sniROM/04_test.py at master · YSiFan/sniROM · GitHub

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"""
Use the trained models to evaluate the performance on the previously unseed test
dataset.
Loading the FNN automatically detects the best hyperparameter configuration from
the tuning.
"""
# Author: Arturs Berzins <berzins@cats.rwth-aachen.de>
# License: BSD 3 clause

import config
import utils
import numpy as np
import pandas as pd

def main():
    dataset = 'test'
    features = utils.load_features(dataset)

    error_table = [] #list of dictionaries

    for component in config.components:
        L = config.num_basis[component]
        D, denom_sq = utils.load_POD_D_and_denom_sq(component)
        eps_pod_sqs = utils.load_error_POD_sq(dataset, component)

        targets = utils.load_targets(dataset, component)
        outputs = {}
        for model_key, model_constructor in utils.models.items():
            model = model_constructor()
            model.load(utils.model_dir, component)
            outputs[model_key] = model.evaluate(features)

        ## for each sample in test set
        for i in range(len(targets)):

            for l in range(L+1):
                eps_pod_sq = eps_pod_sqs[l,i]
                line = {'component': component,
                        'l': l,
                        'sample': i,
                        'dataset': dataset,
                        'eps_pod_sq': eps_pod_sq
                       }

                for model_key in utils.models:
                    q_rb_model = outputs[model_key][i][:l]
                    q_rb_truth = targets[i][:l]
                    D_l = D[:l]
                    eps_reg_sq = np.sum((D_l*(q_rb_model-q_rb_truth))**2) / denom_sq
                    eps_sq = eps_pod_sq + eps_reg_sq
                    eps_key = F'eps_pod{model_key.lower()}_sq'
                    line[eps_key] = eps_sq

                error_table.append(line)

    df = pd.DataFrame(error_table)
    utils.save_error_table(df, dataset)

if __name__ == '__main__':
    main()