Outsourced run command for SM rule 'test_model' into script.

scripts/test_model.py

+# -*- coding: utf-8 -*-
+"""Script to test ANN models.
+
+@author: Laura C. Kühle
+"""
+import sys
+
+from tcd.ANN_Training import ModelTrainer, evaluate_models
+
+
+def main() -> None:
+    """Test ANN models."""
+    models = {}
+    with open(str(snakemake.log[0]), 'w', encoding='utf-8') as logfile:
+        sys.stdout = logfile
+        sys.stderr = logfile
+
+        # Initialize models to be evaluated
+        for model in snakemake.params['models']:
+            trainer = ModelTrainer(
+                {'model_name': model, 'dir': snakemake.params['directory'],
+                 'model_dir': snakemake.params['directory'],
+                 'random_seed': snakemake.params['random_seed'],
+                 **snakemake.params['models'][model]})
+            models[model] = trainer
+
+        # Evaluate models
+        evaluate_models(
+            models=models, directory=snakemake.params['directory'],
+            num_iterations=snakemake.params['num_iterations'],
+            compare_normalization=snakemake.params['compare_normalization'])
+
+
+if __name__ == '__main__':
+    if "snakemake" in locals():
+        main()
+    else:
+        print('Not Defined.')

workflows/ANN_training.smk

 import sys
 
-from scripts.tcd import ANN_Training
-from scripts.tcd.ANN_Training import *
 from scripts.tcd.Plotting import plot_evaluation_results
 
 configfile: 'config.yaml'
@@ -44,23 +42,14 @@ rule test_model:
     params:
         num_iterations = config['num_iterations'],
         compare_normalization = config['compare_normalization'],
-        random_seed = config['random_seed']
+        random_seed = config['random_seed'],
+        directory = DIR,
+        models = config['models']
     log:
         DIR+'/log/test_model.log'
-    run:
-        models = {}
-        with open(str(log), 'w') as logfile:
-            sys.stdout = logfile
-            sys.stderr = logfile
-            for model in MODELS:
-                trainer= ANN_Training.ModelTrainer(
-                    {'model_name': model, 'dir': DIR, 'model_dir': DIR,
-                     'random_seed': params.random_seed,
-                     **MODELS[model]})
-                models[model] = trainer
-            evaluate_models(models=models, directory=DIR,
-                num_iterations=params.num_iterations,
-                compare_normalization=params.compare_normalization)
+    script:
+        '../scripts/test_model.py'
+
 
 rule train_model:
     input: