This code fits a regression tree in python. I want to convert this text based output to a table format.
Have looked into this ( Convert a decision tree to a table ) however the given solution doesn't work.
import pandas as pd
import numpy as np
from sklearn.tree import DecisionTreeRegressor
from sklearn import tree
dataset = np.array(
[['Asset Flip', 100, 1000],
['Text Based', 500, 3000],
['Visual Novel', 1500, 5000],
['2D Pixel Art', 3500, 8000],
['2D Vector Art', 5000, 6500],
['Strategy', 6000, 7000],
['First Person Shooter', 8000, 15000],
['Simulator', 9500, 20000],
['Racing', 12000, 21000],
['RPG', 14000, 25000],
['Sandbox', 15500, 27000],
['Open-World', 16500, 30000],
['MMOFPS', 25000, 52000],
['MMORPG', 30000, 80000]
])
X = dataset[:, 1:2].astype(int)
y = dataset[:, 2].astype(int)
regressor = DecisionTreeRegressor(random_state = 0)
regressor.fit(X, y)
text_rule = tree.export_text(regressor )
print(text_rule)
Output I am getting is like this
print(text_rule)
|--- feature_0 <= 20750.00
| |--- feature_0 <= 7000.00
| | |--- feature_0 <= 1000.00
| | | |--- feature_0 <= 300.00
| | | | |--- value: [1000.00]
| | | |--- feature_0 > 300.00
| | | | |--- value: [3000.00]
| | |--- feature_0 > 1000.00
| | | |--- feature_0 <= 2500.00
| | | | |--- value: [5000.00]
| | | |--- feature_0 > 2500.00
| | | | |--- feature_0 <= 4250.00
| | | | | |--- value: [8000.00]
| | | | |--- feature_0 > 4250.00
| | | | | |--- feature_0 <= 5500.00
| | | | | | |--- value: [6500.00]
| | | | | |--- feature_0 > 5500.00
| | | | | | |--- value: [7000.00]
| |--- feature_0 > 7000.00
| | |--- feature_0 <= 13000.00
| | | |--- feature_0 <= 8750.00
| | | | |--- value: [15000.00]
| | | |--- feature_0 > 8750.00
| | | | |--- feature_0 <= 10750.00
| | | | | |--- value: [20000.00]
| | | | |--- feature_0 > 10750.00
| | | | | |--- value: [21000.00]
| | |--- feature_0 > 13000.00
| | | |--- feature_0 <= 16000.00
| | | | |--- feature_0 <= 14750.00
| | | | | |--- value: [25000.00]
| | | | |--- feature_0 > 14750.00
| | | | | |--- value: [27000.00]
| | | |--- feature_0 > 16000.00
| | | | |--- value: [30000.00]
|--- feature_0 > 20750.00
| |--- feature_0 <= 27500.00
| | |--- value: [52000.00]
| |--- feature_0 > 27500.00
| | |--- value: [80000.00]
I want to convert this rule in a pandas table something similar to the following form. How to do this ?
Plot version of the rule is something like this ( for reference ). Please note in table I have showed the left most part of the rule.
Modifying the the code from the linked answer:
import sklearn
import pandas as pd
def tree_to_df(reg_tree, feature_names):
tree_ = reg_tree.tree_
feature_name = [
feature_names[i] if i != sklearn.tree._tree.TREE_UNDEFINED else "undefined!"
for i in tree_.feature
]
def recurse(node, row, ret):
if tree_.feature[node] != sklearn.tree._tree.TREE_UNDEFINED:
name = feature_name[node]
threshold = tree_.threshold[node]
# Add rule to row and search left branch
row[-1].append(name + " <= " + str(threshold))
recurse(tree_.children_left[node], row, ret)
# Add rule to row and search right branch
row[-1].append(name + " > " + str(threshold))
recurse(tree_.children_right[node], row, ret)
else:
# Add output rules and start a new row
label = tree_.value[node]
ret.append("return " + str(label[0][0]))
row.append([])
# Initialize
rules = [[]]
vals = []
# Call recursive function with initial values
recurse(0, rules, vals)
# Convert to table and output
df = pd.DataFrame(rules).dropna(how='all')
df['Return'] = pd.Series(vals)
return df
This will return a pandas dataframe:
0 1 2 3 Return
0 feature <= 20750.0 feature <= 7000.0 feature <= 1000.0 feature <= 300.0 return 1000.0
1 feature > 300.0 None None None return 3000.0
2 feature > 1000.0 feature <= 2500.0 None None return 5000.0
3 feature > 2500.0 feature <= 4250.0 None None return 8000.0
4 feature > 4250.0 feature <= 5500.0 None None return 6500.0
5 feature > 5500.0 None None None return 7000.0
6 feature > 7000.0 feature <= 13000.0 feature <= 8750.0 None return 15000.0
7 feature > 8750.0 feature <= 10750.0 None None return 20000.0
8 feature > 10750.0 None None None return 21000.0
9 feature > 13000.0 feature <= 16000.0 feature <= 14750.0 None return 25000.0
10 feature > 14750.0 None None None return 27000.0
11 feature > 16000.0 None None None return 30000.0
12 feature > 20750.0 feature <= 27500.0 None None return 52000.0
13 feature > 27500.0 None None None return 80000.0
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