diff --git a/src/mednet/scripts/upload.py b/src/mednet/scripts/upload.py
index 0b0f1417c5c45ebda7a1e9e2da8d429cc3d96271..0b4f01e74dd97befdc6f7a48694b0e125e1cbf60 100644
--- a/src/mednet/scripts/upload.py
+++ b/src/mednet/scripts/upload.py
@@ -2,17 +2,16 @@
#
# SPDX-License-Identifier: GPL-3.0-or-later
-import os
+import configparser
import json
+import os
import pathlib
import shutil
import tempfile
-import configparser
+import click
import gitlab
import mlflow
-
-import click
from clapper.click import ResourceOption, verbosity_option
from clapper.logging import setup
@@ -20,15 +19,16 @@ from .click import ConfigCommand
logger = setup(__name__.split(".")[0], format="%(levelname)s: %(message)s")
+
def get_config():
"""Return an instance of the Gitlab object for remote operations.
-
+
Returns
-------
Gitlab entry and credential.
"""
- cfg = pathlib.Path(os.path.expanduser("~/.python-gitlab.cfg"))
+ cfg = pathlib.Path("~/.python-gitlab.cfg").expanduser()
if cfg.exists():
gl = gitlab.Gitlab.from_config("idiap", [str(cfg)])
config = configparser.ConfigParser()
@@ -41,8 +41,9 @@ def get_config():
return gl, config
+
def create_temp_copy(source, target):
- """ Create a copy of original file in temp folder.
+ """Create a copy of original file in temp folder.
Parameters
----------
@@ -56,11 +57,12 @@ def create_temp_copy(source, target):
Path to target file in temp folder.
"""
- temp_dir = tempfile.gettempdir()
- target = os.path.join(temp_dir, target)
+ temp_dir = pathlib.Path(tempfile.gettempdir())
+ target = temp_dir / target
shutil.copy2(source, target)
return target
+
@click.command(
entry_point_group="mednet.config",
cls=ConfigCommand,
@@ -86,30 +88,30 @@ def create_temp_copy(source, target):
""",
)
@click.option(
- "--output-folder",
- "-o",
- help="Directory in which to upload results from",
- required=True,
- type=click.Path(
- file_okay=False,
- dir_okay=True,
- path_type=pathlib.Path,
- ),
- default="results",
- cls=ResourceOption,
- )
+ "--output-folder",
+ "-o",
+ help="Directory in which to upload results from",
+ required=True,
+ type=click.Path(
+ file_okay=False,
+ dir_okay=True,
+ path_type=pathlib.Path,
+ ),
+ default="results",
+ cls=ResourceOption,
+)
@click.option(
- "--experiment-name",
- "-e",
- help='A string indicating the experiment name (e.g. "exp-pasa_mc" or "exp-densenet_mc-ch")',
- cls=ResourceOption,
- )
+ "--experiment-name",
+ "-e",
+ help='A string indicating the experiment name (e.g. "exp-pasa_mc" or "exp-densenet_mc-ch")',
+ cls=ResourceOption,
+)
@click.option(
- "--run-name",
- "-r",
- help='A string indicating the run name (e.g. "run-1")',
- cls=ResourceOption,
- )
+ "--run-name",
+ "-r",
+ help='A string indicating the run name (e.g. "run-1")',
+ cls=ResourceOption,
+)
@verbosity_option(logger=logger, cls=ResourceOption, expose_value=False)
def upload(
output_folder: pathlib.Path,
@@ -119,10 +121,9 @@ def upload(
) -> None: # numpydoc ignore=PR01
"""Upload results from an experiment output folder."""
- logger.info(f"Getting Gitlab credentials for accessing to MLFlow server...")
+ logger.info("Getting Gitlab credentials for accessing to MLFlow server...")
gitlab, config = get_config()
- project = gitlab.projects.get('biosignal/software/mednet')
- tracking_uri = gitlab.api_url + f"/projects/{project.id}/ml/mlflow"
+ project = gitlab.projects.get("biosignal/software/mednet")
os.environ["MLFLOW_TRACKING_TOKEN"] = config["idiap"]["private_token"]
os.environ["MLFLOW_TRACKING_URI"] = (
gitlab.api_url + f"/projects/{project.id}/ml/mlflow"
@@ -132,12 +133,14 @@ def upload(
train_output_folder = output_folder / "model"
train_meta_file = train_output_folder / "meta.json"
train_log_file = train_output_folder / "trainlog.pdf"
- train_model_file = [str(f) for f in train_output_folder.glob("*lowest*")][0]
- train_model_temp_file = train_model_file.split(os.sep)[-1].replace("=", "_")
- train_model_temp_file = create_temp_copy(train_model_file, train_model_temp_file)
+ train_model_file = [f for f in train_output_folder.glob("*lowest*")][0]
+ train_model_temp_file = train_model_file.parts[-1].replace("=", "_")
+ train_model_temp_file = create_temp_copy(
+ train_model_file, train_model_temp_file
+ )
with train_meta_file.open("r") as f:
meta_data = json.load(f)
-
+
# prepare evaluation files
evaluation_file = output_folder / "evaluation.json"
evaluation_meta_file = output_folder / "evaluation.meta.json"
@@ -147,29 +150,34 @@ def upload(
test_data = evaluation_data["test"]
# prepare experiment and run names
- experiment_name = experiment_name if experiment_name else f'{meta_data["model-name"]}_{meta_data["database-name"]}'
+ experiment_name = (
+ experiment_name
+ if experiment_name
+ else f'{meta_data["model-name"]}_{meta_data["database-name"]}'
+ )
run_name = run_name if run_name else meta_data["datetime"]
- logger.info(f"Setting experiment and run names on the MLFlow server...")
+ logger.info("Setting experiment and run names on the MLFlow server...")
mlflow.set_experiment(experiment_name=experiment_name)
- with mlflow.start_run(run_name=run_name) as run:
+ with mlflow.start_run(run_name=run_name):
# upload metrics
- logger.info(f"Uploading metrics to MLFlow server...")
+ logger.info("Uploading metrics to MLFlow server...")
mlflow.log_metric("threshold", test_data["threshold"])
mlflow.log_metric("precision", test_data["precision"])
mlflow.log_metric("recall", test_data["recall"])
mlflow.log_metric("f1_score", test_data["f1_score"])
- mlflow.log_metric("average_precision_score", test_data["average_precision_score"])
+ mlflow.log_metric(
+ "average_precision_score", test_data["average_precision_score"]
+ )
mlflow.log_metric("specificity", test_data["specificity"])
mlflow.log_metric("auc_score", test_data["auc_score"])
mlflow.log_metric("accuracy", test_data["accuracy"])
mlflow.log_param("version", meta_data["package-version"])
# upload artifacts
- logger.info(f"Uploading artifacts to MLFlow server...")
+ logger.info("Uploading artifacts to MLFlow server...")
mlflow.log_artifact(train_meta_file)
mlflow.log_artifact(train_log_file)
mlflow.log_artifact(train_model_temp_file)
mlflow.log_artifact(evaluation_file)
mlflow.log_artifact(evaluation_meta_file)
mlflow.log_artifact(evaluation_log_file)
-