57 lines
1.5 KiB
Python
57 lines
1.5 KiB
Python
![]() |
import joblib
|
||
|
import matplotlib.pyplot as plt
|
||
|
import pandas
|
||
|
from sklearn import tree
|
||
|
from sklearn.tree import DecisionTreeClassifier
|
||
|
|
||
|
attributes = ["dis_dump", "dis_trash", "mass", "space", "trash_mass", "trash_space"]
|
||
|
decisions = ["decision"]
|
||
|
|
||
|
|
||
|
def learning_tree():
|
||
|
dataset = pandas.read_csv('./tree_dataset.csv')
|
||
|
|
||
|
x = dataset[attributes]
|
||
|
y = dataset[decisions]
|
||
|
|
||
|
decision_tree = DecisionTreeClassifier()
|
||
|
decision_tree = decision_tree.fit(x, y)
|
||
|
|
||
|
return decision_tree
|
||
|
|
||
|
|
||
|
def making_decision(decision_tree, distance_to_bin, distance_to_trash, filling_mass, filling_space, trash_mass,
|
||
|
trash_space):
|
||
|
decision = decision_tree.predict(
|
||
|
[[distance_to_bin, distance_to_trash, filling_mass, filling_space, trash_mass, trash_space]])
|
||
|
|
||
|
return decision
|
||
|
|
||
|
|
||
|
def save_all(decision_tree):
|
||
|
save_tree_to_png(decision_tree)
|
||
|
save_tree_to_txt(decision_tree)
|
||
|
save_tree_to_structure(decision_tree)
|
||
|
|
||
|
|
||
|
def save_tree_to_txt(decision_tree):
|
||
|
with open('./tree_in_txt.txt', "w") as file:
|
||
|
file.write(tree.export_text(decision_tree))
|
||
|
|
||
|
|
||
|
def save_tree_to_png(decision_tree):
|
||
|
fig = plt.figure(figsize=(25, 20))
|
||
|
_ = tree.plot_tree(decision_tree, feature_names=attributes, filled=True)
|
||
|
fig.savefig('./decision_tree.png')
|
||
|
|
||
|
|
||
|
def save_tree_to_structure(decision_tree):
|
||
|
joblib.dump(decision_tree, './tree_model')
|
||
|
|
||
|
|
||
|
def load_tree_from_structure(file):
|
||
|
return joblib.load(file)
|
||
|
|
||
|
if __name__ == '__main__':
|
||
|
tre = learning_tree()
|
||
|
save_all(tre)
|