style: apply ruff format to all files

mlflow_utils/experiment_comparison.py

@@ -51,6 +51,7 @@ logger = logging.getLogger(__name__)
 @dataclass
 class RunComparison:
     """Comparison result for multiple MLflow runs."""
+
     run_ids: List[str]
     experiment_name: str
 
@@ -111,6 +112,7 @@ class RunComparison:
 @dataclass
 class PromotionRecommendation:
     """Recommendation for model promotion."""
+
     model_name: str
     version: Optional[int]
     recommended: bool
@@ -262,13 +264,9 @@ class ExperimentAnalyzer:
 
             # Metadata
             comparison.run_names[run_id] = run.info.run_name or run_id[:8]
-            comparison.start_times[run_id] = datetime.fromtimestamp(
-                run.info.start_time / 1000
-            )
+            comparison.start_times[run_id] = datetime.fromtimestamp(run.info.start_time / 1000)
             if run.info.end_time:
-                comparison.durations[run_id] = (
-                    run.info.end_time - run.info.start_time
-                ) / 1000
+                comparison.durations[run_id] = (run.info.end_time - run.info.start_time) / 1000
 
             # Metrics
             for key, value in run.data.metrics.items():
@@ -288,10 +286,7 @@ class ExperimentAnalyzer:
                 continue
 
             # Determine if lower is better based on metric name
-            minimize = any(
-                term in metric_name.lower()
-                for term in ["latency", "error", "loss", "time"]
-            )
+            minimize = any(term in metric_name.lower() for term in ["latency", "error", "loss", "time"])
 
             if minimize:
                 best_id = min(values.keys(), key=lambda k: values[k])
@@ -330,10 +325,7 @@ class ExperimentAnalyzer:
         )
 
         # Filter to only runs that have the metric
-        runs_with_metric = [
-            r for r in runs
-            if metric in r.data.metrics
-        ]
+        runs_with_metric = [r for r in runs if metric in r.data.metrics]
 
         return runs_with_metric[0] if runs_with_metric else None