Also, have a look at the documentation here
https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/tree/_tree.pyx#L3205
to understand the structure of the tree_ object.
On 31 August 2015 at 08:55, Gilles Louppe <g.lou...@gmail.com> wrote:
> 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
>>>>>>
>>>>>>
>>>>>>
>>>>>>
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