hookeai.miscellaneous.pruning.pruning_dataset¶

Pruning procedure of time series data set.

Functions¶

prune_time_series_dataset: Prune time series data set.
setup_pruning_dirs: Setup main pruning process directories.
set_pruning_iter_dir: Setup pruning iteration directory.
set_default_pruning_parameters: Set default pruning parameters.
get_n_prun_sample: Get number of pruned samples.
perform_pruning_step: Perform pruning step.
set_pruning_step_dir: Setup pruning iteration directory.
write_summary_file: Write summary file.
load_full_dataset: Load full data set.
read_samples_loss_from_dir: Read loss samples from prediction directory.
plot_pruning_iterative_data: Plot pruning iterative data.
display_pruning_data: Display data from pruning data file.
preview_pruning_iterations: Preview pruning iterations.

Functions

`display_pruning_data`(pruning_data_file_path)	Display data from pruning data file.
`find_unique_file_with_regex`(directory, regex)	Find file in directory based on regular expression.
`get_n_prun_sample`(prun_scheduler_type, ...)	Get number of pruned samples.
`get_parent_dataset_indices`(subset, ...)	Get parent data set indices from subset indices.
`load_dataset`(dataset_file_path)	Load PyTorch time series data set.
`load_full_dataset`(full_dataset_dir)	Load full data set.
`make_directory`(directory[, is_overwrite])	Create a directory.
`perform_model_prediction`(predict_directory, ...)	Perform prediction with RNN-based model.
`perform_model_standard_training`(...[, ...])	Perform standard training of RNN-based model.
`perform_pruning_step`(prun_datasets_dir, ...)	Perform pruning step.
`plot_pruning_iterative_data`(pruning_dir, ...)	Plot pruning iterative data.
`plot_xy_data`(data_xy[, data_labels, ...])	Plot data in xy axes.
`preview_pruning_iterations`(pruning_dir[, ...])	Preview pruning iterations.
`prune_time_series_dataset`(pruning_dir, ...)	Prune time series data set.
`read_mean_metrics_results_file`(file_path[, ...])	Read mean prediction metrics from file.
`read_samples_loss_from_dir`(predictions_dir)	Read loss samples from prediction directory.
`save_dataset`(dataset, dataset_basename, ...)	Save PyTorch time series data set to file.
`save_figure`(figure, filename[, height, ...])	Save Matplotlib figure.
`set_default_pruning_parameters`()	Set default pruning parameters.
`set_pruning_iter_dir`(prun_datasets_dir, ...)	Setup pruning iteration directory.
`set_pruning_step_dir`(prun_datasets_dir, ...)	Setup pruning iteration directory.
`setup_pruning_dirs`(pruning_dir, testing_types)	Setup main pruning process directories.
`split_dataset`(dataset, split_sizes[, ...])	Randomly split data set into non-overlapping subsets.
`write_summary_file`(pruning_dir, pruning_params)	Write summary file.

Classes

TimeSeriesDatasetInMemory(dataset_samples)

Time series data set (in-memory storage only).