Add arguments to scPRINT and enable tests

src/methods/scprint/config.vsh.yaml

@@ -1,4 +1,4 @@
-__merge__: /src/api/base_method.yaml
+__merge__: /src/api/comp_method.yaml
 
 name: scprint
 label: scPRINT
@@ -38,6 +38,11 @@ info:
       model_name: "medium"
     scprint_small:
       model_name: "small"
+  test_setup:
+    run:
+      model_name: small
+      batch_size: 16
+      max_len: 100
 
 arguments:
   - name: "--model_name"
@@ -49,6 +54,14 @@ arguments:
     type: file
     description: Path to the scPRINT model.
     required: false
+  - name: --batch_size
+    type: integer
+    description: The size of the batches to be used in the DataLoader.
+    default: 64
+  - name: --max_len
+    type: integer
+    description: The maximum length of the gene sequence.
+    default: 4000
 
 resources:
   - type: python_script

src/methods/scprint/script.py

@@ -1,12 +1,13 @@
-import anndata as ad
-from scdataloader import Preprocessor
+import os
 import sys
-from huggingface_hub import hf_hub_download
-from scprint.tasks import Embedder
-from scprint import scPrint
+
+import anndata as ad
 import scprint
 import torch
-import os
+from huggingface_hub import hf_hub_download
+from scdataloader import Preprocessor
+from scprint import scPrint
+from scprint.tasks import Embedder
 
 ## VIASH START
 par = {
@@ -19,8 +20,8 @@
 ## VIASH END
 
 sys.path.append(meta["resources_dir"])
-from read_anndata_partial import read_anndata
 from exit_codes import exit_non_applicable
+from read_anndata_partial import read_anndata
 
 print(f"====== scPRINT version {scprint.__version__} ======", flush=True)
 
@@ -41,7 +42,7 @@
 
 print("\n>>> Preprocessing data...", flush=True)
 preprocessor = Preprocessor(
-    min_valid_genes_id=min(0.9 * adata.n_vars, 10000), # 90% of features up to 10,000
+    min_valid_genes_id=min(0.9 * adata.n_vars, 10000),  # 90% of features up to 10,000
     # Turn off cell filtering to return results for all cells
     filter_cell_by_counts=False,
     min_nnz_genes=False,
@@ -77,7 +78,8 @@
 print(f"Using {n_cores_available} worker cores")
 embedder = Embedder(
     how="random expr",
-    max_len=4000,
+    batch_size=par["batch_size"],
+    max_len=par["max_len"],
     add_zero_genes=0,
     num_workers=n_cores_available,
     doclass=False,