'batch_size': BATCH_SIZE,
'epochs': EPOCHS,
'verbose': 1,
'callbacks': [es],
'use_multiprocessing': True
}
# ==================================================================================
# Training and prediction
training2(img_list, pctls, model_func, feat_list_new, uncertainty, data_path,
batch, DROPOUT_RATE, HOLDOUT, **model_params)
prediction(img_list, pctls, feat_list_new, data_path, batch, remove_perm=True)
viz_params = {
'img_list': img_list,
'pctls': pctls,
'data_path': data_path,
'uncertainty': uncertainty,
'batch': batch,
'feat_list_new': feat_list_new
}
viz = VizFuncs(viz_params)
viz.metric_plots()
viz.time_plot()
# viz.metric_plots_multi()
viz.false_map()
# viz.time_size()
accuracy.append(accuracy_score(y_test, preds))
precision.append(precision_score(y_test, preds))
recall.append(recall_score(y_test, preds))
f1.append(f1_score(y_test, preds))
del preds, p_hat, aleatoric, epistemic, X_test, y_test, model, data_test, data_vector_test, data_ind_test
metrics = pd.DataFrame(
np.column_stack([pctls, accuracy, precision, recall, f1]),
columns=['cloud_cover', 'accuracy', 'precision', 'recall', 'f1'])
metrics.to_csv(metrics_path / 'metrics.csv', index=False)
times = [float(i) for i in times]
times_df = pd.DataFrame(np.column_stack([pctls, times]),
columns=['cloud_cover', 'testing_time'])
times_df.to_csv(metrics_path / 'testing_times.csv', index=False)
# ======================================================================================================================
training_BNN_gen_model(img_list_train, feat_list_new, model_func, data_path,
batch, dropout_rate, **model_params)
prediction_BNN_gen_model(img_list_test, pctls, feat_list_new, data_path, batch,
MC_passes, **model_params)
viz = VizFuncs(viz_params)
viz.metric_plots()
viz.metric_plots_multi()
viz.time_plot()
viz.false_map(probs=False, save=False)
viz.false_map_borders()
viz.fpfn_map()
viz.uncertainty_map_NN()