Source code for Orange.preprocess.fss

import random
from itertools import takewhile
from operator import itemgetter

import numpy as np

import Orange
from Orange.util import Reprable
from Orange.preprocess.score import ANOVA, GainRatio, \

__all__ = ["SelectBestFeatures", "SelectRandomFeatures"]

[docs]class SelectBestFeatures(Reprable): """ A feature selector that builds a new dataset consisting of either the top `k` features (if `k` is an `int`) or a proportion (if `k` is a `float` between 0.0 and 1.0), or all those that exceed a given `threshold`. Features are scored using the provided feature scoring `method`. By default it is assumed that feature importance decreases with decreasing scores. If both `k` and `threshold` are set, only features satisfying both conditions will be selected. If `method` is not set, it is automatically selected when presented with the dataset. Datasets with both continuous and discrete features are scored using a method suitable for the majority of features. Parameters ---------- method : Orange.preprocess.score.ClassificationScorer, Orange.preprocess.score.SklScorer Univariate feature scoring method. k : int or float The number or propotion of top features to select. threshold : float A threshold that a feature should meet according to the provided method. decreasing : boolean The order of feature importance when sorted from the most to the least important feature. """ def __init__(self, method=None, k=None, threshold=None, decreasing=True): self.method = method self.k = k self.threshold = threshold self.decreasing = decreasing def __call__(self, data): n_attrs = len(data.domain.attributes) if isinstance(self.k, float): effective_k = np.round(self.k * n_attrs).astype(int) or 1 else: effective_k = self.k method = self.method # select default method according to the provided data if method is None: autoMethod = True discr_ratio = (sum(a.is_discrete for a in data.domain.attributes) / len(data.domain.attributes)) if data.domain.has_discrete_class: if discr_ratio >= 0.5: method = GainRatio() else: method = ANOVA() else: method = UnivariateLinearRegression() features = data.domain.attributes try: scores = method(data) except ValueError: scores = self.score_only_nice_features(data, method) best = sorted(zip(scores, features), key=itemgetter(0), reverse=self.decreasing) if self.k: best = best[:effective_k] if self.threshold: pred = ((lambda x: x[0] >= self.threshold) if self.decreasing else (lambda x: x[0] <= self.threshold)) best = takewhile(pred, best) domain =[f for s, f in best], data.domain.class_vars, data.domain.metas) return data.transform(domain) def score_only_nice_features(self, data, method): mask = np.array([isinstance(a, method.feature_type) for a in data.domain.attributes]) features = [f for f in data.domain.attributes if isinstance(f, method.feature_type)] scores = [method(data, f) for f in features] bad = float('-inf') if self.decreasing else float('inf') all_scores = np.array([bad] * len(data.domain.attributes)) all_scores[mask] = scores return all_scores
class SelectRandomFeatures(Reprable): """ A feature selector that selects random `k` features from an input dataset and returns a dataset with selected features. Parameter `k` is either an integer (number of feature) or float (from 0.0 to 1.0, proportion of retained features). Parameters ---------- k : int or float (default = 0.1) The number or proportion of features to retain. """ def __init__(self, k=0.1): self.k = k def __call__(self, data): if isinstance(self.k, float): effective_k = int(len(data.domain.attributes) * self.k) else: effective_k = self.k domain = random.sample(data.domain.attributes, min(effective_k, len(data.domain.attributes))), data.domain.class_vars, data.domain.metas) return data.transform(domain)