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https://github.com/protti/featts

FeatTS is a Semi-Supervised Clustering method that leverages features extracted from the raw time series to create clusters that reflect the original time series.

https://github.com/protti/featts

Science Score: 13.0%

This score indicates how likely this project is to be science-related based on various indicators:

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    CITATION.cff file
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    codemeta.json file
    Found codemeta.json file
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    .zenodo.json file
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    DOI references
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    Scientific vocabulary similarity
    Low similarity (7.7%) to scientific vocabulary

Keywords

clustering features-extraction papers time-series time-series-clustering
Last synced: 5 months ago · JSON representation

Repository

FeatTS is a Semi-Supervised Clustering method that leverages features extracted from the raw time series to create clusters that reflect the original time series.

Basic Info
  • Host: GitHub
  • Owner: protti
  • License: gpl-3.0
  • Language: Python
  • Default Branch: master
  • Homepage:
  • Size: 22 MB
Statistics
  • Stars: 17
  • Watchers: 1
  • Forks: 2
  • Open Issues: 1
  • Releases: 0
Topics
clustering features-extraction papers time-series time-series-clustering
Created over 5 years ago · Last pushed over 1 year ago
Metadata Files
Readme License

README.md

FeatTS

Paper

At this link you can find the paper related at this code: http://openproceedings.org/2021/conf/edbt/p270.pdf

Running

The package could be installed with the following command:

python pip install FeatTS

Usage

In order to play with FeatTS, please check the UCR Archive. We depict below a code snippet demonstrating how to use FeatTS.

```python from aeon.datasets import loadclassification from sklearn.metrics import adjustedmutualinfoscore import numpy as np from FeatTS import FeatTS

if name == 'main':

dataCof = load_classification("Coffee")
X = np.squeeze(dataCof[0], axis=1)
y = dataCof[1].astype(int)
labels = {0: y[0], 1: y[1], 5: y[5], 6: y[0]}  # semi-supervised mode
n_clusters = 2  # Number of clusters

featTS = FeatTS(n_clusters=2)
featTS.fit(X,labels=labels)
print(adjusted_mutual_info_score(featTS.labels_,y))

```

It is also possible to add some external features to the computation. These features will help the choice of the best features:

```python from aeon.datasets import loadclassification from sklearn.metrics import adjustedmutualinfoscore import numpy as np import pandas as pd from FeatTS import FeatTS if name == 'main':

dataCof = load_classification("Coffee")
X = np.squeeze(dataCof[0], axis=1)
y = dataCof[1].astype(int)
labels = {0: y[0], 1: y[1], 5: y[5], 6: y[0]}  # semi-supervised mode
external_feat = pd.DataFrame({'LEN':y})

featTS = FeatTS(n_clusters=2)
featTS.fit(X,labels=labels, external_feat=external_feat)
print(adjusted_mutual_info_score(featTS.labels_,y))
print(featTS.feats_selected_)

```

Owner

  • Name: Donato Tiano
  • Login: protti
  • Kind: user

GitHub Events

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  • Watch event: 2
  • Pull request event: 1
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Last synced: 6 months ago

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  • Development Distribution Score (DDS): 0.492
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Name Email Commits
DonaTProject 6****t@u****m 31
protti d****o@g****m 21
Donato b****i@g****m 7
dependabot[bot] 4****]@u****m 2

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Last synced: 6 months ago

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Top Authors
Issue Authors
  • protti (1)
Pull Request Authors
  • dependabot[bot] (9)
  • giacomoguiduzzi (1)
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dependencies (9)

Packages

  • Total packages: 1
  • Total downloads:
    • pypi 14 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 0
  • Total versions: 4
  • Total maintainers: 1
pypi.org: featts

A new method for clustering time series by adopting the best statistical features.

  • Versions: 4
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 14 Last month
Rankings
Dependent packages count: 10.8%
Average: 35.8%
Dependent repos count: 60.8%
Maintainers (1)
dontia
Last synced: 6 months ago

Dependencies

requirements.txt pypi
  • fastdtw ==0.3.4
  • networkx ==2.5.1
  • numpy ==1.21.0
  • pandas ==1.3.0
  • pyclustering ==0.10.1.2
  • scikit_learn ==0.24.2
  • scipy ==1.7.0
  • tsfresh ==0.18.0