ITMO_FS.filters.multivariate.MIMAGA

class ITMO_FS.filters.multivariate.MIMAGA(mim_size, pop_size, max_iter=20, f_target=0.8, k1=0.6, k2=0.3, k3=0.9, k4=0.001)
__init__(mim_size, pop_size, max_iter=20, f_target=0.8, k1=0.6, k2=0.3, k3=0.9, k4=0.001)
Parameters:
  • mim_size – desirable number of filtered features after MIM
  • pop_size – initial population size
  • max_iter – maximum number of iterations in algorithm
  • f_target – desirable fitness value
  • k1 – consts to determine crossover probability
  • k2 – consts to determine crossover probability
  • k3 – consts to determine mutation probability
  • k4 – consts to determine mutation probability

See also

https()
//www.sciencedirect.com/science/article/abs/pii/S0925231217304150
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
mimaga_filter(genes, classes)

The main function to run algorithm :param genes: initial dataset in format: samples are rows, features are columns :param classes: distribution pf initial dataset :return: filtered with MIMAGA dataset, fitness value

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