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 does not work (and will 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 object oriented API.

sklearn.decomposition.PCA

Further removes the linear correlation across features with ‘whiten=True’.

Notes

See examples/preprocessing/plot_robust_scaling.py for an example.

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. .. versionadded:: 0.17 scale_ attribute.

Methods

fit(sequences[, 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)¶

Methods

__init__([with_centering, with_scaling, ...])
fit(sequences[, 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(sequences, y=None)¶

Fit Preprocessing to X.

Parameters:	sequences : list of array-like, each of shape [sequence_length, n_features] A list of multivariate timeseries. Each sequence may have a different length, but they all must have the same number of features. 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
Returns:	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)