Add detections validator

MANIFEST.in

@@ -12,5 +12,5 @@ recursive-exclude examples *
 recursive-include docs *
 
 # Include json schemas
-recursive-include ethology/io/annotations/json_schemas/schemas *.json
-recursive-include ethology/io/annotations/json_schemas/schemas *.md
+recursive-include ethology/validators/json_schemas/schemas *.json
+recursive-include ethology/validators/json_schemas/schemas *.md

docs/source/_templates/autosummary/class.rst

@@ -3,11 +3,9 @@
 .. currentmodule:: {{ module }}
 
 .. autoclass:: {{ objname }}
-   :members:
-   :show-inheritance:
-   :inherited-members:
-   :exclude-members: Config
-
+   {% if objname != 'ValidDataset' %}:members:{% endif %}
+   {% if objname != 'ValidDataset' %}:inherited-members:{% endif %}
+   {% if objname == 'ValidBboxAnnotationsDataFrame' %}:exclude-members: Config{% endif %}
 
    {% block methods %}
    {% set ns = namespace(has_public_methods=false) %}

docs/source/conf.py

@@ -70,7 +70,7 @@
 # Automatically generate stub pages for API
 autosummary_generate = True
 autosummary_generate_overwrite = False
-autodoc_default_flags = ["members", "inherited-members"]
+autodoc_default_options = {"show-inheritance": True}  # applies to all classes
 
 # Prefix section labels with the document name
 autosectionlabel_prefix_document = True
@@ -182,6 +182,10 @@
     "pandera": ("https://pandera.readthedocs.io/en/stable/", None),
     "movement": ("https://movement.neuroinformatics.dev/latest/", None),
     "sklearn": ("https://scikit-learn.org/stable/", None),
+    "jsonschema": (
+        "https://python-jsonschema.readthedocs.io/en/stable/",
+        None,
+    ),
 }
 
 

ethology/io/annotations/load_bboxes.py

@@ -10,16 +10,16 @@
 import xarray as xr
 from pandera.typing.pandas import DataFrame
 
-from ethology.io.annotations.validate import (
-    ValidBboxesDataFrame,
-    ValidBboxesDataset,
+from ethology.validators.annotations import (
+    ValidBboxAnnotationsDataFrame,
+    ValidBboxAnnotationsDataset,
     ValidCOCO,
     ValidVIA,
-    _check_output,
 )
+from ethology.validators.utils import _check_output
 
 
-@_check_output(ValidBboxesDataset)
+@_check_output(ValidBboxAnnotationsDataset)
 def from_files(
     file_paths: Path | str | list[Path | str],
     format: Literal["VIA", "COCO"],
@@ -138,7 +138,7 @@ def from_files(
 
 
 def _get_map_attributes_from_df(
-    df: DataFrame[ValidBboxesDataFrame],
+    df: DataFrame[ValidBboxAnnotationsDataFrame],
 ) -> tuple[dict, dict]:
     """Get the map attributes from the dataframe.
 
@@ -179,7 +179,7 @@ def _get_map_attributes_from_df(
 @pa.check_types
 def _df_from_multiple_files(
     list_filepaths: list[Path | str], format: Literal["VIA", "COCO"]
-) -> DataFrame[ValidBboxesDataFrame]:
+) -> DataFrame[ValidBboxAnnotationsDataFrame]:
     """Read annotations from multiple files as a valid intermediate dataframe.
 
     Parameters
@@ -242,7 +242,7 @@ def _df_from_multiple_files(
 @pa.check_types
 def _df_from_single_file(
     file_path: Path | str, format: Literal["VIA", "COCO"]
-) -> DataFrame[ValidBboxesDataFrame]:
+) -> DataFrame[ValidBboxAnnotationsDataFrame]:
     """Read annotations from a single file as a valid intermediate dataframe.
 
     Parameters
@@ -374,7 +374,7 @@ def _df_rows_from_valid_VIA_file(file_path: Path) -> list[dict]:
 
             else:
                 supercategory, category, category_id = (
-                    ValidBboxesDataFrame.get_empty_values()[key]
+                    ValidBboxAnnotationsDataFrame.get_empty_values()[key]
                     for key in ["supercategory", "category", "category_id"]
                 )
 
@@ -428,7 +428,7 @@ def _get_image_shape_attr_as_integer(
         ValidBboxesDataFrame.get_empty_values().
 
     """
-    default_value = ValidBboxesDataFrame.get_empty_values()[
+    default_value = ValidBboxAnnotationsDataFrame.get_empty_values()[
         f"image_{attr_name}"
     ]
     try:
@@ -557,7 +557,9 @@ def _df_rows_from_valid_COCO_file(file_path: Path) -> list[dict]:
 
 
 @pa.check_types
-def _df_to_xarray_ds(df: DataFrame[ValidBboxesDataFrame]) -> xr.Dataset:
+def _df_to_xarray_ds(
+    df: DataFrame[ValidBboxAnnotationsDataFrame],
+) -> xr.Dataset:
     """Convert a bounding box annotations dataframe to an xarray dataset.
 
     Parameters
@@ -585,7 +587,7 @@ def _df_to_xarray_ds(df: DataFrame[ValidBboxesDataFrame]) -> xr.Dataset:
 
     """
     # Drop columns if all values in that column are empty
-    default_values = ValidBboxesDataFrame.get_empty_values()
+    default_values = ValidBboxAnnotationsDataFrame.get_empty_values()
     list_empty_cols = [
         col for col in default_values if all(df[col] == default_values[col])
     ]

ethology/io/annotations/save_bboxes.py

@@ -11,17 +11,16 @@
 import xarray as xr
 from pandera.typing.pandas import DataFrame
 
-from ethology.io.annotations.validate import (
-    ValidBboxesDataFrameCOCO,
-    ValidBboxesDataset,
+from ethology.validators.annotations import (
+    ValidBboxAnnotationsCOCO,
+    ValidBboxAnnotationsDataset,
     ValidCOCO,
-    _check_input,
-    _check_output,
 )
+from ethology.validators.utils import _check_input, _check_output
 
 
-@_check_input(validator=ValidBboxesDataset)
-@_check_output(validator=ValidCOCO)  # check output is ethology importable
+@_check_input(validator=ValidBboxAnnotationsDataset)
+@_check_output(validator=ValidCOCO)  # check output is ethology-importable
 def to_COCO_file(dataset: xr.Dataset, output_filepath: str | Path):
     """Save an ``ethology`` bounding box annotations dataset to a COCO file.
 
@@ -56,11 +55,11 @@ def to_COCO_file(dataset: xr.Dataset, output_filepath: str | Path):
     return output_filepath
 
 
-@_check_input(validator=ValidBboxesDataset)
+@_check_input(validator=ValidBboxAnnotationsDataset)
 @pa.check_types
 def _to_COCO_exportable_df(
     ds: xr.Dataset,
-) -> DataFrame[ValidBboxesDataFrameCOCO]:
+) -> DataFrame[ValidBboxAnnotationsCOCO]:
     """Convert dataset of bounding boxes annotations to a COCO-exportable df.
 
     The returned dataframe is validated using ValidBBoxesDataFrameCOCO.
@@ -98,7 +97,7 @@ def _to_COCO_exportable_df(
     return df[cols_to_select]
 
 
-@_check_input(validator=ValidBboxesDataset)
+@_check_input(validator=ValidBboxAnnotationsDataset)
 def _get_raw_df_from_ds(ds: xr.Dataset) -> pd.DataFrame:
     """Get preliminary dataframe from a dataset of bounding boxes annotations.
 
@@ -164,7 +163,7 @@ def _get_raw_df_from_ds(ds: xr.Dataset) -> pd.DataFrame:
 @pa.check_types
 def _add_COCO_data_to_df(
     df: pd.DataFrame, ds_attrs: dict
-) -> DataFrame[ValidBboxesDataFrameCOCO]:
+) -> DataFrame[ValidBboxAnnotationsCOCO]:
     """Add COCO-required data to preliminary dataframe.
 
     The input dataframe is obtained from a dataset of bounding boxes
@@ -266,7 +265,9 @@ def _add_COCO_data_to_df(
 
 
 @pa.check_types
-def _create_COCO_dict(df: DataFrame[ValidBboxesDataFrameCOCO]) -> dict:
+def _create_COCO_dict(
+    df: DataFrame[ValidBboxAnnotationsCOCO],
+) -> dict:
     """Extract COCO dictionary from a COCO-exportable dataframe.
 
     Parameters
@@ -282,7 +283,7 @@ def _create_COCO_dict(df: DataFrame[ValidBboxesDataFrameCOCO]) -> dict:
     """
     COCO_dict: dict[str, Any] = {}
     map_columns_to_COCO_fields = (
-        ValidBboxesDataFrameCOCO.map_df_columns_to_COCO_fields()
+        ValidBboxAnnotationsCOCO.map_df_columns_to_COCO_fields()
     )
     for sections in ["images", "categories", "annotations"]:
         # Extract and rename required columns for this section

ethology/io/annotations/validate.py → ethology/validators/annotations.py

@@ -1,22 +1,20 @@
 """Validators for annotation files and datasets."""
 
 import json
-from collections.abc import Callable
-from functools import wraps
 from pathlib import Path
 
 import pandas as pd
 import pandera.pandas as pa
-import xarray as xr
 from attrs import define, field
 from pandera.typing import Index
 
-from ethology.io.annotations.json_schemas.utils import (
+from ethology.validators.json_schemas.utils import (
     _check_file_is_json,
     _check_file_matches_schema,
     _check_required_keys_in_dict,
     _get_default_schema,
 )
+from ethology.validators.utils import ValidDataset
 
 
 @define
@@ -227,25 +225,39 @@ def _file_contains_unique_image_IDs(self, attribute, value):
 
 
 @define
-class ValidBboxesDataset:
+class ValidBboxAnnotationsDataset(ValidDataset):
     """Class for valid ``ethology`` bounding box annotations datasets.
 
-    It checks that the input dataset has:
+    This class validates that the input dataset:
+
+    - is an xarray Dataset,
+    - has ``image_id``, ``space``, ``id`` as dimensions,
+    - has ``position`` and ``shape`` as data variables,
+    - both data variables span at least the dimensions ``image_id``,
+      ``space`` and ``id``.
 
-    - ``image_id``, ``space``, ``id`` as dimensions
-    - ``position`` and ``shape`` as data variables
 
     Attributes
     ----------
     dataset : xarray.Dataset
         The xarray dataset to validate.
+    required_dims : set[str]
+        The set of required dimension names: ``image_id``, ``space`` and
+        ``id``.
+    required_data_vars : dict[str, set]
+        A dictionary mapping data variable names to their required minimum
+        dimensions:
+
+        - ``position`` maps to ``image_id``, ``space`` and ``id``,
+        - ``shape`` maps to ``image_id``, ``space`` and ``id``.
 
     Raises
     ------
     TypeError
         If the input is not an xarray Dataset.
     ValueError
-        If the dataset is missing required data variables or dimensions.
+        If the dataset is missing required data variables or dimensions,
+        or if any required dimensions are missing for any data variable.
 
     Notes
     -----
@@ -254,46 +266,21 @@ class ValidBboxesDataset:
 
     """
 
-    dataset: xr.Dataset = field()
-
-    # Minimum requirements for annotations datasets holding bboxes
+    # Minimum requirements for a bbox dataset holding detections
     required_dims: set = field(
         default={"image_id", "space", "id"},
         init=False,
     )
-    required_data_vars: set = field(
-        default={"position", "shape"},
+    required_data_vars: dict = field(
+        default={
+            "position": {"image_id", "space", "id"},
+            "shape": {"image_id", "space", "id"},
+        },
         init=False,
     )
 
-    @dataset.validator
-    def _check_dataset_type(self, attribute, value):
-        """Ensure the input is an xarray Dataset."""
-        if not isinstance(value, xr.Dataset):
-            raise TypeError(
-                f"Expected an xarray Dataset, but got {type(value)}."
-            )
-
-    @dataset.validator
-    def _check_required_data_variables(self, attribute, value):
-        """Ensure the dataset has all required data variables."""
-        missing_vars = self.required_data_vars - set(value.data_vars)
-        if missing_vars:
-            raise ValueError(
-                f"Missing required data variables: {sorted(missing_vars)}"
-            )
-
-    @dataset.validator
-    def _check_required_dimensions(self, attribute, value):
-        """Ensure the dataset has all required dimensions."""
-        missing_dims = self.required_dims - set(value.dims)
-        if missing_dims:
-            raise ValueError(
-                f"Missing required dimensions: {sorted(missing_dims)}"
-            )
-
 
-class ValidBboxesDataFrame(pa.DataFrameModel):
+class ValidBboxAnnotationsDataFrame(pa.DataFrameModel):
     """Class for valid bounding boxes intermediate dataframes.
 
     We use this dataframe internally as an intermediate step in the process of
@@ -422,7 +409,7 @@ def get_empty_values() -> dict:
         }
 
 
-class ValidBboxesDataFrameCOCO(pa.DataFrameModel):
+class ValidBboxAnnotationsCOCO(pa.DataFrameModel):
     """Class for COCO-exportable bounding box annotations dataframes.
 
     The validation checks the required columns exist and their types are
@@ -573,38 +560,3 @@ def check_idx_and_annotation_id(cls, df: pd.DataFrame) -> bool:
 
         """
         return all(df.index == df["annotation_id"])
-
-
-def _check_output(validator: type):
-    """Return a decorator that validates the output of a function."""
-
-    def decorator(function: Callable) -> Callable:
-        @wraps(function)  # to preserve function metadata
-        def wrapper(*args, **kwargs):
-            result = function(*args, **kwargs)
-            validator(result)
-            return result
-
-        return wrapper
-
-    return decorator
-
-
-def _check_input(validator: type, input_index: int = 0):
-    """Return a decorator that validates a specific input of a function.
-
-    By default, the first input is validated. If the input index is
-    larger than the number of inputs, no validation is performed.
-    """
-
-    def decorator(function: Callable) -> Callable:
-        @wraps(function)
-        def wrapper(*args, **kwargs):
-            if len(args) > input_index:
-                validator(args[input_index])
-            result = function(*args, **kwargs)
-            return result
-
-        return wrapper
-
-    return decorator