Preprocessing
- class hana_ml.algorithms.pal.auto_ml.Preprocessing(name, **kwargs)
Preprocessing class. Similar to the function preprocessing.
- Parameters
- namestr
The preprocessing algorithm name. The options are:
"OneHotEncoder"
"FeatureNormalizer"
"KBinsDiscretizer"
"Imputer"
"Discretize"
"MDS"
"SMOTE"
"SMOTETomek"
"TomekLinks"
"Sampling"
- **kwargs: dict
A dict of the keyword args passed to the object. Please refer to the documentation of the specific preprocessing algorithm for parameter information.
"OneHotEncoder": no additional parameter is required.
Examples
>>> result = Preprocessing(name="FeatureNormalizer").fit_transform(data=data, key="ID", features=["BMI"])
- Attributes
fit_hdbprocedureReturns the generated hdbprocedure for fit.
predict_hdbprocedureReturns the generated hdbprocedure for predict.
Methods
fit_transform(data[, key, features])Execute the preprocessing algorithm and return the transformed dataset.
- property fit_hdbprocedure
Returns the generated hdbprocedure for fit.
- property predict_hdbprocedure
Returns the generated hdbprocedure for predict.
- fit_transform(data, key=None, features=None, **kwargs)
Execute the preprocessing algorithm and return the transformed dataset.
- Parameters
- dataDataFrame
Input data.
- keystr, optional
Name of the ID column.
Defaults to the index column of
data(i.e. data.index) if it is set.- featureslist, optional
The columns to be preprocessed.
Defaults to None.
- **kwargs: dict
A dict of the keyword args passed to the fit_transform function. Please refer to the documentation of the specific preprocessing for parameter information.