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datasets.py
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from copy import deepcopy
from os.path import isfile
from sklearn.datasets import fetch_openml, fetch_covtype
import random
import numpy as np
import requests
# store some datasets globally because loading them from file again and again is too slow
try:
mnist_X = np.load("mnist_X.npy", allow_pickle=True)
mnist_y = np.load("mnist_y.npy", allow_pickle=True)
except FileNotFoundError:
print("Downloading mnist dataset ...")
mnist_X, mnist_y = fetch_openml("mnist_784", version=1, return_X_y=True, as_frame=False)
np.save("mnist_X.npy", mnist_X)
np.save("mnist_y.npy", mnist_y)
try:
covtype_X = np.load("covtype_X.npy")
except FileNotFoundError:
print("Downloading forest covertype dataset ...")
covtype_X = fetch_covtype()["data"]
assert covtype_X.shape[0] == 581012
assert covtype_X.shape[1] == 54
np.save("covtype_X.npy", covtype_X)
if not isfile("rialto.npy") or not isfile("rialto_labels.npy"):
# Download from https://github.com/vlosing/driftDatasets/tree/master/realWorld/rialto
print("Downloading rialto dataset ...")
open("rialto.data", "wb").write(requests.get("https://raw.githubusercontent.com/vlosing/driftDatasets/master/realWorld/rialto/rialto.data").content)
open("rialto.labels", "wb").write(requests.get("https://raw.githubusercontent.com/vlosing/driftDatasets/master/realWorld/rialto/rialto.labels").content)
X = np.genfromtxt('rialto.data', dtype=float, delimiter=' ')
y = np.genfromtxt('rialto.labels', dtype=int, delimiter=' ')
assert X.shape[0] == 82250
assert X.shape[1] == 27
assert X.shape[0] == y.shape[0]
np.save("rialto.npy", X)
np.save("rialto_labels.npy", y)
if not isfile("music.npy"):
try:
full_dataset = np.genfromtxt('music.csv', dtype=float, delimiter=',')[1:, :] # First line is table header
except FileNotFoundError:
print("Downloading music dataset ...")
open("music.csv", "wb").write(requests.get("https://raw.githubusercontent.com/scikit-multiflow/streaming-datasets/master/music.csv").content)
full_dataset = np.genfromtxt('music.csv', dtype=float, delimiter=',')[1:, :] # First line is table header
for i in range(6, full_dataset.shape[1]):
data_min = np.nanpercentile(a=full_dataset[:, i], q=0.1)
data_max = np.nanpercentile(a=full_dataset[:, i], q=99.9)
if (data_max - data_min) > 0:
full_dataset[:, i] = np.minimum(1.0, np.maximum(0.0, (full_dataset[:, i] - data_min) * (1.0 / (data_max - data_min))))
np.save("music.npy", full_dataset)
def mnist_dataset(length, drifts):
assert mnist_X.shape[1] == 784
data = np.zeros(shape=(length, 14*14))
if isinstance(drifts, int):
if length == 1600 and drifts == 2:
real_drifts = [random.randint(380, 700), random.randint(900, 1220)]
else:
real_drifts = [random.randint(int(((i+1)/(drifts+1)-0.1)*length), int(((i+1)/(drifts+1)+0.1)*length)) for i in range(drifts)]
elif isinstance(drifts, list):
real_drifts = drifts
else:
exit()
concept = random.randint(0, 4)
desired_label = str(random.randint(0, 9))
for i in range(length):
if i in real_drifts:
concept = (concept + 1) % 5
j = random.randint(0, mnist_X.shape[0]-1)
while mnist_y[j] != desired_label:
j = (j+1) % mnist_X.shape[0]
digit = deepcopy(mnist_X[j, :])
digit = digit.reshape(28, 28)
if concept == 0:
pass
elif concept == 1:
digit[0:27, :] = digit[1:28, :]
digit[27, :] = 0
elif concept == 2:
digit[:, 0:27] = digit[:, 1:28]
digit[:, 27] = 0
elif concept == 3:
digit[1:28, :] = digit[0:27, :]
digit[0, :] = 0
elif concept == 4:
digit[:, 1:28] = digit[:, 0:27]
digit[:, 0] = 0
else:
print("ERROR: bad concept")
exit()
data[i, :] = digit[0:28:2, 0:28:2].reshape(14*14)
data *= (1.0/255.0)
assert np.min(data) == 0.0
assert np.max(data) == 1.0
return data, np.array(real_drifts)-0.5
def covtype_dataset(length, drifts):
assert covtype_X.shape[1] == 54
data = np.zeros((length, 54))
if isinstance(drifts, int):
if length == 1600 and drifts == 2:
real_drifts = [random.randint(380, 700), random.randint(900, 1220)]
else:
real_drifts = [random.randint(int(((i + 1) / (drifts + 1) - 0.1) * length), int(((i + 1) / (drifts + 1) + 0.1) * length)) for i in range(drifts)]
elif isinstance(drifts, list):
real_drifts = drifts
else:
exit()
X_std = np.array([279.9844933, 111.91362469, 7.48823537, 212.54917268, 58.29518146, 1559.25352805, 26.76986577, 19.76868014, 38.27449629, 1324.19407022])
assert X_std.shape[0] == 10
shift_mean = []
invert_feature = []
for i in range(length):
if i in real_drifts:
new_shift_mean = random.randint(0, 9)
while len(shift_mean) > 0 and new_shift_mean in shift_mean:
new_shift_mean = random.randint(0, 9)
shift_mean.append(new_shift_mean)
new_invert_feature = random.randint(10, 53)
while len(invert_feature) > 0 and new_invert_feature in invert_feature:
new_invert_feature = random.randint(10, 53)
invert_feature.append(new_invert_feature)
randnr = random.randint(0, covtype_X.shape[0]-1)
data[i, :] = covtype_X[randnr, :]
for j in shift_mean:
data[i, j] += 1.0*X_std[j]
for j in invert_feature:
data[i, j] = 1 - data[i, j]
for i in range(data.shape[1]):
data_min = np.nanpercentile(a=data[:, i], q=0.1)
data_max = np.nanpercentile(a=data[:, i], q=99.9)
if (data_max-data_min) > 0:
data[:, i] = np.minimum(1.0, np.maximum(0.0, (data[:, i] - data_min) * (1.0/(data_max-data_min))))
return data, np.array(real_drifts)-0.5
def rialto_dataset(length, drifts):
X = np.load("rialto.npy")
y = np.load("rialto_labels.npy")
data = np.zeros((length, X.shape[1]))
if isinstance(drifts, int):
if length == 1600 and drifts == 2:
real_drifts = [random.randint(380, 700), random.randint(900, 1220)]
else:
real_drifts = [random.randint(int(((i + 1) / (drifts + 1) - 0.1) * length), int(((i + 1) / (drifts + 1) + 0.1) * length)) for i in range(drifts)]
elif isinstance(drifts, list):
real_drifts = drifts
else:
exit()
concept = random.randint(0, 9)
for i in range(length):
if i in real_drifts:
new_concept = random.randint(0, 9)
while concept == new_concept:
new_concept = random.randint(0, 9)
concept = new_concept
j = random.randint(0, X.shape[0]-1)
while y[j] != concept:
j = (j+1) % X.shape[0]
data[i, :] = X[j, :]
return data, np.array(real_drifts)-0.5
def music_dataset(length: int = 1600, drifts: int = 2) -> tuple[np.ndarray, list]:
assert drifts == 2
full_dataset = np.load("music.npy")
X = full_dataset[:, 6:]
y = full_dataset[:, :6]
assert X.shape[1] == 72
data = np.zeros((length, 72))
if isinstance(drifts, int):
if length == 1600 and drifts == 2:
real_drifts = [random.randint(380, 700), random.randint(900, 1220)]
else:
real_drifts = [random.randint(int(((i + 1) / (drifts + 1) - 0.1) * length), int(((i + 1) / (drifts + 1) + 0.1) * length)) for i in range(drifts)]
elif isinstance(drifts, list):
real_drifts = drifts
else:
exit()
concept = 0
for i in range(length):
if i in real_drifts: # Possible alternative: 1-4-5
if concept == 0:
concept = 3
elif concept == 3:
concept = 5
else:
print("Error")
return
j = random.randint(0, X.shape[0] - 1)
if random.random() > 0.333:
while y[j, concept] != 1:
j = (j + 1) % X.shape[0]
data[i, :] = X[j, :]
else:
while y[j, 0] != 0 and y[j, 3] != 0 and y[j, 5] != 0:
j = (j + 1) % X.shape[0]
data[i, :] = X[j, :]
return data, np.array(real_drifts)-0.5
def trivial_dataset(length=1600, drifts=2):
data = np.zeros((length, 100))
if isinstance(drifts, int):
if length == 1600 and drifts == 2:
real_drifts = [random.randint(380, 700), random.randint(900, 1220)]
else:
real_drifts = [random.randint(int(((i + 1) / (drifts + 1) - 0.1) * length), int(((i + 1) / (drifts + 1) + 0.1) * length)) for i in range(drifts)]
elif isinstance(drifts, list):
real_drifts = drifts
else:
exit()
for i in range(1, length):
if i in real_drifts:
if data[i-1, 0] > 0.5:
data[i, :] = 0.1*random.random()
else:
data[i, :] = 1.0 - 0.1*random.random()
else:
if data[i-1, 0] > 0.5:
data[i, :] = 1.0 - 0.1*random.random()
else:
data[i, :] = 0.1*random.random()
return data, np.array(real_drifts)-0.5