Fix running ICEBERG from an installed package

src/ms_pred/autoregr_gen/hyperopt.py

@@ -37,7 +37,7 @@ def score_function(config, base_args, orig_dir=""):
 
     kwargs = copy.deepcopy(base_args)
     kwargs.update(config)
-    pl.utilities.seed.seed_everything(kwargs.get("seed"))
+    pl.seed_everything(kwargs.get("seed"))
 
     # Get dataset
     # Load smiles dataset and split into 3 subsets

src/ms_pred/autoregr_gen/predict.py

@@ -56,7 +56,7 @@ def predict():
 
     save_dir = kwargs["save_dir"]
     common.setup_logger(save_dir, log_name="autoregr_pred.log", debug=kwargs["debug"])
-    pl.utilities.seed.seed_everything(kwargs.get("seed"))
+    pl.seed_everything(kwargs.get("seed"))
 
     # Dump args
     yaml_args = yaml.dump(kwargs)

src/ms_pred/autoregr_gen/train.py

@@ -83,7 +83,7 @@ def train_model():
 
     save_dir = kwargs["save_dir"]
     common.setup_logger(save_dir, log_name="autoregr_train.log", debug=kwargs["debug"])
-    pl.utilities.seed.seed_everything(kwargs.get("seed"))
+    pl.seed_everything(kwargs.get("seed"))
 
     # Dump args
     yaml_args = yaml.dump(kwargs)

src/ms_pred/ffn_pred/ffn_hyperopt.py

@@ -40,7 +40,7 @@ def score_function(config, base_args, orig_dir=""):
 
     kwargs = copy.deepcopy(base_args)
     kwargs.update(config)
-    pl.utilities.seed.seed_everything(kwargs.get("seed"))
+    pl.seed_everything(kwargs.get("seed"))
 
     # Get dataset
     # Load smiles dataset and split into 3 subsets

src/ms_pred/ffn_pred/predict.py

@@ -58,7 +58,7 @@ def predict():
 
     save_dir = kwargs["save_dir"]
     common.setup_logger(save_dir, log_name="ffn_pred.log", debug=kwargs["debug"])
-    pl.utilities.seed.seed_everything(kwargs.get("seed"))
+    pl.seed_everything(kwargs.get("seed"))
 
     # Dump args
     yaml_args = yaml.dump(kwargs)

src/ms_pred/glacier/predict_inten_joint.py

@@ -54,7 +54,7 @@ def predict():
 
     save_dir = kwargs["save_dir"]
     common.setup_logger(save_dir, log_name="inten_pred.log", debug=kwargs["debug"])
-    pl.utilities.seed.seed_everything(kwargs.get("seed"))
+    pl.seed_everything(kwargs.get("seed"))
     binned_out = kwargs["binned_out"]
 
     # Dump args

src/ms_pred/gnn_pred/gnn_hyperopt.py

@@ -40,7 +40,7 @@ def score_function(config, base_args, orig_dir=""):
 
     kwargs = copy.deepcopy(base_args)
     kwargs.update(config)
-    pl.utilities.seed.seed_everything(kwargs.get("seed"))
+    pl.seed_everything(kwargs.get("seed"))
 
     # Get dataset
     # Load smiles dataset and split into 3 subsets

src/ms_pred/gnn_pred/predict.py

@@ -59,7 +59,7 @@ def predict():
 
     save_dir = kwargs["save_dir"]
     common.setup_logger(save_dir, log_name="gnn_pred.log", debug=kwargs["debug"])
-    pl.utilities.seed.seed_everything(kwargs.get("seed"))
+    pl.seed_everything(kwargs.get("seed"))
 
     # Dump args
     yaml_args = yaml.dump(kwargs)

src/ms_pred/graff_ms/graff_ms_hyperopt.py

@@ -40,7 +40,7 @@ def score_function(config, base_args, orig_dir=""):
 
     kwargs = copy.deepcopy(base_args)
     kwargs.update(config)
-    pl.utilities.seed.seed_everything(kwargs.get("seed"))
+    pl.seed_everything(kwargs.get("seed"))
 
     # Get dataset
     # Load smiles dataset and split into 3 subsets

src/ms_pred/graff_ms/predict.py

@@ -59,7 +59,7 @@ def predict():
 
     save_dir = kwargs["save_dir"]
     common.setup_logger(save_dir, log_name="graff_ms_pred.log", debug=kwargs["debug"])
-    pl.utilities.seed.seed_everything(kwargs.get("seed"))
+    pl.seed_everything(kwargs.get("seed"))
 
     # Dump args
     yaml_args = yaml.dump(kwargs)

src/ms_pred/iceberg/gen_hyperopt.py

@@ -40,7 +40,7 @@ def score_function(config, base_args, orig_dir=""):
 
     kwargs = copy.deepcopy(base_args)
     kwargs.update(config)
-    pl.utilities.seed.seed_everything(kwargs.get("seed"))
+    pl.seed_everything(kwargs.get("seed"))
 
     # Get dataset
     # Load smiles dataset and split into 3 subsets

src/ms_pred/iceberg/iceberg_elucidation.py

@@ -334,7 +334,7 @@ def iceberg_prediction(
         df.to_csv(save_dir / f'cands_df_{exp_name}.tsv', sep='\t', index=False)
 
         # run iceberg to generate in-silico spectrum
-        cmd = (f'''{python_path} src/ms_pred/iceberg/predict_smis.py \\
+        cmd = (f'''{python_path} {Path(__file__).resolve().parent / "predict_smis.py"} \\
                --batch-size {batch_size} \\
                --num-gpu-workers {num_gpu_workers} \\
                --num-cpu-workers {num_cpu_workers} \\

src/ms_pred/iceberg/inten_hyperopt.py

@@ -40,7 +40,7 @@ def score_function(config, base_args, orig_dir=""):
 
     kwargs = copy.deepcopy(base_args)
     kwargs.update(config)
-    pl.utilities.seed.seed_everything(kwargs.get("seed"))
+    pl.seed_everything(kwargs.get("seed"))
 
     # Get dataset
     # Load smiles dataset and split into 3 subsets

src/ms_pred/iceberg/predict_inten.py

@@ -58,7 +58,7 @@ def predict():
 
     save_dir = kwargs["save_dir"]
     common.setup_logger(save_dir, log_name="inten_pred.log", debug=kwargs["debug"])
-    pl.utilities.seed.seed_everything(kwargs.get("seed"))
+    pl.seed_everything(kwargs.get("seed"))
     binned_out = kwargs["binned_out"]
 
     # Dump args

src/ms_pred/marason/gen_hyperopt.py

@@ -40,7 +40,7 @@ def score_function(config, base_args, orig_dir=""):
 
     kwargs = copy.deepcopy(base_args)
     kwargs.update(config)
-    pl.utilities.seed.seed_everything(kwargs.get("seed"))
+    pl.seed_everything(kwargs.get("seed"))
 
     # Get dataset
     # Load smiles dataset and split into 3 subsets

src/ms_pred/marason/inten_hyperopt.py

@@ -44,7 +44,7 @@ def score_function(config, base_args, orig_dir=""):
 
     kwargs = copy.deepcopy(base_args)
     kwargs.update(config)
-    pl.utilities.seed.seed_everything(kwargs.get("seed"))
+    pl.seed_everything(kwargs.get("seed"))
 
     # Get dataset
     # Load smiles dataset and split into 3 subsets

src/ms_pred/marason/predict_inten.py

@@ -70,7 +70,7 @@ def predict():
 
     save_dir = kwargs["save_dir"]
     common.setup_logger(save_dir, log_name="inten_pred.log", debug=kwargs["debug"])
-    pl.utilities.seed.seed_everything(kwargs.get("seed"))
+    pl.seed_everything(kwargs.get("seed"))
     binned_out = kwargs["binned_out"]
 
     # Dump args

src/ms_pred/massformer_pred/massformer_hyperopt.py

@@ -40,7 +40,7 @@ def score_function(config, base_args, orig_dir=""):
 
     kwargs = copy.deepcopy(base_args)
     kwargs.update(config)
-    pl.utilities.seed.seed_everything(kwargs.get("seed"))
+    pl.seed_everything(kwargs.get("seed"))
 
     # Get dataset
     # Load smiles dataset and split into 3 subsets

src/ms_pred/massformer_pred/predict.py

@@ -58,7 +58,7 @@ def predict():
 
     save_dir = kwargs["save_dir"]
     common.setup_logger(save_dir, log_name="massformer_pred.log", debug=kwargs["debug"])
-    pl.utilities.seed.seed_everything(kwargs.get("seed"))
+    pl.seed_everything(kwargs.get("seed"))
 
     # Dump args
     yaml_args = yaml.dump(kwargs)

src/ms_pred/molnetms/molnetms_hyperopt.py

@@ -40,7 +40,7 @@ def score_function(config, base_args, orig_dir=""):
 
     kwargs = copy.deepcopy(base_args)
     kwargs.update(config)
-    pl.utilities.seed.seed_everything(kwargs.get("seed"))
+    pl.seed_everything(kwargs.get("seed"))
 
     # Get dataset
     # Load smiles dataset and split into 3 subsets

src/ms_pred/molnetms/predict.py

@@ -59,7 +59,7 @@ def predict():
 
     save_dir = kwargs["save_dir"]
     common.setup_logger(save_dir, log_name="molnetms_pred.log", debug=kwargs["debug"])
-    pl.utilities.seed.seed_everything(kwargs.get("seed"))
+    pl.seed_everything(kwargs.get("seed"))
 
     # Dump args
     yaml_args = yaml.dump(kwargs)

src/ms_pred/nn_utils/base_hyperopt.py

@@ -65,7 +65,7 @@ def run_hyperopt(
     common.setup_logger(
         save_dir, log_name="hyperopt.log", debug=kwargs.get("debug", False)
     )
-    pl.utilities.seed.seed_everything(kwargs.get("seed"))
+    pl.seed_everything(kwargs.get("seed"))
 
     # Define score function
     trainable = tune.with_parameters(

src/ms_pred/scarf_pred/gen_hyperopt.py

@@ -38,7 +38,7 @@ def score_function(config, base_args, orig_dir=""):
 
     kwargs = copy.deepcopy(base_args)
     kwargs.update(config)
-    pl.utilities.seed.seed_everything(kwargs.get("seed"))
+    pl.seed_everything(kwargs.get("seed"))
 
     # Get dataset
     # Load smiles dataset and split into 3 subsets

src/ms_pred/scarf_pred/inten_hyperopt.py

@@ -38,7 +38,7 @@ def score_function(config, base_args, orig_dir=""):
 
     kwargs = copy.deepcopy(base_args)
     kwargs.update(config)
-    pl.utilities.seed.seed_everything(kwargs.get("seed"))
+    pl.seed_everything(kwargs.get("seed"))
 
     # Get dataset
     # Load smiles dataset and split into 3 subsets

src/ms_pred/scarf_pred/predict_gen.py

@@ -57,7 +57,7 @@ def predict():
 
     save_dir = kwargs["save_dir"]
     common.setup_logger(save_dir, log_name="scarf_pred.log", debug=kwargs["debug"])
-    pl.utilities.seed.seed_everything(kwargs.get("seed"))
+    pl.seed_everything(kwargs.get("seed"))
 
     # Dump args
     yaml_args = yaml.dump(kwargs)

src/ms_pred/scarf_pred/predict_inten.py

@@ -61,7 +61,7 @@ def predict():
 
     save_dir = kwargs["save_dir"]
     common.setup_logger(save_dir, log_name="inten_pred.log", debug=kwargs["debug"])
-    pl.utilities.seed.seed_everything(kwargs.get("seed"))
+    pl.seed_everything(kwargs.get("seed"))
 
     # Dump args
     yaml_args = yaml.dump(kwargs)

src/ms_pred/scarf_pred/predict_smis.py

@@ -74,7 +74,7 @@ def predict():
     common.setup_logger(
         save_dir, log_name="scarf_pred_binned.log", debug=kwargs["debug"]
     )
-    pl.utilities.seed.seed_everything(kwargs.get("seed"))
+    pl.seed_everything(kwargs.get("seed"))
 
     # Dump args
     yaml_args = yaml.dump(kwargs)