Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

https://github.com/IBCNServices/GENDIS

Contains an implementation (sklearn API) of the algorithm proposed in "GENDIS: GEnetic DIscovery of Shapelets" and code to reproduce all experiments.
https://github.com/IBCNServices/GENDIS

data-mining evolutionary-algorithms shapelets time-series-analysis timeseries-analysis

Last synced: about 1 month ago
JSON representation

Contains an implementation (sklearn API) of the algorithm proposed in "GENDIS: GEnetic DIscovery of Shapelets" and code to reproduce all experiments.

Lists

README 

        
# GENDIS [![Build Status](https://travis-ci.org/IBCNServices/GENDIS.svg?branch=master)](https://travis-ci.org/IBCNServices/GENDIS) [![PyPI version](https://badge.fury.io/py/GENDIS.svg)](https://badge.fury.io/py/GENDIS) [![Read The Docs](https://readthedocs.org/projects/gendis/badge/?version=latest)](https://gendis.readthedocs.io/en/latest/?badge=latest) [![Downloads](https://pepy.tech/badge/gendis)](https://pepy.tech/project/gendis)

## GENetic DIscovery of Shapelets 





  






  




In the time series classification domain, shapelets are small subseries that are discriminative for a certain class. It has been shown that by projecting the original dataset to a distance space, where each axis corresponds to the distance to a certain shapelet, classifiers are able to achieve state-of-the-art results on a plethora of datasets.



This repository contains an implementation of `GENDIS`, an algorithm that searches for a set of shapelets in a genetic fashion. The algorithm is insensitive to its parameters (such as population size, crossover and mutation probability, ...) and can quickly extract a small set of shapelets that is able to achieve predictive performances similar (or better) to that of other shapelet techniques.



## Installation



We currently support Python 3.5 & Python 3.6. For installation, there are two alternatives:



1. Clone the repository `https://github.com/IBCNServices/GENDIS.git` and run `(python3 -m) pip -r install requirements.txt`

2. GENDIS is hosted on PyPi. You can just run `(python3 -m) pip install gendis` to add gendis to your dist-packages (you can use it from everywhere).



**Make sure NumPy and Cython is already installed (`pip install numpy` and `pip install Cython`), since that is required for the setup script.**



## Tutorial & Example



### 1. Loading & preprocessing the datasets



In a first step, we need to construct at least a matrix with timeseries (`X_train`) and a vector with labels (`y_train`). Additionally, test data can be loaded as well in order to evaluate the pipeline in the end.



```python

import pandas as pd

# Read in the datafiles

train_df = pd.read_csv()

test_df = pd.read_csv()

# Split into feature matrices and label vectors

X_train = train_df.drop('target', axis=1)

y_train = train_df['target']

X_test = test_df.drop('target', axis=1)

y_test = test_df['target']

```



### 2. Creating a `GeneticExtractor` object



Construct the object. For a list of all possible parameters, and a description, please refer to the documentation in the [code](gendis/genetic.py)



```python

from gendis.genetic import GeneticExtractor

genetic_extractor = GeneticExtractor(population_size=50, iterations=25, verbose=True, 

                                     mutation_prob=0.3, crossover_prob=0.3, 

                                     wait=10, max_len=len(X_train) // 2)

```



### 3. Fit the `GeneticExtractor` and construct distance matrix



```python

shapelets = genetic_extractor.fit(X_train, y_train)

distances_train = genetic_extractor.transform(X_train)

distances_test = genetic_extractor.transform(X_test)

```



### 4. Fit ML classifier on constructed distance matrix



```python

from sklearn.linear_model import LogisticRegression

from sklearn.metrics import accuracy_score

lr = LogisticRegression()

lr.fit(distances_train, y_train)



print('Accuracy = {}'.format(accuracy_score(y_test, lr.predict(distances_test))))

```



### Example notebook



A simple example is provided in [this notebook](gendis/example.ipynb)



## Data



All datasets in this repository are downloaded from [timeseriesclassification](http://timeseriesclassification.com). Please refer to them appropriately when using any dataset.



## Paper experiments



In order to reproduce the results from the corresponding paper, please check out [this directory](gendis/experiments).



## Tests



We provide a few doctests and unit tests. To run the doctests: `python3 -m doctest -v `, where `` is the Python file you want to run the doctests from. To run unit tests: `nose2 -v`



## Contributing, Citing and Contact



If you have any questions, are experiencing bugs in the GENDIS implementation, or would like to contribute, please feel free to create an issue/pull request in this repository or take contact with me at gilles(dot)vandewiele(at)ugent(dot)be



If you use GENDIS in your work, please use the following citation:

```bibtex

@article{vandewiele2021gendis,

  title={GENDIS: Genetic Discovery of Shapelets},

  author={Vandewiele, Gilles and Ongenae, Femke and Turck, Filip De},

  journal={Sensors},

  volume={21},

  number={4},

  pages={1059},

  year={2021},

  publisher={Multidisciplinary Digital Publishing Institute}

}

```