sktime.transformations.panel.summarize¶
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class
sktime.transformations.panel.summarize.
DerivativeSlopeTransformer
[source]¶ Bases:
sktime.transformations.base._PanelToPanelTransformer
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class
sktime.transformations.panel.summarize.
FittedParamExtractor
(forecaster, param_names, n_jobs=None)[source]¶ Bases:
sktime.transformations.base._PanelToTabularTransformer
Extract parameters of a fitted forecaster as features for a subsequent tabular learning task. This class first fits a forecaster to the given time series and then returns the fitted parameters. The fitted parameters can be used as features for a tabular estimator (e.g. classification).
- Parameters
forecaster (estimator object) – sktime estimator to extract features from
param_names (str) – Name of parameters to extract from the forecaster.
n_jobs (int, optional (default=None)) – Number of jobs to run in parallel. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors.
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class
sktime.transformations.panel.summarize.
PlateauFinder
(value=nan, min_length=2)[source]¶ Bases:
sktime.transformations.base._PanelToPanelTransformer
Transformer that finds segments of the same given value, plateau in the time series, and returns the starting indices and lengths.
- Parameters
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class
sktime.transformations.panel.summarize.
RandomIntervalFeatureExtractor
(n_intervals='sqrt', min_length=None, max_length=None, features=None, random_state=None)[source]¶ Bases:
sktime.transformations.base._PanelToTabularTransformer
Transformer that segments time-series into random intervals and subsequently extracts series-to-primitives features from each interval.
n_intervals: str{‘sqrt’, ‘log’, ‘random’}, int or float, optional ( default=’sqrt’)
Number of random intervals to generate, where m is length of time series: - If “log”, log of m is used. - If “sqrt”, sqrt of m is used. - If “random”, random number of intervals is generated. - If int, n_intervals intervals are generated. - If float, int(n_intervals * m) is used with n_intervals giving the fraction of intervals of the time series length.
For all arguments relative to the length of the time series, the generated number of intervals is always at least 1.
- features: list of functions, optional (default=None)
Applies each function to random intervals to extract features. If None, the mean is extracted.
- random_state:int, RandomState instance, optional (default=None)
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.
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fit
(X, y=None)[source]¶ Fit transformer, generating random interval indices.
- Parameters
X (pandas DataFrame of shape [n_samples, n_features]) – Input data
y (pandas Series, shape (n_samples, ..), optional) – Targets for supervised learning.
- Returns
self – This estimator
- Return type
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transform
(X, y=None)[source]¶ Transform X, segments time-series in each column into random intervals using interval indices generated during fit and extracts features from each interval.
- Parameters
X (nested pandas.DataFrame of shape [n_samples, n_features]) – Nested dataframe with time-series in cells.
- Returns
Xt – Transformed pandas DataFrame with same number of rows and one column for each generated interval.
- Return type
pandas.DataFrame