src/display/utils.py

"""
Display utilities and column definitions for MUSEval Leaderboard
"""

from dataclasses import dataclass
from typing import List, Dict, Any
from enum import Enum

# Column definitions for model information
@dataclass
class ModelInfoColumn:
    name: str
    type: str = "str"
    displayed_by_default: bool = True
    never_hidden: bool = False
    hidden: bool = False

# Model information columns
model_info_columns = [
    ModelInfoColumn("model", "str", True, True, False),
    ModelInfoColumn("organization", "str", True, False, False),
    ModelInfoColumn("submission_date", "str", True, False, False),
    ModelInfoColumn("task", "str", True, False, False),
    ModelInfoColumn("dataset_version", "str", True, False, False),
    ModelInfoColumn("paper_url", "str", False, False, False),
    ModelInfoColumn("code_url", "str", False, False, False),
    ModelInfoColumn("domains", "number", True, False, False),
    ModelInfoColumn("categories", "number", True, False, False),
    ModelInfoColumn("datasets", "number", True, False, False),
]

# Benchmark columns (metrics)
BENCHMARK_COLS = [
    "MAE", "Uni-MAE", "RMSE", "MAPE", "R²", "SMAPE", "Uni-Multi"
]

# Evaluation columns
EVAL_COLS = [
    "model", "submitter", "submission_date", "domain", "category", "dataset",
    "task", "dataset_version", "paper_url", "code_url"
]

# Evaluation types
EVAL_TYPES = ["multivariate_forecasting"]

# Model types
class ModelType(Enum):
    FOUNDATION = "Foundation Model"
    TRADITIONAL = "Traditional"
    NEURAL = "Neural Network"
    TRANSFORMER = "Transformer"

# Weight types
class WeightType(Enum):
    LIGHTWEIGHT = "Lightweight"
    MEDIUM = "Medium"
    HEAVY = "Heavy"

# Precision types
class Precision(Enum):
    FLOAT16 = "FP16"
    FLOAT32 = "FP32"
    MIXED = "Mixed"

# Fields function for dataclass
def fields(cls):
    """Get fields from dataclass"""
    return cls.__dataclass_fields__.values() if hasattr(cls, '__dataclass_fields__') else []