OneOfDict

class deeptrack.features.OneOfDict(collection: dict[Any, Feature], key: Any | None | Callable[[...], Any | None] = None, **kwargs: Any)

Bases: Feature

Resolve one feature from a dictionary and apply it to an input.

This feature selects a feature from a dictionary and applies it to an input. The selection is made randomly by default, but it can be controlled using the key argument.

If key is not specified, a random key from the dictionary is selected, and the corresponding feature is applied. Otherwise, the feature mapped to key is resolved.

Parameters

collection: dict[Any, Feature]

A dictionary where keys are identifiers and values are features.

key: PropertyLike[Any | None], optional

The key of the feature to resolve from the dictionary. If None, a random key is selected.

**kwargs: Any

Additional parameters passed to the parent Feature class.

Attributes

__distributed__: bool

Set to False, indicating that this feature’s .get() method processes the entire input at once even if it is a list, rather than distributing calls for each item of the list.

Methods

_process_properties(propertydict) -> dict

It determines which feature to use based on key.

get(inputs, key, _ID, **kwargs) -> Any

It resolves the selected feature and applies it to the input.

Examples

>>> import deeptrack as dt

Define a dictionary of features:

>>> features_dict = {
...     "add": dt.Add(value=10),
...     "multiply": dt.Multiply(value=2),
... }

Create a OneOfDict feature that randomly selects a transformation:

>>> one_of_dict_feature = dt.OneOfDict(features_dict)

Creare an array:

>>> import numpy as np
>>>
>>> input_array = np.array([1, 2, 3])

Apply a randomly selected feature to the array:

>>> output_array = one_of_dict_feature(input_array)
>>> output_array  # The output depends on the randomly selected feature
array([2, 4, 6])  # Alternatively: array([11, 12, 13])

Potentially select a different feature:

>>> output_array = one_of_dict_feature.new(input_array)
>>> output_array

Use a specific key to apply a predefined feature:

>>> controlled_feature = dt.OneOfDict(features_dict, key="add")
>>> output_array = controlled_feature(input_array)
>>> output_array
array([11, 12, 13])

Methods Summary

get(inputs, key[, _ID])

Resolve the selected feature and apply it to the input.

Methods Documentation

get(inputs: Any, key: Any, _ID: tuple[int, ...] = (), **kwargs: Any) → Any

Resolve the selected feature and apply it to the input.

Parameters

inputs: Any

The input data to be processed.

key: Any

The key of the feature to apply from the dictionary.

_ID: tuple[int, …], optional

A unique identifier for caching and parallel execution.

**kwargs: Any

Additional parameters passed to the selected feature.

Returns

Any

The output of the selected feature applied to the input.