Fix embed_cell_types by removing jitter

CHANGELOG.md

@@ -75,12 +75,13 @@
 * Give `methods/fadvi` a `gpu` label. Without one it was scheduled on the CPU partition with no GPU attached, so it
     trained on the CPU and hit its walltime on every dataset.
 * Give `methods/scalex` a `midcpu` label instead of `lowcpu`. It is CPU-bound only because its engine has no CUDA.
-
 * Split Scanorama into two methods/scores
-    - Split Scanorama into embedding (integrate) and count-correction (correct) modes, instead of running both together. 
-        This makes clear what the reported score(s) are describing, and also corrects the misleadingly low score that 
-        the combined method receives. The scores for each component  are in line with their scores from v1, where the modes 
-        were separated.  
+    - Split Scanorama into embedding (integrate) and count-correction (correct) modes, instead of running both together.
+        This makes clear what the reported score(s) are describing, and also corrects the misleadingly low score that
+        the combined method receives. The scores for each component  are in line with their scores from v1, where the modes
+        were separated.
+* Remove jitter from the `embed_cell_types` control method, distinguishing its behavior from
+    `embed_cell_types_jittered` (PR #102).
 
 # task_batch_integration 2.0.0
 

src/control_methods/utils.py

@@ -40,7 +40,7 @@ def _randomize_graph(adata, partition=None, neighbors_key="neighbors"):
     return adata
 
 
-def _perfect_embedding(partition, jitter=0.01):
+def _perfect_embedding(partition, jitter=None):
     """
     Taken and adapted from opsca-v1:
     https://github.com/openproblems-bio/openproblems/blob/acf5c95a7306b819c4a13972783433d0a48f769b/openproblems/tasks/_batch_integration/_common/methods/baseline.py#L37
@@ -51,6 +51,7 @@ def _perfect_embedding(partition, jitter=0.01):
     embedding = OneHotEncoder().fit_transform(
         LabelEncoder().fit_transform(partition)[:, None]
     )
+    embedding = embedding.toarray()
     if jitter is not None:
         embedding = embedding + np.random.uniform(-1 * jitter, jitter, embedding.shape)
     return np.asarray(embedding)