# -*- coding: utf-8 -*-
# This file as well as the whole tsfresh package are licenced under the MIT licence (see the LICENCE.txt)
# Maximilian Christ (maximilianchrist.com), 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]_.
References
----------
.. [4] https://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones
.. [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 = "https://github.com/MaxBenChrist/human-activity-dataset/blob/master/UCI%20HAR%20Dataset.zip?raw=True"
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)
)