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cerlymarco / linear-tree

A python library to build Model Trees with Linear Models at the leaves.
MIT License
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Which traversing method does linear tree use to find the left and right node ? #19

Closed akhilkapil closed 2 years ago

akhilkapil commented 2 years ago

Hi all, I am having a hard time finding out which method is used by linear tree to traverse the whole linear tree. Cause sometimes when I am plotting the tree plot and comparing it with the summary, the mapping makes no sense. For some left node the plot is displaying it as right and vice-versa. you guys can compare the summary with the plot and let me know if I am incorrect somewhere. output

0: {'col': 1, 'th': 0.0127, 'loss': 0.1937, 'samples': 160, 'children': (1, 2), 'models': (RidgeClassifier(), RidgeClassifier())}, 1: {'col': 6, 'th': 0.1461, 'loss': 0.1, 'samples': 80, 'children': (3, 4), 'models': (RidgeClassifier(), RidgeClassifier())}, 2: {'col': 0, 'th': 2.6051, 'loss': 0.05, 'samples': 80, 'children': (9, 10), 'models': (RidgeClassifier(), RidgeClassifier())}, 4: {'col': 0, 'th': -0.0708, 'loss': 0.0364, 'samples': 55, 'children': (5, 6), 'models': (RidgeClassifier(), RidgeClassifier())}, 6: {'col': 2, 'th': -0.7986, 'loss': 0.0, 'samples': 32, 'children': (7, 8), 'models': (RidgeClassifier(), RidgeClassifier())}, 9: {'col': 2, 'th': -0.0865, 'loss': 0.0, 'samples': 59, 'children': (11, 12), 'models': (RidgeClassifier(), RidgeClassifier())}, 3: {'loss': 0.08, 'samples': 25, 'models': RidgeClassifier(), 'classes': array([0, 1, 2])}, 5: {'loss': 0.0, 'samples': 23, 'models': RidgeClassifier(), 'classes': array([0, 1, 2])}, 7: {'loss': 0.0, 'samples': 16, 'models': RidgeClassifier(), 'classes': array([0, 1])}, 8: {'loss': 0.0, 'samples': 16, 'models': RidgeClassifier(), 'classes': array([0, 1, 2])}, 11: {'loss': 0.0, 'samples': 32, 'models': RidgeClassifier(), 'classes': array([0, 1, 2])}, 12: {'loss': 0.0, 'samples': 27, 'models': RidgeClassifier(), 'classes': array([0, 1, 2])}, 10: {'loss': 0.0476, 'samples': 21, 'models': RidgeClassifier(), 'classes': array([0, 1, 2])}}

cerlymarco commented 2 years ago

Hi, thanks for your feedback but the plot you reported seems mapped one to one with the summary below.

_Starting from the root, the id_node_0 is evaluated and id_node_1 and id_node_2 are created. Then id_node_1 is evaluated and id_node_3 and id_node_4 are created. Then id_node_3 is evaluated but we find no utility in splitting. Then id_node_4 is evaluated and id_node_5 and id_node6 are created....

What I reported is the order used when evaluating the splits. If u mean that id_node_3 and id_node_4 should be changed in the plot (according to the order they are evaluated during fitting), I can do nothing for this since it's pydot that creates the plot. It displays the circles/squares of each node according to the place at disposal.

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