Argument Parser updated to use action="store_true" instead of 0/1 for boolean arguments.

CHANGELOG.md

@@ -35,7 +35,11 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### Changed
 
-  Optional multi-core/GPU support for statistics calculation in `create_parameter_weights.py`
+- Argument Parser updated to use action="store_true" instead of 0/1 for boolean arguments.
+  (https://github.com/mllam/neural-lam/pull/72)
+  @ErikLarssonDev
+
+-  Optional multi-core/GPU support for statistics calculation in `create_parameter_weights.py`
   [\#22](https://github.com/mllam/neural-lam/pull/22)
   @sadamov
 

README.md

@@ -118,7 +118,7 @@ Run `python -m neural_lam.create_mesh` with suitable options to generate the gra
 The graphs used for the different models in the [paper](https://arxiv.org/abs/2309.17370) can be created as:
 
 * **GC-LAM**: `python -m neural_lam.create_mesh --graph multiscale`
-* **Hi-LAM**: `python -m neural_lam.create_mesh --graph hierarchical --hierarchical 1` (also works for Hi-LAM-Parallel)
+* **Hi-LAM**: `python -m neural_lam.create_mesh --graph hierarchical --hierarchical` (also works for Hi-LAM-Parallel)
 * **L1-LAM**: `python -m neural_lam.create_mesh --graph 1level --levels 1`
 
 The graph-related files are stored in a directory called `graphs`.

neural_lam/create_mesh.py

@@ -169,10 +169,9 @@ def main(input_args=None):
     )
     parser.add_argument(
         "--plot",
-        type=int,
-        default=0,
+        action="store_true",
         help="If graphs should be plotted during generation "
-        "(default: 0 (false))",
+        "(default: False)",
     )
     parser.add_argument(
         "--levels",
@@ -182,9 +181,8 @@ def main(input_args=None):
     )
     parser.add_argument(
         "--hierarchical",
-        type=int,
-        default=0,
-        help="Generate hierarchical mesh graph (default: 0, no)",
+        action="store_true",
+        help="Generate hierarchical mesh graph (default: False)",
     )
     args = parser.parse_args(input_args)
 

neural_lam/create_parameter_weights.py

@@ -156,9 +156,8 @@ def main():
     )
     parser.add_argument(
         "--distributed",
-        type=int,
-        default=0,
-        help="Run the script in distributed mode (1) or not (0) (default: 0)",
+        action="store_true",
+        help="Run the script in distributed mode (default: False)",
     )
     args = parser.parse_args()
     distributed = bool(args.distributed)

neural_lam/train_model.py

@@ -41,10 +41,9 @@ def main(input_args=None):
     )
     parser.add_argument(
         "--subset_ds",
-        type=int,
-        default=0,
+        action="store_true",
         help="Use only a small subset of the dataset, for debugging"
-        "(default: 0=false)",
+        "(default: false)",
     )
     parser.add_argument(
         "--seed", type=int, default=42, help="random seed (default: 42)"
@@ -71,10 +70,9 @@ def main(input_args=None):
     )
     parser.add_argument(
         "--restore_opt",
-        type=int,
-        default=0,
+        action="store_true",
         help="If optimizer state should be restored with model "
-        "(default: 0 (false))",
+        "(default: false)",
     )
     parser.add_argument(
         "--precision",
@@ -118,11 +116,10 @@ def main(input_args=None):
     )
     parser.add_argument(
         "--output_std",
-        type=int,
-        default=0,
+        action="store_true",
         help="If models should additionally output std.-dev. per "
         "output dimensions "
-        "(default: 0 (no))",
+        "(default: False (no))",
     )
 
     # Training options
@@ -135,10 +132,9 @@ def main(input_args=None):
     )
     parser.add_argument(
         "--control_only",
-        type=int,
-        default=0,
+        action="store_true",
         help="Train only on control member of ensemble data "
-        "(default: 0 (False))",
+        "(default: False)",
     )
     parser.add_argument(
         "--loss",
@@ -233,7 +229,7 @@ def main(input_args=None):
             pred_length=args.ar_steps,
             split="train",
             subsample_step=args.step_length,
-            subset=bool(args.subset_ds),
+            subset=args.subset_ds,
             control_only=args.control_only,
         ),
         args.batch_size,
@@ -247,7 +243,7 @@ def main(input_args=None):
             pred_length=max_pred_length,
             split="val",
             subsample_step=args.step_length,
-            subset=bool(args.subset_ds),
+            subset=args.subset_ds,
             control_only=args.control_only,
         ),
         args.batch_size,
@@ -313,7 +309,7 @@ def main(input_args=None):
                     pred_length=max_pred_length,
                     split="test",
                     subsample_step=args.step_length,
-                    subset=bool(args.subset_ds),
+                    subset=args.subset_ds,
                 ),
                 args.batch_size,
                 shuffle=False,

plot_graph.py

@@ -38,9 +38,8 @@ def main():
     )
     parser.add_argument(
         "--show_axis",
-        type=int,
-        default=0,
-        help="If the axis should be displayed (default: 0 (No))",
+        action="store_true",
+        help="If the axis should be displayed (default: False)",
     )
 
     args = parser.parse_args()

tests/test_mllam_dataset.py

@@ -118,7 +118,7 @@ def test_load_reduced_meps_dataset(meps_example_reduced_filepath):
 def test_create_graph_reduced_meps_dataset():
     args = [
         "--graph=hierarchical",
-        "--hierarchical=1",
+        "--hierarchical",
         "--data_config=data/meps_example_reduced/data_config.yaml",
         "--levels=2",
     ]