return data
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def one_level_normalizing(self, raw_data: typing.List, indx: int, key: str) -> pd.DataFrame:
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"""Extracts the one-level nested data in the list ``raw_data`` at the index ``indx`` at the key ``key``.
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:param raw_data: List of Dicts
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:type raw_data: List
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:param indx: The index of the array from which the data have to be extracted
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:type indx: int
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:param key: the key for the Dicts from which exctract data
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:type key: string
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:return: A normalized dataframe
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:rtype: pandas.Datframe
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"""
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return pd.DataFrame(raw_data[indx][key])
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def normalize_trajectories(self, raw_data: typing.List, indx: int, trajectories_key: str) -> typing.List:
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"""
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Extracts the trajectories in ``raw_data`` at the index ``index`` at the key ``trajectories key``.
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:param raw_data: List of Dicts
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:type raw_data: List
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:param indx: The index of the array from which the data have to be extracted
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:type indx: int
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:param trajectories_key: the key of the trajectories objects
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:type trajectories_key: string
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:return: A list of daframes containg the trajectories
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:rtype: List
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"""
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dataframe = pd.DataFrame
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smps = raw_data[indx][trajectories_key]
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df_samples_list = [dataframe(sample) for sample in smps]
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return df_samples_list
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def build_sorter(self, sample_frame: pd.DataFrame) -> typing.List:
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"""Implements the abstract method build_sorter of the :class:`AbstractImporter` for this dataset.
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"""
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columns_header = list(sample_frame.columns.values)
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columns_header.remove(self._time_key)
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return columns_header
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def clear_data_frame_list(self) -> None:
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"""Removes all values present in the dataframes in the list ``_df_samples_list``.
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"""
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for indx in range(len(self._df_samples_list)):
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self._df_samples_list[indx] = self._df_samples_list[indx].iloc[0:0]
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def dataset_id(self) -> object:
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return self._array_indx
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def import_sampled_cims(self, raw_data: typing.List, indx: int, cims_key: str) -> typing.Dict:
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"""Imports the synthetic CIMS in the dataset in a dictionary, using variables labels
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as keys for the set of CIMS of a particular node.
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:param raw_data: List of Dicts
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:type raw_data: List
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:param indx: The index of the array from which the data have to be extracted
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:type indx: int
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:param cims_key: the key where the json object cims are placed
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:type cims_key: string
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:return: a dictionary containing the sampled CIMS for all the variables in the net
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:rtype: Dictionary
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"""
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cims_for_all_vars = {}
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for var in raw_data[indx][cims_key]:
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sampled_cims_list = []
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cims_for_all_vars[var] = sampled_cims_list
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for p_comb in raw_data[indx][cims_key][var]:
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cims_for_all_vars[var].append(pd.DataFrame(raw_data[indx][cims_key][var][p_comb]).to_numpy())
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return cims_for_all_vars
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