def __init__(
self,
cardinality: int = 2,
verbose: bool = True,
device: str = "cpu",
metric: str = "accuracy",
tie_break_policy: str = "abstain",
n_epochs: int = 100,
lr: float = 0.01,
l2: float = 0.0,
optimizer: str = "sgd",
optimizer_config: Optional[OptimizerConfig] = None,
lr_scheduler: str = "constant",
lr_scheduler_config: Optional[LRSchedulerConfig] = None,
prec_init: float = 0.7,
seed: int = np.random.randint(1e6),
log_freq: int = 10,
mu_eps: Optional[float] = None,
class_balance: Optional[List[float]] = None,
**kwargs: Any,
) -> None:
self.cardinality = cardinality
self.verbose = verbose
self.device = device
self.metric = metric
self.tie_break_policy = tie_break_policy
self.n_epochs = n_epochs
self.lr = lr
self.l2 = l2
self.optimizer = optimizer
self.optimizer_config = (
optimizer_config if optimizer_config is not None else
OptimizerConfig() # type: ignore
)
self.lr_scheduler = lr_scheduler
self.lr_scheduler_config = (
lr_scheduler_config if lr_scheduler_config is not None else
LRSchedulerConfig() # type: ignore
)
self.prec_init = prec_init
self.seed = seed
self.log_freq = log_freq
self.mu_eps = mu_eps
self.class_balance = class_balance
self.label_model = LabelModel(cardinality=self.cardinality,
verbose=self.verbose,
device=self.device)
class TrainConfig(Config):
"""Settings for the fit() method of LabelModel.
Parameters
----------
n_epochs
The number of epochs to train (where each epoch is a single optimization step)
lr
Base learning rate (will also be affected by lr_scheduler choice and settings)
l2
Centered L2 regularization strength
optimizer
Which optimizer to use (one of ["sgd", "adam", "adamax"])
optimizer_config
Settings for the optimizer
lr_scheduler
Which lr_scheduler to use (one of ["constant", "linear", "exponential", "step"])
lr_scheduler_config
Settings for the LRScheduler
prec_init
LF precision initializations / priors
seed
A random seed to initialize the random number generator with
log_freq
Report loss every this many epochs (steps)
mu_eps
Restrict the learned conditional probabilities to [mu_eps, 1-mu_eps]
"""
n_epochs: int = 100
lr: float = 0.01
l2: float = 0.0
optimizer: str = "sgd"
optimizer_config: OptimizerConfig = OptimizerConfig() # type: ignore
lr_scheduler: str = "constant"
lr_scheduler_config: LRSchedulerConfig = LRSchedulerConfig(
) # type: ignore
prec_init: Union[float, List[float], np.ndarray, torch.Tensor] = 0.7
seed: int = np.random.randint(1e6)
log_freq: int = 10
mu_eps: Optional[float] = None
class TrainerConfig(Config):
"""Settings for the Trainer.
Parameters
----------
seed
A random seed to set before training; if None, no seed is set
n_epochs
The number of epochs to train
lr
Base learning rate (will also be affected by lr_scheduler choice and settings)
l2
L2 regularization coefficient (weight decay)
grad_clip
The value that the gradient norm will be clipped to if it exceeds it
train_split
The name of the split to use as the training set
valid_split
The name of the split to use as the validation set
test_split
The name of the split to use as the test set
progress_bar
If True, print a tqdm progress bar during training
model_config
Settings for the MultitaskClassifier
log_manager_config
Settings for the LogManager
checkpointing
If True, use a Checkpointer to save the best model during training
checkpointer_config
Settings for the Checkpointer
logging
If True, log metrics (to file or Tensorboard) during training
log_writer
The type of LogWriter to use (one of ["json", "tensorboard"])
log_writer_config
Settings for the LogWriter
optimizer
Which optimizer to use (one of ["sgd", "adam", "adamax"])
optimizer_config
Settings for the optimizer
lr_scheduler
Which lr_scheduler to use (one of ["constant", "linear", "exponential", "step"])
lr_scheduler_config
Settings for the LRScheduler
batch_scheduler
Which batch scheduler to use (in what order batches will be drawn from multiple
tasks)
"""
seed: Optional[int] = None
n_epochs: int = 1
lr: float = 0.01
l2: float = 0.0
grad_clip: float = 1.0
train_split: str = "train"
valid_split: str = "valid"
test_split: str = "test"
progress_bar: bool = True
model_config: ClassifierConfig = ClassifierConfig() # type:ignore
log_manager_config: LogManagerConfig = LogManagerConfig() # type:ignore
checkpointing: bool = False
checkpointer_config: CheckpointerConfig = CheckpointerConfig(
) # type:ignore
logging: bool = False
log_writer: str = "tensorboard"
log_writer_config: LogWriterConfig = LogWriterConfig() # type:ignore
optimizer: str = "adam"
optimizer_config: OptimizerConfig = OptimizerConfig() # type:ignore
lr_scheduler: str = "constant"
lr_scheduler_config: LRSchedulerConfig = LRSchedulerConfig() # type:ignore
batch_scheduler: str = "shuffled"