def commit(self):
self.Error.unexpected_error.clear()
self.Warning.model_not_appropriate.clear()
self.model.clear()
data = self.data
if not data or not self._models:
return
try:
with self.progressBar(len(self._models) * (self.n_folds + 1) +
1) as progress:
res = model_evaluation(data,
list(self._models.values()),
self.n_folds,
self.forecast_steps,
callback=progress.advance)
except ValueError as e:
self.Error.unexpected_error(e.args[0])
return
res = np.array(res, dtype=object)
if res.ndim > 1:
self.model.setHorizontalHeaderLabels(res[0, 1:].tolist())
self.model.setVerticalHeaderLabels(res[1:, 0].tolist())
self.model.wrap(res[1:, 1:].tolist())
else:
self.Warning.model_not_appropriate()
def commit(self):
self.Error.unexpected_error.clear()
self.model.clear()
data = self.data
if not data or not self._models:
return
try:
with self.progressBar(len(self._models) * (self.n_folds + 1) + 1) as progress:
res = model_evaluation(data, list(self._models.values()),
self.n_folds, self.forecast_steps,
callback=progress.advance)
except ValueError as e:
self.Error.unexpected_error(e.args[0])
return
res = np.array(res, dtype=object)
self.model.setHorizontalHeaderLabels(res[0, 1:].tolist())
self.model.setVerticalHeaderLabels(res[1:, 0].tolist())
self.model.wrap(res[1:, 1:].tolist())
def test_model_evaluation(self):
models = [ARIMA((1, 1, 0)), ARIMA((2, 1, 1)), VAR(1), VAR(3)]
results = model_evaluation(data, models, n_folds=10, forecast_steps=3)
results = np.array(results, dtype=object)
self.assertEqual(results.shape, (4 * 2 + 1, 8))
np.testing.assert_equal(results[1:, 1:].astype(float) > 0, True)