Fix callback iteration index monotonicity

Signed-off-by: Nandana Dileep <nandanadileep29@gmail.com>

Author: nandanadileep

python/interpret-core/interpret/glassbox/_ebm/_boost.py

@@ -86,7 +86,8 @@ def boost(
                     else None
                 )
 
-                step_idx = 0
+                step_idx = 0  # number of accepted boosting updates
+                callback_idx = 0  # number of callback invocations (attempted steps)
                 cur_metric = np.nan
 
                 _log.info("Start boosting")
@@ -369,12 +370,13 @@ def boost(
 
                         if callback is not None:
                             is_done = callback(
-                                bag_idx, step_idx, make_progress, cur_metric
+                                bag_idx, callback_idx, make_progress, cur_metric
                             )
                             if is_done:
                                 if stop_flag is not None:
                                     stop_flag[0] = True
                                 break
+                            callback_idx += 1
 
                         state_idx = state_idx + 1
                         if len(term_features) <= state_idx:

python/interpret-core/interpret/glassbox/_ebm/_ebm.py

@@ -3086,12 +3086,13 @@ class ExplainableBoostingClassifier(ClassifierMixin, EBMModel):
         amount of overfitting of the individual models can improve the accuracy of
         the ensemble as a whole.
     callback : Optional[Callable[[int, int, bool, float], bool]], default=None
-        A user-defined function that is invoked at the end of each boosting step to determine
+        A user-defined function that is invoked at the end of each boosting step attempt to determine
         whether to terminate boosting or continue. If it returns True, the boosting loop is
         stopped immediately. By default, no callback is used and training proceeds according
         to the early stopping settings.  The callback function receives:
-        (1) the bag index, (2) the number of boosting steps completed,
-        (3) a boolean indicating whether progress was made in the current step, and (4) the current best score.
+        (1) the bag index, (2) the callback step index (increments on every callback, even when
+        no update is applied), (3) a boolean indicating whether progress was made in the current step,
+        and (4) the current best score.
     min_samples_leaf : int, default=4
         Minimum number of samples allowed in the leaves.
     min_hessian : float, default=1e-4
@@ -3596,12 +3597,13 @@ class ExplainableBoostingRegressor(RegressorMixin, EBMModel):
         amount of overfitting of the individual models can improve the accuracy of
         the ensemble as a whole.
     callback : Optional[Callable[[int, int, bool, float], bool]], default=None
-        A user-defined function that is invoked at the end of each boosting step to determine
+        A user-defined function that is invoked at the end of each boosting step attempt to determine
         whether to terminate boosting or continue. If it returns True, the boosting loop is
         stopped immediately. By default, no callback is used and training proceeds according
         to the early stopping settings.  The callback function receives:
-        (1) the bag index, (2) the number of boosting steps completed,
-        (3) a boolean indicating whether progress was made in the current step, and (4) the current best score.
+        (1) the bag index, (2) the callback step index (increments on every callback, even when
+        no update is applied), (3) a boolean indicating whether progress was made in the current step,
+        and (4) the current best score.
     min_samples_leaf : int, default=4
         Minimum number of samples allowed in the leaves.
     min_hessian : float, default=0.0

python/interpret-core/tests/glassbox/ebm/test_callback.py

@@ -0,0 +1,45 @@
+# Copyright (c) 2026 The InterpretML Contributors
+# Distributed under the MIT software license
+
+import numpy as np
+import pytest
+
+from interpret.glassbox import ExplainableBoostingClassifier
+from interpret.utils._native import Native
+
+# Skip if the native lib is not available (common in source checkouts without build artifacts)
+try:
+    Native._get_ebm_lib_path()
+except Exception:  # pragma: no cover - just gating environments without libebm
+    pytest.skip("libebm shared library not available", allow_module_level=True)
+
+
+def test_callback_iteration_is_monotonic():
+    """Ensure callback receives strictly increasing iteration indexes even when no progress is made."""
+
+    X = np.array([[0], [1], [0], [1]], dtype=np.float64)
+    y = np.array([0, 1, 0, 1], dtype=np.int64)
+
+    iterations = []
+
+    def cb(bag_idx, iteration_idx, made_progress, metric):
+        iterations.append(iteration_idx)
+        # stop early to keep test fast; plenty of callback invocations happen before the first boost
+        return len(iterations) >= 15
+
+    ebm = ExplainableBoostingClassifier(
+        interactions=0,
+        max_rounds=1,
+        cyclic_progress=0.1,  # forces several no-progress iterations up front
+        outer_bags=1,
+        max_bins=2,
+        max_leaves=2,
+        min_samples_leaf=2,
+        n_jobs=1,
+        random_state=1,
+        callback=cb,
+    )
+
+    ebm.fit(X, y)
+
+    assert iterations == sorted(set(iterations))