space for time

app.py

@@ -461,7 +461,8 @@ def create_evaluation_tabs(results_dfs: Dict[str, pd.DataFrame]) -> None:
     """
     # Create Overview tab first
     overview_df = create_overview_dataframe(results_dfs)
-    create_dataframe_tab("Overview", overview_df, widths=["10%"])
+    n_overview_columns = len(overview_df.columns)
+    create_dataframe_tab("Overview", overview_df, widths=["20%"]+["10%"]*(n_overview_columns-1))
 
     # Create individual evaluation tabs
     for eval_type in OTHER_EVAL_TYPES:

evaluator.py

@@ -5,7 +5,6 @@ from typing import Dict, List, Tuple
 import numpy as np
 import torch
 import json
-from fairchem.core.modules.evaluator import Evaluator
 
 from fairchem.data.omol.modules.evaluator import (
     ligand_pocket,
@@ -95,21 +94,12 @@ def s2ef_metrics(
     submission_filename: Path,
     subsets: list = ["all"],
 ) -> Dict[str, float]:
-    eval_metrics = {
-        "energy": ["mae"],
-        "forces": ["mae"],
-    }
-    evaluator = Evaluator(eval_metrics=eval_metrics)
-
-    # Get order once for all subsets
     order = get_order(submission_filename, annotations_path)
 
     with np.load(submission_filename) as data:
         forces = data["forces"]
         energy = data["energy"][order]
-        natoms = data["natoms"]
-        forces = np.array(np.split(forces, np.cumsum(natoms)[:-1]), dtype=object)[order]
-        natoms = natoms[order]
+        forces = np.array(np.split(forces, np.cumsum(data["natoms"])[:-1]), dtype=object)[order]
 
     if len(set(np.where(np.isinf(energy))[0])) != 0:
         inf_energy_ids = list(set(np.where(np.isinf(energy))[0]))
@@ -132,36 +122,19 @@ def s2ef_metrics(
                 [data_id in allowed_ids for data_id in target_data_ids]
             )
 
-        sub_energy = torch.from_numpy(energy[subset_mask])
-        sub_forces = torch.from_numpy(np.concatenate(forces[subset_mask]))
-        sub_natoms = torch.from_numpy(natoms[subset_mask])
-
-        submission_data = {
-            "energy": sub_energy,
-            "forces": sub_forces,
-            "natoms": sub_natoms,
-        }
+        sub_energy = energy[subset_mask]
+        sub_target_energy = target_energy[subset_mask]
+        energy_mae = np.mean(np.abs(sub_target_energy - sub_energy))
+        metrics[f"{subset}_energy_mae"] = energy_mae
 
-        target_energy_tensor = torch.from_numpy(target_energy[subset_mask])
-        target_force_tensors = torch.from_numpy(
-            np.concatenate(target_forces[subset_mask])
-        )
-        target_natoms_tensor = torch.tensor(
-            [force_array.shape[0] for force_array in target_forces[subset_mask]],
-            dtype=torch.long,
-        )
+        forces_mae = 0
+        natoms = 0
+        for sub_forces, sub_target_forces in zip(forces[subset_mask], target_forces[subset_mask]):
+            forces_mae += np.sum(np.abs(sub_target_forces - sub_forces))
+            natoms += sub_forces.shape[0]
+        forces_mae /= (3*natoms)
 
-        annotations_data = {
-            "energy": target_energy_tensor,
-            "forces": target_force_tensors,
-            "natoms": target_natoms_tensor,
-        }
-
-        subset_metrics = evaluator.eval(
-            submission_data, annotations_data, prev_metrics={}
-        )
-        for key in ["energy_mae", "forces_mae"]:
-            metrics[f"{subset}_{key}"] = subset_metrics[key]["metric"]
+        metrics[f"{subset}_forces_mae"] = forces_mae
 
     return metrics
 
@@ -217,4 +190,4 @@ def evaluate(
     else:
         raise ValueError(f"Unknown eval_type: {eval_type}")
 
-    return metrics
+    return metrics