msmbuilder.feature_selection.VarianceThreshold¶

class msmbuilder.feature_selection.VarianceThreshold(threshold=0.0)¶

Feature selector that removes all low-variance features.

This feature selection algorithm looks only at the features (X), not the desired outputs (y), and can thus be used for unsupervised learning.

Read more in the User Guide.

Parameters:	threshold : float, optional Features with a training-set variance lower than this threshold will be removed. The default is to keep all features with non-zero variance, i.e. remove the features that have the same value in all samples.

Examples

The following dataset has integer features, two of which are the same in every sample. These are removed with the default setting for threshold:

>>> X = [[0, 2, 0, 3], [0, 1, 4, 3], [0, 1, 1, 3]]
>>> selector = VarianceThreshold()
>>> selector.fit_transform(X)
array([[2, 0],
       [1, 4],
       [1, 1]])

Attributes

variances_

(array, shape (n_features,)) Variances of individual features.

Methods

fit(sequences[, y])	Fit the model
fit_transform(sequences[, y])	Fit the model and apply dimensionality reduction
get_params([deep])	Get parameters for this estimator.
get_support([indices])	Get a mask, or integer index, of the features selected
inverse_transform(X)	Reverse the transformation operation
partial_transform(sequence)	Apply dimensionality reduction 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 dimensionality reduction to sequences

__init__(threshold=0.0)¶

Methods

__init__([threshold])
fit(sequences[, y])	Fit the model
fit_transform(sequences[, y])	Fit the model and apply dimensionality reduction
get_params([deep])	Get parameters for this estimator.
get_support([indices])	Get a mask, or integer index, of the features selected
inverse_transform(X)	Reverse the transformation operation
partial_transform(sequence)	Apply dimensionality reduction 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 dimensionality reduction to sequences

fit(sequences, y=None)¶

Fit the model

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 dimensionality reduction

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.

get_support(indices=False)¶

Get a mask, or integer index, of the features selected

Parameters:	indices : boolean (default False) If True, the return value will be an array of integers, rather than a boolean mask.
Returns:	support : array An index that selects the retained features from a feature vector. If indices is False, this is a boolean array of shape [# input features], in which an element is True iff its corresponding feature is selected for retention. If indices is True, this is an integer array of shape [# output features] whose values are indices into the input feature vector.

inverse_transform(X)¶

Reverse the transformation operation

Parameters:	X : array of shape [n_samples, n_selected_features] The input samples.
Returns:	X_r : array of shape [n_samples, n_original_features] X with columns of zeros inserted where features would have been removed by transform.

partial_transform(sequence)¶

Apply dimensionality reduction 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 former 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 dimensionality reduction 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)