docs/changes/newsfragments/472.enh

@@ -0,0 +1 @@
+Simplify ``Storage`` interface and implementations by `Synchon Mandal`_

docs/understanding/storage.rst

@@ -17,11 +17,11 @@ Storage is meant to be used inside the DataGrabber context but you can operate
 on them outside the context as long as the processed data is in the memory and
 the Python runtime has not garbage-collected it.
 
-The :ref:`Markers <marker>` are responsible for defining what *storage kind*
-(``matrix``, ``vector``, ``timeseries``, ``scalar_table``) they support for
-which :ref:`data type <data_types>` by overriding its ``get_output_type``
-method. The storage object in turn declares and provides implementation for
-specific *storage kind*. For example, :class:`.SQLiteFeatureStorage` supports
+The :ref:`Markers <marker>` are responsible for mapping the input
+:ref:`data type <data_types>` to its output :ref:`storage type <storage_types>`
+as shown :ref:`here <extending_markers_input_output>`.
+The storage object in turn declares and provides implementation for
+specific *storage type*. For example, :class:`.SQLiteFeatureStorage` supports
 saving ``matrix``, ``vector`` and ``timeseries`` via ``store_matrix``,
 ``store_vector`` and ``store_timeseries`` methods respectively.
 
@@ -57,6 +57,10 @@ Storage Types
      - A 2D square or non-square matrix of scalar values with column names
      - ``col_names``
      - :meth:`.BaseFeatureStorage.store_timeseries`
+   * - ``timeseries_2d``
+     - A 3D(2D+1D) square or non-square matrix of scalar values with column names across sessions
+     - ``col_names``
+     - :meth:`.BaseFeatureStorage.store_timeseries_2d`
    * - ``scalar_table``
      - | A 2D square or non-square matrix of scalar values with row name, column
        | name and row header column name
@@ -83,4 +87,4 @@ Storage Interfaces
    * - :class:`.HDF5FeatureStorage`
      - ``.hdf5``
      - HDF5
-     - ``matrix``, ``vector``, ``timeseries``, ``scalar_table``
+     - ``matrix``, ``vector``, ``timeseries``, ``timeseries_2d``, ``scalar_table``

junifer/storage/base.py

@@ -5,14 +5,14 @@
 # License: AGPL
 
 from abc import ABC, abstractmethod
-from collections.abc import Iterable
+from collections.abc import Sequence
 from pathlib import Path
-from typing import Any, Optional, Union
+from typing import Any, ClassVar, Optional, Union
 
 import numpy as np
 import pandas as pd
 
-from ..utils import raise_error
+from ..utils import logger, raise_error
 from .utils import process_meta
 
 
@@ -22,44 +22,47 @@ __all__ = ["BaseFeatureStorage"]
 class BaseFeatureStorage(ABC):
     """Abstract base class for feature storage.
 
-    For every interface that is required, one needs to provide a concrete
+    For every storage, one needs to provide a concrete
     implementation of this abstract class.
 
     Parameters
     ----------
     uri : str or pathlib.Path
         The path to the storage.
-    storage_types : str or list of str
-        The available storage types for the class.
     single_output : bool, optional
         Whether to have single output (default True).
 
     Raises
     ------
-    ValueError
-        If required storage type(s) is(are) missing from ``storage_types``.
+    AttributeError
+        If the storage does not have `_STORAGE_TYPES` attribute.
 
     """
 
+    _STORAGE_TYPES: ClassVar[Sequence[str]]
+
     def __init__(
         self,
         uri: Union[str, Path],
-        storage_types: Union[list[str], str],
         single_output: bool = True,
     ) -> None:
-        self.uri = uri
-        # Convert storage_types to list
-        if not isinstance(storage_types, list):
-            storage_types = [storage_types]
-        # Check if required inputs are found
-        if any(x not in self.get_valid_inputs() for x in storage_types):
-            wrong_storage_types = [
-                x for x in storage_types if x not in self.get_valid_inputs()
-            ]
+        # Check for missing storage types attribute
+        if not hasattr(self, "_STORAGE_TYPES"):
             raise_error(
-                f"{self.__class__.__name__} cannot store {wrong_storage_types}"
+                msg="Missing `_STORAGE_TYPES` for the storage",
+                klass=AttributeError,
             )
-        self._valid_inputs = storage_types
+        # Convert str to Path
+        if not isinstance(uri, Path):
+            uri = Path(uri)
+        self.uri = uri
+        # Create parent directories if not present
+        if not self.uri.parent.exists():
+            logger.info(
+                f"Output directory: '{self.uri.parent.resolve()}' "
+                "does not exist, creating now"
+            )
+            self.uri.parent.mkdir(parents=True, exist_ok=True)
         self.single_output = single_output
 
     def get_valid_inputs(self) -> list[str]:
@@ -69,13 +72,10 @@ class BaseFeatureStorage(ABC):
         -------
         list of str
             The list of storage types that can be used as input for this
-            storage interface.
+            storage.
 
         """
-        raise_error(
-            msg="Concrete classes need to implement get_valid_inputs().",
-            klass=NotImplementedError,
-        )
+        return list(self._STORAGE_TYPES)
 
     def validate(self, input_: list[str]) -> None:
         """Validate the input to the pipeline step.
@@ -91,11 +91,12 @@ class BaseFeatureStorage(ABC):
             If the ``input_`` is invalid.
 
         """
-        if not any(x in input_ for x in self._valid_inputs):
+        # self._STORAGE_TYPES should be there already
+        if not any(x in input_ for x in self._STORAGE_TYPES):
             raise_error(
                 "Input does not have the required data."
                 f"\t Input: {input}"
-                f"\t Required (any of): {self._valid_inputs}"
+                f"\t Required (any of): {self._STORAGE_TYPES}"
             )
 
     @abstractmethod
@@ -113,7 +114,7 @@ class BaseFeatureStorage(ABC):
         raise_error(
             msg="Concrete classes need to implement list_features().",
             klass=NotImplementedError,
-        )
+        )  # pragma: no cover
 
     @abstractmethod
     def read(
@@ -141,7 +142,7 @@ class BaseFeatureStorage(ABC):
         raise_error(
             msg="Concrete classes need to implement read().",
             klass=NotImplementedError,
-        )
+        )  # pragma: no cover
 
     @abstractmethod
     def read_df(
@@ -167,7 +168,7 @@ class BaseFeatureStorage(ABC):
         raise_error(
             msg="Concrete classes need to implement read_df().",
             klass=NotImplementedError,
-        )
+        )  # pragma: no cover
 
     @abstractmethod
     def store_metadata(self, meta_md5: str, element: dict, meta: dict) -> None:
@@ -186,7 +187,7 @@ class BaseFeatureStorage(ABC):
         raise_error(
             msg="Concrete classes need to implement store_metadata().",
             klass=NotImplementedError,
-        )
+        )  # pragma: no cover
 
     def store(self, kind: str, **kwargs) -> None:
         """Store extracted features data.
@@ -206,14 +207,16 @@ class BaseFeatureStorage(ABC):
         """
         # Do the check before calling the abstract methods, otherwise the
         # meta might be stored even if the data is not stored.
-        if kind not in self._valid_inputs:
+        if kind not in self._STORAGE_TYPES:
             raise_error(
                 msg=f"I don't know how to store {kind}.",
                 klass=ValueError,
             )
+        # Process and store metadata
         t_meta = kwargs.pop("meta")
         meta_md5, t_meta, t_element = process_meta(t_meta)
         self.store_metadata(meta_md5=meta_md5, element=t_element, meta=t_meta)
+        # Store data
         if kind == "matrix":
             self.store_matrix(meta_md5=meta_md5, element=t_element, **kwargs)
         elif kind == "timeseries":
@@ -236,8 +239,8 @@ class BaseFeatureStorage(ABC):
         meta_md5: str,
         element: dict,
         data: np.ndarray,
-        col_names: Optional[Iterable[str]] = None,
-        row_names: Optional[Iterable[str]] = None,
+        col_names: Optional[Sequence[str]] = None,
+        row_names: Optional[Sequence[str]] = None,
         matrix_kind: str = "full",
         diagonal: bool = True,
     ) -> None:
@@ -251,9 +254,9 @@ class BaseFeatureStorage(ABC):
             The element as a dictionary.
         data : numpy.ndarray
             The matrix data to store.
-        col_names : list or tuple of str, optional
+        col_names : list-like of str, optional
             The column labels (default None).
-        row_names : str, optional
+        row_names : list-like of str, optional
             The row labels (default None).
         matrix_kind : str, optional
             The kind of matrix:
@@ -278,7 +281,7 @@ class BaseFeatureStorage(ABC):
         meta_md5: str,
         element: dict,
         data: Union[np.ndarray, list],
-        col_names: Optional[Iterable[str]] = None,
+        col_names: Optional[Sequence[str]] = None,
     ) -> None:
         """Store vector.
 
@@ -290,7 +293,7 @@ class BaseFeatureStorage(ABC):
             The element as a dictionary.
         data : numpy.ndarray or list
             The vector data to store.
-        col_names : list or tuple of str, optional
+        col_names : list-like of str, optional
             The column labels (default None).
 
         """
@@ -304,7 +307,7 @@ class BaseFeatureStorage(ABC):
         meta_md5: str,
         element: dict,
         data: np.ndarray,
-        col_names: Optional[Iterable[str]] = None,
+        col_names: Optional[Sequence[str]] = None,
     ) -> None:
         """Store timeseries.
 
@@ -316,7 +319,7 @@ class BaseFeatureStorage(ABC):
             The element as a dictionary.
         data : numpy.ndarray
             The timeseries data to store.
-        col_names : list or tuple of str, optional
+        col_names : list-like of str, optional
             The column labels (default None).
 
         """
@@ -330,8 +333,8 @@ class BaseFeatureStorage(ABC):
         meta_md5: str,
         element: dict,
         data: np.ndarray,
-        col_names: Optional[Iterable[str]] = None,
-        row_names: Optional[Iterable[str]] = None,
+        col_names: Optional[Sequence[str]] = None,
+        row_names: Optional[Sequence[str]] = None,
     ) -> None:
         """Store 2D timeseries.
 
@@ -343,9 +346,9 @@ class BaseFeatureStorage(ABC):
             The element as a dictionary.
         data : numpy.ndarray
             The timeseries data to store.
-        col_names : list or tuple of str, optional
+        col_names : list-like of str, optional
             The column labels (default None).
-        row_names : list or tuple of str, optional
+        row_names : list-like of str, optional
             The row labels (default None).
 
         """
@@ -359,8 +362,8 @@ class BaseFeatureStorage(ABC):
         meta_md5: str,
         element: dict,
         data: np.ndarray,
-        col_names: Optional[Iterable[str]] = None,
-        row_names: Optional[Iterable[str]] = None,
+        col_names: Optional[Sequence[str]] = None,
+        row_names: Optional[Sequence[str]] = None,
         row_header_col_name: Optional[str] = "feature",
     ) -> None:
         """Store table with scalar values.
@@ -373,9 +376,9 @@ class BaseFeatureStorage(ABC):
             The element as a dictionary.
         data : numpy.ndarray
             The timeseries data to store.
-        col_names : list or tuple of str, optional
+        col_names : list-like of str, optional
             The column labels (default None).
-        row_names : str, optional
+        row_names : list-like of str, optional
             The row labels (default None).
         row_header_col_name : str, optional
             The column name for the row header column (default "feature").
@@ -392,7 +395,7 @@ class BaseFeatureStorage(ABC):
         raise_error(
             msg="Concrete classes need to implement collect().",
             klass=NotImplementedError,
-        )
+        )  # pragma: no cover
 
     def __str__(self) -> str:
         """Represent object as string.

junifer/storage/hdf5.py

@@ -5,9 +5,9 @@
 # License: AGPL
 
 from collections import defaultdict
-from collections.abc import Iterable
+from collections.abc import Sequence
 from pathlib import Path
-from typing import Any, Optional, Union
+from typing import Any, ClassVar, Optional, Union
 
 import numpy as np
 import pandas as pd
@@ -135,6 +135,14 @@ class HDF5FeatureStorage(BaseFeatureStorage):
 
     """
 
+    _STORAGE_TYPES: ClassVar[Sequence[str]] = [
+        "vector",
+        "timeseries",
+        "matrix",
+        "scalar_table",
+        "timeseries_2d",
+    ]
+
     def __init__(
         self,
         uri: Union[str, Path],
@@ -144,60 +152,19 @@ class HDF5FeatureStorage(BaseFeatureStorage):
         force_float32: bool = True,
         chunk_size: int = 100,
     ) -> None:
-        # Convert str to Path
-        if not isinstance(uri, Path):
-            uri = Path(uri)
-
-        # Create parent directories if not present
-        if not uri.parent.exists():
-            logger.info(
-                f"Output directory: '{uri.parent.resolve()}' "
-                "does not exist, creating now"
-            )
-            uri.parent.mkdir(parents=True, exist_ok=True)
-
-        # Available storage kinds
-        storage_types = [
-            "vector",
-            "timeseries",
-            "matrix",
-            "scalar_table",
-            "timeseries_2d",
-        ]
-
-        super().__init__(
-            uri=uri,
-            storage_types=storage_types,
-            single_output=single_output,
-        )
-
         self.overwrite = overwrite
         self.compression = compression
         self.force_float32 = force_float32
         self.chunk_size = chunk_size
-
-    def get_valid_inputs(self) -> list[str]:
-        """Get valid storage types for input.
-
-        Returns
-        -------
-        list of str
-            The list of storage types that can be used as input for this
-            storage.
-
-        """
-        return [
-            "matrix",
-            "vector",
-            "timeseries",
-            "scalar_table",
-            "timeseries_2d",
-        ]
+        super().__init__(
+            uri=uri,
+            single_output=single_output,
+        )
 
     def _fetch_correct_uri_for_io(self, element: Optional[dict]) -> str:
-        """Return proper URI for I/O based on `element`.
+        """Return proper URI for I/O based on ``element``.
 
-        If `element` is None, will return `self.uri`.
+        If ``element`` is None, will return ``self.uri``.
 
         Parameters
         ----------
@@ -209,22 +176,25 @@ class HDF5FeatureStorage(BaseFeatureStorage):
         str
             Formatted URI for accessing metadata and data.
 
+        Raises
+        ------
+        RuntimeError
+            If ``element=None`` when ``single_output=False``.
+
         """
-        if not self.single_output and not element:
+        if not self.single_output and element is None:
             raise_error(
-                msg=(
-                    "`element` must be provided when `single_output` is False"
-                ),
+                msg="`element` must be provided when `single_output=False`",
                 klass=RuntimeError,
             )
-        elif not self.single_output and element:
+        elif not self.single_output and element is not None:
             # element access for multi output only
             prefix = element_to_prefix(element=element)
         else:
             # parent access for single output, ignore element
             prefix = ""
         # Format URI based on prefix
-        return f"{self.uri.parent}/{prefix}{self.uri.name}"  # type: ignore
+        return f"{self.uri.parent}/{prefix}{self.uri.name}"
 
     def _read_metadata(
         self, element: Optional[dict[str, str]] = None
@@ -291,7 +261,7 @@ class HDF5FeatureStorage(BaseFeatureStorage):
         """
         # Read metadata
         metadata = read_hdf5(
-            fname=str(self.uri.resolve()),  # type: ignore
+            fname=str(self.uri.resolve()),
             title="meta",
             slash="ignore",
         )
@@ -374,8 +344,14 @@ class HDF5FeatureStorage(BaseFeatureStorage):
 
         Raises
         ------
+        ValueError
+            If both ``feature_md5`` and ``feature_name`` are provided or
+            if none of ``feature_md5`` or ``feature_name`` is provided.
         IOError
             If HDF5 file does not exist.
+        RuntimeError
+            If feature is not found or
+            if duplicate feature is found with the same name.
 
         """
         # Parameter conflict
@@ -396,7 +372,7 @@ class HDF5FeatureStorage(BaseFeatureStorage):
             )
         # Parameter check pass; read metadata
         metadata = read_hdf5(
-            fname=str(self.uri.resolve()),  # type: ignore
+            fname=str(self.uri.resolve()),
             title="meta",
             slash="ignore",
         )
@@ -411,15 +387,18 @@ class HDF5FeatureStorage(BaseFeatureStorage):
             )
             # Validate MD5
             if feature_md5 in metadata:
-                md5 = feature_md5  # type: ignore
+                md5 = feature_md5
             else:
-                raise_error(msg=f"Feature MD5 '{feature_md5}' not found")
+                raise_error(
+                    msg=f"Feature MD5 '{feature_md5}' not found",
+                    klass=RuntimeError,
+                )
 
         # Consider feature_name
         elif feature_name:
             logger.debug(
                 f"Validating feature name '{feature_name}' in metadata "
-                f"for: {self.uri.resolve()} ..."  # type: ignore
+                f"for: {self.uri.resolve()} ..."
             )
             # Retrieve MD5 for feature_name
             # Implicit counter for duplicate feature_name with different
@@ -445,7 +424,7 @@ class HDF5FeatureStorage(BaseFeatureStorage):
 
         # Read data from HDF5
         hdf_data = read_hdf5(
-            fname=str(self.uri.resolve()),  # type: ignore
+            fname=str(self.uri.resolve()),
             title=md5,
             slash="ignore",
         )
@@ -573,7 +552,7 @@ class HDF5FeatureStorage(BaseFeatureStorage):
             reshaped_data = np.concatenate(all_data, axis=0)
 
         # Create dataframe for index
-        idx_df = pd.DataFrame(data=element_idx)  # type: ignore
+        idx_df = pd.DataFrame(data=element_idx)
         # Create multiindex from dataframe
         hdf_data_idx = pd.MultiIndex.from_frame(df=idx_df)
         logger.debug(
@@ -589,7 +568,7 @@ class HDF5FeatureStorage(BaseFeatureStorage):
         df = pd.DataFrame(
             data=reshaped_data,
             index=hdf_data_idx,
-            columns=columns,  # type: ignore
+            columns=columns,
             dtype=reshaped_data.dtype,
         )
         logger.debug(
@@ -621,7 +600,7 @@ class HDF5FeatureStorage(BaseFeatureStorage):
         write_hdf5(
             fname=fname,
             data=processed_data,
-            overwrite=self.overwrite,  # type: ignore
+            overwrite=self.overwrite,
             compression=self.compression,
             title=title,
             slash="error",
@@ -837,8 +816,8 @@ class HDF5FeatureStorage(BaseFeatureStorage):
         meta_md5: str,
         element: dict[str, str],
         data: np.ndarray,
-        col_names: Optional[Iterable[str]] = None,
-        row_names: Optional[Iterable[str]] = None,
+        col_names: Optional[Sequence[str]] = None,
+        row_names: Optional[Sequence[str]] = None,
         matrix_kind: str = "full",
         diagonal: bool = True,
         row_header_col_name: str = "ROI",
@@ -856,9 +835,9 @@ class HDF5FeatureStorage(BaseFeatureStorage):
             The element as dictionary.
         data : numpy.ndarray
             The matrix data to store.
-        col_names : list or tuple of str, optional
+        col_names : list-like of str, optional
             The column labels (default None).
-        row_names : list or tuple of str, optional
+        row_names : list-like of str, optional
             The row labels (default None).
         matrix_kind : str, optional
             The kind of matrix:
@@ -895,8 +874,8 @@ class HDF5FeatureStorage(BaseFeatureStorage):
             matrix_kind=matrix_kind,
             diagonal=diagonal,
             data_shape=data.shape,
-            row_names_len=len(row_names),  # type: ignore
-            col_names_len=len(col_names),  # type: ignore
+            row_names_len=len(row_names),
+            col_names_len=len(col_names),
         )
         # Store
         self._store_data(
@@ -914,9 +893,9 @@ class HDF5FeatureStorage(BaseFeatureStorage):
     def store_vector(
         self,
         meta_md5: str,
-        element: dict[str, str],
+        element: dict,
         data: Union[np.ndarray, list],
-        col_names: Optional[Iterable[str]] = None,
+        col_names: Optional[Sequence[str]] = None,
     ) -> None:
         """Store vector.
 
@@ -928,7 +907,7 @@ class HDF5FeatureStorage(BaseFeatureStorage):
             The element as dictionary.
         data : numpy.ndarray or list
             The vector data to store.
-        col_names : list or tuple of str, optional
+        col_names : list-like of str, optional
             The column labels (default None).
 
         """
@@ -957,9 +936,9 @@ class HDF5FeatureStorage(BaseFeatureStorage):
     def store_timeseries(
         self,
         meta_md5: str,
-        element: dict[str, str],
+        element: dict,
         data: np.ndarray,
-        col_names: Optional[Iterable[str]] = None,
+        col_names: Optional[Sequence[str]] = None,
     ) -> None:
         """Store timeseries.
 
@@ -971,7 +950,7 @@ class HDF5FeatureStorage(BaseFeatureStorage):
             The element as dictionary.
         data : numpy.ndarray
             The timeseries data to store.
-        col_names : list or tuple of str, optional
+        col_names : list-like of str, optional
             The column labels (default None).
 
         """
@@ -987,31 +966,31 @@ class HDF5FeatureStorage(BaseFeatureStorage):
     def store_timeseries_2d(
         self,
         meta_md5: str,
-        element: dict[str, str],
+        element: dict,
         data: np.ndarray,
-        col_names: Optional[Iterable[str]] = None,
-        row_names: Optional[Iterable[str]] = None,
+        col_names: Optional[Sequence[str]] = None,
+        row_names: Optional[Sequence[str]] = None,
     ) -> None:
-        """Store a 2D timeseries.
+        """Store 2D timeseries.
 
         Parameters
         ----------
         meta_md5 : str
             The metadata MD5 hash.
         element : dict
-            The element as dictionary.
+            The element as a dictionary.
         data : numpy.ndarray
             The 2D timeseries data to store.
-        col_names : list or tuple of str, optional
+        col_names : list-like of str, optional
             The column labels (default None).
-        row_names : list or tuple of str, optional
+        row_names : list-like of str, optional
             The row labels (default None).
 
         """
         store_timeseries_2d_checks(
             data_shape=data.shape,
-            row_names_len=len(row_names),  # type: ignore
-            col_names_len=len(col_names),  # type: ignore
+            row_names_len=len(row_names) if row_names is not None else 0,
+            col_names_len=len(col_names) if col_names is not None else 0,
         )
         self._store_data(
             kind="timeseries_2d",
@@ -1027,8 +1006,8 @@ class HDF5FeatureStorage(BaseFeatureStorage):
         meta_md5: str,
         element: dict,
         data: np.ndarray,
-        col_names: Optional[Iterable[str]] = None,
-        row_names: Optional[Iterable[str]] = None,
+        col_names: Optional[Sequence[str]] = None,
+        row_names: Optional[Sequence[str]] = None,
         row_header_col_name: Optional[str] = "feature",
     ) -> None:
         """Store table with scalar values.
@@ -1041,9 +1020,9 @@ class HDF5FeatureStorage(BaseFeatureStorage):
             The element as a dictionary.
         data : numpy.ndarray
             The scalar table data to store.
-        col_names : list or tuple of str, optional
+        col_names : list-like of str, optional
             The column labels (default None).
-        row_names : str, optional
+        row_names : list-like of str, optional
             The row labels (default None).
         row_header_col_name : str, optional
             The column name for the row header column (default "feature").
@@ -1079,22 +1058,19 @@ class HDF5FeatureStorage(BaseFeatureStorage):
                 klass=NotImplementedError,
             )
 
-        # Glob files
-        globbed_files = list(
-            self.uri.parent.glob(f"*_{self.uri.name}")  # type: ignore
-        )
-
         # Create new storage instance
         out_storage = HDF5FeatureStorage(uri=self.uri, overwrite="update")
 
         # Run loop to collect metadata
         logger.info(
-            f"Collecting metadata from {self.uri.parent}/*_{self.uri.name}"  # type: ignore
+            f"Collecting metadata from {self.uri.parent}/*_{self.uri.name}"
         )
         # Collect element files per feature MD5
         elements_per_feature_md5 = defaultdict(list)
         out_metadata = {}
-        for file_ in tqdm(globbed_files, desc="file-metadata"):
+        for file_ in tqdm(
+            self.uri.parent.glob(f"*_{self.uri.name}"), desc="file-metadata"
+        ):
             logger.debug(f"Reading HDF5 file: {file_} ...")
             # Create new storage instance to load metadata
             in_storage = HDF5FeatureStorage(uri=file_)
@@ -1113,7 +1089,7 @@ class HDF5FeatureStorage(BaseFeatureStorage):
         logger.info("Writing metadata to HDF5 file ...")
         # Save metadata out metadata
         out_storage._write_processed_data(
-            fname=str(self.uri.resolve()),  # type: ignore
+            fname=str(self.uri.resolve()),
             processed_data=out_metadata,
             title="meta",
         )
@@ -1194,9 +1170,9 @@ class HDF5FeatureStorage(BaseFeatureStorage):
 
                     # Write to HDF5
                     write_hdf5(
-                        fname=str(self.uri.resolve()),  # type: ignore
+                        fname=str(self.uri.resolve()),
                         data=to_write,
-                        overwrite="update",  # type: ignore
+                        overwrite="update",
                         compression=0,
                         title=feature_md5,
                         slash="error",

junifer/storage/pandas_base.py

@@ -5,9 +5,9 @@
 # License: AGPL
 
 import json
-from collections.abc import Iterable
+from collections.abc import Sequence
 from pathlib import Path
-from typing import Optional, Union
+from typing import ClassVar, Optional, Union
 
 import numpy as np
 import pandas as pd
@@ -31,8 +31,6 @@ class PandasBaseFeatureStorage(BaseFeatureStorage):
         The path to the storage.
     single_output : bool, optional
         Whether to have single output (default True).
-    **kwargs
-        Keyword arguments passed to superclass.
 
     See Also
     --------
@@ -40,22 +38,18 @@ class PandasBaseFeatureStorage(BaseFeatureStorage):
 
     """
 
+    _STORAGE_TYPES: ClassVar[Sequence[str]] = [
+        "vector",
+        "timeseries",
+        "matrix",
+    ]
+
     def __init__(
-        self, uri: Union[str, Path], single_output: bool = True, **kwargs
+        self,
+        uri: Union[str, Path],
+        single_output: bool = True,
     ) -> None:
-        super().__init__(uri=uri, single_output=single_output, **kwargs)
-
-    def get_valid_inputs(self) -> list[str]:
-        """Get valid storage types for input.
-
-        Returns
-        -------
-        list of str
-            The list of storage types that can be used as input for this
-            storage interface.
-
-        """
-        return ["matrix", "vector", "timeseries"]
+        super().__init__(uri=uri, single_output=single_output)
 
     def _meta_row(self, meta: dict, meta_md5: str) -> pd.DataFrame:
         """Convert the metadata to a pandas DataFrame.
@@ -102,7 +96,7 @@ class PandasBaseFeatureStorage(BaseFeatureStorage):
 
         """
         # Make mapping between element access keys and values
-        elem_idx: dict[str, Iterable[str]] = {
+        elem_idx: dict[str, Sequence[str]] = {
             k: [v] * n_rows for k, v in element.items()
         }
 
@@ -160,7 +154,7 @@ class PandasBaseFeatureStorage(BaseFeatureStorage):
         meta_md5: str,
         element: dict,
         data: Union[np.ndarray, list],
-        col_names: Optional[Iterable[str]] = None,
+        col_names: Optional[Sequence[str]] = None,
         rows_col_name: Optional[str] = None,
     ) -> None:
         """Store 2D data.
@@ -173,7 +167,7 @@ class PandasBaseFeatureStorage(BaseFeatureStorage):
             The element as a dictionary.
         data : numpy.ndarray or list
             The data to store.
-        col_names : list or tuple of str, optional
+        col_names : list-like of str, optional
             The column labels (default None).
         rows_col_name : str, optional
             The column name to use in case number of rows greater than 1.
@@ -199,7 +193,7 @@ class PandasBaseFeatureStorage(BaseFeatureStorage):
         meta_md5: str,
         element: dict,
         data: Union[np.ndarray, list],
-        col_names: Optional[Iterable[str]] = None,
+        col_names: Optional[Sequence[str]] = None,
     ) -> None:
         """Store vector.
 
@@ -211,7 +205,7 @@ class PandasBaseFeatureStorage(BaseFeatureStorage):
             The element as a dictionary.
         data : numpy.ndarray or list
             The vector data to store.
-        col_names : list or tuple of str, optional
+        col_names : list-like of str, optional
             The column labels (default None).
 
         """
@@ -237,7 +231,7 @@ class PandasBaseFeatureStorage(BaseFeatureStorage):
         meta_md5: str,
         element: dict,
         data: np.ndarray,
-        col_names: Optional[Iterable[str]] = None,
+        col_names: Optional[Sequence[str]] = None,
     ) -> None:
         """Store timeseries.
 
@@ -249,7 +243,7 @@ class PandasBaseFeatureStorage(BaseFeatureStorage):
             The element as a dictionary.
         data : numpy.ndarray
             The timeseries data to store.
-        col_names : list or tuple of str, optional
+        col_names : list-like of str, optional
             The column labels (default None).
 
         """

junifer/storage/sqlite.py

@@ -5,13 +5,14 @@
 # License: AGPL
 
 import json
+from collections.abc import Sequence
 from pathlib import Path
 from typing import TYPE_CHECKING, Any, Optional, Union
 
 import numpy as np
 import pandas as pd
 from pandas.core.base import NoNewAttributesMixin
-from pandas.io.sql import pandasSQL_builder  # type: ignore
+from pandas.io.sql import pandasSQL_builder
 from sqlalchemy import create_engine, inspect
 from tqdm import tqdm
 
@@ -46,8 +47,6 @@ class SQLiteFeatureStorage(PandasBaseFeatureStorage):
     upsert : {"ignore", "update"}, optional
         Upsert mode. If "ignore" is used, the existing elements are ignored.
         If "update", the existing elements are updated (default "update").
-    **kwargs : dict
-            The keyword arguments passed to the superclass.
 
     See Also
     --------
@@ -61,9 +60,8 @@ class SQLiteFeatureStorage(PandasBaseFeatureStorage):
         uri: Union[str, Path],
         single_output: bool = True,
         upsert: str = "update",
-        **kwargs: str,
     ) -> None:
-        # Check upsert argument value
+        # Check and set upsert argument value
         if upsert not in ["update", "ignore"]:
             raise_error(
                 msg=(
@@ -71,26 +69,11 @@ class SQLiteFeatureStorage(PandasBaseFeatureStorage):
                     "Must be either 'update' or 'ignore'."
                 )
             )
-        # Convert str to Path
-        if not isinstance(uri, Path):
-            uri = Path(uri)
-        # Create parent directories if not present
-        if not uri.parent.exists():
-            logger.info(
-                f"Output directory ({uri.parent.absolute()!s}) "
-                "does not exist, creating now."
-            )
-            uri.parent.mkdir(parents=True, exist_ok=True)
-        # Available storage kinds
-        storage_types = ["vector", "timeseries", "matrix"]
+        self.upsert = upsert
         super().__init__(
             uri=uri,
-            storage_types=storage_types,
             single_output=single_output,
-            **kwargs,
         )
-        # Set upsert
-        self._upsert = upsert
 
     def get_engine(self, element: Optional[dict] = None) -> "Engine":
         """Get engine.
@@ -105,19 +88,23 @@ class SQLiteFeatureStorage(PandasBaseFeatureStorage):
         sqlalchemy.engine.Engine
             The sqlalchemy engine.
 
+        Raises
+        ------
+        ValueError
+            If ``element=None`` when ``single_output=False``.
+
         """
         # Prefixed elements
         prefix = ""
         if self.single_output is False:
             if element is None:
-                msg = "element must be specified when single_output is False."
-                raise_error(msg)
-            prefix = element_to_prefix(element)
+                raise_error(
+                    "`element` cannot be None when `single_output=False`"
+                )
+            else:
+                prefix = element_to_prefix(element)
         # Format URI for engine creation
-        uri = (
-            f"sqlite:///{self.uri.parent}/"  # type: ignore
-            f"{prefix}{self.uri.name}"  # type: ignore
-        )
+        uri = f"sqlite:///{self.uri.parent}/{prefix}{self.uri.name}"
         return create_engine(uri, echo=False)
 
     def _save_upsert(
@@ -147,8 +134,8 @@ class SQLiteFeatureStorage(PandasBaseFeatureStorage):
         Raises
         ------
         ValueError
-            If the table exists and if_exists is "fail" or if invalid option is
-            passed to `if_exists`.
+            If the table exists and ``if_exists="fail"`` or
+            if invalid option is passed to ``if_exists``.
 
         """
         # Get index names
@@ -187,12 +174,12 @@ class SQLiteFeatureStorage(PandasBaseFeatureStorage):
                         warn_with_log(
                             f"Some rows (n={len(existing)}) are already "
                             "present in the database. The storage is "
-                            f"configured to {self._upsert} the existing "
+                            f"configured to {self.upsert} the existing "
                             f"elements. The new rows (n={len(new)}) will be "
                             "appended. This warning is shown because normally "
                             "all of the elements should be updated."
                         )
-                    if self._upsert == "update":
+                    if self.upsert == "update":
                         update_stmts = _generate_update_statements(
                             table, index_col, existing
                         )
@@ -257,10 +244,14 @@ class SQLiteFeatureStorage(PandasBaseFeatureStorage):
         dict
             The stored feature as a dictionary.
 
+        Raises
+        ------
+        NotImplementedError
+
         """
         raise_error(
             msg=(
-                "read() for SQLiteFeatureStorage is not currently planned to "
+                "SQLiteFeatureStorage.read() is not currently planned to "
                 "be implemented in the near future. If you need the "
                 "functionality, contact the junifer developers "
                 "(https://juaml.github.io/junifer/main/help.html)."
@@ -434,8 +425,8 @@ class SQLiteFeatureStorage(PandasBaseFeatureStorage):
         meta_md5: str,
         element: dict,
         data: np.ndarray,
-        col_names: Optional[list[str]] = None,
-        row_names: Optional[list[str]] = None,
+        col_names: Optional[Sequence[str]] = None,
+        row_names: Optional[Sequence[str]] = None,
         matrix_kind: str = "full",
         diagonal: bool = True,
     ) -> None:
@@ -451,9 +442,9 @@ class SQLiteFeatureStorage(PandasBaseFeatureStorage):
             The matrix data to store.
         meta : dict
             The metadata as a dictionary.
-        col_names : list or tuple of str, optional
+        col_names : list-like of str, optional
             The column labels (default None).
-        row_names : str, optional
+        row_names : list-like of str, optional
             The row labels (optional None).
         matrix_kind : str, optional
             The kind of matrix:
@@ -479,8 +470,8 @@ class SQLiteFeatureStorage(PandasBaseFeatureStorage):
             matrix_kind=matrix_kind,
             diagonal=diagonal,
             data_shape=data.shape,
-            row_names_len=len(row_names),  # type: ignore
-            col_names_len=len(col_names),  # type: ignore
+            row_names_len=len(row_names),
+            col_names_len=len(col_names),
         )
         # Matrix to vector conversion
         flat_data, columns = matrix_to_vector(
@@ -533,13 +524,11 @@ class SQLiteFeatureStorage(PandasBaseFeatureStorage):
                 msg="collect() is not implemented for single output.",
                 klass=IOError,
             )
-        logger.info(
-            f"Collecting data from {self.uri.parent}/*{self.uri.name}"  # type: ignore
-        )
+        logger.info(f"Collecting data from {self.uri.parent}/*{self.uri.name}")
         # Create new instance
         out_storage = SQLiteFeatureStorage(uri=self.uri, upsert="ignore")
         # Glob files
-        files = self.uri.parent.glob(f"*{self.uri.name}")  # type: ignore
+        files = self.uri.parent.glob(f"*{self.uri.name}")
         for elem in tqdm(files, desc="file"):
             logger.debug(f"Reading from {elem.absolute()!s}")
             in_storage = SQLiteFeatureStorage(uri=elem)

junifer/storage/tests/test_hdf5.py

@@ -24,13 +24,13 @@ from junifer.storage.utils import (
 def test_get_valid_inputs() -> None:
     """Test valid inputs."""
     storage = HDF5FeatureStorage(uri="/tmp")
-    assert storage.get_valid_inputs() == [
+    assert set(storage.get_valid_inputs()) == {
         "matrix",
         "vector",
         "timeseries",
         "scalar_table",
         "timeseries_2d",
-    ]
+    }
 
 
 def test_single_output(tmp_path: Path) -> None:
@@ -285,7 +285,7 @@ def test_read_df_params_error(tmp_path: Path) -> None:
     with pytest.raises(ValueError, match="At least one of"):
         storage.read_df()
 
-    with pytest.raises(ValueError, match="Feature MD5"):
+    with pytest.raises(RuntimeError, match="Feature MD5"):
         storage.read_df(feature_md5="md5")
 
 

junifer/storage/tests/test_sqlite.py

@@ -111,7 +111,7 @@ def test_get_engine_multi_output(tmp_path: Path) -> None:
     storage = SQLiteFeatureStorage(
         uri=uri, single_output=False, upsert="ignore"
     )
-    with pytest.raises(ValueError, match="element must be specified"):
+    with pytest.raises(ValueError, match="`element` cannot be None"):
         storage.get_engine()
 
 

junifer/storage/tests/test_storage_base.py

@@ -4,6 +4,9 @@
 #          Synchon Mandal <s.mandal@fz-juelich.de>
 # License: AGPL
 
+from collections.abc import Sequence
+from typing import ClassVar
+
 import pytest
 
 from junifer.storage.base import BaseFeatureStorage
@@ -12,10 +15,7 @@ from junifer.storage.base import BaseFeatureStorage
 def test_BaseFeatureStorage_abstractness() -> None:
     """Test BaseFeatureStorage is abstract base class."""
     with pytest.raises(TypeError, match=r"abstract"):
-        BaseFeatureStorage(
-            uri="/tmp",
-            storage_types=["matrix"],  # type: ignore
-        )
+        BaseFeatureStorage(uri="/tmp")
 
 
 def test_BaseFeatureStorage() -> None:
@@ -25,17 +25,19 @@ def test_BaseFeatureStorage() -> None:
     class MyFeatureStorage(BaseFeatureStorage):
         """Implement concrete class."""
 
+        _STORAGE_TYPES: ClassVar[Sequence[str]] = [
+            "matrix",
+            "vector",
+            "timeseries",
+            "timeseries_2d",
+        ]
+
         def __init__(self, uri, single_output=True):
-            storage_types = ["matrix", "vector", "timeseries", "timeseries_2d"]
             super().__init__(
                 uri=uri,
-                storage_types=storage_types,
                 single_output=single_output,
             )
 
-        def get_valid_inputs(self):
-            return ["matrix", "vector", "timeseries", "timeseries_2d"]
-
         def list_features(self):
             super().list_features()
 
@@ -107,4 +109,4 @@ def test_BaseFeatureStorage() -> None:
     with pytest.raises(ValueError):
         st.store(kind="lego", meta=meta)
 
-    assert st.uri == "/tmp"
+    assert str(st.uri) == "/tmp"

junifer/storage/utils.py

@@ -6,7 +6,7 @@
 
 import hashlib
 import json
-from collections.abc import Iterable
+from collections.abc import Sequence
 from importlib.metadata import PackageNotFoundError, version
 
 import numpy as np
@@ -36,6 +36,11 @@ def get_dependency_version(dependency: str) -> str:
     str
         The version of the dependency.
 
+    Raises
+    ------
+    PackageNotFoundError
+        If ``dependency`` is not found.
+
     """
     dep_version = ""
     try:
@@ -141,6 +146,11 @@ def element_to_prefix(element: dict) -> str:
     str
         The element converted to prefix.
 
+    Raises
+    ------
+    ValueError
+        If ``element`` is not a dict.
+
     """
     logger.debug(f"Converting element {element} to prefix.")
     prefix = "element"
@@ -208,13 +218,13 @@ def store_matrix_checks(
                 klass=ValueError,
             )
     # Row label validation
-    if row_names_len != data_shape[0]:  # type: ignore
+    if row_names_len != data_shape[0]:
         raise_error(
             msg="Number of row names does not match number of rows",
             klass=ValueError,
         )
     # Column label validation
-    if col_names_len != data_shape[1]:  # type: ignore
+    if col_names_len != data_shape[1]:
         raise_error(
             msg="Number of column names does not match number of columns",
             klass=ValueError,
@@ -245,7 +255,7 @@ def store_timeseries_2d_checks(
         If the number of column names does not match the number of columns
 
     """
-    # data validation
+    # Data validation
     if len(data_shape) != 3:
         raise_error(
             msg="Data must be a 3D array",
@@ -253,13 +263,13 @@ def store_timeseries_2d_checks(
         )
 
     # Row label validation
-    if row_names_len != data_shape[1]:  # type: ignore
+    if row_names_len != data_shape[1]:
         raise_error(
             msg="Number of row names does not match number of rows",
             klass=ValueError,
         )
     # Column label validation
-    if col_names_len != data_shape[2]:  # type: ignore
+    if col_names_len != data_shape[2]:
         raise_error(
             msg="Number of column names does not match number of columns",
             klass=ValueError,
@@ -268,8 +278,8 @@ def store_timeseries_2d_checks(
 
 def matrix_to_vector(
     data: np.ndarray,
-    col_names: Iterable[str],
-    row_names: Iterable[str],
+    col_names: Sequence[str],
+    row_names: Sequence[str],
     matrix_kind: str,
     diagonal: bool,
 ) -> tuple[np.ndarray, list[str]]:
@@ -279,9 +289,9 @@ def matrix_to_vector(
     ----------
     data : 2D / 3D numpy.ndarray
         The matrix / tensor data to store / read.
-    col_names : list or tuple of str
+    col_names : list-like of str
         The column labels.
-    row_names : list or tuple of str
+    row_names : list-like of str
         The row labels.
     matrix_kind : str
         The kind of matrix:
@@ -317,7 +327,7 @@ def matrix_to_vector(
     flat_data = data[data_idx]
     # Generate flat 1D row X column names
     columns = [
-        f"{row_names[i]}~{col_names[j]}"  # type: ignore
+        f"{row_names[i]}~{col_names[j]}"
         for i, j in zip(data_idx[0], data_idx[1])
     ]
 
@@ -326,8 +336,8 @@ def matrix_to_vector(
 
 def timeseries2d_to_vector(
     data: np.ndarray,
-    col_names: Iterable[str],
-    row_names: Iterable[str],
+    col_names: Sequence[str],
+    row_names: Sequence[str],
 ) -> tuple[np.ndarray, list[str]]:
     """Convert matrix to vector based on parameters.
 
@@ -335,9 +345,9 @@ def timeseries2d_to_vector(
     ----------
     data : 2D / 3D numpy.ndarray
         The matrix / tensor data to store / read.
-    col_names : list or tuple of str
+    col_names : list-like of str
         The column labels.
-    row_names : list or tuple of str
+    row_names : list-like of str
         The row labels.
 
     Returns