Metadata-Version: 2.1
Name: movelets
Version: 0.1b0
Summary: Movelets for Multiple Aspect Trajectory Data Mining
Home-page: https://github.com/ttportela/movelets
Author: Tarlis Tortelli Portela
Author-email: Tarlis Tortelli Portela <tarlis@tarlis.com.br>
Maintainer-email: Tarlis Tortelli Portela <tarlis@tarlis.com.br>
License: GPL Version 3 or superior (see LICENSE file)
Project-URL: Homepage, https://github.com/ttportela/movelets
Project-URL: Repository, https://github.com/ttportela/movelets
Project-URL: Documentation, https://github.com/ttportela/movelets/blob/main/README.md
Project-URL: Download, https://pypi.org/project/movelets/#files
Project-URL: Bug Tracker, https://github.com/ttportela/movelets/issues
Keywords: data-science,machine-learning,data-mining,trajectory,multiple-trajectory,trajectory-classification,movelet,movelet-visualization
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)
Classifier: Programming Language :: Python
Classifier: Topic :: Software Development
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Scientific/Engineering :: Visualization
Classifier: Operating System :: OS Independent
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Requires-Python: <3.10,>=3.7
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: glob2 (==0.7)
Requires-Dist: numpy
Requires-Dist: pandas (==1.3.4)
Requires-Dist: scikit-learn
Provides-Extra: all_extras
Requires-Dist: python-dateutil (==2.8.2) ; extra == 'all_extras'
Requires-Dist: cycler (==0.11.0) ; extra == 'all_extras'
Requires-Dist: matplotlib (==3.5.1) ; extra == 'all_extras'
Requires-Dist: notebook (==6.4.6) ; extra == 'all_extras'
Requires-Dist: tensorflow ; extra == 'all_extras'
Requires-Dist: pm4py ; extra == 'all_extras'
Requires-Dist: geohash ; extra == 'all_extras'
Provides-Extra: binder
Requires-Dist: jupyter ; extra == 'binder'
Provides-Extra: dev
Requires-Dist: pre-commit ; extra == 'dev'
Requires-Dist: pytest ; extra == 'dev'
Requires-Dist: pytest-cov ; extra == 'dev'
Requires-Dist: pytest-xdist ; extra == 'dev'
Requires-Dist: wheel ; extra == 'dev'
Provides-Extra: dl
Requires-Dist: tensorflow ; extra == 'dl'
Provides-Extra: docs
Requires-Dist: jupyter ; extra == 'docs'
Requires-Dist: numpydoc ; extra == 'docs'

# Movelets: Movelets for Multiple Aspect Trajectory Data Mining
---

\[[Publication](#)\] \[[citation.bib](citation.bib)\] \[[GitHub](https://github.com/ttportela/movelets)\] \[[PyPi](https://pypi.org/project/movelets/)\]


The present application offers a tool, to support the user in the classification task of multiple aspect trajectories, specifically for extracting and visualizing the movelets, the parts of the trajectory that better discriminate a class. It integrates into a unique platform the fragmented approaches available for multiple aspects trajectories and in general for multidimensional sequence classification into a unique web-based and python library system. Offers both movelets visualization and classification methods.

Created on May, 2023
Copyright (C) 2023, License GPL Version 3 or superior (see LICENSE file)

### Main Modules

- [Methods](/methods): Methods for trajectory classification and movelet extraction;
- [Tutorial](/tutorial): Tutorial on how to use Automatise as a Python library.


### Available Classifiers (needs update):

* **MLP (Movelet)**: Multilayer-Perceptron (MLP) with movelets features. The models were implemented using the Python language, with the keras, fully-connected hidden layer of 100 units, Dropout Layer with dropout rate of 0.5, learning rate of 10−3 and softmax activation function in the Output Layer. Adam Optimization is used to avoid the categorical cross entropy loss, with 200 of batch size, and a total of 200 epochs per training. [REFERENCE*]
* **RF (Movelet)**: Random Forest (RF) with movelets features, that consists of an ensemble of 300 decision trees. The models were implemented using the Python language, with the keras. [REFERENCE*]
* **SVN (Movelet)**: Support Vector Machine (SVM) with movelets features. The models were implemented using the Python language, with the keras, linear kernel and default structure. Other structure details are default settings. [REFERENCE*]

### Installation

Install directly from PyPi repository, or, download from github. (python >= 3.7 required)

```bash
    pip install movelets
```

### Citing

If you use `automatize` please cite the following paper:

    Tarlis Tortelli Portela; Jonata Tyska Carvalho; Vania Bogorny. HiPerMovelets: high-performance movelet extraction for trajectory classification, International Journal of Geographical Information Science, 2022. DOI: 10.1080/13658816.2021.2018593.

[Bibtex](citation.bib):

```bash
@article{Portela2022,
    author = {Tarlis Tortelli Portela and Jonata Tyska Carvalho and Vania Bogorny},
    title = {HiPerMovelets: high-performance movelet extraction for trajectory classification},
    journal = {International Journal of Geographical Information Science},
    volume = {0},
    number = {0},
    pages = {1-25},
    year  = {2022},
    publisher = {Taylor & Francis},
    doi = {10.1080/13658816.2021.2018593},
    URL = {https://doi.org/10.1080/13658816.2021.2018593}
}
```

### Collaborate with us

Any contribution is welcome. This is an active project and if you would like to include your algorithm in `movelets`, feel free to fork the project, open an issue and contact us.

Feel free to contribute in any form, such as scientific publications referencing `movelets`, teaching material and workshop videos.

### Related packages

- [automatize](https://github.com/ttportela/automatize): Automatize: Multiple Aspect Trajectory Data Mining Tool Library;

### Change Log

This is a package under construction:
 
*Dec. 2023:*
 - 
 
 *TODO*:
 - Comments on all public interface funcions and modules
