dicee.callbacks

Classes

AccumulateEpochLossCallback	Abstract class for Callback class for knowledge graph embedding models
PrintCallback	Abstract class for Callback class for knowledge graph embedding models
KGESaveCallback	Abstract class for Callback class for knowledge graph embedding models
PseudoLabellingCallback	Abstract class for Callback class for knowledge graph embedding models
ASWA	Adaptive stochastic weight averaging
Eval	Abstract class for Callback class for knowledge graph embedding models
KronE	Abstract class for Callback class for knowledge graph embedding models
Perturb	A callback for a three-Level Perturbation
PeriodicEvalCallback	Callback to periodically evaluate the model and optionally save checkpoints during training.
LRScheduler	Callback for managing learning rate scheduling and model snapshots.

Functions

estimate_q(eps)	estimate rate of convergence q from sequence esp
compute_convergence(seq, i)

Module Contents

class dicee.callbacks.AccumulateEpochLossCallback(path: str)

Bases: dicee.abstracts.AbstractCallback

Abstract class for Callback class for knowledge graph embedding models

Parameter

path

on_fit_end(trainer, model) → None

Store epoch loss

Parameter

trainer:

model:

rtype:

None

class dicee.callbacks.PrintCallback

Bases: dicee.abstracts.AbstractCallback

Abstract class for Callback class for knowledge graph embedding models

Parameter

start_time

on_fit_start(trainer, pl_module)

Call at the beginning of the training.

Parameter

trainer:

model:

rtype:

None

on_fit_end(trainer, pl_module)

Call at the end of the training.

Parameter

trainer:

model:

rtype:

None

on_train_batch_end(*args, **kwargs)

Call at the end of each mini-batch during the training.

Parameter

trainer:

model:

rtype:

None

on_train_epoch_end(*args, **kwargs)

Call at the end of each epoch during training.

Parameter

trainer:

model:

rtype:

None

class dicee.callbacks.KGESaveCallback(every_x_epoch: int, max_epochs: int, path: str)

Bases: dicee.abstracts.AbstractCallback

Abstract class for Callback class for knowledge graph embedding models

Parameter

every_x_epoch

max_epochs

epoch_counter = 0

path

on_train_batch_end(*args, **kwargs)

Call at the end of each mini-batch during the training.

Parameter

trainer:

model:

rtype:

None

on_fit_start(trainer, pl_module)

Call at the beginning of the training.

Parameter

trainer:

model:

rtype:

None

on_train_epoch_end(*args, **kwargs)

Call at the end of each epoch during training.

Parameter

trainer:

model:

rtype:

None

on_fit_end(*args, **kwargs)

Call at the end of the training.

Parameter

trainer:

model:

rtype:

None

on_epoch_end(model, trainer, **kwargs)

class dicee.callbacks.PseudoLabellingCallback(data_module, kg, batch_size)

Bases: dicee.abstracts.AbstractCallback

Abstract class for Callback class for knowledge graph embedding models

Parameter

data_module

kg

num_of_epochs = 0

unlabelled_size

batch_size

create_random_data()

on_epoch_end(trainer, model)

dicee.callbacks.estimate_q(eps)

estimate rate of convergence q from sequence esp

dicee.callbacks.compute_convergence(seq, i)

class dicee.callbacks.ASWA(num_epochs, path)

Bases: dicee.abstracts.AbstractCallback

Adaptive stochastic weight averaging ASWE keeps track of the validation performance and update s the ensemble model accordingly.

path

num_epochs

initial_eval_setting = None

epoch_count = 0

alphas = []

val_aswa = -1

on_fit_end(trainer, model)

Call at the end of the training.

Parameter

trainer:

model:

rtype:

None

static compute_mrr(trainer, model) → float

get_aswa_state_dict(model)

decide(running_model_state_dict, ensemble_state_dict, val_running_model, mrr_updated_ensemble_model)

Perform Hard Update, software or rejection

Parameters:

• running_model_state_dict

• ensemble_state_dict

• val_running_model

• mrr_updated_ensemble_model

on_train_epoch_end(trainer, model)

Call at the end of each epoch during training.

Parameter

trainer:

model:

rtype:

None

class dicee.callbacks.Eval(path, epoch_ratio: int = None)

Bases: dicee.abstracts.AbstractCallback

Abstract class for Callback class for knowledge graph embedding models

Parameter

path

reports = []

epoch_ratio = None

epoch_counter = 0

on_fit_start(trainer, model)

Call at the beginning of the training.

Parameter

trainer:

model:

rtype:

None

on_fit_end(trainer, model)

Call at the end of the training.

Parameter

trainer:

model:

rtype:

None

on_train_epoch_end(trainer, model)

Call at the end of each epoch during training.

Parameter

trainer:

model:

rtype:

None

on_train_batch_end(*args, **kwargs)

Call at the end of each mini-batch during the training.

Parameter

trainer:

model:

rtype:

None

class dicee.callbacks.KronE

Bases: dicee.abstracts.AbstractCallback

Abstract class for Callback class for knowledge graph embedding models

Parameter

f = None

static batch_kronecker_product(a, b)

Kronecker product of matrices a and b with leading batch dimensions. Batch dimensions are broadcast. The number of them mush :type a: torch.Tensor :type b: torch.Tensor :rtype: torch.Tensor

get_kronecker_triple_representation(indexed_triple: torch.LongTensor)

Get kronecker embeddings

on_fit_start(trainer, model)

Call at the beginning of the training.

Parameter

trainer:

model:

rtype:

None

class dicee.callbacks.Perturb(level: str = 'input', ratio: float = 0.0, method: str = None, scaler: float = None, frequency=None)

Bases: dicee.abstracts.AbstractCallback

A callback for a three-Level Perturbation

Input Perturbation: During training an input x is perturbed by randomly replacing its element. In the context of knowledge graph embedding models, x can denote a triple, a tuple of an entity and a relation, or a tuple of two entities. A perturbation means that a component of x is randomly replaced by an entity or a relation.

Parameter Perturbation:

Output Perturbation:

level = 'input'

ratio = 0.0

method = None

scaler = None

frequency = None

on_train_batch_start(trainer, model, batch, batch_idx)

Called when the train batch begins.

class dicee.callbacks.PeriodicEvalCallback(experiment_path: str, max_epochs: int, eval_every_n_epoch: int = 0, eval_at_epochs: list = None, save_model_every_n_epoch: bool = True, n_epochs_eval_model: str = 'val_test')

Bases: dicee.abstracts.AbstractCallback

Callback to periodically evaluate the model and optionally save checkpoints during training.

Evaluates at regular intervals (every N epochs) or at explicitly specified epochs. Stores evaluation reports and model checkpoints.

experiment_dir

max_epochs

epoch_counter = 0

save_model_every_n_epoch = True

reports

n_epochs_eval_model = 'val_test'

default_eval_model = None

eval_epochs

on_fit_end(trainer, model)

Called at the end of training. Saves final evaluation report.

on_train_epoch_end(trainer, model)

Called at the end of each training epoch. Performs evaluation and checkpointing if scheduled.

Parameters:

• trainer (object) – The training controller.

• model (torch.nn.Module) – The model being trained.

class dicee.callbacks.LRScheduler(adaptive_lr_config: dict, total_epochs: int, experiment_dir: str, eta_max: float = 0.1, snapshot_dir: str = 'snapshots')

Bases: dicee.abstracts.AbstractCallback

Callback for managing learning rate scheduling and model snapshots.

Supports cosine annealing (“cca”), MMCCLR (“mmcclr”), and their deferred (warmup) variants: - “deferred_cca” - “deferred_mmcclr”

At the end of each learning rate cycle, the model can optionally be saved as a snapshot.

total_epochs

experiment_dir

snapshot_dir

batches_per_epoch = None

total_steps = None

cycle_length = None

warmup_steps = None

lr_lambda = None

scheduler = None

step_count = 0

snapshot_loss

on_train_start(trainer, model)

Initialize training parameters and LR scheduler at start of training.

on_train_batch_end(trainer, model, outputs, batch, batch_idx)

Step the LR scheduler and save model snapshot if needed after each batch.

on_fit_end(trainer, model)

Call at the end of the training.

Parameter

trainer:

model:

rtype:

None