def compareModelsTable(original, models_fo, models_ho):
fig = plt.figure(figsize=[12, 4])
fig.suptitle("Comparação de modelos ")
columns = ['Modelo', 'Ordem', 'Partições', 'RMSE', 'MAPE (%)']
rows = []
for model in models_fo:
fts = model["model"]
error_r = Measures.rmse(model["forecasted"], original)
error_m = round(Measures.mape(model["forecasted"], original) * 100, 2)
rows.append(
[model["name"], fts.order,
len(fts.sets), error_r, error_m])
for model in models_ho:
fts = model["model"]
error_r = Measures.rmse(model["forecasted"][fts.order:],
original[fts.order:])
error_m = round(
Measures.mape(model["forecasted"][fts.order:],
original[fts.order:]) * 100, 2)
rows.append(
[model["name"], fts.order,
len(fts.sets), error_r, error_m])
ax1 = fig.add_axes([0, 0, 1, 1]) # left, bottom, width, height
ax1.set_xticks([])
ax1.set_yticks([])
ax1.table(cellText=rows,
colLabels=columns,
cellLoc='center',
bbox=[0, 0, 1, 1])
sup = "\\begin{tabular}{"
header = ""
body = ""
footer = ""
for c in columns:
sup = sup + "|c"
if len(header) > 0:
header = header + " & "
header = header + "\\textbf{" + c + "} "
sup = sup + "|} \\hline\n"
header = header + "\\\\ \\hline \n"
for r in rows:
lin = ""
for c in r:
if len(lin) > 0:
lin = lin + " & "
lin = lin + str(c)
body = body + lin + "\\\\ \\hline \n"
return sup + header + body + "\\end{tabular}"
def evaluate_individual_model(model, partitioner, train, test, window_size, time_displacement):
import numpy as np
from pyFTS.partitioners import Grid
from pyFTS.benchmarks import Measures
try:
model.train(train, sets=partitioner.sets, order=model.order, parameters=window_size)
forecasts = model.forecast(test, time_displacement=time_displacement, window_size=window_size)
_rmse = Measures.rmse(test[model.order:], forecasts[:-1])
_mape = Measures.mape(test[model.order:], forecasts[:-1])
_u = Measures.UStatistic(test[model.order:], forecasts[:-1])
except Exception as e:
print(e)
_rmse = np.nan
_mape = np.nan
_u = np.nan
return {'model': model.shortname, 'partitions': partitioner.partitions, 'order': model.order,
'rmse': _rmse, 'mape': _mape, 'u': _u}
model.fit(train)
forecasts1 = model.predict(test, type='multivariate')
forecasts2 = model.predict(test,
type='multivariate',
generators={'date': time_generator},
steps_ahead=100)
for var in ['temperature', 'load']:
row = [order, knn, var, len(model)]
for horizon in [1, 25, 50, 75, 100]:
if horizon == 1:
row.append(
Measures.mape(
test[var].values[model.order:model.order + 10],
forecasts1[var].values[:10]))
else:
row.append(
Measures.mape(test[var].values[:horizon],
forecasts2[var].values[:horizon]))
print(row)
rows.append(row)
columns = ['Order', 'knn', 'var', 'Rules']
for horizon in [1, 25, 50, 75, 100]:
columns.append('h{}'.format(horizon))
final = pd.DataFrame(rows, columns=columns)
final.to_csv('gmvfts_gefcom12.csv', sep=';', index=False)