first commit

data_provider/m4.py

@@ -0,0 +1,138 @@
+# This source code is provided for the purposes of scientific reproducibility
+# under the following limited license from Element AI Inc. The code is an
+# implementation of the N-BEATS model (Oreshkin et al., N-BEATS: Neural basis
+# expansion analysis for interpretable time series forecasting,
+# https://arxiv.org/abs/1905.10437). The copyright to the source code is
+# licensed under the Creative Commons - Attribution-NonCommercial 4.0
+# International license (CC BY-NC 4.0):
+# https://creativecommons.org/licenses/by-nc/4.0/.  Any commercial use (whether
+# for the benefit of third parties or internally in production) requires an
+# explicit license. The subject-matter of the N-BEATS model and associated
+# materials are the property of Element AI Inc. and may be subject to patent
+# protection. No license to patents is granted hereunder (whether express or
+# implied). Copyright © 2020 Element AI Inc. All rights reserved.
+
+"""
+M4 Dataset
+"""
+import logging
+import os
+from collections import OrderedDict
+from dataclasses import dataclass
+from glob import glob
+
+import numpy as np
+import pandas as pd
+import patoolib
+from tqdm import tqdm
+import logging
+import os
+import pathlib
+import sys
+from urllib import request
+
+
+def url_file_name(url: str) -> str:
+    """
+    Extract file name from url.
+
+    :param url: URL to extract file name from.
+    :return: File name.
+    """
+    return url.split('/')[-1] if len(url) > 0 else ''
+
+
+def download(url: str, file_path: str) -> None:
+    """
+    Download a file to the given path.
+
+    :param url: URL to download
+    :param file_path: Where to download the content.
+    """
+
+    def progress(count, block_size, total_size):
+        progress_pct = float(count * block_size) / float(total_size) * 100.0
+        sys.stdout.write('\rDownloading {} to {} {:.1f}%'.format(url, file_path, progress_pct))
+        sys.stdout.flush()
+
+    if not os.path.isfile(file_path):
+        opener = request.build_opener()
+        opener.addheaders = [('User-agent', 'Mozilla/5.0')]
+        request.install_opener(opener)
+        pathlib.Path(os.path.dirname(file_path)).mkdir(parents=True, exist_ok=True)
+        f, _ = request.urlretrieve(url, file_path, progress)
+        sys.stdout.write('\n')
+        sys.stdout.flush()
+        file_info = os.stat(f)
+        logging.info(f'Successfully downloaded {os.path.basename(file_path)} {file_info.st_size} bytes.')
+    else:
+        file_info = os.stat(file_path)
+        logging.info(f'File already exists: {file_path} {file_info.st_size} bytes.')
+
+
+@dataclass()
+class M4Dataset:
+    ids: np.ndarray
+    groups: np.ndarray
+    frequencies: np.ndarray
+    horizons: np.ndarray
+    values: np.ndarray
+
+    @staticmethod
+    def load(training: bool = True, dataset_file: str = '../dataset/m4') -> 'M4Dataset':
+        """
+        Load cached dataset.
+
+        :param training: Load training part if training is True, test part otherwise.
+        """
+        info_file = os.path.join(dataset_file, 'M4-info.csv')
+        train_cache_file = os.path.join(dataset_file, 'training.npz')
+        test_cache_file = os.path.join(dataset_file, 'test.npz')
+        m4_info = pd.read_csv(info_file)
+        return M4Dataset(ids=m4_info.M4id.values,
+                         groups=m4_info.SP.values,
+                         frequencies=m4_info.Frequency.values,
+                         horizons=m4_info.Horizon.values,
+                         values=np.load(
+                             train_cache_file if training else test_cache_file,
+                             allow_pickle=True))
+
+
+@dataclass()
+class M4Meta:
+    seasonal_patterns = ['Yearly', 'Quarterly', 'Monthly', 'Weekly', 'Daily', 'Hourly']
+    horizons = [6, 8, 18, 13, 14, 48]
+    frequencies = [1, 4, 12, 1, 1, 24]
+    horizons_map = {
+        'Yearly': 6,
+        'Quarterly': 8,
+        'Monthly': 18,
+        'Weekly': 13,
+        'Daily': 14,
+        'Hourly': 48
+    }  # different predict length
+    frequency_map = {
+        'Yearly': 1,
+        'Quarterly': 4,
+        'Monthly': 12,
+        'Weekly': 1,
+        'Daily': 1,
+        'Hourly': 24
+    }
+    history_size = {
+        'Yearly': 1.5,
+        'Quarterly': 1.5,
+        'Monthly': 1.5,
+        'Weekly': 10,
+        'Daily': 10,
+        'Hourly': 10
+    }  # from interpretable.gin
+
+
+def load_m4_info() -> pd.DataFrame:
+    """
+    Load M4Info file.
+
+    :return: Pandas DataFrame of M4Info.
+    """
+    return pd.read_csv(INFO_FILE_PATH)