ITMO_FS.hybrid.FilterWrapperHybrid

class ITMO_FS.hybrid.FilterWrapperHybrid(filter_, wrapper)

Perform the filter + wrapper hybrid algorithm by first running the filter algorithm on the full dataset, leaving the selected features and running the wrapper algorithm on the cut dataset.

Parameters:

• filter (object) – A feature selection model that should have a fit(X, y) method and a selected_features_ attribute available after fitting.
• wrapper (object) – A feature selection model that should have a fit(X, y) method, selected_features_ and best_score_ attributes available after fitting and a predict(X) method.

Notes

This class doesn’t require the first algorithm to be a filter (the only requirements are a fit(X, y) method and a selected_features_ attribute) but it is recommended to use a fast algorithm first to remove a lot of unnecessary features before processing the resulting dataset with a more time-consuming algorithm (e.g. a wrapper).

Examples

>>> import numpy as np
>>> from sklearn.linear_model import LogisticRegression
>>> from ITMO_FS.wrappers.deterministic import BackwardSelection
>>> from ITMO_FS.filters.univariate import UnivariateFilter
>>> from ITMO_FS.hybrid import FilterWrapperHybrid
>>> from sklearn.datasets import make_classification
>>> dataset = make_classification(n_samples=100, n_features=20,
... n_informative=5, n_redundant=0, shuffle=False, random_state=42)
>>> x, y = np.array(dataset[0]), np.array(dataset[1])
>>> filter_ = UnivariateFilter('FRatio', ("K best", 10))
>>> wrapper = BackwardSelection(LogisticRegression(), 5, measure='f1_macro')
>>> model = FilterWrapperHybrid(filter_, wrapper).fit(x, y)
>>> model.selected_features_
array([ 1,  3,  4, 10,  7], dtype=int64)

__init__(filter_, wrapper)

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

predict(X)

Predict class labels for the input data.

Parameters:X (array-like, shape (n_samples, n_features)) – The input samples. Returns:array-like, shape (n_samples,) Return type:class labels

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