Source code for tsfresh.examples.har_dataset

# -*- coding: utf-8 -*-
# This file as well as the whole tsfresh package are licenced under the MIT licence (see the LICENCE.txt)
# Maximilian Christ (, Blue Yonder Gmbh, 2016

This module implements functions to download and load the Human Activity Recognition dataset [4]_.
A description of the data set can be found in [5]_.


.. [4]
.. [5] Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra and Jorge L. Reyes-Ortiz. (2013)
        A Public Domain Dataset for Human Activity Recognition Using Smartphones.
        21th European Symposium on Artificial Neural Networks,
        Computational Intelligence and Machine Learning, ESANN 2013. Bruges, Belgium 24-26 April 2013.


import logging
import os
import shutil
from io import BytesIO
from zipfile import ZipFile

import pandas as pd
import requests

_logger = logging.getLogger(__name__)

module_path = os.path.dirname(__file__)
data_file_name = os.path.join(module_path, "data", "UCI HAR Dataset")

[docs] def download_har_dataset(folder_name=data_file_name): """ Download human activity recognition dataset from UCI ML Repository and store it at /tsfresh/notebooks/data. Examples ======== >>> from tsfresh.examples import har_dataset >>> har_dataset.download_har_dataset() """ zipurl = "" if not os.access(module_path, os.W_OK): raise RuntimeError( "You don't have the necessary permissions to download the Human Activity Dataset " "Set into the module path. Consider installing the module in a virtualenv you " "own or run this function with appropriate permissions." ) if os.path.exists(os.path.join(folder_name, "UCI HAR Dataset")): _logger.warning("You have already downloaded the Human Activity Data Set.") return os.makedirs(folder_name, exist_ok=True) r = requests.get(zipurl, stream=True) if r.status_code != 200: raise RuntimeError( "Could not download the Human Activity Data Set from GitHub." "HTTP status code: {}".format(r.status_code) ) with ZipFile(BytesIO(r.content)) as zfile: zfile.extractall(path=folder_name)
[docs] def load_har_dataset(folder_name=data_file_name): data_file_name_dataset = os.path.join( folder_name, "UCI HAR Dataset", "train", "Inertial Signals", "body_acc_x_train.txt", ) try: return pd.read_csv(data_file_name_dataset, delim_whitespace=True, header=None) except OSError: raise OSError( "File {} was not found. Have you downloaded the dataset with download_har_dataset() " "before?".format(data_file_name_dataset) )
[docs] def load_har_classes(folder_name=data_file_name): data_file_name_classes = os.path.join( folder_name, "UCI HAR Dataset", "train", "y_train.txt" ) try: return pd.read_csv( data_file_name_classes, delim_whitespace=True, header=None ).squeeze() except OSError: raise OSError( "File {} was not found. Have you downloaded the dataset with download_har_dataset() " "before?".format(data_file_name_classes) )