Hi Joel andJeff Thanks for your valuable comment, i got that to work
On 8 February 2017 at 08:13, Jeff Blackburne <[email protected]> wrote: > Nixon, > > If you are using version 0.18 or later, you can reconstruct the > information you need using the `decision_path` method: > > http://scikit-learn.org/stable/auto_examples/tree/ > plot_unveil_tree_structure.html > > -Jeff > > > On Tue, Feb 7, 2017 at 3:21 PM, Joel Nothman <[email protected]> > wrote: > >> I don't think putting that array of indices in a visualisation is a great >> idea! >> >> If you use my_tree.apply(X) you will be able to determine which leaf each >> instance in X lands up at, and potentially trace up the tree from there. >> >> On 8 February 2017 at 01:26, Nixon Raj <[email protected]> wrote: >> >>> >>> For Example, In the below decision tree dot file, I have 223 samples >>> which splits into [174, 49] in the first split and [110, 1] in the 2nd split >>> >>> I would like to get the array of indices for the values of each split >>> like >>> >>> *[174, 49] and their corresponding indices (idx) like [[0, 1 ,5, >>> 7,....,200,221], [3, 4, 6, ....., 199,222,223]]* >>> >>> *[110, 1] and their corresponding indices (idx) like [[0,5,....200,221], >>> [7]]* >>> >>> Please help me >>> >>> node [shape=box] ; >>> 0 [label="X[0] <= 13.9191\nentropy = 0.7597\nsamples = 223\nvalue = >>> [174, 49]"] ; >>> 1 [label="X[1] <= 3.1973\nentropy = 0.0741\nsamples = 111\nvalue = [110, >>> 1]"] ; >>> 0 -> 1 [labeldistance=2.5, labelangle=45, headlabel="True"] ; >>> 2 [label="entropy = 0.0\nsamples = 109\nvalue = [109, 0]"] ; >>> 1 -> 2 ; >>> 3 [label="entropy = 1.0\nsamples = 2\nvalue = [1, 1]"] ; >>> 1 -> 3 ; >>> 4 [label="X[1] <= 3.1266\nentropy = 0.9852\nsamples = 112\nvalue = [64, >>> 48]"] ; >>> 0 -> 4 [labeldistance=2.5, labelangle=-45, headlabel="False"] ; >>> 5 [label="X[2] <= -0.4882\nentropy = 0.7919\nsamples = 63\nvalue = [48, >>> 15]"] ; >>> 4 -> 5 ; >>> 6 [label="entropy = 0.684\nsamples = 11\nvalue = [2, 9]"] ; >>> 5 -> 6 ; >>> 7 [label="X[2] <= 0.5422\nentropy = 0.5159\nsamples = 52\nvalue = [46, >>> 6]"] ; >>> 5 -> 7 ; >>> 8 [label="entropy = 0.0\nsamples = 18\nvalue = [18, 0]"] ; >>> 7 -> 8 ; >>> 9 [label="X[2] <= 0.6497\nentropy = 0.6723\nsamples = 34\nvalue = [28, >>> 6]"] ; >>> 7 -> 9 ; >>> 10 [label="entropy = 0.0\nsamples = 1\nvalue = [0, 1]"] ; >>> 9 -> 10 ; >>> 11 [label="X[2] <= 1.887\nentropy = 0.6136\nsamples = 33\nvalue = [28, >>> 5]"] ; >>> 9 -> 11 ; >>> 12 [label="entropy = 0.0\nsamples = 12\nvalue = [12, 0]"] ; >>> 11 -> 12 ; >>> 13 [label="X[2] <= 2.6691\nentropy = 0.7919\nsamples = 21\nvalue = [16, >>> 5]"] ; >>> 11 -> 13 ; >>> 14 [label="entropy = 0.8113\nsamples = 4\nvalue = [1, 3]"] ; >>> 13 -> 14 ; >>> 15 [label="entropy = 0.5226\nsamples = 17\nvalue = [15, 2]"] ; >>> 13 -> 15 ; >>> 16 [label="X[0] <= 17.3284\nentropy = 0.9113\nsamples = 49\nvalue = [16, >>> 33]"] ; >>> 4 -> 16 ; >>> 17 [label="entropy = 0.9183\nsamples = 6\nvalue = [4, 2]"] ; >>> 16 -> 17 ; >>> 18 [label="X[2] <= 19.7048\nentropy = 0.8542\nsamples = 43\nvalue = [12, >>> 31]"] ; >>> 16 -> 18 ; >>> 19 [label="X[2] <= 5.8511\nentropy = 0.8296\nsamples = 42\nvalue = [11, >>> 31]"] ; >>> 18 -> 19 ; >>> 20 [label="X[0] <= 31.8916\nentropy = 0.878\nsamples = 37\nvalue = [11, >>> 26]"] ; >>> 19 -> 20 ; >>> 21 [label="X[1] <= 3.3612\nentropy = 0.6666\nsamples = 23\nvalue = [4, >>> 19]"] ; >>> 20 -> 21 ; >>> 22 [label="entropy = 0.8905\nsamples = 13\nvalue = [4, 9]"] ; >>> 21 -> 22 ; >>> 23 [label="entropy = 0.0\nsamples = 10\nvalue = [0, 10]"] ; >>> 21 -> 23 ; >>> 24 [label="entropy = 1.0\nsamples = 14\nvalue = [7, 7]"] ; >>> 20 -> 24 ; >>> 25 [label="entropy = 0.0\nsamples = 5\nvalue = [0, 5]"] ; >>> 19 -> 25 ; >>> 26 [label="entropy = 0.0\nsamples = 1\nvalue = [1, 0]"] ; >>> 18 -> 26 ; >>> } >>> >>> >>> _______________________________________________ >>> scikit-learn mailing list >>> [email protected] >>> https://mail.python.org/mailman/listinfo/scikit-learn >>> >>> >> >> _______________________________________________ >> scikit-learn mailing list >> [email protected] >> https://mail.python.org/mailman/listinfo/scikit-learn >> >> > > _______________________________________________ > scikit-learn mailing list > [email protected] > https://mail.python.org/mailman/listinfo/scikit-learn > > -- Regards Nixon Raj N Department of Biological Science and Technology Institute of Bioinformatics and Systems Biology National Chiao Tung University 208 Lab Building 1, 75 Bo-Ai St. Dong District, Hsinchu, Taiwan 30062 (R.O.C.) Mob:+886-989353921 0ffice ext: 56997
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