FeatureUnion

class ibex.sklearn.pipeline.FeatureUnion(transformer_list, n_jobs=1, transformer_weights=None, as_index=True)

Bases: sklearn.pipeline.FeatureUnion, sklearn.base.TransformerMixin, ibex._base.FrameMixin

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:

• transformer_list – list 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_jobs – int, optional. Number of jobs to run in parallel (default 1).
• transformer_weights – dict, optional Multiplicative weights for features per transformer. Keys are transformer names, values the weights.

Example

>>> import pandas as pd
>>> X = pd.DataFrame({'a': [1, 2, 3], 'b': [10, -3, 4]})

>>> from ibex.sklearn import preprocessing as pd_preprocessing
>>> from ibex.sklearn import pipeline as pd_pipeline

>>> trn = pd_pipeline.FeatureUnion([
...     ('std', pd_preprocessing.StandardScaler()),
...     ('abs', pd_preprocessing.MaxAbsScaler())])
>>> trn.fit_transform(X)
      std                 abs
        a         b         a    b
0 -1.224745  1.192166  0.333333  1.0
1  0.000000 -1.254912  0.666667 -0.3
2  1.224745  0.062746  1.000000  0.4

>>> from ibex import trans
>>>
>>> trn = pd_preprocessing.StandardScaler() + pd_preprocessing.MaxAbsScaler()
>>> trn.fit_transform(X)
      standardscaler           maxabsscaler
        a         b         a    b
0 -1.224745  1.192166  0.333333  1.0
1  0.000000 -1.254912  0.666667 -0.3
2  1.224745  0.062746  1.000000  0.4

>>> trn = trans(pd_preprocessing.StandardScaler(), out_cols=['std_scale_a', 'std_scale_b'])
>>> trn += trans(pd_preprocessing.MaxAbsScaler(), out_cols=['max_scale_a', 'max_scale_b'])
>>> trn.fit_transform(X)
functiontransformer_0             functiontransformer_1
std_scale_a  std_scale_b  max_scale_a  max_scale_b
0    -1.224745     1.192166     0.333333          1.0
1     0.000000    -1.254912     0.666667         -0.3
2     1.224745     0.062746     1.000000          0.4

fit_transform(X, y=None, **fit_params)

Fits the transformer using X (and possibly y). Transforms X using the transformers, uses pandas.concat() to horizontally concatenate the results.

Returns:self

transform(X, *args, **kwargs)

Transforms X using the transformers, uses pandas.concat() to horizontally concatenate the results.