from pathlib import Path
import logging
import joblib
import os
from typing import Optional, Union
import zipfile
import numpy as np
from sklearn.datasets import _base
from sklearn.utils import check_random_state, Bunch
from cblearn.preprocessing import query_from_columns
ARCHIVE = _base.RemoteFileMetadata(
filename='nature_and_vogue_triplets.zip',
url='http://anttiukkonen.com/nature_and_vogue_triplets.zip',
checksum=('db4718bb742a2492dee87e94daead3070b2afa3a9174aa717be336940e47e3cc'))
logger = logging.getLogger(__name__)
[docs]
def fetch_nature_scene_similarity(data_home: Optional[os.PathLike] = None, download_if_missing: bool = True,
shuffle: bool = True, random_state: Optional[np.random.RandomState] = None,
return_triplets: bool = False) -> Union[Bunch, np.ndarray]:
""" Load the nature scene similarity dataset (odd-one-out).
=================== =====================
Triplets 3355
Objects (Scenes) 120
=================== =====================
See :ref:`nature_vogue_dataset` for a detailed description.
>>> dataset = fetch_nature_scene_similarity(shuffle=True) # doctest: +REMOTE_DATA
>>> dataset.image_label[[0, -1]].tolist() # doctest: +REMOTE_DATA
['art114.jpg', 'n344019.jpg']
>>> dataset.triplet.shape # doctest: +REMOTE_DATA
(3355, 3)
Args:
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
shuffle: default = True
Shuffle the order of triplet constraints.
random_state: optional, default = None
Initialization for shuffle random generator
return_triplets : boolean, default=False.
If True, returns numpy array instead of a Bunch object.
Returns:
dataset : :class:`~sklearn.utils.Bunch`
Dictionary-like object, with the following attributes.
triplet : ndarray, shape (n_triplets, 3)
Each row corresponding odd-one-out query.
The columns represent the odd image and two others.
class_label : ndarray, shape (120, )
Names of the scene images.
DESCR : string
Description of the dataset.
triplets : numpy arrays (n_triplets, 3)
Only present when `return_triplets=True`.
Raises:
IOError: If the data is not locally available, but download_if_missing=False
"""
return _fetch_nature_vogue('nature', data_home, download_if_missing, shuffle, random_state, return_triplets)
[docs]
def fetch_vogue_cover_similarity(data_home: Optional[os.PathLike] = None, download_if_missing: bool = True,
shuffle: bool = True, random_state: Optional[np.random.RandomState] = None,
return_triplets: bool = False) -> Union[Bunch, np.ndarray]:
""" Load the vogue cover similarity dataset (odd-one-out).
=================== =====================
Triplets 1107
Objects (Covers) 60
=================== =====================
See :ref:`nature_vogue_dataset` for a detailed description.
>>> dataset = fetch_vogue_cover_similarity(shuffle=True) # doctest: +REMOTE_DATA
>>> dataset.image_label[[0, -1]].tolist() # doctest: +REMOTE_DATA
['Cover_uk_VOgue_MAY10_V_29mar10_bt_268x353.jpg', 'voguecoverapr11_bt_268x353.jpg']
>>> dataset.triplet.shape # doctest: +REMOTE_DATA
(1107, 3)
Args:
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
shuffle: default = True
Shuffle the order of triplet constraints.
random_state: optional, default = None
Initialization for shuffle random generator
return_triplets : boolean, default=False.
If True, returns numpy array instead of a Bunch object.
Returns:
dataset : :class:`~sklearn.utils.Bunch`
Dictionary-like object, with the following attributes.
triplet : ndarray, shape (n_triplets, 3)
Each row corresponding odd-one-out query.
The columns represent the odd image and two others.
class_label : ndarray, shape (120, )
Names of the scene images.
DESCR : string
Description of the dataset.
triplets : numpy arrays (n_triplets, 3)
Only present when `return_triplets=True`.
Raises:
IOError: If the data is not locally available, but download_if_missing=False
"""
return _fetch_nature_vogue('vogue', data_home, download_if_missing, shuffle, random_state, return_triplets)
def _fetch_nature_vogue(dataset: str, data_home: Optional[os.PathLike] = None, download_if_missing: bool = True,
shuffle: bool = True, random_state: Optional[np.random.RandomState] = None,
return_triplets: bool = False) -> Union[Bunch, np.ndarray]:
data_home = Path(_base.get_data_home(data_home=data_home))
if not data_home.exists():
data_home.mkdir()
filepath = Path(_base._pkl_filepath(data_home, 'nature_vogue_similarity.pkz'))
if not filepath.exists():
if not download_if_missing:
raise IOError("Data not found and `download_if_missing` is False")
logger.info('Downloading material similarity data from {} to {}'.format(ARCHIVE.url, data_home))
archive_path = _base._fetch_remote(ARCHIVE, dirname=data_home)
with zipfile.ZipFile(archive_path) as zf:
with zf.open('nature/nature_triplets.txt', 'r') as f:
nature_data = np.loadtxt(f, dtype=str)
with zf.open('vogue/vogue_triplets.txt', 'r') as f:
vogue_data = np.loadtxt(f, dtype=str)
joblib.dump((nature_data, vogue_data), filepath, compress=6)
os.remove(archive_path)
else:
(nature_data, vogue_data) = joblib.load(filepath)
if dataset == 'nature':
data = nature_data[:, [2, 0, 1]]
elif dataset == 'vogue':
data = vogue_data[:, [2, 0, 1]]
triplets, encoder = query_from_columns(data, [0, 1, 2], return_transformer=True)
classes = encoder.encoder_.classes_
if shuffle:
random_state = check_random_state(random_state)
triplets = random_state.permutation(triplets)
if return_triplets:
return triplets
module_path = Path(__file__).parent
with module_path.joinpath('descr', 'nature_vogue_similarity.rst').open() as rst_file:
fdescr = rst_file.read()
return Bunch(triplet=triplets,
image_label=classes,
DESCR=fdescr)
