{"action":"created","comment":{"url":"https://api.github.com/repos/scikit-learn/scikit-learn/pulls/comments/122839212","pull_request_review_id":44981793,"id":122839212,"diff_hunk":"@@ -381,10 +381,105 @@ The normalizer instance can then be used on sample vectors as any transformer::\n   efficient Cython routines. To avoid unnecessary memory copies, it is\n   recommended to choose the CSR representation upstream.\n \n-.. _preprocessing_binarization:\n+.. _preprocessing_categorical_features:\n+\n+Encoding categorical features\n+=============================\n+Often features are not given as continuous values but categorical.\n+For example a person could have features ``[\"male\", \"female\"]``,\n+``[\"from Europe\", \"from US\", \"from Asia\"]``,\n+``[\"uses Firefox\", \"uses Chrome\", \"uses Safari\", \"uses Internet Explorer\"]``.\n+Such features can be efficiently coded as integers, for instance\n+``[\"male\", \"from US\", \"uses Internet Explorer\"]`` could be expressed as\n+``[0, 1, 3]`` while ``[\"female\", \"from Asia\", \"uses Chrome\"]`` would be\n+``[1, 2, 1]``.\n+\n+Such integer representation can not be used directly with scikit-learn estimators, as these\n+expect continuous input, and would interpret the categories as being ordered, which is often\n+not desired (i.e. the set of browsers was ordered arbitrarily).\n+\n+One possibility to convert categorical features to features that can be used\n+with scikit-learn estimators is to use a one-of-K or one-hot encoding, which is\n+implemented in :class:`OneHotEncoder`.  This estimator transforms each\n+categorical feature with ``m`` possible values into ``m`` binary features, with\n+only one active.\n+\n+Continuing the example above::\n+\n+  >>> enc = preprocessing.OneHotEncoder()\n+  >>> enc.fit([[0, 0, 3], [1, 1, 0], [0, 2, 1], [1, 0, 2]])  # doctest: +ELLIPSIS\n+  OneHotEncoder(categorical_features='all', dtype=<... 'numpy.float64'>,\n+         handle_unknown='error', n_values='auto', sparse=True)\n+  >>> enc.transform([[0, 1, 3]]).toarray()\n+  array([[ 1.,  0.,  0.,  1.,  0.,  0.,  0.,  0.,  1.]])\n+\n+By default, how many values each feature can take is inferred automatically from the dataset.\n+It is possible to specify this explicitly using the parameter ``n_values``.\n+There are two genders, three possible continents and four web browsers in our\n+dataset.\n+Then we fit the estimator, and transform a data point.\n+In the result, the first two numbers encode the gender, the next set of three\n+numbers the continent and the last four the web browser.\n+\n+Note that, if there is a possibilty that the training data might have missing categorical\n+features, one has to explicitly set ``n_values``. For example,\n+\n+    >>> enc = preprocessing.OneHotEncoder(n_values=[2, 3, 4])\n+    >>> # Note that there are missing categorical values for the 2nd and 3rd\n+    >>> # features\n+    >>> enc.fit([[1, 2, 3], [0, 2, 0]])  # doctest: +ELLIPSIS\n+    OneHotEncoder(categorical_features='all', dtype=<... 'numpy.float64'>,\n+           handle_unknown='error', n_values=[2, 3, 4], sparse=True)\n+    >>> enc.transform([[1, 0, 0]]).toarray()\n+    array([[ 0.,  1.,  1.,  0.,  0.,  1.,  0.,  0.,  0.]])\n+\n+See :ref:`dict_feature_extraction` for categorical features that are represented\n+as a dict, not as integers.\n+\n+.. _discretization:\n+\n+Discretization\n+==============\n+\n+`Discretization <https://en.wikipedia.org/wiki/Discretization_of_continuous_features>`_\n+(otherwise known as quantization or binning) provides a way to partition continuous\n+features into discrete values. Certain datasets with continuous features\n+may benefit from discretization, because discretization can transform the dataset\n+of continuous attributes to one with only nominal attributes.\n+\n+K-bins discretization\n+---------------------\n+\n+Scikit-learn allows users to discretize features into ``k``\n+equal width bins using a :class:`KBinsDiscretizer`::\n+\n+  >>> X = np.array([[ -3., 5., 15 ],\n+  ...               [  0., 6., 14 ],\n+  ...               [  6., 3., 11 ]])\n+  >>> est = preprocessing.KBinsDiscretizer(n_bins=[3, 3, 2]).fit(X)\n+\n+In this example, the \"bin edges\" (boundaries for the discretization criteria)\n+for each of the three features are as follows:\n+\n+ - ``0`` and ``3``\n+ - ``4`` and ``5``\n+ - ``13``\n \n-Binarization\n-============\n+Based on these bin edges, we transform ``X`` as follows::\n+","path":"doc/modules/preprocessing.rst","position":92,"original_position":92,"commit_id":"0189c298acf71f0872f9f1bd0d701fb076712f80","original_commit_id":"0189c298acf71f0872f9f1bd0d701fb076712f80","user":{"login":"glemaitre","id":7454015,"avatar_url":"https://avatars1.githubusercontent.com/u/7454015?v=3","gravatar_id":"","url":"https://api.github.com/users/glemaitre","html_url":"https://github.com/glemaitre","followers_url":"https://api.github.com/users/glemaitre/followers","following_url":"https://api.github.com/users/glemaitre/following{/other_user}","gists_url":"https://api.github.com/users/glemaitre/gists{/gist_id}","starred_url":"https://api.github.com/users/glemaitre/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/glemaitre/subscriptions","organizations_url":"https://api.github.com/users/glemaitre/orgs","repos_url":"https://api.github.com/users/glemaitre/repos","events_url":"https://api.github.com/users/glemaitre/events{/privacy}","received_events_url":"https://api.github.com/users/glemaitre/received_events","type":"User","site_admin":false},"body":"```\r\nBased on these bin intervals, ``X`` is transformed as follows::\r\n```","created_at":"2017-06-19T22:17:51Z","updated_at":"2017-06-19T22:21:36Z","html_url":"https://github.com/scikit-learn/scikit-learn/pull/7668#discussion_r122839212","pull_request_url":"https://api.github.com/repos/scikit-learn/scikit-learn/pulls/7668","_links":{"self":{"href":"https://api.github.com/repos/scikit-learn/scikit-learn/pulls/comments/122839212"},"html":{"href":"https://github.com/scikit-learn/scikit-learn/pull/7668#discussion_r122839212"},"pull_request":{"href":"https://api.github.com/repos/scikit-learn/scikit-learn/pulls/7668"}}},"pull_request":{"url":"https://api.github.com/repos/scikit-learn/scikit-learn/pulls/7668","id":89353098,"html_url":"https://github.com/scikit-learn/scikit-learn/pull/7668","diff_url":"https://github.com/scikit-learn/scikit-learn/pull/7668.diff","patch_url":"https://github.com/scikit-learn/scikit-learn/pull/7668.patch","issue_url":"https://api.github.com/repos/scikit-learn/scikit-learn/issues/7668","number":7668,"state":"open","locked":false,"title":"[MRG+1] Add fixed width discretization to scikit-learn","user":{"login":"hlin117","id":1865885,"avatar_url":"https://avatars0.githubusercontent.com/u/1865885?v=3","gravatar_id":"","url":"https://api.github.com/users/hlin117","html_url":"https://github.com/hlin117","followers_url":"https://api.github.com/users/hlin117/followers","following_url":"https://api.github.com/users/hlin117/following{/other_user}","gists_url":"https://api.github.com/users/hlin117/gists{/gist_id}","starred_url":"https://api.github.com/users/hlin117/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/hlin117/subscriptions","organizations_url":"https://api.github.com/users/hlin117/orgs","repos_url":"https://api.github.com/users/hlin117/repos","events_url":"https://api.github.com/users/hlin117/events{/privacy}","received_events_url":"https://api.github.com/users/hlin117/received_events","type":"User","site_admin":false},"body":"# Summary\r\n\r\nThis PR addresses #5778. It adds the `KBinsDiscretizer` class to scikit-learn.\r\n\r\nThis PR closes #5825 as a much more straightforward discretization algorithm. It is a WIP, please feel free to leave comments about the current code before the code gets hairy and hard to read 😄 \r\n# Roadmap\r\n\r\nPermission\r\n- [x] Package owner approval of algorithm\r\n\r\nFeatures and code\r\n- [x] Uniform discretization strategy for 2D arrays\r\n- [x] `inverse_transform` function\r\n- [x] Unit tests (duh)\r\n\r\nDocumentation\r\n- [x] User guide\r\n- [x] Docs in `doc/modules/classes.rst`\r\n- [x] Docs in `doc/modules/preprocessing.rst`\r\n- [x] Docstring tests\r\n","created_at":"2016-10-14T09:37:04Z","updated_at":"2017-06-19T22:21:36Z","closed_at":null,"merged_at":null,"merge_commit_sha":"af3e5aa84214660c9b52a3358e1aae809efbd96f","assignee":{"login":"jnothman","id":78827,"avatar_url":"https://avatars1.githubusercontent.com/u/78827?v=3","gravatar_id":"","url":"https://api.github.com/users/jnothman","html_url":"https://github.com/jnothman","followers_url":"https://api.github.com/users/jnothman/followers","following_url":"https://api.github.com/users/jnothman/following{/other_user}","gists_url":"https://api.github.com/users/jnothman/gists{/gist_id}","starred_url":"https://api.github.com/users/jnothman/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/jnothman/subscriptions","organizations_url":"https://api.github.com/users/jnothman/orgs","repos_url":"https://api.github.com/users/jnothman/repos","events_url":"https://api.github.com/users/jnothman/events{/privacy}","received_events_url":"https://api.github.com/users/jnothman/received_events","type":"User","site_admin":false},"assignees":[{"login":"jnothman","id":78827,"avatar_url":"https://avatars1.githubusercontent.com/u/78827?v=3","gravatar_id":"","url":"https://api.github.com/users/jnothman","html_url":"https://github.com/jnothman","followers_url":"https://api.github.com/users/jnothman/followers","following_url":"https://api.github.com/users/jnothman/following{/other_user}","gists_url":"https://api.github.com/users/jnothman/gists{/gist_id}","starred_url":"https://api.github.com/users/jnothman/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/jnothman/subscriptions","organizations_url":"https://api.github.com/users/jnothman/orgs","repos_url":"https://api.github.com/users/jnothman/repos","events_url":"https://api.github.com/users/jnothman/events{/privacy}","received_events_url":"https://api.github.com/users/jnothman/received_events","type":"User","site_admin":false}],"requested_reviewers":[],"milestone":{"url":"https://api.github.com/repos/scikit-learn/scikit-learn/milestones/21","html_url":"https://github.com/scikit-learn/scikit-learn/milestone/21","labels_url":"https://api.github.com/repos/scikit-learn/scikit-learn/milestones/21/labels","id":1970916,"number":21,"title":"0.19","description":"","creator":{"login":"amueller","id":449558,"avatar_url":"https://avatars0.githubusercontent.com/u/449558?v=3","gravatar_id":"","url":"https://api.github.com/users/amueller","html_url":"https://github.com/amueller","followers_url":"https://api.github.com/users/amueller/followers","following_url":"https://api.github.com/users/amueller/following{/other_user}","gists_url":"https://api.github.com/users/amueller/gists{/gist_id}","starred_url":"https://api.github.com/users/amueller/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/amueller/subscriptions","organizations_url":"https://api.github.com/users/amueller/orgs","repos_url":"https://api.github.com/users/amueller/repos","events_url":"https://api.github.com/users/amueller/events{/privacy}","received_events_url":"https://api.github.com/users/amueller/received_events","type":"User","site_admin":false},"open_issues":72,"closed_issues":121,"state":"open","created_at":"2016-08-29T22:34:33Z","updated_at":"2017-06-19T20:35:09Z","due_on":"2017-07-04T07:00:00Z","closed_at":null},"commits_url":"https://api.github.com/repos/scikit-learn/scikit-learn/pulls/7668/commits","review_comments_url":"https://api.github.com/repos/scikit-learn/scikit-learn/pulls/7668/comments","review_comment_url":"https://api.github.com/repos/scikit-learn/scikit-learn/pulls/comments{/number}","comments_url":"https://api.github.com/repos/scikit-learn/scikit-learn/issues/7668/comments","statuses_url":"https://api.github.com/repos/scikit-learn/scikit-learn/statuses/0189c298acf71f0872f9f1bd0d701fb076712f80","head":{"label":"hlin117:dis2","ref":"dis2","sha":"0189c298acf71f0872f9f1bd0d701fb076712f80","user":{"login":"hlin117","id":1865885,"avatar_url":"https://avatars0.githubusercontent.com/u/1865885?v=3","gravatar_id":"","url":"https://api.github.com/users/hlin117","html_url":"https://github.com/hlin117","followers_url":"https://api.github.com/users/hlin117/followers","following_url":"https://api.github.com/users/hlin117/following{/other_user}","gists_url":"https://api.github.com/users/hlin117/gists{/gist_id}","starred_url":"https://api.github.com/users/hlin117/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/hlin117/subscriptions","organizations_url":"https://api.github.com/users/hlin117/orgs","repos_url":"https://api.github.com/users/hlin117/repos","events_url":"https://api.github.com/users/hlin117/events{/privacy}","received_events_url":"https://api.github.com/users/hlin117/received_events","type":"User","site_admin":false},"repo":{"id":36590365,"name":"scikit-learn","full_name":"hlin117/scikit-learn","owner":{"login":"hlin117","id":1865885,"avatar_url":"https://avatars0.githubusercontent.com/u/1865885?v=3","gravatar_i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