Integration with other Python packages

tslearn is a general-purpose Python machine learning library for time series that offers tools for pre-processing and feature extraction as well as dedicated models for clustering, classification and regression. To ensure compatibility with more specific Python packages, we provide utilities to convert data sets from and to other formats.

tslearn.utils.to_time_series_dataset() is a general function that transforms an array-like object into a three-dimensional array of shape (n_ts, sz, d) with the following conventions:

  • the fist axis is the sample axis, n_ts being the number of time series;
  • the second axis is the time axis, sz being the maximum number of time points;
  • the third axis is the dimension axis, d being the number of dimensions.

This is how a data set of time series is represented in tslearn.

The following sections briefly explain how to transform a data set from tslearn to another supported Python package and vice versa.

scikit-learn

scikit-learn is a popular Python package for machine learning. tslearn.utils.to_sklearn_dataset() converts a data set from tslearn format to scikit-learn format. To convert a data set from scikit-learn, you can use tslearn.utils.to_time_series_dataset().

>>> from tslearn.utils import to_sklearn_dataset
>>> to_sklearn_dataset([[1, 2], [1, 4, 3]])
array([[ 1.,  2., nan],
       [ 1.,  4.,  3.]])
>>> to_time_series_dataset([[ 1.,  2., None], [ 1.,  4.,  3.]])
array([[[ 1.],
    [ 2.],
    [nan]],

   [[ 1.],
    [ 4.],
    [ 3.]]])

pyts

pyts is a Python package dedicated to time series classification. tslearn.utils.to_pyts_dataset() and tslearn.utils.from_pyts_dataset() allow users to convert a data set from tslearn format to pyts format and vice versa.

>>> from tslearn.utils import from_pyts_dataset, to_pyts_dataset
>>> from_pyts_dataset([[1, 2], [1, 4]])
array([[[1],
        [2]],

       [[1],
        [4]]])

>>> to_pyts_dataset([[[1], [2]], [[1], [4]]])
array([[1., 2.],
       [1., 4.]])

seglearn

seglearn is a python package for machine learning time series or sequences. tslearn.utils.to_seglearn_dataset() and tslearn.utils.from_seglearn_dataset() allow users to convert a data set from tslearn format to seglearn format and vice versa.

>>> from tslearn.utils import from_seglearn_dataset, to_seglearn_dataset
>>> from_seglearn_dataset([[1, 2], [1, 4, 3]])
array([[[ 1.],
        [ 2.],
        [nan]],

       [[ 1.],
        [ 4.],
        [ 3.]]])
>>> to_seglearn_dataset([[[1], [2], [None]], [[1], [4], [3]]])
array([array([[1.],
       [2.]]),
       array([[1.],
       [4.],
       [3.]])], dtype=object)

stumpy

stumpy is a powerful and scalable Python library for computing a Matrix Profile, which can be used for a variety of time series data mining tasks. tslearn.utils.to_stumpy_dataset() and tslearn.utils.from_stumpy_dataset() allow users to convert a data set from tslearn format to stumpy format and vice versa.

>>> import numpy as np
>>> from tslearn.utils import from_stumpy_dataset, to_stumpy_dataset
>>> from_stumpy_dataset([np.array([1, 2]), np.array([1, 4, 3])])
array([[[ 1.],
        [ 2.],
        [nan]],

       [[ 1.],
        [ 4.],
        [ 3.]]])
>>> to_stumpy_dataset([[[1], [2], [None]], [[1], [4], [3]]])
[array([1., 2.]), array([1., 4., 3.])]

sktime

sktime is a scikit-learn compatible Python toolbox for learning with time series. tslearn.utils.to_sktime_dataset() and tslearn.utils.from_sktime_dataset() allow users to convert a data set from tslearn format to sktime format and vice versa. pandas is a required dependency to use these functions.

>>> import pandas as pd
>>> from tslearn.utils import from_sktime_dataset, to_sktime_dataset
>>> df = pd.DataFrame()
>>> df["dim_0"] = [pd.Series([1, 2]), pd.Series([1, 4, 3])]
>>> from_sktime_dataset(df)
array([[[ 1.],
        [ 2.],
        [nan]],

       [[ 1.],
        [ 4.],
        [ 3.]]])
>>> to_sktime_dataset([[[1], [2], [None]], [[1], [4], [3]]]).shape
(2, 1)

pyflux

pyflux is a library for time series analysis and prediction. tslearn.utils.to_pyflux_dataset() and tslearn.utils.from_pyflux_dataset() allow users to convert a data set from tslearn format to pyflux format and vice versa. pandas is a required dependency to use these functions.

>>> import pandas as pd
>>> from tslearn.utils import from_pyflux_dataset, to_pyflux_dataset
>>> df = pd.DataFrame([1, 2], columns=["dim_0"])
>>> from_pyflux_dataset(df)
array([[[1.],
        [2.]]])
>>> to_pyflux_dataset([[[1], [2]]]).shape
(2, 1)

tsfresh

tsfresh iis a python package automatically calculating a large number of time series characteristics. tslearn.utils.to_tsfresh_dataset() and tslearn.utils.from_tsfresh_dataset() allow users to convert a data set from tslearn format to tsfresh format and vice versa. pandas is a required dependency to use these functions.

>>> import pandas as pd
>>> from tslearn.utils import from_tsfresh_dataset, to_tsfresh_dataset
>>> df = pd.DataFrame([[0, 0, 1.0],
...                    [0, 1, 2.0],
...                    [1, 0, 1.0],
...                    [1, 1, 4.0],
...                    [1, 2, 3.0]], columns=['id', 'time', 'dim_0'])
>>> from_tsfresh_dataset(df)
array([[[ 1.],
    [ 2.],
    [nan]],

   [[ 1.],
    [ 4.],
    [ 3.]]])
>>> to_tsfresh_dataset([[[1], [2], [None]], [[1], [4], [3]]]).shape
(5, 3)

cesium

cesium is an open-source platform for time series inference. tslearn.utils.to_cesium_dataset() and tslearn.utils.from_cesium_dataset() allow users to convert a data set from tslearn format to cesium format and vice versa. cesium is a required dependency to use these functions.

>>> from tslearn.utils import from_cesium_dataset, to_cesium_dataset
>>> from cesium.data_management import TimeSeries
>>> from_cesium_dataset([TimeSeries(m=[1, 2]), TimeSeries(m=[1, 4, 3])])
array([[[ 1.],
        [ 2.],
        [nan]],

       [[ 1.],
        [ 4.],
        [ 3.]]])
>>> len(to_cesium_dataset([[[1], [2], [None]], [[1], [4], [3]]]))
2