cursus.processing.temporal.sequence_padding_processor

Sequence Padding Processor for Temporal Self-Attention Model

This module provides atomic sequence padding/truncation for temporal sequences. Extracted from TSA preprocess_functions.py logic.

class SequencePaddingProcessor(target_length=51, padding_strategy='pre', truncation_strategy='post', padding_value=0, axis=0)

Bases: Processor

Pads or truncates sequences to a target length.

Extracted from TSA preprocess_functions.py: - seq_cat_mtx = np.pad(seq_cat_mtx, [(seq_len - 1 - len(seq_cat_vars_mtx), 0), (0, 0)])

Parameters:

• target_length (int) – Desired sequence length

• padding_strategy (str) – ‘pre’, ‘post’

• truncation_strategy (str) – ‘pre’, ‘post’

• padding_value (int | float) – Value to use for padding

• axis (int) – Axis along which to pad/truncate

fit(data)

No fitting required for padding

process(input_data)

Apply sequence padding/truncation

get_config()

Return processor configuration