Transformer
ClaSPTransformer
ClaSP (Classification Score Profile) Transformer.
Implementation of the Classification Score Profile of a time series. ClaSP hierarchically splits a TS into two parts, where each split point is determined by training a binary TS classifier for each possible split point and selecting the one with highest accuracy, i.e., the one that is best at identifying subsequences to be from either of the partitions.
Schnellstart
python
from sktime.transformations.clasp import ClaSPTransformer
estimator = ClaSPTransformer(window_length=10, scoring_metric='ROC_AUC', exclusion_radius=0.05)Tags
Fähigkeiten
- Reihen ungleicher Länge
- Multivariat: Nicht unterstützt
- Inverse Transformation: Nicht unterstützt
- Fehlende Werte: Nicht unterstützt
- Entfernt fehlende Werte: Nicht unterstützt
- Gleicht Reihenlängen an: Nicht unterstützt
Eigenschaften
- Eingabetypscitype:transform-input
- Series
- Ausgabetypscitype:transform-output
- Series
- Label-Typscitype:transform-labels
- None
- Fit ist leerfit_is_empty
- Ja
- Behält den Zeitindextransform-returns-same-time-index
- Nein
- Benötigt Xrequires_X
- Ja
- Benötigt yrequires_y
- Nein
- X und y brauchen denselben IndexX-y-must-have-same-index
- Nein
Parameter(3)
- window_lengthint, default = 10
- size of window for sliding.
- scoring_metricstring, default = ROC_AUC
- the scoring metric to use in ClaSP - choose from ROC_AUC or F1
- exclusion_radiusint
- Exclusion Radius for change points to be non-trivial matches
Beispiele
>>> from sktime.transformations.clasp import ClaSPTransformer
>>> from sktime.detection.clasp import find_dominant_window_sizes
>>> from sktime.datasets import load_electric_devices_segmentation
>>> X, true_period_size, true_cps = load_electric_devices_segmentation ()
>>> dominant_period_size = find_dominant_window_sizes (X)
>>> clasp = ClaSPTransformer (window_length = dominant_period_size)
>>> clasp. fit (X)
>>> profile = clasp. transform (X)