Ah yeah, they are. Nevermind.
On Sun, Aug 30, 2015 at 8:00 AM, Trevor Stephens <trev.steph...@gmail.com>
wrote:
> 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 <jmschreibe...@gmail.com>
> 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 <trev.steph...@gmail.com
>> > 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 <dnsr...@gmail.com> 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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