method_to_transform¶
-
gtda.mapper.
method_to_transform
(cls, method_name)[source]¶ Wrap a class to add a
transform
method as an alias to an existing method.An example of use is for classes possessing a
score
method such as kernel density estimators and anomaly/novelty detection estimators, allow for these estimators are to be used as steps in a pipeline.Note that 1D array outputs are reshaped into 2D column vectors before being returned by the new
transform
.- Parameters
cls (object) – Class to be wrapped. If method_name is not one of its methods,
transform
always returnsNone
.method_name (str) – Name of the method in cls to which
transform
will be an alias. The fist argument of this method (afterself
) becomes theX
input fortransform
.
- Returns
wrapped_cls – New class inheriting from
sklearn.base.TransformerMixin
, so that bothtransform
andfit_transform
are available. Its name is the name of cls prepended with'Extended'
.- Return type
object
Examples
>>> import numpy as np >>> from sklearn.neighbors import KernelDensity >>> from gtda.mapper import method_to_transform >>> X = np.random.random((100, 2)) >>> kde = KernelDensity()
Extend
KernelDensity
to give it atransform
method as an alias ofscore_samples
(up to output shape). The new class is instantiated with the same parameters as the original one.>>> ExtendedKDE = method_to_transform(KernelDensity, 'score_samples') >>> extended_kde = ExtendedKDE() >>> Xt = kde.fit(X).score_samples(X) >>> print(Xt.shape) (100,) >>> Xt_extended = extended_kde.fit_transform(X) >>> print(Xt_extended.shape) (100, 1) >>> np.array_equal(Xt, Xt_extended.flatten()) True