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* upstream/master: FIX YeoJohnson transform lambda bounds (scikit-learn#12522) [MRG] Additional Warnings in case OpenML auto-detected a problem with dataset (scikit-learn#12541) ENH Prefer threads for IsolationForest (scikit-learn#12543) joblib 0.13.0 (scikit-learn#12531) DOC tweak KMeans regarding cluster_centers_ convergence (scikit-learn#12537) DOC (0.21) Make sure plot_tree docs are generated and fix link in whatsnew (scikit-learn#12533) ALL Add HashingVectorizer to __all__ (scikit-learn#12534) BLD we should ensure continued support for joblib 0.11 (scikit-learn#12350) fix typo in whatsnew Fix dead link to numpydoc (scikit-learn#12532) [MRG] Fix segfault in AgglomerativeClustering with read-only mmaps (scikit-learn#12485) MNT (0.21) OPTiCS change the default `algorithm` to `auto` (scikit-learn#12529) FIX SkLearn `.score()` method generating error with Dask DataFrames (scikit-learn#12462) MNT KBinsDiscretizer.transform should not mutate _encoder (scikit-learn#12514)
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‎.travis.yml

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NUMPY_VERSION="1.10.4" SCIPY_VERSION="0.16.1" CYTHON_VERSION="0.25.2"
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PILLOW_VERSION="4.0.0" COVERAGE=true
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if: type != cron
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# Python 3.5 build
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- env: DISTRIB="conda" PYTHON_VERSION="3.5" INSTALL_MKL="false"
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NUMPY_VERSION="1.10.4" SCIPY_VERSION="0.16.1" CYTHON_VERSION="0.25.2"
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PILLOW_VERSION="4.0.0" COVERAGE=true
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SKLEARN_SITE_JOBLIB=1 JOBLIB_VERSION="0.11"
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if: type != cron
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# This environment tests the latest available dependencies.
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# It runs tests requiring pandas and PyAMG.
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# It also runs with the site joblib instead of the vendored copy of joblib.

‎doc/glossary.rst

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We try to adhere to `PEP257
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<https://www.python.org/dev/peps/pep-0257/>`_, and follow `NumpyDoc
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conventions <numpydoc.readthedocs.io/en/latest/format.html>`_.
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conventions <https://numpydoc.readthedocs.io/en/latest/format.html>`_.
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double underscore
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double underscore notation

‎doc/modules/classes.rst

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:template: function.rst
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tree.export_graphviz
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tree.plot_tree
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.. _utils_ref:

‎doc/modules/computing.rst

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scikit-learn uses the site joblib rather than its vendored version.
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Consequently, joblib must be installed for scikit-learn to run.
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Note that using the site joblib is at your own risks: the versions of
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scikt-learn and joblib need to be compatible. In addition, dumps from
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joblib.Memory might be incompatible, and you might loose some caches
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and have to redownload some datasets.
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scikit-learn and joblib need to be compatible. Currently, joblib 0.11+
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is supported. In addition, dumps from joblib.Memory might be incompatible,
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and you might loose some caches and have to redownload some datasets.
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:SKLEARN_ASSUME_FINITE:
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‎doc/whats_new/v0.20.rst

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:mod:`sklearn.preprocessing`
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........................
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- |Fix| Fixed bug in :class:`preprocessing.OrdinalEncoder` when passing
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manually specified categories. :issue:`12365` by `Joris Van den Bossche`_.
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- |Fix| Fixed bug in :class:`preprocessing.KBinsDiscretizer` where the
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``transform`` method mutates the ``_encoder`` attribute. The ``transform``
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method is now thread safe. :issue:`12514` by
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:user:`Hanmin Qin <qinhanmin2014>`.
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- |API| The default value of the :code:`method` argument in
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:func:`preprocessing.power_transform` will be changed from :code:`box-cox`
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to :code:`yeo-johnson` to match :class:`preprocessing.PowerTransformer`
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in version 0.23. A FutureWarning is raised when the default value is used.
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:issue:`12317` by :user:`Eric Chang <chang>`.
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- |Fix| Fixed bug in :class:`preprocessing.OrdinalEncoder` when passing
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manually specified categories. :issue:`12365` by `Joris Van den Bossche`_.
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- |Fix| Fixed a bug in :class:`preprocessing.PowerTransformer` where the
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Yeo-Johnson transform was incorrect for lambda parameters outside of `[0, 2]`
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:issue:`12522` by :user:`Nicolas Hug<NicolasHug>`.
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:mod:`sklearn.utils`
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........................
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precision issues in :class:`preprocessing.StandardScaler` and
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:class:`decomposition.IncrementalPCA` when using float32 datasets.
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:issue:`12338` by :user:`bauks <bauks>`.
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Miscellaneous
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.............
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- |Fix| When using site joblib by setting the environment variable
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`SKLEARN_SITE_JOBLIB`, added compatibility with joblib 0.11 in addition
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to 0.12+. :issue:`12350` by `Joel Nothman`_ and `Roman Yurchak`_.
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Miscellaneous
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.............
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happens immediately (i.e., without a deprecation cycle).
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:issue:`11741` by `Olivier Grisel`_.
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- |Fix| Fixed a bug in validation helpers where passing a Dask DataFrame results
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in an error. :issue:`12462` by :user:`Zachariah Miller <zwmiller>`
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Changes to estimator checks
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---------------------------
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‎doc/whats_new/v0.21.rst

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:class:`linear_model.MultiTaskLasso` which were breaking when
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``warm_start = True``. :issue:`12360` by :user:`Aakanksha Joshi <joaak>`.
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:mod:`sklearn.ensemble`
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.......................
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- |Efficiency| Make :class:`ensemble.IsolationForest` prefer threads over
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processes when running with ``n_jobs > 1`` as the underlying decision tree
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fit calls do release the GIL. This changes reduces memory usage and
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communication overhead. :issue:`12543` by :user:`Isaac Storch <istorch>`
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and `Olivier Grisel`_.
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:mod:`sklearn.metrics`
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......................
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:mod:`sklearn.tree`
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...................
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- Decision Trees can now be plotted with matplotlib using
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:func:`tree.export.plot_tree` without relying on the ``dot`` library,
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:func:`tree.plot_tree` without relying on the ``dot`` library,
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removing a hard-to-install dependency. :issue:`8508` by `Andreas Müller`_.
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- |Feature| ``get_n_leaves()`` and ``get_depth()`` have been added to
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- Add ``check_fit_idempotent`` to
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:func:`~utils.estimator_checks.check_estimator`, which checks that
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when `fit` is called twice with the same data, the ouput of
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`predit`, `predict_proba`, `transform`, and `decision_function` does not
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`predict`, `predict_proba`, `transform`, and `decision_function` does not
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change. :issue:`12328` by :user:`Nicolas Hug <NicolasHug>`

‎sklearn/cluster/hierarchical.py

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'retain the lower branches required '
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'for the specified number of clusters',
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stacklevel=2)
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X = np.require(X, requirements="W")
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out = hierarchy.ward(X)
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children_ = out[:, :2].astype(np.intp)
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‎sklearn/cluster/k_means_.py

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Attributes
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----------
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cluster_centers_ : array, [n_clusters, n_features]
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Coordinates of cluster centers
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Coordinates of cluster centers. If the algorithm stops before fully
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converging (see ``tol`` and ``max_iter``), these will not be
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consistent with ``labels_``.
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labels_ :
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Labels of each point
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clustering algorithms available), but it falls in local minima. That's why
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it can be useful to restart it several times.
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If the algorithm stops before fully converging (because of ``tol`` of
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``max_iter``), ``labels_`` and ``means_`` will not be consistent, i.e. the
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``means_`` will not be the means of the points in each cluster.
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Also, the estimator will reassign ``labels_`` after the last iteration to
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make ``labels_`` consistent with ``predict`` on the training set.
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If the algorithm stops before fully converging (because of ``tol`` or
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``max_iter``), ``labels_`` and ``cluster_centers_`` will not be consistent,
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i.e. the ``cluster_centers_`` will not be the means of the points in each
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cluster. Also, the estimator will reassign ``labels_`` after the last
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iteration to make ``labels_`` consistent with ``predict`` on the training
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set.
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"""
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‎sklearn/cluster/optics_.py

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p=2, metric_params=None, maxima_ratio=.75,
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rejection_ratio=.7, similarity_threshold=0.4,
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significant_min=.003, min_cluster_size=.005,
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min_maxima_ratio=0.001, algorithm='ball_tree',
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min_maxima_ratio=0.001, algorithm='auto',
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leaf_size=30, n_jobs=None):
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algorithm : {'auto', 'ball_tree', 'kd_tree', 'brute'}, optional
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Algorithm used to compute the nearest neighbors:
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- 'ball_tree' will use :class:`BallTree` (default)
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- 'ball_tree' will use :class:`BallTree`
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- 'kd_tree' will use :class:`KDTree`
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- 'brute' will use a brute-force search.
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- 'auto' will attempt to decide the most appropriate algorithm
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based on the values passed to :meth:`fit` method.
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based on the values passed to :meth:`fit` method. (default)
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algorithm : {'auto', 'ball_tree', 'kd_tree', 'brute'}, optional
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- 'ball_tree' will use :class:`BallTree` (default)
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- 'ball_tree' will use :class:`BallTree`
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- 'kd_tree' will use :class:`KDTree`
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- 'brute' will use a brute-force search.
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- 'auto' will attempt to decide the most appropriate algorithm
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based on the values passed to :meth:`fit` method.
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based on the values passed to :meth:`fit` method. (default)
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p=2, metric_params=None, maxima_ratio=.75,
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rejection_ratio=.7, similarity_threshold=0.4,
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significant_min=.003, min_cluster_size=.005,
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min_maxima_ratio=0.001, algorithm='ball_tree',
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min_maxima_ratio=0.001, algorithm='auto',
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leaf_size=30, n_jobs=None):
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‎sklearn/datasets/openml.py

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data_description['version'],
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data_description['name'],
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data_description['url']))
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if 'error' in data_description:
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warn("OpenML registered a problem with the dataset. It might be "
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"unusable. Error: {}".format(data_description['error']))
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if 'warning' in data_description:
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warn("OpenML raised a warning on the dataset. It might be "
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"unusable. Warning: {}".format(data_description['warning']))
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# download data features, meta-info about column types
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features_list = _get_data_features(data_id, data_home)

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