def perclass_wlcss_test():
# classes = [406516, 404516, 406505, 404505, 406519, 404519, 407521, 405506, 406520, 404520, 408512]
classes = [48, 49, 50, 51, 52, 53, 54, 55, 56, 57]
# classes = [406516, 404516, 406505, 404505, 406519, 404519, 407521, 405506]
templates, streams, streams_labels = dl.load_training_dataset(
dataset_choice='skoda_mini',
classes=classes,
template_choice_method='mrt_lcs')
streams_labels_sorted_idx = streams_labels.argsort()
streams = [streams[i] for i in streams_labels_sorted_idx]
streams_labels = streams_labels[streams_labels_sorted_idx]
params = [[34, 5, 2], [59, 18, 3], [49, 13, 2], [5, 11, 4], [55, 57, 1],
[55, 38, 0], [24, 8, 2], [47, 16, 2], [30, 0, 8], [39, 9, 9]]
# params = [[63, 3, 1] for _ in range(len(classes))]
thresholds = [500, 994, 907, -698, 962, 405, -643, -74, 859, 480]
# thresholds = [374, 228, 104, 279, 519, 394, 247, 73]
start_time = time.time()
m_wlcss_cuda = WLCSSCuda(templates, streams, np.array(params), False)
mss = m_wlcss_cuda.compute_wlcss()
m_wlcss_cuda.cuda_freemem()
print("Duration: {}".format(
time.strftime("%H:%M:%S.%f", time.gmtime(time.time() - start_time))))
fitness_score = ftf.isolated_fitness_function_params(
mss, streams_labels, thresholds, classes, parameter_to_optimize='f1')
print(fitness_score)
pfe.performance_evaluation_isolated(mss, streams_labels, thresholds,
classes)
plt_creator.plot_isolated_mss(
mss,
thresholds,
'hci_guided',
classes,
streams_labels,
title="Isolated matching score - Params opt. - {}".format(
'hci_guided'))
plt.show()
def __compute_fitness_cuda(self, pop):
params = [[
self.__np_to_int(p[0:self.__bits_reward]),
self.__np_to_int(p[self.__bits_reward:self.__penalty_idx]),
self.__np_to_int(p[self.__penalty_idx:self.__epsilon_idx])
] for p in pop]
thresholds = [[
self.__np_to_int(
p[self.__epsilon_idx +
(j * self.__bits_thresholds):self.__epsilon_idx +
(j + 1) * self.__bits_thresholds]) - self.__scaling_factor
for j in range(len(self.__templates))
] for p in pop]
matching_scores = self.__m_wlcss_cuda.compute_wlcss(params)
fitness_scores = [
fit_fun.isolated_fitness_function_params(
matching_scores[k],
self.__streams_labels,
thresholds[k],
self.__classes,
parameter_to_optimize=self.__fitness_function)
for k in range(self.__num_individuals)
]
return np.array(fitness_scores)
outputconffile.write("Null class extraction: {}\n".format(use_null))
outputconffile.write(
"Null class percentage: {}\n".format(null_class_percentage))
outputconffile.write("Use encoding: {}\n".format(encoding))
outputconffile.write("Duration: {}\n".format(
time.strftime("%H:%M:%S", time.gmtime(elapsed_time))))
print("Results written")
print(output_file_path.replace(".txt", ""))
m_wlcss_cuda = WLCSSCuda(best_templates, streams, params, encoding)
mss = m_wlcss_cuda.compute_wlcss()
m_wlcss_cuda.cuda_freemem()
if optimize_thresholds:
thresholds = best_thresholds
fitness_score = ftf.isolated_fitness_function_params(
mss, streams_labels, thresholds, classes, parameter_to_optimize='f1')
print(fitness_score)
for i, c in enumerate(classes):
tmp_labels = np.copy(streams_labels)
tmp_labels[tmp_labels != c] = 0
templates_fitness_score = ftf.isolated_fitness_function_templates(
mss[:, i],
tmp_labels,
thresholds[i],
parameter_to_optimize=fitness_function)
print(
"Class: {} - Score: {:4.3f} - Good dist: {:4.3f} - Bad dist: {:4.3f} - Thres: {}"
.format(c, templates_fitness_score[0], templates_fitness_score[1],
templates_fitness_score[2], thresholds[i]))
if use_evolved_templates:
if es_results_file is not None:
if use_evolved_thresholds:
templates, thresholds = dl.load_evolved_templates(
es_results_file, classes, use_evolved_thresholds)
else:
templates = dl.load_evolved_templates(es_results_file, classes)
if isolated_case:
m_wlcss_cuda = WLCSSCuda(templates, streams_test, params, encoding)
mss = m_wlcss_cuda.compute_wlcss()
m_wlcss_cuda.cuda_freemem()
print("Perf_F1: {}".format(
ftf.isolated_fitness_function_params(mss,
test_labels,
thresholds,
classes,
parameter_to_optimize='f1')))
pfe.performance_evaluation_isolated(mss, test_labels, thresholds,
classes)
plt_creator.plot_isolated_mss(
mss,
thresholds,
dataset_choice,
classes,
streams_labels=test_labels,
title="Dataset: {} - {} - Evolved_templ: {} - Evolved_thres: {}".
format(dataset_choice, "Isolated", use_evolved_templates,
use_evolved_thresholds))
# plt_creator.plot_gestures(dl.load_dataset(dataset_choice, classes), classes=classes)
else:
