Source code for divik.feature_selection._stat_selector_mixin

import logging
from abc import ABCMeta

import numpy as np
from sklearn.base import BaseEstimator

    from sklearn.feature_selection._base import SelectorMixin
except ModuleNotFoundError:
    from sklearn.feature_selection.base import SelectorMixin

from divik.core import configurable

[docs]class StatSelectorMixin(SelectorMixin, metaclass=ABCMeta): """ Transformer mixin that performs feature selection given a support mask This mixin provides a feature selector implementation with ``transform`` and ``inverse_transform`` functionality given that ``selected_`` is specified during ``fit``. Additionally, provides a ``_to_characteristics`` and ``_to_raw`` implementations given ``stat``, optionally ``use_log`` and ``preserve_high``. """ def _to_characteristics(self, X): """Extract & normalize characteristics from data""" try: neutral = self.neutral except AttributeError: neutral = None if neutral is not None: X = X.copy() X[X == neutral] = np.nan if self.stat == "mean": vals = np.nanmean(X, axis=0) elif self.stat == "var": vals = np.nanvar(X, axis=0) elif self.stat == "cv": vals = np.nanstd(X, axis=0) / np.nanmean(X, axis=0) elif callable(self.stat): vals = self.stat(X) if vals.size != X.shape[1]: raise RuntimeError( "Computed statistic shape mismatch {0}".format(vals.shape) ) else: msg = 'stat must be one of {"cv", "mean", "var"} or callable' logging.error(msg) raise ValueError(msg) if hasattr(self, "use_log") and self.use_log: if np.any(vals < 0): logging.error( "Feature characteristic cannot be negative with log filtering" ) raise ValueError( "Feature characteristic cannot be negative with log filtering" ) vals = np.log(vals) if hasattr(self, "preserve_high") and not self.preserve_high: vals = -vals return vals def _to_raw(self, threshold): """Convert threshold to the feature characteristic space""" if hasattr(self, "preserve_high") and not self.preserve_high: threshold = -threshold if hasattr(self, "use_log") and self.use_log: threshold = np.exp(threshold) return threshold def _get_support_mask(self): """ Get the boolean mask indicating which features are selected Returns ------- support : boolean array of shape [# input features] An element is True iff its corresponding feature is selected for retention. """ return self.selected_
[docs]@configurable class NoSelector(BaseEstimator, StatSelectorMixin): """Dummy selector to use when no selection is supposed to be made.""" def __init__(self): pass
[docs] def fit(self, X, y=None): """Pass data forward Parameters ---------- X : {array-like, sparse matrix}, shape (n_samples, n_features) Sample vectors to pass. y : any Ignored. This parameter exists only for compatibility with sklearn.pipeline.Pipeline. Returns ------- self """ self.selected_ = np.ones((X.shape[1],), dtype=bool) return self