Update order of arguments

python/evalica/__init__.py

@@ -483,24 +483,24 @@ class EloResult(Generic[T]):
     Attributes:
         scores: The element scores.
         index: The index.
-        win_weight: The win weight.
-        tie_weight: The tie weight.
         initial: The initial score of each element.
         base: The base of the exponent.
         scale: The scale factor.
         k: The K-factor.
+        win_weight: The win weight.
+        tie_weight: The tie weight.
         solver: The solver.
 
     """
 
     scores: pd.Series[float]
     index: dict[T, int]
-    win_weight: float
-    tie_weight: float
     initial: float
     base: float
     scale: float
     k: float
+    win_weight: float
+    tie_weight: float
     solver: str
 
 
@@ -509,12 +509,12 @@ def elo(
         ys: Collection[T],
         ws: Collection[Winner],
         index: dict[T, int] | None = None,
-        win_weight: float = 1.0,
-        tie_weight: float = 0.5,
         initial: float = 1000.,
         base: float = 10.,
         scale: float = 400.,
         k: float = 4.,
+        win_weight: float = 1.0,
+        tie_weight: float = 0.5,
         solver: Literal["naive", "pyo3"] = "pyo3",
 ) -> EloResult[T]:
     """
@@ -528,12 +528,12 @@ def elo(
         ys: The right-hand side elements.
         ws: The winner elements.
         index: The index.
-        win_weight: The win weight.
-        tie_weight: The tie weight.
         initial: The initial score of each element.
         base: The base of the exponent.
         scale: The scale factor.
         k: The K-factor.
+        win_weight: The win weight.
+        tie_weight: The tie weight.
         solver: The solver.
 
     Returns:
@@ -545,19 +545,19 @@ def elo(
     assert index is not None, "index is None"
 
     if solver == "pyo3":
-        scores = elo_pyo3(xs_indexed, ys_indexed, ws, len(index), win_weight, tie_weight, initial, base, scale, k)
+        scores = elo_pyo3(xs_indexed, ys_indexed, ws, len(index), initial, base, scale, k, win_weight, tie_weight)
     else:
-        scores = elo_naive(xs_indexed, ys_indexed, ws, len(index), win_weight, tie_weight, initial, base, scale, k)
+        scores = elo_naive(xs_indexed, ys_indexed, ws, len(index), initial, base, scale, k, win_weight, tie_weight)
 
     return EloResult(
         scores=pd.Series(scores, index=index, name=elo.__name__).sort_values(ascending=False, kind="stable"),
         index=index,
-        win_weight=win_weight,
-        tie_weight=tie_weight,
         initial=initial,
         base=base,
         scale=scale,
         k=k,
+        win_weight=win_weight,
+        tie_weight=tie_weight,
         solver=solver,
     )
 

python/evalica/evalica.pyi

@@ -83,12 +83,12 @@ def elo_pyo3(
         ys: npt.ArrayLike,
         ws: Collection[Winner],
         total: int,
-        win_weight: float,
-        tie_weight: float,
         initial: float,
         base: float,
         scale: float,
         k: float,
+        win_weight: float,
+        tie_weight: float,
 ) -> npt.NDArray[np.float64]: ...
 
 

python/evalica/naive.py

@@ -143,12 +143,12 @@ def elo(
         ys: Collection[int],
         ws: Collection[Winner],
         total: int,
-        win_weight: float = 1.0,
-        tie_weight: float = 0.5,
         initial: float = 1000.,
         base: float = 10.,
         scale: float = 400.,
         k: float = 30.,
+        win_weight: float = 1.0,
+        tie_weight: float = 0.5,
 ) -> npt.NDArray[np.float64]:
     if len(xs) != len(ys) or len(xs) != len(ws) or len(ys) != len(ws):
         raise LengthMismatchError

python/evalica/test_evalica.py

@@ -207,12 +207,12 @@ def test_newman(comparison: Comparison, v_init: float) -> None:
 
 @given(
     comparison=comparisons(),
-    win_weight=st.floats(0., 10.),
-    tie_weight=st.floats(0., 10.),
     initial=st.floats(0., 1000.),
     base=st.floats(0., 1000.),
     scale=st.floats(0., 1000.),
     k=st.floats(0., 1000.),
+    win_weight=st.floats(0., 10.),
+    tie_weight=st.floats(0., 10.),
 )
 def test_elo(
         comparison: Comparison,
@@ -227,23 +227,23 @@ def test_elo(
 
     result_pyo3 = evalica.elo(
         xs, ys, ws,
-        win_weight=win_weight,
-        tie_weight=tie_weight,
         initial=initial,
         base=base,
         scale=scale,
         k=k,
+        win_weight=win_weight,
+        tie_weight=tie_weight,
         solver="pyo3",
     )
 
     result_naive = evalica.elo(
         xs, ys, ws,
-        win_weight=win_weight,
-        tie_weight=tie_weight,
         initial=initial,
         base=base,
         scale=scale,
         k=k,
+        win_weight=win_weight,
+        tie_weight=tie_weight,
         solver="naive",
     )
 

src/elo.rs

@@ -10,12 +10,12 @@ pub fn elo<A: Float + AddAssign>(
     ys: &ArrayView1<usize>,
     ws: &ArrayView1<Winner>,
     total: usize,
-    win_weight: A,
-    tie_weight: A,
     initial: A,
     base: A,
     scale: A,
     k: A,
+    win_weight: A,
+    tie_weight: A,
 ) -> Result<Array1<A>, ShapeError> {
     check_lengths!(xs.len(), ys.len(), ws.len());
 

src/python.rs

@@ -195,24 +195,24 @@ fn elo_pyo3<'py>(
     ys: PyArrayLike1<'py, usize>,
     ws: PyArrayLike1<'py, Winner>,
     total: usize,
-    win_weight: f64,
-    tie_weight: f64,
     initial: f64,
     base: f64,
     scale: f64,
     k: f64,
+    win_weight: f64,
+    tie_weight: f64,
 ) -> PyResult<Py<PyArray1<f64>>> {
     match elo(
         &xs.as_array(),
         &ys.as_array(),
         &ws.as_array(),
         total,
-        win_weight,
-        tie_weight,
         initial,
         base,
         scale,
         k,
+        win_weight,
+        tie_weight,
     ) {
         Ok(scores) => Ok(scores.into_pyarray_bound(py).unbind()),
         Err(_) => Err(LengthMismatchError::new_err("mismatching input shapes")),