Transformer
DistanceFeatures
Use distances to training series as features.
In transform, returns tabular features as follows: for i-th series in X, returns all distances to series seen in fit j th column of i-th row is distance between i-th series in transform, and j-the series in fit. Column index is instance index in fit. If fit series was Hierarchical, hierarchy index is preserved.
Schnellstart
python
from sktime.transformations.compose_distance import DistanceFeatures
estimator = DistanceFeatures(distance=None, distance_mtype=None, flatten_hierarchy=False)Tags
Fähigkeiten
- Multivariat
- Fehlende Werte
- Reihen ungleicher Länge
- Inverse Transformation: Nicht unterstützt
- Entfernt fehlende Werte: Nicht unterstützt
- Gleicht Reihenlängen an: Nicht unterstützt
Eigenschaften
- Eingabetypscitype:transform-input
- Series
- Ausgabetypscitype:transform-output
- Primitives
- Label-Typscitype:transform-labels
- None
- Fit ist leerfit_is_empty
- Nein
- 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)
- distance: sktime pairwise panel transform, str, or callable, optional, default=None
- if panel transform, will be used directly as the distance in the algorithm default None = euclidean distance on flattened series, FlatDist(ScipyDist()) if str, will behave as FlatDist(ScipyDist(distance)) = scipy dist on flat series if callable, must be distance_mtype x distance_mtype -> 2D float np.array
- distance_mtypestr, or list of str optional. default = None.
- mtype that distance expects for X and X2, if a callable only set this if distance is not BasePairwiseTransformerPanel descendant
- flatten_hierarchybool, optional, default=False.
- whether column hierarchy in transform return is flattened (using __ concat), in case of a hierarchical series index seen in fit.
Beispiele
>>> from sktime.datasets import load_unit_test
>>> from sktime.transformations.compose_distance import DistanceFeatures
>>> X_train, _ = load_unit_test (return_X_y = True, split = "train")
>>> X, _ = load_unit_test (return_X_y = True, split = "test")
>>> trafo = DistanceFeatures ()
>>> trafo. fit (X_train) DistanceFeatures(
... )
>>> Xt = trafo. transform (X)