<p dir="auto">To reproduce:</p>
<div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics import classification_report
from sklearn.multiclass import OneVsRestClassifier

from sklearn.pipeline import Pipeline
from sklearn.svm import SVC, LinearSVC
from sklearn.datasets import fetch_20newsgroups

data_train = fetch_20newsgroups(subset='train')
data_test = fetch_20newsgroups(subset='test')




clf=OneVsRestClassifier(estimator=SVC(),n_jobs=-1)  ##Error when calling fit
#clf=OneVsRestClassifier(estimator=SVC(),n_jobs=1) ##NO Error if set to 1



pipeLine=Pipeline([('tfidf',TfidfVectorizer(min_df=10)),
    ('clf',clf)])

trainx=data_train.data
trainy=data_train.target
evalx=data_test.data
evaly=data_test.target
pipeLine.fit(trainx,trainy)

predictValue=pipeLine.predict(evalx)

print classification_report(evaly,predictValue)"><pre class="notranslate"><span class="pl-k">from</span> <span class="pl-s1">sklearn</span>.<span class="pl-s1">feature_extraction</span>.<span class="pl-s1">text</span> <span class="pl-k">import</span> <span class="pl-v">TfidfVectorizer</span>
<span class="pl-k">from</span> <span class="pl-s1">sklearn</span>.<span class="pl-s1">metrics</span> <span class="pl-k">import</span> <span class="pl-s1">classification_report</span>
<span class="pl-k">from</span> <span class="pl-s1">sklearn</span>.<span class="pl-s1">multiclass</span> <span class="pl-k">import</span> <span class="pl-v">OneVsRestClassifier</span>

<span class="pl-k">from</span> <span class="pl-s1">sklearn</span>.<span class="pl-s1">pipeline</span> <span class="pl-k">import</span> <span class="pl-v">Pipeline</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">SVC</span>, <span class="pl-v">LinearSVC</span>
<span class="pl-k">from</span> <span class="pl-s1">sklearn</span>.<span class="pl-s1">datasets</span> <span class="pl-k">import</span> <span class="pl-s1">fetch_20newsgroups</span>

<span class="pl-s1">data_train</span> <span class="pl-c1">=</span> <span class="pl-en">fetch_20newsgroups</span>(<span class="pl-s1">subset</span><span class="pl-c1">=</span><span class="pl-s">'train'</span>)
<span class="pl-s1">data_test</span> <span class="pl-c1">=</span> <span class="pl-en">fetch_20newsgroups</span>(<span class="pl-s1">subset</span><span class="pl-c1">=</span><span class="pl-s">'test'</span>)




<span class="pl-s1">clf</span><span class="pl-c1">=</span><span class="pl-v">OneVsRestClassifier</span>(<span class="pl-s1">estimator</span><span class="pl-c1">=</span><span class="pl-v">SVC</span>(),<span class="pl-s1">n_jobs</span><span class="pl-c1">=</span><span class="pl-c1">-</span><span class="pl-c1">1</span>)  <span class="pl-c">##Error when calling fit</span>
<span class="pl-c">#clf=OneVsRestClassifier(estimator=SVC(),n_jobs=1) ##NO Error if set to 1</span>



<span class="pl-s1">pipeLine</span><span class="pl-c1">=</span><span class="pl-v">Pipeline</span>([(<span class="pl-s">'tfidf'</span>,<span class="pl-v">TfidfVectorizer</span>(<span class="pl-s1">min_df</span><span class="pl-c1">=</span><span class="pl-c1">10</span>)),
    (<span class="pl-s">'clf'</span>,<span class="pl-s1">clf</span>)])

<span class="pl-s1">trainx</span><span class="pl-c1">=</span><span class="pl-s1">data_train</span>.<span class="pl-s1">data</span>
<span class="pl-s1">trainy</span><span class="pl-c1">=</span><span class="pl-s1">data_train</span>.<span class="pl-s1">target</span>
<span class="pl-s1">evalx</span><span class="pl-c1">=</span><span class="pl-s1">data_test</span>.<span class="pl-s1">data</span>
<span class="pl-s1">evaly</span><span class="pl-c1">=</span><span class="pl-s1">data_test</span>.<span class="pl-s1">target</span>
<span class="pl-s1">pipeLine</span>.<span class="pl-en">fit</span>(<span class="pl-s1">trainx</span>,<span class="pl-s1">trainy</span>)

<span class="pl-s1">predictValue</span><span class="pl-c1">=</span><span class="pl-s1">pipeLine</span>.<span class="pl-en">predict</span>(<span class="pl-s1">evalx</span>)

<span class="pl-k">print</span> <span class="pl-en">classification_report</span>(<span class="pl-s1">evaly</span>,<span class="pl-s1">predictValue</span>)</pre></div>
<p dir="auto">Output:</p>
<p dir="auto">ValueError: UPDATEIFCOPY base is read-only</p>
<div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="Linux-4.2.0-19-generic-x86_64-with-Ubuntu-15.10-wily
('Python', '2.7.10 (default, Oct 14 2015, 16:09:02) \n[GCC 5.2.1 20151010]')
('NumPy', '1.10.4')
('SciPy', '0.17.0')
('Scikit-Learn', '0.18.dev0')"><pre class="notranslate"><code class="notranslate">Linux-4.2.0-19-generic-x86_64-with-Ubuntu-15.10-wily
('Python', '2.7.10 (default, Oct 14 2015, 16:09:02) \n[GCC 5.2.1 20151010]')
('NumPy', '1.10.4')
('SciPy', '0.17.0')
('Scikit-Learn', '0.18.dev0')
</code></pre></div>
<p dir="auto">PR <a class="issue-link js-issue-link" data-error-text="Failed to load title" data-id="84423486" data-permission-text="Title is private" data-url="https://github.com/scikit-learn/scikit-learn/issues/4807" data-hovercard-type="pull_request" data-hovercard-url="/scikit-learn/scikit-learn/pull/4807/hovercard" href="https://github.com/scikit-learn/scikit-learn/pull/4807">#4807</a> reveals a variety of estimators that fails on memory mapped input, once we allow check_array to process a memory map without copying its content in memory.</p>
<p dir="auto">Estimators failing on read only memory maps :</p>
<ul dir="auto">
<li>the whole PLS family</li>
<li>Factor analysis</li>
<li>Incremental PCA</li>
<li>NuSVC</li>
</ul>
<p dir="auto">Transformers :</p>
<ul dir="auto">
<li>KernelCenterer</li>
<li>MaxAbsScaler</li>
<li>MinAbsScaler</li>
<li>RobustScaler</li>
<li>StandardScaler</li>
</ul>
<p dir="auto">Most of these should be easy to fix (e.g X -= X.mean should be replaced etc.)</p>