Added univariate streaming algorithms

examples/huggingface/text_embedding_monitoring.py

@@ -3,8 +3,10 @@
 from ml3_drift.huggingface.drift_detection_pipeline import (
     HuggingFaceDriftDetectionPipeline,
 )
-from ml3_drift.monitoring.multivariate.bonferroni import BonferroniCorrectionAlgorithm
-from ml3_drift.monitoring.univariate.continuous.ks import KSAlgorithm
+from ml3_drift.monitoring.algorithms.batch.bonferroni import (
+    BonferroniCorrectionAlgorithm,
+)
+from ml3_drift.monitoring.algorithms.batch.ks import KSAlgorithm
 from ml3_drift.callbacks.base import logger_callback
 
 

pyproject.toml

@@ -22,12 +22,14 @@ dependencies = [
 
 sklearn = ["scikit-learn>=1.6.1"]
 
-huggingface = ["scipy>=1.15.2", "transformers[torch]>=4.52.3"]
+huggingface = ["transformers[torch]>=4.52.3"]
 
 polars = ["polars>=1.31.0"]
 
 pandas = ["pandas>=2.2.3"]
 
+river = ["river>=0.22.0"]
+
 
 # -------------------------------------------------
 

src/ml3_drift/analysis/analyzer/base.py

@@ -4,12 +4,14 @@
 from typing_extensions import TypeIs
 
 from ml3_drift.analysis.report import Report
-from ml3_drift.monitoring.base import MonitoringAlgorithm
-from ml3_drift.monitoring.multivariate.bonferroni import BonferroniCorrectionAlgorithm
-from ml3_drift.monitoring.univariate.continuous.ks import KSAlgorithm
-from ml3_drift.monitoring.univariate.discrete.chi_square import (
+from ml3_drift.monitoring.algorithms.batch.bonferroni import (
+    BonferroniCorrectionAlgorithm,
+)
+from ml3_drift.monitoring.algorithms.batch.ks import KSAlgorithm
+from ml3_drift.monitoring.algorithms.batch.chi_square import (
     ChiSquareAlgorithm,
 )
+from ml3_drift.monitoring.base.base import MonitoringAlgorithm
 
 if TYPE_CHECKING:
     import pandas as pd
@@ -158,6 +160,10 @@ def analyze(
         else:
             categorical_columns_ids = []
 
+        if not continuous_columns_ids and not categorical_columns_ids:
+            raise ValueError(
+                "At least one of continuous_columns or categorical_columns must be provided."
+            )
         # Input and target in canonical form
         array_X = self._to_numpy(X)
 

src/ml3_drift/analysis/analyzer/batch.py

@@ -4,7 +4,7 @@
 
 from ml3_drift.analysis.analyzer.base import DataDriftAnalyzer
 from ml3_drift.analysis.report import Report
-from ml3_drift.monitoring.base import MonitoringAlgorithm
+from ml3_drift.monitoring.base.base import MonitoringAlgorithm
 
 from ml3_drift.models.monitoring import (
     MonitoringOutput,
@@ -74,7 +74,7 @@ def _single_scan_data(
         y_categorical: bool,
         first_batch_indexes: tuple[int, int],
         second_batch_indexes: tuple[int, int],
-    ) -> tuple[MonitoringOutput, MonitoringOutput]:
+    ) -> tuple[MonitoringOutput | None, MonitoringOutput | None]:
         """
         Inner helper method that performs a single scan of two batches
         """
@@ -95,16 +95,20 @@ def _single_scan_data(
             y_categorical,
             second_batch_indexes,
         )
-
-        cont_algorithm = deepcopy(self.continuous_monitoring_algorithm).fit(
-            first_batch_cont
-        )
-        cat_algorithm = deepcopy(self.categorical_monitoring_algorithm).fit(
-            first_batch_cat
-        )
-
-        cont_output = cont_algorithm.detect(second_batch_cont)[0]
-        cat_output = cat_algorithm.detect(second_batch_cat)[0]
+        if len(continuous_columns_ids) > 0:
+            cont_algorithm = deepcopy(self.continuous_monitoring_algorithm).fit(
+                first_batch_cont
+            )
+            cont_output = cont_algorithm.detect(second_batch_cont)[0]
+        else:
+            cont_output = None
+        if len(categorical_columns_ids) > 0:
+            cat_algorithm = deepcopy(self.categorical_monitoring_algorithm).fit(
+                first_batch_cat
+            )
+            cat_output = cat_algorithm.detect(second_batch_cat)[0]
+        else:
+            cat_output = None
 
         return cont_output, cat_output
 
@@ -149,7 +153,9 @@ def _scan_data(
                 next_batch_indexes,
             )
 
-            if cont_output.drift_detected | cat_output.drift_detected:
+            if (cont_output is not None and cont_output.drift_detected) | (
+                cat_output is not None and cat_output.drift_detected
+            ):
                 # if a drift is detected then, we close the current batch and open a new one
                 merged_batches.append(
                     (current_batch_start, current_batch_indexes[1] - 1)
@@ -181,7 +187,10 @@ def _scan_data(
 
             # if no drift is detected the two batches are considered to belong to the same distribution
             # and are added to the same distribution list
-            if not (cont_output.drift_detected | cat_output.drift_detected):
+            if not (
+                (cont_output is not None and cont_output.drift_detected)
+                | (cat_output is not None and cat_output.drift_detected)
+            ):
                 same_distributions[pair[0]].append(pair[1])
 
         return Report(concepts=merged_batches, same_distributions=same_distributions)

src/ml3_drift/analysis/analyzer/stream.py

@@ -2,7 +2,7 @@
 
 from ml3_drift.analysis.analyzer.base import DataDriftAnalyzer
 from ml3_drift.analysis.report import Report
-from ml3_drift.monitoring.base import MonitoringAlgorithm
+from ml3_drift.monitoring.base.base import MonitoringAlgorithm
 
 
 class StreamDataDriftAnalyzer(DataDriftAnalyzer):

src/ml3_drift/huggingface/drift_detection_pipeline.py

@@ -2,7 +2,7 @@
 import torch
 from transformers import Pipeline, pipeline
 
-from ml3_drift.monitoring.base import MonitoringAlgorithm
+from ml3_drift.monitoring.base.base import MonitoringAlgorithm
 
 
 class HuggingFaceDriftDetectionPipeline:

src/ml3_drift/monitoring/multivariate/bonferroni.py → src/ml3_drift/monitoring/algorithms/batch/bonferroni.py

@@ -8,13 +8,18 @@
     MonitoringAlgorithmSpecs,
     MonitoringOutput,
 )
-from ml3_drift.monitoring.base import MonitoringAlgorithm
-from ml3_drift.monitoring.univariate.base import UnivariateMonitoringAlgorithm
+from ml3_drift.monitoring.base.base_multivariate import MultivariateMonitoringAlgorithm
+from ml3_drift.monitoring.base.base_univariate import UnivariateMonitoringAlgorithm
+from ml3_drift.monitoring.base.batch_monitoring_algorithm import (
+    BatchMonitoringAlgorithm,
+)
 
 T = TypeVar("T", bound=UnivariateMonitoringAlgorithm)
 
 
-class BonferroniCorrectionAlgorithm(MonitoringAlgorithm):
+class BonferroniCorrectionAlgorithm(
+    BatchMonitoringAlgorithm, MultivariateMonitoringAlgorithm
+):
     """
     Extension of p-value based univariate algorithms with Bonferroni correction
     to handle multivariate data

src/ml3_drift/monitoring/univariate/discrete/chi_square.py → src/ml3_drift/monitoring/algorithms/batch/chi_square.py

@@ -8,10 +8,13 @@
     MonitoringAlgorithmSpecs,
     MonitoringOutput,
 )
-from ml3_drift.monitoring.univariate.base import UnivariateMonitoringAlgorithm
+from ml3_drift.monitoring.base.base_univariate import UnivariateMonitoringAlgorithm
+from ml3_drift.monitoring.base.batch_monitoring_algorithm import (
+    BatchMonitoringAlgorithm,
+)
 
 
-class ChiSquareAlgorithm(UnivariateMonitoringAlgorithm):
+class ChiSquareAlgorithm(BatchMonitoringAlgorithm, UnivariateMonitoringAlgorithm):
     """Monitoring algorithm based on the Chi Square statistic test.
 
     Parameters

src/ml3_drift/monitoring/univariate/continuous/ks.py → src/ml3_drift/monitoring/algorithms/batch/ks.py

@@ -8,10 +8,13 @@
     MonitoringAlgorithmSpecs,
     MonitoringOutput,
 )
-from ml3_drift.monitoring.univariate.base import UnivariateMonitoringAlgorithm
+from ml3_drift.monitoring.base.base_univariate import UnivariateMonitoringAlgorithm
+from ml3_drift.monitoring.base.batch_monitoring_algorithm import (
+    BatchMonitoringAlgorithm,
+)
 
 
-class KSAlgorithm(UnivariateMonitoringAlgorithm):
+class KSAlgorithm(BatchMonitoringAlgorithm, UnivariateMonitoringAlgorithm):
     """Monitoring algorithm based on the Kolmogorov-Smirnov statistic test.
 
     Parameters

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

@@ -0,0 +1,77 @@
+from typing import Callable
+
+import numpy as np
+from ml3_drift.enums.monitoring import DataDimension, DataType, MonitoringType
+from ml3_drift.models.monitoring import (
+    DriftInfo,
+    MonitoringAlgorithmSpecs,
+    MonitoringOutput,
+)
+from ml3_drift.monitoring.base.base_univariate import UnivariateMonitoringAlgorithm
+from ml3_drift.monitoring.base.online_monitorning_algorithm import (
+    OnlineMonitorningAlgorithm,
+)
+
+RIVER = True
+try:
+    from river.drift.adwin import ADWIN as RiverADWIN
+except ModuleNotFoundError:
+    RIVER = False
+
+
+class ADWIN(OnlineMonitorningAlgorithm, UnivariateMonitoringAlgorithm):
+    @classmethod
+    def specs(cls) -> MonitoringAlgorithmSpecs:
+        return MonitoringAlgorithmSpecs(
+            data_dimension=DataDimension.MULTIVARIATE,
+            data_type=DataType.MIX,
+            monitoring_type=MonitoringType.ONLINE,
+        )
+
+    def __init__(
+        self,
+        callbacks: list[Callable[[DriftInfo | None], None]] | None = None,
+        p_value: float = 0.002,
+        clock: float = 32,
+        max_buckets: int = 5,
+        min_window_length: int = 5,
+        grace_period: int = 10,
+        *args,
+        **kwargs,
+    ) -> None:
+        if not RIVER:
+            raise ModuleNotFoundError(
+                "River library is required for ADWIN algorithm. Please install it using pip install/ uv add ml3-drift[river]"
+            )
+        self.p_value = p_value
+        self.clock = clock
+        self.max_buckets = max_buckets
+        self.min_window_length = min_window_length
+        self.grace_period = grace_period
+        self._args = args
+        self._kwargs = kwargs
+        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(
+            delta=self.p_value,
+            clock=self.clock,
+            max_buckets=self.max_buckets,
+            min_window_length=self.min_window_length,
+            grace_period=self.grace_period,
+            *self._args,
+            **self._kwargs,
+        )
+
+    def _fit(self, X: np.ndarray):
+        """Fit the KSWIN algorithm to the data."""
+        self._validate(X)
+        self.reset_internal_parameters()
+        self.is_fitted = True
+
+    def _detect(self):
+        self.drift_agent.update(self.comparison_data)
+        drift_detected = self.drift_agent.drift_detected
+        return MonitoringOutput(drift_detected=drift_detected, drift_info=None)

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

@@ -0,0 +1,74 @@
+from typing import Callable
+
+import numpy as np
+from ml3_drift.enums.monitoring import DataDimension, DataType, MonitoringType
+from ml3_drift.models.monitoring import (
+    DriftInfo,
+    MonitoringAlgorithmSpecs,
+    MonitoringOutput,
+)
+from ml3_drift.monitoring.base.base_univariate import UnivariateMonitoringAlgorithm
+from ml3_drift.monitoring.base.online_monitorning_algorithm import (
+    OnlineMonitorningAlgorithm,
+)
+
+RIVER = True
+try:
+    from river.drift.kswin import KSWIN as RiverKSWIN
+except ModuleNotFoundError:
+    RIVER = False
+
+
+class KSWIN(OnlineMonitorningAlgorithm, UnivariateMonitoringAlgorithm):
+    @classmethod
+    def specs(cls) -> MonitoringAlgorithmSpecs:
+        return MonitoringAlgorithmSpecs(
+            data_dimension=DataDimension.MULTIVARIATE,
+            data_type=DataType.MIX,
+            monitoring_type=MonitoringType.ONLINE,
+        )
+
+    def __init__(
+        self,
+        callbacks: list[Callable[[DriftInfo | None], None]] | None = None,
+        p_value: float = 0.00,
+        window_size: int = 100,
+        stat_size: int = 30,
+        seed: int | None = None,
+        *args,
+        **kwargs,
+    ) -> None:
+        if not RIVER:
+            raise ModuleNotFoundError(
+                "River library is required for KSWIN algorithm. Please install it using pip install/ uv add ml3-drift[river]"
+            )
+        self.p_value = p_value
+        self.window_size = window_size
+        self.stat_size = stat_size
+        self.seed = seed
+        self._args = args
+        self._kwargs = kwargs
+        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(
+            alpha=self.p_value,
+            window_size=self.window_size,
+            stat_size=self.stat_size,
+            seed=self.seed,
+            *self._args,
+            **self._kwargs,
+        )
+
+    def _fit(self, X: np.ndarray):
+        """Fit the KSWIN algorithm to the data."""
+        self._validate(X)
+        self.reset_internal_parameters()
+        self.is_fitted = True
+
+    def _detect(self):
+        self.drift_agent.update(self.comparison_data)
+        drift_detected = self.drift_agent.drift_detected
+        return MonitoringOutput(drift_detected=drift_detected, drift_info=None)