ITMO_FS.ensembles.ranking_based.Mixed

class ITMO_FS.ensembles.ranking_based.Mixed(filters, n_features, fusion_function=<function best_goes_first_fusion>)

Perform feature selection based on several filters, selecting features this way:

Get ranks from every filter from input. Then loops through, on every iteration=i

selects features on i position on every filter then shuffles them, then adds to result list without duplication,

continues until specified number of features

Parameters:
  • filters (collection) – Collection of measure functions with signature measure(X, y) that should return an array of importance values for each feature.
  • n_features (int) – Amount of features to select.
  • fusion_function (callable) – A function with signature (filter_ranks (array-like, shape (n_filters, n_features), k (int)) that should return the indices of k selected features based on the filter rankings.

Examples

>>> from ITMO_FS.filters.univariate.measures import *
>>> from ITMO_FS.ensembles.ranking_based.Mixed import Mixed
>>> import numpy as np
>>> x = np.array([[3, 3, 3, 2, 2], [3, 3, 1, 2, 3], [1, 3, 5, 1, 1],
... [3, 1, 4, 3, 1], [3, 1, 2, 3, 1]])
>>> y = np.array([1, 3, 2, 1, 2])
>>> mixed = Mixed([gini_index, chi2_measure], 2).fit(x, y)
>>> mixed.selected_features_
array([2, 4], dtype=int64)
__init__(filters, n_features, fusion_function=<function best_goes_first_fusion>)

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