Create working components [WIP]:

Update control_method script

src/control_methods/my_control_method/script.py

@@ -1,39 +1,43 @@
 import anndata as ad
+import sklearn.linear_model
 
 ## VIASH START
 # Note: this section is auto-generated by viash at runtime. To edit it, make changes
 # in config.vsh.yaml and then run `viash config inject config.vsh.yaml`.
 par = {
-  'train_h5ad': 'resources_test/task_template/pancreas/train_h5ad.h5ad',
-  'test_h5ad': 'resources_test/task_template/pancreas/test_h5ad.h5ad',
+  'input_train': 'resources_test/task_template/pancreas/train.h5ad',
+  'input_test': 'resources_test/task_template/pancreas/test.h5ad',
   'output': 'output.h5ad'
 }
 meta = {
-  'name': 'my_control_method'
+  'name': 'logistic_regression'
 }
 ## VIASH END
 
 print('Reading input files', flush=True)
-train_h5ad = ad.read_h5ad(par['train_h5ad'])
-test_h5ad = ad.read_h5ad(par['test_h5ad'])
+input_train = ad.read_h5ad(par['input_train'])
+input_test = ad.read_h5ad(par['input_test'])
 
 print('Preprocess data', flush=True)
 # ... preprocessing ...
 
 print('Train model', flush=True)
 # ... train model ...
+classifier = sklearn.linear_model.LogisticRegression()
+classifier.fit(input_train.obsm["X_pca"], input_train.obs["label"].astype(str))
 
 print('Generate predictions', flush=True)
 # ... generate predictions ...
+obs = classifier.predict(input_test.obsm["X_pca"])
 
 print("Write output AnnData to file", flush=True)
 output = ad.AnnData(
   uns={
-    'dataset_id': train_h5ad.uns['dataset_id'],
+    'dataset_id': input_train.uns['dataset_id'],
     'method_id': meta['name']
   },
-  layers={
-    'prediction': layers_prediction
+  obs={
+    'label_pred': obs
   }
 )
 output.write_h5ad(par['output'], compression='gzip')