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from pathlib import Path |
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from typing import Sequence |
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import numpy as np |
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import librosa |
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from sklearn.metrics import mean_absolute_percentage_error |
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GENERATION_TASK_IDS = [ |
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"MEG03", |
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"ECG01", |
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"ECG02", |
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"NEG03", |
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"NEG04", |
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"ENG01", |
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"ENG02", |
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"ENG03", |
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"ENG04", |
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"ENG05", |
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"PHG02", |
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"PHG03", |
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"URG01", |
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"URG02", |
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"URG03", |
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"URG05", |
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"MAG01", |
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] |
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IMPUTATION_TASK_IDS = [ |
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"NEG04", |
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"ENG05", |
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"PHG03", |
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"URG03", |
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] |
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CLASSIFICATION_TASK_IDS = [ |
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"ASU03", |
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"BIU01", |
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"BIU02", |
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"BIU03", |
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"NEU02", |
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"NEU05", |
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"NEU06", |
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"PHU01", |
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"PHU06", |
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"MFU01_MFU02", |
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"RAU01", |
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"RAU02", |
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] |
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EVENT_DETECTION_TASK_IDS = ["ASU01_ASG02", "EAU01_EAG02"] |
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ANOMALY_DETECTION_TASK_IDS = [ |
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"MEU01", |
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"MEU02", |
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"NEU01", |
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"PHU04", |
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"PHU05", |
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"URU04", |
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"MFU03", |
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] |
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MCQ_TASK_IDS = ["MEU04", "ECU03"] |
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DATASET_TO_TASK = { |
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"ASU01_ASG02": "event_detection", |
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"ASU03": "classification", |
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"EAU01_EAG02": "event_detection", |
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"BIU01": "classification", |
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"BIU02": "classification", |
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"BIU03": "classification", |
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"MEU01": "anomaly_detection", |
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"MEU02": "anomaly_detection", |
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"MEG03": "forecasting", |
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"MEU04": "mcq", |
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"ECG01": "forecasting", |
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"ECG02": "forecasting", |
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"ECU03": "mcq", |
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"NEU01": "anomaly_detection", |
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"NEU02": "classification", |
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"NEG03": "forecasting", |
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"NEG04": "imputation", |
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"NEU05": "classification", |
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"NEU06": "classification", |
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"ENG01": "synthesize", |
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"ENG02": "forecasting", |
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"ENG03": "forecasting", |
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"ENG04": "forecasting", |
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"ENG05": "imputation", |
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"PHU01": "classification", |
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"PHG02": "forecasting", |
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"PHG03": "imputation", |
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"PHU04": "anomaly_detection", |
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"PHU05": "anomaly_detection", |
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"PHU06": "classification", |
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"URG01": "forecasting", |
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"URG02": "forecasting", |
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"URG03": "imputation", |
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"URU04": "anomaly_detection", |
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"URG05": "forecasting", |
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"MFU01_MFU02": "classification", |
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"MFU03": "anomaly_detection", |
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"RAU01": "classification", |
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"RAU02": "classification", |
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"MAG01": "forecasting" |
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} |
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def read_time_series_data(path: str | Path) -> Sequence: |
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path_str = path.__str__() |
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data = [] |
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if path_str.endswith(".csv"): |
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with open(path) as raw_data_reader: |
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for line in raw_data_reader.readlines(): |
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line = line.strip("\ufeff") |
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if "," in line: |
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data.append(line.strip().split(",")) |
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else: |
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data.append(line.strip()) |
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if "X" not in data: |
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data = np.array(data, dtype=np.float32) |
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else: |
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data = np.array(data) |
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elif path_str.endswith(".npy"): |
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data = np.load(path) |
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elif path_str.endswith(".wav") or path_str.endswith(".flac"): |
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data, _ = librosa.core.load(path, mono=False) |
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else: |
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raise ValueError(f"Unsupported data type {path_str.endswith()}") |
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return data |
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def concat_base_path(base_path: Path, path: str) -> Path: |
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if (base_path / path).exists(): |
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return base_path / path |
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else: |
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return base_path.parent / path |
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def non_zero_rel_mae(y_true: np.ndarray, y_pred: np.ndarray) -> float: |
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idxs = np.where(y_true != 0)[0] |
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return mean_absolute_percentage_error(y_true[idxs], y_pred[idxs]) |
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