msmbuilder.preprocessing.Binarizer

class msmbuilder.preprocessing.Binarizer(threshold=0.0, copy=True)

Binarize data (set feature values to 0 or 1) according to a threshold

Values greater than the threshold map to 1, while values less than or equal to the threshold map to 0. With the default threshold of 0, only positive values map to 1.

Binarization is a common operation on text count data where the analyst can decide to only consider the presence or absence of a feature rather than a quantified number of occurrences for instance.

It can also be used as a pre-processing step for estimators that consider boolean random variables (e.g. modelled using the Bernoulli distribution in a Bayesian setting).

Read more in the User Guide.

Parameters:

threshold : float, optional (0.0 by default)

Feature values below or equal to this are replaced by 0, above it by 1. Threshold may not be less than 0 for operations on sparse matrices.

copy : boolean, optional, default True

set to False to perform inplace binarization and avoid a copy (if the input is already a numpy array or a scipy.sparse CSR matrix).

See also

binarize
Equivalent function without the object oriented API.

Notes

If the input is a sparse matrix, only the non-zero values are subject to update by the Binarizer class.

This estimator is stateless (besides constructor parameters), the fit method does nothing but is useful when used in a pipeline.

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.
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__(threshold=0.0, copy=True)

Methods

__init__([threshold, 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.
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.
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)