"""
Pulls data from Kaggle API
"""
from kaggle import KaggleApi
api = KaggleApi()
api.authenticate()
api.dataset_download_files("shivamb/Netflix-shows", unzip= True)
api.kernels_output("eugenioscionti/scraping-rotten-tomatoes-to-enrich-netflix-dataset", "./")
def kaggle_dataset_download(api, dataset_name, path):
kag_api.dataset_download_files(api, dataset_name, unzip=True, path=path)
print("[INFO] Dataset downloaded.")
def fetch_pins_people(resize=.5,
min_faces_per_person=0,
color=False,
slice_=(slice(25, 275), slice(25, 275)),
download_if_missing=True):
"""Load PINS dataset.
Use a PINS dataset provided by Kaggle, everage the scikit-learn memory
optimizations.
Args:
resize (float, optional): Image resize factor. Defaults to .5.
min_faces_per_person (int, optional): Minimal number of images per
person. Defaults to 0.
color (bool): Toggle is images should be in RGB or 1 channel.
Defaults to False.
slice_ (tuple, optional): A rectangle to which images are sliced.
Defaults to (slice(70, 195), slice(78, 172)).
download_if_missing (bool, optional): Set if the dataset should be
downloaded if not present on the machine. Defaults to True.
Returns:
sklearn.utils.Bunch: Collection of data set
"""
from kaggle import KaggleApi
# Extract ZIP dataset
kaggle_api = KaggleApi()
kaggle_home = kaggle_api.read_config_file()['path']
path_to_zip = os.path.join(kaggle_home, 'datasets', PINS_DATASET['name'],
PINS_DATASET['zip'])
path_to_files = os.path.join(kaggle_home, 'datasets', PINS_DATASET['name'],
PINS_DATASET['folder'])
# Download if missing
if download_if_missing and not os.path.exists(path_to_zip):
kaggle_api.authenticate()
kaggle_api.dataset_download_files(PINS_DATASET['name'], quiet=False)
if not os.path.exists(path_to_files):
with ZipFile(path_to_zip, 'r') as zipObj:
extraction_path = os.path.join(kaggle_home, 'datasets',
PINS_DATASET['name'])
zipObj.extractall(extraction_path)
# Load data in memory
m = Memory(location=kaggle_home, compress=6, verbose=0)
load_func = m.cache(_fetch_lfw_people)
faces, target, target_names = load_func(
path_to_files,
resize=resize,
min_faces_per_person=min_faces_per_person,
color=color,
slice_=slice_)
X = faces.reshape(len(faces), -1)
# Fix names
with np.nditer(target_names, op_flags=['readwrite']) as it:
for x in it:
x[...] = np.core.defchararray.replace(x, 'pins ', '')
x[...] = np.core.defchararray.replace(x, ' face', '')
x[...] = np.core.defchararray.title(x)
# pack the results as a Bunch instance
return Bunch(data=X,
images=faces,
target=target,
target_names=target_names)