Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
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260 lines
10 KiB
260 lines
10 KiB
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import pandas as pd
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import json
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import typing
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from abstract_importer import AbstractImporter
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class JsonImporter(AbstractImporter):
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"""
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Implements the Interface AbstractImporter and adds all the necessary methods to process and prepare the data in json ext.
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with the following structure:
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[] 0
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|_ dyn.cims
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|_ dyn.str
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|_ samples
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|_ variabels
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:file_path: the path of the file that contains tha data to be imported
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:samples_label: the reference key for the samples in the trajectories
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:structure_label: the reference key for the structure of the network data
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:variables_label: the reference key for the cardinalites of the nodes data
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:time_key: the key used to identify the timestamps in each trajectory
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:variables_key: the key used to identify the names of the variables in the net
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:df_samples_list: a Dataframe list in which every df contains a trajectory
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:df_structure: Dataframe containing the structure of the network (edges)
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:df_variables: Dataframe containing the nodes cardinalities
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:df_concatenated_samples: the concatenation and processing of all the trajectories present in the list df_samples list
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:sorter: the columns header(excluding the time column) of the Dataframe concatenated_samples
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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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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 = []
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self._df_structure = pd.DataFrame()
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self._df_variables = pd.DataFrame()
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self._concatenated_samples = None
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self.sorter = None
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super(JsonImporter, self).__init__(file_path)
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def import_data(self):
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"""
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Imports and prepares all data present needed for susequent computation.
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Parameters:
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void
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Returns:
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void
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"""
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raw_data = self.read_json_file()
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self.import_trajectories(raw_data)
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self.compute_row_delta_in_all_samples_frames(self.time_key)
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self.clear_data_frame_list()
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self.import_structure(raw_data)
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self.import_variables(raw_data, self.sorter)
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def import_trajectories(self, raw_data: typing.List):
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"""
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Imports the trajectories in the list of dicts raw_data.
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Parameters:
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:raw_data: List of Dicts
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Returns:
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void
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"""
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self.normalize_trajectories(raw_data, 0, self.samples_label)
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def import_structure(self, raw_data: typing.List):
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"""
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Imports in a dataframe the data in the list raw_data at the key structure_label
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Parameters:
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raw_data: the data
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Returns:
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void
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"""
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self._df_structure = self.one_level_normalizing(raw_data, 0, self.structure_label)
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def import_variables(self, raw_data: typing.List, sorter: typing.List):
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"""
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Imports the data in raw_data at the key variables_label.
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Sorts the row of the dataframe df_variables using the list sorter.
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Parameters:
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raw_data: the data
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sorter: the list used to sort the dataframe self.df_variables
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Returns:
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void
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"""
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self._df_variables = self.one_level_normalizing(raw_data, 0, self.variables_label)
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#self.sorter = self._df_variables[self.variables_key].to_list()
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#self.sorter.sort()
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#print("Sorter:", self.sorter)
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self._df_variables[self.variables_key] = self._df_variables[self.variables_key].astype("category")
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self._df_variables[self.variables_key] = self._df_variables[self.variables_key].cat.set_categories(self.sorter)
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self._df_variables = self._df_variables.sort_values([self.variables_key])
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self._df_variables.reset_index(inplace=True)
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print("Var Frame", self._df_variables)
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def read_json_file(self) -> typing.List:
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"""
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Reads the first json file in the path self.filePath
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Parameters:
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void
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Returns:
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data: the contents of the json file
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"""
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#try:
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#read_files = glob.glob(os.path.join(self.files_path, "*.json"))
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#if not read_files:
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#raise ValueError('No .json file found in the entered path!')
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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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#except ValueError as err:
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#print(err.args)
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def one_level_normalizing(self, raw_data: typing.List, indx: int, key: str) -> pd.DataFrame:
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"""
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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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Parameters:
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raw_data: List of Dicts
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indx: The index of the array from which the data have to be extracted
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key: the key for the Dicts from which exctract data
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Returns:
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a normalized dataframe
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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):
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"""
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Extracts the traj in raw_data at the index index at the key trajectories key.
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Adds the extracted traj in the dataframe list self._df_samples_list.
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Initializes the list self.sorter.
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Parameters:
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raw_data: the data
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indx: the index of the array from which extract data
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trajectories_key: the key of the trajectories objects
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Returns:
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void
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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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self.df_samples_list = [dataframe(sample) for sample in smps]
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columns_header = list(self.df_samples_list[0].columns.values)
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columns_header.remove(self.time_key)
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self.sorter = columns_header
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def compute_row_delta_sigle_samples_frame(self, sample_frame: pd.DataFrame, time_header_label: str,
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columns_header: typing.List, shifted_cols_header: typing.List) \
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-> pd.DataFrame:
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"""
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Computes the difference between each value present in th time column.
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Copies and shift by one position up all the values present in the remaining columns.
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Parameters:
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sample_frame: the traj to be processed
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time_header_label: the label for the times
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columns_header: the original header of sample_frame
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shifted_cols_header: a copy of columns_header with changed names of the contents
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Returns:
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sample_frame: the processed dataframe
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"""
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sample_frame[time_header_label] = sample_frame[time_header_label].diff().shift(-1)
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shifted_cols = sample_frame[columns_header].shift(-1).fillna(0).astype('int32')
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#print(shifted_cols)
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shifted_cols.columns = shifted_cols_header
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sample_frame = sample_frame.assign(**shifted_cols)
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sample_frame.drop(sample_frame.tail(1).index, inplace=True)
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return sample_frame
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def compute_row_delta_in_all_samples_frames(self, time_header_label: str):
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"""
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Calls the method compute_row_delta_sigle_samples_frame on every dataframe present in the list self.df_samples_list.
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Concatenates the result in the dataframe concatanated_samples
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Parameters:
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time_header_label: the label of the time column
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Returns:
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void
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"""
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"""columns_header = list(self.df_samples_list[0].columns.values)
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columns_header.remove('Time')
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self.sorter = columns_header"""
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shifted_cols_header = [s + "S" for s in self.sorter]
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compute_row_delta = self.compute_row_delta_sigle_samples_frame
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"""for indx, sample in enumerate(self.df_samples_list):
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self.df_samples_list[indx] = self.compute_row_delta_sigle_samples_frame(sample,
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time_header_label, self.sorter, shifted_cols_header)"""
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self.df_samples_list = [compute_row_delta(sample, time_header_label, self.sorter, shifted_cols_header)
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for sample in self.df_samples_list]
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self._concatenated_samples = pd.concat(self.df_samples_list)
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complete_header = self.sorter[:]
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complete_header.insert(0,'Time')
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complete_header.extend(shifted_cols_header)
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#print("Complete Header", complete_header)
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self._concatenated_samples = self._concatenated_samples[complete_header]
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#print("Concat Samples",self._concatenated_samples)
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def build_list_of_samples_array(self, data_frame: pd.DataFrame) -> typing.List:
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"""
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Builds a List containing the columns of dataframe and converts them to a numpy array.
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Parameters:
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:data_frame: the dataframe from which the columns have to be extracted and converted
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Returns:
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:columns_list: the resulting list of numpy arrays
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"""
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columns_list = [data_frame[column].to_numpy() for column in data_frame]
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#for column in data_frame:
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#columns_list.append(data_frame[column].to_numpy())
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return columns_list
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def clear_concatenated_frame(self):
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"""
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Removes all values in the dataframe concatenated_samples
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Parameters:
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void
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Returns:
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void
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"""
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self._concatenated_samples = self._concatenated_samples.iloc[0:0]
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def clear_data_frame_list(self):
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"""
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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)): # Le singole traj non servono più #TODO usare list comprens
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self.df_samples_list[indx] = self.df_samples_list[indx].iloc[0:0]
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def import_sampled_cims(self, raw_data: typing.List, indx: int, cims_key: str) -> typing.Dict:
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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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@property
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def concatenated_samples(self):
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return self._concatenated_samples
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@property
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def variables(self):
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return self._df_variables
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@property
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def structure(self):
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return self._df_structure
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