Update ref_index test

src/copairs/compute.py

@@ -312,7 +312,7 @@ def average_precision(rel_k) -> np.ndarray:
     num_pos = rel_k.shape[1]
     pr_k = np.arange(1, num_pos + 1, dtype=np.float32) / (rel_k + 1)
     ap_values = pr_k.sum(axis=1) / num_pos
-    return ap_values
+    return ap_values.astype(np.float32)
 
 
 def ap_contiguous(
@@ -399,7 +399,7 @@ def random_ap(num_perm: int, num_pos: int, total: int, seed: int):
 
     # Compute Average Precision (AP) scores for each row of the binary matrix
     null_dist = average_precision(rel_k)
-    return null_dist.astype(np.float32)
+    return null_dist
 
 
 def null_dist_cached(

src/copairs/map/map.py

@@ -1,4 +1,5 @@
 import logging
+from typing import Optional
 
 import numpy as np
 import pandas as pd
@@ -16,7 +17,7 @@ def mean_average_precision(
     null_size: int,
     threshold: float,
     seed: int,
-    max_workers: int = 32,
+    max_workers: Optional[int] = None,
 ) -> pd.DataFrame:
     """Calculate the Mean Average Precision (mAP) score and associated p-values.
 
@@ -38,6 +39,8 @@ def mean_average_precision(
         p-value threshold for identifying significant MaP scores.
     seed : int
         Random seed for reproducibility.
+    max_workers : int
+        Number of workers used. Default defined by tqdm's `thread_map`
 
     Returns:
     -------

tests/test_reference_index.py

@@ -1,5 +1,5 @@
 """Tests for assign reference index helper function."""
-
+import pytest
 import numpy as np
 import pandas as pd
 
@@ -8,21 +8,26 @@
 from tests.helpers import simulate_random_dframe
 
 
+@pytest.mark.filterwarnings("ignore:invalid value encountered in divide")
 def test_assign_reference_index():
     SEED = 42
-    length = 20
-    vocab_size = {"p": 5, "w": 3, "l": 2}
+    length = 200
+    vocab_size = {"p": 5, "w": 3, "l": 4}
     n_feats = 5
     pos_sameby = ["l"]
     pos_diffby = []
     neg_sameby = []
     neg_diffby = ["l"]
     rng = np.random.default_rng(SEED)
     meta = simulate_random_dframe(length, vocab_size, pos_sameby, pos_diffby, rng)
+    # p: Plate, w: Well, l: PerturbationID, t: PerturbationType (is control?)
+    meta.eval("t=(l=='l1')", inplace=True)
     length = len(meta)
     feats = rng.uniform(size=(length, n_feats))
 
-    ap = average_precision(meta, feats, pos_sameby, pos_diffby, neg_sameby, neg_diffby)
+    ap = average_precision(
+        meta, feats, pos_sameby + ["t"], pos_diffby, neg_sameby, neg_diffby + ["t"]
+    )
 
     ap_ri = average_precision(
         assign_reference_index(meta, "l=='l1'"),