syne_tune.optimizer.schedulers.transfer_learning.quantile_based.normalization_transforms module

class syne_tune.optimizer.schedulers.transfer_learning.quantile_based.normalization_transforms.GaussianTransform(y, random_state=None)

Bases: object

Transform data into Gaussian by applying psi = Phi^{-1} o F where F is the truncated empirical CDF. :type y: array :param y: shape (n, dim) :type random_state: Optional[RandomState] :param random_state: If specified, randomize the rank when consecutive values exists between extreme values.

If none use lowest rank of duplicated values.

static z_transform(series, values_sorted, random_state=None)

Parameters:

• series – shape (n, dim)

• values_sorted – series sorted on the first axis

• random_state (Optional[RandomState]) – if not None, ranks are drawn uniformly for values with consecutive ranges

Returns:

data with same shape as input series where distribution is normalized on all dimensions

transform(y)

Parameters:

y (array) – shape (n, dim)

Returns:

shape (n, dim), distributed along a normal

class syne_tune.optimizer.schedulers.transfer_learning.quantile_based.normalization_transforms.StandardTransform(y)

Bases: object

transform(y)

syne_tune.optimizer.schedulers.transfer_learning.quantile_based.normalization_transforms.from_string(name, random_state=None)