Return top-k predictions for the ``dataset``, using the trained model.
        Predictions are returned as an SFrame with three columns: `id`,
        `class`, and `probability`, `margin`,  or `rank`, depending on the ``output_type``
        parameter. Input dataset size must be the same as for training of the model.

        Parameters
        ----------
        dataset : SFrame
            A dataset that has the same columns that were used during training.
            If the target column exists in ``dataset`` it will be ignored
            while making predictions.

        output_type : {'probability', 'rank', 'margin'}, optional
            Choose the return type of the prediction:

            - `probability`: Probability associated with each label in the prediction.
            - `rank`       : Rank associated with each label in the prediction.
            - `margin`     : Margin associated with each label in the prediction.

        k : int, optional
            Number of classes to return for each input example.

        missing_value_action : str, optional
            Action to perform when missing values are encountered. Can be
            one of:

            - 'auto': By default the model will treat missing value as is.
            - 'impute': Proceed with evaluation by filling in the missing
              values with the mean of the training data. Missing
              values are also imputed if an entire column of data is
              missing during evaluation.
            - 'error': Do not proceed with evaluation and terminate with
              an error message.

        Returns
        -------
        out : SFrame
            An SFrame with model predictions.

        See Also
        --------
        predict, classify, evaluate

        Examples
        --------
        >>> pred = m.predict_topk(validation_data, k=3)
        >>> pred
        +--------+-------+-------------------+
        | id     | class |   probability     |
        +--------+-------+-------------------+
        |   0    |   4   |   0.995623886585  |
        |   0    |   9   |  0.0038311756216  |
        |   0    |   7   | 0.000301006948575 |
        |   1    |   1   |   0.928708016872  |
        |   1    |   3   |  0.0440889261663  |
        |   1    |   2   |  0.0176190119237  |
        |   2    |   3   |   0.996967732906  |
        |   2    |   2   |  0.00151345680933 |
        |   2    |   7   | 0.000637513934635 |
        |   3    |   1   |   0.998070061207  |
        |  ...   |  ...  |        ...        |
        +--------+-------+-------------------+
        [35688 rows x 3 columns]

def predict_topk(self, dataset, output_type="probability", k=3, missing_value_action='auto'):
        """
        Return top-k predictions for the ``dataset``, using the trained model.
        Predictions are returned as an SFrame with three columns: `id`,
        `class`, and `probability`, `margin`,  or `rank`, depending on the ``output_type``
        parameter. Input dataset size must be the same as for training of the model.

        Parameters
        ----------
        dataset : SFrame
            A dataset that has the same columns that were used during training.
            If the target column exists in ``dataset`` it will be ignored
            while making predictions.

        output_type : {'probability', 'rank', 'margin'}, optional
            Choose the return type of the prediction:

            - `probability`: Probability associated with each label in the prediction.
            - `rank`       : Rank associated with each label in the prediction.
            - `margin`     : Margin associated with each label in the prediction.

        k : int, optional
            Number of classes to return for each input example.

        missing_value_action : str, optional
            Action to perform when missing values are encountered. Can be
            one of:

            - 'auto': By default the model will treat missing value as is.
            - 'impute': Proceed with evaluation by filling in the missing
              values with the mean of the training data. Missing
              values are also imputed if an entire column of data is
              missing during evaluation.
            - 'error': Do not proceed with evaluation and terminate with
              an error message.

        Returns
        -------
        out : SFrame
            An SFrame with model predictions.

        See Also
        --------
        predict, classify, evaluate

        Examples
        --------
        >>> pred = m.predict_topk(validation_data, k=3)
        >>> pred
        +--------+-------+-------------------+
        | id     | class |   probability     |
        +--------+-------+-------------------+
        |   0    |   4   |   0.995623886585  |
        |   0    |   9   |  0.0038311756216  |
        |   0    |   7   | 0.000301006948575 |
        |   1    |   1   |   0.928708016872  |
        |   1    |   3   |  0.0440889261663  |
        |   1    |   2   |  0.0176190119237  |
        |   2    |   3   |   0.996967732906  |
        |   2    |   2   |  0.00151345680933 |
        |   2    |   7   | 0.000637513934635 |
        |   3    |   1   |   0.998070061207  |
        |  ...   |  ...  |        ...        |
        +--------+-------+-------------------+
        [35688 rows x 3 columns]
        """
        _check_categorical_option_type('output_type', output_type, ['rank', 'margin', 'probability'])
        if missing_value_action == 'auto':
            missing_value_action = _sl.select_default_missing_value_policy(self, 'predict')

        # Low latency path
        if isinstance(dataset, list):
            return self.__proxy__.fast_predict_topk(
                dataset, missing_value_action, output_type, k)
        if isinstance(dataset, dict):
            return self.__proxy__.fast_predict_topk(
                [dataset], missing_value_action, output_type, k)
        # Fast path
        _raise_error_if_not_sframe(dataset, "dataset")
        return self.__proxy__.predict_topk(
            dataset, missing_value_action, output_type, k)