added comment to clarify comparison size = 1 in online algorithm, fixed multivariate edge case validation

Author: gloriadesideri

src/ml3_drift/monitoring/algorithms/online/adwin.py

@@ -50,8 +50,9 @@ def __init__(
         self.grace_period = grace_period
         self._args = args
         self._kwargs = kwargs
-        super().__init__(comparison_size=1, callbacks=callbacks)
-        
+        super().__init__(
+            comparison_size=1, callbacks=callbacks
+        )  # since we add only one sample per step and river handles building the window internally we set comparison_size to 1
 
     def _reset_internal_parameters(self):
         self.drift_agent = RiverADWIN(
@@ -61,7 +62,7 @@ def _reset_internal_parameters(self):
             min_window_length=self.min_window_length,
             grace_period=self.grace_period,
             *self._args,
-            **self._kwargs
+            **self._kwargs,
         )
 
     def _fit(self, X: np.ndarray):

src/ml3_drift/monitoring/algorithms/online/kswin.py

@@ -48,7 +48,9 @@ def __init__(
         self.seed = seed
         self._args = args
         self._kwargs = kwargs
-        super().__init__(comparison_size=1, callbacks=callbacks)
+        super().__init__(
+            comparison_size=1, callbacks=callbacks
+        )  # since we add only one sample per step and river handles building the window internally we set comparison_size to 1
 
     def _reset_internal_parameters(self):
         self.drift_agent = RiverKSWIN(
@@ -57,7 +59,7 @@ def _reset_internal_parameters(self):
             stat_size=self.stat_size,
             seed=self.seed,
             *self._args,
-            **self._kwargs
+            **self._kwargs,
         )
 
     def _fit(self, X: np.ndarray):

src/ml3_drift/monitoring/algorithms/online/page_hinkley.py

@@ -53,7 +53,9 @@ def __init__(
         self.seed = seed
         self._args = args
         self._kwargs = kwargs
-        super().__init__(callbacks=callbacks, comparison_size=1)
+        super().__init__(
+            callbacks=callbacks, comparison_size=1
+        )  # since we add only one sample per step and river handles building the window internally we set comparison_size to 1
 
     def _reset_internal_parameters(self):
         self.drift_agent = RiverPageHinkley(
@@ -63,7 +65,7 @@ def _reset_internal_parameters(self):
             threshold=self.threshold,
             mode=self.mode,
             *self._args,
-            **self._kwargs
+            **self._kwargs,
         )
 
     def _fit(self, X: np.ndarray):

src/ml3_drift/monitoring/base/base_multivariate.py

@@ -12,7 +12,7 @@ class MultivariateMonitoringAlgorithm(MonitoringAlgorithm, ABC):
     """
 
     def _is_valid(self, X: np.ndarray) -> tuple[bool, str]:
-        if X.shape[1] > 1:
+        if X.shape[1] >= 1:
             return True, ""
         else:
             return False, f"X must be multi-dimensional vector. Got {X.shape}"