deepnog.inference

Author: Roman Feldbauer

Date: 2020-02-19

Description:

Predict orthologous groups of protein sequences.

deepnog.inference.load_nn(architecture, model_dict, device='cpu')[source]

Import NN architecture and set loaded parameters.

Parameters

  • architecture (str) – Name of neural network module and class to import.

  • model_dict (dict) – Dictionary holding all parameters and hyper-parameters of the model.

  • device ([str, torch.device]) – Device to load the model into.

Returns

model – Neural network object of type architecture with parameters loaded from model_dict and moved to device.

Return type

torch.nn.Module

deepnog.inference.predict(model, dataset, device='cpu', batch_size=16, num_workers=4, verbose=3)[source]

Use model to predict zero-indexed labels of dataset.

Also handles communication with ProteinIterators used to load data to log how many sequences have been skipped due to having empty sequence ids.

Parameters

  • model (nn.Module) – Trained neural network model.

  • dataset (ProteinDataset) – Data to predict protein families for.

  • device ([str, torch.device]) – Device of model.

  • batch_size (int) – Forward batch_size proteins through neural network at once.

  • num_workers (int) – Number of workers for data loading.

  • verbose (int) – Define verbosity.

Returns

  • preds (torch.Tensor, shape (n_samples,)) – Stores the index of the output-node with the highest activation

  • confs (torch.Tensor, shape (n_samples,)) – Stores the confidence in the prediction

  • ids (list[str]) – Stores the (possible empty) protein labels extracted from data file.

  • indices (list[int]) – Stores the unique indices of sequences mapping to their position in the file