skltemplate.TemplateTransformer¶
-
class
skltemplate.TemplateTransformer(demo_param='demo')[source]¶ An example transformer that returns the element-wise square root.
For more information regarding how to build your own transformer, read more in the User Guide.
- Parameters
- demo_paramstr, default=’demo’
A parameter used for demonstation of how to pass and store paramters.
- Attributes
- n_features_int
The number of features of the data passed to
fit().
-
__init__(self, demo_param='demo')[source]¶ Initialize self. See help(type(self)) for accurate signature.
-
fit(self, X, y=None)[source]¶ A reference implementation of a fitting function for a transformer.
- Parameters
- X{array-like, sparse matrix}, shape (n_samples, n_features)
The training input samples.
- yNone
There is no need of a target in a transformer, yet the pipeline API requires this parameter.
- Returns
- selfobject
Returns self.
-
fit_transform(self, X, y=None, **fit_params)¶ Fit to data, then transform it.
Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.
- Parameters
- Xnumpy array of shape [n_samples, n_features]
Training set.
- ynumpy array of shape [n_samples]
Target values.
- **fit_paramsdict
Additional fit parameters.
- Returns
- X_newnumpy array of shape [n_samples, n_features_new]
Transformed array.
-
get_params(self, deep=True)¶ Get parameters for this estimator.
- Parameters
- deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators.
- Returns
- paramsmapping of string to any
Parameter names mapped to their values.
-
set_params(self, **params)¶ Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form
<component>__<parameter>so that it’s possible to update each component of a nested object.- Parameters
- **paramsdict
Estimator parameters.
- Returns
- selfobject
Estimator instance.
