class HighDG(ADG):
def __init__(self, work_path, fmt, res_type, nn_size):
super().__init__(work_path, fmt)
self.mode = 'one'
self.generated_num = 0
self.nn_size = nn_size
self.base_path = os.path.join(work_path, 'net')
self.base_power = Power(fmt=fmt)
self.base_power.load_power(self.base_path, fmt=fmt)
self.input_fmt = load_input_fmt(os.path.join(work_path, 'cct', 'predict', 'input.txt'),
input_mode=True)
self.feature_model = load_model(os.path.join(work_path, res_type, 'feature'),
'', suffix='tf')
self.data_set = GHData(work_path, '', '')
self.data_set.load_data(work_path)
self.features = pd.DataFrame(
self.feature_model.predict(self.data_set.input_data.values),
index=self.data_set.input_data.index)
self.init_assess = self.distribution_assess(nn_size)
def distribution_assess(self, nn_size=-1):
dists = pairwise_distances(self.features)
if nn_size < 1:
return np.average(dists) / np.max(dists)
return np.average(np.partition(dists, nn_size + 1)[:, 1:nn_size + 1])
def choose_samples(self, size=1):
idx = np.arange(self.features.shape[0])
np.random.shuffle(idx)
return self.features.index[idx[:size]]
def generate_one(self, power, idx, out_path):
print(idx, out_path)
if not power:
self.input_fmt['value'] = self.data_set.ori_data.loc[idx].values
power = restore_power_input(self.input_fmt, self.base_power, normalize=False)
print('old=', self.input_fmt['value'])
# TODO
self.input_fmt['value'] *= np.random.normal(loc=1.0, scale=0.1,
size=self.input_fmt.shape[0])
power = restore_power_input(self.input_fmt, power, normalize=False)
print('new=', self.input_fmt['value'])
shutil.rmtree(out_path, ignore_errors=True)
power.save_power(out_path, fmt=self.fmt, lp=False)
shutil.copy(os.path.join(self.base_path, 'LF.L0'), out_path)
shutil.copy(os.path.join(self.base_path, 'ST.S0'), out_path)
call_wmlf(out_path)
ret = check_lfcal(out_path)
if ret:
idx = os.path.split(out_path)[-1]
new_data = load_power_input(self.input_fmt, power)
new_data = self.data_set.normalize_it(new_data)
new_feature = self.feature_model.predict(new_data[np.newaxis, :])
self.features.loc[idx] = new_feature.reshape(-1)
self.generated_num += 1
return ret
def remove_samples(self):
pass
def done(self):
assess = self.distribution_assess(self.nn_size)
print('num=%d, init=%.6f, now=%.6f' % (self.generated_num, self.init_assess, assess))
return self.generated_num > 0
def restore_dataset(path, input_fmt, res_type):
data_set = GHData(path, path + "/net", input_fmt)
data_set.load_data(path + '/' + res_type)
return data_set