FIX make joblib utils private, and remove mentions of externals.joblib (scikit-learn#12345)

Author: tomMoral

README.rst

@@ -58,7 +58,8 @@ scikit-learn requires:
 Scikit-learn 0.21 and later will require Python 3.5 or newer.
 
 For running the examples Matplotlib >= 1.4 is required. A few examples
-require scikit-image >= 0.11.3 and a few examples require pandas >= 0.17.1.
+require scikit-image >= 0.11.3, a few examples require pandas >= 0.17.1
+and a few example require joblib >= 0.11.
 
 scikit-learn also uses CBLAS, the C interface to the Basic Linear Algebra
 Subprograms library. scikit-learn comes with a reference implementation, but

benchmarks/bench_covertype.py

@@ -50,6 +50,7 @@
 from time import time
 import argparse
 import numpy as np
+from joblib import Memory
 
 from sklearn.datasets import fetch_covtype, get_data_home
 from sklearn.svm import LinearSVC
@@ -59,7 +60,6 @@
 from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier
 from sklearn.ensemble import GradientBoostingClassifier
 from sklearn.metrics import zero_one_loss
-from sklearn.utils import Memory
 from sklearn.utils import check_array
 
 # Memoize the data extraction and memory map the resulting

benchmarks/bench_mnist.py

@@ -35,13 +35,13 @@
 from time import time
 import argparse
 import numpy as np
+from joblib import Memory
 
 from sklearn.datasets import fetch_mldata
 from sklearn.datasets import get_data_home
 from sklearn.ensemble import ExtraTreesClassifier
 from sklearn.ensemble import RandomForestClassifier
 from sklearn.dummy import DummyClassifier
-from sklearn.utils import Memory
 from sklearn.kernel_approximation import Nystroem
 from sklearn.kernel_approximation import RBFSampler
 from sklearn.metrics import zero_one_loss

benchmarks/bench_plot_nmf.py

@@ -14,6 +14,7 @@
 
 import numpy as np
 import matplotlib.pyplot as plt
+from joblib import Memory
 import pandas
 
 from sklearn.utils.testing import ignore_warnings
@@ -22,7 +23,6 @@
 from sklearn.decomposition.nmf import _initialize_nmf
 from sklearn.decomposition.nmf import _beta_divergence
 from sklearn.decomposition.nmf import INTEGER_TYPES, _check_init
-from sklearn.utils import Memory
 from sklearn.exceptions import ConvergenceWarning
 from sklearn.utils.extmath import safe_sparse_dot, squared_norm
 from sklearn.utils import check_array

benchmarks/bench_rcv1_logreg_convergence.py

@@ -4,11 +4,11 @@
 # License: BSD 3 clause
 
 import matplotlib.pyplot as plt
+from joblib import Memory
 import numpy as np
 import gc
 import time
 
-from sklearn.utils import Memory
 from sklearn.linear_model import (LogisticRegression, SGDClassifier)
 from sklearn.datasets import fetch_rcv1
 from sklearn.linear_model.sag import get_auto_step_size

benchmarks/bench_saga.py

@@ -7,12 +7,12 @@
 import time
 from os.path import expanduser
 
+from joblib import delayed, Parallel, Memory
 import matplotlib.pyplot as plt
 import numpy as np
 
 from sklearn.datasets import fetch_rcv1, load_iris, load_digits, \
  fetch_20newsgroups_vectorized
-from sklearn.utils import delayed, Parallel, Memory
 from sklearn.linear_model import LogisticRegression
 from sklearn.metrics import log_loss
 from sklearn.model_selection import train_test_split

benchmarks/bench_tsne_mnist.py

@@ -14,8 +14,8 @@
 import numpy as np
 import json
 import argparse
+from joblib import Memory
 
-from sklearn.utils import Memory
 from sklearn.datasets import fetch_mldata
 from sklearn.manifold import TSNE
 from sklearn.neighbors import NearestNeighbors

build_tools/circle/build_doc.sh

@@ -120,7 +120,8 @@ conda update --yes --quiet conda
 conda create -n $CONDA_ENV_NAME --yes --quiet python="${PYTHON_VERSION:-*}" \
  numpy="${NUMPY_VERSION:-*}" scipy="${SCIPY_VERSION:-*}" cython \
  pytest coverage matplotlib="${MATPLOTLIB_VERSION:-*}" sphinx=1.6.2 pillow \
- scikit-image="${SCIKIT_IMAGE_VERSION:-*}" pandas="${PANDAS_VERSION:-*}"
+ scikit-image="${SCIKIT_IMAGE_VERSION:-*}" pandas="${PANDAS_VERSION:-*}" \
+ joblib
 
 source activate testenv
 pip install sphinx-gallery

doc/modules/classes.rst

@@ -1480,31 +1480,32 @@ Low-level methods
 
 Utilities from joblib:
 
-.. autosummary::
- :toctree: generated/
- :template: class.rst
-
- utils.Memory
- utils.Parallel
-
 .. autosummary::
  :toctree: generated/
  :template: function.rst
 
- utils.cpu_count
- utils.delayed
  utils.parallel_backend
+ utils.register_parallel_backend
 
 Recently deprecated
 ===================
 
 To be removed in 0.23
 ---------------------
 
+.. autosummary::
+ :toctree: generated/
+ :template: deprecated_class.rst
+
+ utils.Memory
+ utils.Parallel
+
 .. autosummary::
  :toctree: generated/
  :template: deprecated_function.rst
 
+ utils.cpu_count
+ utils.delayed
  metrics.calinski_harabaz_score
 
 

doc/modules/model_persistence.rst

@@ -35,25 +35,25 @@ persistence model, namely `pickle <https://docs.python.org/2/library/pickle.html
  >>> y[0]
  0
 
-In the specific case of scikit-learn, it may be better to use
-joblib's replacement of pickle (``joblib.dump`` & ``joblib.load``),
-which is more efficient on objects that carry large numpy arrays internally as
-is often the case for fitted scikit-learn estimators, but can only pickle to the
-disk and not to a string::
+In the specific case of scikit-learn, it may be better to use joblib's
+replacement of pickle (``dump`` & ``load``), which is more efficient on
+objects that carry large numpy arrays internally as is often the case for
+fitted scikit-learn estimators, but can only pickle to the disk and not to a
+string::
 
- >>> from sklearn.externals import joblib
- >>> joblib.dump(clf, 'filename.joblib') # doctest: +SKIP
+ >>> from joblib import dump, load
+ >>> dump(clf, 'filename.joblib') # doctest: +SKIP
 
 Later you can load back the pickled model (possibly in another Python process)
 with::
 
- >>> clf = joblib.load('filename.joblib') # doctest:+SKIP
+ >>> clf = load('filename.joblib') # doctest:+SKIP
 
 .. note::
 
- ``joblib.dump`` and ``joblib.load`` functions also accept file-like object
+ ``dump`` and ``load`` functions also accept file-like object
  instead of filenames. More information on data persistence with Joblib is
- available `here <https://pythonhosted.org/joblib/persistence.html>`_.
+ available `here <https://joblib.readthedocs.io/en/latest/persistence.html>`_.
 
 .. _persistence_limitations: