process/infer_eval_utils.py

from pathlib import Path
from typing import Sequence

import numpy as np
import librosa
from sklearn.metrics import mean_absolute_percentage_error


GENERATION_TASK_IDS = [
    "MEG03",
    "ECG01",
    "ECG02",
    "NEG03",
    "NEG04",
    "ENG01",
    "ENG02",
    "ENG03",
    "ENG04",
    "ENG05",
    "PHG02",
    "PHG03",
    "URG01",
    "URG02",
    "URG03",
    "URG05",
    "MAG01",
]

IMPUTATION_TASK_IDS = [
    "NEG04",
    "ENG05",
    "PHG03",
    "URG03",
]

CLASSIFICATION_TASK_IDS = [
    "ASU03",
    "BIU01",
    "BIU02",
    "BIU03",
    "NEU02",
    "NEU05",
    "NEU06",
    "PHU01",
    "PHU06",
    "MFU01_MFU02",
    "RAU01",
    "RAU02",
]

EVENT_DETECTION_TASK_IDS = ["ASU01_ASG02", "EAU01_EAG02"]

ANOMALY_DETECTION_TASK_IDS = [
    "MEU01",
    "MEU02",
    "NEU01",
    "PHU04",
    "PHU05",
    "URU04",
    "MFU03",
]

MCQ_TASK_IDS = ["MEU04", "ECU03"]

DATASET_TO_TASK = {
    "ASU01_ASG02": "event_detection",
    "ASU03": "classification",
    "EAU01_EAG02": "event_detection",
    "BIU01": "classification",
    "BIU02": "classification",
    "BIU03": "classification",
    "MEU01": "anomaly_detection",
    "MEU02": "anomaly_detection",
    "MEG03": "forecasting",
    "MEU04": "mcq",
    "ECG01": "forecasting",
    "ECG02": "forecasting",
    "ECU03": "mcq",
    "NEU01": "anomaly_detection",
    "NEU02": "classification",
    "NEG03": "forecasting",
    "NEG04": "imputation",
    "NEU05": "classification",
    "NEU06": "classification",
    "ENG01": "synthesize",
    "ENG02": "forecasting",
    "ENG03": "forecasting",
    "ENG04": "forecasting",
    "ENG05": "imputation",
    "PHU01": "classification",
    "PHG02": "forecasting",
    "PHG03": "imputation",
    "PHU04": "anomaly_detection",
    "PHU05": "anomaly_detection",
    "PHU06": "classification",
    "URG01": "forecasting",
    "URG02": "forecasting",
    "URG03": "imputation",
    "URU04": "anomaly_detection",
    "URG05": "forecasting",
    "MFU01_MFU02": "classification",
    "MFU03": "anomaly_detection",
    "RAU01": "classification",
    "RAU02": "classification",
    "MAG01": "forecasting"
}


def read_time_series_data(path: str | Path) -> Sequence:
    path_str = path.__str__()
    data = []
    if path_str.endswith(".csv"):
        with open(path) as raw_data_reader:
            for line in raw_data_reader.readlines():
                line = line.strip("\ufeff")
                if "," in line:
                    data.append(line.strip().split(","))
                else:
                    data.append(line.strip())
        if "X" not in data:
            data = np.array(data, dtype=np.float32)
        else:
            data = np.array(data)
    elif path_str.endswith(".npy"):
        data = np.load(path)
    elif path_str.endswith(".wav") or path_str.endswith(".flac"):
        data, _ = librosa.core.load(path, mono=False)
    else:
        raise ValueError(f"Unsupported data type {path_str.endswith()}")
    return data


def concat_base_path(base_path: Path, path: str) -> Path:
    if (base_path / path).exists():
        return base_path / path
    else:
        return base_path.parent / path


def non_zero_rel_mae(y_true: np.ndarray, y_pred: np.ndarray) -> float:
    idxs = np.where(y_true != 0)[0]
    return mean_absolute_percentage_error(y_true[idxs], y_pred[idxs])