intensity model

.idea/misc.xml

@@ -3,5 +3,5 @@
   <component name="Black">
     <option name="sdkName" value="Python 3.9 (LC-MS-RT-prediction)" />
   </component>
-  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.9 (LC-MS-RT-prediction)" project-jdk-type="Python SDK" />
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.10 (LC-MS-RT-prediction)" project-jdk-type="Python SDK" />
 </project>
\ No newline at end of file

requirements.txt

@@ -3,4 +3,5 @@ h5py~=3.10.0
 pandas~=2.2.0
 numpy~=1.26.2
 matplotlib~=3.8.2
-wandb~=0.16.2
\ No newline at end of file
+wandb~=0.16.2
+torchmetrics~=1.3.0.post0
\ No newline at end of file

test.py

@@ -6,6 +6,7 @@ import torch
 from dataloader import load_data, load_split_intensity, Intentsity_Dataset, load_intensity_from_files
 from model import RT_pred_model, Intensity_pred_model_multi_head, RT_pred_model_self_attention
 from config import load_args
+from loss import masked_cos_sim, masked_pearson_correlation_distance
 
 
 def train(model, data_train, epoch, optimizer, criterion, cuda=False):
@@ -30,7 +31,7 @@ def train(model, data_train, epoch, optimizer, criterion, cuda=False):
           distance / len(data_train))
 
 
-def eval(model, data_test, epoch, criterion=nn.MSELoss(reduction='mean'), cuda=False):
+def eval(model, data_test, epoch, criterion=masked_cos_sim, cuda=False):
     losses = 0.
     distance = 0.
     for data, target in data_test:
@@ -57,7 +58,7 @@ def save(model, optimizer, epoch, checkpoint_name):
     }, os.path.join('checkpoints', checkpoint_name))
 
 
-def run(epochs, eval_inter, save_inter, model, data_train, data_test, optimizer, criterion=nn.MSELoss(reduction='mean'),
+def run(epochs, eval_inter, save_inter, model, data_train, data_test, optimizer, criterion=masked_cos_sim,
         cuda=False):
     for e in range(1, epochs + 1):
         train(model, data_train, e, optimizer, criterion, cuda=cuda)
@@ -68,30 +69,12 @@ def run(epochs, eval_inter, save_inter, model, data_train, data_test, optimizer,
 
 
 def main(args):
+
     os.environ["WANDB_API_KEY"] = 'b4a27ac6b6145e1a5d0ee7f9e2e8c20bd101dccd'
     os.environ["WANDB_MODE"] = "offline"
+    os.environ["WANDB_DIR"] = os.path.abspath("./wandb_run")
 
-    config = {
-        "model": "RT prediction GRU/selfAtt+ GRU",
-        "learning_rate": args.lr,
-        "batch_size": args.batch_size,
-    }
-
-    # wdb.init(project="RT prediction", dir='wandb_run')
-
-    print('Cuda : ', torch.cuda.is_available())
-    data_train, data_test = load_data(args.batch_size, args.n_train, args.n_test, data_source='database/data.csv')
-    print('\nData loaded')
-    model = RT_pred_model_self_attention()
-    if torch.cuda.is_available():
-        model = model.cuda()
-    optimizer = optim.Adam(model.parameters(), lr=args.lr)
-    print('\nModel initialised')
-    run(args.epochs, args.eval_inter, args.save_inter, model, data_train, data_test, optimizer=optimizer, cuda=True)
-    # wdb.finish()
-
-
-def main2(args):
+    wdb.init(project="RT prediction", dir='./wandb_run')
     print(torch.cuda.is_available())
     # sources_train = ('data/intensity/sequence_train.npy',
     #                  'data/intensity/intensity_train.npy',
@@ -111,9 +94,7 @@ def main2(args):
                'data/intensity/collision_energy_header.npy',
                'data/intensity/precursor_charge_header.npy')
 
-    data_train, data_test, data_validation = load_split_intensity(sources, (0.5, 0.25, 0.25))
-    train = Intentsity_Dataset(data_train)
-    test = Intentsity_Dataset(data_test)
+    data_train, data_test, data_validation = load_split_intensity(sources,32, (0.5, 0.25, 0.25))
 
 
 
@@ -123,10 +104,11 @@ def main2(args):
         model = model.cuda()
     optimizer = optim.Adam(model.parameters(), lr=0.001)
     print('\nModel initialised')
-    run(args.epochs, args.eval_inter, args.save_inter, model, train, test, optimizer=optimizer, cuda=True)
+    run(args.epochs, args.eval_inter, args.save_inter, model, data_train, data_test, optimizer=optimizer, cuda=True)
 
+    wdb.finish()
 
 if __name__ == "__main__":
     args = load_args()
     print(args)
-    main2(args)
+    main(args)