# test_data = DataProvider(TEST_FILE_NAME, TEST_INIT_DATA_FILE_NAME).get_data()
# assert(calc_class_re())
app = QApplication(sys.argv)
data = dataProvider.get_data()
# print data
classifier = c45(data, max_repeat_var=10)
form = MainWindow(data, classifier)
## print data
# classifier = c45(data, max_repeat_var=10)
# pos_sum = 0
# for row, target in zip(data.data, data.target):
# pos = 0
# for l, c in classifier.get_labels_count(row).items():
# pos += 1
# if l == target:
# pos_sum += pos
# print pos
# else:
# print "=========="
# print "---------------"
# print pos_sum / data.n_samples
print get_classification_error(classifier.get_likely_label, data)
form.show()
code = app.exec_()
dataProvider.save()
sys.exit(code)
# except Exception as e:
# log.error(e.message)
# raise e
def error_of_likely_class_is(step, error):
got = get_classification_error(world.c45.get_likely_label, world.data_set)
assert got == float(error), "correct classification error"
def error_less_or_eq_for_set(step, error, ds):
data_set = world.tr_set if ds == "training" else world.tst_set
got = get_classification_error(world.c45.get_likely_label, data_set)
assert got <= float(error), "correct classification error"
def try_to_calc_error(step):
world.was_exception = False
try:
world.error = get_classification_error(world.classifier, world.data_set)
except Exception:
world.was_exception = True
