dabl.EasyPreprocessor

class dabl.EasyPreprocessor(scale=True, force_imputation=True, verbose=0, types=None)[source]

A simple preprocessor.

Detects variable types, encodes everything as floats for use with sklearn.

Applies one-hot encoding, missing value imputation and scaling.

Parameters
scaleboolean, default=True

Whether to scale continuous data.

force_imputationbool, default=True

Whether to create imputers even if no training data is missing.

verboseint, default=0

Control output verbosity.

Attributes
ct_ColumnTransformer

Main container for all transformations.

columns_pandas columns

Columns of training data.

dtypes_Series of dtypes

Dtypes of training data columns.

types_something

Inferred input types.

__init__(scale=True, force_imputation=True, verbose=0, types=None)[source]

Initialize self. See help(type(self)) for accurate signature.

fit(X, y=None)[source]

A reference implementation of a fitting function for a transformer.

Parameters
Xarray-like or sparse matrix of 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(X, y=None, **fit_params)[source]

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_params(deep=True)[source]

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_params(**params)[source]

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.

transform(X)[source]

A reference implementation of a transform function.

Parameters
Xarray-like of shape = [n_samples, n_features]

The input samples.

Returns
X_transformedarray of int of shape = [n_samples, n_features]

The array containing the element-wise square roots of the values in X.