My commit
Browse files- aphantasia_drawing_dataset.py +192 -0
aphantasia_drawing_dataset.py
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# -*- coding: utf-8 -*-
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"""aphantasia_drawing_dataset.ipynb
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Automatically generated by Colaboratory.
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Original file is located at
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https://colab.research.google.com/drive/1FHMQJWfjSzSrtEpARqh7IKVrfF7LwyRA
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"""
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#!pip install datasets -q
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#from google.colab import drive
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#drive.mount('/content/drive')
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#path = os.getcwd() + "/drive/MyDrive/Duke/huggingface_project/aphantasia_drawing"
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# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# TODO: Address all TODOs and remove all explanatory comments
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"""TODO: Add a description here."""
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#import csv
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import base64
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from PIL import Image
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import io
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import json
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import os
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from typing import List
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import datasets
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import logging
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# TODO: Add BibTeX citation
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# Find for instance the citation on arxiv or on the dataset repo/website
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_CITATION = """\
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@misc{Bainbridge_Pounder_Eardley_Baker_2023,
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title={Quantifying Aphantasia through drawing: Those without visual imagery show deficits in object but not spatial memory},
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url={osf.io/cahyd},
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publisher={OSF},
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author={Bainbridge, Wilma A and Pounder, Zoë and Eardley, Alison and Baker, Chris I},
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year={2023},
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month={Sep}
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}
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"""
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# TODO: Add description of the dataset here
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# You can copy an official description
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_DESCRIPTION = """\
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This dataset comes from the Brain Bridge Lab from the University of Chicago.
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It is from an online memory drawing experiment with 61 individuals with aphantasia
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and 52 individuals with normal imagery. In the experiment participants 1) studied 3 separate
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scene photographs presented one after the other, 2) then drew them from memory,
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3) completed a recognition task, 4) copied the images while viewing them, 5) filled out
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a VVIQ and OSIQ questionnaire and also demographics questions. The data from the experiment
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was made available on the OSF website linked above. It was created July 31, 2020 and last
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updated September 27, 2023.
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"""
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# TODO: Add a link to an official homepage for the dataset here
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_HOMEPAGE = "https://osf.io/cahyd/"
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# TODO: Add the licence for the dataset here if you can find it
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_LICENSE = ""
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# TODO: Add link to the official dataset URLs here
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# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
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# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
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url = "https://drive.google.com/file/d/1aRhQlKPDk29yYPkx2kPhqaMwec5QZ4JE/view?usp=sharing"
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def _get_drive_url(url):
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base_url = 'https://drive.google.com/uc?id='
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split_url = url.split('/')
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return base_url + split_url[5]
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_URL = {"train": _get_drive_url(url)}
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# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
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class AphantasiaDrawingDataset(datasets.GeneratorBasedBuilder):
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"""TODO: Short description of my dataset."""
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_URL = _URL
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VERSION = datasets.Version("1.1.0")
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def _info(self):
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return datasets.DatasetInfo(
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description=_DESCRIPTION,
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features=datasets.Features({
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"subject": {
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"sub_id": datasets.Value("int32"),
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"treatment": datasets.ClassLabel(names=["aphantasia", "control"]),
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"demographics": {
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"country": datasets.Value("string"),
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"age": datasets.Value("float"),
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"gender": datasets.Value("string"),
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"occupation": datasets.Value("string"),
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"art_ability": datasets.Value("int32"),
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"art_experience": datasets.Value("string"),
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"device": datasets.Value("string"),
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"input": datasets.Value("string"),
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"difficult": datasets.Value("string"),
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"diff_explaination": datasets.Value("string"),
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"vviq_score": datasets.Value("int32"),
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"osiq_score": datasets.Value("int32")
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},
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"drawings": {
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"kitchen": {
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"perception": datasets.Image(decode=True, id=None),
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"memory": datasets.Image(decode=True, id=None)
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},
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"livingroom": {
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"perception": datasets.Image(decode=True, id=None),
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"memory": datasets.Image(decode=True, id=None)
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},
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"bedroom": {
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"perception": datasets.Image(decode=True, id=None),
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"memory": datasets.Image(decode=True, id=None)
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}
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},
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"image": {
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"kitchen": datasets.Image(decode=True, id=None),
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"livingroom": datasets.Image(decode=True, id=None),
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"bedroom": datasets.Image(decode=True, id=None)
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}
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}
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}),
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# No default supervised_keys (as we have to pass both question
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# and context as input).
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supervised_keys=None,
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homepage=_HOMEPAGE,
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citation=_CITATION,
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)
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def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
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url_to_download = self._URL
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downloaded_file = dl_manager.download_and_extract(url_to_download)
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return [
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datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={
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"filepath": downloaded_file["train"]
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})
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]
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def _generate_examples(self, filepath):
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"""This function returns the examples in the raw (text) form."""
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logging.info("generating examples from = %s", filepath)
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with open(filepath, "r") as subjects_file:
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subjects_data = json.load(subjects_file)
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idx = 0
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for sub in subjects_data:
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s = subjects_data[sub]
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for room in subjects_data[sub]["drawings"].keys():
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if subjects_data[sub]["drawings"][room]["perception"] != "":
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img_byt = base64.b64decode(subjects_data[sub]["drawings"][room]["perception"])
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img = Image.open(io.BytesIO(img_byt))
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subjects_data[sub]["drawings"][room]["perception"] = img
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else:
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subjects_data[sub]["drawings"][room]["perception"] = None
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if subjects_data[sub]["drawings"][room]["memory"] != "":
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img_byt = base64.b64decode(subjects_data[sub]["drawings"][room]["memory"])
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img = Image.open(io.BytesIO(img_byt))
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subjects_data[sub]["drawings"][room]["memory"] = img
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else:
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subjects_data[sub]["drawings"][room]["memory"] = None
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for room in subjects_data[sub]["image"].keys():
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img_byt = base64.b64decode(s["image"][room])
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img = Image.open(io.BytesIO(img_byt))
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subjects_data[sub]["image"][room] = img
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idx += 1
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yield idx-1, {
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sub: subjects_data[sub]
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}
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