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czytanie/odczytywanie drzewa z/do pliku za pomocą pickle

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Tomasz Adamczyk 2021-05-19 15:04:17 +02:00
parent 4b73064f4e
commit 7cabeb6cbe
7 changed files with 35 additions and 26 deletions
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32
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resources/mydecisiontree.png → resources/mytree.png
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treelearn.py
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@ -2,22 +2,27 @@ import matplotlib.image as pltimg
import matplotlib.pyplot as plt
import os
import pandas
import pickle
import pydotplus
from sklearn import tree
from sklearn.tree import DecisionTreeClassifier
def treelearn(): #zwraca utworzone drzewo decyzyjne
df = pandas.read_csv(os.path.join('resources', 'data.csv')) #czytanie danych do nauki drzewa z pliku .csv
features = ['amount of seeds', 'collected plants', 'fertilizer', 'fuel', 'water level']
x = df[features] #wczytanie atrybutów, z których ma się uczyć drzewo
y = df['back to station'] #podjęte decyzje
dtree = DecisionTreeClassifier() #tworzy obiekt drzewa
dtree = dtree.fit(x, y) #uczy drzewo
data = tree.export_graphviz(dtree, out_file=None, feature_names=features)
graph = pydotplus.graph_from_dot_data(data)
graph.write_png(os.path.join('resources', 'mydecisiontree.png'))
img = pltimg.imread(os.path.join('resources', 'mydecisiontree.png'))
imgplot = plt.imshow(img)
plt.show() #wyświetl drzewo decyzyjne
if os.path.exists("resources/tree.pkl"): #jeżeli drzewo jest zapisane w pliku to odczytaj
dtree = pickle.load(open(os.path.join('resources', "tree.pkl"), "rb"))
else: #w przeciwnym razie utwórz drzewo od początku i zapisz do pliku
df = pandas.read_csv(os.path.join('resources', 'data.csv')) #czytanie danych do nauki drzewa z pliku .csv
features = ['amount of seeds', 'collected plants', 'fertilizer', 'fuel', 'water level']
x = df[features] #wczytanie atrybutów, z których ma się uczyć drzewo
y = df['back to station'] #podjęte decyzje
dtree = DecisionTreeClassifier() #klasyfikuje drzewo
dtree = dtree.fit(x, y) #uczy drzewo
pickle.dump(dtree, open(os.path.join('resources', "tree.pkl"), "wb"))
data = tree.export_graphviz(dtree, out_file=None, feature_names=features)
graph = pydotplus.graph_from_dot_data(data)
graph.write_png(os.path.join('resources', 'mytree.png'))
img = pltimg.imread(os.path.join('resources', 'mytree.png'))
imgplot = plt.imshow(img)
plt.show() #wyświetl drzewo decyzyjne
return dtree
def make_decision(tree, amount_of_seeds, collected_plants, fertilizer, fuel, water_level): #zwraca decyzję o powrocie do stacji (0 : NIE, 1 : TAK)
decision = tree.predict([[amount_of_seeds, collected_plants, fertilizer, fuel, water_level]]) #podejmij decyzję na podstawie aktualnych parametrów traktora o powrocie do stacji lub nie