hider.py

"""
The hider module containing the `hider(...)` function.
"""
# pylint: disable=fixme
from typing import Dict, Union, Tuple, Optional
import numpy as np
from utils.data_preprocess import preprocess_data


def hider(
    input_dict: Dict,
) -> Union[
    np.ndarray,  # return generated_data
    Tuple[np.ndarray, Optional[np.ndarray]],  # return generated_data, generated_padding_mask
    Tuple[np.ndarray, Optional[int]],  # return generated_data, 3
    Tuple[np.ndarray, Optional[np.ndarray], Optional[int]],  # return generated_data, generated_padding_mask, 3
    str,  # return "rescore"
]:
    """Solution hider function.

    Args:
        input_dict (Dict): Dictionary that contains the hider function inputs, as below:
            * "seed" (int): Random seed provided by the competition, use for reproducibility.
            * "data" (np.ndarray of float): Input data, shape [num_examples, max_seq_len, num_features].
            * "padding_mask" (np.ndarray of bool): Padding mask of bools, same shape as data.

    Returns:
        Return format is:
            np.ndarray (of float) [, np.ndarray (of bool), int]
        * First argument is the hider generated data, expected shape [num_examples, max_seq_len, num_features]);
        * Second optional argument is the corresponding padding mask, same shape;
        * Third optional argument is the number of seeds to use in hider evaluation step, expected range [0, 5], where 
            0 means that evaluation is skipped.
        
        Alternatively, may return a str "rescore" if there has been a previous successful submission and wishing to 
        just re-run the vs-seekers scoring step.
    """

    # Get the inputs.
    seed = input_dict["seed"]  # Random seed provided by the competition, use for reproducibility.
    data = input_dict["data"]  # Input data, shape [num_examples, max_seq_len, num_features].
    padding_mask = input_dict["padding_mask"]  # Padding mask of bools, same shape as data.

    # Get processed and imputed data, if desired:
    data_preproc, data_imputed = preprocess_data(data, padding_mask)

    # TODO: Put your hider code to replace Example 1 below.
    # Feel free play around with Examples 1 (add_noise) and 2 (timegan) below.

    # --- Example 1: add_noise ---
    from examples.hider.add_noise import add_noise

    generated_data = add_noise.add_noise(data_imputed, noise_size=0.1)
    generated_padding_mask = np.copy(padding_mask)
    return generated_data, generated_padding_mask

    # --- Example 2: timegan ---
    # from utils.misc import tf115_found
    # assert tf115_found is True, "TensorFlow 1.15 not found, which is required to run timegan."
    # from examples.hider.timegan import timegan
    # generated_data = timegan.timegan(data_imputed)
    # return generated_data