# X_train = scaler.fit_transform(X_train.T).T
# X_val = scaler.transform(X_val.T).T
################### TRAIN THE LSTM MODELS ###################
in_shape = 3 * N
out_shape = 2 * N
# Build training and validation sets.
train_data = core_1.build_lstm_dataloader(X_train, PQ_meas_tr, V_meas_tr, x_i,
k_nn, batch_size)
val_data = core_1.build_lstm_dataloader(X_val, PQ_meas_val, V_meas_val, x_i,
k_nn, batch_size)
# Initialize and train the models.
model = lstm.LSTM(in_shape, hidden_layer_size, out_shape, batch_size)
model, val_loss = lstm_utils.train(model,
train_data,
val_data,
lr=1e-3,
epochs=lstm_epoch_max,
l2=0.)
ts = ts_val[k_nn - 1:-1]
################### VISUALIZE RESULTS ON VALIDATION SET ###################
# Run inference.
S, y_pred, y_true = lstm.predict(model, val_data, batch_size)
fig = plt.figure(figsize=(10, 5))
gs = fig.add_gridspec(3, 4)
def run(x_i, sensor_class, seed):
####################### PARAMETERS #######################
# General parameters.
dataset = 'cigre13'
sc_train = sensor_class
# Training (12h), validation (6h), testing (6h) splits.
ts_train = np.arange(0, 12 * 3600)
ts_val = np.arange(12 * 3600, 18 * 3600)
ts_test = np.arange(18 * 3600, 24 * 3600 - 1)
T_train, T_val, T_test = len(ts_train), len(ts_val), len(ts_test)
# Feedforward neural network parameters.
k_nn = 10
nn_epoch_max = 10
hidden_shape = [128, 64]
# LSTM parameters.
k_lstm = 50
hidden_layer_size = 64
lstm_epoch_max = 10
batch_size = 64
# Least squares model.
tau = 1000
freq = 50
####################### SET RANDOM SEED #######################
torch.manual_seed(seed)
np.random.seed(seed)
####################### LOAD TRUE DATA #######################
# Load true load flow.
V_org, I_org, P_org, Q_org, current_idx = load_true_data(dataset)
# Remove slack measurements and create measurement matrices used in Model 2.
V_org_magn = np.abs(V_org[1:])
N, T = V_org_magn.shape
# Create measurement delta matrix to be used as target.
dV_org_magn = np.diff(V_org_magn, axis=1)
################### GENERATE NOISY DATA FOR TRAINING ###################
# Add noise to load flow data.
V_meas, _, P_meas, Q_meas, std_abs, std_ang, std_re, std_im, _, _ = \
simulate_noisy_meas(sc_train, V_org, I_org, current_idx)
# Remove slack measurements and create measurement matrices used in Model 2.
V_meas = V_meas[1:]
PQ_meas = np.vstack((P_meas[1:], Q_meas[1:]))
################### SELECT TYPE OF COEFFICIENT ###################
# Select type of dependent variable.
V_meas = np.abs(V_meas)
# Load true coefficients.
coefficients = load_coefficients(dataset)
Kp_true = coefficients['vmagn_p'][x_i - 1, x_i - 1]
Kq_true = coefficients['vmagn_q'][x_i - 1, x_i - 1]
################### SPLIT TRAINING, VALIDATION AND TESTING DATA ###################
# Train matrices
V_meas_tr = V_meas[:, ts_train]
PQ_meas_tr = PQ_meas[:, ts_train]
X_train = np.vstack((V_meas_tr, PQ_meas_tr))
# Validation matrices.
V_meas_val = V_meas[:, ts_val]
PQ_meas_val = PQ_meas[:, ts_val]
X_val = np.vstack((V_meas_val, PQ_meas_val))
################### PRE-PROCESS DATA ###################
# Normalize training input data.
norm_scaler = MinMaxScaler()
X_train = norm_scaler.fit_transform(X_train.T).T
X_val = norm_scaler.transform(X_val.T).T
################### FEEDFORWARD NEURAL NET ###################
print('Training feedforward neural net...')
in_shape = 3 * N * k_nn
out_shape = 2 * N
# Build training, validation, and test sets.
train_data = nn.build_training_dataloader(X_train, PQ_meas_tr, V_meas_tr, x_i, k_nn)
val_data = nn.build_training_dataloader(X_val, PQ_meas_val, V_meas_val, x_i, k_nn)
# Initialize and train the models.
nn_model = nn.FeedForward(in_shape, hidden_shape, out_shape, k_nn)
nn_model, _ = fc.nn.train(nn_model, train_data, val_data, epochs=nn_epoch_max)
################### LSTM NEURAL NET ###################
print('\nTraining LSTMs...')
in_shape = 3 * N
out_shape = 2 * N
# Build training and validation sets.
train_data = lstm.build_dataloader(X_train, PQ_meas_tr, V_meas_tr, x_i, k_lstm, batch_size)
val_data = lstm.build_dataloader(X_val, PQ_meas_val, V_meas_val, x_i, k_lstm, batch_size)
# Initialize and train the models.
lstm_model = lstm.LSTM(in_shape, hidden_layer_size, out_shape, batch_size)
lstm_model, _ = fc.lstm.train(lstm_model, train_data, val_data, lr=1e-3, epochs=lstm_epoch_max, l2=0.)
for sc_test in [0., 0.2, 0.5, 1.0]:
folder = f'cross_trained_{dataset}_train{sc_train}_test{sc_test}'
logger = ComparisonLogger(folder)
################### GENERATE NOISY DATA FOR TESTING ###################
# Add noise to load flow data.
V_meas, _, P_meas, Q_meas, std_abs, std_ang, std_re, std_im, _, _ = \
simulate_noisy_meas(sc_test, V_org, I_org, current_idx)
# Remove slack measurements and create measurement matrices used in Model 2.
V_meas = V_meas[1:]
PQ_meas = np.vstack((P_meas[1:], Q_meas[1:]))
PQ_meas_bias = np.vstack((PQ_meas, np.ones(T)))
################### SELECT TYPE OF COEFFICIENT ###################
# Select type of dependent variable.
V_meas = np.abs(V_meas)
dPQ_meas = np.diff(PQ_meas, axis=1)
################### SPLIT TESTING DATA ###################
# Testing matrices.
V_meas_test = V_meas[:, ts_test]
PQ_meas_test = PQ_meas[:, ts_test]
X_test = np.vstack((V_meas_test, PQ_meas_test))
PQ_meas_bias_test = PQ_meas_bias[:, ts_test]
dPQ_meas_test = dPQ_meas[:, ts_test]
################### PRE-PROCESS DATA ###################
# Normalize training input data.
X_test = norm_scaler.transform(X_test.T).T
################### INFERENCE WITH PRE-TRAINED MODELS ###################
# Feedforward model.
test_data = nn.build_testing_dataloader(X_test, PQ_meas_test, V_meas_test, x_i, k_nn)
S_nn, y_pred_nn, _ = nn_model.predict(test_data)
ts_nn = np.arange(k_nn-1, T_test-1)
# LSTM model.
test_data = lstm.build_dataloader(X_test, PQ_meas_test, V_meas_test, x_i, k_lstm, batch_size)
S_lstm, y_pred_lstm, _ = lstm.predict(lstm_model, test_data, batch_size)
ts_lstm = np.arange(k_nn-1, T_test-1)
################### LEAST SQUARES MODEL ###################
print('\tLeast squares estimation...')
which_i = np.array([x_i])
valid_timesteps = np.ones(T_test - 1).astype(np.bool)
use_sigma = False
k_pcr = None
qr = False
S_ls, ts_ls, _ = linear.linear_model(V_meas_test, PQ_meas_bias_test, use_sigma,
tau, freq, which_i, k_pcr, qr, valid_timesteps)
# Remove bias terms.
S_ls = {a: b[:-1] for a, b in S_ls.items()}
y_pred_ls = fc.linear.lm_estimate_dVmagn(dPQ_meas_test[:, ts_ls], S_ls, None, False)
S_ls, y_pred_ls = S_ls[x_i], y_pred_ls[x_i]
################### VISUALIZE RESULTS ON TEST SET ###################
ts_all = ts_test[ts_ls]
ts_all_hour = ts_all / 3600
x_nn = ts_ls - k_nn + 1
x_lstm = ts_ls - k_lstm + 1
fig = plt.figure(figsize=(10, 5))
gs = fig.add_gridspec(3, 4, hspace=0.05)
# Plot Kp coefficients.
ax = fig.add_subplot(gs[0, :-1])
ax.plot(ts_all_hour, Kp_true[ts_all], label='True')
ax.plot(ts_all_hour, S_ls[x_i - 1], label='LS')
ax.plot(ts_all_hour, S_nn[x_i - 1, x_nn], label='NN')
ax.plot(ts_all_hour, S_lstm[x_i - 1, x_lstm], label='LSTM')
ax.set_ylabel(r'$\partial |V_{%d}|/\partial P_{%d}$' % (x_i, x_i))
ax.set_xticks([])
# Plot Kq coefficients.
ax = fig.add_subplot(gs[1, :-1])
ax.plot(ts_all_hour, Kq_true[ts_all], label='True')
ax.plot(ts_all_hour, S_ls[x_i - 1 + N], label='LS')
ax.plot(ts_all_hour, S_nn[x_i - 1 + N, x_nn], label='NN')
ax.plot(ts_all_hour, S_lstm[x_i - 1 + N, x_lstm], label='LSTM')
ax.legend(loc='upper right')
ax.set_ylabel(r'$\partial |V_{%d}|/\partial Q_{%d}$' % (x_i, x_i))
ax.set_xticks([])
# Plot dV.
ax = fig.add_subplot(gs[2, :-1])
ax.plot(ts_all_hour[::2], dV_org_magn[x_i - 1, ts_all[::2]], label='True')
ax.plot(ts_all_hour[::2], y_pred_ls[::2], label='LS')
ax.plot(ts_all_hour[::2], y_pred_nn[x_nn[::2]], label='NN')
ax.plot(ts_all_hour[::2], y_pred_lstm[x_lstm[::2]], label='LSTM')
ax.set_ylabel(r'$\Delta |V_{%d}|$' % (x_i))
ax.set_xlabel('Time (h)')
# Plot Kp errors.
ax = fig.add_subplot(gs[0, -1])
e_ls = 100 * norm_e(Kp_true[ts_all], S_ls[x_i - 1])
e_nn = 100 * norm_e(Kp_true[ts_all], S_nn[x_i - 1, x_nn])
e_lstm = 100 * norm_e(Kp_true[ts_all], S_lstm[x_i - 1, x_lstm])
ax.boxplot([e_ls, e_nn, e_lstm], labels=['LS', 'NN', 'LSTM'])
ax.set_xticks([])
# Plot Kq errors.
ax = fig.add_subplot(gs[1, -1])
e_ls = 100 * norm_e(Kq_true[ts_all], S_ls[x_i - 1 + N])
e_nn = 100 * norm_e(Kq_true[ts_all], S_nn[x_i - 1 + N, x_nn])
e_lstm = 100 * norm_e(Kq_true[ts_all], S_lstm[x_i - 1 + N, x_lstm])
ax.boxplot([e_ls, e_nn, e_lstm], labels=['LS', 'NN', 'LSTM'])
ax.set_ylabel('Normalized error [%]')
ax.set_xticks([])
# Plot d|V| errors.
ax = fig.add_subplot(gs[2, -1])
e_ls = 100 * norm_e(dV_org_magn[x_i - 1, ts_all], y_pred_ls)
e_nn = 100 * norm_e(dV_org_magn[x_i - 1, ts_all], y_pred_nn[x_nn])
e_lstm = 100 * norm_e(dV_org_magn[x_i - 1, ts_all], y_pred_lstm[x_lstm])
ax.boxplot([e_ls, e_nn, e_lstm], labels=['LS', 'NN', 'LSTM'], showfliers=False)
gs.tight_layout(fig)
plt.show()
logger.save_fig(fig, f'x_{x_i}_s{seed}.png')
print('Done!')