I am using scikit's regression tree function and graphviz to generate the wonderful, easy to interpret visuals of some decision trees:
dot_data = tree.export_graphviz(Run.reg, out_file=None,
feature_names=Xvar,
filled=True, rounded=True,
special_characters=True)
graph = pydotplus.graph_from_dot_data(dot_data)
graph.write_png('CART.png')
graph.write_svg("CART.svg")
This runs perfectly, but I'd like to change the color scheme if possible? The plot represents CO2 fluxes, so I'd like to make the negative values green and positive brown. I can export as svg instead and alter everything manually, but when I do, the text doesn't quite line up with the boxes so changing the colors manually and fixing all the text adds a very tedious step to my workflow that I would really like to avoid!
Also, I've seen some trees where the length of the lines connecting nodes is proportional to the % variance explained by the split. I'd love to be able to do that too if possible?
graph.get_edge_list()
set_fillcolor()
import pydotplus
from sklearn.datasets import load_iris
from sklearn import tree
import collections
clf = tree.DecisionTreeClassifier(random_state=42)
iris = load_iris()
clf = clf.fit(iris.data, iris.target)
dot_data = tree.export_graphviz(clf,
feature_names=iris.feature_names,
out_file=None,
filled=True,
rounded=True)
graph = pydotplus.graph_from_dot_data(dot_data)
colors = ('brown', 'forestgreen')
edges = collections.defaultdict(list)
for edge in graph.get_edge_list():
edges[edge.get_source()].append(int(edge.get_destination()))
for edge in edges:
edges[edge].sort()
for i in range(2):
dest = graph.get_node(str(edges[edge][i]))[0]
dest.set_fillcolor(colors[i])
graph.write_png('tree.png')
Also, i've seen some trees where the length of the lines connecting nodes is proportional to the % varriance explained by the split. I'd love to be able to do that too if possible!?
You could play with set_weight()
and set_len()
but that's a bit more tricky and needs some fiddling to get it right but here is some code to get you started.
for edge in edges:
edges[edge].sort()
src = graph.get_node(edge)[0]
total_weight = int(src.get_attributes()['label'].split('samples = ')[1].split('<br/>')[0])
for i in range(2):
dest = graph.get_node(str(edges[edge][i]))[0]
weight = int(dest.get_attributes()['label'].split('samples = ')[1].split('<br/>')[0])
graph.get_edge(edge, str(edges[edge][0]))[0].set_weight((1 - weight / total_weight) * 100)
graph.get_edge(edge, str(edges[edge][0]))[0].set_len(weight / total_weight)
graph.get_edge(edge, str(edges[edge][0]))[0].set_minlen(weight / total_weight)