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post_processing_utils

Utilities for post-processing.

Module​

Functions​

create_postprocessor​

def create_postprocessor(    config: dict[str, Any],) ‑> Optional[PostProcessor]:

Create a postprocessor from a configuration dictionary.

Arguments

  • config: Postprocessor configuration with 'type' and other parameters

Returns An instance of the requested postprocessor or None if there is an error when creating it.

create_postprocessors​

def create_postprocessors(    postprocessor_configs: Optional[list[dict[str, Any]]] = None,) ‑> list[PostProcessor]:

Create a list of postprocessors from configurations.

Arguments

  • postprocessor_configs: Configuration for postprocessors, either: - None (returns an empty list) - A list of dicts, each with 'type' and other parameters

Returns List of PostProcessors.

Classes​

ColumnRenamerPostProcessor​

class ColumnRenamerPostProcessor(column_mapping: dict[str, str]):

Renames columns in a DataFrame.

Initialize the column renamer.

Arguments

  • column_mapping: Mapping from old column names to new column names.

Ancestors​

Methods​

process​

def process(self, predictions: Any) ‑> Any:

Process the predictions by renaming columns.

Arguments

  • predictions: The model output to process.

Returns Processed predictions with renamed columns.

CompoundPostProcessor​

class CompoundPostProcessor(    postprocessing_sequence: list[dict[str, Any]], name: Optional[str] = None,):

Applies multiple postprocessors in sequence.

Initialize the compound postprocessor.

Arguments

  • postprocessing_sequence: List of postprocessor configurations to apply in a sequence
  • name: Name as a string for the for this transformation pipeline. Default to None.

Ancestors​

Methods​

process​

def process(self, predictions: Any) ‑> Any:

Apply all postprocessors in sequence.

JSONFieldRestructuringPostProcessor​

class JSONFieldRestructuringPostProcessor(    column_patterns: list[str],    field_mappings: list[dict[str, str]],    keep_original: bool = False,):

Restructures JSON data by moving fields between different levels.

This is done for example to move fields from one json level to another as for example from {"key1": {"key2": "value", "key3": "val"}} to {"key1": {"key2": "value"}, "key3": "val"} and vice-versa.

Initialize the JSON field restructurer.

Arguments

  • column_patterns: List of regex patterns matching columns to process
  • field_mappings: List of mappings, each with: - 'source_path': JSON path to extract (using dot notation, e.g., 'key1.key2' or 'key2') - 'target_path': Path to place the value (dot notation, e.g., ''key1.key2' or 'key2'')
  • keep_original: Whether to keep the original fields

Ancestors​

Methods​

process​

def process(self, predictions: Any) ‑> Any:

Process predictions by restructuring JSON fields.

JSONKeyRenamerPostProcessor​

class JSONKeyRenamerPostProcessor(    column_patterns: list[str],    key_mappings: list[dict[str, str]],    recursive: bool = True,):

Renames keys within JSON data stored in columns.

Initialize the JSON key renamer.

Arguments

  • column_patterns: List of regex patterns matching columns to process
  • key_mappings: List of mappings, each with: - 'source_key': Original key name - 'target_key': New key name
  • recursive: Whether to rename keys recursively throughout nested objects

Ancestors​

Methods​

process​

def process(self, predictions: Any) ‑> Any:

Process predictions by renaming JSON keys.

PostProcessor​

class PostProcessor():

Base class that all postprocessors inherit from.

Subclasses​

Methods​

process​

def process(    self, predictions: Union[PredictReturnType, pd.DataFrame],) ‑> Union[bitfount.types.PredictReturnType, pandas.core.frame.DataFrame, Any]:

Process the model predictions.

PostprocessorType​

class PostprocessorType(    value, names=None, *, module=None, qualname=None, type=None, start=1,):

Types of built-in postprocessors.

Ancestors​

Variables​

  • static COMPOUND
  • static JSON_KEY_RENAME
  • static JSON_RESTRUCTURE
  • static RENAME
  • static STRING_TO_JSON
  • static TRANSFORM

StringToJSONPostProcessor​

class StringToJSONPostProcessor(column_patterns: list[str]):

Converts string columns containing JSON data to actual JSON/dict objects.

Initialize the string to JSON converter.

Arguments

  • column_patterns: List of regex patterns matching column names to process

Ancestors​

Methods​

process​

def process(self, predictions: Any) ‑> Any:

Process predictions by converting string columns to JSON objects.

TransformationApplierPostProcessor​

class TransformationApplierPostProcessor(transformations: list[Transformation]):

Applies transformations from the transformations module.

Initialize the transformation applier.

Arguments

  • transformations: List of transformations to apply.

Ancestors​

Methods​

process​

def process(self, predictions: Any) ‑> Any:

Process the predictions using the transformations.

Arguments

  • predictions: Model output to process

Returns Processed predictions