msmbuilder.preprocessing.RobustScaler

class msmbuilder.preprocessing.RobustScaler(with_centering=True, with_scaling=True, quantile_range=(25.0, 75.0), copy=True)

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 (or each sample, depending on the axis argument) 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 : boolean, True by default

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 : boolean, True by default

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 : boolean, optional, default is 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.

See also

robust_scale

Equivalent function without the estimator API.

sklearn.decomposition.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.

https://en.wikipedia.org/wiki/Median_(statistics) https://en.wikipedia.org/wiki/Interquartile_range

Attributes:

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.

Methods

fit(X[, y])	Fit Preprocessing to X.
fit_transform(sequences[, y])	Fit the model and apply preprocessing
get_params([deep])	Get parameters for this estimator.
inverse_transform(X)	Scale back the data to the original representation
partial_fit(sequence[, y])	Fit Preprocessing to X.
partial_transform(sequence)	Apply preprocessing to single sequence
set_params(**params)	Set the parameters of this estimator.
summarize()	Return some diagnostic summary statistics about this Markov model
transform(sequences)	Apply preprocessing to sequences

__init__(with_centering=True, with_scaling=True, quantile_range=(25.0, 75.0), copy=True)

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

Methods

__init__([with_centering, with_scaling, …])	Initialize self.
fit(X[, y])	Fit Preprocessing to X.
fit_transform(sequences[, y])	Fit the model and apply preprocessing
get_params([deep])	Get parameters for this estimator.
inverse_transform(X)	Scale back the data to the original representation
partial_fit(sequence[, y])	Fit Preprocessing to X.
partial_transform(sequence)	Apply preprocessing to single sequence
set_params(**params)	Set the parameters of this estimator.
summarize()	Return some diagnostic summary statistics about this Markov model
transform(sequences)	Apply preprocessing to sequences

fit(X, y=None)

Fit Preprocessing to X.

Parameters:

sequence : array-like, [sequence_length, n_features]

A multivariate timeseries.

y : None

Ignored

Returns:

self

fit_transform(sequences, y=None)

Fit the model and apply preprocessing

Parameters:

sequences: list of array-like, each of shape (n_samples_i, n_features)

Training data, where n_samples_i in the number of samples in sequence i and n_features is the number of features.

y : None

Ignored

Returns:

sequence_new : list of array-like, each of shape (n_samples_i, n_components)

get_params(deep=True)

Get parameters for this estimator.

Parameters:

deep : boolean, optional

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

params : mapping of string to any

Parameter names mapped to their values.

inverse_transform(X)

Scale back the data to the original representation

Parameters:

X : array-like

The data used to scale along the specified axis.

partial_fit(sequence, y=None)

Fit Preprocessing to X. Parameters ———- sequence : array-like, [sequence_length, n_features]

A multivariate timeseries.

y : None

Ignored

self

partial_transform(sequence)

Apply preprocessing to single sequence

Parameters:

sequence: array like, shape (n_samples, n_features)

A single sequence to transform

Returns:

out : array like, shape (n_samples, n_features)

set_params(**params)

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.

Returns:

self

summarize()

Return some diagnostic summary statistics about this Markov model

transform(sequences)

Apply preprocessing to sequences

Parameters:

sequences: list of array-like, each of shape (n_samples_i, n_features)

Sequence data to transform, where n_samples_i in the number of samples in sequence i and n_features is the number of features.

Returns:

sequence_new : list of array-like, each of shape (n_samples_i, n_components)