Here is a sample code on how to retrieve the nodes traversed by a given sample:
from sklearn.tree import DecisionTreeClassifier
from sklearn.datasets import load_iris
iris = load_iris()
X, y = iris.data, iris.target
clf = DecisionTreeClassifier().fit(X, y)
def path(tree, sample):
nodes = []
features = []
node = 0
while tree.children_right[node] != -1:
nodes.append(node)
if sample[tree.feature[node]] <= tree.threshold[node]:
node = tree.children_left[node]
else:
node = tree.children_right[node]
return nodes
path(clf.tree_, X[100])
# [0, 2, 12]
Now to derive statistics like the number of samples reaching each
node, you can iterate over your data X and increment counters, e.g.,
by doing counters[path(clf.tree_, X[i])] += 1, where counters is a
numpy array of size tree_.node_count.
Hope this helps,
Gilles
On 30 August 2015 at 22:37, Rex X <dnsr...@gmail.com> wrote:
> Jacob, this modification seems not easy. After fetching the decision rules
> leading to the node of interest, a following Pandas groupby script can
> compute these numbers through. Thank you. :)
>
>
>
> 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
>>>>>
>>>>>
>>>>>
>>>>>
>>>>> ------------------------------------------------------------------------------
>>>>>
>>>>> _______________________________________________
>>>>> Scikit-learn-general mailing list
>>>>> Scikit-learn-general@lists.sourceforge.net
>>>>> https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
>>>>>
>>>>
>>>>
>>>>
>>>> ------------------------------------------------------------------------------
>>>>
>>>> _______________________________________________
>>>> Scikit-learn-general mailing list
>>>> Scikit-learn-general@lists.sourceforge.net
>>>> https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
>>>>
>>>
>>>
>>>
>>> ------------------------------------------------------------------------------
>>>
>>> _______________________________________________
>>> Scikit-learn-general mailing list
>>> Scikit-learn-general@lists.sourceforge.net
>>> https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
>>>
>>
>>
>>
>> ------------------------------------------------------------------------------
>>
>> _______________________________________________
>> Scikit-learn-general mailing list
>> Scikit-learn-general@lists.sourceforge.net
>> https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
>>
>
>
> ------------------------------------------------------------------------------
>
> _______________________________________________
> Scikit-learn-general mailing list
> Scikit-learn-general@lists.sourceforge.net
> https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
>
------------------------------------------------------------------------------
_______________________________________________
Scikit-learn-general mailing list
Scikit-learn-general@lists.sourceforge.net
https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
