RTEB Open — unified schema
Collection
The 17 open RTEB(beta) retrieval datasets, normalised to one strict schema with full provenance. • 16 items • Updated
Datasets:
Tasks:
Text Retrieval
Modalities:
Text
Formats:
parquet
Sub-tasks:
document-retrieval
Languages:
English
Size:
1K - 10K
License:
id stringlengths 9 9 | title stringclasses 1
value | text stringlengths 312 2.4k |
|---|---|---|
000000000 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
from scipy import sparse
V, x = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
V.data += x
#print(V)
with open('result/result_{}.pkl'.format(args.test_ca... | |
000000001 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
from scipy import sparse
sa = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
result = (sa.count_nonzero()==0)
#print(result)
with open('result/result_{}... | |
000000002 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
from scipy import stats
x, mu, stddev = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
result = stats.lognorm(s=stddev, scale=np.exp(mu)).... | |
000000003 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
import scipy.ndimage
x, shape = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
result = scipy.ndimage.zoom(x, zoom=(shape[0]/x.shape[0], ... | |
000000004 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
from scipy import stats
import random
import numpy as np
def poisson_simul(rate, T):
time = random.expovariate(rate)
times = [0]
while (times[-1] < T):
... | |
000000005 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
from scipy import sparse
import numpy as np
sa, sb = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
result = sa.multiply(sb)
#print(result)
with open('r... | |
000000006 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import scipy.optimize
import numpy as np
a, x_true, y, x0, x_lower_bounds = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
def residual_ans(x, a, y):
s =... | |
000000007 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import scipy.integrate
c, low, high = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
def f(c=5, low=0, high=1):
result = scipy.integrate.quadrature(lam... | |
000000008 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import pandas as pd
import io
import numpy as np
from scipy import stats
df = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
indices = [('1415777_at Pnlipr... | |
000000009 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
a = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
kurtosis_result = (sum((a - np.mean(a)) ** 4)/len(a)) / np.std(a)**4
#print(kurtosis... | |
000000010 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
import scipy
x, y = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
result = np.polyfit(np.log(x), y, 1)[::-1]
#print(result)
with open... | |
000000011 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
import scipy as sp
from scipy import integrate,stats
def bekkers(x, a, m, d):
p = a*np.exp((-1*(x**(1/3) - m)**2)/(2*d**2))*x**(-2/3)
return(p... | |
000000012 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import pandas as pd
import io
from scipy import stats
df = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
result = pd.DataFrame(data=stats.zscore(df, axis ... | |
000000013 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
import scipy.spatial.distance
example_array = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
def f(example_array):
import itertools
... | |
000000014 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import scipy.optimize as optimize
from math import *
initial_guess = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
def g(params):
import numpy as np
... | |
000000015 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import scipy.optimize as sciopt
import numpy as np
import pandas as pd
a = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
weights = (a.values / a.values.sum... | |
000000016 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
from scipy.optimize import fsolve
def eqn(x, a, b):
return x + 2*a - b**2
xdata, bdata = pickle.load(open(f"input/input{args.test_case}.pkl", "rb... | |
000000017 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
from scipy import stats
import random
import numpy as np
def poisson_simul(rate, T):
time = random.expovariate(rate)
times = [0]
while (times[-1] < T):
... | |
000000018 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
from scipy.sparse import csr_matrix
arr = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
M = csr_matrix(arr)
result = M.A.diagonal(0)
#p... | |
000000019 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
from scipy import stats
import random
import numpy as np
def poisson_simul(rate, T):
time = random.expovariate(rate)
times = [0]
while (times[-1] < T):
... | |
000000020 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import scipy
import scipy.optimize
import numpy as np
def test_func(x):
return (x[0])**2+(x[1])**2
def test_grad(x):
return [2*x[0],2*x[1]]
starting_point, dire... | |
000000021 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
import scipy.stats
z_scores, mu, sigma = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
temp = np.array(z_scores)
p_values = scipy.stats... | |
000000022 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
from scipy.sparse import lil_matrix
from scipy import sparse
M = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
rows, cols = M.nonzero(... | |
000000023 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
from scipy import sparse
c1, c2 = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
Feature = sparse.vstack((c1, c2))
#print(Feature)
with open('result/resu... | |
000000024 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
from scipy.spatial import distance
shape = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
xs, ys = np.indices(shape)
xs = xs.reshape(shap... | |
000000025 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
import scipy.spatial.distance
example_array = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
import itertools
n = example_array.max()+1
in... | |
000000026 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import pandas as pd
import numpy as np
import scipy.stats as stats
df = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
import itertools as IT
for col1, col2... | |
000000027 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
from scipy import sparse
import numpy as np
sA, sB = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
def f(sA, sB):
result = sA.multiply(sB)
return ... | |
000000028 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
import scipy.ndimage
square = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
def filter_isolated_cells(array, struct):
filtered_array... | |
000000029 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
from scipy import sparse
c1, c2 = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
Feature = sparse.hstack((c1, c2)).tocsr()
#print(Feature)
with open('res... | |
000000030 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
import scipy
x, y = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
result = np.polyfit(np.log(x), y, 1)
#print(result)
with open('resu... | |
000000031 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
from scipy.sparse import csr_matrix
col = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
Max, Min = col.max(), col.min()
#print(Max)
#p... | |
000000032 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
from scipy import sparse
sa = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
result = (sa.count_nonzero()==0)
#print(result)
with open('result/result_{}... | |
000000033 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
from scipy.optimize import curve_fit
import numpy as np
z, Ua, tau, degree = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
def fourier(x, *a):
ret = a[... | |
000000034 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
import scipy.optimize
x, y, p0 = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
result = scipy.optimize.curve_fit(lambda t,a,b, c: a*np.e... | |
000000035 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import scipy
import numpy as np
a = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
a = 1-np.sign(a)
#print(a)
with open('result/result_{}.pkl'.format(ar... | |
000000036 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import scipy.spatial
import numpy as np
centroids, data, k = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
def find_k_closest(centroids, data, k=1, distan... | |
000000037 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
import scipy.ndimage
a = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
b = scipy.ndimage.median_filter(a, size=(3, 3), origin=(0, 1))
... | |
000000038 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
from scipy import sparse
sa, sb = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
result = sparse.hstack((sa, sb)).tocsr()
#print(result)
with open('resu... | |
000000039 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
from scipy import sparse
c1, c2 = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
Feature = sparse.hstack((c1, c2)).tocsr()
#print(Feature)
with open('res... | |
000000040 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
import scipy.stats
N, p = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
n = np.arange(N + 1, dtype=np.int64)
dist = scipy.stats.binom(p=... | |
000000041 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
import scipy.stats as ss
x1, x2 = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
s, c_v, s_l = ss.anderson_ksamp([x1,x2])
result = c_v[2] ... | |
000000042 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
import scipy.spatial
import scipy.optimize
np.random.seed(100)
points1, N, points2 = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
C = s... | |
000000043 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
import scipy.optimize as sciopt
fp = lambda p, x: p[0]*x[0]+p[1]*x[1]
e = lambda p, x, y: ((fp(p,x)-y)**2).sum()
pmin, pmax, x, y = pickle.load(op... | |
000000044 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
from scipy import signal
arr, n = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
res = signal.argrelextrema(arr, np.less_equal, order=n, a... | |
000000045 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
import scipy.spatial
centroids, data = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
def find_k_closest(centroids, data, k=1, distance_n... | |
000000046 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import scipy.optimize
import numpy as np
a, y, x0 = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
def residual_ans(x, a, y):
s = ((y - a.dot(x**2))**2)... | |
000000047 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import scipy.spatial
points, extraPoints = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
vor = scipy.spatial.Voronoi(points)
kdtree = scipy.spatial.cKDTree(... | |
000000048 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import scipy.spatial
import numpy as np
centroids, data = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
def find_k_closest(centroids, data, k=1, distance... | |
000000049 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
import scipy.interpolate
s, t = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
x, y = np.ogrid[-1:1:10j,-2:0:10j]
z = (x + y)*np.exp(-6.0 ... | |
000000050 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import scipy.spatial
points, extraPoints = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
vor = scipy.spatial.Voronoi(points)
kdtree = scipy.spatial.cKDTree(... | |
000000051 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import scipy.integrate
import numpy as np
N0, time_span = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
def dN1_dt (t, N1):
return -100 * N1 + np.sin(t)... | |
000000052 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
import scipy.interpolate
x, array, x_new = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
new_array = scipy.interpolate.interp1d(x, array,... | |
000000053 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import scipy
import numpy as np
a = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
a = np.sign(a)
#print(a)
with open('result/result_{}.pkl'.format(args... | |
000000054 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
import scipy.interpolate
points, V, request = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
result = scipy.interpolate.griddata(points,... | |
000000055 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
import scipy.stats
z_scores = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
temp = np.array(z_scores)
p_values = scipy.stats.norm.cdf(te... | |
000000056 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import scipy.integrate
import numpy as np
N0, time_span = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
def dN1_dt (t, N1):
return -100 * N1 + np.sin(t... | |
000000057 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
import scipy.sparse as sparse
vectors, max_vector_size = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
result = sparse.lil_matrix((len(... | |
000000058 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
from scipy.spatial import distance
shape = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
def f(shape = (6, 6)):
xs, ys = np.indices(... | |
000000059 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
from scipy import sparse
import numpy as np
matrix = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
result = sparse.spdiags(matrix, (1, 0, -1), 5, 5).T.A
... | |
000000060 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
from scipy.optimize import minimize
def function(x):
return -1*(18*x[0]+16*x[1]+12*x[2]+11*x[3])
I = pickle.load(open(f"input/input{args.test_cas... | |
000000061 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import pandas as pd
import io
from scipy import integrate
df = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
df.Time = pd.to_datetime(df.Time, format='%Y-%m... | |
000000062 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
from scipy import stats
pre_course_scores, during_course_scores = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
p_value = stats.ranksums... | |
000000063 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
from scipy import sparse
V, x, y = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
V = V.copy()
V.data += x
V.eliminate_zeros()
V.data += y
V.eliminate_zeros... | |
000000064 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
from scipy.sparse import lil_matrix
def f(sA):
rows, cols = sA.nonzero()
sA[cols, rows] = sA[rows, cols]
return sA
sA = pickle.load(open... | |
000000065 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
from scipy import stats
def f(pre_course_scores, during_course_scores):
p_value = stats.ranksums(pre_course_scores, during_course_scores).pvalue
... | |
000000066 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
from scipy import sparse
sa, sb = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
result = sparse.vstack((sa, sb)).tocsr()
#print(result)
with open('resu... | |
000000067 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
import scipy.ndimage
square = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
def filter_isolated_cells(array, struct):
filtered_array... | |
000000068 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
import scipy.stats as ss
x1, x2, x3, x4 = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
statistic, critical_values, significance_level = ... | |
000000069 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import pandas as pd
import io
from scipy import stats
df = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
result = pd.DataFrame(data=stats.zscore(df, axis =... | |
000000070 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import scipy.integrate
import math
import numpy as np
x, u, o2 = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
def NDfx(x):
return((1/math.sqrt((2*math.... | |
000000071 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
import scipy.interpolate
s, t = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
def f(s, t):
x, y = np.ogrid[-1:1:10j,-2:0:10j]
z =... | |
000000072 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
import scipy.stats
a = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
kurtosis_result = scipy.stats.kurtosis(a)
#print(kurtosis_result)... | |
000000073 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
import scipy as sp
from scipy import integrate,stats
def bekkers(x, a, m, d):
p = a*np.exp((-1*(x**(1/3) - m)**2)/(2*d**2))*x**(-2/3)
return(p... | |
000000074 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
from scipy import ndimage
def f(img):
threshold = 0.75
blobs = img > threshold
labels, result = ndimage.label(blobs)
return result
i... | |
000000075 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
from scipy import signal
arr, n = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
result = signal.argrelextrema(arr, np.less_equal, order=n... | |
000000076 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
from scipy.sparse import csr_matrix
col = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
n = col.shape[0]
val = col.data
for i in range... | |
000000077 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
from scipy.spatial import distance
shape = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
xs, ys = np.indices(shape)
xs = xs.reshape(shap... | |
000000078 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
from scipy import sparse
V, x = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
V._update(zip(V.keys(), np.array(list(V.values())) + x))
#... | |
000000079 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
import scipy.spatial
import scipy.optimize
points1, N, points2 = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
C = scipy.spatial.distanc... | |
000000080 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
from scipy.optimize import fsolve
def eqn(x, a, b):
return x + 2*a - b**2
xdata, adata = pickle.load(open(f"input/input{args.test_case}.pkl", "rb... | |
000000081 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
from scipy import sparse
import numpy as np
import math
sa = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
sa = sparse.csr_matrix(sa.toarray() / np.sqrt(np.... | |
000000082 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
from scipy import stats
mu, stddev = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
expected_value = np.exp(mu + stddev ** 2 / 2)
median =... | |
000000083 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
from scipy import ndimage
img = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
threshold = 0.75
blobs = img < threshold
labels, result =... | |
000000084 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
from scipy import ndimage
img = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
threshold = 0.75
blobs = img > threshold
labels, nlabels ... | |
000000085 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
import scipy.interpolate
points, V, request = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
result = scipy.interpolate.griddata(points,... | |
000000086 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
from scipy import stats
import numpy as np
x, y, alpha = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
s, p = stats.ks_2samp(x, y)
result = (p <= alpha)
... | |
000000087 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
from scipy import sparse
import numpy as np
a, b = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
b = sparse.csr_matrix(a)
b.setdiag(0)
b.eliminate_zeros()
... | |
000000088 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
from scipy import misc
from scipy.ndimage import rotate
import numpy as np
data_orig, x0, y0, angle = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
def rot... | |
000000089 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
from scipy import stats
import numpy as np
np.random.seed(42)
x, y = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
statistic, p_value = stats.ks_2samp(x, y)... | |
000000090 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import pandas as pd
import io
from scipy import stats
df = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
indices = [('1415777_at Pnliprp1', 'data'), ('141... | |
000000091 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import scipy.interpolate
x, y, eval = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
result = scipy.interpolate.griddata(x, y, eval)
#print(result)
with... | |
000000092 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
from scipy import stats
import pandas as pd
import numpy as np
LETTERS = list('ABCDEFGHIJKLMNOPQRSTUVWXYZ')
df = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"... | |
000000093 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import scipy.integrate
import math
import numpy as np
x, u, o2 = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
def NDfx(x):
return((1/math.sqrt((2*math.... | |
000000094 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
import scipy.spatial.distance
example_array = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
import itertools
n = example_array.max()+1
in... | |
000000095 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
from scipy import interpolate
import numpy as np
x, y, x_val = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
result = np.zeros((5, 100))
for i in range(5):
... | |
000000096 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import scipy.integrate
c, low, high = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
result = scipy.integrate.quadrature(lambda x: 2*c*x, low, high)[0]
#p... | |
000000097 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
from scipy.sparse import csr_matrix
col = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
mean = col.mean()
N = col.shape[0]
sqr = col.c... | |
000000098 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import scipy.integrate
import numpy as np
N0, time_span = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
def dN1_dt(t, N1):
input = 1-np.cos(t) if 0<t<2*... | |
000000099 |
import pickle
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--test_case", type=int, default=1)
args = parser.parse_args()
import numpy as np
import scipy.stats
p_values = pickle.load(open(f"input/input{args.test_case}.pkl", "rb"))
z_scores = scipy.stats.norm.ppf(p_values)
#print(z_scores)
... |
End of preview. Expand in Data Studio
DS1000Retrieval — RTEB open subset, unified schema
A normalised copy of the dataset behind the mteb task DS1000Retrieval, one of the 17 open tasks in the
RTEB(beta) retrieval benchmark. Same queries, documents and relevance
judgements as the benchmark evaluates — reshaped into one strict schema shared by all 17.
| Source | embedding-benchmark/DS1000 @ 25cd4dc8172e (the revision pinned in mteb) |
| Domain · languages | code · eng |
| Queries / documents / qrels | 1,998 / 1,998 / 1,998 |
| Qrels per query | min 1 · mean 1.0 · max 1 |
| Score values | 1 ×1,998 |
| Layout | queries · corpus · qrels, split test |
| License | cc-by-sa-4.0 |
Schema
| config | columns | rules |
|---|---|---|
queries |
id: string, text: string |
ids unique and non-empty; every query has ≥ 1 qrel |
corpus |
id: string, title: string, text: string |
title is always present ("" when the source has none) |
qrels |
query-id: string, corpus-id: string, score: int32 |
referential integrity to both tables; no duplicate pairs; no floats |
Files are Parquet, sorted by id, zstd-compressed, sharded at 500 MB. Every rule above is enforced by a validator
before publishing; provenance.json records the source file hashes, what changed, and the output file hashes.
What changed from the source
- byte-preserved all text — no whitespace or newline normalisation (code dataset)
- added a
titlecolumn filled with""(the source has none) - cast
qrels.scoredouble -> int32 - renamed source splits (
queries←queries/queries,corpus←corpus/corpus,qrels←default/test) totest
Load it
from datasets import load_dataset
queries = load_dataset("Hyukkyu/rteb-DS1000Retrieval", "queries", split="test")
corpus = load_dataset("Hyukkyu/rteb-DS1000Retrieval", "corpus", split="test")
qrels = load_dataset("Hyukkyu/rteb-DS1000Retrieval", "qrels", split="test")
License and attribution
The data is redistributed under the source's terms — cc-by-sa-4.0. All credit belongs to the
original authors; see the source repository and the references in mteb's task metadata (https://huggingface.co/datasets/embedding-benchmark/DS1000).
This repository is an independent repackaging and is not affiliated with the RTEB or MTEB maintainers.
License discrepancy. The upstream dataset card declares
cc-by-sa-4.0whilemteb'sTaskMetadatadeclaresmit. This card carries the upstream value as the more conservative choice.
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