constants.py

import tensorflow as tf
import kerastuner

TRAIN_STEPS = 1000
EVAL_STEPS = 100

BATCH_SIZE = 128

INPUT_WINDOW_SIZE = 7
OUTPUT_WINDOW_SIZE = 1

# === Feature Directory ===
DENSE_FLOAT_FEATURE_KEYS = ['a', 'b', 'c', 'd', 'e']

# === Feature Selection ===
FEATURE_KEYS = DENSE_FLOAT_FEATURE_KEYS  # feature inputs for model
PREDICT_FEATURE_KEYS = FEATURE_KEYS  # features that model predicts (if equal to feature_keys it can predict multiple timesteps ahead)

INPUT_FEATURE_KEYS = list(set(FEATURE_KEYS + PREDICT_FEATURE_KEYS))


# === Hyperparameters ===
HP_LR = 'learning_rate'
HP_HIDDEN_LAYER_NUM = 'hidden_layer_num'
HP_HIDDEN_LATENT_DIM = 'hidden_latent_dim'
HP_PRE_OUTPUT_UNITS = 'pre_output_units'


def _get_hyperparameters() -> kerastuner.HyperParameters:
    hp = kerastuner.HyperParameters()
    # todo: move string value definitions to constants
    hp.Fixed(HP_LR, 1e-2)
    hp.Fixed(HP_HIDDEN_LAYER_NUM, 1)
    hp.Fixed(HP_HIDDEN_LATENT_DIM, 64)
    #hp.Choice('dropout', [0.2, 0.3, 0.5], default=0.2)
    hp.Fixed(HP_PRE_OUTPUT_UNITS, 32)

    return hp


HYPERPARAMETERS = _get_hyperparameters()
HYPERPARAM_NUM_STEPS = 10