def feature_construction(data_matrix_original, selectors):
from eden.selector import CompositeSelector
selector = CompositeSelector(selectors)
from eden.selector import Projector
projector = Projector(selector, metric='cosine')
data_matrix = projector.fit_transform(data_matrix_original)
return data_matrix
def randomize(self, data_matrix, amount=.5):
random.seed(self.random_state)
inclusion_threshold = random.uniform(amount, 1)
selectors = []
if random.random() > inclusion_threshold:
selectors.append(
SparseSelector(random_state=random.randint(1, 1e9)))
if random.random() > inclusion_threshold:
selectors.append(
MaxVolSelector(random_state=random.randint(1, 1e9)))
if random.random() > inclusion_threshold:
selectors.append(
QuickShiftSelector(random_state=random.randint(1, 1e9)))
if random.random() > inclusion_threshold:
selectors.append(
DensitySelector(random_state=random.randint(1, 1e9)))
if random.random() > inclusion_threshold:
selectors.append(
OnionSelector(random_state=random.randint(1, 1e9)))
if not selectors:
selectors.append(
DensitySelector(random_state=random.randint(1, 1e9)))
selectors.append(
SparseSelector(random_state=random.randint(1, 1e9)))
selector = CompositeSelector(selectors=selectors)
selector.randomize(data_matrix, amount=amount)
self.selectors = deepcopy(selector.selectors)
self.metric = 'rbf'
self.kwds = {'gamma': random.choice([10**x for x in range(-3, 3)])}
if random.random() > inclusion_threshold:
self.n_nearest_neighbors = random.randint(3, 20)
else:
self.n_nearest_neighbors = None
self.n_links = random.randint(1, 5)
self.random_state = self.random_state ^ random.randint(1, 1e9)
