Logging is not just for training

src/batdetect2/logging.py

@@ -15,6 +15,7 @@ from typing import (
 )
 
 import numpy as np
+import pandas as pd
 from lightning.pytorch.loggers import (
     CSVLogger,
     Logger,
@@ -56,7 +57,7 @@ class TensorBoardLoggerConfig(BaseLoggerConfig):
 
 class MLFlowLoggerConfig(BaseLoggerConfig):
     name: Literal["mlflow"] = "mlflow"
-    tracking_uri: Optional[str] = None
+    tracking_uri: Optional[str] = "http://localhost:5000"
     tags: Optional[dict[str, Any]] = None
     log_model: bool = False
 
@@ -160,6 +161,9 @@ def create_tensorboard_logger(
 
     name = run_name
 
+    if name is None:
+        name = experiment_name
+
     if run_name is not None and experiment_name is not None:
         name = str(Path(experiment_name) / run_name)
 
@@ -239,10 +243,10 @@ def build_logger(
     )
 
 
-Plotter = Callable[[str, Figure, int], None]
+PlotLogger = Callable[[str, Figure, int], None]
 
 
-def get_image_plotter(logger: Logger) -> Optional[Plotter]:
+def get_image_logger(logger: Logger) -> Optional[PlotLogger]:
     if isinstance(logger, TensorBoardLogger):
         return logger.experiment.add_figure
 
@@ -250,6 +254,7 @@ def get_image_plotter(logger: Logger) -> Optional[Plotter]:
 
         def plot_figure(name, figure, step):
             image = _convert_figure_to_array(figure)
+            name = name.replace("/", "_")
             return logger.experiment.log_image(
                 logger.run_id,
                 image,
@@ -263,6 +268,37 @@ def get_image_plotter(logger: Logger) -> Optional[Plotter]:
         return partial(save_figure, dir=Path(logger.log_dir))
 
 
+TableLogger = Callable[[str, pd.DataFrame, int], None]
+
+
+def get_table_logger(logger: Logger) -> Optional[TableLogger]:
+    if isinstance(logger, TensorBoardLogger):
+        return partial(save_table, dir=Path(logger.log_dir))
+
+    if isinstance(logger, MLFlowLogger):
+
+        def plot_figure(name: str, df: pd.DataFrame, step: int):
+            return logger.experiment.log_table(
+                logger.run_id,
+                data=df,
+                artifact_file=f"{name}_step_{step}.json",
+            )
+
+        return plot_figure
+
+    if isinstance(logger, CSVLogger):
+        return partial(save_table, dir=Path(logger.log_dir))
+
+
+def save_table(name: str, df: pd.DataFrame, step: int, dir: Path) -> None:
+    path = dir / "tables" / f"{name}_step_{step}.csv"
+
+    if not path.parent.exists():
+        path.parent.mkdir(parents=True)
+
+    df.to_csv(path, index=False)
+
+
 def save_figure(name: str, fig: Figure, step: int, dir: Path) -> None:
     path = dir / "plots" / f"{name}_step_{step}.png"