diff --git a/.coverage b/.coverage
index c9d78c7..c3df57d 100644
Binary files a/.coverage and b/.coverage differ
diff --git a/guillory21_doc/__pycache__/doc.cpython-311.pyc b/guillory21_doc/__pycache__/doc.cpython-311.pyc
new file mode 100644
index 0000000..a98676f
Binary files /dev/null and b/guillory21_doc/__pycache__/doc.cpython-311.pyc differ
diff --git a/guillory21_doc/doc.py b/guillory21_doc/doc.py
new file mode 100644
index 0000000..9b59883
--- /dev/null
+++ b/guillory21_doc/doc.py
@@ -0,0 +1,4 @@
+import numpy as np
+
+def get_doc(probs1, probs2):
+    return np.mean(probs2) - np.mean(probs1) 
\ No newline at end of file
diff --git a/quacc/baseline.py b/quacc/baseline.py
index 32dcacc..c508dce 100644
--- a/quacc/baseline.py
+++ b/quacc/baseline.py
@@ -1,16 +1,13 @@
+from ast import get_docstring
 from statistics import mean
 from typing import Dict
 from sklearn.base import BaseEstimator
 from sklearn.model_selection import cross_validate
 from quapy.data import LabelledCollection
-from garg22_ATC.ATC_helper import (
-    find_ATC_threshold,
-    get_ATC_acc,
-    get_entropy,
-    get_max_conf,
-)
+import garg22_ATC.ATC_helper as atc
 import numpy as np
-from jiang18_trustscore.trustscore import TrustScore
+import jiang18_trustscore.trustscore as trustscore
+import guillory21_doc.doc as doc
 
 
 def kfcv(c_model: BaseEstimator, validation: LabelledCollection) -> Dict:
@@ -19,7 +16,7 @@ def kfcv(c_model: BaseEstimator, validation: LabelledCollection) -> Dict:
     return {"f1_score": mean(scores["test_f1_macro"])}
 
 
-def ATC_MC(
+def atc_mc(
     c_model: BaseEstimator,
     validation: LabelledCollection,
     test: LabelledCollection,
@@ -34,21 +31,23 @@ def ATC_MC(
     test_probs = c_model_predict(test.X)
 
     ## score function, e.g., negative entropy or argmax confidence
-    val_scores = get_max_conf(val_probs)
+    val_scores = atc.get_max_conf(val_probs)
+   #pred_idxv1           #calib_probsv1/probsv1
     val_preds = np.argmax(val_probs, axis=-1)
-
-    test_scores = get_max_conf(test_probs)
-
-    _, ATC_thres = find_ATC_threshold(val_scores, val_labels == val_preds)
-    ATC_accuracy = get_ATC_acc(ATC_thres, test_scores)
+   #pred_probs_new            #probs_new
+    test_scores = atc.get_max_conf(test_probs)
+                                     #pred_probsv1  #labelsv1     #pred_idxv1
+    _, atc_thres = atc.find_ATC_threshold(val_scores,    val_labels == val_preds)
+                              #calib_thres_balance #pred_probs_new
+    atc_accuracy = atc.get_ATC_acc(atc_thres,           test_scores)
 
     return {
         "true_acc": 100 * np.mean(np.argmax(test_probs, axis=-1) == test.y),
-        "pred_acc": ATC_accuracy,
+        "pred_acc": atc_accuracy,
     }
 
 
-def ATC_NE(
+def atc_ne(
     c_model: BaseEstimator,
     validation: LabelledCollection,
     test: LabelledCollection,
@@ -63,17 +62,17 @@ def ATC_NE(
     test_probs = c_model_predict(test.X)
 
     ## score function, e.g., negative entropy or argmax confidence
-    val_scores = get_entropy(val_probs)
+    val_scores = atc.get_entropy(val_probs)
     val_preds = np.argmax(val_probs, axis=-1)
 
-    test_scores = get_entropy(test_probs)
+    test_scores = atc.get_entropy(test_probs)
 
-    _, ATC_thres = find_ATC_threshold(val_scores, val_labels == val_preds)
-    ATC_accuracy = get_ATC_acc(ATC_thres, test_scores)
+    _, atc_thres = atc.find_ATC_threshold(val_scores, val_labels == val_preds)
+    atc_accuracy = atc.get_ATC_acc(atc_thres, test_scores)
 
     return {
         "true_acc": 100 * np.mean(np.argmax(test_probs, axis=-1) == test.y),
-        "pred_acc": ATC_accuracy,
+        "pred_acc": atc_accuracy,
     }
 
 
@@ -87,8 +86,25 @@ def trust_score(
 
     test_pred = c_model_predict(test.X)
 
-    trust_model = TrustScore()
+    trust_model = trustscore.TrustScore()
     trust_model.fit(validation.X, validation.y)
 
     return trust_model.get_score(test.X, test_pred)
 
+
+def doc_feat(
+    c_model: BaseEstimator,
+    validation: LabelledCollection,
+    test: LabelledCollection,
+    predict_method="predict_proba",
+):
+    c_model_predict = getattr(c_model, predict_method)
+
+    val_probs, val_labels = c_model_predict(validation.X), validation.y
+    test_probs = c_model_predict(test.X)
+    val_scores = np.max(val_probs, axis=-1)
+    test_scores = np.max(test_probs, axis=-1)
+    val_preds = np.argmax(val_probs, axis=-1)
+
+    v1acc = np.mean(val_preds == val_labels)*100
+    return v1acc + doc.get_doc(val_scores, test_scores)