del preds, probs, pred_probs, upper, lower, X_test, y_test, \
trained_model, data_test, data_vector_test, data_ind_test
metrics = pd.DataFrame(
np.column_stack([pctls, accuracy, precision, recall, f1, roc_auc]),
columns=[
'cloud_cover', 'accuracy', 'precision', 'recall', 'f1', 'auc'
])
metrics.to_csv(metrics_path / 'metrics.csv', index=False)
times = [
float(i) for i in times
] # Convert time objects to float, otherwise valMetrics will be non-numeric
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)
# ======================================================================================================================
log_reg_training_sample(img_list, pctls, feat_list_new, feat_list_all,
data_path, batch, n_flood, n_nonflood)
log_reg_prediction_sample(img_list, pctls, feat_list_all, data_path, batch)
viz = VizFuncs(viz_params)
viz.metric_plots()
viz.metric_plots_multi()
viz.time_plot()
viz.false_map(probs=True, save=False)
viz.false_map_borders()
viz.uncertainty_map_LR()
viz.fpfn_map(probs=True)
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()
'img_list': img_list,
'pctls': pctls,
'data_path': data_path,
'batch': batch,
'feat_list_new': feat_list_new
}
# NUM_PARALLEL_EXEC_UNITS = 8
# config = tf.compat.v1.ConfigProto(intra_op_parallelism_threads=NUM_PARALLEL_EXEC_UNITS, inter_op_parallelism_threads=4,
# allow_soft_placement=True, device_count={'CPU': NUM_PARALLEL_EXEC_UNITS})
# session = tf.compat.v1.Session(config=config)
# tf.compat.v1.keras.backend.set_session(session)
# os.environ["KMP_BLOCKTIME"] = "30"
# os.environ["KMP_SETTINGS"] = "1"
# os.environ["KMP_AFFINITY"] = "granularity=fine,verbose,compact,1,0"
# os.environ['MKL_NUM_THREADS'] = str(NUM_PARALLEL_EXEC_UNITS)
# os.environ['GOTO_NUM_THREADS'] = str(NUM_PARALLEL_EXEC_UNITS)
# os.environ['OMP_NUM_THREADS'] = str(NUM_PARALLEL_EXEC_UNITS)
# ======================================================================================================================
training_bnn_kwon(img_list, pctls, model_func, feat_list_new, data_path, batch,
dropout_rate, **model_params)
prediction_bnn_kwon(img_list, pctls, feat_list_new, data_path, batch,
MC_passes, **model_params)
viz = VizFuncs(viz_params)
viz.time_plot()
viz.false_map(probs=False, save=False)
viz.false_map_borders()
viz.uncertainty_map_NN()
viz.fpfn_map(probs=False)