import random
positive_digit = 6
name = 'covtype-%d' % positive_digit
num_folds = 5
budget = 150
npos = 500
nneg = 1000
search = {'logC': [-8, 1], 'logGamma': [-8, 1]}
covtype = sklearn.datasets.fetch_covtype()
n = covtype.data.shape[0]
positive_idx = [i for i in range(n) if covtype.target[i] == positive_digit]
negative_idx = [i for i in range(n) if not covtype.target[i] == positive_digit]
# draw random subsamples
positive_idx = random.sample(positive_idx, npos)
negative_idx = random.sample(negative_idx, nneg)
original_data = covtype.data[positive_idx + negative_idx, ...]
data = original_data # + 10 * np.random.randn(original_data.shape[0], original_data.shape[1])
labels = [True] * len(positive_idx) + [False] * len(negative_idx)
objfun = hpo.make_svm_objfun(data, labels, num_folds)
hpo_search = hpo.svm_search_space(**search)
hpo.setup_hpolib(hpo.negate(objfun), hpo_search, budget, name)
import optunity
import numpy as np
import hpolib_generator as hpo
import cloudpickle as pickle
from sklearn.datasets import load_digits
positive_digit = 6
name='digits-%d' % positive_digit
num_folds=5
budget = 150
search={'logC': [-8, 1], 'logGamma': [-8, 1]}
digits = load_digits()
n = digits.data.shape[0]
positive_idx = [i for i in range(n) if digits.target[i] == positive_digit]
negative_idx = [i for i in range(n) if not digits.target[i] == positive_digit]
original_data = digits.data[positive_idx + negative_idx, ...]
data = original_data + 10 * np.random.randn(original_data.shape[0], original_data.shape[1])
labels = [True] * len(positive_idx) + [False] * len(negative_idx)
objfun = hpo.make_svm_objfun(data, labels, num_folds)
hpo_search = hpo.svm_search_space(**search)
hpo.setup_hpolib(hpo.negate(objfun), hpo_search, budget, name)