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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 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
df = pandas.read_csv(os.path.join('resources', 'data.csv')) #czytanie danych do nauki drzewa z pliku .csv if os.path.exists("resources/tree.pkl"): #jeżeli drzewo jest zapisane w pliku to odczytaj
features = ['amount of seeds', 'collected plants', 'fertilizer', 'fuel', 'water level'] dtree = pickle.load(open(os.path.join('resources', "tree.pkl"), "rb"))
x = df[features] #wczytanie atrybutów, z których ma się uczyć drzewo else: #w przeciwnym razie utwórz drzewo od początku i zapisz do pliku
y = df['back to station'] #podjęte decyzje df = pandas.read_csv(os.path.join('resources', 'data.csv')) #czytanie danych do nauki drzewa z pliku .csv
dtree = DecisionTreeClassifier() #tworzy obiekt drzewa features = ['amount of seeds', 'collected plants', 'fertilizer', 'fuel', 'water level']
dtree = dtree.fit(x, y) #uczy drzewo x = df[features] #wczytanie atrybutów, z których ma się uczyć drzewo
data = tree.export_graphviz(dtree, out_file=None, feature_names=features) y = df['back to station'] #podjęte decyzje
graph = pydotplus.graph_from_dot_data(data) dtree = DecisionTreeClassifier() #klasyfikuje drzewo
graph.write_png(os.path.join('resources', 'mydecisiontree.png')) dtree = dtree.fit(x, y) #uczy drzewo
img = pltimg.imread(os.path.join('resources', 'mydecisiontree.png')) pickle.dump(dtree, open(os.path.join('resources', "tree.pkl"), "wb"))
imgplot = plt.imshow(img) data = tree.export_graphviz(dtree, out_file=None, feature_names=features)
plt.show() #wyświetl drzewo decyzyjne 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 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) 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 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