mlresearch.preprocessing
.PipelineEncoder¶
- class mlresearch.preprocessing.PipelineEncoder(features=None, encoder=None, **kwargs)[source]¶
Pipeline-compatible wrapper of Scikit-learn’s Transformer objects. Used to pass encoding of non-metric features and scalers (when there are categorical features) within a pipeline.
When
encoder
is None,sklearn.preprocessing.OneHotEncoder
will be used. In that case, kwargs can be passed to define its parameters. Otherwise, it is ignored.The fitted encoder object from Scikit-learn is stored in
self.encoder_
.- Parameters:
- featuresndarray of shape (n_cat_features,) or (n_features,)
Specifies which features to transform. Can either be:
array of indices specifying the features to transform.
mask array of shape (n_features, ) and
bool
dtype for whichTrue
indicates the features to transform.array of shape (n_transf_features,) and
str
dtype with the names of the features to transform. In this case,X
must be a dataframe. Raises an error otherwise.
- encoderencoder object, default=None
Encoder object to be used for transforming the features. If None, defaults to sklearn’s OneHotEncoder with default parameters, which can be modified with keyword arguments.
Warning
The
encoder
object must be compatible with sklearn’s API.
Notes
In most situations,
sklearn.compose.ColumnTransformer
can be used as an alternative toPipelineEncoder
.- Attributes:
- features_ndarray of shape (n_features,)
Mask array of shape (n_features, ) and
bool
dtype for whichTrue
indicates the features to transform.- encoded_features_names_out_ndarray of str objects
Output feature names after transformation.
- encoded_features_idx_out_ndarray of int objects
Indices of encoded features after transformation.
- fit(X, y=None)[source]¶
Fit PipelineEncoder to X.
- Parameters:
- Xarray-like of shape (n_samples, n_features)
The data to determine the categories of each feature.
- yNone
Ignored. This parameter exists only for compatibility with
Pipeline
.
- Returns:
- self
Fitted encoder.
- 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:
- Xarray-like of shape (n_samples, n_features)
Input samples.
- yarray-like of shape (n_samples,) or (n_samples, n_outputs), default=None
Target values (None for unsupervised transformations).
- **fit_paramsdict
Additional fit parameters.
- Returns:
- X_newndarray array of shape (n_samples, n_features_new)
Transformed array.
- get_metadata_routing()¶
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
- Returns:
- routingMetadataRequest
A
MetadataRequest
encapsulating routing information.
- get_params(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:
- paramsdict
Parameter names mapped to their values.
- 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
“polars”: Polars output
None: Transform configuration is unchanged
New in version 1.4: “polars” option was added.
- Returns:
- selfestimator instance
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:
- **paramsdict
Estimator parameters.
- Returns:
- selfestimator instance
Estimator instance.
- steps: List[Any]¶
- transform(X)[source]¶
Transform X using the
encoder
object.- Parameters:
- Xarray-like of shape (n_samples, n_features_to_encode + n_remaining)
Data containing the features to encode.
- Returns:
- X_out{ndarray, sparse matrix} of shape (n_samples, n_encoded_features + n_remaining)
Transformed input. Regardless of sparse_output, a dense matrix will be returned.