cobras-ts 0.0.3

Semi-supervised time series clustering with COBRAS

Library for semi-supervised time series clustering using pairwise constraints.

Installation

This package is available on PyPi:

$ pip install cobras_ts

Usage

Examples can also be found in the examples subdirectory.

Running COBRAS_kShape:

import os
import numpy as np
from cobras_ts import cobras_kshape

ucr_path = '/path/to/UCR/archive'
dataset = 'ECG200'
budget = 100

data = np.loadtxt(os.path.join(ucr_path,dataset,dataset + '_TEST'), delimiter=',')
series = data[:,1:]
labels = data[:,0]

clusterer = cobras_kshape.COBRAS_kShape(series, labels, budget)
clusterings, runtimes, ml, cl = clusterer.cluster()

Running COBRAS_DTW:

This uses the dtaidistance package to compute the DTW distance matrix. Note that constructing this matrix is typically the most time consuming step, and significant speedups can be achieved by using the C implementation in the dtaidistance package.

import os
import numpy as np
from cobras_ts import cobras_dtw
from dtaidistance import dtw

ucr_path = '/path/to/UCR/archive'
dataset = 'ECG200'
budget = 100
alpha = 0.5
window = 10

data = np.loadtxt(os.path.join(ucr_path,dataset,dataset + '_TEST'), delimiter=',')
series = data[:,1:]
labels = data[:,0]

dists = dtw.distance_matrix(series, window=int(0.01 * window * series.shape[1]))
dists[dists == np.inf] = 0
dists = dists + dists.T - np.diag(np.diag(dists))
affinities = np.exp(-dists * alpha)

clusterer = cobras_dtw.COBRAS_DTW(affinities, labels, budget)
clusterings, runtimes, ml, cl = clusterer.cluster()

Dependencies

This package uses Python3, numpy, scikit-learn, kshape and dtaidistance.

Contact

Toon Van Craenendonck at [e-mail]

License

COBRAS code for semi-supervised time series clustering.

Copyright 2018 KU Leuven, DTAI Research Group

Licensed under the Apache License, Version 2.0 (the “License”); you may not use this file except in compliance with the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an “AS IS” BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.