pyprobound.rounds.Round

class Round(aggregate, reference_round, train_depth=True, log_depth=0, library_concentration=-1, name='')

Bases: BaseRound

Base class for sequenced rounds with an aggregate object.

aggregate

The binding components driving enrichment.

Type:

Aggregate

reference_round

The previous round for cumulative enrichment.

Type:

BaseRound

log_depth

The sequencing depth \(\eta\) in log space.

Type:

Tensor

__init__(aggregate, reference_round, train_depth=True, log_depth=0, library_concentration=-1, name='')

Initializes the round.

Parameters:

• aggregate (Aggregate) – The binding components driving enrichment.

• reference_round (BaseRound | None) – The previous round used in cumulative enrichment.

• train_depth (bool) – Whether to train the sequencing depth \(\eta\).

• log_depth (float) – The initial value of log_depth.

• library_concentration (float) – The total library concentration, used for estimating the free protein concentration.

• name (str) – A string used to describe the round.

Methods

cache(fun)	Decorator for a function to cache its output.
check_length_consistency()	Checks that input lengths of Binding components are consistent.
components()	Iterator of child components.
forward(seqs)	Predicts the log relative count.
freeze()	Turns off gradient calculation for all parameters.
from_binding(binding, reference_round[, ...])	Creates a new instance from binding components and reference round.
log_aggregate(seqs)	Predicts the log aggregate \(\log Z_i\).
log_cumulative_enrichment(seqs)	Predicts the log cumulative enrichment.
log_enrichment(seqs)	Predicts the log enrichment ratio.
max_embedding_size()	The maximum number of bytes needed to encode a sequence.
optim_procedure([ancestry, current_order])	The sequential optimization procedure for all Binding components.
reload(checkpoint)	Loads the model from a checkpoint file.
reload_from_state_dict(state_dict)	Loads the model from a state dict.
save(checkpoint[, flank_lengths])	Saves the model to a file with "state_dict" and "metadata" fields.
unfreeze([parameter])	Turns on gradient calculation for the specified parameter.

Attributes

library_concentration	The total library concentration \([\text{library}]\).
unfreezable	alias of Literal['all', 'depth']

Non-Inherited Members

classmethod from_binding(binding, reference_round, train_depth=True, log_depth=0, train_concentration=False, target_concentration=1, library_concentration=-1, activity_heuristic=0.05, name='')

Creates a new instance from binding components and reference round.

Parameters:

• binding (Iterable[Binding]) – The binding components driving enrichment.

• reference_round (BaseRound | None) – The previous round used in cumulative enrichment.

• train_depth (bool) – Whether to train the sequencing depth \(\eta\).

• log_depth (float) – The initial value of log_depth.

• train_concentration (bool) – Whether to train the protein concentration.

• target_concentration (float) – Protein concentration, used for estimating the free protein concentration.

• library_concentration (float) – Library concentration, used for estimating the free protein concentration.

• activity_heuristic (float) – The fraction of the total aggregate that the contribution will be set to when it is first optimized.

• name (str) – A string used to describe the round.

Return type:

Self

components()

Iterator of child components.

Return type:

Iterator[Aggregate]

log_aggregate(seqs)

Predicts the log aggregate \(\log Z_i\).

Parameters:

seqs (Tensor) – A sequence tensor of shape \((\text{minibatch},\text{length})\) or \((\text{minibatch},\text{in_channels},\text{length})\).

Return type:

Tensor

Returns:

The log aggregate tensor of shape \((\text{minibatch},)\).