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