Categorial features are currently not supported for trees. You can have
discrete features, but not strictly categorical ones.
On Wed, Sep 2, 2015 at 5:39 PM, Rex X <dnsr...@gmail.com> wrote:
> Gilles,
>
> Here is one further minor question. How to check the feature type, whether
> it is categorical or numerical?
>
> When we traverse the tree, and get
> tree.feature[node]
>
> This can return a feature id.
> Is there any easy way to know if it is related to a categorical or
> numerical attribute?
>
>
> Thanks again,
> Rex
>
>
> On Sun, Aug 30, 2015 at 11:57 PM, Gilles Louppe <g.lou...@gmail.com>
> wrote:
>
>> 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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