mlnext.pipeline.ZeroVarianceDropper#

class mlnext.pipeline.ZeroVarianceDropper(verbose: bool = False)[source]#

Bases: BaseEstimator, TransformerMixin

Removes all columns that are numeric and have zero variance. Needs to be fitted first. Gives a warning if a column that was registered as zero variance deviates.

Example

>>> data = pd.DataFrame({'a': [0.0, 0.0], 'b': [1.0, 0.0]})
>>> ZeroVarianceDropper().fit_transform(data)
pd.DataFrame({'b': [1.0, 0.0]})

Methods

`fit`	Finds all columns with zero variance.
`fit_transform`	Fit to data, then transform it.
`get_params`	Get parameters for this estimator.
`set_output`	Set output container.
`set_params`	Set the parameters of this estimator.
`transform`	Drops all columns found by fit with zero variance.

fit(X, y=None)[source]#

Finds all columns with zero variance.

Parameters:

X (pd.DataFrame) – Dataframe.
y (array-like, optional) – Labels. Defaults to None.

Returns:

Returns self.

Return type:

ZeroVarianceDropper

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

transform(X)[source]#

Drops all columns found by fit with zero variance.

Parameters:: X (pd.DataFrame) – Dataframe.
Returns:: Returns the new dataframe.
Return type:: pd.DataFrame