saves results of eval of model

Author: jcapp4

chebai/loss/boost_bce.py

@@ -1,7 +1,4 @@
 import torch
-import sys
-sys.path.insert(1,'/home/programmer/Bachelorarbeit/python-chebai')
-
 import extras.weight_loader as f
 
 

chebai/models/base.py

@@ -7,8 +7,6 @@
 
 from chebai.preprocessing.structures import XYData
 
-import sys
-sys.path.insert(1,'/home/programmer/Bachelorarbeit/python-chebai')
 
 import extras.weight_loader as f
 

chebai/preprocessing/datasets/base.py

@@ -18,8 +18,6 @@
 from torch.utils.data import DataLoader
 
 from chebai.preprocessing import reader as dr
-import sys
-sys.path.insert(1,'/home/programmer/Bachelorarbeit/python-chebai')
 
 import extras.weight_loader as f
 

chebai/result/utils.py

@@ -14,6 +14,8 @@
 from chebai.preprocessing.datasets.base import XYBaseDataModule
 from chebai.preprocessing.datasets.chebi import _ChEBIDataExtractor
 
+from extras.ev_model import create_weight_dict
+
 
 def get_checkpoint_from_wandb(
     epoch: int,
@@ -117,6 +119,7 @@ def evaluate_model(
     data_list = data_list[: data_module.data_limit]
     preds_list = []
     labels_list = []
+    weights_list = []
     if buffer_dir is not None:
         os.makedirs(buffer_dir, exist_ok=True)
     save_ind = 0
@@ -132,6 +135,8 @@ def evaluate_model(
             preds, labels = _run_batch(data_list[i : i + batch_size], model, collate)
             preds_list.append(preds)
             labels_list.append(labels)
+            for j in range(i,i+batch_size):
+                weights_list.append(data_list[j])
 
             if buffer_dir is not None:
                 if n_saved * batch_size >= save_batch_size:
@@ -170,6 +175,68 @@ def evaluate_model(
             )
 
 
+def evaluate_model_weights(
+    model: ChebaiBaseNet,
+    data_module: XYBaseDataModule,
+    filename: Optional[str] = None,
+    buffer_dir: Optional[str] = None,
+    batch_size: int = 32,
+    skip_existing_preds: bool = False,
+    kind: str = "test",
+) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
+    """
+    Runs the model on the test set of the data module or on the dataset found in the specified file.
+    If buffer_dir is set, results will be saved in buffer_dir.
+
+    Note:
+        No need to provide "filename" parameter for Chebi dataset, "kind" parameter should be provided.
+
+    Args:
+        model: The model to evaluate.
+        data_module: The data module containing the dataset.
+        filename: Optional file name for the dataset.
+        buffer_dir: Optional directory to save the results.
+        batch_size: The batch size for evaluation.
+        skip_existing_preds: Whether to skip evaluation if predictions already exist.
+        kind: Kind of split of the data to be used for testing the model. Default is `test`.
+
+    Returns:
+        Tensors with predictions and labels.
+    """
+    model.eval()
+    collate = data_module.reader.COLLATOR()
+
+    if isinstance(data_module, _ChEBIDataExtractor):
+        # As the dynamic split change is implemented only for chebi-dataset as of now
+        data_df = data_module.dynamic_split_dfs[kind]
+        data_list = data_df.to_dict(orient="records")
+    else:
+        data_list = data_module.load_processed_data("test", filename)
+    data_list = data_list[: data_module.data_limit]
+    preds_list = []
+    labels_list = []
+    weights_list = []
+    if buffer_dir is not None:
+        os.makedirs(buffer_dir, exist_ok=True)
+    save_ind = 0
+    save_batch_size = 128
+    n_saved = 1
+
+    print("")
+    for i in tqdm.tqdm(range(0, len(data_list), batch_size)):
+        if not (
+            skip_existing_preds
+            and os.path.isfile(os.path.join(buffer_dir, f"preds{save_ind:03d}.pt"))
+        ):
+            preds, labels = _run_batch(data_list[i : i + batch_size], model, collate)
+            preds_list.append(preds)
+            labels_list.append(labels)
+
+    result = create_weight_dict(preds_list,labels_list,data_list)
+    torch.save(result,"./result.pt")
+    
+
+
 def load_results_from_buffer(
     buffer_dir: str, device: torch.device
 ) -> Tuple[Optional[torch.Tensor], Optional[torch.Tensor]]:

extras/ev_model.py

@@ -0,0 +1,15 @@
+def create_weight_dict(p,l,data_list):
+    result = []
+    i = 0
+    for j in range(0,len(p)):
+        for k in range(0,len(p[j])):
+            d = {}
+            pred = p[j][k]
+            label = l[j][k]
+            ident = data_list[i]["idents"]
+            d["pred"]= pred
+            d["label"]= label
+            d["ident"]= ident
+            result.append(d)
+            i = i + 1
+    return result