Abstract Base Classes

class pysgmcmc.sampling.Sampler

Enumeration type for all samplers we support.

_member_type_

alias of object

classmethod get_sampler(sampling_method, **sampler_args)

Return a sampler object for supported sampling_method, where all

default values for parameters in keyword dictionary sampler_args are overwritten.

Parameters:

• sampling_method (Sampler) – Enum corresponding to sampling method to return a sampler for.
• **sampler_args (dict) – Keyword arguments that contain all input arguments to the desired the constructor of the sampler for the specified sampling_method.

Returns:

sampler – A sampler instance that implements the specified sampling_method and is initialized with inputs sampler_args.

Return type:

Subclass of sampling.MCMCSampler

Examples

We can use this method to construct a sampler for a given sampling method and override default values by providing them as keyword arguments:

>>> import tensorflow as tf
>>> params = [tf.Variable(0.)]
>>> cost_fun = lambda params: tf.reduce_sum(params) # dummy cost function
>>> session=tf.Session()
>>> with tf.Session() as session: sampler = Sampler.get_sampler(Sampler.SGHMC, session=session, params=params, cost_fun=cost_fun, dtype=tf.float32)
>>> type(sampler)
<class 'pysgmcmc.samplers.sghmc.SGHMCSampler'>
>>> sampler.dtype
tf.float32
>>> session.close()

Construction of SGLD sampler:

>>> import tensorflow as tf
>>> params = [tf.Variable(0.)]
>>> cost_fun = lambda params: tf.reduce_sum(params) # dummy cost function
>>> session=tf.Session()
>>> with tf.Session() as session: sampler = Sampler.get_sampler(Sampler.SGLD, session=session, params=params, cost_fun=cost_fun, dtype=tf.float32)
>>> type(sampler)
<class 'pysgmcmc.samplers.sgld.SGLDSampler'>
>>> sampler.dtype
tf.float32
>>> session.close()

Construction of Relativistic SGHMC sampler:

>>> import tensorflow as tf
>>> params = [tf.Variable(0.)]
>>> cost_fun = lambda params: tf.reduce_sum(params) # dummy cost function
>>> session=tf.Session()
>>> with tf.Session() as session: sampler = Sampler.get_sampler(Sampler.RelativisticSGHMC, session=session, params=params, cost_fun=cost_fun, dtype=tf.float32)
>>> type(sampler)
<class 'pysgmcmc.samplers.relativistic_sghmc.RelativisticSGHMCSampler'>
>>> sampler.dtype
tf.float32
>>> session.close()

Sampler arguments that do not have a default must be provided as keyword argument, otherwise this method will raise an exception:

>>> sampler = Sampler.get_sampler(Sampler.SGHMC, dtype=tf.float32)
Traceback (most recent call last):
  ...
ValueError: sampling.Sampler.get_sampler: params was not overwritten as sampler argument in `sampler_args` and does not have any default value in SGHMCSampler.__init__Please pass an explicit value for this parameter.

If an optional argument is not provided as keyword argument, the corresponding default value is used. If we do not provide/overwrite the dtype keyword argument, the samplers default value of tf.float64 is used:

>>> import tensorflow as tf
>>> params = [tf.Variable(0., dtype=tf.float64)]
>>> cost_fun = lambda params: tf.reduce_sum(params) # dummy cost function
>>> with tf.Session() as session: sampler = Sampler.get_sampler(Sampler.SGHMC, session=session, params=params, cost_fun=cost_fun)
>>> sampler.dtype
tf.float64

If a keyword argument that is provided does not represent a valid parameter of the corresponding sampling_method, a ValueError is raised:

>>> import tensorflow as tf
>>> params = [tf.Variable(0., dtype=tf.float64)]
>>> cost_fun = lambda params: tf.reduce_sum(params) # dummy cost function
>>> with tf.Session() as session: sampler = Sampler.get_sampler(Sampler.SGHMC, unknown_argument=None, session=session, params=params, cost_fun=cost_fun)
Traceback (most recent call last):
  ...
ValueError: sampling.Sampler.get_sampler: 'SGHMCSampler' does not take any parameter with name 'unknown_argument' which was specified as argument to this sampler. Please ensure, that you only specify sampler arguments that fit the corresponding sampling method.
For your choice of sampling method ('Sampler.SGHMC'), supported parameters are:
-params
-cost_fun
-batch_generator
-stepsize_schedule
-burn_in_steps
-mdecay
-scale_grad
-session
-dtype
-seed

static is_burn_in_mcmc(sampling_method)

Static method that returns true if sampling_method is a burn_in sampler (e.g. there is an entry for it in Sampler enum).

Examples

Burn-in sampling methods give True:

>>> Sampler.is_burn_in_mcmc(Sampler.SGHMC)
True

Other sampling methods give False:

>>> Sampler.is_burn_in_mcmc(Sampler.RelativisticSGHMC)
False

Other input types give False:

>>> Sampler.is_burn_in_mcmc(0)
False
>>> Sampler.is_burn_in_mcmc("test")
False

static is_supported(sampling_method)

Static method that returns true if sampling_method is a supported sampler (e.g. there is an entry for it in Sampler enum).

Examples

Supported sampling methods give True:

>>> Sampler.is_supported(Sampler.SGHMC)
True

Other input types give False:

>>> Sampler.is_supported(0)
False
>>> Sampler.is_supported("test")
False