HAR.py

# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Human Activity Recognition."""


import os

import numpy as np
import datasets


_CITATION = """\
@misc{misc_smartphone-based_recognition_of_human_activities_and_postural_transitions_341,
  author       = {Reyes-Ortiz,Jorge, Anguita,Davide, Oneto,Luca, and Parra,Xavier},
  title        = {{Smartphone-Based Recognition of Human Activities and Postural Transitions}},
  year         = {2015},
  howpublished = {UCI Machine Learning Repository},
  note         = {{DOI}: https://doi.org/10.24432/C54G7M}
}
"""
_DESCRIPTION = """\
The Human Activity Recognition dataset.
"""
_HOMEPAGE = "http://archive.ics.uci.edu/dataset/341/smartphone+based+recognition+of+human+activities+and+postural+transitions"
_LICENSE = "Creative Commons Attribution 4.0 International (CC BY 4.0) license"
_URL = 'https://archive.ics.uci.edu/ml/machine-learning-databases/00341/HAPT%20Data%20Set.zip'
_LABEL_NAMES = ["WALKING", "WALKING_UPSTAIRS", "WALKING_DOWNSTAIRS", "SITTING", "STANDING", "LAYING", "STAND_TO_SIT", "SIT_TO_STAND", "SIT_TO_LIE", "LIE_TO_SIT", "STAND_TO_LIE", "LIE_TO_STAND"]


class HARDataset(datasets.GeneratorBasedBuilder):
    """Human Activity Recognition."""

    VERSION = datasets.Version("1.1.0")

    def _info(self):
        return datasets.DatasetInfo(
            # This is the description that will appear on the datasets page.
            description=_DESCRIPTION,
            # This defines the different columns of the dataset and their types
            features=datasets.Features({
                "features": datasets.Sequence(feature=datasets.Value("float32"), length=561),
                "labels": datasets.ClassLabel(12, names=_LABEL_NAMES),
                "subject id": datasets.Value("uint8"),
            }),
            # Homepage of the dataset for documentation
            homepage=_HOMEPAGE,
            # License for the dataset if available
            license=_LICENSE,
            # Citation for the dataset
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        data_dir = dl_manager.download_and_extract(_URL)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": os.path.join(data_dir, "Train"),
                    "split": "train",
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": os.path.join(data_dir, "Test"),
                    "split": "test"
                },
            ),
        ]

    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
    def _generate_examples(self, filepath, split):
        X = np.genfromtxt(f"{filepath}/X_{split}.txt")
        Y = np.genfromtxt(f"{filepath}/y_{split}.txt").astype(int) - 1
        subject_ids = np.genfromtxt(f"{filepath}/subject_id_{split}.txt").astype(int)
        for key, (x, y, id) in enumerate(zip(X, Y, subject_ids)):
            yield key, {
                "features": x,
                "labels": y,
                "subject id": id,
            }