ZipfFrequencyExtractor#

Defined in: voxatlas.features.lexical.frequency.zipf_frequency

class voxatlas.features.lexical.frequency.zipf_frequency.ZipfFrequencyExtractor[source]#

Bases: BaseExtractor

Extract the lexical.frequency.zipf feature within the VoxAtlas pipeline.

This public extractor defines the reusable API for computing lexical.frequency.zipf 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 converts raw lexical frequencies to the Zipf scale commonly used in psycholinguistic reporting.

  1. Dependency retrieval The upstream lookup stage provides raw frequency values \(f_i\) for each token.

  2. Logarithmic transformation VoxAtlas computes

    \[z_i = \log_{10}(f_i) + 3,\]

    which places frequent words on a compact approximately human-interpretable scale.

  3. Packaging Non-finite inputs remain missing, and the transformed values are returned on the token index.

Notes

This extractor declares the upstream dependencies [‘lexical.frequency.lookup’] 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.lexical.frequency.zipf_frequency import ZipfFrequencyExtractor
>>> from voxatlas.pipeline.feature_store import FeatureStore
>>> table = pd.DataFrame({"id": [1], "frequency": [1000.0]})
>>> store = FeatureStore()
>>> store.add("lexical.frequency.lookup", TableFeatureOutput(feature="lexical.frequency.lookup", unit="token", values=table))
>>> out = ZipfFrequencyExtractor().compute(FeatureInput(audio=None, units=None, context={"feature_store": store}), {})
>>> float(out.values.loc[1])
6.0
name: str = 'lexical.frequency.zipf'#
input_units: str | None = 'token'#
output_units: str | None = 'token'#
dependencies: list[str] = ['lexical.frequency.lookup']#
default_config: dict = {}#
compute(feature_input, params)[source]#

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:

Structured output aligned to the token unit level when applicable.

Return type:

FeatureOutput

Examples

>>> import pandas as pd
>>> from voxatlas.features.feature_input import FeatureInput
>>> from voxatlas.features.feature_output import TableFeatureOutput
>>> from voxatlas.features.lexical.frequency.zipf_frequency import ZipfFrequencyExtractor
>>> from voxatlas.pipeline.feature_store import FeatureStore
>>> table = pd.DataFrame({"id": [1], "frequency": [1000.0]})
>>> store = FeatureStore()
>>> store.add("lexical.frequency.lookup", TableFeatureOutput(feature="lexical.frequency.lookup", unit="token", values=table))
>>> result = ZipfFrequencyExtractor().compute(FeatureInput(audio=None, units=None, context={"feature_store": store}), {})
>>> result.unit
'token'