Merge branch 'dev' into feature/test_chebai_cli

Author: aditya0by0

chebai/result/analyse_sem.py

@@ -2,6 +2,7 @@
 import os
 import traceback
 from datetime import datetime
+from pathlib import Path
 from typing import List, LiteralString, Optional, Tuple
 
 import pandas as pd
@@ -155,9 +156,11 @@ def get_disjoint_groups(disjoint_files):
         disjoint_files = os.path.join("data", "chebi-disjoints.owl")
     disjoint_pairs, disjoint_groups = [], []
     for file in disjoint_files:
-        if file.split(".")[-1] == "csv":
+        if isinstance(file, Path):
+            file = str(file)
+        if file.endswith(".csv"):
             disjoint_pairs += pd.read_csv(file, header=None).values.tolist()
-        elif file.split(".")[-1] == "owl":
+        elif file.endswith(".owl"):
             with open(file, "r") as f:
                 plaintext = f.read()
                 segments = plaintext.split("<")
@@ -217,10 +220,16 @@ def set_label_names(self, label_names):
             self.label_successors = self.label_successors.unsqueeze(0)
 
     def __call__(self, preds):
+        if preds.shape[1] == 0:
+            # no labels predicted
+            return preds
+        # preds shape: (n_samples, n_labels)
         preds_sum_orig = torch.sum(preds)
         # step 1: apply implications: for each class, set prediction to max of itself and all successors
         preds = preds.unsqueeze(1)
         preds_masked_succ = torch.where(self.label_successors, preds, 0)
+        # preds_masked_succ shape: (n_samples, n_labels, n_labels)
+
         preds = preds_masked_succ.max(dim=2).values
         if torch.sum(preds) != preds_sum_orig:
             print(f"Preds change (step 1): {torch.sum(preds) - preds_sum_orig}")

pyproject.toml

@@ -1,10 +1,10 @@
 [build-system]
-requires = ["setuptools>=61.0", "wheel"]
+requires = ["setuptools >= 77.0.3", "wheel"]
 build-backend = "setuptools.build_meta"
 
 [project]
 name = "chebai"
-version = "1.0.2"
+version = "1.0.3"
 description = "ChEBai is a deep learning library designed for the integration of deep learning methods with chemical ontologies, particularly ChEBI."
 authors = [
     { name = "MGlauer", email = "[email protected]" }