import logging
import os
import zipfile
from pathlib import Path
from typing import Optional, Union
import joblib
import numpy as np
import scipy.io
from sklearn.datasets import _base
from sklearn.utils import Bunch
ARCHIVE = _base.RemoteFileMetadata(
filename='all.zip',
url='https://files.osf.io/v1/resources/ey9vp/providers/osfstorage/'
'5e7a7065d2927f006fdd1cf9?action=download&direct&version=1',
checksum=('8c799cdebb00192ecb63f3e28c6eeee0e2f64fcb8dad3bc68982e551f2ae5b1c'))
logger = logging.getLogger(__name__)
AVAILABLE_SIMILARITIES = [
'fruit2_romney', 'nonsense_romney', 'furniture_romney', 'kinship_kimrosenberg', 'rectangle_kruschke',
'vegetables2_romney', 'animalpictures5', 'auditory', 'druguse', 'faces11', 'fruits', 'dotpatterns',
'furniture2_romney', 'bodies_viken', 'textures', 'sport_romney', 'bankwiring', 'morsenumbers',
'faces_busey', 'letters', 'vehicles_romney', 'vehicles2_romney', 'birds_romney', 'fruit_romney', 'risks',
'morseall', 'texturemit_heaps', 'cartoonfaces', 'country_robinsonhefner', 'congress', 'phonemes',
'toys_romney', 'colour', 'countriessim', 'faces5', 'tools_romney', 'lines_cohen', 'abstractnumbers',
'countriesdis', 'animalnames11', 'faces_steyvers', 'weapons2_romney', 'texturebrodatz_heaps',
'fish_romney', 'flowerpots', 'sizeangle_treat', 'clothing2_romney', 'weapons_romney', 'clothing_romney',
'animalnames5', 'vegetables_romney', 'animalpictures11']
[docs]
def fetch_similarity_matrix(name: str, data_home: Optional[os.PathLike] = None, download_if_missing: bool = True
) -> Union[Bunch, np.ndarray]:
""" Load human similarity judgements, aggregated to a similarity matrix.
This function provides access to the following similarity matrices:
`fruit2_romney, nonsense_romney, furniture_romney, kinship_kimrosenberg, rectangle_kruschke, vegetables2_romney,
animalpictures5, auditory, druguse, faces11, fruits, dotpatterns, furniture2_romney, bodies_viken,
textures, sport_romney, bankwiring, morsenumbers, faces_busey, letters, vehicles_romney, vehicles2_romney,
birds_romney, fruit_romney, risks, morseall, texturemit_heaps, cartoonfaces, country_robinsonhefner, congress,
phonemes, toys_romney, colour, countriessim, faces5, tools_romney, lines_cohen, abstractnumbers, countriesdis,
animalnames11, faces_steyvers, weapons2_romney, texturebrodatz_heaps, fish_romney, flowerpots, sizeangle_treat,
clothing2_romney, weapons_romney, clothing_romney, animalnames5, vegetables_romney, animalpictures11`.
See :ref:`similarity_matrix_dataset` for a detailed description.
>>> dataset = fetch_similarity_matrix('colour') # doctest: +REMOTE_DATA
>>> dataset.labels[:2].tolist() # doctest: +REMOTE_DATA
['434', '445']
>>> dataset.similarity.shape # doctest: +REMOTE_DATA
(14, 14)
Args:
name: Name of the similarity dataset
data_home : optional, default: None
Specify another download and cache folder for the datasets. By default
all scikit-learn data is stored in '~/scikit_learn_data' subfolders.
download_if_missing : optional, default=True
Returns:
dataset : :class:`~sklearn.utils.Bunch`
Dictionary-like object, with the following attributes.
similarity : ndarray, shape (n_objects, n_objects)
Symmetric matrix of normalized object similarities.
None for some datasets.
proximity : ndarray, shape (n_objects, n_objects)
Symmetric matrix of normalized pairwise proximities.
None for some datasets.
n_objects: int
Number of objects
labels : (n_objects,)
Single word describing each object
sigma: float
Uncertainty of the similarity values.
Not available for all datasets.
DESCR : string
Description of the dataset.
Raises:
IOError: If the data is not locally available, but download_if_missing=False
"""
if name not in AVAILABLE_SIMILARITIES:
raise ValueError(f"Unexpected similarity name = {name}. Use one of {AVAILABLE_SIMILARITIES}.")
data_home = Path(_base.get_data_home(data_home=data_home))
if not data_home.exists():
data_home.mkdir()
basepath = Path(_base._pkl_filepath(data_home, 'similarity_collection/'))
filepath = basepath.joinpath(f'{name}.pkz')
if not filepath.exists():
if not download_if_missing:
raise IOError("Data not found and `download_if_missing` is False")
logger.info('Downloading imagenet similarity data from {} to {}'.format(ARCHIVE.url, data_home))
archive_path = _base._fetch_remote(ARCHIVE, dirname=data_home)
if not basepath.exists():
basepath.mkdir(parents=True)
with zipfile.ZipFile(archive_path) as zf:
for _this_name in AVAILABLE_SIMILARITIES:
with zf.open(f'{_this_name}.mat', 'r') as f:
_raw = scipy.io.loadmat(f)
_this_dict = {
'similarity': np.array(_raw.get('s', None)),
'proximity': np.array(_raw.get('d', None)),
'n_objects': int(_raw['n'][0, 0]),
'labels': np.array(_raw['labs'], dtype=str),
'sigma': float(_raw.get('sigma_emp', np.array([[np.nan]]))[0, 0]),
}
_this_filepath = basepath.joinpath(f'{_this_name}.pkz')
joblib.dump(_this_dict, _this_filepath, compress=6)
if name == _this_name:
data_dict = _this_dict
os.remove(archive_path)
else:
data_dict = joblib.load(filepath)
module_path = Path(__file__).parent
with module_path.joinpath('descr', 'similarity_matrix.rst').open() as rst_file:
fdescr = rst_file.read()
return Bunch(**data_dict,
DESCR=fdescr)
