Pushing the docs to dev/ for branch: master, commit faa940608befaeca99db501609c6db796739f30f

Author: sklearn-ci

dev/_downloads/auto_examples_jupyter.zip

@@ -51,7 +51,7 @@
       },
       "outputs": [],
       "source": [
-        "from tempfile import mkdtemp\nfrom shutil import rmtree\nfrom joblib import Memory\n\n# Create a temporary folder to store the transformers of the pipeline\ncachedir = mkdtemp()\nmemory = Memory(___location=cachedir, verbose=10)\ncached_pipe = Pipeline([('reduce_dim', PCA()),\n                        ('classify', LinearSVC(dual=False, max_iter=10000))],\n                       memory=memory)\n\n# This time, a cached pipeline will be used within the grid search\ngrid = GridSearchCV(cached_pipe, cv=5, n_jobs=1, param_grid=param_grid)\ndigits = load_digits()\ngrid.fit(digits.data, digits.target)\n\n# Delete the temporary cache before exiting\nrmtree(cachedir)"
+        "from joblib import Memory\nfrom shutil import rmtree\n\n# Create a temporary folder to store the transformers of the pipeline\n___location = 'cachedir'\nmemory = Memory(___location=___location, verbose=10)\ncached_pipe = Pipeline([('reduce_dim', PCA()),\n                        ('classify', LinearSVC(dual=False, max_iter=10000))],\n                       memory=memory)\n\n# This time, a cached pipeline will be used within the grid search\ngrid = GridSearchCV(cached_pipe, cv=5, n_jobs=1, param_grid=param_grid)\ndigits = load_digits()\ngrid.fit(digits.data, digits.target)\n\n# Delete the temporary cache before exiting\nmemory.clear(warn=False)\nrmtree(___location)"
       ]
     },
     {

dev/_downloads/auto_examples_python.zip

@@ -102,13 +102,12 @@
 #     cache. Hence, use the ``memory`` constructor parameter when the fitting
 #     of a transformer is costly.
 
-from tempfile import mkdtemp
-from shutil import rmtree
 from joblib import Memory
+from shutil import rmtree
 
 # Create a temporary folder to store the transformers of the pipeline
-cachedir = mkdtemp()
-memory = Memory(___location=cachedir, verbose=10)
+___location = 'cachedir'
+memory = Memory(___location=___location, verbose=10)
 cached_pipe = Pipeline([('reduce_dim', PCA()),
                         ('classify', LinearSVC(dual=False, max_iter=10000))],
                        memory=memory)
@@ -119,7 +118,8 @@
 grid.fit(digits.data, digits.target)
 
 # Delete the temporary cache before exiting
-rmtree(cachedir)
+memory.clear(warn=False)
+rmtree(___location)
 
 ###############################################################################
 # The ``PCA`` fitting is only computed at the evaluation of the first