Fix/predict single value (#108)

* fix(TorchForecastingModel): solved bug at TorchForecastingModel.predict(n) with n = 1

* feature(testing): added tests for length 1 predictions for RNN and TCN, set torch random seed for TCN test

Co-authored-by: pennfranc <flaessig@student.ethz.ch>
Co-authored-by: TheMP <marek.pasieka@gmail.com>

darts/models/torch_forecasting_model.py

@@ -320,8 +320,7 @@ def predict(self, n: int) -> TimeSeries:
             pred_in[:, -1, :] = out[:, self.first_prediction_index]
             test_out.append(out.cpu().detach().numpy()[0, self.first_prediction_index])
         test_out = np.stack(test_out)
-
-        return self._build_forecast_series(test_out.squeeze())
+        return self._build_forecast_series(test_out.squeeze(1))
 
     @property
     def first_prediction_index(self) -> int:

darts/tests/test_RNN.py

@@ -45,4 +45,8 @@ def test_fit(self):
         pred3 = model3.predict(n=6)
         self.assertNotEqual(sum(pred1.values() - pred3.values()), 0.)
 
+        # test short predict
+        pred4 = model3.predict(n=1)
+        self.assertEqual(len(pred4), 1)
+
         shutil.rmtree('.darts')

darts/tests/test_TCN.py

@@ -33,7 +33,12 @@ def test_fit(self):
         pred2 = model2.predict(n=2).values()[0]
         self.assertTrue(abs(pred2 - 10) < abs(pred - 10))
 
+        # test short predict
+        pred3 = model2.predict(n=1)
+        self.assertEqual(len(pred3), 1)
+
     def test_coverage(self):
+        torch.manual_seed(0)
         input_lengths = range(20, 50)
         kernel_sizes = range(2, 5)
         dilation_bases = range(2, 5)