rollout_metrics_noisy_model

Attributes

logger
description

Classes

Objective

Optuna objective class for hyperparameter optimization.

Functions

compute_spread_skill_metric(all_files, ensemble_size)
compute_metrics(metrics, y_pred, y, date_time, ...)	Compute metrics and update metrics_results.
calculate_ensemble_metrics(ensemble_preds, true_values)	Calculate RMSE and STD per channel
predict(rank, world_size, conf[, backend, p, trial, ...])

Module Contents

rollout_metrics_noisy_model.logger

rollout_metrics_noisy_model.compute_spread_skill_metric(all_files, ensemble_size)

rollout_metrics_noisy_model.compute_metrics(metrics, y_pred, y, date_time, forecast_step, utc_datetime)

Compute metrics and update metrics_results.

rollout_metrics_noisy_model.calculate_ensemble_metrics(ensemble_preds, true_values)

Calculate RMSE and STD per channel

Parameters:

• ensemble_preds – (n_members, batch, channels, 1, height, width)

• true_values – (1, batch, channels, 1, height, width)

rollout_metrics_noisy_model.predict(rank, world_size, conf, backend=None, p=None, trial=None, num_samples=10)

class rollout_metrics_noisy_model.Objective(config, metric='val_loss', device='cpu')

Bases: echo.src.base_objective.BaseObjective

Optuna objective class for hyperparameter optimization.

config

Configuration dictionary containing training parameters.

Type:

dict

metric

Metric to optimize, defaults to “val_loss”.

Type:

str

device

Device for training, defaults to “cpu”.

Type:

str

train(trial, conf)

Train the model using the given trial configuration.

Parameters:

• trial (optuna.trial.Trial) – Optuna trial object.

• conf (dict) – Configuration dictionary for the current trial.

Returns:

The result of the training process.

Return type:

Any

rollout_metrics_noisy_model.description = 'Rollout AI-NWP forecasts'