Merge pull request pycaret#3599 from pycaret/ts_plot_model_fix

Fixes TS Plotting Issue

pycaret/internal/plots/utils/time_series.py

@@ -951,7 +951,7 @@ def _resolve_hoverinfo(
     hoverinfo: Optional[str],
     threshold: int,
     data: Optional[pd.Series],
-    X: Optional[pd.DataFrame],
+    X: Optional[List[pd.DataFrame]],
 ) -> str:
     """Decide whether data tip obtained by hovering over a Plotly plot should be
     enabled or disabled based user settings and size of data. If user provides the
@@ -989,7 +989,7 @@ def _resolve_renderer(
     renderer: Optional[str],
     threshold: int,
     data: Optional[pd.Series],
-    X: Optional[pd.DataFrame],
+    X: Optional[List[pd.DataFrame]],
 ) -> str:
     """Decide the renderer to use for the Plotly plot based user settings and
     size of data. If user provides the `renderer` option, it is honored, else it

pycaret/time_series/forecasting/oop.py

@@ -3778,6 +3778,11 @@ def _plot_model(
                     # Disable Prediction Intervals if more than 1 estimator is provided.
                     return_pred_int = False
 
+                if X is not None:
+                    X = _reformat_dataframes_for_plots(
+                        data=[X], labels_suffix=["original"]
+                    )
+
             elif plot == "insample":
                 # Try to get insample forecasts if possible
                 model_results = [

tests/test_time_series_plots.py

@@ -13,6 +13,7 @@
     _return_model_names_for_plots_stats,
 )
 
+from pycaret.datasets import get_data
 from pycaret.time_series import TSForecastingExperiment
 
 pytestmark = pytest.mark.filterwarnings("ignore::UserWarning")
@@ -352,3 +353,28 @@ def test_plot_multiple_model_overlays(
     assert (
         "Please provide a label corresponding to each model to proceed." in exceptionmsg
     )
+
+
+def test_plot_final_model_exo():
+    """Tests running plot model after running finalize_model when exogenous
+    variables are present. Fix for https://github.com/pycaret/pycaret/issues/3565
+    """
+    data = get_data("uschange")
+    target = "Consumption"
+    FH = 3
+    train = data.iloc[: int(len(data) - FH)]
+    test = data.iloc[int(len(data)) - FH :]
+    test = test.drop(columns=[target], axis=1)
+
+    exp = TSForecastingExperiment()
+    exp.setup(data=train, target=target, fh=FH, session_id=42)
+    model = exp.create_model("arima")
+    final_model = exp.finalize_model(model)
+
+    # Previous issue coming from renderer resolution due to X
+
+    # This should not give an error (passing X explicitly)
+    exp.plot_model(final_model, data_kwargs={"X": test})
+
+    # Also, plotting without explicit passing X should also pass
+    exp.plot_model()