msmbuilder.preprocessing.MaxAbsScaler¶

class msmbuilder.preprocessing.MaxAbsScaler(copy=True)¶

Scale each feature by its maximum absolute value.

This estimator scales and translates each feature individually such that the maximal absolute value of each feature in the training set will be 1.0. It does not shift/center the data, and thus does not destroy any sparsity.

This scaler can also be applied to sparse CSR or CSC matrices.

New in version 0.17.

Parameters:

copy : boolean, optional, default is True

Set to False to perform inplace scaling and avoid a copy (if the input is already a numpy array).

See also

maxabs_scale: Equivalent function without the object oriented API.

Attributes

scale_	(ndarray, shape (n_features,)) Per feature relative scaling of the data. .. versionadded:: 0.17 scale_ attribute.
max_abs_	(ndarray, shape (n_features,)) Per feature maximum absolute value.
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)	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)¶

Methods

`__init__`([copy])
`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, sparse matrix}

The data that should be transformed back.

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)