vak.config.learncurve.LearncurveConfig

class vak.config.learncurve.LearncurveConfig(model, num_epochs, batch_size, root_results_dir, dataset: DatasetConfig, trainer: TrainerConfig, results_dirname=None, standardize_frames=False, num_workers=2, shuffle=True, val_step=None, ckpt_step=None, patience=None, checkpoint_path=None, frames_standardizer_path=None, post_tfm_kwargs: dict | None = None)

Bases: TrainConfig

Class that represents [vak.learncurve] table in configuration file.

model

The model to use: its name, and the parameters to configure it. Must be an instance of vak.config.ModelConfig

Type:

vak.config.ModelConfig

num_epochs

number of training epochs. One epoch = one iteration through the entire training set.

Type:

int

batch_size

number of samples per batch presented to models during training.

Type:

int

root_results_dir

directory in which results will be created. The vak.cli.train function will create a subdirectory in this directory each time it runs.

Type:

str

dataset

The dataset to use: the path to it, and optionally a path to a file representing splits, and the name, if it is a built-in dataset. Must be an instance of vak.config.DatasetConfig.

Type:

vak.config.DatasetConfig

trainer

Configuration for lightning.pytorch.Trainer. Must be an instance of vak.config.TrainerConfig.

Type:

vak.config.TrainerConfig

num_workers

Number of processes to use for parallel loading of data. Argument to torch.DataLoader.

Type:

int

shuffle

if True, shuffle training data before each epoch. Default is True.

Type:

bool

standardize_frames

if True, use vak.transforms.FramesStandardizer to standardize the frames. Normalization is done by subtracting off the mean for each row of the training set and then dividing by the std for that frequency bin. This same normalization is then applied to validation + test data.

Type:

bool

val_step

Step on which to estimate accuracy using validation set. If val_step is n, then validation is carried out every time the global step / n is a whole number, i.e., when val_step modulo the global step is 0. Default is None, in which case no validation is done.

Type:

int

ckpt_step

step/epoch at which to save to checkpoint file. Default is None, in which case checkpoint is only saved at the last epoch.

Type:

int

patience

number of epochs to wait without the error dropping before stopping the training. Default is None, in which case training continues for num_epochs

Type:

int

post_tfm_kwargs

Keyword arguments to post-processing transform. If None, then no additional clean-up is applied when transforming labeled timebins to segments, the default behavior. The transform used is vak.transforms.frame_labels.ToSegmentsWithPostProcessing`. Valid keyword argument names are 'majority_vote' and 'min_segment_dur', and should be appropriate values for those arguments: Boolean for ``majority_vote, a float value for min_segment_dur. See the docstring of the transform for more details on these arguments and how they work.

Type:

dict

__init__(model, num_epochs, batch_size, root_results_dir, dataset: DatasetConfig, trainer: TrainerConfig, results_dirname=None, standardize_frames=False, num_workers=2, shuffle=True, val_step=None, ckpt_step=None, patience=None, checkpoint_path=None, frames_standardizer_path=None, post_tfm_kwargs: dict | None = None) → None

Method generated by attrs for class LearncurveConfig.

Methods

__init__(model, num_epochs, batch_size, ...)	Method generated by attrs for class LearncurveConfig.
from_config_dict(config_dict)	Return LearncurveConfig instance from a dict.

Attributes

post_tfm_kwargs
batch_size
checkpoint_path
ckpt_step
dataset
frames_standardizer_path
model
num_epochs
num_workers
patience
results_dirname
root_results_dir
shuffle
standardize_frames
trainer
val_step

classmethod from_config_dict(config_dict: dict) → LearncurveConfig

Return LearncurveConfig instance from a dict.

The dict passed in should be the one found by loading a valid configuration toml file with vak.config.parse.from_toml_path(), and then using key learncurve, i.e., LearncurveConfig.from_config_dict(config_dict['learncurve']).