ITMO_FS.wrappers.randomized.SimulatedAnnealing

class ITMO_FS.wrappers.randomized.SimulatedAnnealing(estimator, measure, seed=42, iteration_number=100, c=1, init_number_of_features=None, cv=3)

Simulated Annealing algorithm.

Parameters:
  • estimator (object) – A supervised learning estimator that should have a fit(X, y) method and a predict(X) method.
  • measure (string or callable) – A standard estimator metric (e.g. ‘f1’ or ‘roc_auc’) or a callable with signature measure(estimator, X, y) which should return only a single value.
  • seed (int) – Random seed used to initialize np.random.default_rng().
  • iteration_number (int) – Number of iterations of the algorithm.
  • c (int) – A constant that is used to control the rate of feature perturbation.
  • init_number_of_features (int) – The number of features to initialize start features subset with, by default 5-10 percents of features is used.
  • cv (int) – Number of folds in cross-validation.

Notes

For more details see this paper.

Examples

>>> from sklearn.datasets import make_classification
>>> from sklearn.linear_model import LogisticRegression
>>> from ITMO_FS.wrappers.randomized import SimulatedAnnealing
>>> 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])
>>> sa = SimulatedAnnealing(LogisticRegression(), measure='f1_macro',
... iteration_number=50).fit(x, y)
>>> sa.selected_features_
array([ 1,  4,  3, 17, 10, 16, 11, 14,  5], dtype=int64)
__init__(estimator, measure, seed=42, iteration_number=100, c=1, init_number_of_features=None, cv=3)

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