def query(self, dataset, label=None, k=5, radius=None, verbose=True):
        """
        For each row of the input 'dataset', retrieve the nearest neighbors
        from the model's stored data. In general, the query dataset does not
        need to be the same as the reference data stored in the model, but if
        it is, the 'include_self_edges' parameter can be set to False to
        exclude results that match query points to themselves.

        Parameters
        ----------
        dataset : SFrame
            Query data. Must contain columns with the same names and types as
            the features used to train the model. Additional columns are
            allowed, but ignored. Please see the nearest neighbors
            :func:`~turicreate.nearest_neighbors.create` documentation for more
            detail on allowable data types.

        label : str, optional
            Name of the query SFrame column with row labels. If 'label' is not
            specified, row numbers are used to identify query dataset rows in
            the output SFrame.

        k : int, optional
            Number of nearest neighbors to return from the reference set for
            each query observation. The default is 5 neighbors, but setting it
            to ``None`` will return all neighbors within ``radius`` of the
            query point.

        radius : float, optional
            Only neighbors whose distance to a query point is smaller than this
            value are returned. The default is ``None``, in which case the
            ``k`` nearest neighbors are returned for each query point,
            regardless of distance.

        verbose: bool, optional
            If True, print progress updates and model details.

        Returns
        -------
        out : SFrame
            An SFrame with the k-nearest neighbors of each query observation.
            The result contains four columns: the first is the label of the
            query observation, the second is the label of the nearby reference
            observation, the third is the distance between the query and
            reference observations, and the fourth is the rank of the reference
            observation among the query's k-nearest neighbors.

        See Also
        --------
        similarity_graph

        Notes
        -----
        - The `dataset` input to this method *can* have missing values (in
          contrast to the reference dataset used to create the nearest
          neighbors model). Missing numeric values are imputed to be the mean
          of the corresponding feature in the reference dataset, and missing
          strings are imputed to be empty strings.

        - If both ``k`` and ``radius`` are set to ``None``, each query point
          returns all of the reference set. If the reference dataset has
          :math:`n` rows and the query dataset has :math:`m` rows, the output
          is an SFrame with :math:`nm` rows.

        - For models created with the 'lsh' method, the query results may have
          fewer query labels than input query points. Because LSH is an
          approximate method, a query point may have fewer than 'k' neighbors.
          If LSH returns no neighbors at all for a query, the query point is
          omitted from the results.

        Examples
        --------
        First construct a toy SFrame and create a nearest neighbors model:

        >>> sf = turicreate.SFrame({'label': range(3),
        ...                       'feature1': [0.98, 0.62, 0.11],
        ...                       'feature2': [0.69, 0.58, 0.36]})
        >>> model = turicreate.nearest_neighbors.create(sf, 'label')

        A new SFrame contains query observations with same schema as the
        reference SFrame. This SFrame is passed to the ``query`` method.

        >>> queries = turicreate.SFrame({'label': range(3),
        ...                            'feature1': [0.05, 0.61, 0.99],
        ...                            'feature2': [0.06, 0.97, 0.86]})
        >>> model.query(queries, 'label', k=2)
        +-------------+-----------------+----------------+------+
        | query_label | reference_label |    distance    | rank |
        +-------------+-----------------+----------------+------+
        |      0      |        2        | 0.305941170816 |  1   |
        |      0      |        1        | 0.771556867638 |  2   |
        |      1      |        1        | 0.390128184063 |  1   |
        |      1      |        0        | 0.464004310325 |  2   |
        |      2      |        0        | 0.170293863659 |  1   |
        |      2      |        1        | 0.464004310325 |  2   |
        +-------------+-----------------+----------------+------+
        """

        ## Validate the 'dataset' input
        _tkutl._raise_error_if_not_sframe(dataset, "dataset")
        _tkutl._raise_error_if_sframe_empty(dataset, "dataset")

        ## Get model features
        ref_features = self.features
        sf_features = _tkutl._toolkits_select_columns(dataset, ref_features)

        ## Validate and preprocess the 'label' input
        if label is None:
            query_labels = _turicreate.SArray.from_sequence(len(dataset))

        else:
            if not label in dataset.column_names():
                raise ValueError(
                    "Input 'label' must be a string matching the name of a " +\
                    "column in the reference SFrame 'dataset'.")

            if not dataset[label].dtype == str and not dataset[label].dtype == int:
                raise TypeError("The label column must contain integers or strings.")

            if label in ref_features:
                raise ValueError("The label column cannot be one of the features.")

            query_labels = dataset[label]


        ## Validate neighborhood parameters 'k' and 'radius'
        if k is not None:
            if not isinstance(k, int):
                raise ValueError("Input 'k' must be an integer.")

            if k <= 0:
                raise ValueError("Input 'k' must be larger than 0.")

        if radius is not None:
            if not isinstance(radius, (int, float)):
                raise ValueError("Input 'radius' must be an integer or float.")

            if radius < 0:
                raise ValueError("Input 'radius' must be non-negative.")


        ## Set k and radius to special values to indicate 'None'
        if k is None:
            k = -1

        if radius is None:
            radius = -1.0

        opts = {'model': self.__proxy__,
                'model_name': self.__name__,
                'features': sf_features,
                'query_labels': query_labels,
                'k': k,
                'radius': radius}

        with QuietProgress(verbose):
            result = _turicreate.extensions._nearest_neighbors.query(opts)

        return result['neighbors']
For each row of the input 'dataset', retrieve the nearest neighbors
        from the model's stored data. In general, the query dataset does not
        need to be the same as the reference data stored in the model, but if
        it is, the 'include_self_edges' parameter can be set to False to
        exclude results that match query points to themselves.

        Parameters
        ----------
        dataset : SFrame
            Query data. Must contain columns with the same names and types as
            the features used to train the model. Additional columns are
            allowed, but ignored. Please see the nearest neighbors
            :func:`~turicreate.nearest_neighbors.create` documentation for more
            detail on allowable data types.

        label : str, optional
            Name of the query SFrame column with row labels. If 'label' is not
            specified, row numbers are used to identify query dataset rows in
            the output SFrame.

        k : int, optional
            Number of nearest neighbors to return from the reference set for
            each query observation. The default is 5 neighbors, but setting it
            to ``None`` will return all neighbors within ``radius`` of the
            query point.

        radius : float, optional
            Only neighbors whose distance to a query point is smaller than this
            value are returned. The default is ``None``, in which case the
            ``k`` nearest neighbors are returned for each query point,
            regardless of distance.

        verbose: bool, optional
            If True, print progress updates and model details.

        Returns
        -------
        out : SFrame
            An SFrame with the k-nearest neighbors of each query observation.
            The result contains four columns: the first is the label of the
            query observation, the second is the label of the nearby reference
            observation, the third is the distance between the query and
            reference observations, and the fourth is the rank of the reference
            observation among the query's k-nearest neighbors.

        See Also
        --------
        similarity_graph

        Notes
        -----
        - The `dataset` input to this method *can* have missing values (in
          contrast to the reference dataset used to create the nearest
          neighbors model). Missing numeric values are imputed to be the mean
          of the corresponding feature in the reference dataset, and missing
          strings are imputed to be empty strings.

        - If both ``k`` and ``radius`` are set to ``None``, each query point
          returns all of the reference set. If the reference dataset has
          :math:`n` rows and the query dataset has :math:`m` rows, the output
          is an SFrame with :math:`nm` rows.

        - For models created with the 'lsh' method, the query results may have
          fewer query labels than input query points. Because LSH is an
          approximate method, a query point may have fewer than 'k' neighbors.
          If LSH returns no neighbors at all for a query, the query point is
          omitted from the results.

        Examples
        --------
        First construct a toy SFrame and create a nearest neighbors model:

        >>> sf = turicreate.SFrame({'label': range(3),
        ...                       'feature1': [0.98, 0.62, 0.11],
        ...                       'feature2': [0.69, 0.58, 0.36]})
        >>> model = turicreate.nearest_neighbors.create(sf, 'label')

        A new SFrame contains query observations with same schema as the
        reference SFrame. This SFrame is passed to the ``query`` method.

        >>> queries = turicreate.SFrame({'label': range(3),
        ...                            'feature1': [0.05, 0.61, 0.99],
        ...                            'feature2': [0.06, 0.97, 0.86]})
        >>> model.query(queries, 'label', k=2)
        +-------------+-----------------+----------------+------+
        | query_label | reference_label |    distance    | rank |
        +-------------+-----------------+----------------+------+
        |      0      |        2        | 0.305941170816 |  1   |
        |      0      |        1        | 0.771556867638 |  2   |
        |      1      |        1        | 0.390128184063 |  1   |
        |      1      |        0        | 0.464004310325 |  2   |
        |      2      |        0        | 0.170293863659 |  1   |
        |      2      |        1        | 0.464004310325 |  2   |
        +-------------+-----------------+----------------+------+