Transformer
FeatureUnion
Concatenates results of multiple transformer objects.
This estimator applies a list of transformer objects in parallel to the input data, then concatenates the results. This is useful to combine several feature extraction mechanisms into a single transformer. Parameters of the transformations may be set using its name and the parameter name separated by a ‘__’. A transformer may be replaced entirely by setting the parameter with its name to another transformer, or removed by setting to ‘drop’ or None.
Quickstart
python
from sktime.transformations.compose import FeatureUnion
estimator = FeatureUnion(transformer_list, n_jobs=None, transformer_weights=None, flatten_transform_index=True)Tags
Capabilities
- Multivariate
- Unequal-length series
- Inverse transform: Not supported
- Missing values: Not supported
- Removes missing values: Not supported
- Equalizes series length: Not supported
Properties
- Input typescitype:transform-input
- Series
- Output typescitype:transform-output
- Series
- Label typescitype:transform-labels
- None
- Fit is emptyfit_is_empty
- No
- Keeps the time indextransform-returns-same-time-index
- No
- Requires Xrequires_X
- Yes
- Requires yrequires_y
- No
- X and y need the same indexX-y-must-have-same-index
- No
Parameters(4)
- transformer_listlist of (string, transformer) tuples
- List of transformer objects to be applied to the data. The first half of each tuple is the name of the transformer.
- n_jobsint or None, optional (default=None)
Number of jobs to run in parallel.
Nonemeans 1 unless in ajoblib.parallel_backendcontext.-1means using all processors.- transformer_weightsdict, optional
- Multiplicative weights for features per transformer. Keys are transformer names, values the weights.
- flatten_transform_indexbool, optional (default=True)
- if True, columns of return DataFrame are flat, by “transformer__variablename” if False, columns are MultiIndex (transformer, variablename) has no effect if return mtype is one without column names