{"action":"created","comment":{"url":"https://api.github.com/repos/scikit-learn/scikit-learn/pulls/comments/130746751","pull_request_review_id":53656824,"id":130746751,"diff_hunk":"@@ -0,0 +1,237 @@\n+# Authors: Andreas Mueller <andreas.mueller@columbia.edu>\n+#          Guillaume Lemaitre <guillaume.lemaitre@inria.fr>\n+# License: BSD 3 clause\n+\n+import numpy as np\n+\n+from ..base import BaseEstimator, RegressorMixin, clone\n+from ..utils.validation import check_is_fitted, has_fit_parameter\n+from ..utils import check_X_y, check_random_state\n+from ._function_transformer import FunctionTransformer\n+\n+__all__ = ['TransformTargetRegressor']\n+\n+\n+class TransformTargetRegressor(BaseEstimator, RegressorMixin):\n+    \"\"\"Meta-estimator to regress on a transformed target.\n+\n+    Useful for applying a non-linear transformation in regression\n+    problems. This transformation can be given as a Transformer such as the\n+    QuantileTransformer or as a function and its inverse such as ``np.log`` and\n+    ``np.exp``.\n+\n+    The computation during ``fit`` is::\n+\n+        regressor.fit(X, func(y))\n+\n+    or::\n+\n+        regressor.fit(X, transformer.transform(y))\n+\n+    The computation during ``predict`` is::\n+\n+        inverse_func(regressor.predict(X))\n+\n+    or::\n+\n+        transformer.inverse_transform(regressor.predict(X))\n+\n+    Read more in the :ref:`User Guide <preprocessing_targets>`.\n+\n+    Parameters\n+    ----------\n+    regressor : object, (default=LinearRegression())\n+        Regressor object such as derived from ``RegressorMixin``. This\n+        regressor will be cloned during fitting.\n+\n+    transformer : object, (default=None)\n+        Estimator object such as derived from ``TransformerMixin``. Cannot be\n+        set at the same time as ``func`` and ``inverse_func``. If\n+        ``transformer`` is ``None`` as well as ``func`` and ``inverse_func``,\n+        the transformer will be an identity transformer. Note that the\n+        transformer will be cloned during fitting.\n+\n+    func : function, optional\n+        Function to apply to ``y`` before passing to ``fit``. Cannot be set at\n+        the same time as ``transformer``. If ``func`` is ``None``, the function\n+        used will be the identity function.\n+\n+    inverse_func : function, optional\n+        Function to apply to the prediction of the regressor. Cannot be set at\n+        the same time as ``transformer`` as well. If ``inverse_func is\n+        ``None``, the function used will be the identity function. The inverse\n+        function is used to return to the same space of the original training\n+        labels during prediction.\n+\n+    check_inverse : bool, (default=True)\n+        Whether to check that ``transform`` followed by ``inverse_transform``\n+        or ``func`` followed by ``inverse_func`` leads to the original targets.\n+\n+    random_state : int, RandomState instance or None, optional (default=None)\n+        If int, random_state is the seed used by the random number generator;\n+        If RandomState instance, random_state is the random number generator;\n+        If None, the random number generator is the RandomState instance used\n+        by `np.random`. The random state is used for subsampling in\n+        ``check_inverse``.\n+\n+    Attributes\n+    ----------\n+    regressor_ : object\n+        Fitted regressor.\n+\n+    transformer_ : object\n+        Transformer used in ``fit`` and ``predict``.\n+\n+    Examples\n+    --------\n+    >>> import numpy as np\n+    >>> from sklearn.linear_model import LinearRegression\n+    >>> from sklearn.preprocessing import TransformTargetRegressor\n+    >>> tt = TransformTargetRegressor(regressor=LinearRegression(),\n+    ...                                 func=np.log, inverse_func=np.exp)\n+    >>> X = np.arange(4).reshape(-1, 1)\n+    >>> y = np.exp(2 * X).ravel()\n+    >>> tt.fit(X, y)\n+    ... # doctest: +NORMALIZE_WHITESPACE\n+    TransformTargetRegressor(check_inverse=True,\n+                             func=<ufunc 'log'>,\n+                             inverse_func=<ufunc 'exp'>,\n+                             random_state=None,\n+                             regressor=LinearRegression(copy_X=True,\n+                                                        fit_intercept=True,\n+                                                        n_jobs=1,\n+                                                        normalize=False),\n+                             transformer=None)\n+    >>> tt.score(X, y)\n+    1.0\n+    >>> tt.regressor_.coef_\n+    array([ 2.])\n+\n+    Notes\n+    -----\n+    The benefit of transforming the target before fitting a regression model is\n+    highlighted in :ref:`examples/preprocessing/plot_transform_target.py\n+    <sphx_glr_auto_examples_preprocessing_plot_transform_target.py> `.\n+\n+    See also :ref:`User guide <scaling_regression>`\n+\n+    \"\"\"\n+    def __init__(self, regressor=None, transformer=None,\n+                 func=None, inverse_func=None, check_inverse=True,\n+                 random_state=None):\n+        self.regressor = regressor\n+        self.transformer = transformer\n+        self.func = func\n+        self.inverse_func = inverse_func\n+        self.check_inverse = check_inverse\n+        self.random_state = random_state\n+\n+    def _fit_transformer(self, y):\n+        if (self.transformer is not None and\n+                (self.func is not None or self.inverse_func is not None)):\n+            raise ValueError(\"'transformer' and functions 'func'/\"\n+                             \"'inverse_func' cannot both be set.\")\n+        elif self.transformer is not None:\n+            self.transformer_ = clone(self.transformer)\n+        else:\n+            self.transformer_ = FunctionTransformer(\n+                func=self.func, inverse_func=self.inverse_func, validate=False)\n+        # XXX: sample_weight is not currently passed to the\n+        # transformer. However, if transformer starts using sample_weight, the\n+        # code should be modified accordingly. At the time to consider the\n+        # sample_prop feature, it is also a good use case to be considered.\n+        self.transformer_.fit(y)\n+        if self.check_inverse:\n+            random_state = check_random_state(self.random_state)\n+            n_subsample = min(10, y.shape[0])\n+            subsample_idx = random_state.choice(range(y.shape[0]),\n+                                                size=n_subsample,\n+                                                replace=False)\n+            if not np.allclose(\n+                    y[subsample_idx],\n+                    self.transformer_.inverse_transform(\n+                        self.transformer_.transform(y[subsample_idx]))):\n+                raise ValueError(\"The provided functions or transformer are\"\n+                                 \" not strictly inverse of each other. If\"\n+                                 \" you are sure you want to proceed regardless\"\n+                                 \", set 'check_inverse=False'\")\n+\n+    def fit(self, X, y, sample_weight=None):\n+        \"\"\"Fit the model according to the given training data.\n+\n+        Parameters\n+        ----------\n+        X : {array-like, sparse matrix}, shape (n_samples, n_features)\n+            Training vector, where n_samples is the number of samples and\n+            n_features is the number of features.\n+\n+        y : array-like, shape (n_samples,)\n+            Target values.\n+\n+        sample_weight : array-like, shape (n_samples,) optional\n+            Array of weights that are assigned to individual samples.\n+            If not provided, then each sample is given unit weight.\n+\n+        Returns\n+        -------\n+        self : object\n+            Returns self.\n+        \"\"\"\n+        X, y = check_X_y(X, y, multi_output=True, y_numeric=True)","path":"sklearn/preprocessing/target.py","position":180,"original_position":180,"commit_id":"0ddfee0e8dbeca6918058f51a66decda38399dff","original_commit_id":"9e07197a8e262ed6593cfea1d825a5d1cc64248e","user":{"login":"jnothman","id":78827,"avatar_url":"https://avatars2.githubusercontent.com/u/78827?v=4","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},"body":"Okay. Just keep the validation","created_at":"2017-08-01T22:37:08Z","updated_at":"2017-08-01T22:37:08Z","html_url":"https://github.com/scikit-learn/scikit-learn/pull/9041#discussion_r130746751","pull_request_url":"https://api.github.com/repos/scikit-learn/scikit-learn/pulls/9041","_links":{"self":{"href":"https://api.github.com/repos/scikit-learn/scikit-learn/pulls/comments/130746751"},"html":{"href":"https://github.com/scikit-learn/scikit-learn/pull/9041#discussion_r130746751"},"pull_request":{"href":"https://api.github.com/repos/scikit-learn/scikit-learn/pulls/9041"}}},"pull_request":{"url":"https://api.github.com/repos/scikit-learn/scikit-learn/pulls/9041","id":124427850,"html_url":"https://github.com/scikit-learn/scikit-learn/pull/9041","diff_url":"https://github.com/scikit-learn/scikit-learn/pull/9041.diff","patch_url":"https://github.com/scikit-learn/scikit-learn/pull/9041.patch","issue_url":"https://api.github.com/repos/scikit-learn/scikit-learn/issues/9041","number":9041,"state":"open","locked":false,"title":"[MRG + 1] TransformedTargetRegressor","user":{"login":"glemaitre","id":7454015,"avatar_url":"https://avatars2.githubusercontent.com/u/7454015?v=4","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":"Continutation of #8988 \r\n\r\nTODO:\r\n\r\n- [x] User guide doc\r\n- [x] API doc\r\n- [x] What's new entry\r\n\r\n","created_at":"2017-06-07T14:02:03Z","updated_at":"2017-08-01T22:37:08Z","closed_at":null,"merged_at":null,"merge_commit_sha":"a6e4f6723cfa9aacf1a6c20140f12ce3681d751c","assignee":null,"assignees":[],"requested_reviewers":[],"milestone":{"url":"https://api.github.com/repos/scikit-learn/scikit-learn/milestones/24","html_url":"https://github.com/scikit-learn/scikit-learn/milestone/24","labels_url":"https://api.github.com/repos/scikit-learn/scikit-learn/milestones/24/labels","id":2577444,"number":24,"title":"0.20","description":"","creator":{"login":"jnothman","id":78827,"avatar_url":"https://avatars2.githubusercontent.com/u/78827?v=4","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},"open_issues":77,"closed_issues":7,"state":"open","created_at":"2017-06-12T11:07:06Z","updated_at":"2017-07-26T08:28:48Z","due_on":"2018-01-31T08:00:00Z","closed_at":null},"commits_url":"https://api.github.com/repos/scikit-learn/scikit-learn/pulls/9041/commits","review_comments_url":"https://api.github.com/repos/scikit-learn/scikit-learn/pulls/9041/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/9041/comments","statuses_url":"https://api.github.com/repos/scikit-learn/scikit-learn/statuses/0ddfee0e8dbeca6918058f51a66decda38399dff","head":{"label":"glemaitre:targettransformer","ref":"targettransformer","sha":"0ddfee0e8dbeca6918058f51a66decda38399dff","user":{"login":"glemaitre","id":7454015,"avatar_url":"https://avatars2.githubusercontent.com/u/7454015?v=4","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},"repo":{"id":49886894,"name":"scikit-learn","full_name":"glemaitre/scikit-learn","owner":{"login":"glemaitre","id":7454015,"avatar_url":"https://avatars2.githubusercontent.com/u/7454015?v=4","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},"private":false,"html_url":"https://github.com/glemaitre/scikit-learn","description":"scikit-learn: 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