msmbuilder.preprocessing.StandardScaler

class msmbuilder.preprocessing.StandardScaler(copy=True, with_mean=True, with_std=True)

Standardize features by removing the mean and scaling to unit variance

Centering and scaling happen independently on each feature by computing the relevant statistics on the samples in the training set. Mean and standard deviation 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: they might behave badly if the individual feature do not more or less look like standard normally distributed data (e.g. Gaussian with 0 mean and unit variance).

For instance many elements used in the objective function of a learning algorithm (such as the RBF kernel of Support Vector Machines or the L1 and L2 regularizers of linear models) assume that all features are centered around 0 and have variance in the same order. If a feature has a variance that is orders of magnitude larger that others, it might dominate the objective function and make the estimator unable to learn from other features correctly as expected.

This scaler can also be applied to sparse CSR or CSC matrices by passing with_mean=False to avoid breaking the sparsity structure of the data.

Read more in the User Guide.

Parameters:

copy : boolean, optional, 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.

with_mean : 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_std : boolean, True by default

If True, scale the data to unit variance (or equivalently, unit standard deviation).

See also

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.

Examples

>>> from sklearn.preprocessing import StandardScaler
>>>
>>> data = [[0, 0], [0, 0], [1, 1], [1, 1]]
>>> scaler = StandardScaler()
>>> print(scaler.fit(data))
StandardScaler(copy=True, with_mean=True, with_std=True)
>>> print(scaler.mean_)
[ 0.5  0.5]
>>> print(scaler.transform(data))
[[-1. -1.]
 [-1. -1.]
 [ 1.  1.]
 [ 1.  1.]]
>>> print(scaler.transform([[2, 2]]))
[[ 3.  3.]]

Attributes:

scale_ : ndarray, shape (n_features,)

Per feature relative scaling of the data.

New in version 0.17: scale_

mean_ : array of floats with shape [n_features]

The mean value for each feature in the training set.

var_ : array of floats with shape [n_features]

The variance for each feature in the training set. Used to compute scale_

n_samples_seen_ : int

The number of samples processed by the estimator. Will be reset on new calls to fit, but increments across partial_fit calls.

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[, copy])	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__(copy=True, with_mean=True, with_std=True)

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

Methods

__init__([copy, with_mean, with_std])	Initialize self.
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[, copy])	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, copy=None)

Scale back the data to the original representation

Parameters:

X : array-like, shape [n_samples, n_features]

The data used to scale along the features axis.

copy : bool, optional (default: None)

Copy the input X or not.

Returns:

X_tr : array-like, shape [n_samples, n_features]

Transformed array.

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)