Source code for voxatlas.features.syntax.local_structure.pos

import pandas as pd

from voxatlas.features.base_extractor import BaseExtractor
from voxatlas.features.feature_output import ScalarFeatureOutput
from voxatlas.registry.feature_registry import registry




class SyntaxLocalStructurePOSExtractor(BaseExtractor):
    r"""
    Extract the ``syntax.local_structure.pos`` feature within the VoxAtlas pipeline.
    
    This public extractor defines the reusable API for computing ``syntax.local_structure.pos`` from VoxAtlas structured inputs. It consumes ``token`` units and produces values aligned to ``token`` units, making the extractor a stable pipeline node that can be cited independently of the surrounding execution machinery.
    
    Algorithm
    ---------
    The extractor works directly from the dependency-annotated token table produced earlier in the pipeline.
    
    1. Parsed token retrieval
       Token rows, head identifiers, and dependency metadata are loaded from the upstream syntax table.
    
    2. Local-structure computation
       The feature projects part-of-speech labels from the dependency or token annotation table.
    
    3. Packaging
       Values are returned at token resolution so they can be aggregated into sentence-level complexity measures later in the pipeline.
    
    Notes
    -----
    This extractor declares the upstream dependencies ['syntax.dependencies'] and is executed only after those features are available in the pipeline feature store.
    
    Examples
    --------
    >>> import pandas as pd
    >>> from voxatlas.features.feature_input import FeatureInput
    >>> from voxatlas.features.feature_output import TableFeatureOutput
    >>> from voxatlas.features.syntax.local_structure.pos import SyntaxLocalStructurePOSExtractor
    >>> from voxatlas.pipeline.feature_store import FeatureStore
    >>> deps = pd.DataFrame({"token_id": [1], "pos": ["NOUN"]})
    >>> store = FeatureStore()
    >>> store.add("syntax.dependencies", TableFeatureOutput(feature="syntax.dependencies", unit="token", values=deps))
    >>> out = SyntaxLocalStructurePOSExtractor().compute(FeatureInput(audio=None, units=None, context={"feature_store": store}), {})
    >>> out.values.loc[1]
    'NOUN'
    """
    name = "syntax.local_structure.pos"
    input_units = "token"
    output_units = "token"
    dependencies = ["syntax.dependencies"]
    default_config = {}



    def compute(self, feature_input, params):
        """
        Compute the extractor output for a single pipeline invocation.
        
        This method is the reusable execution entry point for the extractor. It receives the standard ``FeatureInput`` bundle, applies the configured algorithm, and returns feature values aligned to the extractor output units for storage in the pipeline feature store.
        
        Parameters
        ----------
        feature_input : object
            Structured extractor input bundling audio, hierarchical units, and execution context for this feature computation.
        params : object
            Resolved feature configuration for this invocation. Keys are feature-specific and merged from defaults and pipeline settings.
        
        Returns
        -------
        FeatureOutput
            Structured output aligned to the ``token`` unit level when applicable.
        
        Examples
        --------
        >>> import pandas as pd
        >>> from voxatlas.features.feature_input import FeatureInput
        >>> from voxatlas.features.feature_output import TableFeatureOutput
        >>> from voxatlas.features.syntax.local_structure.pos import SyntaxLocalStructurePOSExtractor
        >>> from voxatlas.pipeline.feature_store import FeatureStore
        >>> deps = pd.DataFrame({"token_id": [1], "pos": ["NOUN"]})
        >>> store = FeatureStore()
        >>> store.add("syntax.dependencies", TableFeatureOutput(feature="syntax.dependencies", unit="token", values=deps))
        >>> result = SyntaxLocalStructurePOSExtractor().compute(FeatureInput(audio=None, units=None, context={"feature_store": store}), {})
        >>> result.unit
        'token'
        """
        table = feature_input.context["feature_store"].get("syntax.dependencies").values
        values = pd.Series(table["pos"].values, index=table["token_id"], dtype="object")

        return ScalarFeatureOutput(feature=self.name, unit="token", values=values)




registry.register(SyntaxLocalStructurePOSExtractor)