106 lines
3.8 KiB
Python
106 lines
3.8 KiB
Python
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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