def do_evaluation(self, modeleval: ModelEvaluation, round_value=False,
make_nonnegative=False):
ts_pred = self.fit_and_forecast(modeleval)
if round_value:
ts_pred = np.around(ts_pred)
if make_nonnegative:
ts_pred[ts_pred < 0] = 0
modeleval.evaluate(modeleval.ts_test, ts_pred)
def do_evaluation(self, modeleval: ModelEvaluation, round_value=False,
make_nonnegative=False):
# print("Fitting:", self.__class__)
ts_pred = self.fit(modeleval)
if round_value:
ts_pred = np.around(ts_pred)
if make_nonnegative:
ts_pred[ts_pred < 0] = 0
modeleval.evaluate(modeleval.ts_test, ts_pred)
def do_evaluation(self, modeleval: ModelEvaluation):
ts_pred = self.fit(modeleval)
modeleval.evaluate(modeleval.ts_test, ts_pred)