bird_cloud_gnn.cross_validation

Helper functions for cross validation.

Module Contents

Functions

get_dataloaders(dataset, train_idx, test_idx, batch_size)

Returns train and test dataloaders for a given dataset, train indices, test indices, and batch size.

kfold_evaluate(dataset, layers_data[, n_splits, ...])

Evaluate the model on a dataset using StratifiedKFold.

leave_one_origin_out_evaluate(dataset, layers_data[, ...])

Evaluate the model on a dataset by looping over each origin, and training the data with
bird_cloud_gnn.cross_validation.get_dataloaders(dataset, train_idx, test_idx, batch_size)[source]

Returns train and test dataloaders for a given dataset, train indices, test indices, and batch size.

Parameters:

  • dataset (torch_geometric.datasets) – The dataset to use for creating dataloaders.

  • train_idx (list) – The indices to use for training.

  • test_idx (list) – The indices to use for testing.

  • batch_size (int) – The batch size to use for the dataloaders.

Returns:

A tuple containing the train and test dataloaders.

Return type:

tuple

bird_cloud_gnn.cross_validation.kfold_evaluate(dataset, layers_data, n_splits=5, learning_rate=0.01, num_epochs=100, batch_size=512)[source]

Evaluate the model on a dataset using StratifiedKFold.

Parameters:

  • dataset (RadarDataset) – The dataset

  • layers_data (list) – The list of input size and activation

  • n_splits (int, optional) – Number of folds. Defaults to 5.

  • learning_rate (float, optional) – Learning rate. Defaults to 0.01.

  • num_epochs (int, optional) – Training epochs. Defaults to 20.

  • batch_size (int, optional) – Batch size used in the data loaders. Defaults to 512.

Returns:

None

bird_cloud_gnn.cross_validation.leave_one_origin_out_evaluate(dataset, layers_data, learning_rate=0.01, num_epochs=100, batch_size=512)[source]

Evaluate the model on a dataset by looping over each origin, and training the data with all data not from that origin, and testing with data from that origin. In other words, doing a leave one out validation on the origins.

Parameters:

  • dataset (RadarDataset) – The dataset.

  • layers_data (list) – The list of input size and activation

  • n_splits (int, optional) – Number of folds. Defaults to 5.

  • learning_rate (float, optional) – Learning rate. Defaults to 0.01.

  • num_epochs (int, optional) – Training epochs. Defaults to 20.

  • batch_size (int, optional) – Batch size used in the data loaders. Defaults to 512.