Convert a generic tree regressor model to the protobuf spec.

    This currently supports:
      * Decision tree regression
      * Gradient boosted tree regression
      * Random forest regression
      * Decision tree classifier.
      * Gradient boosted tree classifier.
      * Random forest classifier.

    ----------
    Parameters
    model: [DecisionTreeRegressor | GradientBoostingRegression | RandomForestRegressor]
        A scikit learn tree model.

    feature_names : list of strings, optional (default=None)
        Names of each of the features.

    target: str
        Name of the output column.

    base_prediction: double
        Base prediction value.

    mode: str in ['regressor', 'classifier']
        Mode of the tree model.

    class_labels: list[int]
        List of classes

    post_evaluation_transform: list[int]
        Post evaluation transform

    Returns
    -------
    model_spec: An object of type Model_pb.
        Protobuf representation of the model

def convert_tree_ensemble(model, input_features,
                          output_features = ('predicted_class', float),
                          mode = 'regressor',
                          base_prediction = None,
                          class_labels = None,
                          post_evaluation_transform = None):
    """
    Convert a generic tree regressor model to the protobuf spec.

    This currently supports:
      * Decision tree regression
      * Gradient boosted tree regression
      * Random forest regression
      * Decision tree classifier.
      * Gradient boosted tree classifier.
      * Random forest classifier.

    ----------
    Parameters
    model: [DecisionTreeRegressor | GradientBoostingRegression | RandomForestRegressor]
        A scikit learn tree model.

    feature_names : list of strings, optional (default=None)
        Names of each of the features.

    target: str
        Name of the output column.

    base_prediction: double
        Base prediction value.

    mode: str in ['regressor', 'classifier']
        Mode of the tree model.

    class_labels: list[int]
        List of classes

    post_evaluation_transform: list[int]
        Post evaluation transform

    Returns
    -------
    model_spec: An object of type Model_pb.
        Protobuf representation of the model
    """

    num_dimensions = get_input_dimension(model)
    features = process_or_validate_features(input_features, num_dimensions)

    n_classes = None
    if mode == 'classifier':
        n_classes = model.n_classes_
        if class_labels is None:
            class_labels = range(n_classes)
        else:
            if len(class_labels) != n_classes:
                raise ValueError("Number of classes in model (%d) does not match "
                                 "length of supplied class list (%d)."
                                 % (n_classes, len(class_labels)))

        coreml_tree = TreeEnsembleClassifier(input_features, class_labels, output_features)
        if post_evaluation_transform is not None:
            coreml_tree.set_post_evaluation_transform(post_evaluation_transform)

        # Base prediction not provided
        if base_prediction is None:
            if n_classes == 2:
                base_prediction = [0.0]
            else:
                base_prediction = [0.0 for c in range(n_classes)]
        coreml_tree.set_default_prediction_value(base_prediction)
    else:
        if base_prediction is None:
            base_prediction = 0.0
        coreml_tree = TreeEnsembleRegressor(input_features, output_features)
        coreml_tree.set_default_prediction_value(base_prediction)

    # Single tree
    if hasattr(model, 'tree_'):
        _recurse(coreml_tree, model.tree_, tree_id = 0, node_id = 0,
                               mode = mode, n_classes = n_classes)

    # Multiple trees
    elif hasattr(model, 'estimators_'):
        is_ensembling_in_separate_trees = False
        if type(model.estimators_) != list:
            is_ensembling_in_separate_trees = len(model.estimators_.shape) > 0 and model.estimators_.shape[1] > 1
            estimators = model.estimators_.flatten()
        else:
            estimators = model.estimators_

        scaling = model.learning_rate if hasattr(model, 'learning_rate') else 1.0 / len(estimators)
        for tree_id, base_model in enumerate(estimators):
            if is_ensembling_in_separate_trees:
                tree_index = tree_id % n_classes
            else:
                tree_index = 0
            _recurse(coreml_tree, base_model.tree_, tree_id, node_id = 0,
                    scaling = scaling, mode = mode, n_classes = n_classes, tree_index = tree_index)
    else:
        raise TypeError('Unknown scikit-learn tree model type.')

    return coreml_tree.spec