a069c9a874
- Add Dataset_PEMS and Dataset_Solar classes for PEMS and Solar datasets - Update data_factory.py to include new dataset mappings - Fix M4 dataset handling with proper numpy array dtype - Add PEMS-specific loss function (L1Loss) and inverse transform support - Update validation logic for PEMS dataset with inverse scaling - Fix M4 data loader insample mask calculation bug Changes support new traffic and solar energy datasets while maintaining backward compatibility with existing datasets.
140 lines
4.4 KiB
Python
140 lines
4.4 KiB
Python
# This source code is provided for the purposes of scientific reproducibility
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# under the following limited license from Element AI Inc. The code is an
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# implementation of the N-BEATS model (Oreshkin et al., N-BEATS: Neural basis
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# expansion analysis for interpretable time series forecasting,
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# https://arxiv.org/abs/1905.10437). The copyright to the source code is
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# licensed under the Creative Commons - Attribution-NonCommercial 4.0
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# International license (CC BY-NC 4.0):
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# https://creativecommons.org/licenses/by-nc/4.0/. Any commercial use (whether
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# for the benefit of third parties or internally in production) requires an
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# explicit license. The subject-matter of the N-BEATS model and associated
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# materials are the property of Element AI Inc. and may be subject to patent
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# protection. No license to patents is granted hereunder (whether express or
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# implied). Copyright © 2020 Element AI Inc. All rights reserved.
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"""
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M4 Dataset
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"""
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import logging
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import os
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from collections import OrderedDict
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from dataclasses import dataclass
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from glob import glob
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import numpy as np
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import pandas as pd
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import patoolib
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from tqdm import tqdm
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import logging
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import os
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import pathlib
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import sys
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from urllib import request
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def url_file_name(url: str) -> str:
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"""
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Extract file name from url.
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:param url: URL to extract file name from.
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:return: File name.
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"""
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return url.split('/')[-1] if len(url) > 0 else ''
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def download(url: str, file_path: str) -> None:
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"""
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Download a file to the given path.
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:param url: URL to download
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:param file_path: Where to download the content.
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"""
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def progress(count, block_size, total_size):
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progress_pct = float(count * block_size) / float(total_size) * 100.0
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sys.stdout.write('\rDownloading {} to {} {:.1f}%'.format(url, file_path, progress_pct))
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sys.stdout.flush()
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if not os.path.isfile(file_path):
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opener = request.build_opener()
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opener.addheaders = [('User-agent', 'Mozilla/5.0')]
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request.install_opener(opener)
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pathlib.Path(os.path.dirname(file_path)).mkdir(parents=True, exist_ok=True)
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f, _ = request.urlretrieve(url, file_path, progress)
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sys.stdout.write('\n')
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sys.stdout.flush()
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file_info = os.stat(f)
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logging.info(f'Successfully downloaded {os.path.basename(file_path)} {file_info.st_size} bytes.')
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else:
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file_info = os.stat(file_path)
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logging.info(f'File already exists: {file_path} {file_info.st_size} bytes.')
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@dataclass()
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class M4Dataset:
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ids: np.ndarray
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groups: np.ndarray
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frequencies: np.ndarray
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horizons: np.ndarray
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values: np.ndarray
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@staticmethod
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def load(training: bool = True, dataset_file: str = '../dataset/m4') -> 'M4Dataset':
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"""
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Load cached dataset.
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:param training: Load training part if training is True, test part otherwise.
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"""
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info_file = os.path.join(dataset_file, 'M4-info.csv')
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train_cache_file = os.path.join(dataset_file, 'training.npz')
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test_cache_file = os.path.join(dataset_file, 'test.npz')
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m4_info = pd.read_csv(info_file)
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return M4Dataset(ids=m4_info.M4id.values,
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groups=m4_info.SP.values,
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frequencies=m4_info.Frequency.values,
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horizons=m4_info.Horizon.values,
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values=np.load(
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train_cache_file if training else test_cache_file,
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allow_pickle=True))
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@dataclass()
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class M4Meta:
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seasonal_patterns = ['Yearly', 'Quarterly', 'Monthly', 'Weekly', 'Daily', 'Hourly']
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horizons = [6, 8, 18, 13, 14, 48]
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frequencies = [1, 4, 12, 1, 1, 24]
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horizons_map = {
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'Yearly': 6,
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'Quarterly': 8,
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'Monthly': 18,
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'Weekly': 13,
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'Daily': 14,
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'Hourly': 48
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} # different predict length
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frequency_map = {
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'Yearly': 1,
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'Quarterly': 4,
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'Monthly': 12,
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'Weekly': 1,
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'Daily': 1,
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'Hourly': 24
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}
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history_size = {
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'Yearly': 1.5,
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'Quarterly': 1.5,
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'Monthly': 1.5,
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'Weekly': 10,
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'Daily': 10,
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'Hourly': 10
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} # from interpretable.gin
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def load_m4_info(info_file_path: str) -> pd.DataFrame:
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"""
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Load M4Info file.
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:param info_file_path: Path to M4-info.csv file
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:return: Pandas DataFrame of M4Info.
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"""
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return pd.read_csv(info_file_path)
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