use default output path for metcalf scoring

src/nplinker/scoring/metcalf_scoring.py

@@ -3,6 +3,7 @@
 from typing import TYPE_CHECKING
 import numpy as np
 import pandas as pd
+from nplinker.defaults import OUTPUT_DEFAULT_PATH
 from nplinker.genomics import GCF
 from nplinker.logconfig import LogConfig
 from nplinker.metabolomics import MolecularFamily
@@ -31,11 +32,13 @@ class MetcalfScoring(ScoringMethod):
         DATALINKS: The DataLinks object to use for scoring.
         LINKFINDER: The LinkFinder object to use for scoring.
         NAME: The name of the scoring method. This is set to 'metcalf'.
+        CACHE: The name of the cache file to use for storing the MetcalfScoring.
     """
 
     DATALINKS = None
     LINKFINDER = None
     NAME = "metcalf"
+    CACHE = "cache_metcalf_scoring.pckl"
 
     def __init__(self, npl: NPLinker) -> None:
         """Create a MetcalfScoring object.
@@ -69,9 +72,8 @@ def setup(npl: NPLinker):
             )
         )
 
-        cache_dir = os.path.join(npl.root_dir, "metcalf")
-        cache_file = os.path.join(cache_dir, "metcalf_scores.pckl")
-        os.makedirs(cache_dir, exist_ok=True)
+        OUTPUT_DEFAULT_PATH.mkdir(exist_ok=True)
+        cache_file = OUTPUT_DEFAULT_PATH / MetcalfScoring.CACHE
 
         # the metcalf preprocessing can take a long time for large datasets, so it's
         # better to cache as the data won't change unless the number of objects does

tests/integration/test_nplinker_local.py

@@ -27,7 +27,7 @@ def npl() -> NPLinker:
     npl.load_data()
     # remove cached score results before running tests
     root_dir = Path(npl.root_dir)
-    score_cache = root_dir / "metcalf" / "metcalf_scores.pckl"
+    score_cache = root_dir / "output" / "cache_metcalf_scoring.pckl"
     score_cache.unlink(missing_ok=True)
     return npl