Forked from ubershmekel/decision_tree_code_gen.py
Created
September 14, 2017 14:05
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Generate python code from orange-canvas decision trees
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""" | |
How to use: | |
1. Place a "Python Script" node on your canvas | |
2. Hook your "Classification Tree" node out into the "Python Script" input | |
3. Paste this gist in the python script and hit "Execute" | |
The output should be similar to this: | |
# slope (<32.000, 34.000>) | |
if slope <=1.008: return False #(<7.000, 0.000>) | |
if slope >1.008: | |
# peak_i (<25.000, 34.000>) | |
if peak_i <=39.500: return False #(<19.000, 2.000>) | |
if peak_i >39.500: return True #(<6.000, 32.000>) | |
Based on http://orange.biolab.si/doc/reference/Orange.classification.tree/#tree-structure | |
""" | |
def print_tree0(node, level): | |
if not node: | |
print " "*level + "<null node>" | |
return | |
if node.branch_selector: | |
node_desc = node.branch_selector.class_var.name | |
node_cont = node.distribution | |
indent = " " * level | |
print "\n" + indent + "# %s (%s)" % (node_desc, node_cont), | |
for i in range(len(node.branches)): | |
print "\n{indent}if {var} {op}:".format(indent=indent, var=node_desc, op=node.branch_descriptions[i]), | |
print_tree0(node.branches[i], level+1) | |
else: | |
node_cont = node.distribution | |
major_class = node.node_classifier.default_value | |
print "return %s #(%s) " % (major_class, node_cont), | |
print_tree0(in_classifier.tree, 0) |
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