matmodel.util package
Submodules
matmodel.util.filters module
MAT-Tools: Python Framework for Multiple Aspect Trajectory Data Mining
The present application offers a tool, to support the user in the modeling of multiple aspect trajectory data. It integrates into a unique framework for multiple aspects trajectories and in general for multidimensional sequence data mining methods. Copyright (C) 2022, MIT license (this portion of code is subject to licensing from source project distribution)
Created on Apr, 2024 Copyright (C) 2024, License GPL Version 3 or superior (see LICENSE file)
- Authors:
Tarlis Portela
Vanessa Lago Machado
- matmodel.util.filters.attributes2names(attributes_desc)[source]
Extracts the names (text field) of all attributes from the provided list of attribute descriptors.
Args:
- attributes_desc (list):
List of attribute descriptors, each containing a ‘text’ field.
Returns:
- list:
List of attribute names (strings).
- matmodel.util.filters.names2indexes(sel_attributes, attributes_desc)[source]
Converts a list of selected attribute names into their corresponding indexes based on a provided list of attribute descriptors.
Args:
- sel_attributes (list):
List of selected attribute names (strings).
- attributes_desc (list):
List of attribute descriptors, each containing a ‘text’ field.
Returns:
- list:
List of indexes corresponding to the selected attribute names within attributes_desc.
matmodel.util.parsers module
MAT-Tools: Python Framework for Multiple Aspect Trajectory Data Mining
The present application offers a tool, to support the user in the modeling of multiple aspect trajectory data. It integrates into a unique framework for multiple aspects trajectories and in general for multidimensional sequence data mining methods. Copyright (C) 2022, MIT license (this portion of code is subject to licensing from source project distribution)
Created on Apr, 2024 Copyright (C) 2024, License GPL Version 3 or superior (see LICENSE file)
- Authors:
Tarlis Portela
Vanessa Lago Machado
- matmodel.util.parsers.df2trajectory(df, data_desc=None, tid_col='tid', label_col='label')[source]
Convert a DataFrame to a list of Trajectory objects.
Parameters:
- dfpandas.DataFrame
The DataFrame to be converted.
- data_descstr, optional
The data descriptor file, a file path for the data descriptor JSON. If None, a descriptor is generated from the DataFrame (default None).
- tid_colstr, optional
The name of the column representing trajectory IDs (default ‘tid’).
- label_colstr, optional
The name of the column representing class labels (default ‘label’).
Returns:
- list of Trajectory
The list of converted Trajectory objects.
- DataDescriptor
The data descriptor object used for reading the dataset trajectories.
- matmodel.util.parsers.json2movelet(file, name='movelets', count=0, load_distances=False)[source]
Parses a JSON movelets file and converts it into a list of Movelet objects.
Args:
- file (file path / file object):
The JSON file containing movelet or shapelet data.
- name (str, optional):
The key in the JSON file that holds the movelet data. Defaults to ‘movelets’.
- count (int, optional):
An initial count for the movelets. Defaults to 0. Used for reading multiple files.
- load_distances (bool, optional):
Whether to load the distances associated with the movelet. Defaults to False.
Returns:
- list:
A list of Movelet objects parsed from the JSON file.
Example:
movelets = json2movelet(‘moveletsOnTrain.json’)