| 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]) |
|
|
