epitome.models.EpitomeModel

class epitome.models.EpitomeModel(*args, **kwargs)

__init__(*args, **kwargs)

Creates a new model with 4 layers with 100 unites each.

To resume model training on an old model, call:

model = EpitomeModel(checkpoint=path_to_saved_model)

Methods

__init__(*args, **kwargs)	Creates a new model with 4 layers with 100 unites each.
body_fn()
create_model(**kwargs)	Creates an Epitome model.
eval_vector(matrix, indices)	Evaluates a new cell type based on its chromatin (DNase or ATAC-seq) vector, as well as any other similarity targets (acetylation, methylation, etc.).
g(p[, a, B, y])	Normalization Function.
loss_fn(y_true, y_pred, weights)	Loss function for Epitome.
run_predictions(num_samples, iter_[, …])	Runs predictions on num_samples records
save(checkpoint_path)	Saves model.
score_matrix(accessilibility_peak_matrix, …)	Runs predictions on a matrix of accessibility peaks, where columns are samples and rows are regions from regions_peak_file.
score_peak_file(similarity_peak_files, …)	Runs predictions on a set of peaks defined in a bed or narrowPeak file.
score_whole_genome(similarity_peak_files, …)	Runs a whole genome scan for all available genomic regions in the dataset (about 3.2Million regions) Takes about 1 hour on entire genome.
test(num_samples[, mode, calculate_metrics])	Tests model on valid and test dataset handlers.
test_from_generator(num_samples, ds[, …])	Runs test given a specified data generator.
train(max_train_batches[, patience, min_delta])	Trains an Epitome model.

Attributes

predict_step_generator
predict_step_matrix