Merge branch 'main' into custom-lr-scheduler

neuralprophet/forecaster.py

@@ -572,7 +572,6 @@ def __init__(
         self.data_params = None
 
         # Pytorch Lightning Trainer
-        self.metrics_logger = MetricsLogger(save_dir=os.getcwd())
         self.accelerator = accelerator
 
         # set during prediction
@@ -934,6 +933,7 @@ def fit(
         early_stopping: bool = False,
         minimal: bool = False,
         metrics: Optional[np_types.CollectMetricsMode] = None,
+        metrics_log_dir: Optional[str] = None,
         progress: Optional[str] = "bar",
         checkpointing: bool = False,
         num_workers: int = 0,
@@ -1049,16 +1049,29 @@ def fit(
                         number of epochs to train for."
                 )
 
-        if metrics:
+        # Setup Metrics
+        if metrics is not None:
             self.metrics = utils_metrics.get_metrics(metrics)
 
-        if progress == "plot" and not metrics:
-            log.info("Progress plot requires metrics to be enabled. Disabling progress plot.")
-            progress = None
+        if progress == "plot" and not self.metrics:
+            log.info("Progress plot requires metrics to be enabled. Setting progress to bar.")
+            progress = "bar"
 
         if not self.config_normalization.global_normalization:
             log.info("When Global modeling with local normalization, metrics are displayed in normalized scale.")
 
+        if metrics_log_dir is not None and not self.metrics:
+            log.error("Metrics are disabled. Ignoring provided logging directory.")
+            metrics_log_dir = None
+        if metrics_log_dir is None and self.metrics:
+            log.warning("Metrics are enabled. Please provide valid metrics logging directory. Setting to CWD")
+            metrics_log_dir = os.getcwd()
+
+        if self.metrics:
+            self.metrics_logger = MetricsLogger(save_dir=metrics_log_dir)
+        else:
+            self.metrics_logger = None
+
         # Pre-processing
         # Copy df and save list of unique time series IDs (the latter for global-local modelling if enabled)
         df, _, _, self.id_list = df_utils.prep_or_copy_df(df)

neuralprophet/utils_metrics.py

@@ -27,7 +27,9 @@ def get_metrics(metric_input):
             Dict of names of torchmetrics.Metric metrics
     """
     if metric_input is None:
-        return {}
+        return False
+    elif metric_input is False:
+        return False
     elif metric_input is True:
         return {"MAE": METRICS["MAE"], "RMSE": METRICS["RMSE"]}
     elif isinstance(metric_input, str):
@@ -51,5 +53,5 @@ def get_metrics(metric_input):
                 "All metrics must be valid names of torchmetrics.Metric objects."
             )
         return {k: [v, {}] for k, v in metric_input.items()}
-    elif metric_input is not False:
+    else:
         raise ValueError("Received unsupported argument for collect_metrics.")

tests/test_utils.py

@@ -38,3 +38,9 @@ def test_create_dummy_datestamps():
     m = NeuralProphet(epochs=EPOCHS, batch_size=BATCH_SIZE, learning_rate=LR)
     _ = m.fit(df_dummy)
     _ = m.make_future_dataframe(df_dummy, periods=365, n_historic_predictions=True)
+
+
+def test_no_log():
+    df = pd.read_csv(PEYTON_FILE, nrows=NROWS)
+    m = NeuralProphet(epochs=EPOCHS, batch_size=BATCH_SIZE, learning_rate=LR)
+    _ = m.fit(df, metrics=False, metrics_log_dir=False)