def test_is_sa(self):
nd = np.array([[1, 2, 3], [4, 5, 6]], dtype=int)
dtype = np.dtype({'names': map('f{}'.format, xrange(3)),
'formats': [float] * 3})
sa = np.array([(-1.0, 2.0, -1.0), (0.0, -1.0, 2.0)], dtype=dtype)
self.assertFalse(utils.is_sa(nd))
self.assertTrue(utils.is_sa(sa))
def test_is_sa(self):
nd = np.array([[1, 2, 3], [4, 5, 6]], dtype=int)
dtype = np.dtype({
'names': map('f{}'.format, xrange(3)),
'formats': [float] * 3
})
sa = np.array([(-1.0, 2.0, -1.0), (0.0, -1.0, 2.0)], dtype=dtype)
self.assertFalse(utils.is_sa(nd))
self.assertTrue(utils.is_sa(sa))
def __init__(
self,
M,
y,
clfs=[{'clf': RandomForestClassifier}],
subsets=[{'subset': SubsetNoSubset}],
cvs=[{'cv': NoCV}],
trials=None):
if utils.is_sa(M):
self.col_names = M.dtype.names
self.M = utils.cast_np_sa_to_nd(M)
else: # assuming an nd_array
self.M = M
self.col_names = ['f{}'.format(i) for i in xrange(M.shape[1])]
self.y = y
self.clfs = clfs
self.subsets = subsets
self.cvs = cvs
self.trials = trials
