mlnext.pipeline.Clip#
- class mlnext.pipeline.Clip(lower: float = 0.0, upper: float = 1.0)[source]#
Bases:
BaseEstimator
,TransformerMixin
Transformer that clips values by a lower and upper bound.
Example
>>> data = pd.DataFrame({'a': [-0.1, 1.2], 'b': [0.5, 0.6]}) >>> Clip().transform(data) pd.DataFrame({'a': [0, 1], 'b': [0.5, 0.6]})
Methods
fit
Fit to data, then transform it.
Get parameters for this estimator.
Set output container.
Set the parameters of this estimator.
transform
- fit_transform(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:
X (array-like of shape (n_samples, n_features)) – Input samples.
y (array-like of shape (n_samples,) or (n_samples, n_outputs), default=None) – Target values (None for unsupervised transformations).
**fit_params (dict) – Additional fit parameters.
- Returns:
X_new – Transformed array.
- Return type:
ndarray array of shape (n_samples, n_features_new)
- get_params(deep=True)#
Get parameters for this estimator.
- Parameters:
deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.
- Returns:
params – Parameter names mapped to their values.
- Return type:
dict
- set_output(*, transform=None)#
Set output container.
See sphx_glr_auto_examples_miscellaneous_plot_set_output.py for an example on how to use the API.
- Parameters:
transform ({"default", "pandas"}, default=None) –
Configure output of transform and fit_transform.
”default”: Default output format of a transformer
”pandas”: DataFrame output
None: Transform configuration is unchanged
- Returns:
self – Estimator instance.
- Return type:
estimator instance
- set_params(**params)#
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as
Pipeline
). The latter have parameters of the form<component>__<parameter>
so that it’s possible to update each component of a nested object.- Parameters:
**params (dict) – Estimator parameters.
- Returns:
self – Estimator instance.
- Return type:
estimator instance