daart Modules¶

daart.callbacks Module¶

Callback classes to control training.

Classes¶

`BaseCallback`()	Abstract base class for callbacks.
`EarlyStopping`([patience, delta])	Stop training when a monitored quantity has stopped improving.
`AnnealHparam`(hparams, key, epoch_start, ...)	Linearly increase value in an hparam dict.
`PseudoLabels`([prob_threshold, epoch_start])	Implement PseudoLabels algorithm.
`UPS`([prob_threshold, variance_threshold, ...])	Implement uncertainty-aware pseudo-labels algorithm.

daart.data Module¶

Classes for splitting and serving data to models.

The data generator classes contained in this module inherit from the torch.utils.data.Dataset class. The user-facing class is the DataGenerator, which can manage one or more datasets. Each dataset is composed of trials, which are split into training, validation, and testing trials using the split_trials(). The default data generator can handle the following data types:

markers: i.e. DLC/DGP markers
labels_strong: discrete behavioral labels
labels_weak: noisy discrete behavioral labels

Functions¶

`split_trials`(n_trials[, rng_seed, train_tr, ...])	Split trials into train/val/test blocks.
`compute_sequences`(data, sequence_length[, ...])	Compute sequences of temporally contiguous data points.
`compute_sequence_pad`(hparams)	Compute padding needed to account for convolutions.
`load_marker_csv`(filepath)	Load markers from csv file assuming DLC format.
`load_feature_csv`(filepath)	Load markers from csv file assuming the following format.
`load_marker_h5`(filepath)	Load markers from hdf5 file assuming DLC format.
`load_label_csv`(filepath)	Load labels from csv file assuming a standard format.
`load_label_pkl`(filepath)	Load labels from pkl file assuming a standard format.

Classes¶

`SingleDataset`(id, signals, transforms, paths)	Dataset class for a single dataset.
`DataGenerator`(ids_list, signals_list, ...[, ...])	Dataset generator for serving pytorch models.

daart.eval Module¶

Evaluation functions for the daart package.

Functions¶

`get_precision_recall`(true_classes, pred_classes)	Compute precision and recall for classifier.
`int_over_union`(array1, array2)	Compute intersection over union for two 1D arrays.
`run_lengths`(array)	Compute distribution of run lengths for an array with integer entries.
`plot_training_curves`(metrics_file[, dtype, ...])	Create training plots for each term in the objective function.
`load_metrics_csv_as_df`(metric_file, metrics_list)	Load metrics csv file and return as a pandas dataframe for easy plotting.

daart.io Module¶

File IO for daart package.

Functions¶

`get_subdirs`(path)	Get all first-level subdirectories in a given path (no recursion).
`get_expt_dir`(base_dir, expt_ids)	Construct experiment directory given base directory and list of experiment ids.
`get_model_dir`(base_dir, model_params)	Helper function to construct model directory from model param dict.
`get_model_params`(hparams)	Returns dict containing all params considered essential for defining a model of that type.
`find_experiment`(hparams[, verbose, ...])	Search testtube versions to find if experiment with the same hyperparameters has been fit.
`read_expt_info_from_csv`(expt_file)	Read csv file that contains expt id info.
`export_expt_info_to_csv`(expt_dir, ids_list)	Export list of expt ids to csv file.
`export_hparams`(hparams[, filename])	Export hyperparameter dictionary as a yaml file.
`make_dir_if_not_exists`(save_file)	Utility function for creating necessary dictories for a specified filename.

daart.losses Module¶

Custom losses for PyTorch models.

Functions¶

kl_div_to_std_normal(mu, logvar)

Compute element-wise KL(q(z) || N(0, 1)) where q(z) is a normal parameterized by mu, logvar.

daart.testtube Module¶

Test-tube helper functions for use in fitting scripts.

Functions¶

`get_all_params`()
`print_hparams`(hparams)	Nicely formatted hparams string.
`create_tt_experiment`(hparams)	Create test-tube experiment for organizing model fits.
`clean_tt_dir`(hparams)	Delete all (unnecessary) subdirectories in the model directory (created by test-tube)

daart.train Module¶

Helper functions for model training.

Classes¶

`Logger`([n_datasets, save_path])	Base class for logging loss metrics.
`Trainer`([learning_rate, l2_reg, min_epochs, ...])

daart.transforms Module¶

Tranform classes to process data.

Data generator objects can apply these transforms to data upon loading.

Classes¶

`Compose`(transforms)	Composes several transforms together.
`Transform`()	Abstract base class for transforms.
`BlockShuffle`(rng_seed)	Shuffle blocks of contiguous discrete states within each trial.
`MakeOneHot`([n_classes])	Turn a categorical vector into a one-hot vector.
`MotionEnergy`()	Compute motion energy across batch dimension.
`Unitize`()	Place each channel (mostly) in [0, 1].
`ZScore`()	z-score channel activity.

daart.utils Module¶

Utility functions for daart package.

Functions¶

`build_data_generator`(hparams)	Helper function to build a data generator from hparam dict.
`collect_callbacks`(hparams)	Helper function to build a list of callbacks from hparam dict.