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'add-card' #228

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714978f
'add-card'
superarthurlx Feb 12, 2025
d036e6b
'update-card'
superarthurlx Feb 12, 2025
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158 changes: 158 additions & 0 deletions baselines/CARD/ETTm2.py
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import os
import sys
from easydict import EasyDict
sys.path.append(os.path.abspath(__file__ + '/../../..'))
from basicts.metrics import masked_mae, masked_mse
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 CARD
from .loss import card_loss

############################## Hot Parameters ##############################
# Dataset & Metrics configuration
DATA_NAME = 'ETTm2' # 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 = CARD
NUM_NODES = 7
MODEL_PARAM = {
"enc_in": NUM_NODES, # num nodes
"dec_in": NUM_NODES,
"c_out": NUM_NODES,
"seq_len": INPUT_LEN,
"pred_len": OUTPUT_LEN, # prediction sequence length
"patch_len": 16,
"stride": 8,
"d_model": 16,
"d_ff": 32,
"use_statistic": False,
"e_layers": 2,
"n_heads": 2,
"dropout": 0.2,
"momentum": 0.1,
"dp_rank": 8,
"merge_size": 2,
"alpha": 0.5,
"over_hidden": False,
"trianable_smooth": False,
"untoken": False,
"use_h_loss": True,
"model_token_number": 0,
"num_time_features": 4, # number of used time features
"time_of_day_size": 96,
"day_of_week_size": 7,
"day_of_month_size": 31,
"day_of_year_size": 366
}
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, 3, 4]
CFG.MODEL.TARGET_FEATURES = [0]

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

CFG.METRICS = EasyDict()
# Metrics settings
CFG.METRICS.FUNCS = EasyDict({
'MAE': masked_mae,
'MSE': masked_mse
})
CFG.METRICS.TARGET = 'MSE'
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 = card_loss
# Optimizer settings
CFG.TRAIN.OPTIM = EasyDict()
CFG.TRAIN.OPTIM.TYPE = "Adam"
CFG.TRAIN.OPTIM.PARAM = {
"lr": 0.0001,
}
# Learning rate scheduler settings
CFG.TRAIN.LR_SCHEDULER = EasyDict()
CFG.TRAIN.LR_SCHEDULER.TYPE = "MultiStepLR"
CFG.TRAIN.LR_SCHEDULER.PARAM = {
"milestones": [1, 25, 50],
"gamma": 0.5
}
CFG.TRAIN.CLIP_GRAD_PARAM = {
'max_norm': 5.0
}
# Train data loader settings
CFG.TRAIN.DATA = EasyDict()
CFG.TRAIN.DATA.BATCH_SIZE = 32
CFG.TRAIN.DATA.SHUFFLE = True

############################## Validation Configuration ##############################
CFG.VAL = EasyDict()
CFG.VAL.INTERVAL = 1
CFG.VAL.DATA = EasyDict()
CFG.VAL.DATA.BATCH_SIZE = 32

############################## Test Configuration ##############################
CFG.TEST = EasyDict()
CFG.TEST.INTERVAL = 1
CFG.TEST.DATA = EasyDict()
CFG.TEST.DATA.BATCH_SIZE = 32

############################## Evaluation Configuration ##############################

CFG.EVAL = EasyDict()

# Evaluation parameters
CFG.EVAL.HORIZONS = [12, 24, 48, 96]
CFG.EVAL.USE_GPU = True # Whether to use GPU for evaluation. Default: True
158 changes: 158 additions & 0 deletions baselines/CARD/Electricity.py
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import os
import sys
from easydict import EasyDict
sys.path.append(os.path.abspath(__file__ + '/../../..'))
from basicts.metrics import masked_mae, masked_mse
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 CARD
from .loss import card_loss

############################## Hot Parameters ##############################
# Dataset & Metrics configuration
DATA_NAME = 'Electricity' # 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 = CARD
NUM_NODES = 321
MODEL_PARAM = {
"enc_in": NUM_NODES, # num nodes
"dec_in": NUM_NODES,
"c_out": NUM_NODES,
"seq_len": INPUT_LEN,
"pred_len": OUTPUT_LEN, # prediction sequence length
"patch_len": 16,
"stride": 8,
"d_model": 128,
"d_ff": 256,
"use_statistic": False,
"e_layers": 2,
"n_heads": 16,
"dropout": 0.2,
"momentum": 0.1,
"dp_rank": 8,
"merge_size": 2,
"alpha": 0.5,
"over_hidden": False,
"trianable_smooth": False,
"untoken": False,
"use_h_loss": True,
"model_token_number": 0,
"num_time_features": 4, # number of used time features
"time_of_day_size": 24,
"day_of_week_size": 7,
"day_of_month_size": 31,
"day_of_year_size": 366
}
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, 3, 4]
CFG.MODEL.TARGET_FEATURES = [0]

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

CFG.METRICS = EasyDict()
# Metrics settings
CFG.METRICS.FUNCS = EasyDict({
'MAE': masked_mae,
'MSE': masked_mse
})
CFG.METRICS.TARGET = 'MSE'
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 = card_loss
# Optimizer settings
CFG.TRAIN.OPTIM = EasyDict()
CFG.TRAIN.OPTIM.TYPE = "Adam"
CFG.TRAIN.OPTIM.PARAM = {
"lr": 0.0001,
}
# Learning rate scheduler settings
CFG.TRAIN.LR_SCHEDULER = EasyDict()
CFG.TRAIN.LR_SCHEDULER.TYPE = "MultiStepLR"
CFG.TRAIN.LR_SCHEDULER.PARAM = {
"milestones": [1, 25, 50],
"gamma": 0.5
}
CFG.TRAIN.CLIP_GRAD_PARAM = {
'max_norm': 5.0
}
# Train data loader settings
CFG.TRAIN.DATA = EasyDict()
CFG.TRAIN.DATA.BATCH_SIZE = 32
CFG.TRAIN.DATA.SHUFFLE = True

############################## Validation Configuration ##############################
CFG.VAL = EasyDict()
CFG.VAL.INTERVAL = 1
CFG.VAL.DATA = EasyDict()
CFG.VAL.DATA.BATCH_SIZE = 32

############################## Test Configuration ##############################
CFG.TEST = EasyDict()
CFG.TEST.INTERVAL = 1
CFG.TEST.DATA = EasyDict()
CFG.TEST.DATA.BATCH_SIZE = 32

############################## Evaluation Configuration ##############################

CFG.EVAL = EasyDict()

# Evaluation parameters
CFG.EVAL.HORIZONS = [12, 24, 48, 96]
CFG.EVAL.USE_GPU = True # Whether to use GPU for evaluation. Default: True
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