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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
.idea/workspace.xml
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resources/mydecisiontree.png → resources/mytree.png
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treelearn.py
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@ -2,20 +2,25 @@ import matplotlib.image as pltimg
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
import os import os
import pandas import pandas
import pickle
import pydotplus import pydotplus
from sklearn import tree from sklearn import tree
from sklearn.tree import DecisionTreeClassifier from sklearn.tree import DecisionTreeClassifier
def treelearn(): #zwraca utworzone drzewo decyzyjne def treelearn(): #zwraca utworzone 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 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'] features = ['amount of seeds', 'collected plants', 'fertilizer', 'fuel', 'water level']
x = df[features] #wczytanie atrybutów, z których ma się uczyć drzewo x = df[features] #wczytanie atrybutów, z których ma się uczyć drzewo
y = df['back to station'] #podjęte decyzje y = df['back to station'] #podjęte decyzje
dtree = DecisionTreeClassifier() #tworzy obiekt drzewa dtree = DecisionTreeClassifier() #klasyfikuje drzewo
dtree = dtree.fit(x, y) #uczy 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) data = tree.export_graphviz(dtree, out_file=None, feature_names=features)
graph = pydotplus.graph_from_dot_data(data) graph = pydotplus.graph_from_dot_data(data)
graph.write_png(os.path.join('resources', 'mydecisiontree.png')) graph.write_png(os.path.join('resources', 'mytree.png'))
img = pltimg.imread(os.path.join('resources', 'mydecisiontree.png')) img = pltimg.imread(os.path.join('resources', 'mytree.png'))
imgplot = plt.imshow(img) imgplot = plt.imshow(img)
plt.show() #wyświetl drzewo decyzyjne plt.show() #wyświetl drzewo decyzyjne
return dtree return dtree