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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