def plot_test_prediction(target, predictions, model_name, data_desc,
error_func):
save_plot = options.save_plot
if save_plot:
pp_path = get_path(options, "prediction_%s" % data_desc, "pdf")
pp = PdfPages(pp_path)
error = concat_and_calc_error(predictions, target, error_func)
if np.any(target == 0):
mapestr = "no MAPE due to 0's in observations"
else:
mapestr = 'MAPE: %3.2f' % concat_and_calc_error(
predictions, target, sg.utils.mape)
title = 'Test data prediction, %s, %s, %i days, model error: ' \
'%3.2f, RMSE: %3.2f, %s' % \
(model_name, data_desc, len(predictions), error,
concat_and_calc_error(predictions, target, Oger.utils.rmse), mapestr)
print "%s." % title
plt.figure()
plt.title(title)
plot_target_predictions(target, predictions)
if save_plot:
pp.savefig()
pp.close()
if not options.no_show_plot:
plt.ioff() # In case --live-plot is chosen, since that sets ion().
plt.show()
def plot_test_prediction(target, predictions,
model_name, data_desc, error_func):
save_plot = options.save_plot
if save_plot:
pp_path = get_path(options, "prediction_%s" % data_desc, "pdf")
pp = PdfPages(pp_path)
error = concat_and_calc_error(predictions, target, error_func)
if np.any(target == 0):
mapestr = "no MAPE due to 0's in observations"
else:
mapestr = 'MAPE: %3.2f' % concat_and_calc_error(predictions, target,
sg.utils.mape)
title = 'Test data prediction, %s, %s, %i days, model error: ' \
'%3.2f, RMSE: %3.2f, %s' % \
(model_name, data_desc, len(predictions), error,
concat_and_calc_error(predictions, target, Oger.utils.rmse), mapestr)
print "%s." % title
plt.figure()
plt.title(title)
plot_target_predictions(target, predictions)
if save_plot:
pp.savefig()
pp.close()
if not options.no_show_plot:
plt.ioff() # In case --live-plot is chosen, since that sets ion().
plt.show()
def _save_test_prediction(target, predictions, data_desc):
path = get_path(options, "test_series_%s" % data_desc, "pickle")
with open(path, "a+") as f:
pickle.dump((target, predictions), f)
def _save_test_prediction(target,
predictions, data_desc):
path = get_path(options, "test_series_%s" % data_desc, "pickle")
with open(path, "a+") as f:
pickle.dump((target, predictions), f)