feat: add ColumnsSelectorTransformer

Author: iraedeus

pysatl_cpd/benchmark/core/benchmark_logger.py

@@ -6,7 +6,7 @@
 import logging
 from typing import Any
 
-__author__ = "PySATL contributors"
+__author__ = "Danil Totmyanin"
 __copyright__ = "Copyright (c) 2026 PySATL project"
 __license__ = "SPDX-License-Identifier: MIT"
 

pysatl_cpd/core/algorithm_entry.py

@@ -3,12 +3,13 @@
 Container for benchmark algorithm execution entries.
 """
 
-__author__ = "PySATL contributors"
+__author__ = "Danil Totmyanin"
 __copyright__ = "Copyright (c) 2026 PySATL project"
 __license__ = "SPDX-License-Identifier: MIT"
 
 from collections.abc import Sequence
 from dataclasses import dataclass
+from typing import Any
 
 from pysatl_cpd.core.data_transformers.idata_transformer import IDataTransformer
 from pysatl_cpd.core.online.ionline_algorithm import OnlineAlgorithm, OnlineAlgorithmConfiguration, OnlineAlgorithmState
@@ -36,7 +37,7 @@ class AlgorithmEntry[DataT, ConfigT: OnlineAlgorithmConfiguration, StateT: Onlin
 
     algorithm: OnlineAlgorithm[DataT, ConfigT, StateT]
     thresholds: Sequence[float]
-    transformer: IDataTransformer | None = None
+    transformer: IDataTransformer[Any, Any] | None = None
 
     @property
     def full_name(self) -> str:

pysatl_cpd/core/data_transformers/columns_selector_transformer.py

@@ -0,0 +1,91 @@
+# -*- coding: ascii -*-
+
+"""
+Columns Selector Transformer Implementation.
+
+This module provides a transformer that allows selecting specific columns
+from multivariate time series data.
+"""
+
+__author__ = "Danil Totmyanin"
+__copyright__ = "Copyright (c) 2026 PySATL project"
+__license__ = "SPDX-License-Identifier: MIT"
+
+import numpy as np
+
+from pysatl_cpd.core.data_providers.idata_provider import DataProvider
+from pysatl_cpd.core.data_providers.numpy_data_provider import (
+    NDArrayMultivariateProvider,
+    NDArrayUnivariateProvider,
+)
+from pysatl_cpd.core.data_transformers.idata_transformer import IDataTransformer
+
+
+class ColumnsSelectorTransformer(IDataTransformer[np.ndarray, np.ndarray | float]):
+    """
+    Transformer for selecting specific columns from multivariate data.
+
+    If a single integer index is provided, it transforms multivariate data
+    into univariate data. If a list of indices is provided, it returns
+    multivariate data containing only the specified columns.
+
+    Parameters
+    ----------
+    columns : list[int] or int
+        Indices of columns to select from the input multivariate array.
+    """
+
+    def __init__(self, columns: list[int] | int) -> None:
+        self.cols = columns
+
+    @property
+    def name(self) -> str:
+        """
+        Return a unique name including selected column indices.
+
+        Returns
+        -------
+        str
+            Formatted name like 'Col_0' or 'Cols_0_2_3'.
+        """
+        if isinstance(self.cols, int):
+            return f"Col_{self.cols}"
+        cols_str = "_".join(map(str, self.cols))
+        return f"Cols_{cols_str}"
+
+    def transform(self, provider: DataProvider[np.ndarray]) -> DataProvider[np.ndarray | float]:
+        """
+        Extract selected columns and wrap into a new NumPy data provider.
+
+        Parameters
+        ----------
+        provider : DataProvider[np.ndarray]
+            Multivariate data provider yielding 1-D NumPy arrays.
+
+        Returns
+        -------
+        DataProvider[Any]
+            NDArrayUnivariateProvider if `columns` is int,
+            NDArrayMultivariateProvider if `columns` is list[int].
+
+        Raises
+        ------
+        ValueError
+            If the data provided by the source is not 2-dimensional.
+        """
+        raw_nd_data = np.array(list(provider))
+
+        if raw_nd_data.ndim < 2:
+            raise ValueError(
+                f"ColumnsSelectorTransformer expects 2D data, "
+                f"got {raw_nd_data.ndim}D data from provider '{provider.name}'."
+            )
+
+        cols_data = raw_nd_data[:, self.cols]
+
+        new_provider_name = f"{provider.name}_{self.name}"
+
+        if isinstance(self.cols, int):
+            return NDArrayUnivariateProvider(data=cols_data, name=new_provider_name)
+
+        return NDArrayMultivariateProvider(data=cols_data, name=new_provider_name)

pysatl_cpd/core/data_transformers/idata_transformer.py

@@ -7,17 +7,16 @@
 feeding data into change-point detection algorithms.
 """
 
-__author__ = "PySATL contributors"
+__author__ = "Danil Totmyanin"
 __copyright__ = "Copyright (c) 2026 PySATL project"
 __license__ = "SPDX-License-Identifier: MIT"
 
 from abc import ABC, abstractmethod
-from typing import Any
 
 from pysatl_cpd.core.data_providers.idata_provider import DataProvider
 
 
-class IDataTransformer(ABC):
+class IDataTransformer[DataInT, DataOutT](ABC):
     """
     Abstract base class for data transformers.
 
@@ -27,24 +26,23 @@ class IDataTransformer(ABC):
     """
 
     @abstractmethod
-    def transform(self, provider: DataProvider[Any]) -> DataProvider[Any]:
+    def transform(self, provider: DataProvider[DataInT]) -> DataProvider[DataOutT]:
         """
         Apply transformation to the given data provider.
 
         Parameters
         ----------
-        provider : DataProvider[Any]
+        provider : DataProvider[DataInT]
             The source data provider.
 
         Returns
         -------
-        DataProvider[Any]
+        DataProvider[DataOut]
             A new data provider yielding transformed observations.
         """
         raise NotImplementedError
 
     @property
-    @abstractmethod
     def name(self) -> str:
         """
         Return the human-readable name of the transformer.
@@ -54,7 +52,7 @@ def name(self) -> str:
         str
             Transformer identifier used for logging and caching.
         """
-        raise NotImplementedError
+        return type(self).__name__
 
     def __hash__(self) -> int:
         """