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.
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__init__(scale=True, force_imputation=True, verbose=0, types=None)[source]¶ Initialize self. See help(type(self)) for accurate signature.
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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.
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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
- X{array-like, sparse matrix, dataframe} of shape (n_samples, n_features)
- yndarray of shape (n_samples,), default=None
Target values.
- **fit_paramsdict
Additional fit parameters.
- Returns
- X_newndarray array of shape (n_samples, n_features_new)
Transformed array.
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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
- paramsmapping of string to any
Parameter names mapped to their values.
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set_params(**params)[source]¶ 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.