#183 ruff reformat

MannLabs · Jul 23, 2024 · dc0326c · dc0326c
1 parent 503ca25
commit dc0326c
Showing 1 changed file with 20 additions and 18 deletions.
diff --git a/peptdeep/hla/hla_class1.py b/peptdeep/hla/hla_class1.py
@@ -380,19 +380,17 @@ def predict_from_proteins(
         peptide_df["sequence"] = get_seq_series(peptide_df, self._cat_protein_sequence)
         return peptide_df
 
-    def _concat_neg_df(self, precursor_df, column_to_train='HLA'):
+    def _concat_neg_df(self, precursor_df, column_to_train="HLA"):
         precursor_df = append_nAA_column_if_missing(precursor_df)
         precursor_df[column_to_train] = 1
         df_list = [precursor_df]
-        for nAA, group_df in precursor_df.groupby('nAA'):
+        for nAA, group_df in precursor_df.groupby("nAA"):
             rnd_seqs = get_random_sequences(
-                self.protein_df, 
-                n=len(group_df),
-                pep_len = nAA
+                self.protein_df, n=len(group_df), pep_len=nAA
+            )
+            df_list.append(
+                pd.DataFrame({"sequence": rnd_seqs, "nAA": nAA, column_to_train: 0})
             )
-            df_list.append(pd.DataFrame(
-                {'sequence':rnd_seqs,'nAA':nAA,column_to_train:0}
-            ))
         return pd.concat(df_list).reset_index(drop=True)
 
     def test(self, precursor_df):
@@ -402,17 +400,21 @@ def test(self, precursor_df):
         precision_list = []
         recall_list = []
         fp_list = []
-        for prob in [0.5,0.6,0.7,0.8, 0.9]:
+        for prob in [0.5, 0.6, 0.7, 0.8, 0.9]:
             prob_list.append(prob)
-            precision_list.append(df[df.HLA_prob_pred>prob].HLA.mean())
-            recall_list.append(df[df.HLA_prob_pred>prob].HLA.sum()/len(df)*2)
-            fp_list.append(1-(1-df[df.HLA_prob_pred<prob].HLA).sum()/len(df)*2)
-        return pd.DataFrame(dict(
-            HLA_prob_pred=prob_list,
-            precision=precision_list,
-            recall=recall_list,
-            false_positive=fp_list
-            ))    
+            precision_list.append(df[df.HLA_prob_pred > prob].HLA.mean())
+            recall_list.append(df[df.HLA_prob_pred > prob].HLA.sum() / len(df) * 2)
+            fp_list.append(
+                1 - (1 - df[df.HLA_prob_pred < prob].HLA).sum() / len(df) * 2
+            )
+        return pd.DataFrame(
+            dict(
+                HLA_prob_pred=prob_list,
+                precision=precision_list,
+                recall=recall_list,
+                false_positive=fp_list,
+            )
+        )
 
     def _download_pretrained_hla_model(self):
         download_models(url=self._model_url, target_path=self._model_zip)