Rename rest to reset_state

Author: Ashley Scillitoe

alibi_detect/cd/base_online.py

@@ -1,6 +1,7 @@
 import os
 from pathlib import Path
 import logging
+import warnings
 from abc import abstractmethod
 from typing import Any, Callable, Dict, List, Optional, Union, Tuple
 
@@ -219,6 +220,15 @@ def _initialise_state(self) -> None:
         self.drift_preds = np.array([])  # type: ignore[var-annotated]
 
     def reset(self) -> None:
+        """
+        Deprecated reset method. This method will be repurposed or removed in the future. To reset the detector to
+        its initial state (`t=0`) use :meth:`reset_state`.
+        """
+        self.reset_state()
+        warnings.warn('This method is deprecated and will be removed/repurposed in the future. To reset the detector '
+                      'to its initial state use `reset_state`.', DeprecationWarning)
+
+    def reset_state(self) -> None:
         """
         Resets the detector to its initial state (`t=0`). This does not include reconfiguring thresholds.
         """
@@ -524,6 +534,15 @@ def _check_drift(self, test_stats: np.ndarray, thresholds: np.ndarray) -> int:
         pass
 
     def reset(self) -> None:
+        """
+        Deprecated reset method. This method will be repurposed or removed in the future. To reset the detector to
+        its initial state (`t=0`) use :meth:`reset_state`.
+        """
+        self.reset_state()
+        warnings.warn('This method is deprecated and will be removed/repurposed in the future. To reset the detector '
+                      'to its initial state use `reset_state`.', DeprecationWarning)
+
+    def reset_state(self) -> None:
         """
         Resets the detector to its initial state (`t=0`). This does not include reconfiguring thresholds.
         """

alibi_detect/cd/lsdd_online.py

@@ -113,9 +113,11 @@ def test_stats(self):
     def thresholds(self):
         return [self._detector.thresholds[min(s, self._detector.window_size-1)] for s in range(self.t)]
 
-    def reset(self):
-        "Resets the detector but does not reconfigure thresholds."
-        self._detector.reset()
+    def reset_state(self):
+        """
+        Resets the detector to its initial state (`t=0`). This does not include reconfiguring thresholds.
+        """
+        self._detector.reset_state()
 
     def predict(self, x_t: Union[np.ndarray, Any], return_test_stat: bool = True) \
             -> Dict[Dict[str, str], Dict[str, Union[int, float]]]:

alibi_detect/cd/mmd_online.py

@@ -113,9 +113,11 @@ def test_stats(self):
     def thresholds(self):
         return [self._detector.thresholds[min(s, self._detector.window_size-1)] for s in range(self.t)]
 
-    def reset(self):
-        "Resets the detector but does not reconfigure thresholds."
-        self._detector.reset()
+    def reset_state(self):
+        """
+        Resets the detector to its initial state (`t=0`). This does not include reconfiguring thresholds.
+        """
+        self._detector.reset_state()
 
     def predict(self, x_t: Union[np.ndarray, Any], return_test_stat: bool = True) \
             -> Dict[Dict[str, str], Dict[str, Union[int, float]]]:

alibi_detect/cd/pytorch/tests/test_lsdd_online_pt.py

@@ -87,12 +87,12 @@ def test_lsdd_online(lsdd_online_params, seed):
         test_stats_h0.append(pred_t['data']['test_stat'])
         if pred_t['data']['is_drift']:
             detection_times_h0.append(pred_t['data']['time'])
-            cd.reset()
+            cd.reset_state()
     average_delay_h0 = np.array(detection_times_h0).mean()
     test_stats_h0 = [ts for ts in test_stats_h0 if ts is not None]
     assert ert/3 < average_delay_h0 < 3*ert
 
-    cd.reset()
+    cd.reset_state()
 
     detection_times_h1 = []
     test_stats_h1 = []
@@ -103,7 +103,7 @@ def test_lsdd_online(lsdd_online_params, seed):
         test_stats_h1.append(pred_t['data']['test_stat'])
         if pred_t['data']['is_drift']:
             detection_times_h1.append(pred_t['data']['time'])
-            cd.reset()
+            cd.reset_state()
     average_delay_h1 = np.array(detection_times_h1).mean()
     test_stats_h1 = [ts for ts in test_stats_h1 if ts is not None]
     assert np.abs(average_delay_h1) < ert/2
@@ -139,7 +139,7 @@ def test_lsdd_online_state_online(tmp_path, seed):
         test_stats_1.append(preds['data']['test_stat'])
 
     # Reset and check state cleared
-    dd.reset()
+    dd.reset_state()
     for key, orig_val in state_dict_t0.items():
         np.testing.assert_array_equal(orig_val, getattr(dd, key))  # use np.testing here as it handles torch.Tensor etc
 

alibi_detect/cd/pytorch/tests/test_mmd_online_pt.py

@@ -87,12 +87,12 @@ def test_mmd_online(mmd_online_params, seed):
         test_stats_h0.append(pred_t['data']['test_stat'])
         if pred_t['data']['is_drift']:
             detection_times_h0.append(pred_t['data']['time'])
-            cd.reset()
+            cd.reset_state()
     average_delay_h0 = np.array(detection_times_h0).mean()
     test_stats_h0 = [ts for ts in test_stats_h0 if ts is not None]
     assert ert/3 < average_delay_h0 < 3*ert
 
-    cd.reset()
+    cd.reset_state()
 
     detection_times_h1 = []
     test_stats_h1 = []
@@ -103,7 +103,7 @@ def test_mmd_online(mmd_online_params, seed):
         test_stats_h1.append(pred_t['data']['test_stat'])
         if pred_t['data']['is_drift']:
             detection_times_h1.append(pred_t['data']['time'])
-            cd.reset()
+            cd.reset_state()
     average_delay_h1 = np.array(detection_times_h1).mean()
     test_stats_h1 = [ts for ts in test_stats_h1 if ts is not None]
     assert np.abs(average_delay_h1) < ert/2
@@ -139,7 +139,7 @@ def test_mmd_online_state_online(tmp_path, seed):
         test_stats_1.append(preds['data']['test_stat'])
 
     # Reset and check state cleared
-    dd.reset()
+    dd.reset_state()
     for key, orig_val in state_dict_t0.items():
         np.testing.assert_array_equal(orig_val, getattr(dd, key))  # use np.testing here as it handles torch.Tensor etc
 

alibi_detect/cd/tensorflow/tests/test_lsdd_online_tf.py

@@ -98,12 +98,12 @@ def test_lsdd_online(lsdd_online_params, seed):
         test_stats_h0.append(pred_t['data']['test_stat'])
         if pred_t['data']['is_drift']:
             detection_times_h0.append(pred_t['data']['time'])
-            cd.reset()
+            cd.reset_state()
     average_delay_h0 = np.array(detection_times_h0).mean()
     test_stats_h0 = [ts for ts in test_stats_h0 if ts is not None]
     assert ert/3 < average_delay_h0 < 3*ert
 
-    cd.reset()
+    cd.reset_state()
 
     detection_times_h1 = []
     test_stats_h1 = []
@@ -114,7 +114,7 @@ def test_lsdd_online(lsdd_online_params, seed):
         test_stats_h1.append(pred_t['data']['test_stat'])
         if pred_t['data']['is_drift']:
             detection_times_h1.append(pred_t['data']['time'])
-            cd.reset()
+            cd.reset_state()
     average_delay_h1 = np.array(detection_times_h1).mean()
     test_stats_h1 = [ts for ts in test_stats_h1 if ts is not None]
     assert np.abs(average_delay_h1) < ert/2
@@ -150,7 +150,7 @@ def test_lsdd_online_state_online(tmp_path, seed):
         test_stats_1.append(preds['data']['test_stat'])
 
     # Reset and check state cleared
-    dd.reset()
+    dd.reset_state()
     for key, orig_val in state_dict_t0.items():
         np.testing.assert_array_equal(orig_val, getattr(dd, key))  # use np.testing here as it handles torch.Tensor etc
 

alibi_detect/cd/tensorflow/tests/test_mmd_online_tf.py

@@ -98,12 +98,12 @@ def test_mmd_online(mmd_online_params, seed):
         test_stats_h0.append(pred_t['data']['test_stat'])
         if pred_t['data']['is_drift']:
             detection_times_h0.append(pred_t['data']['time'])
-            cd.reset()
+            cd.reset_state()
     average_delay_h0 = np.array(detection_times_h0).mean()
     test_stats_h0 = [ts for ts in test_stats_h0 if ts is not None]
     assert ert/3 < average_delay_h0 < 3*ert
 
-    cd.reset()
+    cd.reset_state()
 
     detection_times_h1 = []
     test_stats_h1 = []
@@ -114,7 +114,7 @@ def test_mmd_online(mmd_online_params, seed):
         test_stats_h1.append(pred_t['data']['test_stat'])
         if pred_t['data']['is_drift']:
             detection_times_h1.append(pred_t['data']['time'])
-            cd.reset()
+            cd.reset_state()
     average_delay_h1 = np.array(detection_times_h1).mean()
     print(detection_times_h0, average_delay_h0)
     test_stats_h1 = [ts for ts in test_stats_h1 if ts is not None]
@@ -151,7 +151,7 @@ def test_mmd_online_state_online(tmp_path, seed):
         test_stats_1.append(preds['data']['test_stat'])
 
     # Reset and check state cleared
-    dd.reset()
+    dd.reset_state()
     for key, orig_val in state_dict_t0.items():
         np.testing.assert_array_equal(orig_val, getattr(dd, key))  # use np.testing here as it handles torch.Tensor etc
 

alibi_detect/cd/tests/test_cvm_online.py

@@ -39,21 +39,21 @@ def test_cvmdriftonline(window_sizes, batch_size, n_feat, seed):
         test_stats_h0.append(pred_t['data']['test_stat'])
         if pred_t['data']['is_drift']:
             detection_times_h0.append(pred_t['data']['time'])
-            cd.reset()
+            cd.reset_state()
     art = np.array(detection_times_h0).mean() - np.min(window_sizes) + 1
     test_stats_h0 = [ts for ts in test_stats_h0 if ts is not None]
     assert ert/3 < art < 3*ert
 
     # Drifted data
-    cd.reset()
+    cd.reset_state()
     detection_times_h1 = []
     test_stats_h1 = []
     for x_t in x_h1:
         pred_t = cd.predict(x_t, return_test_stat=True)
         test_stats_h1.append(pred_t['data']['test_stat'])
         if pred_t['data']['is_drift']:
             detection_times_h1.append(pred_t['data']['time'])
-            cd.reset()
+            cd.reset_state()
     add = np.array(detection_times_h1).mean() - np.min(window_sizes)
     test_stats_h1 = [ts for ts in test_stats_h1 if ts is not None]
     assert add < ert/2
@@ -91,7 +91,7 @@ def test_cvm_online_state_online(n_feat, tmp_path, seed):
         test_stats_1.append(preds['data']['test_stat'])
 
     # Reset and check state cleared
-    dd.reset()
+    dd.reset_state()
     for key, orig_val in state_dict_t0.items():
         np.testing.assert_array_equal(orig_val, getattr(dd, key))  # use np.testing here as it handles torch.Tensor etc
 

alibi_detect/cd/tests/test_fet_online.py

@@ -42,7 +42,7 @@ def test_fetdriftonline(alternative, n_feat, seed):
             assert cd.t - t0 == 1  # This checks state updated (self.t at least)
             if pred_t['data']['is_drift']:
                 detection_times_h0.append(pred_t['data']['time'])
-                cd.reset()
+                cd.reset_state()
 
         # Drifted data
         if alternative == 'less':
@@ -52,15 +52,15 @@ def test_fetdriftonline(alternative, n_feat, seed):
             p_h1 = 0.9
             x_h1 = partial(np.random.choice, (0, 1), size=n_feat, p=[1-p_h1, p_h1])
 
-        cd.reset()
+        cd.reset_state()
         count = 0
         while len(detection_times_h1) < n_reps and count < int(1e6):
             count += 1
             x_t = x_h1().reshape(1, 1) if n_feat == 1 else x_h1()  # test shape (1,1) in 1D case here
             pred_t = cd.predict(x_t)
             if pred_t['data']['is_drift']:
                 detection_times_h1.append(pred_t['data']['time'])
-                cd.reset()
+                cd.reset_state()
 
     art = np.array(detection_times_h0).mean() - np.min(window_sizes) + 1
     add = np.array(detection_times_h1).mean() - np.min(window_sizes)
@@ -100,7 +100,7 @@ def test_fet_online_state_online(n_feat, tmp_path, seed):
         test_stats_1.append(preds['data']['test_stat'])
 
     # Reset and check state cleared
-    dd.reset()
+    dd.reset_state()
     for key, orig_val in state_dict_t0.items():
         np.testing.assert_array_equal(orig_val, getattr(dd, key))  # use np.testing here as it handles torch.Tensor etc
 

doc/source/cd/methods/onlinecvmdrift.ipynb

@@ -115,12 +115,6 @@
     "\n",
     "```python\n",
     "preds = cd.predict(x_t, return_test_stat=True)\n",
-    "```\n",
-    "\n",
-    "Resetting the detector with the same reference data and thresholds but with a new and empty test-window is straight-forward:\n",
-    "\n",
-    "```python\n",
-    "cd.reset()\n",
     "```"
    ]
   },
@@ -146,7 +140,7 @@
     "cd.load_state('checkpoint_t1')\n",
     "```\n",
     "\n",
-    "At any point, the state may be reset with the `reset` method."
+    "At any point, the state may be reset with the `reset_state` method."
    ]
   }
  ],
@@ -169,7 +163,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.5"
+   "version": "3.8.11"
   }
  },
  "nbformat": 4,