from abc import abstractmethod
import numpy as np
from sklearn.exceptions import NotFittedError
class ScoreSelectorMixin:
"""
Mixin that adds getter for calculation of scores of features and checks that scores are calculated
"""
@abstractmethod
def _get_scores(self):
"""
Get calculated scores for features
Returns
------
scores: ndarray
Numpy Array with length equal to the initial number of features in dataset. Every element
of the array is the score of this feature
"""
pass
def _check_scores_set(self):
"""
Checks that scores for features are computed
Returns
-------
check: bool
Flag that indicates that scores are computed
"""
return self._get_scores() is not None
class ThresholdSelectorMixin(ScoreSelectorMixin):
"""
Mixin that selects features according to some threshold. That means that
all features whose score is higher than threshold are selected
"""
@abstractmethod
def _get_threshold(self):
"""
Get the value of threshold
Returns
-------
threshold: float
The value of threshold
"""
pass
def _get_support_mask(self):
if not self._check_scores_set():
raise NotFittedError('Feature Selector is not fitted')
return self._get_scores() > self._get_threshold()
class KBestSelectorMixin(ScoreSelectorMixin):
"""
Mixin that selects K best features according to their scores
"""
@abstractmethod
def _get_k(self):
"""
Get number of features to select
Returns
-------
k: int
Number of features to select
"""
pass
def _get_support_mask(self):
if not self._check_scores_set():
raise NotFittedError('Feature Selector is not fitted')
mask = np.zeros(self._get_scores().shape, dtype=bool)
mask[np.argsort(self._get_scores(), kind="mergesort")[-self._get_k():]] = 1
return mask
