def sliding_window_simple_search(data, windowsize, model, partitions, orders,
**kwargs):
_3d = len(orders) > 1
ret = []
errors = np.array([[0 for k in range(len(partitions))]
for kk in range(len(orders))])
forecasted_best = []
figsize = kwargs.get('figsize', [10, 15])
fig = plt.figure(figsize=figsize)
plotforecasts = kwargs.get('plotforecasts', False)
if plotforecasts:
ax0 = fig.add_axes([0, 0.4, 0.9, 0.5]) # left, bottom, width, height
ax0.set_xlim([0, len(data)])
ax0.set_ylim([min(data) * 0.9, max(data) * 1.1])
ax0.set_title('Forecasts')
ax0.set_ylabel('F(T)')
ax0.set_xlabel('T')
min_rmse = 1000000.0
best = None
intervals = kwargs.get('intervals', False)
threshold = kwargs.get('threshold', 0.5)
progressbar = kwargs.get('progressbar', None)
rng1 = enumerate(partitions, start=0)
if progressbar:
from tqdm import tqdm
rng1 = enumerate(tqdm(partitions), start=0)
for pc, p in rng1:
fs = Grid.GridPartitioner(data=data, npart=p)
rng2 = enumerate(orders, start=0)
if progressbar:
rng2 = enumerate(tqdm(orders), start=0)
for oc, o in rng2:
_error = []
for ct, train, test in Util.sliding_window(data, windowsize, 0.8,
**kwargs):
fts = model("q = " + str(p) + " n = " + str(o), partitioner=fs)
fts.fit(train, order=o)
if not intervals:
forecasted = fts.forecast(test)
if not fts.has_seasonality:
_error.append(
Measures.rmse(np.array(test[o:]),
np.array(forecasted[:-1])))
else:
_error.append(
Measures.rmse(np.array(test[o:]),
np.array(forecasted)))
for kk in range(o):
forecasted.insert(0, None)
if plotforecasts: ax0.plot(forecasted, label=fts.name)
else:
forecasted = fts.forecast_interval(test)
_error.append(1.0 - Measures.rmse_interval(
np.array(test[o:]), np.array(forecasted[:-1])))
error = np.nanmean(_error)
errors[oc, pc] = error
if (min_rmse - error) > threshold:
min_rmse = error
best = fts
forecasted_best = forecasted
# print(min_rmse)
if plotforecasts:
# handles0, labels0 = ax0.get_legend_handles_labels()
# ax0.legend(handles0, labels0)
elev = kwargs.get('elev', 30)
azim = kwargs.get('azim', 144)
ax0.plot(test, label="Original", linewidth=3.0, color="black")
if _3d: ax1 = Axes3D(fig, rect=[0, 1, 0.9, 0.9], elev=elev, azim=azim)
if not plotforecasts:
ax1 = Axes3D(fig, rect=[0, 1, 0.9, 0.9], elev=elev, azim=azim)
# ax1 = fig.add_axes([0.6, 0.5, 0.45, 0.45], projection='3d')
if _3d:
ax1.set_title('Error Surface')
ax1.set_ylabel('Model order')
ax1.set_xlabel('Number of partitions')
ax1.set_zlabel('RMSE')
X, Y = np.meshgrid(partitions, orders)
surf = ax1.plot_surface(X,
Y,
errors,
rstride=1,
cstride=1,
antialiased=True)
else:
ax1 = fig.add_axes([0, 1, 0.9, 0.9])
ax1.set_title('Error Curve')
ax1.set_ylabel('Number of partitions')
ax1.set_xlabel('RMSE')
ax0.plot(errors, partitions)
ret.append(best)
ret.append(forecasted_best)
# plt.tight_layout()
file = kwargs.get('file', None)
save = kwargs.get('save', False)
Util.show_and_save_image(fig, file, save)
return ret
def simpleSearch_RMSE(train,
test,
model,
partitions,
orders,
save=False,
file=None,
tam=[10, 15],
plotforecasts=False,
elev=30,
azim=144,
intervals=False,
parameters=None,
partitioner=Grid.GridPartitioner,
transformation=None,
indexer=None):
_3d = len(orders) > 1
ret = []
if _3d:
errors = np.array([[0 for k in range(len(partitions))]
for kk in range(len(orders))])
else:
errors = []
forecasted_best = []
fig = plt.figure(figsize=tam)
# fig.suptitle("Comparação de modelos ")
if plotforecasts:
ax0 = fig.add_axes([0, 0.4, 0.9, 0.5]) # left, bottom, width, height
ax0.set_xlim([0, len(train)])
ax0.set_ylim([min(train) * 0.9, max(train) * 1.1])
ax0.set_title('Forecasts')
ax0.set_ylabel('F(T)')
ax0.set_xlabel('T')
min_rmse = 1000000.0
best = None
for pc, p in enumerate(partitions, start=0):
sets = partitioner(data=train, npart=p,
transformation=transformation).sets
for oc, o in enumerate(orders, start=0):
fts = model("q = " + str(p) + " n = " + str(o))
fts.append_transformation(transformation)
fts.train(train, sets=sets, order=o, parameters=parameters)
if not intervals:
forecasted = fts.forecast(test)
if not fts.has_seasonality:
error = Measures.rmse(np.array(test[o:]),
np.array(forecasted[:-1]))
else:
error = Measures.rmse(np.array(test[o:]),
np.array(forecasted))
for kk in range(o):
forecasted.insert(0, None)
if plotforecasts: ax0.plot(forecasted, label=fts.name)
else:
forecasted = fts.forecast_interval(test)
error = 1.0 - Measures.rmse_interval(np.array(test[o:]),
np.array(forecasted[:-1]))
if _3d:
errors[oc, pc] = error
else:
errors.append(error)
if error < min_rmse:
min_rmse = error
best = fts
forecasted_best = forecasted
# print(min_rmse)
if plotforecasts:
# handles0, labels0 = ax0.get_legend_handles_labels()
# ax0.legend(handles0, labels0)
ax0.plot(test, label="Original", linewidth=3.0, color="black")
if _3d: ax1 = Axes3D(fig, rect=[0, 1, 0.9, 0.9], elev=elev, azim=azim)
if _3d and not plotforecasts:
ax1 = Axes3D(fig, rect=[0, 1, 0.9, 0.9], elev=elev, azim=azim)
ax1.set_title('Error Surface')
ax1.set_ylabel('Model order')
ax1.set_xlabel('Number of partitions')
ax1.set_zlabel('RMSE')
X, Y = np.meshgrid(partitions, orders)
surf = ax1.plot_surface(X,
Y,
errors,
rstride=1,
cstride=1,
antialiased=True)
else:
ax1 = fig.add_axes([0, 1, 0.9, 0.9])
ax1.set_title('Error Curve')
ax1.set_xlabel('Number of partitions')
ax1.set_ylabel('RMSE')
ax1.plot(partitions, errors)
ret.append(best)
ret.append(forecasted_best)
ret.append(min_rmse)
# plt.tight_layout()
cUtil.show_and_save_image(fig, file, save)
return ret