Hi,
The simplest method to get you are looking for is to re-propagate the
training samples into the tree and keep track of the nodes they
traverse. You should have a look at the implementation of `apply` to
get started.
Hope this helps,
Gilles
On 30 August 2015 at 21:55, Rex X <dnsr...@gmail.com> wrote:
> Hi Trevor,
>
> Yes, unfortunately I am using sample_weight. So clf.tree_.value is a
> weighted sum. We cannot do such a division to get the number of samples.
> Because we can't track which samples belonging to one node, there is no way
> to get the sample_weights. If we know, we don't need to get into this step.
>
> It is good to know the apply method for DecisionTreeClassifier. But when we
> used sample_weight, can this way work?
>
>
> Thank you.
>
> Rex
>
>
> On Sun, Aug 30, 2015 at 12:41 PM, Trevor Stephens <trev.steph...@gmail.com>
> wrote:
>>
>> As Jacob mentions, the tree object is written in cython, and is pretty
>> heavy going.
>>
>> However,
>>
>> clf.tree_.value / clf.class_weight.values()
>>
>> might work for you?
>>
>> If using the sample_weight as well, you would need to additionally scale
>> along the other axis too.
>>
>> Alternatively, if only interested in the leaf nodes, the
>> DecisionTreeClassifier has an apply() method which returns the leaf ID for
>> any data passed to it. Use the original data, and then some light Pandas
>> pivoting should get you to what you need.
>>
>>
>>
>> On Sun, Aug 30, 2015 at 11:54 AM, Jacob Schreiber
>> <jmschreibe...@gmail.com> wrote:
>>>
>>> You would have to modify sklearn/tree/_tree.pyx. See the Tree class near
>>> the bottom, and its list of properties. An issue may be that you would have
>>> to extensively modify the code, as you would need to modify both splitter
>>> and criterion objects as well. If you are doing this for your own personal
>>> use, it may be easier to write a small script which successively applies the
>>> rules of the tree to your data to see how many points from each class are
>>> present.
>>>
>>> On Sun, Aug 30, 2015 at 10:50 AM, Rex X <dnsr...@gmail.com> wrote:
>>>>
>>>> Hi Jacob and Trevor,
>>>>
>>>> Which part of the source code we can modify to add a new attribute to
>>>> DecisionTreeClassifier.tree_, to count the number of samples of each class
>>>> within each node?
>>>>
>>>> Could you point me the right direction?
>>>>
>>>> Best,
>>>> Rex
>>>>
>>>>
>>>>
>>>>
>>>> On Sun, Aug 30, 2015 at 8:12 AM, Jacob Schreiber
>>>> <jmschreibe...@gmail.com> wrote:
>>>>>
>>>>> This value is computed while building the tree, but is not kept in the
>>>>> tree.
>>>>>
>>>>> On Sun, Aug 30, 2015 at 7:02 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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