<p dir="auto">sklearn/tests/test_common.py::test_non_meta_estimators[LinearSVR-LinearSVR-check2214] Makefile:27: recipe for target 'test-code' failed</p>
<p dir="auto">Linux-4.15.0-22-generic-x86_64-with-debian-buster-sid<br>
Python 3.6.4 |Anaconda, Inc.| (default, Jan 16 2018, 18:10:19)<br>
[GCC 7.2.0]<br>
NumPy 1.14.0<br>
SciPy 1.0.0<br>
Scikit-Learn 0.20.dev0<br>
Cython version 0.27.3</p>
<p dir="auto">Deterministic segfault :-/ I need to investigate<br>
reproduce:</p>
<div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="from sklearn.svm import LinearSVR
from sklearn.utils.estimator_checks import check_regressors_train

check_regressors_train(&quot;LinearSVR&quot;, LinearSVR(), readonly_memmap=True)"><pre class="notranslate"><span class="pl-k">from</span> <span class="pl-s1">sklearn</span>.<span class="pl-s1">svm</span> <span class="pl-k">import</span> <span class="pl-v">LinearSVR</span>
<span class="pl-k">from</span> <span class="pl-s1">sklearn</span>.<span class="pl-s1">utils</span>.<span class="pl-s1">estimator_checks</span> <span class="pl-k">import</span> <span class="pl-s1">check_regressors_train</span>

<span class="pl-en">check_regressors_train</span>(<span class="pl-s">"LinearSVR"</span>, <span class="pl-v">LinearSVR</span>(), <span class="pl-s1">readonly_memmap</span><span class="pl-c1">=</span><span class="pl-c1">True</span>)</pre></div>
<p dir="auto">looks like that never worked.</p>
<p dir="auto">This seems to be data-dependent and the following snippet only fails on master (not 0.19.1):</p>
<div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="from sklearn.externals import joblib
from sklearn.utils.estimator_checks import _boston_subset
from sklearn.svm import LinearSVR

X, y = _boston_subset()

joblib.dump(y, '/tmp/test.pkl')
y = joblib.load('/tmp/test.pkl', mmap_mode='r')

LinearSVR().fit(X, y)"><pre class="notranslate"><span class="pl-k">from</span> <span class="pl-s1">sklearn</span>.<span class="pl-s1">externals</span> <span class="pl-k">import</span> <span class="pl-s1">joblib</span>
<span class="pl-k">from</span> <span class="pl-s1">sklearn</span>.<span class="pl-s1">utils</span>.<span class="pl-s1">estimator_checks</span> <span class="pl-k">import</span> <span class="pl-s1">_boston_subset</span>
<span class="pl-k">from</span> <span class="pl-s1">sklearn</span>.<span class="pl-s1">svm</span> <span class="pl-k">import</span> <span class="pl-v">LinearSVR</span>

<span class="pl-v">X</span>, <span class="pl-s1">y</span> <span class="pl-c1">=</span> <span class="pl-en">_boston_subset</span>()

<span class="pl-s1">joblib</span>.<span class="pl-en">dump</span>(<span class="pl-s1">y</span>, <span class="pl-s">'/tmp/test.pkl'</span>)
<span class="pl-s1">y</span> <span class="pl-c1">=</span> <span class="pl-s1">joblib</span>.<span class="pl-en">load</span>(<span class="pl-s">'/tmp/test.pkl'</span>, <span class="pl-s1">mmap_mode</span><span class="pl-c1">=</span><span class="pl-s">'r'</span>)

<span class="pl-v">LinearSVR</span>().<span class="pl-en">fit</span>(<span class="pl-v">X</span>, <span class="pl-s1">y</span>)</pre></div>
<p dir="auto">Trying some information from gdb:</p>
<div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="gdb --args python -- /tmp/test-linear-svr.py"><pre class="notranslate"><code class="notranslate">gdb --args python -- /tmp/test-linear-svr.py
</code></pre></div>
<div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="Program received signal SIGSEGV, Segmentation fault.
0x00007fffe4dc76a8 in train_one (prob=prob@entry=0x55555616fde0, param=param@entry=0x555556618010, w=w@entry=0x55555600f9f0, 
    Cp=Cp@entry=0, Cn=Cn@entry=0) at sklearn/svm/src/liblinear/linear.cpp:2255
2255            for(int i=0;i&lt;prob-&gt;l;i++)
(gdb) bt
#0  0x00007fffe4dc76a8 in train_one (prob=prob@entry=0x55555616fde0, param=param@entry=0x555556618010, w=w@entry=0x55555600f9f0, 
    Cp=Cp@entry=0, Cn=Cn@entry=0) at sklearn/svm/src/liblinear/linear.cpp:2255
#1  0x00007fffe4dca952 in train (prob=prob@entry=0x55555616fde0, param=param@entry=0x555556618010)
    at sklearn/svm/src/liblinear/linear.cpp:2386
#2  0x00007fffe4dbc20d in __pyx_pf_7sklearn_3svm_9liblinear_train_wrap (__pyx_v_X=__pyx_v_X@entry=0x7fffe3f6c4e0, 
    __pyx_v_Y=__pyx_v_Y@entry=0x7fffe3f6c440, __pyx_v_is_sparse=__pyx_v_is_sparse@entry=0, 
    __pyx_v_solver_type=__pyx_v_solver_type@entry=13, __pyx_v_eps=__pyx_v_eps@entry=0.0001, __pyx_v_bias=__pyx_v_bias@entry=1, 
    __pyx_v_C=__pyx_v_C@entry=1, __pyx_v_class_weight=__pyx_v_class_weight@entry=0x7ffff6946580, __pyx_v_max_iter=1000, 
    __pyx_v_random_seed=1682099873, __pyx_v_epsilon=0, __pyx_v_sample_weight=0x7fffe3f6c260, __pyx_self=&lt;optimized out&gt;)
    at sklearn/svm/liblinear.c:2279
#3  0x00007fffe4dbf756 in __pyx_pw_7sklearn_3svm_9liblinear_1train_wrap (__pyx_self=&lt;optimized out&gt;, __pyx_args=&lt;optimized out&gt;, 
    __pyx_kwds=&lt;optimized out&gt;) at sklearn/svm/liblinear.c:1920
#4  0x000055555565fb24 in _PyCFunction_FastCallDict ()
#5  0x00005555556f939c in call_function ()
#6  0x0000555555715b3a in _PyEval_EvalFrameDefault ()
#7  0x00005555556b6614 in _PyEval_EvalCodeWithName ()
#8  0x00005555556b74d1 in fast_function ()
#9  0x00005555556f9475 in call_function ()
#10 0x000055555571684c in _PyEval_EvalFrameDefault ()
#11 0x00005555556b6614 in _PyEval_EvalCodeWithName ()
#12 0x00005555556b74d1 in fast_function ()
#13 0x00005555556f9475 in call_function ()
#14 0x0000555555715b3a in _PyEval_EvalFrameDefault ()
#15 0x00005555556c8bf9 in PyEval_EvalCodeEx ()
#16 0x00005555556c999c in PyEval_EvalCode ()
#17 0x00005555557693c4 in run_mod ()
#18 0x00005555557697c1 in PyRun_FileExFlags ()
#19 0x00005555557699c4 in PyRun_SimpleFileExFlags ()
#20 0x000055555576d4f8 in Py_Main ()
#21 0x000055555563426e in main ()"><pre class="notranslate"><code class="notranslate">Program received signal SIGSEGV, Segmentation fault.
0x00007fffe4dc76a8 in train_one (prob=prob@entry=0x55555616fde0, param=param@entry=0x555556618010, w=w@entry=0x55555600f9f0, 
    Cp=Cp@entry=0, Cn=Cn@entry=0) at sklearn/svm/src/liblinear/linear.cpp:2255
2255            for(int i=0;i&lt;prob-&gt;l;i++)
(gdb) bt
#0  0x00007fffe4dc76a8 in train_one (prob=prob@entry=0x55555616fde0, param=param@entry=0x555556618010, w=w@entry=0x55555600f9f0, 
    Cp=Cp@entry=0, Cn=Cn@entry=0) at sklearn/svm/src/liblinear/linear.cpp:2255
#1  0x00007fffe4dca952 in train (prob=prob@entry=0x55555616fde0, param=param@entry=0x555556618010)
    at sklearn/svm/src/liblinear/linear.cpp:2386
#2  0x00007fffe4dbc20d in __pyx_pf_7sklearn_3svm_9liblinear_train_wrap (__pyx_v_X=__pyx_v_X@entry=0x7fffe3f6c4e0, 
    __pyx_v_Y=__pyx_v_Y@entry=0x7fffe3f6c440, __pyx_v_is_sparse=__pyx_v_is_sparse@entry=0, 
    __pyx_v_solver_type=__pyx_v_solver_type@entry=13, __pyx_v_eps=__pyx_v_eps@entry=0.0001, __pyx_v_bias=__pyx_v_bias@entry=1, 
    __pyx_v_C=__pyx_v_C@entry=1, __pyx_v_class_weight=__pyx_v_class_weight@entry=0x7ffff6946580, __pyx_v_max_iter=1000, 
    __pyx_v_random_seed=1682099873, __pyx_v_epsilon=0, __pyx_v_sample_weight=0x7fffe3f6c260, __pyx_self=&lt;optimized out&gt;)
    at sklearn/svm/liblinear.c:2279
#3  0x00007fffe4dbf756 in __pyx_pw_7sklearn_3svm_9liblinear_1train_wrap (__pyx_self=&lt;optimized out&gt;, __pyx_args=&lt;optimized out&gt;, 
    __pyx_kwds=&lt;optimized out&gt;) at sklearn/svm/liblinear.c:1920
#4  0x000055555565fb24 in _PyCFunction_FastCallDict ()
#5  0x00005555556f939c in call_function ()
#6  0x0000555555715b3a in _PyEval_EvalFrameDefault ()
#7  0x00005555556b6614 in _PyEval_EvalCodeWithName ()
#8  0x00005555556b74d1 in fast_function ()
#9  0x00005555556f9475 in call_function ()
#10 0x000055555571684c in _PyEval_EvalFrameDefault ()
#11 0x00005555556b6614 in _PyEval_EvalCodeWithName ()
#12 0x00005555556b74d1 in fast_function ()
#13 0x00005555556f9475 in call_function ()
#14 0x0000555555715b3a in _PyEval_EvalFrameDefault ()
#15 0x00005555556c8bf9 in PyEval_EvalCodeEx ()
#16 0x00005555556c999c in PyEval_EvalCode ()
#17 0x00005555557693c4 in run_mod ()
#18 0x00005555557697c1 in PyRun_FileExFlags ()
#19 0x00005555557699c4 in PyRun_SimpleFileExFlags ()
#20 0x000055555576d4f8 in Py_Main ()
#21 0x000055555563426e in main ()
</code></pre></div>