docstring updates

tsfm_public/toolkit/visualization.py

@@ -224,26 +224,38 @@ def plot_predictions(
 
     User should pass either:
 
-        - input_df and predictions_df: context will be extracted from input_df, and predictions will be extracted from predictions_df. Predictions_df is expected to have rows containing lists of predictions.
-        - input_df and exploded_predictions_df: context will be extracted from input_df, and predictions from exploded_predictions_df will be plotted
+        - input_df and predictions_df: context will be extracted from input_df, and predictions will be extracted from
+            predictions_df. Predictions_df is expected to have rows containing lists of predictions.
+        - input_df and exploded_predictions_df: context will be extracted from input_df, and predictions from
+            exploded_predictions_df will be plotted
         - dset and model: model will be used to produce predictions from records selected from dset
 
+    If exploded_predictions_df is passed, indices and num_plots are ignored, the assumption is that there are only one
+        set of predictions passed for plotting.
 
     Args:
-        input_df (Optional[pd.DataFrame], optional): The input dataframe from which the predictions are generated, containing timestamp and target columns. Defaults to None.
-        predictions_df (Optional[pd.DataFrame], optional): The predictions dataframe, where each row contains starting timestamp and a list of predictions for each target column. Defaults to None.
-        exploded_predictions_df (Optional[pd.DataFrame], optional): The predictions dataframe, containing timestamp and predicted target columns. Defaults to None.
-        dset (Optional[Dataset], optional): Torch dataset containing the context data to use as input for the model. Defaults to None.
+        input_df (Optional[pd.DataFrame], optional): The input dataframe from which the predictions are generated,
+            containing timestamp and target columns. Defaults to None.
+        predictions_df (Optional[pd.DataFrame], optional): The predictions dataframe, where each row contains starting
+            timestamp and a list of predictions for each target column. Defaults to None.
+        exploded_predictions_df (Optional[pd.DataFrame], optional): The predictions dataframe, containing timestamp
+            and predicted target columns. Defaults to None.
+        dset (Optional[Dataset], optional): Torch dataset containing the context data to use as input for the model.
+            Defaults to None.
         model (Optional[PreTrainedModel], optional): The pre-trained time series model. Defaults to None.
-        freq (Optional[str], optional): Frequency of the time series data, using Pandas string abbreviations (https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases). Defaults to None.
+        freq (Optional[str], optional): Frequency of the time series data, using Pandas string abbreviations
+            (https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases). Defaults to None.
         timestamp_column (Optional[str], optional): Name of timestamp column in the dataframe. Defaults to None.
-        id_columns (Optional[List[str]], optional):  (For future use) List of id columns in the dataframe. Defaults to None.
-        plot_context (Optional[int], optional): Integer representing the number of time points of historical data to plot. Defaults to None.
+        id_columns (Optional[List[str]], optional):  (For future use) List of id columns in the dataframe. Defaults to
+            None.
+        plot_context (Optional[int], optional): Integer representing the number of time points of historical data to
+            plot. Defaults to None.
         plot_dir (str, optional): Directory where plots are saved. Defaults to None.
         num_plots (int, optional): Number of subplots to plot in the figure. Defaults to 10.
         plot_prefix (str, optional): Prefix to put on the plot file names. Defaults to "valid".
         channel (Union[int, str], optional): Channel, i.e., target column or its index, to plot. Defaults to None.
-        indices (List[int], optional): List of indices to plot. If None, random examples will be chosen. Defaults to None.
+        indices (List[int], optional): List of indices to plot. If None, random examples will be chosen. Defaults to
+            None.
     """
     if indices is not None:
         num_plots = len(indices)
@@ -263,6 +275,7 @@ def plot_predictions(
         plot_context = len(input_df)
         using_pipeline = True
         plot_test_data = False
+        indices = [-1]  # indices not used in exploded case
     elif input_df is not None and predictions_df is not None:
         # 2) input_df and predictions plus column information is provided