无法从管道中绘制树

Question

我有以下用于决策树分类的代码，我能够看到该模型的预测结果但无法绘制树

from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.impute import SimpleImputer
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.compose import make_column_selector as selector
from sklearn.tree import plot_tree
from sklearn.tree import DecisionTreeClassifier

# Scale numeric values
num_piepline = Pipeline([("imputer", SimpleImputer(missing_values=np.nan,
                                          strategy="median",
                                          )),
                           ('scalar1',StandardScaler()),
                           
                      ])

# One-hot encode categorical values
cat_pipeline = Pipeline([('onehot', OneHotEncoder(handle_unknown='ignore'))])

full_pipeline = ColumnTransformer(
    transformers=[
        ('num', num_piepline, ['a', 'b', 'c', 'd']),
        ('cat', cat_pipeline, ['e'])
        
    ])

decisiontree_entropy_model = Pipeline(steps=[
    ('dt_preprocessor', full_pipeline),
    ('dt_classifier', DecisionTreeClassifier(random_state=2021, max_depth=3, criterion='entropy'))])

decisiontree_entropy_model.fit(X_train, y_train)

dte_y_pred = decisiontree_entropy_model.predict(X_train)

fig = plt.figure(figsize=(25,20))
plot_tree(decisiontree_entropy_model_clf)

我得到以下错误堆栈跟踪。

---------------------------------------------------------------------------
NotFittedError                            Traceback (most recent call last)
<ipython-input-151-da85340c2477> in <module>
      1 from sklearn.tree import plot_tree
      2 fig = plt.figure(figsize=(25,20))
----> 3 plot_tree(decisiontree_entropy_model_clf)
      4 
      5 # from IPython.display import Image

~\Anaconda3\lib\site-packages\sklearn\utils\validation.py in inner_f(*args, **kwargs)
     70                           FutureWarning)
     71         kwargs.update({k: arg for k, arg in zip(sig.parameters, args)})
---> 72         return f(**kwargs)
     73     return inner_f
     74 

~\Anaconda3\lib\site-packages\sklearn\tree\_export.py in plot_tree(decision_tree, max_depth, feature_names, class_names, label, filled, impurity, node_ids, proportion, rotate, rounded, precision, ax, fontsize)
    178     """
    179 
--> 180     check_is_fitted(decision_tree)
    181 
    182     if rotate != 'deprecated':

~\Anaconda3\lib\site-packages\sklearn\utils\validation.py in inner_f(*args, **kwargs)
     70                           FutureWarning)
     71         kwargs.update({k: arg for k, arg in zip(sig.parameters, args)})
---> 72         return f(**kwargs)
     73     return inner_f
     74 

~\Anaconda3\lib\site-packages\sklearn\utils\validation.py in check_is_fitted(estimator, attributes, msg, all_or_any)
   1017 
   1018     if not attrs:
-> 1019         raise NotFittedError(msg % {'name': type(estimator).__name__})
   1020 
   1021 

NotFittedError: This Pipeline instance is not fitted yet. Call 'fit' with appropriate arguments before using this estimator.

这里我运行适合模型，我可以在模型上看到 classification_report，但是打印出来的是不适合的错误。调用fit一次后pipeline istance不存在了吗？不确定为什么它在实际用于从分类报告中导出性能指标时仅在绘制树时失败

Answer 1

您的代码中没有名为 decisiontree_entropy_model_clf 的内容；从管道绘制决策树，你应该使用

plot_tree(decisiontree_entropy_model['dt_classifier'])

安装管道后（安装前树甚至不存在）。

一般访问管道的各种属性，请参阅。

无法从管道中绘制树

Not able to plot tree from pipeline

python

pipeline

decision-tree

scikit-learn