Question

好奇的边缘行为。在此示例中，KNN exists被打印，但Random Forest exists没有。

在检查是否存在模型时发现它，当模型是随机森林时，未触发if model: ...。

from sklearn.ensemble import RandomForestClassifier
from sklearn.neighbors import KNeighborsClassifier

if KNeighborsClassifier(4):
    print('KNN exists')

if RandomForestClassifier(n_estimators=10, max_depth=4):
    print('Random Forest exists')

为什么会这样？

Answer 1

啊哈！这是因为Random实现了__len__：

In [1]: from sklearn.ensemble import RandomForestClassifier
   ...: from sklearn.neighbors import KNeighborsClassifier
   ...:

In [2]: knn =  KNeighborsClassifier(4)

In [3]: forest = RandomForestClassifier(n_estimators=10, max_depth=4)

In [4]: knn.__bool__
---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-4-ef1cfe16be77> in <module>()
----> 1 knn.__bool__

AttributeError: 'KNeighborsClassifier' object has no attribute '__bool__'

In [5]: knn.__len__
---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-5-dc98bf8c50e0> in <module>()
----> 1 knn.__len__

AttributeError: 'KNeighborsClassifier' object has no attribute '__len__'

In [6]: forest.__bool__
---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-6-fbdd7f01e843> in <module>()
----> 1 forest.__bool__

AttributeError: 'RandomForestClassifier' object has no attribute '__bool__'

In [7]: forest.__len__
Out[7]:
<bound method BaseEnsemble.__len__ of RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
            max_depth=4, max_features='auto', max_leaf_nodes=None,
            min_impurity_split=1e-07, min_samples_leaf=1,
            min_samples_split=2, min_weight_fraction_leaf=0.0,
            n_estimators=10, n_jobs=1, oob_score=False, random_state=None,
            verbose=0, warm_start=False)>

In [8]: len(forest)
Out[8]: 0

而且，根据Python Data Model：

object.__bool__(self)

被要求实施真值测试和内置操作   bool();应该返回False或True。如果未定义此方法，   __len__()被调用，如果已定义，则，如果对象的结果非零，则该对象被视为true。如果类未定义__len__()   并且__bool__()，其所有实例都被认为是真实的。

正如人们所预料的那样，len的{{1}}是估算器的数量，但只有在之后才是RandomForestClassifier：

.fit

Scikit随机森林分类器未评估为True

1 个答案: