Code review suggestions

docs/normalization.md

@@ -7,6 +7,7 @@ The normalization strategy to apply before fusion can be defined through the `no
 | **Normalization Strategies**                   | **Alias**         |
 |------------------------------------------------|-------------------|
 | [Min-Max Norm][min-max-norm]                   | min-max           |
+| [Min-Max-Inverted Norm][min-max-norm-inverted] | min-max-inverted  |
 | [Max Norm][max-norm]                           | max               |
 | [Sum Norm][sum-norm]                           | sum               |
 | [ZMUV Norm][zmuv-norm]                         | zmuv              |
@@ -22,9 +23,9 @@ $$
 \operatorname{MinMaxNorm(s)}=\frac{s - s_{min}}{s_{max} - s_{min}}
 $$
 
-Min-Max Norm accepts an optional boolean parameter `invert`, which, when set to true,
-Min-Max Norm scales the scores (s) of a result list between 0 and 1,
-setting the maximum score ($s_{max}$) to 0 and the minimum score ($s_{min}$) to 1.
+## Min-Max Inverted Norm
+---
+Min-Max Norm scales the scores (s) of a result list between 0 and 1, scaling to 1 the minimum score ($s_{min}$) and 0 the maximum score ($s_{max}$).
 
 $$
 \operatorname{MinMaxNorm(s)}=\frac{s_{max} - s}{s_{max} - s_{min}}

ranx/normalization/__init__.py

@@ -21,7 +21,9 @@ def norm_switch(method: str = "min-max"):
     elif method == "max":
         return max_norm
     elif method in {"min_max", "min-max"}:
-        return min_max_norm
+        return lambda run: min_max_norm(run, invert=False)
+    elif method in {"min_max_inverted", "min_max-inverted"}:
+        return lambda run: min_max_norm(run, invert=True)
     elif method == "rank":
         return rank_norm
     elif method == "sum":

tests/unit/ranx/normalization/min_max_norm_test.py

@@ -1,7 +1,7 @@
 import pytest
 
 from ranx import Run
-from ranx.normalization import min_max_norm
+from ranx.normalization import min_max_norm, norm_switch
 
 
 # FIXTURES =====================================================================
@@ -43,11 +43,9 @@ def test_min_max_norm(run):
     assert norm_run["q2"]["d2"] == (2 - 1) / (2 - 1)
 
 
-def test_min_max_norm_with_invert(run):
+@pytest.mark.parametrize("norm_name", ["min_max_inverted", "min_max-inverted"])
+def test_min_max_norm_with_invert(run, norm_name):
     run_copy = run.run.copy()
-
-    norm_run = min_max_norm(run, True)
-
     assert run.run == run_copy
 
     assert len(run.run) == 2
@@ -60,11 +58,13 @@ def test_min_max_norm_with_invert(run):
     assert run.run["q2"]["d2"] == 2
     assert run.size == 2
 
+    norm_run = norm_switch(norm_name)(run)
+
     assert len(norm_run) == 2
     assert len(norm_run["q1"]) == 3
     assert len(norm_run["q2"]) == 2
     assert norm_run["q1"]["d1"] == (3 - 1) / (3 - 1)
     assert norm_run["q1"]["d2"] == (3 - 2) / (3 - 1)
     assert norm_run["q1"]["d3"] == (3 - 3) / (3 - 1)
-    assert norm_run["q2"]["d1"] == (3 - 1) / (2 - 1)
-    assert norm_run["q2"]["d2"] == (3 - 2) / (2 - 1)
+    assert norm_run["q2"]["d1"] == (2 - 1) / (2 - 1)
+    assert norm_run["q2"]["d2"] == (2 - 2) / (2 - 1)