Question

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")

如何使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，而无需完全使用新脚本重新创建轮盘？

Answer 1

CoreML（现在）是一个全新的东西，因此目前没有任何已知的第三方转换脚本来源。

＆＃34;模特＆＃34; coremltools documentation部分提供了有关如何使用Python生成CoreML模型的大量文档。话虽这么说，您可以使用文档中提供的模型接口将任何机器学习模型转换为CoreML模型。

目前，coremltools不支持多输出回归模型。如果您不想重新发明轮子，则需要通过引入与当前预测所针对的输出相对应的新输入，将模型转换为单个输出模型。

无论哪种方式，文档都在那里，所以应该让你开始。

scikit-learn：将多输出决策树转换为CoreML模型

1 个答案: