sup3r.preprocessing.batch_handlers.factory.BatchHandlerMom2

class BatchHandlerMom2(train_containers, *, val_containers=None, sample_shape=None, batch_size=16, n_batches=64, s_enhance=1, t_enhance=1, means=None, stds=None, queue_cap=None, transform_kwargs=None, mode='lazy', feature_sets=None, time_enhance_mode='constant', lower_models=None, s_padding=0, t_padding=0, end_t_padding=False, **kwargs)

Bases: QueueMom2

BatchHandler object built from two lists of Container objects, one with training data and one with validation data. These lists will be used to initialize lists of class:Sampler objects that will then be used to build batches at run time.

Notes

These lists of containers can contain data from the same underlying data source (e.g. CONUS WTK) (e.g. initialize train / val containers with different time period and / or regions, or they can be used to sample from completely different data sources (e.g. train on CONUS WTK while validating on Canada WTK).

See also

Sampler, AbstractBatchQueue, StatsCollection

Parameters:

• train_containers (List[Container]) – List of objects with a .data attribute, which will be used to initialize Sampler objects and then used to initialize a batch queue of training data. The data can be a Sup3rX or Sup3rDataset object.

• val_containers (List[Container]) – List of objects with a .data attribute, which will be used to initialize Sampler objects and then used to initialize a batch queue of validation data. The data can be a Sup3rX or a Sup3rDataset object.

• batch_size (int) – Number of observations / samples in a batch

• n_batches (int) – Number of batches in an epoch, this sets the iteration limit for this object.

• s_enhance (int) – Integer factor by which the spatial axes is to be enhanced.

• t_enhance (int) – Integer factor by which the temporal axes is to be enhanced.

• means (str | dict | None) – Usually a file path for loading / saving results, or None for just calculating stats and not saving. Can also be a dict.

• stds (str | dict | None) – Usually a file path for loading / saving results, or None for just calculating stats and not saving. Can also be a dict.

• queue_cap (int) – Maximum number of batches the batch queue can store.

• transform_kwargs (Union[Dict, None]) – Dictionary of kwargs to be passed to self.transform. This method performs smoothing / coarsening.

• mode (str) – Loading mode. Default is ‘lazy’, which only loads data into memory as batches are queued. ‘eager’ will load all data into memory right away.

• feature_sets (Optional[dict]) – Optional dictionary describing how the full set of features is split between lr_only_features and hr_exo_features.

  featureslist | tuple

  List of full set of features to use for sampling. If no entry is provided then all data_vars from data will be used.

  lr_only_featureslist | tuple

  List of feature names or patt*erns that should only be included in the low-res training set and not the high-res observations.

  hr_exo_featureslist | tuple

  List of feature names or patt*erns that should be included in the high-resolution observation but not expected to be output from the generative model. An example is high-res topography that is to be injected mid-network.

• kwargs (dict) – Keyword arguments for parent class

• sample_shape (tuple) – Size of arrays to sample from the contained data.

• time_enhance_mode (str) – [constant, linear] Method to enhance temporally when constructing subfilter. At every temporal location, a low-res temporal data is subtracted from the high-res temporal data predicted. constant will assume that the low-res temporal data is constant between landmarks. linear will linearly interpolate between landmarks to generate the low-res data to remove from the high-res.

• lower_models (Dict[int, Sup3rCondMom] | None) – Dictionary of models that predict lower moments. For example, if this queue is part of a handler to estimate the 3rd moment lower_models could include models that estimate the 1st and 2nd moments. These lower moments can be required in higher order moment calculations.

• s_padding (int | None) – Width of spatial padding to predict only middle part. If None, no padding is used

• t_padding (int | None) – Width of temporal padding to predict only middle part. If None, no padding is used

• end_t_padding (bool | False) – Zero pad the end of temporal space. Ensures that loss is calculated only if snapshot is surrounded by temporal landmarks. False by default

Methods

check_enhancement_factors()	Make sure the enhancement factors evenly divide the sample_shape.
check_features()	Make sure all samplers have the same sets of features.
check_shared_attr(attr)	Check if all containers have the same value for attr.
enqueue_batch()	Build batch and send to queue.
enqueue_batches()	Callback function for queue thread.
get_batch()	Get batch from queue or directly from a Sampler through sample_batch.
get_container_index()	Get random container index based on weights
get_queue()	Return FIFO queue for storing batches.
get_random_container()	Get random container based on container weights
init_samplers(train_containers, ...)	Initialize samplers from given data containers.
log_queue_info()	Log info about queue size.
make_mask(high_res)	Make mask for output.
make_output(samples)
post_init_log([args_dict])	Log additional arguments after initialization.
post_proc(samples)	Returns normalized collection of samples / observations along with mask and target output for conditional moment estimation.
preflight()	Run checks before kicking off the queue.
sample_batch()	Get random sampler from collection and return a batch of samples from that sampler.
start()	Start the val data batch queue in addition to the train batch queue.
stop()	Stop the val data batch queue in addition to the train batch queue.
transform(samples[, smoothing, ...])	Coarsen high res data to get corresponding low res batch.
wrap(data)	Return a Sup3rDataset object or tuple of such.

Attributes

container_weights	Get weights used to sample from different containers based on relative sizes
data	Return underlying data.
features	Get all features contained in data.
hr_shape	Shape of high resolution sample in a low-res / high-res pair.
lr_shape	Shape of low resolution sample in a low-res / high-res pair.
queue_shape	Shape of objects stored in the queue.
queue_thread	Get new queue thread.
running	Boolean to check whether to keep enqueueing batches.
shape	Get shape of underlying data.

class Batch(low_res, high_res)

Bases: tuple

Create new instance of Batch(low_res, high_res)

__add__(value, /)

Return self+value.

__mul__(value, /)

Return self*value.

count(value, /)

Return number of occurrences of value.

high_res

Alias for field number 1

index(value, start=0, stop=9223372036854775807, /)

Return first index of value.

Raises ValueError if the value is not present.

low_res

Alias for field number 0

class ConditionalBatch(low_res, high_res, output, mask)

Bases: tuple

Create new instance of ConditionalBatch(low_res, high_res, output, mask)

__add__(value, /)

Return self+value.

__mul__(value, /)

Return self*value.

count(value, /)

Return number of occurrences of value.

high_res

Alias for field number 1

index(value, start=0, stop=9223372036854775807, /)

Return first index of value.

Raises ValueError if the value is not present.

low_res

Alias for field number 0

mask

Alias for field number 3

output

Alias for field number 2

SAMPLER

alias of Sampler

TRAIN_QUEUE

alias of QueueMom2

VAL_QUEUE

alias of QueueMom2

check_enhancement_factors()

Make sure the enhancement factors evenly divide the sample_shape.

check_features()

Make sure all samplers have the same sets of features.

check_shared_attr(attr)

Check if all containers have the same value for attr. If they do the collection effectively inherits those attributes.

property container_weights

Get weights used to sample from different containers based on relative sizes

property data

Return underlying data.

Returns:

Sup3rDataset

See also

wrap()

enqueue_batch()

Build batch and send to queue.

enqueue_batches() → None

Callback function for queue thread. While training, the queue is checked for empty spots and filled. In the training thread, batches are removed from the queue.

property features

Get all features contained in data.

get_batch() → Batch

Get batch from queue or directly from a Sampler through sample_batch.

get_container_index()

Get random container index based on weights

get_queue()

Return FIFO queue for storing batches.

get_random_container()

Get random container based on container weights

property hr_shape

Shape of high resolution sample in a low-res / high-res pair. (e.g. (spatial_1, spatial_2, temporal, features))

init_samplers(train_containers, val_containers, sample_shape, feature_sets, batch_size, sampler_kwargs)

Initialize samplers from given data containers.

log_queue_info()

Log info about queue size.

property lr_shape

Shape of low resolution sample in a low-res / high-res pair. (e.g. (spatial_1, spatial_2, temporal, features))

make_mask(high_res)

Make mask for output. This is used to ensure consistency when training conditional moments.

Note

Consider the case of learning E(HR|LR) where HR is the high_res and LR is the low_res. In theory, the conditional moment estimation works if the full LR is passed as input and predicts the full HR. In practice, only the LR data that overlaps and surrounds the HR data is useful, ie E(HR|LR) = E(HR|LR_nei) where LR_nei is the LR data that surrounds the HR data. Physically, this is equivalent to saying that data far away from a region of interest does not matter. This allows learning the conditional moments on spatial and temporal chunks only if one restricts the high_res output as being overlapped and surrounded by the input low_res. The role of the mask is to ensure that the input low_res always surrounds the output high_res.

Parameters:

high_res (Union[np.ndarray, da.core.Array]) – 4D | 5D array (batch_size, spatial_1, spatial_2, features) (batch_size, spatial_1, spatial_2, temporal, features)

Returns:

mask (Union[np.ndarray, da.core.Array]) – 4D | 5D array (batch_size, spatial_1, spatial_2, features) (batch_size, spatial_1, spatial_2, temporal, features)

make_output(samples)

Returns:

(HR - <HR|LR>)**2 (Union[np.ndarray, da.core.Array]) – 4D | 5D array (batch_size, spatial_1, spatial_2, features) (batch_size, spatial_1, spatial_2, temporal, features) HR is high-res and LR is low-res

post_init_log(args_dict=None)

Log additional arguments after initialization.

post_proc(samples)

Returns normalized collection of samples / observations along with mask and target output for conditional moment estimation. Performs coarsening on high-res data if Collection consists of Sampler objects and not DualSampler objects

Returns:

namedtuple – Named tuple with low_res, high_res, mask, and output attributes

preflight()

Run checks before kicking off the queue.

property queue_shape

Shape of objects stored in the queue.

property queue_thread

Get new queue thread.

property running

Boolean to check whether to keep enqueueing batches.

sample_batch()

Get random sampler from collection and return a batch of samples from that sampler.

Notes

These samples are wrapped in an np.asarray call, so they have been loaded into memory.

property shape

Get shape of underlying data.

start()

Start the val data batch queue in addition to the train batch queue.

stop()

Stop the val data batch queue in addition to the train batch queue.

transform(samples, smoothing=None, smoothing_ignore=None, temporal_coarsening_method='subsample')

Coarsen high res data to get corresponding low res batch.

Parameters:

• samples (Union[np.ndarray, da.core.Array]) – High resolution batch of samples. 4D | 5D array (batch_size, spatial_1, spatial_2, features) (batch_size, spatial_1, spatial_2, temporal, features)

• smoothing (float | None) – Standard deviation to use for gaussian filtering of the coarse data. This can be tuned by matching the kinetic energy of a low resolution simulation with the kinetic energy of a coarsened and smoothed high resolution simulation. If None no smoothing is performed.

• smoothing_ignore (list | None) – List of features to ignore for the smoothing filter. None will smooth all features if smoothing kwarg is not None

• temporal_coarsening_method (str) – Method to use for temporal coarsening. Can be subsample, average, min, max, or total

Returns:

• low_res (Union[np.ndarray, da.core.Array]) – 4D | 5D array (batch_size, spatial_1, spatial_2, features) (batch_size, spatial_1, spatial_2, temporal, features)

• high_res (Union[np.ndarray, da.core.Array]) – 4D | 5D array (batch_size, spatial_1, spatial_2, features) (batch_size, spatial_1, spatial_2, temporal, features)

wrap(data)

Return a Sup3rDataset object or tuple of such. This is a tuple when the .data attribute belongs to a Collection object like BatchHandler. Otherwise this is Sup3rDataset object, which is either a wrapped 2-tuple or 1-tuple (e.g. len(data) == 2 or len(data) == 1). This is a 2-tuple when .data belongs to a dual container object like DualSampler and a 1-tuple otherwise.