def fetch_GLASS(path, valid_size=0.2, test_size=0.2, seed=None):
path = Path(path)
data_path = path / 'glass.data'
if not data_path.exists():
path.mkdir(parents=True)
download(
'https://archive.ics.uci.edu/ml/machine-learning-databases/glass/glass.data',
data_path)
data = np.genfromtxt(data_path, delimiter=',')
X, Y = (data[:, 1:-1]).astype(np.float32), (data[:, -1] - 1).astype(int)
X_train, X_test, y_train, y_test = train_test_split(X,
Y,
stratify=Y,
test_size=test_size,
random_state=seed)
X_train, X_val, y_train, y_val = train_test_split(X_train,
y_train,
stratify=y_train,
test_size=valid_size /
(1 - test_size),
random_state=seed)
return dict(X_train=X_train,
y_train=y_train,
X_valid=X_val,
y_valid=y_val,
X_test=X_test,
y_test=y_test)
def fetch_MICROSOFT(path, **kwargs):
train_path = os.path.join(path, 'msrank_train.tsv')
test_path = os.path.join(path, 'msrank_test.tsv')
if not all(os.path.exists(fname) for fname in (train_path, test_path)):
os.makedirs(path, exist_ok=True)
download(
"https://www.dropbox.com/s/izpty5feug57kqn/msrank_train.tsv?dl=1",
train_path)
download(
"https://www.dropbox.com/s/tlsmm9a6krv0215/msrank_test.tsv?dl=1",
test_path)
for fname in (train_path, test_path):
raw = open(fname).read().replace('\\t', '\t')
with open(fname, 'w') as f:
f.write(raw)
data_train = pd.read_csv(train_path, header=None, skiprows=1, sep='\t')
data_test = pd.read_csv(test_path, header=None, skiprows=1, sep='\t')
train_idx_path = os.path.join(path, 'train_idx.txt')
valid_idx_path = os.path.join(path, 'valid_idx.txt')
if not all(
os.path.exists(fname)
for fname in (train_idx_path, valid_idx_path)):
download(
"https://www.dropbox.com/s/pba6dyibyogep46/train_idx.txt?dl=1",
train_idx_path)
download(
"https://www.dropbox.com/s/yednqu9edgdd2l1/valid_idx.txt?dl=1",
valid_idx_path)
train_idx = pd.read_csv(train_idx_path, header=None)[0].values
valid_idx = pd.read_csv(valid_idx_path, header=None)[0].values
X_train, y_train, query_train = data_train.iloc[
train_idx,
2:].values, data_train.iloc[train_idx,
0].values, data_train.iloc[train_idx,
1].values
X_valid, y_valid, query_valid = data_train.iloc[
valid_idx,
2:].values, data_train.iloc[valid_idx,
0].values, data_train.iloc[valid_idx,
1].values
X_test, y_test, query_test = data_test.iloc[:,
2:].values, data_test.iloc[:,
0].values, data_test.iloc[:,
1].values
return dict(
X_train=X_train.astype(np.float32),
y_train=y_train.astype(np.int64),
query_train=query_train,
X_valid=X_valid.astype(np.float32),
y_valid=y_valid.astype(np.int64),
query_valid=query_valid,
X_test=X_test.astype(np.float32),
y_test=y_test.astype(np.int64),
query_test=query_test,
)
def fetch_COVTYPE(path, valid_size=0.2, test_size=0.2, seed=None):
path = Path(path)
data_path = path / 'covtype.data'
if not data_path.exists():
path.mkdir(parents=True)
archive_path = path / 'covtype.data.gz'
download(
'https://archive.ics.uci.edu/ml/machine-learning-databases/covtype/covtype.data.gz',
archive_path)
with gzip.open(archive_path, 'rb') as f_in:
with open(data_path, 'wb') as f_out:
shutil.copyfileobj(f_in, f_out)
data = np.genfromtxt(data_path, delimiter=',')
X, Y = (data[:, :-1]).astype(np.float32), (data[:, -1] - 1).astype(int)
X_train, X_test, y_train, y_test = train_test_split(X,
Y,
stratify=Y,
test_size=test_size,
random_state=seed)
X_train, X_val, y_train, y_val = train_test_split(X_train,
y_train,
stratify=y_train,
test_size=valid_size /
(1 - test_size),
random_state=seed)
return dict(X_train=X_train,
y_train=y_train,
X_valid=X_val,
y_valid=y_val,
X_test=X_test,
y_test=y_test)
def fetch_HIGGS(path,
train_size=None,
valid_size=None,
test_size=5 * 10**5,
**kwargs):
data_path = os.path.join(path, 'higgs.csv')
if not os.path.exists(data_path):
os.makedirs(path, exist_ok=True)
archive_path = os.path.join(path, 'HIGGS.csv.gz')
download(
'https://archive.ics.uci.edu/ml/machine-learning-databases/00280/HIGGS.csv.gz',
archive_path)
with gzip.open(archive_path, 'rb') as f_in:
with open(data_path, 'wb') as f_out:
shutil.copyfileobj(f_in, f_out)
n_features = 29
types = {i: (np.float32 if i != 0 else np.int) for i in range(n_features)}
data = pd.read_csv(data_path, header=None, dtype=types)
data_train, data_test = data.iloc[:-test_size], data.iloc[-test_size:]
X_train, y_train = data_train.iloc[:, 1:].values, data_train.iloc[:,
0].values
X_test, y_test = data_test.iloc[:, 1:].values, data_test.iloc[:, 0].values
if all(sizes is None for sizes in (train_size, valid_size)):
train_idx_path = os.path.join(path, 'stratified_train_idx.txt')
valid_idx_path = os.path.join(path, 'stratified_valid_idx.txt')
if not all(
os.path.exists(fname)
for fname in (train_idx_path, valid_idx_path)):
download(
"https://www.dropbox.com/s/i2uekmwqnp9r4ix/stratified_train_idx.txt?dl=1",
train_idx_path)
download(
"https://www.dropbox.com/s/wkbk74orytmb2su/stratified_valid_idx.txt?dl=1",
valid_idx_path)
train_idx = pd.read_csv(train_idx_path, header=None)[0].values
valid_idx = pd.read_csv(valid_idx_path, header=None)[0].values
else:
assert train_size, "please provide either train_size or none of sizes"
if valid_size is None:
valid_size = len(X_train) - train_size
assert valid_size > 0
if train_size + valid_size > len(X_train):
warnings.warn(
'train_size + valid_size = {} exceeds dataset size: {}.'.
format(train_size + valid_size, len(X_train)), Warning)
shuffled_indices = np.random.permutation(np.arange(len(X_train)))
train_idx = shuffled_indices[:train_size]
valid_idx = shuffled_indices[train_size:train_size + valid_size]
return dict(
X_train=X_train[train_idx],
y_train=y_train[train_idx],
X_valid=X_train[valid_idx],
y_valid=y_train[valid_idx],
X_test=X_test,
y_test=y_test,
)
def fetch_TICTACTOE(path, valid_size=0.2, test_size=0.2, seed=None):
path = Path(path)
data_path = path / 'tic-tac-toe.data'
if not data_path.exists():
path.mkdir(parents=True, exist_ok=True)
download(
'https://archive.ics.uci.edu/ml/machine-learning-databases/tic-tac-toe/tic-tac-toe.data',
data_path)
data = pd.read_csv(data_path, names=np.arange(10))
encoder = OrdinalEncoder(return_df=False)
data = encoder.fit_transform(data)
X, Y = (data[:, :-1]).astype(np.float32), (data[:, -1] - 1).astype(int)
X_train, X_test, y_train, y_test = train_test_split(X,
Y,
stratify=Y,
test_size=test_size,
random_state=seed)
X_train, X_val, y_train, y_val = train_test_split(X_train,
y_train,
stratify=y_train,
test_size=valid_size /
(1 - test_size),
random_state=seed)
return dict(X_train=X_train,
y_train=y_train,
X_valid=X_val,
y_valid=y_val,
X_test=X_test,
y_test=y_test)
def fetch_YEAR(path,
train_size=None,
valid_size=None,
test_size=51630,
**kwargs):
data_path = os.path.join(path, 'data.csv')
if not os.path.exists(data_path):
os.makedirs(path, exist_ok=True)
download(
'https://www.dropbox.com/s/l09pug0ywaqsy0e/YearPredictionMSD.txt?dl=1',
data_path)
n_features = 91
types = {i: (np.float32 if i != 0 else np.int) for i in range(n_features)}
data = pd.read_csv(data_path, header=None, dtype=types)
data_train, data_test = data.iloc[:-test_size], data.iloc[-test_size:]
X_train, y_train = data_train.iloc[:, 1:].values, data_train.iloc[:,
0].values
X_test, y_test = data_test.iloc[:, 1:].values, data_test.iloc[:, 0].values
if all(sizes is None for sizes in (train_size, valid_size)):
train_idx_path = os.path.join(path, 'stratified_train_idx.txt')
valid_idx_path = os.path.join(path, 'stratified_valid_idx.txt')
if not all(
os.path.exists(fname)
for fname in (train_idx_path, valid_idx_path)):
download(
"https://www.dropbox.com/s/00u6cnj9mthvzj1/stratified_train_idx.txt?dl=1",
train_idx_path)
download(
"https://www.dropbox.com/s/420uhjvjab1bt7k/stratified_valid_idx.txt?dl=1",
valid_idx_path)
train_idx = pd.read_csv(train_idx_path, header=None)[0].values
valid_idx = pd.read_csv(valid_idx_path, header=None)[0].values
else:
assert train_size, "please provide either train_size or none of sizes"
if valid_size is None:
valid_size = len(X_train) - train_size
assert valid_size > 0
if train_size + valid_size > len(X_train):
warnings.warn(
'train_size + valid_size = {} exceeds dataset size: {}.'.
format(train_size + valid_size, len(X_train)), Warning)
shuffled_indices = np.random.permutation(np.arange(len(X_train)))
train_idx = shuffled_indices[:train_size]
valid_idx = shuffled_indices[train_size:train_size + valid_size]
return dict(
X_train=X_train[train_idx],
y_train=y_train[train_idx],
X_valid=X_train[valid_idx],
y_valid=y_train[valid_idx],
X_test=X_test,
y_test=y_test,
)
def fetch_ALOI(path, valid_size=0.2, test_size=0.2, seed=None):
from PIL import Image
from tqdm import tqdm
path = Path(path)
data_path = path / 'aloi_red4'
npz_path = path / 'aloi_red4.npz'
if not data_path.exists():
path.mkdir(parents=True, exist_ok=True)
for data_type in ["ill", "col", "view", "stereo"]:
archive_path = path / f'aloi_red4_{data_type}.tar'
download(
f'http://aloi.science.uva.nl/tars/aloi_red4_{data_type}.tar',
archive_path)
with tarfile.open(archive_path, 'r') as f_in:
f_in.extractall(path=data_path)
if not npz_path.exists():
X = np.empty((110250, 3, 144, 192), dtype=np.uint8)
Y = np.empty(110250, dtype=np.uint16)
# loop over classes
i = 0
for c in tqdm(range(1000), desc='Converting ALOI png to npy'):
c_path = data_path / "png4" / str(c + 1)
# loop over class instances
for i_path in c_path.glob('*.png'):
im_frame = Image.open(i_path)
X[i] = np.transpose(np.array(im_frame), (2, 0, 1))
Y[i] = c
i += 1
np.savez_compressed(npz_path, X=X, Y=Y)
else:
data = np.load(npz_path)
X, Y = data['X'], data['Y']
X_train, X_test, y_train, y_test = train_test_split(X,
Y,
stratify=Y,
test_size=test_size,
random_state=seed)
X_train, X_val, y_train, y_val = train_test_split(X_train,
y_train,
stratify=y_train,
test_size=valid_size /
(1 - test_size),
random_state=seed)
return dict(X_train=X_train,
y_train=y_train,
X_valid=X_val,
y_valid=y_val,
X_test=X_test,
y_test=y_test)
def fetch_EPSILON(path,
train_size=None,
valid_size=None,
test_size=None,
**kwargs):
train_path = os.path.join(path, 'epsilon_normalized')
test_path = os.path.join(path, 'epsilon_normalized.t')
if not all(os.path.exists(fname) for fname in (train_path, test_path)):
os.makedirs(path, exist_ok=True)
train_archive_path = os.path.join(path, 'epsilon_normalized.bz2')
test_archive_path = os.path.join(path, 'epsilon_normalized.t.bz2')
if not all(
os.path.exists(fname)
for fname in (train_archive_path, test_archive_path)):
download(
"https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary/epsilon_normalized.bz2",
train_archive_path)
download(
"https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary/epsilon_normalized.t.bz2",
test_archive_path)
print("unpacking dataset")
for file_name, archive_name in zip(
(train_path, test_path), (train_archive_path, test_archive_path)):
zipfile = bz2.BZ2File(archive_name)
with open(file_name, 'wb') as f:
f.write(zipfile.read())
print("reading dataset (it may take a long time)")
X_train, y_train = load_svmlight_file(train_path,
dtype=np.float32,
n_features=2000)
X_test, y_test = load_svmlight_file(test_path,
dtype=np.float32,
n_features=2000)
X_train, X_test = X_train.toarray(), X_test.toarray()
y_train, y_test = y_train.astype(np.int), y_test.astype(np.int)
y_train[y_train == -1] = 0
y_test[y_test == -1] = 0
if all(sizes is None for sizes in (train_size, valid_size, test_size)):
train_idx_path = os.path.join(path, 'stratified_train_idx.txt')
valid_idx_path = os.path.join(path, 'stratified_valid_idx.txt')
if not all(
os.path.exists(fname)
for fname in (train_idx_path, valid_idx_path)):
download(
"https://www.dropbox.com/s/wxgm94gvm6d3xn5/stratified_train_idx.txt?dl=1",
train_idx_path)
download(
"https://www.dropbox.com/s/fm4llo5uucdglti/stratified_valid_idx.txt?dl=1",
valid_idx_path)
train_idx = pd.read_csv(train_idx_path, header=None)[0].values
valid_idx = pd.read_csv(valid_idx_path, header=None)[0].values
else:
assert train_size, "please provide either train_size or none of sizes"
if valid_size is None:
valid_size = len(X_train) - train_size
assert valid_size > 0
if train_size + valid_size > len(X_train):
warnings.warn(
'train_size + valid_size = {} exceeds dataset size: {}.'.
format(train_size + valid_size, len(X_train)), Warning)
if test_size is not None:
warnings.warn('Test set is fixed for this dataset.', Warning)
shuffled_indices = np.random.permutation(np.arange(len(X_train)))
train_idx = shuffled_indices[:train_size]
valid_idx = shuffled_indices[train_size:train_size + valid_size]
return dict(X_train=X_train[train_idx],
y_train=y_train[train_idx],
X_valid=X_train[valid_idx],
y_valid=y_train[valid_idx],
X_test=X_test,
y_test=y_test)
def fetch_YAHOO(path, *args):
train_path = os.path.join(path, 'yahoo_train.tsv')
valid_path = os.path.join(path, 'yahoo_valid.tsv')
test_path = os.path.join(path, 'yahoo_test.tsv')
if not all(
os.path.exists(fname)
for fname in (train_path, valid_path, test_path)):
os.makedirs(path, exist_ok=True)
train_archive_path = os.path.join(path, 'yahoo_train.tsv.gz')
valid_archive_path = os.path.join(path, 'yahoo_valid.tsv.gz')
test_archive_path = os.path.join(path, 'yahoo_test.tsv.gz')
if not all(
os.path.exists(fname)
for fname in (train_archive_path, valid_archive_path,
test_archive_path)):
download(
"https://www.dropbox.com/s/7rq3ki5vtxm6gzx/yahoo_set_1_train.gz?dl=1",
train_archive_path)
download(
"https://www.dropbox.com/s/3ai8rxm1v0l5sd1/yahoo_set_1_validation.gz?dl=1",
valid_archive_path)
download(
"https://www.dropbox.com/s/3d7tdfb1an0b6i4/yahoo_set_1_test.gz?dl=1",
test_archive_path)
for file_name, archive_name in zip(
(train_path, valid_path, test_path),
(train_archive_path, valid_archive_path, test_archive_path)):
with gzip.open(archive_name, 'rb') as f_in:
with open(file_name, 'wb') as f_out:
shutil.copyfileobj(f_in, f_out)
for fname in (train_path, valid_path, test_path):
raw = open(fname).read().replace('\\t', '\t')
with open(fname, 'w') as f:
f.write(raw)
data_train = pd.read_csv(train_path, header=None, skiprows=1, sep='\t')
data_valid = pd.read_csv(valid_path, header=None, skiprows=1, sep='\t')
data_test = pd.read_csv(test_path, header=None, skiprows=1, sep='\t')
X_train, y_train, query_train = data_train.iloc[:,
2:].values, data_train.iloc[:,
0].values, data_train.iloc[:,
1].values
X_valid, y_valid, query_valid = data_valid.iloc[:,
2:].values, data_valid.iloc[:,
0].values, data_valid.iloc[:,
1].values
X_test, y_test, query_test = data_test.iloc[:,
2:].values, data_test.iloc[:,
0].values, data_test.iloc[:,
1].values
return dict(
X_train=X_train.astype(np.float32),
y_train=y_train,
query_train=query_train,
X_valid=X_valid.astype(np.float32),
y_valid=y_valid,
query_valid=query_valid,
X_test=X_test.astype(np.float32),
y_test=y_test,
query_test=query_test,
)
def fetch_A9A(path,
train_size=None,
valid_size=None,
test_size=None,
**kwargs):
train_path = os.path.join(path, 'a9a')
test_path = os.path.join(path, 'a9a.t')
if not all(os.path.exists(fname) for fname in (train_path, test_path)):
os.makedirs(path, exist_ok=True)
download("https://www.dropbox.com/s/9cqdx166iwonrj9/a9a?dl=1",
train_path)
download("https://www.dropbox.com/s/sa0ds895c0v4xc6/a9a.t?dl=1",
test_path)
X_train, y_train = load_svmlight_file(train_path,
dtype=np.float32,
n_features=123)
X_test, y_test = load_svmlight_file(test_path,
dtype=np.float32,
n_features=123)
X_train, X_test = X_train.toarray(), X_test.toarray()
y_train[y_train == -1] = 0
y_test[y_test == -1] = 0
y_train, y_test = y_train.astype(np.int), y_test.astype(np.int)
if all(sizes is None for sizes in (train_size, valid_size, test_size)):
train_idx_path = os.path.join(path, 'stratified_train_idx.txt')
valid_idx_path = os.path.join(path, 'stratified_valid_idx.txt')
if not all(
os.path.exists(fname)
for fname in (train_idx_path, valid_idx_path)):
download(
"https://www.dropbox.com/s/xy4wwvutwikmtha/stratified_train_idx.txt?dl=1",
train_idx_path)
download(
"https://www.dropbox.com/s/nthpxofymrais5s/stratified_test_idx.txt?dl=1",
valid_idx_path)
train_idx = pd.read_csv(train_idx_path, header=None)[0].values
valid_idx = pd.read_csv(valid_idx_path, header=None)[0].values
else:
assert train_size, "please provide either train_size or none of sizes"
if valid_size is None:
valid_size = len(X_train) - train_size
assert valid_size > 0
if train_size + valid_size > len(X_train):
warnings.warn(
'train_size + valid_size = {} exceeds dataset size: {}.'.
format(train_size + valid_size, len(X_train)), Warning)
if test_size is not None:
warnings.warn('Test set is fixed for this dataset.', Warning)
shuffled_indices = np.random.permutation(np.arange(len(X_train)))
train_idx = shuffled_indices[:train_size]
valid_idx = shuffled_indices[train_size:train_size + valid_size]
return dict(X_train=X_train[train_idx],
y_train=y_train[train_idx],
X_valid=X_train[valid_idx],
y_valid=y_train[valid_idx],
X_test=X_test,
y_test=y_test)
def fetch_PROTEIN(path,
train_size=None,
valid_size=None,
test_size=None,
**kwargs):
"""
https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multiclass.html#protein
"""
train_path = os.path.join(path, 'protein')
test_path = os.path.join(path, 'protein.t')
if not all(os.path.exists(fname) for fname in (train_path, test_path)):
os.makedirs(path, exist_ok=True)
download("https://www.dropbox.com/s/pflp4vftdj3qzbj/protein.tr?dl=1",
train_path)
download("https://www.dropbox.com/s/z7i5n0xdcw57weh/protein.t?dl=1",
test_path)
for fname in (train_path, test_path):
raw = open(fname).read().replace(' .', '0.')
with open(fname, 'w') as f:
f.write(raw)
X_train, y_train = load_svmlight_file(train_path,
dtype=np.float32,
n_features=357)
X_test, y_test = load_svmlight_file(test_path,
dtype=np.float32,
n_features=357)
X_train, X_test = X_train.toarray(), X_test.toarray()
y_train, y_test = y_train.astype(np.int), y_test.astype(np.int)
if all(sizes is None for sizes in (train_size, valid_size, test_size)):
train_idx_path = os.path.join(path, 'stratified_train_idx.txt')
valid_idx_path = os.path.join(path, 'stratified_valid_idx.txt')
if not all(
os.path.exists(fname)
for fname in (train_idx_path, valid_idx_path)):
download(
"https://www.dropbox.com/s/wq2v9hl1wxfufs3/small_stratified_train_idx.txt?dl=1",
train_idx_path)
download(
"https://www.dropbox.com/s/7o9el8pp1bvyy22/small_stratified_valid_idx.txt?dl=1",
valid_idx_path)
train_idx = pd.read_csv(train_idx_path, header=None)[0].values
valid_idx = pd.read_csv(valid_idx_path, header=None)[0].values
else:
assert train_size, "please provide either train_size or none of sizes"
if valid_size is None:
valid_size = len(X_train) - train_size
assert valid_size > 0
if train_size + valid_size > len(X_train):
warnings.warn(
'train_size + valid_size = {} exceeds dataset size: {}.'.
format(train_size + valid_size, len(X_train)), Warning)
if test_size is not None:
warnings.warn('Test set is fixed for this dataset.', Warning)
shuffled_indices = np.random.permutation(np.arange(len(X_train)))
train_idx = shuffled_indices[:train_size]
valid_idx = shuffled_indices[train_size:train_size + valid_size]
return dict(X_train=X_train[train_idx],
y_train=y_train[train_idx],
X_valid=X_train[valid_idx],
y_valid=y_train[valid_idx],
X_test=X_test,
y_test=y_test)
query_train=query_train,
X_valid=X_valid.astype(np.float32),
y_valid=y_valid,
query_valid=query_valid,
X_test=X_test.astype(np.float32),
y_test=y_test,
query_test=query_test,
)
def fetch_CLICK(path, valid_size=100_000, seed=None, **kwargs):
# based on: https://www.kaggle.com/slamnz/primer-airlines-delay
csv_path = os.path.join(path, 'click.csv')
if not os.path.exists(csv_path):
os.makedirs(path, exist_ok=True)
download('https://www.dropbox.com/s/w43ylgrl331svqc/click.csv?dl=1',
csv_path)
data = pd.read_csv(csv_path, index_col=0)
X, y = data.drop(columns=['target']), data['target']
X_train, X_test = X[:-100_000].copy(), X[-100_000:].copy()
y_train, y_test = y[:-100_000].copy(), y[-100_000:].copy()
y_train = (y_train.values.reshape(-1) == 1).astype('int64')
y_test = (y_test.values.reshape(-1) == 1).astype('int64')
cat_features = [
'url_hash', 'ad_id', 'advertiser_id', 'query_id', 'keyword_id',
'title_id', 'description_id', 'user_id'
]
X_train, X_val, y_train, y_val = train_test_split(X_train,