import numpy as np
from sklearn.base import BaseEstimator
from ._stat_selector_mixin import StatSelectorMixin
# noinspection PyAttributeOutsideInit
[docs]class PercentageSelector(BaseEstimator, StatSelectorMixin):
"""Feature selector that removes / preserves top some percent of features
This feature selection algorithm looks only at the features (X), not the
desired outputs (y), and can thus be used for unsupervised learning.
Parameters
----------
stat: {'mean', 'var'}
Kind of statistic to be computed out of the feature.
use_log: bool, optional, default: False
Whether to use the logarithm of feature characteristic instead of the
characteristic itself. This may improve feature filtering performance,
depending on the distribution of features, however all the
characteristics (mean, variance) have to be positive for that -
filtering will fail otherwise. This is useful for specific cases in
biology where the distribution of data may actually require this option
for any efficient filtering.
keep_top: bool, optional, default: True
When True, keeps features with highest value of the characteristic.
p: float, optional, default: 0.2
Rate of features to keep.
Attributes
----------
vals_: array, shape (n_features,)
Computed characteristic of each feature.
threshold_: float
Value of the threshold used for filtering
selected_: array, shape (n_features,)
Vector of binary selections of the informative features.
"""
def __init__(self, stat: str, use_log: bool = False, keep_top: bool = True,
p: float = 0.2):
self.stat = stat
self.use_log = use_log
self.keep_top = keep_top
self.p = p
[docs] def fit(self, X, y=None):
"""Learn data-driven feature thresholds from X.
Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
Sample vectors from which to compute feature characteristic.
y : any
Ignored. This parameter exists only for compatibility with
sklearn.pipeline.Pipeline.
Returns
-------
self
"""
self.vals_ = self._to_characteristics(X)
if self.keep_top:
self.threshold_ = np.quantile(self.vals_, q=1 - self.p)
self.selected_ = self.threshold_ <= self.vals_
else:
self.threshold_ = np.quantile(self.vals_, q=self.p)
self.selected_ = self.threshold_ >= self.vals_
return self