[results] Hardcode trainlog and evaluation file names

src/mednet/scripts/evaluate.py

@@ -25,13 +25,13 @@ logger = setup(__name__.split(".")[0], format="%(levelname)s: %(message)s")
 
    .. code:: sh
 
-      mednet evaluate -vv --predictions=path/to/predictions.json --output=evaluation.json
+      mednet evaluate -vv --predictions=path/to/predictions.json
 
 2. Run evaluation on an existing prediction output, tune threshold a priori on the `validation` set:
 
    .. code:: sh
 
-      mednet evaluate -vv --predictions=path/to/predictions.json --output=evaluation.json --threshold=validation
+      mednet evaluate -vv --predictions=path/to/predictions.json --threshold=validation
 """,
 )
 @click.option(
@@ -48,19 +48,18 @@ logger = setup(__name__.split(".")[0], format="%(levelname)s: %(message)s")
     cls=ResourceOption,
 )
 @click.option(
-    "--output",
+    "--output-folder",
     "-o",
-    help="""Path to a JSON file in which to save evaluation results
-    (leading directories are created if they do not exist).""",
+    help="Directory in which to store results (created if does not exist)",
     required=True,
-    default="evaluation.json",
-    cls=ResourceOption,
     type=click.Path(
-        file_okay=True,
-        dir_okay=False,
+        file_okay=False,
+        dir_okay=True,
         writable=True,
         path_type=pathlib.Path,
     ),
+    default="results",
+    cls=ResourceOption,
 )
 @click.option(
     "--threshold",
@@ -106,7 +105,7 @@ logger = setup(__name__.split(".")[0], format="%(levelname)s: %(message)s")
 @verbosity_option(logger=logger, cls=ResourceOption, expose_value=False)
 def evaluate(
     predictions: pathlib.Path,
-    output: pathlib.Path,
+    output_folder: pathlib.Path,
     threshold: str | float,
     binning: str,
     plot: bool,
@@ -128,13 +127,16 @@ def evaluate(
     )
     from .utils import execution_metadata, save_json_with_backup
 
+    evaluation_filename = "evaluation.json"
+    evaluation_file = pathlib.Path(output_folder) / evaluation_filename
+
     with predictions.open("r") as f:
         predict_data = json.load(f)
 
     # register metadata
     json_data: dict[str, typing.Any] = execution_metadata()
     json_data = {k.replace("_", "-"): v for k, v in json_data.items()}
-    save_json_with_backup(output.with_suffix(".meta.json"), json_data)
+    save_json_with_backup(evaluation_file.with_suffix(".meta.json"), json_data)
 
     if threshold in predict_data:
         # it is the name of a split
@@ -166,8 +168,8 @@ def evaluate(
         )
 
     # records full result analysis to a JSON file
-    logger.info(f"Saving evaluation results at `{output}`...")
-    with output.open("w") as f:
+    logger.info(f"Saving evaluation results at `{evaluation_file}`...")
+    with evaluation_file.open("w") as f:
         json.dump(results, f, indent=2, cls=NumpyJSONEncoder)
 
     # dump evaluation results in RST format to screen and file
@@ -181,7 +183,7 @@ def evaluate(
     table = tabulate_results(table_data, fmt="rst")
     click.echo(table)
 
-    table_path = output.with_suffix(".rst")
+    table_path = evaluation_file.with_suffix(".rst")
     logger.info(
         f"Saving evaluation results in table format at `{table_path}`...",
     )
@@ -189,7 +191,7 @@ def evaluate(
         f.write(table)
 
     # dump evaluation plots in file
-    figure_path = output.with_suffix(".pdf")
+    figure_path = evaluation_file.with_suffix(".pdf")
     logger.info(f"Saving evaluation figures at `{figure_path}`...")
 
     if plot:

src/mednet/scripts/experiment.py

@@ -101,11 +101,10 @@ def experiment(
     from .train_analysis import train_analysis
 
     logdir = train_output_folder / "logs"
-    output_pdf = train_output_folder / "trainlog.pdf"
     ctx.invoke(
         train_analysis,
         logdir=logdir,
-        output=output_pdf,
+        output_folder=train_output_folder,
     )
 
     logger.info("Ended train analysis")
@@ -139,12 +138,10 @@ def experiment(
 
     from .evaluate import evaluate
 
-    evaluation_output = output_folder / "evaluation.json"
-
     ctx.invoke(
         evaluate,
         predictions=predictions_output,
-        output=evaluation_output,
+        output=output_folder,
         threshold="validation",
     )
 

src/mednet/scripts/train_analysis.py

@@ -5,7 +5,7 @@
 import pathlib
 
 import click
-from clapper.click import verbosity_option
+from clapper.click import ResourceOption, verbosity_option
 from clapper.logging import setup
 
 # avoids X11/graphical desktop requirement when creating plots
@@ -119,18 +119,23 @@ def create_figures(
     type=click.Path(dir_okay=True, exists=True, path_type=pathlib.Path),
 )
 @click.option(
-    "--output",
+    "--output-folder",
     "-o",
-    help="Name of the output file to create (multi-page .pdf)",
+    help="Directory in which to store results (created if does not exist)",
     required=True,
-    show_default=True,
-    default="trainlog.pdf",
-    type=click.Path(dir_okay=False, file_okay=True, path_type=pathlib.Path),
+    type=click.Path(
+        file_okay=False,
+        dir_okay=True,
+        writable=True,
+        path_type=pathlib.Path,
+    ),
+    default="results",
+    cls=ResourceOption,
 )
 @verbosity_option(logger=logger, expose_value=False)
 def train_analysis(
     logdir: pathlib.Path,
-    output: pathlib.Path,
+    output_folder: pathlib.Path,
 ) -> None:  # numpydoc ignore=PR01
     """Create a plot for each metric in the training logs and saves them in a .pdf file."""
     import matplotlib.pyplot as plt
@@ -138,11 +143,14 @@ def train_analysis(
 
     from ..utils.tensorboard import scalars_to_dict
 
+    train_log_filename = "trainlog.pdf"
+    train_log_file = pathlib.Path(output_folder) / train_log_filename
+
     data = scalars_to_dict(logdir)
 
-    output.parent.mkdir(parents=True, exist_ok=True)
+    train_log_file.parent.mkdir(parents=True, exist_ok=True)
 
-    with PdfPages(output) as pdf:
+    with PdfPages(train_log_file) as pdf:
         for figure in create_figures(data):
             pdf.savefig(figure)
             plt.close(figure)