Successfully run training

qlib/contrib/model/pytorch_general_nn.py

@@ -488,12 +488,40 @@ def metric_fn(self, pred, label):
 
         raise ValueError("unknown metric `%s`" % self.metric)
 
+
+    def _get_fl(self, data: torch.Tensor):
+        """
+        get feature and label from data
+        - Handle the different data shape of time series and tabular data
+
+        Parameters
+        ----------
+        data : torch.Tensor
+            input data which maybe 3 dimension or 2 dimension
+            - 3dim: [batch_size, time_step, feature_dim]
+            - 2dim: [batch_size, feature_dim]
+
+        Returns
+        -------
+        Tuple[torch.Tensor, torch.Tensor]
+        """
+        if data.dim() == 3:
+            # it is a time series dataset
+            feature = data[:, :, 0:-1].to(self.device)
+            label = data[:, -1, -1].to(self.device)
+        elif data.dim() == 2:
+            # it is a tabular dataset
+            feature = data[:, 0:-1].to(self.device)
+            label = data[:, -1].to(self.device)
+        else:
+            raise ValueError("Unsupported data shape.")
+        return feature, label
+
     def train_epoch(self, data_loader):
         self.dnn_model.train()
 
         for data, weight in data_loader:
-            feature = data[:, :, 0:-1].to(self.device)
-            label = data[:, -1, -1].to(self.device)
+            feature , label = self._get_fl(data)
 
             pred = self.dnn_model(feature.float())
             loss = self.loss_fn(pred, label, weight.to(self.device))
@@ -526,19 +554,18 @@ def test_epoch(self, data_loader):
 
     def fit(
         self,
-        dataset,
+        dataset: Union[DatasetH, TSDatasetH],
         evals_result=dict(),
         save_path=None,
         reweighter=None,
     ):
+        ists = isinstance(dataset, TSDatasetH)  # is this time series dataset
+
         dl_train = dataset.prepare("train", col_set=["feature", "label"], data_key=DataHandlerLP.DK_L)
         dl_valid = dataset.prepare("valid", col_set=["feature", "label"], data_key=DataHandlerLP.DK_L)
         if dl_train.empty or dl_valid.empty:
             raise ValueError("Empty data from dataset, please check your dataset config.")
 
-        dl_train.config(fillna_type="ffill+bfill")  # process nan brought by dataloader
-        dl_valid.config(fillna_type="ffill+bfill")  # process nan brought by dataloader
-
         if reweighter is None:
             wl_train = np.ones(len(dl_train))
             wl_valid = np.ones(len(dl_valid))
@@ -548,6 +575,15 @@ def fit(
         else:
             raise ValueError("Unsupported reweighter type.")
 
+        # Preprocess for data.  To align to Dataset Interface for DataLoader
+        if ists:
+            dl_train.config(fillna_type="ffill+bfill")  # process nan brought by dataloader
+            dl_valid.config(fillna_type="ffill+bfill")  # process nan brought by dataloader
+        else:
+            # If it is a tabular, we convert the dataframe to numpy to be indexable by DataLoader
+            dl_train = dl_train.values
+            dl_valid = dl_valid.values
+
         train_loader = DataLoader(
             ConcatDataset(dl_train, wl_train),
             batch_size=self.batch_size,
@@ -562,6 +598,7 @@ def fit(
             num_workers=self.n_jobs,
             drop_last=True,
         )
+        del dl_train, dl_valid, wl_train, wl_valid
 
         save_path = get_or_create_path(save_path)
 
@@ -605,7 +642,7 @@ def fit(
         if self.use_gpu:
             torch.cuda.empty_cache()
 
-    def predict(self, dataset):
+    def predict(self, dataset: Union[DatasetH, TSDatasetH]):
         if not self.fitted:
             raise ValueError("model is not fitted yet!")
 

tests/model/test_general_nn.py

@@ -67,9 +67,16 @@ def test_both_dataset(self):
                     "dropout":0.,
                 },
             ),
+            GeneralPTNN(
+                n_epochs=2,
+                pt_model_uri="qlib.contrib.model.pytorch_nn.Net",  # it is a MLP
+                pt_model_kwargs={
+                    "input_dim":3,
+                },
+            ),
         ]
 
-        for ds, model in zip((tsds, tbds), model_l):
+        for ds, model in reversed(list(zip((tsds, tbds), model_l))):
             model.fit(ds)  # It works
             model.predict(ds)  # It works
             break