Minor fixes

Author: remydubois

illico/utils/ranking.py

@@ -7,7 +7,7 @@
 @njit(nogil=True, cache=False, fastmath=True)
 def _accumulate_group_ranksums_from_argsort(
     arr: np.ndarray, idx: np.ndarray, groups: np.ndarray, ranksums: np.ndarray, zero_values_offset: int = 0
-) -> tuple[np.ndarray, int]:
+) -> tuple[float, int]:
     """From a given array of values, indices of sorted values (result of np.argsort) and groups, accumulate group rank
     sums in the placegolder `ranksums`.
 
@@ -21,7 +21,7 @@ def _accumulate_group_ranksums_from_argsort(
         on non zero values.
 
     Returns:
-        np.ndarray: tie sums
+        float: tie sums
         int: position of zeros in the sorted array (if zero_values_offset > 0)
 
     Author: Rémy Dubois
@@ -93,7 +93,7 @@ def rank_sum_and_ties_from_sorted(A: np.ndarray, B: np.ndarray, zero_values_offs
     k = 0  # number of items processed so far (0-based)
     zero_pos = -1  # Default setting
 
-    sum_ranks_B = 0
+    sum_ranks_B = 0.0
     tie_sum = 0.0
 
     # main sweep

src/dense_ovr.rs

@@ -33,7 +33,7 @@ pub fn dense_ovr_kernel<D: SparseFloat>(
     let mut tie_sums = Array1::zeros(chunk_ub - chunk_lb);
     for j in 0..chunk.dim().1 {
         let sorted_indices = argsort(chunk.column(j));
-        _ = accumulate_rank_and_tie_sums_from_argsort(
+        accumulate_rank_and_tie_sums_from_argsort(
             chunk.column(j),
             sorted_indices,
             grpc.encoded_groups,

src/ranking.rs

@@ -157,7 +157,7 @@ pub fn rank_sum_and_ties<D: SparseFloat>(
     while j < n_tgt {
         let v = tgt[j];
 
-        if (v.to_f64() > 0.) & (zero_values_offset > 0) {
+        if (v.to_f64() > 0.) && (zero_values_offset > 0) {
             zero_pos = k as isize;
             k += zero_values_offset;
             zero_values_offset = 0;
@@ -184,7 +184,7 @@ pub fn rank_sum_and_ties<D: SparseFloat>(
         k += count_tgt;
     }
 
-    if (zero_pos == -1) & (zero_values_offset > 0) {
+    if (zero_pos == -1) && (zero_values_offset > 0) {
         zero_pos = k as isize;
     }
 
@@ -250,7 +250,7 @@ pub fn accumulate_rank_and_tie_sums_from_argsort<D: SparseFloat>(
     while i < n_values {
         // Find tie block
         let mut j = i + 1;
-        if (zero_values_offset > 0) & (x[sorted_indices[i]].to_f64() > 0.) {
+        if (zero_values_offset > 0) && (x[sorted_indices[i]].to_f64() > 0.) {
             zero_pos = i as isize;
             rank += zero_values_offset;
             zero_values_offset = 0;
@@ -278,7 +278,7 @@ pub fn accumulate_rank_and_tie_sums_from_argsort<D: SparseFloat>(
         i = j;
     }
 
-    if (zero_pos == -1) & (zero_values_offset > 0) {
+    if (zero_pos == -1) && (zero_values_offset > 0) {
         zero_pos = n_values as isize;
     }
 

tests/conftest.py

@@ -102,10 +102,12 @@ def rand_adata(request, tmp_path):
     groups = rng.randint(0, n_groups, size=n_cells)  # Add one ref group
 
     # Now cover all possible negative value scenarios: some with all negative values, some with a mix of both
-    # By defaults dense_counts only contains positive values
+    # By default dense_counts only contains positive values
     dense_counts[:, 0] *= -1.0  # all negative in the first column
     dense_counts[groups == 0, 1] *= -1.0  # First group all negative, rest all positives in the second column
     dense_counts[np.isin(groups, [0, 1]), 2] *= -1.0  # Two groups all negative, rest all positives in the third column
+    dense_counts[groups == 0, 3][::2] *= -1.0  # Ref is both pos and neg in the fourth column
+    dense_counts[groups == 1, 4][::2] *= -1.0  # Target is both pos and neg in the fifth column
 
     fmt, lazy = request.param
     if fmt == "dense":

tests/utils/test_ranking.py

@@ -30,7 +30,7 @@ def test_rank_sum_and_ties_from_sorted(format):
         n_zeros_A = (A == 0).sum()
         n_zeros_B = (B == 0).sum()
         n_zeros = n_zeros_A + n_zeros_B
-        A, B = A[A != 0], B[B != 0]  # Keep only positive values to have ties
+        A, B = A[A != 0], B[B != 0]  # Keep only non-null values to have ties
     else:
         n_zeros_A = n_zeros_B = n_zeros = 0
     A.sort()