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Postprocess.py
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import openpyxl
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
# Specify the path to your Excel file
models_path = 'path'# Specify the path to the folder containing the Excel files
rnd_seeds = [0, 100, 300, 700, 1000]
tasks = ['nodeopf']
powergrids = ['ieee24', 'ieee39', 'uk', 'ieee118']
n_bus = [24, 39, 29, 118]
models = ['gcn', 'gin', 'gat', 'transformer']
# Specify the sheet name
sheet_name = 'Metrics'
sheet_name_d = 'Data'
sheet_node = {
'Power grid': ['ieee24', '', '', '', 'ieee39', '', '', '', 'uk', '', '', '', 'ieee118', '', '', ''],
'MPL type': ['gcn', 'gin', 'gat', 'transformer', 'gcn', 'gin', 'gat', 'transformer', 'gcn', 'gin', 'gat', 'transformer', 'gcn', 'gin', 'gat', 'transformer'],
'mse': [],
'rmse': [],
}
#sheet_node = {
# 'Power grid': ['ieee24', '', '', 'ieee39', '', '', 'uk', '', ''],
# 'MPL type': ['gcn', 'gin', 'gat', 'gcn', 'gin', 'gat', 'gcn', 'gin', 'gat'],
# 'mse': [],
# 'rmse': [],
#}
results = {}
smallest_mse = float('inf') # Initialize smallest MSE to positive infinity
best_model = None
best_random_seed = None
for i,powergrid in enumerate(powergrids):
results[powergrid] = {}
for model in models:
results[powergrid][model] = {}
for task in tasks:
results[powergrid][model][task] = {}
if task == tasks[0]:
metrics = ['mse', 'rmse']
results[powergrid][model][task][metrics[0]] = {}
results[powergrid][model][task][metrics[1]] = {}
mse = []
rmsescore = []
for rnd_seed in rnd_seeds:
specific_excel_file_path = models_path + powergrid + '\\' + 'summary' + powergrid + '_' + model + '_' + task + '_3l_16h_' + str(rnd_seed) +'s' + '.xlsx'
print(specific_excel_file_path)
workbook = openpyxl.load_workbook(specific_excel_file_path)
sheet = workbook[sheet_name]
if task == tasks[0]:
mse_value = sheet['B2'].value
mse.append(mse_value)
rmsescore.append(sheet['B3'].value)
if mse_value < smallest_mse:
smallest_mse = mse_value
best_model = model
best_random_seed = rnd_seed
workbook.close()
if task == tasks[0]:
sheet_node[metrics[0]].append(str(np.format_float_scientific(np.mean(mse), precision=4))+'±'+str(np.format_float_scientific(np.std(mse), precision=4)))
sheet_node[metrics[1]].append(str(np.format_float_scientific(np.mean(rmsescore), precision=4))+'±'+str(np.format_float_scientific(np.std(rmsescore), precision=4)))
if task == 'node':
plt.figure(i)
best_excel_file_path = models_path + powergrid + '\\' + 'summary' + powergrid + '_' + best_model + '_' + task + '_3l_16h_' + str(
best_random_seed) + 's' + '.xlsx'
workbookplot = openpyxl.load_workbook(best_excel_file_path)
sheet = workbookplot[sheet_name_d]
# Get data from columns A, B, C, and D
v_targ = [cell.value for cell in sheet['A']]
t_targ = [cell.value for cell in sheet['B']]
pg_targ = [cell.value for cell in sheet['C']]
qg_targ = [cell.value for cell in sheet['D']]
v_pred = [cell.value for cell in sheet['E']]
t_pred = [cell.value for cell in sheet['F']]
pg_pred = [cell.value for cell in sheet['G']]
qg_pred = [cell.value for cell in sheet['H']]
mse_ac = (sum(np.abs(np.array(v_targ[1:n_bus[i]]) - np.array(v_pred[1:n_bus[i]]))))
mse_bd = (sum(np.abs(np.array(t_targ[1:n_bus[i]]) - np.array(t_pred[1:n_bus[i]]))))
mse_pg = sum(np.abs(np.array(pg_targ[1:n_bus[i]] - np.array(pg_pred[1:n_bus[i]]))))
mse_qg = sum(np.abs(np.array(qg_targ[1:n_bus[i]] - np.array(qg_pred[1:n_bus[i]]))))
plt.bar(['V [p.u.]', 'T [degree]', 'Pg [MW]', 'Qg [MVAr]'], [mse_ac, mse_bd, mse_pg, mse_qg])
plt.xlabel('quantity')
plt.ylabel('Mean Absolute Error (MAE)')
plt.title(f'error total test set {powergrid}')
plt.savefig(f'mse_bar_plot_{powergrid}.png')
elif task == 'nodeopf':
plt.figure(i)
best_excel_file_path = models_path + powergrid + '\\' + 'summary' + powergrid + '_' + best_model + '_' + task + '_3l_16h_' + str(
best_random_seed) + 's' + '.xlsx'
workbookplot = openpyxl.load_workbook(best_excel_file_path)
sheet = workbookplot[sheet_name_d]
# Get data from columns A, B, C, and D
v_targ = [cell.value for cell in sheet['A']]
t_targ = [cell.value for cell in sheet['B']]
pg_targ = [cell.value for cell in sheet['C']]
qg_targ = [cell.value for cell in sheet['D']]
v_pred = [cell.value for cell in sheet['E']]
t_pred = [cell.value for cell in sheet['F']]
pg_pred = [cell.value for cell in sheet['G']]
qg_pred = [cell.value for cell in sheet['H']]
# Calculate MSE between columns
mse_ac = sum(np.abs(np.array(v_targ[1:n_bus[i]] - np.array(v_pred[1:n_bus[i]]))))
mse_bd = sum(np.abs(np.array(t_targ[1:n_bus[i]] - np.array(t_pred[1:n_bus[i]]))))
mse_pg = sum(np.abs(np.array(pg_targ[1:n_bus[i]] - np.array(pg_pred[1:n_bus[i]]))))
mse_qg = sum(np.abs(np.array(qg_targ[1:n_bus[i]] - np.array(qg_pred[1:n_bus[i]]))))
print(f"GRID {powergrid} v:{mse_ac},t:{mse_bd}, pg:{mse_pg},qg:{mse_qg}")
plt.bar(['V [p.u.]', 'T [degree]', 'Pg [MW]', 'Qg [MVAr]'], [mse_ac, mse_bd, mse_pg, mse_qg])
plt.xlabel('quantity')
plt.ylabel('Mean Absolute Error (MAE)')
plt.title(f'error total test set {powergrid}')
plt.savefig(f'mse_bar_plot_{powergrid}.png')
df_sheet_Regression = pd.DataFrame(sheet_node)
excel_file_path = f'processed_results_{task}_modelbig.xlsx'
# Create a Pandas Excel writer using XlsxWriter as the engine
with pd.ExcelWriter(excel_file_path, engine='xlsxwriter') as writer:
# Write each DataFrame to a different sheet
df_sheet_Regression.to_excel(writer, sheet_name='node', index=False)
for powergrid in powergrids:
worksheet = writer.sheets['node']
worksheet.insert_image('E1', f'mse_bar_plot_{powergrid}.png')
# Close the workbook when done
workbook.close()