feat: 🎸 support M4 dataset

baselines/MLP/M4.py

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+import os
+import sys
+
+# TODO: remove it when basicts can be installed by pip
+sys.path.append(os.path.abspath(__file__ + "/../../.."))
+from easydict import EasyDict
+from basicts.runners import M4ForecastingRunner
+from basicts.losses import masked_mae
+from basicts.data import M4ForecastingDataset
+
+from .mlp_arch import MultiLayerPerceptron
+
+def get_cfg(seasonal_pattern):
+    assert seasonal_pattern in ["Yearly", "Quarterly", "Monthly", "Weekly", "Daily", "Hourly"]
+    prediction_len = {"Yearly": 6, "Quarterly": 8, "Monthly": 18, "Weekly": 13, "Daily": 14, "Hourly": 48}[seasonal_pattern]
+    history_size = 2
+    history_len = history_size * prediction_len
+
+    CFG = EasyDict()
+
+    # ================= general ================= #
+    CFG.DESCRIPTION = "Multi-layer perceptron model configuration "
+    CFG.RUNNER = M4ForecastingRunner
+    CFG.DATASET_CLS = M4ForecastingDataset
+    CFG.DATASET_NAME = "M4_" + seasonal_pattern
+    CFG.DATASET_INPUT_LEN = history_len
+    CFG.DATASET_OUTPUT_LEN = prediction_len
+    CFG.GPU_NUM = 1
+
+    # ================= environment ================= #
+    CFG.ENV = EasyDict()
+    CFG.ENV.SEED = 1
+    CFG.ENV.CUDNN = EasyDict()
+    CFG.ENV.CUDNN.ENABLED = True
+
+    # ================= model ================= #
+    CFG.MODEL = EasyDict()
+    CFG.MODEL.NAME = "MultiLayerPerceptron"
+    CFG.MODEL.ARCH = MultiLayerPerceptron
+    CFG.MODEL.PARAM = {
+        "history_seq_len": CFG.DATASET_INPUT_LEN,
+        "prediction_seq_len": CFG.DATASET_OUTPUT_LEN,
+        "hidden_dim": 32
+    }
+    CFG.MODEL.FORWARD_FEATURES = [0]
+    CFG.MODEL.TARGET_FEATURES = [0]
+
+    # ================= optim ================= #
+    CFG.TRAIN = EasyDict()
+    CFG.TRAIN.LOSS = masked_mae
+    CFG.TRAIN.OPTIM = EasyDict()
+    CFG.TRAIN.OPTIM.TYPE = "Adam"
+    CFG.TRAIN.OPTIM.PARAM = {
+        "lr": 0.002,
+        "weight_decay": 1.0e-5,
+        "eps": 1.0e-8
+    }
+    CFG.TRAIN.LR_SCHEDULER = EasyDict()
+    CFG.TRAIN.LR_SCHEDULER.TYPE = "MultiStepLR"
+    CFG.TRAIN.LR_SCHEDULER.PARAM = {
+        "milestones": [1, 30, 38, 46, 54, 62, 70, 80],
+        "gamma": 0.5
+    }
+
+    # ================= train ================= #
+    CFG.TRAIN.CLIP_GRAD_PARAM = {
+        "max_norm": 5.0
+    }
+    CFG.TRAIN.NUM_EPOCHS = 5
+    CFG.TRAIN.CKPT_SAVE_DIR = os.path.join(
+        "checkpoints",
+        "_".join([CFG.MODEL.NAME, str(CFG.TRAIN.NUM_EPOCHS)])
+    )
+    # train data
+    CFG.TRAIN.DATA = EasyDict()
+    # read data
+    CFG.TRAIN.DATA.DIR = "datasets/" + CFG.DATASET_NAME
+    # dataloader args, optional
+    CFG.TRAIN.DATA.BATCH_SIZE = 32
+    CFG.TRAIN.DATA.PREFETCH = False
+    CFG.TRAIN.DATA.SHUFFLE = True
+    CFG.TRAIN.DATA.NUM_WORKERS = 2
+    CFG.TRAIN.DATA.PIN_MEMORY = False
+
+    # ================= test ================= #
+    CFG.TEST = EasyDict()
+    CFG.TEST.INTERVAL = CFG.TRAIN.NUM_EPOCHS
+    # evluation
+    # test data
+    CFG.TEST.DATA = EasyDict()
+    # read data
+    CFG.TEST.DATA.DIR = "datasets/" + CFG.DATASET_NAME
+    # dataloader args, optional
+    CFG.TEST.DATA.BATCH_SIZE = 32
+    CFG.TEST.DATA.PREFETCH = False
+    CFG.TEST.DATA.SHUFFLE = False
+    CFG.TEST.DATA.NUM_WORKERS = 2
+    CFG.TEST.DATA.PIN_MEMORY = False
+
+    # ================= evaluate ================= #
+    CFG.EVAL = EasyDict()
+    CFG.EVAL.HORIZONS = []
+    CFG.EVAL.SAVE_PATH = os.path.abspath(__file__ + "/..")
+
+    return CFG