ogrisel

import numpy as np
from subprocess import check_output
import threadpoolctl
from pprint import pprint
from time import perf_counter
from pathlib import Path
import sklearn
from sklearn.neighbors import KNeighborsClassifier
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split

ogrisel

import numpy as np
from subprocess import check_output
import threadpoolctl
from pprint import pprint
from time import perf_counter
from pathlib import Path
import sklearn
from sklearn.neighbors import KNeighborsClassifier
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split

ogrisel

ogrisel

___________________________________________ TestUfuncGenericLoops.test_unary_PyUFunc_O_O_method_full[reciprocal] ____________________________________________

self = <numpy.core.tests.test_ufunc.TestUfuncGenericLoops object at 0x15b5324f0>, ufunc = <ufunc 'reciprocal'>

    @pytest.mark.parametrize("ufunc", UNARY_OBJECT_UFUNCS)
    def test_unary_PyUFunc_O_O_method_full(self, ufunc):
        """Compare the result of the object loop with non-object one"""
        val = np.float64(np.pi/4)
    
        class MyFloat(np.float64):

ogrisel

Running from numpy source directory.
Processing numpy/random/_bounded_integers.pxd.in
Processing numpy/random/_philox.pyx
Processing numpy/random/_bounded_integers.pyx.in
Processing numpy/random/_sfc64.pyx
Processing numpy/random/_mt19937.pyx
Processing numpy/random/bit_generator.pyx
Processing numpy/random/mtrand.pyx
Processing numpy/random/_generator.pyx
Processing numpy/random/_pcg64.pyx

ogrisel

# %%
import numpy as np
from numpy.random import sample
from numpy.testing import assert_allclose
from sklearn.preprocessing import scale
from sklearn.datasets import make_regression
from sklearn.model_selection import train_test_split
from scipy.optimize import fmin_l_bfgs_b, check_grad

ogrisel

import numpy as np
from sklearn.utils.extmath import _incremental_mean_and_var
from sklearn.utils.sparsefuncs import _incr_mean_var_axis0
from sklearn.utils.sparsefuncs import _csc_mean_var_axis0
from sklearn.utils.sparsefuncs import _csr_mean_var_axis0
from scipy.sparse import csr_matrix
from scipy.sparse import csc_matrix

for dtype in [np.float64, np.float32]:
    print(f"## dtype={dtype.__name__}")

ogrisel

# %%
import numpy as np
from numpy.testing import assert_allclose
from sklearn.linear_model import Ridge
from sklearn.datasets import make_regression
from sklearn.model_selection import train_test_split
from scipy.optimize import fmin_l_bfgs_b, check_grad

np.random.seed(0)

ogrisel

# %%
from time import perf_counter
from sklearn.datasets import make_blobs
from sklearn.cluster import KMeans
from sklearn.cluster import MiniBatchKMeans
from sklearn.cluster import kmeans_plusplus
from sklearn.model_selection import train_test_split
from scipy.spatial.distance import cdist
from subprocess import run
from pprint import pprint

ogrisel

NumPy version 0.3.0+24607.gd075ba2ce
NumPy relaxed strides checking option: True
NumPy CPU features: NEON NEON_FP16 NEON_VFPV4 ASIMD ASIMDHP? ASIMDDP?
........................................................................ [  0%]
......................................................x................. [  1%]
........................................................................ [  1%]
................................................x....................... [  2%]
........................................................................ [  2%]
........................................................................ [  3%]
........................................................................ [  3%]