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Customertree.md
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Customertree.md
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Piotr Jakub Dębski 01.06.2020
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## Automatyczny kelner: raport podprojektu indywidualnego
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W tym dokumencie opisane zostały podstawy i najważniejsze informacje dotyczące powstania
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oraz funkcjonowania podprojektu indywidualnego.
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### Cel projektu
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Celem projektu jest dodanie do projektu Zautomatyzowanego Kelnera sztucznej inteligencji u
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klientów restauracji, która na podstawie kilku przypisanych do obiektu klienta cech pozwoli mu
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na samodzielne wybranie zamówionego dania oraz napoju. Osobny moduł będzie miał za zadanie
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stworzyć modele drzewa decyzyjnego, które będą używane w głownym pliku projektu.
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### Biblioteki
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Do wykonania projektu wykorzystane zostały następujące biblioteki Pythona:
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- random
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- joblib
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- pandas
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- sklearn
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- pydot
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oraz dodatkowo:
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- graphviz
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za pomocą którego wygenerowane została reprezentacja wizualna drzew decyzyjnych
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food_tree.pdf oraz drink_tree.pdf znajdujących się w folderze graphs.
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### Dane
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Dane na których podstawie algorytm ma stworzyć modele drzew decyzyjnych umieszczone zostały w
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pliku learning_db.py i podzielone na 6 binarnych kategorii:
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- gender - płeć ( M - mężczyzna K - kobieta )
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- age - wiek ( Adult - dorosły Child - dziecko )
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- outfit - ubiór ( Casual - codzienny Elegant - reprezentacyjny )
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- cash - pieniądze ( "+" - dużo "-" - mało )
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- vege - dieta wegetariańska ( "Yes" "No" )
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- time - czas ( "Afternoon" - popołudnie "Evening" - wieczór )
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oraz na 2 kategorie, które mają zostać sklasyfikowane :
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- food - dania ( 35 wyborów )
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- drink - napoje ( 8 wyborów )
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Dane zawierają 200 przykładów.
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### Tworzenie drzewa
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1. Za pomocą funkcji pandas.DataFrame() program łączy ze sobą wszystkie przykłady w
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dwuwymiarową strukturę danych o wymiarach 9x200 (kategorie+index)x(przykłady).
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2. Za pomocą funkcji pandas.factorize() program dostosowuje dane w każdej kolumnie do naszych
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potrzeb i obliczeń.
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3. Następnie model danych zostaje podzielony na X - zbiór przykładów oraz y - odpowiadający
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przykładom wynik czyli rodzaj dania.
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4. Zestaw danych zostaje podzielony na testowy dzięki funkcji train_test_split()
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( 25% - food , 40% - drink) i treningowy ( 75% - food , 60% - drink )
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5. Dzięki funkcji DecisionTreeClassifier() oraz tą samą funkcją z argumentem wymagającym przyjęcia
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do kryterium entropii tworzą się dwa klasyfikatory (wyniki będą porównywane by wybrać
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dokładniejszą metodę)
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6. Zestaw treningowy przekazany zostaje do funkcji fit() dzięki czemu można teraz
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przeprowadzić predykcję za pomocą funkcji predict(), której w miejsce argumentu wprowadzamy
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zestaw testowy.
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7. W wyniku wielokrotnych porównań dokładności funkcji predict() na klasyfikatorach, entropia
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okazuje się lepszym kryterium od indeksu Giniego na zadanym zestawie danych, więc model drzewa
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korzystający z tego kryterium zostaje zapisany do użycia w głównym ciele projektu.
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8. Kroki 3-7 zostały powtórzone dla drzewa napojów, a odpowiednie wizualizacje drzew decyzyjnych
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zapisane są w folderze graphs.
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### Synchronizacja w projekcie
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Modele food_model i drink_model zostaną załadowane do programu z folderu models.
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Do obsługi stworzonego modelu stworzona jest klasa Client oraz funkcja client_ordering(ctr)
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Klasa Client poprzez swój konstruktor automatycznie losuje wszystkie cechy, na podstawie
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których funkcja predict() wybiera danie oraz napój, natomiast funkcja client_ordering(ctr) wymaga
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w swoim argumencie posiadania obiektu klasy Client, z której pobierze wszystkie wartości cech
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i przeniesie je do funkcji predict() zwracając otrzymane wyniki.
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customertree.py
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customertree.py
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import pandas as pandas
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import graphviz
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import pydot
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from learning_db import *
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from joblib import dump
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from sklearn import tree
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from sklearn.metrics import accuracy_score
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from sklearn.externals.six import StringIO
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from sklearn.tree import DecisionTreeClassifier
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from sklearn.metrics import accuracy_score
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from sklearn.model_selection import train_test_split
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customers = pandas.DataFrame({"gender": gender,
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"age": age,
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"outfit": outfit,
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"cash": cash,
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"time": time,
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"vege": vege,
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"food": food,
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"drink": drink
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})
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customers["gender"], gender_objects = pandas.factorize(customers["gender"])
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customers["age"], age_objects = pandas.factorize(customers["age"])
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customers["outfit"], outfit_objects = pandas.factorize(customers["outfit"])
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customers["cash"], cash_objects = pandas.factorize(customers["cash"])
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customers["time"], time_objects = pandas.factorize(customers["time"])
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customers["vege"], vege_objects = pandas.factorize(customers["vege"])
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customers["food"], food_objects = pandas.factorize(customers["food"])
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customers["drink"], drink_objects = pandas.factorize(customers["drink"])
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objects = []
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objects.append(gender_objects)
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objects.append(age_objects)
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objects.append(outfit_objects)
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objects.append(cash_objects)
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objects.append(time_objects)
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objects.append(vege_objects)
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objects.append(food_objects)
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objects.append(drink_objects)
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#X = customers.drop(["food","drink"], axis=1)
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#y = customers["food"]
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#X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=None)
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#food_classifier = DecisionTreeClassifier(criterion = "entropy", random_state=1)
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#food_classifier.fit(X_train, y_train)
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#porównanie kryterium: index Giniego i entropia
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#food_classifier1 = DecisionTreeClassifier()
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#food_classifier1.fit(X_train, y_train)
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#food_classifier2 = DecisionTreeClassifier(criterion = "entropy")
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#food_classifier2.fit(X_train, y_train)
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#y_pred1 = food_classifier1.predict(X_test)
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#y_pred2 = food_classifier2.predict(X_test)
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#if accuracy_score(y_test, y_pred1) > accuracy_score(y_test, y_pred2):
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# dump(food_classifier,'models/food_model.joblib')
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#else:
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# dump(food_classifier2,'models/food_model.joblib')
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#dot_data=StringIO()
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#tree = tree.export_graphviz(food_classifier, out_file = dot_data,
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# feature_names = X.columns,
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# class_names = food_objects,
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# filled = True, rounded = True)
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#graph = pydot.graph_from_dot_data(dot_data.getvalue())
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#graph[0].write_pdf("graphs/food_model.pdf")
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#X = customers.drop(["food","drink"], axis=1)
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#y = customers["drink"]
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#X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.45, random_state=1)
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#drink_classifier = DecisionTreeClassifier(criterion = "entropy")
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#drink_classifier.fit(X_train, y_train)
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#drink_classifier1 = DecisionTreeClassifier()
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#drink_classifier1.fit(X_train, y_train)
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#drink_classifier2 = DecisionTreeClassifier(criterion = "entropy")
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#drink_classifier2.fit(X_train, y_train)
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#y_pred1 = drink_classifier1.predict(X_test)
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#y_pred2 = drink_classifier2.predict(X_test)
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#if accuracy_score(y_test, y_pred1) > accuracy_score(y_test, y_pred2):
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# dump(drink_classifier1,'models/drink_model.joblib')
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#else:
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# dump(drink_classifier2,'models/drink_model.joblib')
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#dot_data=StringIO()
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#tree = tree.export_graphviz(drink_classifier, out_file = dot_data,
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# feature_names = X.columns,
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# class_names = drink_objects,
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# filled = True, rounded = True)
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#graph = pydot.graph_from_dot_data(dot_data.getvalue())
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#graph[0].write_pdf("graphs/drink_model.pdf")
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graphs/drink_model.pdf
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graphs/drink_model.pdf
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graphs/food_model.pdf
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graphs/food_model.pdf
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learning_db.py
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gender = ["M","M","M","M","M","M","M","M","M","M",
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"M","M","M","M","M","M","M","M","M","M",
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"M","M","M","M","M","M","M","M","M","M",
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"M","M","M","M","M","M","M","M","M","M",
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"M","M","M","M","M","M","M","M","M","M",
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"M","M","M","M","M","M","M","M","M","M",
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"M","M","M","M","M","M","M","M","M","M",
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"M","M","M","M","M","M","M","M","M","M",
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"M","M","M","M","M","M","M","M","M","M",
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"M","M","M","M","M","M","M","M","M","M",
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"W","W","W","W","W","W","W","W","W","W",
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"W","W","W","W","W","W","W","W","W","W",
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"W","W","W","W","W","W","W","W","W","W",
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"W","W","W","W","W","W","W","W","W","W",
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"W","W","W","W","W","W","W","W","W","W",
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"W","W","W","W","W","W","W","W","W","W",
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"W","W","W","W","W","W","W","W","W","W",
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"W","W","W","W","W","W","W","W","W","W",
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"W","W","W","W","W","W","W","W","W","W",
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"W","W","W","W","W","W","W","W","W","W"
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]
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age = ["Adult","Adult","Adult","Adult","Adult","Adult","Adult","Adult","Adult","Adult",
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"Adult","Adult","Adult","Adult","Adult","Adult","Adult","Adult","Adult","Adult",
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"Adult","Adult","Adult","Adult","Adult","Adult","Adult","Adult","Adult","Adult",
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"Adult","Adult","Adult","Adult","Adult","Adult","Adult","Adult","Adult","Adult",
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"Adult","Adult","Adult","Adult","Adult","Adult","Adult","Adult","Adult","Adult",
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"Adult","Adult","Adult","Adult","Adult","Adult","Adult","Adult","Adult","Adult",
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"Adult","Adult","Adult","Adult","Adult","Adult","Adult","Adult","Adult","Adult",
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"Adult","Adult","Adult","Adult","Adult","Adult","Adult","Adult","Adult","Adult",
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"Adult","Adult","Adult","Adult","Adult","Adult","Adult","Adult","Adult","Adult",
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"Child","Child","Child","Child","Child","Child","Child","Child","Child","Child",
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"Adult","Adult","Adult","Adult","Adult","Adult","Adult","Adult","Adult","Adult",
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"Adult","Adult","Adult","Adult","Adult","Adult","Adult","Adult","Adult","Adult",
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"Adult","Adult","Adult","Adult","Adult","Adult","Adult","Adult","Adult","Adult",
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"Adult","Adult","Adult","Adult","Adult","Adult","Adult","Adult","Adult","Adult",
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"Adult","Adult","Adult","Adult","Adult","Adult","Adult","Adult","Adult","Adult",
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"Adult","Adult","Adult","Adult","Adult","Adult","Adult","Adult","Adult","Adult",
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"Adult","Adult","Adult","Adult","Adult","Adult","Adult","Adult","Adult","Adult",
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"Adult","Adult","Adult","Adult","Adult","Adult","Adult","Adult","Adult","Adult",
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"Adult","Adult","Adult","Adult","Adult","Adult","Adult","Adult","Adult","Adult",
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"Child","Child","Child","Child","Child","Child","Child","Child","Child","Child"
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]
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outfit = ["Casual","Casual","Casual","Casual","Casual","Casual","Casual","Casual","Casual","Casual",
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"Casual","Casual","Casual","Casual","Casual","Casual","Casual","Casual","Casual","Casual",
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"Casual","Casual","Casual","Casual","Casual","Casual","Casual","Casual","Casual","Casual",
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"Casual","Casual","Casual","Casual","Casual","Casual","Casual","Casual","Casual","Casual",
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"Casual","Casual","Casual","Casual","Casual","Casual","Casual","Casual","Casual","Casual",
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"Casual","Casual","Casual","Casual","Casual","Casual","Casual","Casual","Casual","Casual",
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"Casual","Casual","Casual","Casual","Casual","Casual","Casual","Casual","Casual","Casual",
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"Elegant","Elegant","Elegant","Elegant","Elegant","Elegant","Elegant","Elegant","Elegant","Elegant",
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"Elegant","Elegant","Elegant","Elegant","Elegant","Elegant","Elegant","Elegant","Elegant","Elegant",
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"Casual","Casual","Casual","Casual","Casual","Elegant","Elegant","Elegant","Elegant","Elegant",
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"Casual","Casual","Casual","Casual","Casual","Casual","Casual","Casual","Casual","Casual",
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"Casual","Casual","Casual","Casual","Casual","Casual","Casual","Casual","Casual","Casual",
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"Casual","Casual","Casual","Casual","Casual","Casual","Casual","Casual","Casual","Casual",
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"Casual","Casual","Casual","Casual","Casual","Casual","Casual","Casual","Casual","Casual",
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"Casual","Casual","Casual","Casual","Casual","Casual","Casual","Casual","Casual","Casual",
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"Casual","Casual","Casual","Casual","Casual","Casual","Casual","Casual","Casual","Casual",
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"Casual","Casual","Casual","Casual","Casual","Casual","Casual","Casual","Casual","Casual",
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"Elegant","Elegant","Elegant","Elegant","Elegant","Elegant","Elegant","Elegant","Elegant","Elegant",
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"Elegant","Elegant","Elegant","Elegant","Elegant","Elegant","Elegant","Elegant","Elegant","Elegant",
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"Casual","Casual","Casual","Casual","Casual","Elegant","Elegant","Elegant","Elegant","Elegant"
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]
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cash = ["+","+","+","+","+","+","+","+","+","+",
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"+","+","+","+","+","+","+","+","+","+",
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"+","+","+","+","+","+","+","+","+","+",
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"-","-","-","-","-","-","-","-","-","-",
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"-","-","-","-","-","-","-","-","-","-",
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"-","-","-","-","-","-","-","-","-","-",
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"+","+","+","+","+","+","+","+","+","+",
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"+","+","+","+","+","+","+","+","+","+",
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"+","+","+","+","+","+","+","+","+","+",
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"-","+","-","+","-","+","-","+","-","+",
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"+","+","+","+","+","+","+","+","+","+",
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"+","+","+","+","+","+","+","+","+","+",
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"+","+","+","+","+","+","+","+","+","+",
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"-","-","-","-","-","-","-","-","-","-",
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"-","-","-","-","-","-","-","-","-","-",
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"-","-","-","-","-","-","-","-","-","-",
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"+","+","+","+","+","+","+","+","+","+",
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"+","+","+","+","+","+","+","+","+","+",
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"+","+","+","+","+","+","+","+","+","+",
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"-","+","-","+","-","+","-","+","-","+"
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]
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time = ["Evening","Evening","Evening","Evening","Evening","Evening","Evening","Evening","Evening","Evening",
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"Afternoon","Afternoon","Afternoon","Afternoon","Afternoon","Afternoon","Afternoon","Afternoon","Afternoon","Afternoon",
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"Evening","Evening","Evening","Evening","Evening","Evening","Evening","Evening","Evening","Evening",
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"Afternoon","Afternoon","Afternoon","Afternoon","Afternoon","Afternoon","Afternoon","Afternoon","Afternoon","Afternoon",
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"Evening","Evening","Evening","Evening","Evening","Evening","Evening","Evening","Evening","Evening",
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"Afternoon","Afternoon","Afternoon","Afternoon","Afternoon","Afternoon","Afternoon","Afternoon","Afternoon","Afternoon",
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"Evening","Evening","Evening","Evening","Evening","Evening","Evening","Evening","Evening","Evening",
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"Afternoon","Afternoon","Afternoon","Afternoon","Afternoon","Afternoon","Afternoon","Afternoon","Afternoon","Afternoon",
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"Evening","Evening","Evening","Evening","Evening","Evening","Evening","Evening","Evening","Evening",
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"Afternoon","Afternoon","Evening","Evening","Afternoon","Afternoon","Evening","Evening","Afternoon","Afternoon",
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"Evening","Evening","Evening","Evening","Evening","Evening","Evening","Evening","Evening","Evening",
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"Afternoon","Afternoon","Afternoon","Afternoon","Afternoon","Afternoon","Afternoon","Afternoon","Afternoon","Afternoon",
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"Evening","Evening","Evening","Evening","Evening","Evening","Evening","Evening","Evening","Evening",
|
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"Afternoon","Afternoon","Afternoon","Afternoon","Afternoon","Afternoon","Afternoon","Afternoon","Afternoon","Afternoon",
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"Evening","Evening","Evening","Evening","Evening","Evening","Evening","Evening","Evening","Evening",
|
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"Afternoon","Afternoon","Afternoon","Afternoon","Afternoon","Afternoon","Afternoon","Afternoon","Afternoon","Afternoon",
|
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"Evening","Evening","Evening","Evening","Evening","Evening","Evening","Evening","Evening","Evening",
|
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"Afternoon","Afternoon","Afternoon","Afternoon","Afternoon","Afternoon","Afternoon","Afternoon","Afternoon","Afternoon",
|
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"Evening","Evening","Evening","Evening","Evening","Evening","Evening","Evening","Evening","Evening",
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"Afternoon","Afternoon","Evening","Evening","Afternoon","Afternoon","Evening","Evening","Afternoon","Afternoon"
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]
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vege = ["No","No","No","No","No","No","No","No","No","No",
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"No","No","No","No","No","No","No","No","No","No",
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"No","No","No","No","No","Yes","Yes","Yes","Yes","Yes",
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"No","No","No","No","No","Yes","Yes","Yes","Yes","Yes",
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"No","No","No","No","No","Yes","Yes","Yes","Yes","Yes",
|
||||
"No","No","No","No","No","No","No","No","No","No",
|
||||
"No","No","No","No","No","Yes","Yes","Yes","Yes","Yes",
|
||||
"No","No","No","No","No","No","No","No","No","No",
|
||||
"No","No","No","No","No","Yes","Yes","Yes","Yes","Yes",
|
||||
"No","No","No","No","No","No","No","No","Yes","Yes",
|
||||
|
||||
"No","No","No","No","No","No","No","No","No","No",
|
||||
"No","No","No","No","No","No","No","No","No","No",
|
||||
"No","No","No","No","No","Yes","Yes","Yes","Yes","Yes",
|
||||
"No","No","No","No","No","Yes","Yes","Yes","Yes","Yes",
|
||||
"No","No","No","No","No","Yes","Yes","Yes","Yes","Yes",
|
||||
"No","No","No","No","No","No","No","No","No","No",
|
||||
"No","No","No","No","No","Yes","Yes","Yes","Yes","Yes",
|
||||
"No","No","No","No","No","No","No","No","No","No",
|
||||
"No","No","No","No","No","Yes","Yes","Yes","Yes","Yes",
|
||||
"No","No","No","No","No","No","No","No","Yes","Yes"
|
||||
]
|
||||
|
||||
food = ['club_sandwich','steak','hot_dog','hamburger','hot_dog',
|
||||
'hot_dog','hot_dog','club_sandwich','steak','club_sandwich',
|
||||
'hamburger','steak','hamburger','club_sandwich','hot_dog',
|
||||
'hot_dog','steak','hot_dog','apple_pie','ice_cream',
|
||||
'pizza','hot_dog','hamburger','steak','club_sandwich',
|
||||
|
||||
'ice_cream','waffles','greek_salad','greek_salad','greek_salad',
|
||||
'lasagna','lasagna','ice_cream','ice_cream','steak',
|
||||
'greek_salad','waffles','greek_salad','apple_pie','waffles',
|
||||
'hamburger','pizza','lasagna','hamburger','hamburger',
|
||||
'ice_cream','waffles','greek_salad','club_sandwich','ice_cream',
|
||||
|
||||
'pizza','ice_cream','pizza','greek_salad','ice_cream',
|
||||
'lasagna','pizza','pizza','hamburger','club_sandwich',
|
||||
'club_sandwich','pizza','club_sandwich','apple_pie','club_sandwich',
|
||||
'ice_cream','waffles','greek_salad','apple_pie','greek_salad',
|
||||
'club_sandwich','steak','pizza','hamburger','hot_dog',
|
||||
|
||||
'hamburger','steak','hot_dog','steak','hot_dog',
|
||||
'hot_dog','hot_dog','hamburger','steak','club_sandwich',
|
||||
'greek_salad','waffles','waffles','apple_pie','greek_salad',
|
||||
'club_sandwich','club_sandwich','pizza','waffles','apple_pie',
|
||||
'hot_dog','ice_cream','greek_salad','apple_pie','ice_cream',
|
||||
|
||||
'club_sandwich','steak','steak','hamburger','hot_dog',
|
||||
'hot_dog','hot_dog','club_sandwich','steak','club_sandwich',
|
||||
'hamburger','steak','steak','lasagna','hot_dog',
|
||||
'hot_dog','ice_cream','greek_salad','pizza','ice_cream',
|
||||
'hot_dog','hot_dog','hamburger','steak','club_sandwich',
|
||||
|
||||
'waffles','waffles','greek_salad','waffles','greek_salad',
|
||||
'lasagna','lasagna','lasagna','lasagna','lasagna',
|
||||
'ice_cream','waffles','waffles','apple_pie','greek_salad',
|
||||
'pizza','pizza','hamburger','hamburger','ice_cream',
|
||||
'ice_cream','waffles','greek_salad','apple_pie','ice_cream',
|
||||
|
||||
'lasagna','ice_cream','hamburger','hamburger','ice_cream',
|
||||
'hamburger','hamburger','pizza','pizza','ice_cream',
|
||||
'lasagna','hot_dog','hamburger','steak','club_sandwich',
|
||||
'ice_cream','waffles','greek_salad','apple_pie','ice_cream',
|
||||
'club_sandwich','steak','hot_dog','hamburger','hot_dog',
|
||||
|
||||
'hamburger','steak','hot_dog','hamburger','hot_dog',
|
||||
'hot_dog','steak','hamburger','steak','club_sandwich',
|
||||
'ice_cream','waffles','greek_salad','apple_pie','greek_salad',
|
||||
'ice_cream','ice_cream','lasagna','apple_pie','apple_pie',
|
||||
'hot_dog','ice_cream','hot_dog','apple_pie','waffles'
|
||||
]
|
||||
|
||||
drink = ["Whisky","Whisky","Whisky","Whisky","Whisky",
|
||||
"Whisky","Whisky","Beer","Beer","Beer",
|
||||
"Cola","Water","Orange Juice","Water","Beer",
|
||||
"Cola","Water","Orange Juice","Cola","Beer",
|
||||
"Beer","Beer","Beer","Beer","Beer",
|
||||
|
||||
"Beer","Beer","Beer","Beer","Beer",
|
||||
"Cola","Water","Orange Juice","Water","Beer",
|
||||
"Cola","Water","Orange Juice","Cola","Beer",
|
||||
"Beer","Beer","Beer","Beer","Beer",
|
||||
"Beer","Beer","Beer","Beer","Beer",
|
||||
|
||||
"Cola","Water","Orange Juice","Water","Beer",
|
||||
"Cola","Water","Orange Juice","Cola","Beer",
|
||||
"Whisky","Whisky","Whisky","Whisky","Whisky",
|
||||
"Whisky","Whisky","Whisky","Beer","Beer",
|
||||
"Beer","Beer","Beer","Beer","Beer",
|
||||
|
||||
"Cola","Water","Orange Juice","Water","Beer",
|
||||
"Beer","Beer","Beer","Beer","Beer",
|
||||
"Beer","Beer","Beer","Beer","Beer",
|
||||
"Orange Juice","Orange Juice","Cola","Cola","Cola",
|
||||
"Cola","Cola","Cola","Cola","Cola",
|
||||
|
||||
"Whisky","Whisky","Whisky","Red Wine","Red Wine",
|
||||
"Red Wine","Red Wine","Red Wine","Red Wine","Whisky",
|
||||
"Water","Water","Orange Juice","Orange Juice","Cola",
|
||||
"Water","Water","Orange Juice","Orange Juice","Cola",
|
||||
"Red Wine","Red Wine","Red Wine","Red Wine","Red Wine",
|
||||
|
||||
"Whisky","Whisky","Whisky","Red Wine","Red Wine",
|
||||
"Water","Water","Orange Juice","Orange Juice","Cola",
|
||||
"Water","Water","Orange Juice","Orange Juice","Cola",
|
||||
"Red Wine","Red Wine","Red Wine","Red Wine","Red Wine",
|
||||
"Red Wine","Red Wine","Red Wine","Red Wine","Red Wine",
|
||||
|
||||
"Water","Water","Orange Juice","Orange Juice","Cola",
|
||||
"Water","Water","Orange Juice","Orange Juice","Cola",
|
||||
"Red Wine","Red Wine","Red Wine","Red Wine","Red Wine",
|
||||
"Red Wine","Red Wine","Whisky","Whisky","Whisky",
|
||||
"Water","Water","Orange Juice","Orange Juice","Cola",
|
||||
|
||||
"Water","Water","Orange Juice","Orange Juice","Cola",
|
||||
"Red Wine","Red Wine","Red Wine","Red Wine","Red Wine",
|
||||
"Red Wine","Red Wine","Red Wine","Red Wine","Red Wine",
|
||||
"Orange Juice","Orange Juice","Orange Juice","Orange Juice","Orange Juice",
|
||||
"Orange Juice","Orange Juice","Orange Juice","Cola","Cola"
|
||||
]
|
||||
87
main.py
87
main.py
@ -10,6 +10,9 @@ import numpy as np
|
||||
from data import *
|
||||
from choice_tree import *
|
||||
|
||||
from joblib import load
|
||||
from customertree import objects
|
||||
|
||||
import tensorflow as tf
|
||||
from keras import *
|
||||
import h5py
|
||||
@ -36,6 +39,9 @@ CATEGORIES = [
|
||||
"waffles"
|
||||
]
|
||||
|
||||
food_model = load("models/food_model.joblib")
|
||||
drink_model = load("models/drink_model.joblib")
|
||||
|
||||
model = tf.keras.models.load_model('final1')
|
||||
|
||||
with h5py.File('food_10_64x3_test.hdf5', "r") as f:
|
||||
@ -104,6 +110,84 @@ def client_ordering():
|
||||
###
|
||||
|
||||
|
||||
class Client:
|
||||
def __init__(self):
|
||||
self.gender = random.choice(["Man","Woman"])
|
||||
self.outfit = random.choice(["Casual","Elegant"])
|
||||
self.cash = random.choice([20,20,20,30,30,50,50,70,80,90,100,100,120,
|
||||
120,150,200,300,500])
|
||||
self.time = random.choice(["Afternoon","Evening"])
|
||||
self.vege = random.choice(["No","No","No","No","Yes"])
|
||||
self.age = random.randint(12,80)
|
||||
|
||||
def __str__(self):
|
||||
return (self.gender + " Age: " + str(self.age) +" "+ self.outfit+
|
||||
" $"+ str(self.cash)+ " Vege: "+ self.vege)
|
||||
|
||||
def order_drink(clt):
|
||||
frame = []
|
||||
if clt.gender == "Man":
|
||||
frame.append(0)
|
||||
else:
|
||||
frame.append(1)
|
||||
if clt.age > 17:
|
||||
frame.append(0)
|
||||
else:
|
||||
frame.append(1)
|
||||
if clt.outfit == "Casual":
|
||||
frame.append(0)
|
||||
else:
|
||||
frame.append(1)
|
||||
if clt.cash > 100:
|
||||
frame.append(0)
|
||||
else:
|
||||
frame.append(1)
|
||||
if clt.time == "Evening":
|
||||
frame.append(0)
|
||||
else:
|
||||
frame.append(1)
|
||||
if clt.vege == "No":
|
||||
frame.append(0)
|
||||
else:
|
||||
frame.append(1)
|
||||
|
||||
drink_predict = drink_model.predict([frame])
|
||||
drink_index = drink_predict[0]
|
||||
|
||||
return objects[-1][drink_index]
|
||||
|
||||
def order_food(clt):
|
||||
frame = []
|
||||
if clt.gender == "Man":
|
||||
frame.append(0)
|
||||
else:
|
||||
frame.append(1)
|
||||
if clt.age > 17:
|
||||
frame.append(0)
|
||||
else:
|
||||
frame.append(1)
|
||||
if clt.outfit == "Casual":
|
||||
frame.append(0)
|
||||
else:
|
||||
frame.append(1)
|
||||
if clt.cash > 100:
|
||||
frame.append(0)
|
||||
else:
|
||||
frame.append(1)
|
||||
if clt.time == "Evening":
|
||||
frame.append(0)
|
||||
else:
|
||||
frame.append(1)
|
||||
if clt.vege == "No":
|
||||
frame.append(0)
|
||||
else:
|
||||
frame.append(1)
|
||||
|
||||
food_predict = food_model.predict([frame])
|
||||
food_index = food_predict[0]
|
||||
|
||||
return objects[-2][food_index]
|
||||
|
||||
###
|
||||
class Node:
|
||||
def __init__(self, state, parent, action):
|
||||
@ -553,7 +637,8 @@ while True:
|
||||
restaurant.tiles[waiter.y][waiter.x].clientState = "wait"
|
||||
waiter.orders = (waiter.x, waiter.y)
|
||||
DEFINE += 1
|
||||
waiter.order_list.insert(0, random.choice(CATEGORIES))
|
||||
cl = Client()
|
||||
waiter.order_list.insert(0,client_ordering_food(cl))
|
||||
if (waiter.x, waiter.y) == KITCHEN:
|
||||
if waiter.orders:
|
||||
restaurant.kitchen.append([waiter.orders[0], waiter.orders[1], 50])
|
||||
|
||||
BIN
models/drink_model.joblib
Normal file
BIN
models/drink_model.joblib
Normal file
Binary file not shown.
BIN
models/food_model.joblib
Normal file
BIN
models/food_model.joblib
Normal file
Binary file not shown.
Loading…
Reference in New Issue
Block a user