make logging of metrics optional and dependent on metrics setting (#1638

)

neuralprophet/forecaster.py

@@ -476,7 +476,7 @@ def __init__(
             log.info(
                 DeprecationWarning(
                     "Providing metrics to collect via `collect_metrics` in NeuralProphet is deprecated and will be "
-                    + "removed in a future version. The metrics are now configure in the `fit()` method via `metrics`."
+                    + "removed in a future version. The metrics are now configured in the `fit()` method via `metrics`."
                 )
             )
         self.metrics = utils_metrics.get_metrics(collect_metrics)
@@ -548,7 +548,6 @@ def __init__(
         self.data_params = None
 
         # Pytorch Lightning Trainer
-        self.metrics_logger = MetricsLogger(save_dir=os.getcwd())
         self.accelerator = accelerator
         self.trainer_config = trainer_config
 
@@ -911,6 +910,7 @@ 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,
         continue_training: bool = False,
@@ -973,10 +973,15 @@ def fit(
             pd.DataFrame
                 metrics with training and potentially evaluation metrics
         """
+        if minimal:
+            checkpointing = False
+            self.metrics = False
+            progress = None
+
         if self.fitted:
             raise RuntimeError("Model has been fitted already. Please initialize a new model to fit again.")
 
-        # Configuration
+        # Train Config overrides
         if epochs is not None:
             self.config_train.epochs = epochs
 
@@ -989,10 +994,7 @@ def fit(
         if early_stopping is not None:
             self.early_stopping = early_stopping
 
-        if metrics is not None:
-            self.metrics = utils_metrics.get_metrics(metrics)
-
-        # Warnings
+        # Warning for early stopping and regularization
         if early_stopping:
             reg_enabled = utils.check_for_regularization(
                 [
@@ -1012,17 +1014,28 @@ def fit(
                         number of epochs to train for."
                 )
 
-        if progress == "plot" and metrics is False:
-            log.info("Progress plot requires metrics to be enabled. Enabling the default metrics.")
-            metrics = utils_metrics.get_metrics(True)
+        # Setup Metrics
+        if metrics is not None:
+            self.metrics = utils_metrics.get_metrics(metrics)
+
+        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 minimal:
-            checkpointing = False
-            self.metrics = False
-            progress = None
+        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)

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

@@ -40,6 +40,12 @@ def test_create_dummy_datestamps():
     _ = 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)
+
+
 def test_save_load():
     df = pd.read_csv(PEYTON_FILE, nrows=NROWS)
     m = NeuralProphet(