fix models/ dir creation

should be the concern of save_checkpoint() who relies on it being there

Author: janosh

aviary/core.py

@@ -1,6 +1,7 @@
 from __future__ import annotations
 
 import gc
+import os
 import shutil
 import sys
 from abc import ABC, abstractmethod
@@ -510,6 +511,7 @@ def save_checkpoint(
         model_name (str): String describing the model.
         run_id (int): Unique identifier of the model run.
     """
+    os.makedirs(f"models/{model_name}", exist_ok=True)
     checkpoint = f"models/{model_name}/checkpoint-r{run_id}.pth.tar"
     best = f"models/{model_name}/best-r{run_id}.pth.tar"
 

examples/cgcnn-example.py

@@ -202,8 +202,6 @@ def main(  # noqa: C901
         "n_hidden": n_hidden,
     }
 
-    os.makedirs(f"models/{model_name}/", exist_ok=True)
-
     if log:
         os.makedirs("runs/", exist_ok=True)
 

examples/roost-example.py

@@ -186,8 +186,6 @@ def main(  # noqa: C901
         "out_hidden": [256, 128, 64],
     }
 
-    os.makedirs(f"models/{model_name}/", exist_ok=True)
-
     if log:
         os.makedirs("runs/", exist_ok=True)
 

examples/wren-example.py

@@ -199,8 +199,6 @@ def main(  # noqa: C901
         "trunk_hidden": [128, 64],
     }
 
-    os.makedirs(f"models/{model_name}/", exist_ok=True)
-
     if log:
         os.makedirs("runs/", exist_ok=True)
 

tests/test_cgcnn_classification.py

@@ -1,5 +1,3 @@
-import os
-
 import numpy as np
 import torch
 from sklearn.metrics import accuracy_score, roc_auc_score
@@ -98,8 +96,6 @@ def test_cgcnn_clf(df_matbench_phonons):
         "n_hidden": n_hidden,
     }
 
-    os.makedirs(f"models/{model_name}", exist_ok=True)
-
     train_ensemble(
         model_class=CrystalGraphConvNet,
         model_name=model_name,

tests/test_cgcnn_regression.py

@@ -1,5 +1,3 @@
-import os
-
 import numpy as np
 import torch
 from sklearn.metrics import r2_score
@@ -98,8 +96,6 @@ def test_cgcnn_regression(df_matbench_phonons):
         "n_hidden": n_hidden,
     }
 
-    os.makedirs(f"models/{model_name}", exist_ok=True)
-
     train_ensemble(
         model_class=CrystalGraphConvNet,
         model_name=model_name,

tests/test_roost_classification.py

@@ -1,5 +1,3 @@
-import os
-
 import numpy as np
 import torch
 from sklearn.metrics import accuracy_score, roc_auc_score
@@ -100,8 +98,6 @@ def test_roost_clf(df_matbench_phonons):
         "out_hidden": [128, 64],
     }
 
-    os.makedirs(f"models/{model_name}", exist_ok=True)
-
     train_ensemble(
         model_class=Roost,
         model_name=model_name,

tests/test_roost_regression.py

@@ -1,5 +1,3 @@
-import os
-
 import numpy as np
 import torch
 from sklearn.metrics import r2_score
@@ -100,8 +98,6 @@ def test_roost_regression(df_matbench_phonons):
         "out_hidden": [128, 64],
     }
 
-    os.makedirs(f"models/{model_name}", exist_ok=True)
-
     train_ensemble(
         model_class=Roost,
         model_name=model_name,

tests/test_wren_classification.py

@@ -1,5 +1,3 @@
-import os
-
 import numpy as np
 import torch
 from sklearn.metrics import accuracy_score, roc_auc_score
@@ -108,8 +106,6 @@ def test_wren_clf(df_matbench_phonons_wyckoff):
         "trunk_hidden": [64],
     }
 
-    os.makedirs(f"models/{model_name}", exist_ok=True)
-
     train_ensemble(
         model_class=Wren,
         model_name=model_name,

tests/test_wren_regression.py

@@ -1,5 +1,3 @@
-import os
-
 import numpy as np
 import torch
 from sklearn.metrics import r2_score
@@ -108,8 +106,6 @@ def test_wren_regression(df_matbench_phonons_wyckoff):
         "trunk_hidden": [64],
     }
 
-    os.makedirs(f"models/{model_name}", exist_ok=True)
-
     train_ensemble(
         model_class=Wren,
         model_name=model_name,