def make_dataset(path, input_fmt, res_type):
net_path = os.path.join(path, 'net')
res_path = os.path.join(path, res_type)
if not os.path.exists(res_path):
os.mkdir(res_path)
data_set = GHData(path, net_path, input_fmt)
data_set.load_x(x_ratio_thr=-1.0, dt_idx=False)
data_set.load_y(res_type, na_value=-1.0)
# data_set.drop_times(['00000027'])
data_set.normalize()
# data_set.column_valid = np.ones((data_set.input_data.shape[1],), dtype=np.bool)
data_set.save_data(res_path)
return data_set
def make_dataset(path, out_path, input_fmt, res_type):
data_set = GHData(path, path + "/net", input_fmt)
data_set.load_x()
data_set.load_y(res_type)
data_set.normalize()
data_set.save_data(out_path)
all_types = ['cct', 'sst', 'vs', 'v_curve']
res_type = 'cct'
res_name = res_type
res_path = path + "/" + res_name
input_dic = {'generator': ['p', 'v'], 'station': ['pl', 'ql']}
dr_percs = [0.5, 0.5, 0.5, 0.5, 0.5, 0.5]
net_path = path + "/net"
net = GHNet("inf", input_dic, dr_percs=dr_percs)
net.load_net(net_path)
net1 = GHNet("inf", input_dic, dr_percs=dr_percs)
net1.load_net(net_path)
data_set = GHData(path, net_path, net.input_layer)
data_set.load_x(x_ratio_thr=-1.0)
data_set.load_y(res_type)
data_set.normalize()
drops = np.array(range(len(data_set.column_valid)))[~data_set.column_valid]
net.drop_inputs(drops)
net1.drop_inputs(drops)
n_batch = 16
n_epochs = 10
n_al_epochs = 10
only_real = True
y_columns = list(range(data_set.y.shape[1]))
y_columns = [2, 11, 23]
net.build_multi_reg_k(len(y_columns),
activation=tf.keras.layers.LeakyReLU())
net1.build_multi_reg_k(len(y_columns),
net_path = os.path.join(path, 'net')
res_type = 'cct'
res_path = os.path.join(path, res_type)
input_dic = {'generator': ['p'],
'load': ['p', 'q']}
fmt = 'off'
power = Power(fmt=fmt)
power.load_power(net_path, fmt, lf=True, lp=False, st=False, station=True)
input_layer = []
for etype in input_dic:
for dtype in input_dic[etype]:
t = '_'.join((etype, dtype))
input_layer.extend([(t, n) for n in power.data[etype]['name']])
data_set = GHData(path, net_path, input_layer)
data_set.load_x(x_ratio_thr=-1.0, dt_idx=False)
data_set.load_y(res_type)
data_set.normalize()
data_set.column_valid = np.ones((data_set.input_data.shape[1], ), dtype=np.bool)
"""
y_columns = list(range(data_set.y.shape[1]))
column_names = data_set.y.columns[y_columns]
print("targets:", column_names)
prelu = tf.keras.layers.ReLU(negative_slope=0.1)
x = Input(shape=(len(input_layer),), dtype='float32', name='x')
fault = Input(shape=(len(y_columns),), dtype='float32', name='fault')
y_ = Input(shape=(len(y_columns),), dtype='float32', name='y_')
pre_model, all_layers = build_mlp(x, [64, 32, len(y_columns)], 'wepri36',
activation=prelu, last_activation=prelu)
feature_model = Model(x, all_layers[-2])