test: Move results folders inside tests/ subdirectories

.gitignore

@@ -3,7 +3,7 @@ CLAUDE.md
 .claude/
 
 # Profiling results
-traces/
+tests/profiling/results/
 
 # Trajectories results
 tests/trajectories/results/
@@ -18,7 +18,7 @@ uv.lock
 .idea/
 
 # Generated images
-images/
+tests/plots/results/
 
 # Byte-compiled / optimized / DLL files
 __pycache__/

tests/paths.py

@@ -1,4 +1,7 @@
 from pathlib import Path
 
 TORCHJD_DIR = Path(__file__).parent.parent
-TRACES_DIR = TORCHJD_DIR / "traces"
+TESTS_DIR = Path(__file__).parent
+PLOTS_RESULTS_DIR = TESTS_DIR / "plots" / "results"
+PROFILING_RESULTS_DIR = TESTS_DIR / "profiling" / "results"
+TRAJECTORIES_RESULTS_DIR = TESTS_DIR / "trajectories" / "results"

tests/plots/static_plotter.py

@@ -1,6 +1,5 @@
-import os
-
 import torch
+from paths import PLOTS_RESULTS_DIR
 from plotly import graph_objects as go
 
 from plots._utils import (
@@ -268,14 +267,14 @@ def main(
     )
     fig.update_yaxes(range=[-0.1, 2.1], showgrid=False, zeroline=False, visible=False)
 
-    os.makedirs("images/", exist_ok=True)
-    fig.write_image(f"images/{filename}.pdf")
+    PLOTS_RESULTS_DIR.mkdir(exist_ok=True)
+    fig.write_image(PLOTS_RESULTS_DIR / f"{filename}.pdf")
     # Alternative: use .svg here and then convert to pdf using rsvg-convert. Install
     # [rsvg-convert](https://manpages.ubuntu.com/manpages/bionic/man1/rsvg-convert.1.html) and run:
     # `rsvg-convert -f pdf -o filename.pdf filename.svg`
     # To do that on all files at ones, run:
     # ```
-    # for file in images/*.svg; do rsvg-convert -f pdf -o "${file%.svg}.pdf" "$file"; done
+    # for file in tests/plots/results/*.svg; do rsvg-convert -f pdf -o "${file%.svg}.pdf" "$file"; done
     # ```
 
 

tests/profiling/plot_memory_timeline.py

@@ -13,7 +13,7 @@
 matplotlib.use("Agg")  # Use non-GUI backend to avoid tkinter dependency
 import matplotlib.pyplot as plt
 import numpy as np
-from paths import TRACES_DIR
+from paths import PROFILING_RESULTS_DIR
 
 
 @dataclass
@@ -63,7 +63,7 @@ def plot_memory_timelines(experiment: str, folders: list[str]) -> None:
     cpu_timelines = []
     cuda_timelines = []
     for folder in folders:
-        path = TRACES_DIR / folder / f"{experiment}.json"
+        path = PROFILING_RESULTS_DIR / folder / f"{experiment}.json"
         cpu_timeline, cuda_timeline = extract_memory_timelines(path)
         cpu_timelines.append(cpu_timeline)
         cuda_timelines.append(cuda_timeline)
@@ -104,7 +104,7 @@ def plot_memory_timelines(experiment: str, folders: list[str]) -> None:
 
     fig.tight_layout()
 
-    output_dir = Path(TRACES_DIR / "memory_timelines")
+    output_dir = Path(PROFILING_RESULTS_DIR / "memory_timelines")
     output_dir.mkdir(parents=True, exist_ok=True)
     output_path = output_dir / f"{experiment}.png"
     print(f"\nSaving plot to: {output_path}")

tests/profiling/run_profiler.py

@@ -23,7 +23,7 @@
 )
 from utils.tensors import make_inputs_and_targets
 
-from tests.paths import TRACES_DIR
+from tests.paths import PROFILING_RESULTS_DIR
 from torchjd.aggregation import UPGrad, UPGradWeighting
 from torchjd.autogram import Engine
 
@@ -98,7 +98,7 @@ def _save_and_print_trace(
     batch_size: int,
 ) -> None:
     filename = f"{factory}-bs{batch_size}-{DEVICE.type}.json"
-    output_dir = TRACES_DIR / method_name
+    output_dir = PROFILING_RESULTS_DIR / method_name
     output_dir.mkdir(parents=True, exist_ok=True)
     trace_path = output_dir / filename
 

tests/trajectories/_paths.py

@@ -1,23 +1,23 @@
 from pathlib import Path
 
-RESULTS_DIR = Path(__file__).parent / "results"
+from tests.paths import TRAJECTORIES_RESULTS_DIR
 
 
 def get_params_dir(objective_key: str) -> Path:
-    return RESULTS_DIR / objective_key / "X"
+    return TRAJECTORIES_RESULTS_DIR / objective_key / "X"
 
 
 def get_values_dir(objective_key: str) -> Path:
-    return RESULTS_DIR / objective_key / "Y"
+    return TRAJECTORIES_RESULTS_DIR / objective_key / "Y"
 
 
 def get_param_plots_dir(objective_key: str) -> Path:
-    return RESULTS_DIR / objective_key / "param_plots"
+    return TRAJECTORIES_RESULTS_DIR / objective_key / "param_plots"
 
 
 def get_value_plots_dir(objective_key: str) -> Path:
-    return RESULTS_DIR / objective_key / "value_plots"
+    return TRAJECTORIES_RESULTS_DIR / objective_key / "value_plots"
 
 
 def get_distance_to_pf_plots_dir(objective_key: str) -> Path:
-    return RESULTS_DIR / objective_key / "distance_to_pf"
+    return TRAJECTORIES_RESULTS_DIR / objective_key / "distance_to_pf"

tests/trajectories/optimize.py

@@ -18,6 +18,7 @@
 import numpy as np
 import torch
 
+from tests.paths import TRAJECTORIES_RESULTS_DIR
 from torchjd.aggregation import Stateful
 from trajectories._constants import (
     AGGREGATORS,
@@ -29,7 +30,7 @@
     OBJECTIVES,
 )
 from trajectories._optimization import optimize
-from trajectories._paths import RESULTS_DIR, get_params_dir, get_values_dir
+from trajectories._paths import get_params_dir, get_values_dir
 
 warnings.filterwarnings("ignore")
 
@@ -88,7 +89,7 @@ def main() -> None:
         "learning_rates": learning_rates,
         "initial_points": initial_points,
     }
-    with open(RESULTS_DIR / objective_key / "metadata.json", "w") as f:
+    with open(TRAJECTORIES_RESULTS_DIR / objective_key / "metadata.json", "w") as f:
         json.dump(metadata, f)
 
     for aggregator_key in aggregator_keys:

tests/trajectories/plot_distance_to_pf.py

@@ -15,9 +15,10 @@
 import numpy as np
 import torch
 
+from tests.paths import TRAJECTORIES_RESULTS_DIR
 from trajectories._constants import AGGREGATOR_ORDER, AGGREGATORS, LATEX_NAMES, OBJECTIVES
 from trajectories._pareto_utils import make_2d_pf_distance_fn
-from trajectories._paths import RESULTS_DIR, get_distance_to_pf_plots_dir, get_values_dir
+from trajectories._paths import get_distance_to_pf_plots_dir, get_values_dir
 from trajectories._plotters import (
     MultiEvolutionPlotter,
     SquareBoxAspectSetter,
@@ -51,7 +52,7 @@ def main() -> None:
     args = parser.parse_args()
     objective_key = args.objective
 
-    with open(RESULTS_DIR / objective_key / "metadata.json") as f:
+    with open(TRAJECTORIES_RESULTS_DIR / objective_key / "metadata.json") as f:
         metadata = json.load(f)
 
     values_dir = get_values_dir(objective_key)

tests/trajectories/plot_params.py

@@ -14,6 +14,7 @@
 import matplotlib.pyplot as plt
 import numpy as np
 
+from tests.paths import TRAJECTORIES_RESULTS_DIR
 from trajectories._constants import (
     AGGREGATOR_ORDER,
     AGGREGATORS,
@@ -24,7 +25,7 @@
 from trajectories._objectives import ElementWiseQuadratic, WithSPSMappingMixin
 from trajectories._optimization import compute_gradient_cosine_similarities
 from trajectories._pareto_utils import sample_2d_spss
-from trajectories._paths import RESULTS_DIR, get_param_plots_dir, get_params_dir
+from trajectories._paths import get_param_plots_dir, get_params_dir
 from trajectories._plotters import (
     AxesPlotter,
     ContentLimAdjuster,
@@ -61,7 +62,7 @@ def main() -> None:
     args = parser.parse_args()
     objective_key = args.objective
 
-    with open(RESULTS_DIR / objective_key / "metadata.json") as f:
+    with open(TRAJECTORIES_RESULTS_DIR / objective_key / "metadata.json") as f:
         metadata = json.load(f)
 
     params_dir = get_params_dir(objective_key)

tests/trajectories/plot_values.py

@@ -14,6 +14,7 @@
 import matplotlib.pyplot as plt
 import numpy as np
 
+from tests.paths import TRAJECTORIES_RESULTS_DIR
 from trajectories._constants import (
     AGGREGATOR_ORDER,
     AGGREGATORS,
@@ -23,7 +24,7 @@
 )
 from trajectories._objectives import WithSPSMappingMixin
 from trajectories._pareto_utils import compute_normalized_2d_pf_distances, sample_2d_pf
-from trajectories._paths import RESULTS_DIR, get_value_plots_dir, get_values_dir
+from trajectories._paths import get_value_plots_dir, get_values_dir
 from trajectories._plotters import (
     ContentLimAdjuster,
     HeatmapPlotter,
@@ -58,7 +59,7 @@ def main() -> None:
     args = parser.parse_args()
     objective_key = args.objective
 
-    with open(RESULTS_DIR / objective_key / "metadata.json") as f:
+    with open(TRAJECTORIES_RESULTS_DIR / objective_key / "metadata.json") as f:
         metadata = json.load(f)
 
     values_dir = get_values_dir(objective_key)