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Olivier Grisel ogrisel

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View float32 issue
## Context: the new `global_dtype` fixture and `SKLEARN_RUN_FLOAT32_TESTS` environment variable
Introduction of low-level computational routines for 32bit motivated an extension of tests to run them on 32bit.
In this regards, https://github.com/scikit-learn/scikit-learn/pull/22690 introduced a new `global_dtype` fixture as well has the `SKLEARN_RUN_FLOAT32_TESTS` env. variable to make it possible to run the test on 32bit data.
Running test on 32bit can be done using `SKLEARN_RUN_FLOAT32_TESTS=1`.
For instance, this run the first `global_dtype`-parametrised test:
View bench_knn_scalability.py
from pathlib import Path
import sys
from time import perf_counter
from threadpoolctl import threadpool_limits
from sklearn.datasets import make_blobs
from sklearn.model_selection import train_test_split
from sklearn.neighbors import NearestNeighbors
from joblib import Memory
import pandas as pd
import matplotlib.pyplot as plt
View scipy_test.log
This file has been truncated, but you can view the full file.
============================= test session starts ==============================
platform darwin -- Python 3.9.7, pytest-6.2.5, py-1.10.0, pluggy-1.0.0 -- /Users/ogrisel/mambaforge/envs/crash-scipy-dev/bin/python
cachedir: .pytest_cache
rootdir: /Users/ogrisel
collecting ... collected 47225 items
mambaforge/envs/crash-scipy-dev/lib/python3.9/site-packages/scipy/_build_utils/tests/test_scipy_version.py::test_valid_scipy_version PASSED [ 0%]
mambaforge/envs/crash-scipy-dev/lib/python3.9/site-packages/scipy/_lib/tests/test__gcutils.py::test_set_gc_state PASSED [ 0%]
mambaforge/envs/crash-scipy-dev/lib/python3.9/site-packages/scipy/_lib/tests/test__gcutils.py::test_gc_state PASSED [ 0%]
@ogrisel
ogrisel / bench_knn.py
Last active Aug 18, 2021
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View bench_knn.py
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
View bench_knn.py
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
View rf_scalability.ipynb
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View numpy_test_log.txt
___________________________________________ 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):
View numpy_build_log.txt
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
View ridge_normalize_false.py
# %%
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
View weighted_variance_stability.py
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__}")