lore_sa.encoder_decoder.EncDec

class lore_sa.encoder_decoder.EncDec(dataset_descriptor)

Abstract base class for encoding and decoding features.

The EncDec class provides an interface for transforming features between their original representation and an encoded representation suitable for machine learning models. This is particularly important for:

• Categorical features: Converting to one-hot encoding or ordinal encoding

• Numerical features: Scaling or normalization

• Feature engineering: Creating derived features

The encoder is used by LORE to: 1. Encode instances before generating neighborhoods 2. Decode synthetic instances back to original feature space 3. Decode rules to make them interpretable

dataset_descriptor

Descriptor containing information about feature types, ranges, and categorical values

Type:

dict

encoded_features

Mapping from encoded feature indices to feature names

Type:

dict

encoded_descriptor

Descriptor for the encoded feature space

Type:

dict

encode()

Transform features to encoded representation

decode()

Transform encoded features back to original representation

encode_target_class()

Encode target class labels

decode_target_class()

Decode target class labels

get_encoded_features()

Get mapping of encoded features

get_encoded_intervals()

Get index intervals for encoded features

Example

>>> from lore_sa.encoder_decoder import ColumnTransformerEnc
>>>
>>> # Create encoder from dataset descriptor
>>> encoder = ColumnTransformerEnc(dataset.descriptor)
>>>
>>> # Encode a sample
>>> encoded = encoder.encode([sample])
>>>
>>> # Decode back to original space
>>> decoded = encoder.decode(encoded)

See also

ColumnTransformerEnc: Concrete implementation using sklearn’s ColumnTransformer

__init__(dataset_descriptor)

Initialize the encoder/decoder.

Parameters:

dataset_descriptor (dict) – Dictionary containing feature information including ‘numeric’, ‘categorical’, and ‘ordinal’ feature descriptors

Methods

__init__(dataset_descriptor)	Initialize the encoder/decoder.
decode(x)	Transform features from encoded to original representation.
decode_target_class(x)
encode(x)	Transform features from original to encoded representation.
encode_target_class(param)
get_encoded_features()	Get a mapping of encoded feature indices to feature names.
get_encoded_intervals()	Get index intervals for each original feature in the encoded space.

abstract decode(x: numpy.array)

Transform features from encoded to original representation.

This method reverses the encoding transformation, converting features from the encoded space back to their original representation. This is essential for making explanations interpretable to users.

Parameters:

x (np.array) – Array of shape (n_samples, n_encoded_features) containing samples in the encoded feature space

Returns:

Decoded array of shape (n_samples, n_features) in the original

feature space

Return type:

np.array

Example

>>> # Encoded:  [[1, 0, 0, 25], [0, 1, 0, 30]]
>>> # Original: [['red', 25], ['blue', 30]]
>>> decoded = encoder.decode(encoded_data)

abstract encode(x: numpy.array)

Transform features from original to encoded representation.

This method applies the encoding transformation to convert features from their original space (e.g., with categorical labels) to an encoded space suitable for machine learning (e.g., with one-hot encoded categorical features).

Parameters:

x (np.array) – Array of shape (n_samples, n_features) containing samples in the original feature space

Returns:

Encoded array of shape (n_samples, n_encoded_features) where

n_encoded_features may be larger than n_features due to one-hot encoding

Return type:

np.array

Example

>>> # Original: [['red', 25], ['blue', 30]]
>>> # Encoded:  [[1, 0, 0, 25], [0, 1, 0, 30]]  # one-hot for color
>>> encoded = encoder.encode(original_data)

abstract get_encoded_features()

Get a mapping of encoded feature indices to feature names.

Returns:

Dictionary mapping encoded feature indices to descriptive names.

For one-hot encoded features, names include the category value (e.g., ‘color=red’, ‘color=blue’).

Return type:

dict

Example

>>> features = encoder.get_encoded_features()
>>> # {0: 'age', 1: 'color=red', 2: 'color=blue', 3: 'color=green'}

get_encoded_intervals()

Get index intervals for each original feature in the encoded space.

This method returns a list of (start, end) tuples indicating the range of encoded indices that correspond to each original feature. This is useful when an original categorical feature is one-hot encoded into multiple columns.

Returns:

List of (start_idx, end_idx) tuples, one for each original feature.

For numerical features, start_idx == end_idx. For one-hot encoded categorical features, the interval spans multiple indices.

Return type:

list

Example

>>> intervals = encoder.get_encoded_intervals()
>>> # [(0, 1), (1, 4), (4, 5)]  # age (1 col), color (3 cols), income (1 col)