Marta miniprojekt + raport
This commit is contained in:
Marta Roszak
parent
27a5abb035
commit
ecd41469ec
@ -1,8 +1,11 @@
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Restaurant/Marta/.idea/Marta.iml
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11
Restaurant/Marta/.idea/Marta.iml
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Restaurant/Marta/.idea/modules.xml
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8
Restaurant/Marta/.idea/modules.xml
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Restaurant/Marta/.idea/vcs.xml
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6
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172
Restaurant/Marta/.idea/workspace.xml
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90
Restaurant/Marta/DecisionTreeGenerate.py
Normal file
90
Restaurant/Marta/DecisionTreeGenerate.py
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@ -0,0 +1,90 @@
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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
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main()
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@ -3,6 +3,7 @@ import random
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import time
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import queue
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import math
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import joblib
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from keras.models import load_model, Sequential
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from keras.preprocessing.image import img_to_array, load_img
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from keras.layers import Conv2D, MaxPooling2D
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@ -123,6 +124,9 @@ class Dish(object):
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self.preparationTime = prepTime
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self.eatingTime = eatTime
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self.price = price
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self.fatContent = random.randint(0, 16)
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self.fiberContent = random.randint(0, 16)
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self.spicy = random.randint(0, 1)
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class Plate(object):
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@ -374,10 +378,10 @@ def classify(a):
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plate_3 = Plate(dish_1, "Sara/data/validation/dirty/dirty001.png")
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plate_4 = Plate(dish_3, "Sara/data/validation/dirty/dirty009.png")
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client_1 = Client(21, 'f', 45, 1)
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client_2 = Client(44, 'm', 22, 4)
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client_3 = Client(27, 'f', 15, 5)
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client_4 = Client(19, 'm', 32, 8)
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client_1 = Client(21, 1, 45, 1)
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client_2 = Client(44, 0, 22, 4)
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client_3 = Client(27, 1, 15, 5)
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client_4 = Client(19, 0, 32, 8)
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client_1.takePlateAndEat(plate_1)
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client_2.takePlateAndEat(plate_2)
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@ -412,6 +416,66 @@ def classify(a):
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pass
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def predictDigest(dish, client, model):
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data = []
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data.append(client.age)
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data.append(dish.fatContent)
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data.append(dish.fiberContent)
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data.append(client.sex)
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data.append(dish.spicy)
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prediction = model.predict([data])
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if prediction == 1:
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messagebox.showinfo("opinia", "Z tym może być ciężko. " + str(data))
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return prediction
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else:
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messagebox.showinfo("opinia", "Z tym nie będzie tak źle! " + str(data))
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return prediction
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def evaluate(b):
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if b == 1:
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loaded_model = joblib.load('Marta/finalized_model.sav')
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# load = Image.open('Marta/digest_entropy.png')
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# load.show()
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messagebox.showinfo("zaczynamy", "Zaczynamy!")
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dish_1 = Dish("obiad", 40, 20, 15)
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dish_2 = Dish("deser", 25, 10, 10)
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dish_3 = Dish("obiad 2", 55, 25, 28)
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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):
|
||||
textSurface = font.render(text, True, black)
|
||||
return textSurface, textSurface.get_rect()
|
||||
@ -491,6 +555,8 @@ def makeImgClassificator(model):
|
||||
optimizer='rmsprop',
|
||||
metrics=['accuracy'])
|
||||
|
||||
|
||||
|
||||
def main():
|
||||
global width, rows, bot, light_beige, beige, white, black, sizeBetween, tables, kitchen, cantwalk, puddles, window, clock, \
|
||||
img_classify
|
||||
@ -545,7 +611,7 @@ def main():
|
||||
pygame.quit()
|
||||
quit()
|
||||
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)
|
||||
|
||||
pygame.quit()
|
||||
|
||||
Loading…
Reference in New Issue
Block a user