# dask_ml.preprocessing.RobustScaler¶

class dask_ml.preprocessing.RobustScaler(*, with_centering=True, with_scaling=True, quantile_range=(25.0, 75.0), copy=True, unit_variance=False)

Scale features using statistics that are robust to outliers.

This Scaler removes the median and scales the data according to the quantile range (defaults to IQR: Interquartile Range). The IQR is the range between the 1st quartile (25th quantile) and the 3rd quartile (75th quantile).

Centering and scaling happen independently on each feature by computing the relevant statistics on the samples in the training set. Median and interquartile range are then stored to be used on later data using the transform method.

Standardization of a dataset is a common requirement for many machine learning estimators. Typically this is done by removing the mean and scaling to unit variance. However, outliers can often influence the sample mean / variance in a negative way. In such cases, the median and the interquartile range often give better results.

New in version 0.17.

Read more in the User Guide.

Parameters: with_centering : bool, default=True If True, center the data before scaling. This will cause transform to raise an exception when attempted on sparse matrices, because centering them entails building a dense matrix which in common use cases is likely to be too large to fit in memory. with_scaling : bool, default=True If True, scale the data to interquartile range. quantile_range : tuple (q_min, q_max), 0.0 < q_min < q_max < 100.0, default=(25.0, 75.0), == (1st quantile, 3rd quantile), == IQR Quantile range used to calculate scale_. New in version 0.18. copy : bool, default=True If False, try to avoid a copy and do inplace scaling instead. This is not guaranteed to always work inplace; e.g. if the data is not a NumPy array or scipy.sparse CSR matrix, a copy may still be returned. unit_variance : bool, default=False If True, scale data so that normally distributed features have a variance of 1. In general, if the difference between the x-values of q_max and q_min for a standard normal distribution is greater than 1, the dataset will be scaled down. If less than 1, the dataset will be scaled up. New in version 0.24. center_ : array of floats The median value for each feature in the training set. scale_ : array of floats The (scaled) interquartile range for each feature in the training set. New in version 0.17: scale_ attribute.

robust_scale
Equivalent function without the estimator API.
PCA
Further removes the linear correlation across features with ‘whiten=True’.

Notes

For a comparison of the different scalers, transformers, and normalizers, see examples/preprocessing/plot_all_scaling.py.

Examples

>>> from sklearn.preprocessing import RobustScaler
>>> X = [[ 1., -2.,  2.],
...      [ -2.,  1.,  3.],
...      [ 4.,  1., -2.]]
>>> transformer = RobustScaler().fit(X)
>>> transformer
RobustScaler()
>>> transformer.transform(X)
array([[ 0. , -2. ,  0. ],
[-1. ,  0. ,  0.4],
[ 1. ,  0. , -1.6]])


Methods

 fit(X, pandas.core.frame.DataFrame, …) Compute the median and quantiles to be used for scaling. fit_transform(X[, y]) Fit to data, then transform it. get_params([deep]) Get parameters for this estimator. inverse_transform(X, …) Scale back the data to the original representation set_params(**params) Set the parameters of this estimator. transform(X, pandas.core.frame.DataFrame, …) Center and scale the data.
__init__(*, with_centering=True, with_scaling=True, quantile_range=(25.0, 75.0), copy=True, unit_variance=False)

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

fit(X: Union[ArrayLike, pandas.core.frame.DataFrame, dask.dataframe.core.DataFrame], y: Union[ArrayLike, dask.dataframe.core.Series, pandas.core.series.Series, None] = None) → dask_ml.preprocessing.data.RobustScaler

Compute the median and quantiles to be used for scaling.

Parameters: X : {array-like, sparse matrix} of shape (n_samples, n_features) The data used to compute the median and quantiles used for later scaling along the features axis. y : None Ignored. self : object Fitted scaler.
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. X_new : ndarray array of shape (n_samples, n_features_new) Transformed array.
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. params : dict Parameter names mapped to their values.
inverse_transform(X: Union[ArrayLike, pandas.core.frame.DataFrame, dask.dataframe.core.DataFrame]) → Union[ArrayLike, pandas.core.frame.DataFrame, dask.dataframe.core.DataFrame]

Scale back the data to the original representation

Parameters: X : array-like The data used to scale along the specified axis. This implementation was copied and modified from Scikit-Learn. See License information here: https://github.com/scikit-learn/scikit-learn/blob/main/README.rst
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. self : estimator instance Estimator instance.
transform(X: Union[ArrayLike, pandas.core.frame.DataFrame, dask.dataframe.core.DataFrame]) → Union[ArrayLike, pandas.core.frame.DataFrame, dask.dataframe.core.DataFrame]

Center and scale the data.

Can be called on sparse input, provided that RobustScaler has been fitted to dense input and with_centering=False.

Parameters: X : {array-like, sparse matrix} The data used to scale along the specified axis. This implementation was copied and modified from Scikit-Learn. See License information here: https://github.com/scikit-learn/scikit-learn/blob/main/README.rst