On the current master branch, using the OP's example:
In [7]: clf.tree_.weighted_n_node_samples
Out[7]:
array([ 50. , 15. , 35. , 16. , 14.1, 1.9, 0.7, 0.4, 0.3,
1.2, 19. , 3.1, 0.7, 0.4, 0.3, 2.4, 15.9, 0.3,
15.6])
On Sun, Aug 30, 2015 at 8:43 AM, Jacob Schreiber <[email protected]>
wrote:
> Trevor, those attributes are present while the tree is being built, but
> are not kept in the final tree object.
>
> On Sun, Aug 30, 2015 at 7:15 AM, Trevor Stephens <[email protected]>
> wrote:
>
>> n_node_samples is the count of actual dataset samples in each
>> node. weighted_n_node_samples is the same, weighted by the class_weight
>> and/or sample_weight.
>>
>> On Sun, Aug 30, 2015 at 8:02 AM, Rex X <[email protected]> wrote:
>>
>>> DecisionTreeClassifier.tree_.n_node_samples is the total number of
>>> samples in all classes of one node, and
>>> DecisionTreeClassifier.tree_.value is the computed weight for each
>>> class of one node. Only if the sample_weight and class_weight of this
>>> DecisionTreeClassifier
>>> is one, then this attribute equals the number of samples of each class of
>>> one node.
>>>
>>> But for the general case with a given sample_weight and class_weight, is
>>> there any attribute telling us the number of samples of each class
>>> within one node?
>>>
>>>
>>> import pandas as pd
>>> from sklearn.datasets import load_iris
>>> from sklearn import tree
>>> import sklearn
>>>
>>> iris = sklearn.datasets.load_iris()
>>> clf = tree.DecisionTreeClassifier(class_weight={0 : 0.30, 1: 0.3,
>>> 2:0.4}, max_features="auto")
>>> clf.fit(iris.data, iris.target)
>>>
>>>
>>> # the total number of samples in all classes of each node
>>> clf.tree_.n_node_samples
>>>
>>> # the computed weight for each class of each node
>>> clf.tree_.value
>>>
>>>
>>>
>>>
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>>>
>>
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