syne_tune.optimizer.schedulers.searchers.bayesopt.gpautograd.gluon_blocks_helpers module

class syne_tune.optimizer.schedulers.searchers.bayesopt.gpautograd.gluon_blocks_helpers.ConstantPositiveVector(param_name, encoding, size_cols, **kwargs)

Bases: Block

Represents constant vector, with positive entry value represented as Gluon parameter, to be used in the context of wrapper classes in gluon_blocks.py. Shape, dtype, and context are determined from the features argument:

• If features.shape = (n, d): shape = (d, 1) if size_cols = True (number cols of features) shape = (n, 1) if size_cols = False (number rows of features)

• dtype = features.dtype, ctx = features.ctx

Encoding and internal Gluon parameter: The positive scalar parameter is encoded via encoding (see ScalarEncodingBase). The internal Gluon parameter (before encoding) has the name param_name + "_internal".

forward(features, param_internal)

Returns constant positive vector

If features.shape = (n, d), the shape of the vector returned is (d, 1) if size_cols = True, (n, 1) otherwise.

Parameters:

• features – Matrix for shape, dtype, ctx

• param_internal – Unwrapped parameter

Returns:

Constant positive vector

set(val)

get()

get_box_constraints_internal()

log_parameters()

get_parameters()

switch_updating(flag)

Is the underlying parameter updated during learning?

By default, the parameter takes part in learning (its grad_req attribute is ‘write’). For flag == False, the attribute is flipped to ‘null’, and the parameter remains constant during learning.

Parameters:

flag – Update parameter during learning?

has_regularizer()

eval_regularizer(features)

class syne_tune.optimizer.schedulers.searchers.bayesopt.gpautograd.gluon_blocks_helpers.PositiveScalarEncoding(lower, constr_upper=None, init_val=None, regularizer=None, dimension=1)

Bases: ScalarEncodingBase

Provides encoding for positive scalar and vector: param > lower. Here, param is represented as gluon.Parameter. The param is with shape (dimension,) where dimension is 1 by default.

The encoding is given as:

param = softrelu(param_internal) + lower, softrelu(x) = log(1 + exp(x))

If constr_upper is used, the constraint

param_internal < dec(constr_upper)

can be enforced by an optimizer. Since dec is increasing, this translates to param < constr_upper. Note: While lower is enforced by the encoding, the upper bound is not, has to be enforced by an optimizer.

get(param_internal)

decode(val, name)

class syne_tune.optimizer.schedulers.searchers.bayesopt.gpautograd.gluon_blocks_helpers.IdentityScalarEncoding(constr_lower=None, constr_upper=None, init_val=None, regularizer=None, dimension=1)

Bases: ScalarEncodingBase

Identity encoding for scalar and vector:

param = param_internal

This does not ensure that param is positive! Use this only if positivity is otherwise guaranteed.

get(param_internal)

decode(val, name)

class syne_tune.optimizer.schedulers.searchers.bayesopt.gpautograd.gluon_blocks_helpers.LogarithmScalarEncoding(constr_lower=None, constr_upper=None, init_val=None, regularizer=None, dimension=1)

Bases: ScalarEncodingBase

Logarithmic encoding for scalar and vector:

param = exp(param_internal), param_internal = param

get(param_internal)

decode(val, name)

syne_tune.optimizer.schedulers.searchers.bayesopt.gpautograd.gluon_blocks_helpers.unwrap_parameter(param_internal, some_arg=None)

syne_tune.optimizer.schedulers.searchers.bayesopt.gpautograd.gluon_blocks_helpers.encode_unwrap_parameter(param_internal, encoding, some_arg=None)

syne_tune.optimizer.schedulers.searchers.bayesopt.gpautograd.gluon_blocks_helpers.param_to_pretty_string(gluon_param, encoding)

Take a gluon parameter and transform it to a string amenable to plotting If need be, the gluon parameter is appropriately encoded (e.g., log-exp transform).

Parameters:

• gluon_param (Parameter) – gluon parameter

• encoding (ScalarEncodingBase) – object in charge of encoding/decoding the gluon_param

Return type:

str

syne_tune.optimizer.schedulers.searchers.bayesopt.gpautograd.gluon_blocks_helpers.register_parameter(params, name, encoding, shape=(1, ), dtype=<class 'numpy.float64'>)

syne_tune.optimizer.schedulers.searchers.bayesopt.gpautograd.gluon_blocks_helpers.create_encoding(encoding_name, init_val, constr_lower, constr_upper, dimension, prior)