Merge pull request #511 from DHI/rel_1.2

Release 1.2.0

Author: ecomodeller

docs/examples/Metrics_custom_metric.qmd

@@ -29,11 +29,13 @@ cc.skill()
 Some metrics has parameters, which require a bit special treatment.
 
 ```{python}
-from modelskill.metrics import hit_ratio
+from modelskill.metrics import hit_ratio, metric
 
+@metric(best="+")
 def hit_ratio_05_pct(obs, model):
     return hit_ratio(obs, model, 0.5) * 100
 
+@metric(best="+")
 def hit_ratio_01_pct(obs, model):
     return hit_ratio(obs, model, 0.1) * 100
 
@@ -44,17 +46,16 @@ cc.skill(metrics=[hit_ratio_05_pct, hit_ratio_01_pct])
 And you are always free to specify your own special metric or import metrics from other libraries, e.g. scikit-learn.
 
 ```{python}
-def my_special_metric_with_long_descriptive_name(obs, model):
+@metric(best="-", has_units=True)
+def mcae(obs, model):
 
     res = obs - model
 
     res_clipped = np.clip(res,0,np.inf)
 
     return np.mean(np.abs(res_clipped))
 
-# short alias to avoid long column names in output
-def mcae(obs, model): return my_special_metric_with_long_descriptive_name(obs, model)
 
-cc.skill(metrics=mcae)
+cc.skill(metrics=mcae).style()
 ```
 

modelskill/__init__.py

@@ -1,3 +1,4 @@
+from importlib.metadata import PackageNotFoundError, version
 from pathlib import Path
 from platform import architecture
 from typing import Union
@@ -19,7 +20,12 @@
 # Dev branch marker is: 'X.Y.dev' or 'X.Y.devN' where N is an integer.
 # 'X.Y.dev0' is the canonical version of 'X.Y.dev'
 #
-__version__ = "1.2.dev0"
+try:
+    # read version from installed package
+    __version__ = version("modelskill")
+except PackageNotFoundError:
+    # package is not installed
+    __version__ = "dev"
 
 if "64" not in architecture()[0]:
     raise Exception("This library has not been tested for a 32 bit system.")

modelskill/metrics.py

@@ -41,14 +41,15 @@ def add_metric(metric: Callable, has_units: bool = False) -> None:
 
 
 def metric(
-    best: Literal["+", "-"] | None = None, has_units: bool = False
+    best: Literal["+", "-", 0, 1] | None = None, has_units: bool = False
 ) -> Callable[[F], F]:
     """Decorator to indicate a function as a metric.
 
     Parameters
     ----------
-    best : {"+", "-"}, optional
+    best : {"+", "-", 0, 1}, optional
         Indicates whether a higher value ("+") or a lower value ("-") is better.
+        Some "metrics" like bias or linear slope has a value where 0 or 1 is the best, and no direction.
         If None, no preference is specified.
     has_units : bool, default False
         Specifies whether the metric has physical units.

pyproject.toml

@@ -12,7 +12,7 @@ allow-direct-references = true
 
 [project]
 name = "modelskill"
-version = "1.2.dev0"
+version = "1.2.0"
 dependencies = [
     "numpy  > 1.24.4",
     "pandas >= 1.4",