scikit-learn：将多输出决策树转换为CoreML模型

我有一个训练有素的scikit-learn模型，它使用多输出决策树（作为 RandomForestRegressor ） . 由于多输出行为是内置的，因此未对Random Forest回归模型明确进行自定义配置以启用多输出行为 . 基本上，只要您将多输出训练数据拟合到模型中，模型就会在幕后切换到多输出模式 .

此外， RandomForestRegressor 是CoreML转换脚本提供的受支持的转换器 . 但是，在转换过程中，我得到了堆栈跟踪的错误：

ValueError：scikit-learn树中只有1个输出 .

Traceback (most recent call last):
  File "/Users/user0/Desktop/model_convert.py", line 7, in <module>
    coreml_model = sklearn_to_ml.convert(model)
  File "/Library/Python/2.7/site-packages/coremltools/converters/sklearn/_converter.py", line 146, in convert
    sk_obj, input_features, output_feature_names, class_labels = None)
  File "/Library/Python/2.7/site-packages/coremltools/converters/sklearn/_converter_internal.py", line 297, in _convert_sklearn_model
    last_spec = last_sk_m.convert(last_sk_obj, current_input_features, output_features)._spec
  File "/Library/Python/2.7/site-packages/coremltools/converters/sklearn/_random_forest_regressor.py", line 53, in convert
    return _MLModel(_convert_tree_ensemble(model, feature_names, target))
  File "/Library/Python/2.7/site-packages/coremltools/converters/sklearn/_tree_ensemble.py", line 195, in convert_tree_ensemble
    scaling = scaling, mode = mode, n_classes = n_classes, tree_index = tree_index)
  File "/Library/Python/2.7/site-packages/coremltools/converters/sklearn/_tree_ensemble.py", line 68, in _recurse
    _recurse(coreml_tree, scikit_tree, tree_id, left_child_id, scaling, mode, n_classes, tree_index)
  File "/Library/Python/2.7/site-packages/coremltools/converters/sklearn/_tree_ensemble.py", line 68, in _recurse
    _recurse(coreml_tree, scikit_tree, tree_id, left_child_id, scaling, mode, n_classes, tree_index)
  File "/Library/Python/2.7/site-packages/coremltools/converters/sklearn/_tree_ensemble.py", line 68, in _recurse
    _recurse(coreml_tree, scikit_tree, tree_id, left_child_id, scaling, mode, n_classes, tree_index)
  File "/Library/Python/2.7/site-packages/coremltools/converters/sklearn/_tree_ensemble.py", line 68, in _recurse
    _recurse(coreml_tree, scikit_tree, tree_id, left_child_id, scaling, mode, n_classes, tree_index)
  File "/Library/Python/2.7/site-packages/coremltools/converters/sklearn/_tree_ensemble.py", line 68, in _recurse
    _recurse(coreml_tree, scikit_tree, tree_id, left_child_id, scaling, mode, n_classes, tree_index)
  File "/Library/Python/2.7/site-packages/coremltools/converters/sklearn/_tree_ensemble.py", line 68, in _recurse
    _recurse(coreml_tree, scikit_tree, tree_id, left_child_id, scaling, mode, n_classes, tree_index)
  File "/Library/Python/2.7/site-packages/coremltools/converters/sklearn/_tree_ensemble.py", line 68, in _recurse
    _recurse(coreml_tree, scikit_tree, tree_id, left_child_id, scaling, mode, n_classes, tree_index)
  File "/Library/Python/2.7/site-packages/coremltools/converters/sklearn/_tree_ensemble.py", line 68, in _recurse
    _recurse(coreml_tree, scikit_tree, tree_id, left_child_id, scaling, mode, n_classes, tree_index)
  File "/Library/Python/2.7/site-packages/coremltools/converters/sklearn/_tree_ensemble.py", line 68, in _recurse
    _recurse(coreml_tree, scikit_tree, tree_id, left_child_id, scaling, mode, n_classes, tree_index)
  File "/Library/Python/2.7/site-packages/coremltools/converters/sklearn/_tree_ensemble.py", line 68, in _recurse
    _recurse(coreml_tree, scikit_tree, tree_id, left_child_id, scaling, mode, n_classes, tree_index)
  File "/Library/Python/2.7/site-packages/coremltools/converters/sklearn/_tree_ensemble.py", line 68, in _recurse
    _recurse(coreml_tree, scikit_tree, tree_id, left_child_id, scaling, mode, n_classes, tree_index)
  File "/Library/Python/2.7/site-packages/coremltools/converters/sklearn/_tree_ensemble.py", line 68, in _recurse
    _recurse(coreml_tree, scikit_tree, tree_id, left_child_id, scaling, mode, n_classes, tree_index)
  File "/Library/Python/2.7/site-packages/coremltools/converters/sklearn/_tree_ensemble.py", line 68, in _recurse
    _recurse(coreml_tree, scikit_tree, tree_id, left_child_id, scaling, mode, n_classes, tree_index)
  File "/Library/Python/2.7/site-packages/coremltools/converters/sklearn/_tree_ensemble.py", line 68, in _recurse
    _recurse(coreml_tree, scikit_tree, tree_id, left_child_id, scaling, mode, n_classes, tree_index)
  File "/Library/Python/2.7/site-packages/coremltools/converters/sklearn/_tree_ensemble.py", line 75, in _recurse
    raise ValueError('Expected only 1 output in the scikit-learn tree.')
ValueError: Expected only 1 output in the scikit-learn tree.

简单的转换代码如下：

from coremltools.converters import sklearn as sklearn_to_ml
from sklearn.externals import joblib

model = joblib.load("ms5000.pkl")

print("Converting model")
coreml_model = sklearn_to_ml.convert(model)

print("Saving CoreML model")
coreml_model.save("ms5000.mlmodel")

我该怎么做才能使CoreML转换脚本能够处理多输出决策树？我是否可以对现有脚本进行更改而不用完全使用新脚本重新创建轮子？