89 lines
2.9 KiB
Python
89 lines
2.9 KiB
Python
import pandas as pd
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try:
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from StringIO import StringIO
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except ImportError:
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from io import StringIO
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from io import StringIO
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from sklearn.tree import DecisionTreeClassifier
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from sklearn.model_selection import train_test_split
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from sklearn import metrics
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from sklearn.tree import export_graphviz
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import joblib
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from IPython.display import Image
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import pydotplus
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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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def main():
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dot_data = StringIO()
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col_names = ['age', 'sex', 'fat', 'fiber', 'spicy', 'number']
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#import danych
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model_tree = pd.read_csv("Nowy.csv", header=None, names=col_names)
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model_tree.head()
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#seperacja danych cechy
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feature_cols = ['age', 'sex', 'fat', 'fiber', 'spicy']
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X = model_tree[feature_cols]
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#separacja danych etykieta
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y = model_tree.number
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#podział danych na zestaw treningowy i testowy; 70% trening 30% test
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3,
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random_state=1)
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###############################
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# stworzenie -obiektu- drzewa Decision Tree classifer
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clf = DecisionTreeClassifier()
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# drzewo treningowe
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clf = clf.fit(X_train, y_train)
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#generowanie wyników dla zbioru testowego
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y_pred = clf.predict(X_test)
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#print(y_pred)
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#Akuratność dla modelu danych
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print("Accuracy:", metrics.accuracy_score(y_test, y_pred))
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dot_data = StringIO()
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#tworzenie graficznego drzewa
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export_graphviz(clf, out_file=dot_data,
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filled=True, rounded=True,
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special_characters=True, feature_names=feature_cols,
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class_names=['1', '2', '3', '4', '5', '6', '7'])
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graph = pydotplus.graph_from_dot_data(dot_data.getvalue())
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graph.write_png('polecanie_1.png')
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Image(graph.create_png())
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# zapisanie modelu danych do pliku
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file_name = 'final_model.sav'
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joblib.dump(clf, file_name)
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# ************************************************************
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# stworzenie -obiektu- drzewa Decision Tree classifer z kryterium entropii
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clf = DecisionTreeClassifier(criterion="entropy")
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#drzewo testowe-z entripią
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clf = clf.fit(X_train, y_train)
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#generowanie wyników dla zbioru testowego
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y_pred = clf.predict(X_test)
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#Akuratność dla modelu danych z warunkiem entropii
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print("Accuracy:", metrics.accuracy_score(y_test, y_pred))
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#stowrzenie graficznego drzewa z warunkiem entropii
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dot_data = StringIO()
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export_graphviz(clf, out_file=dot_data,
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filled=True, rounded=True,
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special_characters=True, feature_names=feature_cols,
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class_names=['1', '2', '3', '4', '5', '6', '7'])
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graph = pydotplus.graph_from_dot_data(dot_data.getvalue())
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graph.write_png('polecanie_2_entropia.png')
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Image(graph.create_png())
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return
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main() |