Binarizer

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

Bases: sklearn.preprocessing.data.Binarizer, ibex._base.FrameMixin

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

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.

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).

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.

binarize: Equivalent function without the estimator API.

fit(X, y=None)[source]

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Do nothing and return the estimator unchanged

This method is just there to implement the usual API and hence work in pipelines.

X : array-like

fit_transform(X, y=None, **fit_params)

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

X : numpy array of shape [n_samples, n_features]
Training set.
y : numpy array of shape [n_samples]
Target values.
X_new : numpy array of shape [n_samples, n_features_new]
Transformed array.
transform(X, y='deprecated', copy=None)[source]

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Binarize each element of X

X : {array-like, sparse matrix}, shape [n_samples, n_features]
The data to binarize, element by element. scipy.sparse matrices should be in CSR format to avoid an un-necessary copy.
y : (ignored)

Deprecated since version 0.19: This parameter will be removed in 0.21.

copy : bool
Copy the input X or not.