ITMO_FS.filters.multivariate.FCBFDiscreteFilter

class ITMO_FS.filters.multivariate.FCBFDiscreteFilter(delta=0.1)

Create FCBF (Fast Correlation Based filter) feature selection filter based on mutual information criteria for data with discrete features. This filter finds best set of features by searching for a feature, which provides the most information about classification problem on given dataset at each step and then eliminating features which are less relevant than redundant.

Parameters:delta (float) – Symmetric uncertainty value threshold.

Notes

For more details see this paper.

Examples

>>> from ITMO_FS.filters.multivariate import FCBFDiscreteFilter
>>> import numpy as np
>>> X = np.array([[1, 2, 3, 3, 1], [2, 2, 3, 3, 2], [1, 3, 3, 1, 3],
... [3, 1, 3, 1, 4], [4, 4, 3, 1, 5]])
>>> y = np.array([1, 2, 3, 4, 5])
>>> fcbf = FCBFDiscreteFilter().fit(X, y)
>>> fcbf.selected_features_
array([4], dtype=int64)
__init__(delta=0.1)

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

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

Fit the algorithm.

Parameters:
  • X (array-like, shape (n_samples, n_features)) – The training input samples.
  • y (array-like, shape (n_samples,), optional) – The class labels.
  • fit_params (dict, optional) – Additional parameters to pass to underlying _fit function.
Returns:
Return type:

Self, i.e. the transformer object.
fit_transform(X, y=None, **fit_params)

Fit to data, then transform it.

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

Parameters:
  • X ({array-like, sparse matrix, dataframe} of shape (n_samples, n_features)) –
  • y (ndarray of shape (n_samples,), default=None) – Target values.
  • **fit_params (dict) – Additional fit parameters.
Returns:

X_new – Transformed array.
Return type:

ndarray array of shape (n_samples, n_features_new)
get_params(deep=True)

Get parameters for this estimator.

Parameters:deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns:params – Parameter names mapped to their values.
Return type:mapping of string to any
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.

Parameters:**params (dict) – Estimator parameters.
Returns:self – Estimator instance.
Return type:object
transform(X)

Transform given data by slicing it with selected features.

Parameters:X (array-like, shape (n_samples, n_features)) – The training input samples.
Returns:
Return type:Transformed 2D numpy array