wildboar.transform._interval#

Module Contents#

Classes#

FeatureTransform

Transform a time series as a number of features

IntervalTransform

Embed a time series as a collection of features per interval.
class wildboar.transform._interval.FeatureTransform(*, summarizer='catch22', n_jobs=None)[source]#

Bases: IntervalTransform

Transform a time series as a number of features

Parameters:

  • summarizer (str or list, optional) –

    The method to summarize each interval.

    • if str, the summarizer is determined by _SUMMARIZERS.keys().

    • if list, the summarizer is a list of functions f(x) -> float, where x is a numpy array.

    The default summarizer summarizes each time series using catch22-features

  • n_jobs (int, optional) – The number of cores to use on multi-core.

References

Lubba, Carl H., Sarab S. Sethi, Philip Knaute, Simon R. Schultz, Ben D. Fulcher, and Nick S. Jones.

catch22: Canonical time-series characteristics. Data Mining and Knowledge Discovery 33, no. 6 (2019): 1821-1852.

class wildboar.transform._interval.IntervalTransform(n_intervals='sqrt', *, intervals='fixed', sample_size=0.5, min_size=0.0, max_size=1.0, summarizer='mean_var_slope', n_jobs=None, random_state=None)[source]#

Bases: wildboar.transform.base.BaseFeatureEngineerTransform

Embed a time series as a collection of features per interval.

Examples

>>> from wildboar.datasets import load_dataset
>>> x, y = load_dataset("GunPoint")
>>> t = IntervalTransform(n_intervals=10, summarizer="mean")
>>> t.fit_transform(x)

Each interval (15 timepoints) are transformed to their mean.

>>> t = IntervalTransform(n_intervals="sqrt", summarizer=[np.mean, np.std])
>>> t.fit_transform(x)

Each interval (150 // 12 timepoints) are transformed to two features. The mean and the standard deviation.

Parameters:

  • n_intervals (str, int or float, optional) –

    The number of intervals to use for the transform.

    • if “log”, the number of intervals is log2(n_timestep).

    • if “sqrt”, the number of intervals is sqrt(n_timestep).

    • if int, the number of intervals is n_intervals.

    • if float, the number of intervals is n_intervals * n_timestep, with 0 < n_intervals < 1.

  • intervals (str, optional) –

    The method for selecting intervals

    • if “fixed”, n_intervals non-overlapping intervals.

    • if “sample”, n_intervals * sample_size non-overlapping intervals.

    • if “random”, n_intervals possibly overlapping intervals of randomly sampled in [min_size * n_timestep, max_size * n_timestep]

  • sample_size (float, optional) – The sample size of fixed intervals if intervals="sample"

  • min_size (float, optional) – The minimum interval size if intervals="random"

  • max_size (float, optional) – The maximum interval size if intervals="random"

  • summarizer (str or list, optional) –

    The method to summarize each interval.

    • if str, the summarizer is determined by _SUMMARIZERS.keys().

    • if list, the summarizer is a list of functions f(x) -> float, where x is a numpy array.

    The default summarizer summarizes each interval as its mean, standard deviation and slope.

  • n_jobs (int, optional) – The number of cores to use on multi-core.

  • random_state (int or RandomState) –

    • If int, random_state is the seed used by the random number generator

    • If RandomState instance, random_state is the random number generator

    • If None, the random number generator is the RandomState instance used by np.random.