[ENH] MoleculeLoader support for PSeAAC

pyaptamer/pseaac/_pseaac_aptanet.py

@@ -5,6 +5,7 @@
 
 import numpy as np
 
+from pyaptamer.data import MoleculeLoader
 from pyaptamer.pseaac._props import aa_props
 from pyaptamer.utils._pseaac_utils import AMINO_ACIDS, clean_protein_seq
 
@@ -163,6 +164,25 @@ def _avg_theta_val(self, seq_vec, seq_len, n, prop_group):
         return np.mean(diffs**2)
 
     def transform(self, protein_sequence):
+        """Get PseAAC features for protein sequence or MoleculeLoader.
+
+        Parameters
+        ----------
+        protein_sequence : str or MoleculeLoader
+            Sequence string, or a MoleculeLoader.
+
+        Returns
+        -------
+        np.ndarray or list of np.ndarray
+            If input is a str: 1D array of PseAAC features.
+            If input is a MoleculeLoader: list of 1D arrays, one per sequence.
+        """
+        if isinstance(protein_sequence, MoleculeLoader):
+            seqs = protein_sequence.to_df_seq()["sequence"]
+            return [self._transform(seq) for seq in seqs]
+        return self._transform(protein_sequence)
+
+    def _transform(self, protein_sequence):
         """
         Generate the PseAAC feature vector for the given protein sequence.
 

pyaptamer/pseaac/_pseaac_general.py

@@ -5,6 +5,7 @@
 
 import numpy as np
 
+from pyaptamer.data import MoleculeLoader
 from pyaptamer.pseaac._props import aa_props
 from pyaptamer.utils._pseaac_utils import AMINO_ACIDS, clean_protein_seq
 
@@ -206,6 +207,25 @@ def _avg_theta_val(self, seq_vec, seq_len, n, prop_group):
         return np.mean(diffs**2)
 
     def transform(self, protein_sequence):
+        """Get PseAAC features for protein sequence or MoleculeLoader.
+
+        Parameters
+        ----------
+        protein_sequence : str or MoleculeLoader
+            Sequence string, or a MoleculeLoader.
+
+        Returns
+        -------
+        np.ndarray or list of np.ndarray
+            If input is a str: 1D array of PseAAC features.
+            If input is a MoleculeLoader: list of 1D arrays, one per sequence.
+        """
+        if isinstance(protein_sequence, MoleculeLoader):
+            seqs = protein_sequence.to_df_seq()["sequence"]
+            return [self._transform(seq) for seq in seqs]
+        return self._transform(protein_sequence)
+
+    def _transform(self, protein_sequence):
         """
         Generate the PseAAC feature vector for the given protein sequence.