Marta miniprojekt + raport
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11
Restaurant/Marta/.idea/Marta.iml
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Restaurant/Marta/.idea/Marta.iml
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Restaurant/Marta/.idea/encodings.xml
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Restaurant/Marta/.idea/misc.xml
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Restaurant/Marta/.idea/modules.xml
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Restaurant/Marta/.idea/modules.xml
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Restaurant/Marta/.idea/vcs.xml
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Restaurant/Marta/.idea/vcs.xml
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Restaurant/Marta/.idea/workspace.xml
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90
Restaurant/Marta/DecisionTreeGenerate.py
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90
Restaurant/Marta/DecisionTreeGenerate.py
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import pandas as pd
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import joblib
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import pydotplus
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from IPython.display import Image
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from sklearn import tree
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from sklearn.model_selection import train_test_split
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from sklearn.metrics import accuracy_score
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import os
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os.environ["PATH"] += os.pathsep + r'C:\Program Files (x86)\Graphviz2.38\bin'
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# import danych
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def dataImport():
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dataset = pd.read_csv('learnData4.csv', sep=',', header=None)
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# print(dataset)
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return dataset
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# zamiana dataset na dwie tablice zawierające cechy i wyniki
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def splitDataSet(dataset):
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X = dataset.values[:, 0:5]
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Y = dataset.values[:, 5]
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# podział na zbiory do nauki i testowe
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x_train, x_test, y_train, y_test = train_test_split(
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X, Y, test_size=0.3, random_state=100)
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return X, Y, x_train, x_test, y_train, y_test
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def main():
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data = dataImport()
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X, Y, x_train, x_test, y_train, y_test = splitDataSet(data)
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# utworzenie modelu drzewa decyzyjnego
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model = tree.DecisionTreeClassifier()
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model2 = tree.DecisionTreeClassifier(criterion="entropy")
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# przeprowadzenie "nauki" na zbiorach do nauki
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model.fit(x_train, y_train)
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model2.fit(x_train, y_train)
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# generowanie wyników dla zbioru testowego
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pred = model.predict(x_test)
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pred2 = model2.predict(x_test)
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# porównanie z faktycznymi wynikami = wyliczenie dokładności
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acc = accuracy_score(y_test, pred) * 100 # aprox. 77.78%
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acc2 = accuracy_score(y_test, pred2) * 100 # aprox. 83.33%
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print("akuratnosc dla modelu Gini: " + str(acc))
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print("akuratnosc dla modelu Entropy: " + str(acc2))
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# przekazanie wygenerowanego modelu do pliku
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filename = 'finalized_model.sav'
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joblib.dump(model2, filename)
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kluseczka = [22, 4, 4, 1, 0]
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wynik = model.predict([kluseczka])
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print(wynik)
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wynik2 = model2.predict([kluseczka])
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print(wynik2)
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# wygenerowanie i zapisanie graficznej reprezentacji drzewa
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# dot_data = tree.export_graphviz(model, out_file=None,
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# feature_names=["age", "fat", "fiber", "sex", "spicy"],
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# class_names=["0", "1"],
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# filled=True, rounded=True,
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# special_characters=True)
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dot_data2 = tree.export_graphviz(model2, out_file=None,
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feature_names=["age", "fat", "fiber", "sex", "spicy"],
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class_names=["easy to digest", "hard to digest"],
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filled=True, rounded=True,
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special_characters=True)
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# wygenerowanie grafiki przedstawiającej drzewo
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# graph = pydotplus.graph_from_dot_data(dot_data)
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# Image(graph.create_png())
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# graph.write_png("digest.png")
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graph2 = pydotplus.graph_from_dot_data(dot_data2)
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Image(graph2.create_png())
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graph2.write_png("digest_entropy.png")
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|
||||||
|
return
|
||||||
|
|
||||||
|
main()
|
@ -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()
|
||||||
|
Loading…
Reference in New Issue
Block a user