regular_config.py

import os
import sys
from easydict import EasyDict
sys.path.append(os.path.abspath(__file__ + '/../../..'))

from basicts.metrics import masked_mae, masked_mape, masked_rmse
from basicts.data import TimeSeriesForecastingDataset
from basicts.runners import SimpleTimeSeriesForecastingRunner
from basicts.scaler import ZScoreScaler
from basicts.utils import get_regular_settings
from .arch import MultiLayerPerceptron as MLP

############################## Hot Parameters ##############################
# Dataset & Metrics configuration
DATA_NAME = 'PEMS08'  # Dataset name
regular_settings = get_regular_settings(DATA_NAME)
INPUT_LEN = regular_settings['INPUT_LEN']  # Length of input sequence
OUTPUT_LEN = regular_settings['OUTPUT_LEN']  # Length of output sequence
TRAIN_VAL_TEST_RATIO = regular_settings['TRAIN_VAL_TEST_RATIO']  # Train/Validation/Test split ratios
NORM_EACH_CHANNEL = regular_settings['NORM_EACH_CHANNEL'] # Whether to normalize each channel of the data
RESCALE = regular_settings['RESCALE'] # Whether to rescale the data
NULL_VAL = regular_settings['NULL_VAL'] # Null value in the data
# Model architecture and parameters
MODEL_ARCH = MLP
MODEL_PARAM = {
    'history_seq_len': INPUT_LEN,
    'prediction_seq_len': OUTPUT_LEN,
    'hidden_dim': 64
}
NUM_EPOCHS = 100

############################## General Configuration ##############################
CFG = EasyDict()
# General settings
CFG.DESCRIPTION = 'An Example Config'
CFG.GPU_NUM = 1 # Number of GPUs to use (0 for CPU mode)
# Runner
CFG.RUNNER = SimpleTimeSeriesForecastingRunner

############################## Dataset Configuration ##############################
CFG.DATASET = EasyDict()
# Dataset settings
CFG.DATASET.NAME = DATA_NAME
CFG.DATASET.TYPE = TimeSeriesForecastingDataset
CFG.DATASET.PARAM = EasyDict({
    'dataset_name': DATA_NAME,
    'train_val_test_ratio': TRAIN_VAL_TEST_RATIO,
    'input_len': INPUT_LEN,
    'output_len': OUTPUT_LEN,
    # 'mode' is automatically set by the runner
})

############################## Scaler Configuration ##############################
CFG.SCALER = EasyDict()
# Scaler settings
CFG.SCALER.TYPE = ZScoreScaler # Scaler class
CFG.SCALER.PARAM = EasyDict({
    'dataset_name': DATA_NAME,
    'train_ratio': TRAIN_VAL_TEST_RATIO[0],
    'norm_each_channel': NORM_EACH_CHANNEL,
    'rescale': RESCALE,
})

############################## Model Configuration ##############################
CFG.MODEL = EasyDict()
# Model settings
CFG.MODEL.NAME = MODEL_ARCH.__name__
CFG.MODEL.ARCH = MODEL_ARCH
CFG.MODEL.PARAM = MODEL_PARAM
CFG.MODEL.FORWARD_FEATURES = [0, 1, 2]
CFG.MODEL.TARGET_FEATURES = [0]

############################## Metrics Configuration ##############################

CFG.METRICS = EasyDict()
# Metrics settings
CFG.METRICS.FUNCS = EasyDict({
                                'MAE': masked_mae,
                                'MAPE': masked_mape,
                                'RMSE': masked_rmse,
                            })
CFG.METRICS.TARGET = 'MAE'
CFG.METRICS.NULL_VAL = NULL_VAL

############################## Training Configuration ##############################
CFG.TRAIN = EasyDict()
CFG.TRAIN.NUM_EPOCHS = NUM_EPOCHS
CFG.TRAIN.CKPT_SAVE_DIR = os.path.join(
    'checkpoints',
    MODEL_ARCH.__name__,
    '_'.join([DATA_NAME, str(CFG.TRAIN.NUM_EPOCHS), str(INPUT_LEN), str(OUTPUT_LEN)])
)
CFG.TRAIN.LOSS = masked_mae
# Optimizer settings
CFG.TRAIN.OPTIM = EasyDict()
CFG.TRAIN.OPTIM.TYPE = 'Adam'
CFG.TRAIN.OPTIM.PARAM = {
                            'lr': 0.002,
                            'weight_decay': 0.0001,
                        }
# Train data loader settings
CFG.TRAIN.DATA = EasyDict()
CFG.TRAIN.DATA.BATCH_SIZE = 64
CFG.TRAIN.DATA.SHUFFLE = True

############################## Validation Configuration ##############################
CFG.VAL = EasyDict()
CFG.VAL.INTERVAL = 1
CFG.VAL.DATA = EasyDict()
CFG.VAL.DATA.BATCH_SIZE = 64

############################## Test Configuration ##############################
CFG.TEST = EasyDict()
CFG.TEST.INTERVAL = 1
CFG.TEST.DATA = EasyDict()
CFG.TEST.DATA.BATCH_SIZE = 64