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
ARCHIVE = _base.RemoteFileMetadata(
filename='60_cars_data.zip',
url='http://www.tml.cs.uni-tuebingen.de/team/luxburg/code_and_data/60_cars_data.zip',
checksum=('5fa2ad932d48adf5cfe36bd16a08b25fd88d1519d974908f6ccbba769f629640'))
logger = logging.getLogger(__name__)
[docs]
def fetch_car_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 60-car dataset (most-central triplets).
=================== =====================
Triplets 7097
Objects (Cars) 60
Query 3 cars, most-central
Sessions 146
Queries per Session 30-50
=================== =====================
See :ref:`central_car_dataset` for a detailed description.
>>> dataset = fetch_car_similarity(shuffle=False) # doctest: +REMOTE_DATA
>>> dataset.class_name.tolist() # doctest: +REMOTE_DATA
['OFF-ROAD / SPORT UTILITY VEHICLES', 'ORDINARY CARS', 'OUTLIERS', 'SPORTS CARS']
>>> dataset.triplet.shape # doctest: +REMOTE_DATA
(7097, 3)
>>> rounds, round_count = np.unique(dataset.survey_round, return_counts=True) # doctest: +REMOTE_DATA
>>> len(rounds), round_count.min(), round_count.max() # doctest: +REMOTE_DATA
(146, 30, 50)
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 a triplet constraint.
The columns represent the three indices shown per most-central question.
response : ndarray, shape (n_triplets, )
The car per question (0, 1, or 2) that was selected as "most-central".
survey_round : ndarray of int, shape (n_triplets, )
Survey rounds, grouping responses from a participant during a session.
Some participants responded in multiple rounds at different times.
rt : ndarray of float, shape (n_triplets, )
Reaction time of the response in seconds.
class_id : np.ndarray (60, )
The class assigned to each object.
class_name : list (4)
Names of the classes.
DESCR : string
Description of the dataset.
triplets : numpy array (n_triplets, 3)
Only present when `return_triplets=True`.
Raises:
IOError: If the data is not locally available, but download_if_missing=False
"""
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, 'car_centrality.pkz'))
if not filepath.exists():
if not download_if_missing:
raise IOError("Data not found and `download_if_missing` is False")
logger.info('Downloading 60-car dataset 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('60_cars_data/survey_data.csv', 'r') as f:
survey_data = np.loadtxt(f, dtype=str, delimiter=',', skiprows=1)
joblib.dump(survey_data, filepath, compress=6)
os.remove(archive_path)
else:
survey_data = joblib.load(filepath)
class_map = {
'ORDINARY CARS': [2, 6, 7, 8, 9, 10, 11, 12, 16, 17, 25, 32, 35, 36, 37, 38,
39, 41, 44, 45, 46, 55, 58, 60],
'SPORTS CARS': [15, 19, 20, 28, 40, 42, 47, 48, 49, 50, 51, 52, 54, 56, 59],
'OFF-ROAD / SPORT UTILITY VEHICLES': [1, 3, 4, 5, 13, 14, 18, 22, 24, 26, 27,
29, 31, 33, 34, 43, 57],
'OUTLIERS': [21, 23, 30, 53],
}
class_names = np.asarray(sorted(class_map.keys()))
classes = np.empty(60, dtype=int)
for cls_ix, cls_name in enumerate(class_names):
classes[np.array(class_map[cls_name]) - 1] = cls_ix
if shuffle:
random_state = check_random_state(random_state)
shuffle_ix = random_state.permutation(len(survey_data))
survey_data = survey_data[shuffle_ix]
raw_triplets = survey_data[:, [2, 3, 4]].astype(int)
triplets = raw_triplets - 1
response = (survey_data[:, [1]].astype(int) == raw_triplets).nonzero()[1]
survey_round = survey_data[:, [0]].astype(int)
rt = survey_data[:, [5]].astype(float)
if return_triplets:
return triplets
module_path = Path(__file__).parent
with module_path.joinpath('descr', 'car_similarity.rst').open() as rst_file:
fdescr = rst_file.read()
return Bunch(triplet=triplets,
response=response,
survey_round=survey_round,
rt=rt,
class_id=classes,
class_name=class_names,
DESCR=fdescr)
