Fix checkpointing bug in tabular forecasting (#995)

CHANGELOG.md

@@ -52,6 +52,8 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 - Fixed a bug where additional `DataModule` keyword arguments could not be configured with Flash Zero for some tasks ([#994](https://github.com/PyTorchLightning/lightning-flash/pull/994))
 
+- Fixed a bug where the TabularForecaster would not work with some versions of pandas ([#995](https://github.com/PyTorchLightning/lightning-flash/pull/995))
+
 ### Removed
 
 - Removed `OutputMapping` ([#939](https://github.com/PyTorchLightning/lightning-flash/pull/939))

flash/core/integrations/pytorch_forecasting/adapter.py

@@ -76,7 +76,7 @@ def from_task(
     ) -> Adapter:
         parameters = copy(parameters)
         # Remove the single row of data from the parameters to reconstruct the `time_series_dataset`
-        data = parameters.pop("data_sample")
+        data = DataFrame.from_dict(parameters.pop("data_sample"))
         time_series_dataset = PatchTimeSeriesDataSet.from_parameters(parameters, data)
 
         backbone_kwargs["loss"] = loss_fn

flash/tabular/forecasting/data.py

@@ -62,7 +62,7 @@ def load_data(
             parameters = time_series_dataset.get_parameters()
 
             # Add some sample data so that we can recreate the `TimeSeriesDataSet` later on
-            parameters["data_sample"] = data.iloc[[0]]
+            parameters["data_sample"] = data.iloc[[0]].to_dict()
 
             self.set_state(TimeSeriesDataSetParametersState(parameters))
             self.parameters = parameters

flash/tabular/forecasting/model.py

@@ -37,7 +37,7 @@ def __init__(
         metrics: Union[torchmetrics.Metric, List[torchmetrics.Metric]] = None,
         learning_rate: float = 4e-3,
     ):
-        self.save_hyperparameters(ignore="parameters")
+        self.save_hyperparameters()
 
         if backbone_kwargs is None:
             backbone_kwargs = {}