Marta miniprojekt + raport

This commit is contained in:
Marta Roszak 2020-05-10 18:42:31 +02:00
parent 27a5abb035
commit ecd41469ec
10 changed files with 372 additions and 8 deletions

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@ -0,0 +1,90 @@
import pandas as pd
import joblib
import pydotplus
from IPython.display import Image
from sklearn import tree
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
import os
os.environ["PATH"] += os.pathsep + r'C:\Program Files (x86)\Graphviz2.38\bin'
# import danych
def dataImport():
dataset = pd.read_csv('learnData4.csv', sep=',', header=None)
# print(dataset)
return dataset
# zamiana dataset na dwie tablice zawierające cechy i wyniki
def splitDataSet(dataset):
X = dataset.values[:, 0:5]
Y = dataset.values[:, 5]
# podział na zbiory do nauki i testowe
x_train, x_test, y_train, y_test = train_test_split(
X, Y, test_size=0.3, random_state=100)
return X, Y, x_train, x_test, y_train, y_test
def main():
data = dataImport()
X, Y, x_train, x_test, y_train, y_test = splitDataSet(data)
# utworzenie modelu drzewa decyzyjnego
model = tree.DecisionTreeClassifier()
model2 = tree.DecisionTreeClassifier(criterion="entropy")
# przeprowadzenie "nauki" na zbiorach do nauki
model.fit(x_train, y_train)
model2.fit(x_train, y_train)
# generowanie wyników dla zbioru testowego
pred = model.predict(x_test)
pred2 = model2.predict(x_test)
# porównanie z faktycznymi wynikami = wyliczenie dokładności
acc = accuracy_score(y_test, pred) * 100 # aprox. 77.78%
acc2 = accuracy_score(y_test, pred2) * 100 # aprox. 83.33%
print("akuratnosc dla modelu Gini: " + str(acc))
print("akuratnosc dla modelu Entropy: " + str(acc2))
# przekazanie wygenerowanego modelu do pliku
filename = 'finalized_model.sav'
joblib.dump(model2, filename)
kluseczka = [22, 4, 4, 1, 0]
wynik = model.predict([kluseczka])
print(wynik)
wynik2 = model2.predict([kluseczka])
print(wynik2)
# wygenerowanie i zapisanie graficznej reprezentacji drzewa
# dot_data = tree.export_graphviz(model, out_file=None,
# feature_names=["age", "fat", "fiber", "sex", "spicy"],
# class_names=["0", "1"],
# filled=True, rounded=True,
# special_characters=True)
dot_data2 = tree.export_graphviz(model2, out_file=None,
feature_names=["age", "fat", "fiber", "sex", "spicy"],
class_names=["easy to digest", "hard to digest"],
filled=True, rounded=True,
special_characters=True)
# wygenerowanie grafiki przedstawiającej drzewo
# graph = pydotplus.graph_from_dot_data(dot_data)
# Image(graph.create_png())
# graph.write_png("digest.png")
graph2 = pydotplus.graph_from_dot_data(dot_data2)
Image(graph2.create_png())
graph2.write_png("digest_entropy.png")
return
main()

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@ -3,6 +3,7 @@ import random
import time import time
import queue import queue
import math import math
import joblib
from keras.models import load_model, Sequential from keras.models import load_model, Sequential
from keras.preprocessing.image import img_to_array, load_img from keras.preprocessing.image import img_to_array, load_img
from keras.layers import Conv2D, MaxPooling2D from keras.layers import Conv2D, MaxPooling2D
@ -123,6 +124,9 @@ class Dish(object):
self.preparationTime = prepTime self.preparationTime = prepTime
self.eatingTime = eatTime self.eatingTime = eatTime
self.price = price self.price = price
self.fatContent = random.randint(0, 16)
self.fiberContent = random.randint(0, 16)
self.spicy = random.randint(0, 1)
class Plate(object): class Plate(object):
@ -374,10 +378,10 @@ def classify(a):
plate_3 = Plate(dish_1, "Sara/data/validation/dirty/dirty001.png") plate_3 = Plate(dish_1, "Sara/data/validation/dirty/dirty001.png")
plate_4 = Plate(dish_3, "Sara/data/validation/dirty/dirty009.png") plate_4 = Plate(dish_3, "Sara/data/validation/dirty/dirty009.png")
client_1 = Client(21, 'f', 45, 1) client_1 = Client(21, 1, 45, 1)
client_2 = Client(44, 'm', 22, 4) client_2 = Client(44, 0, 22, 4)
client_3 = Client(27, 'f', 15, 5) client_3 = Client(27, 1, 15, 5)
client_4 = Client(19, 'm', 32, 8) client_4 = Client(19, 0, 32, 8)
client_1.takePlateAndEat(plate_1) client_1.takePlateAndEat(plate_1)
client_2.takePlateAndEat(plate_2) client_2.takePlateAndEat(plate_2)
@ -412,6 +416,66 @@ def classify(a):
pass pass
def predictDigest(dish, client, model):
data = []
data.append(client.age)
data.append(dish.fatContent)
data.append(dish.fiberContent)
data.append(client.sex)
data.append(dish.spicy)
prediction = model.predict([data])
if prediction == 1:
messagebox.showinfo("opinia", "Z tym może być ciężko. " + str(data))
return prediction
else:
messagebox.showinfo("opinia", "Z tym nie będzie tak źle! " + str(data))
return prediction
def evaluate(b):
if b == 1:
loaded_model = joblib.load('Marta/finalized_model.sav')
# load = Image.open('Marta/digest_entropy.png')
# load.show()
messagebox.showinfo("zaczynamy", "Zaczynamy!")
dish_1 = Dish("obiad", 40, 20, 15)
dish_2 = Dish("deser", 25, 10, 10)
dish_3 = Dish("obiad 2", 55, 25, 28)
plate_1 = Plate(dish_1, "Sara/data/validation/food/food004.png")
plate_2 = Plate(dish_2, "Sara/data/validation/food/food002.png")
plate_4 = Plate(dish_3, "Sara/data/validation/dirty/dirty009.png")
client_1 = Client(27, 1, 45, 1)
client_2 = Client(44, 0, 22, 4)
client_4 = Client(16, 0, 32, 8)
client_1.takePlateAndEat(plate_1)
client_2.takePlateAndEat(plate_2)
client_4.takePlateAndEat(plate_4)
bot.goByAStar((tables[1].pos[0] + 1, tables[1].pos[1]))
opinion = predictDigest(dish_1, client_1, loaded_model)
bot.goByAStar((tables[3].pos[0] + 1, tables[3].pos[1]))
opinion = predictDigest(dish_2, client_2, loaded_model)
bot.goByAStar((tables[5].pos[0] + 1, tables[5].pos[1]))
opinion = predictDigest(dish_3, client_4, loaded_model)
messagebox.showinfo("Informacja końcowa", "Zakończono trasę.")
pygame.quit()
exit()
else:
pass
def text_objects(text, font): def text_objects(text, font):
textSurface = font.render(text, True, black) textSurface = font.render(text, True, black)
return textSurface, textSurface.get_rect() return textSurface, textSurface.get_rect()
@ -491,6 +555,8 @@ def makeImgClassificator(model):
optimizer='rmsprop', optimizer='rmsprop',
metrics=['accuracy']) metrics=['accuracy'])
def main(): def main():
global width, rows, bot, light_beige, beige, white, black, sizeBetween, tables, kitchen, cantwalk, puddles, window, clock, \ global width, rows, bot, light_beige, beige, white, black, sizeBetween, tables, kitchen, cantwalk, puddles, window, clock, \
img_classify img_classify
@ -545,7 +611,7 @@ def main():
pygame.quit() pygame.quit()
quit() quit()
button("Rozpoznawanie talerzy", 0, width + 1, 198, 48, light_beige, white, classify) button("Rozpoznawanie talerzy", 0, width + 1, 198, 48, light_beige, white, classify)
button("Ciężkostrawność dań", 200, width + 1, 198, 48, light_beige, white, "nazwa_funkcji") button("Ciężkostrawność dań", 200, width + 1, 198, 48, light_beige, white, evaluate)
redrawWindow(window) redrawWindow(window)
pygame.quit() pygame.quit()