50 lines
1.8 KiB
Python
50 lines
1.8 KiB
Python
import pandas as pd
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from sklearn.tree import DecisionTreeClassifier, plot_tree, export_graphviz
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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 matplotlib.pyplot as plt
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headers = ['adult','active_time','ill','season','guests','hunger','wet_food','dry_food']
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# Wczytanie danych
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data = pd.read_csv('dane.csv', header=0)
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X = data[['adult','active_time','ill','season','guests','hunger','wet_food','dry_food']]
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Y = data['decision']
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X = pd.get_dummies(data=X, columns=['season'])
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X_train, X_test, Y_train, Y_test = train_test_split(X, Y, train_size=0.8)
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clf = DecisionTreeClassifier(random_state=0, min_samples_leaf = 4, min_samples_split=2)
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clf = clf.fit(X_train, Y_train)
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Y_pred = clf.predict(X_test)
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accuracy = accuracy_score(Y_test, Y_pred)
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print("Dokładność:", accuracy)
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#zapisanie drzewa do pliku
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plt.figure(figsize=(50,30))
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plot_tree(clf, filled=True, feature_names=X.columns, class_names=['nie karmi', 'karmi mokrą karmą', 'karmi suchą karmą']) # filled=True koloruje węzły
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plt.savefig('tree.png')
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# Nowe dane
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def add_data(adult,active_time,ill,season,guests,hunger,wet_food,dry_food):
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X_new = pd.DataFrame({
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'adult': [adult],
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'active_time': [active_time],
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'ill': [ill],
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'season': [season],
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'guests':[guests],
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'hunger': [hunger],
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'wet_food': [wet_food],
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'dry_food': [dry_food]
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})
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X_new_encoded = pd.get_dummies(X_new)
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missing_columns = set(X.columns) - set(X_new.columns)
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for col in missing_columns:
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X_new_encoded[col] = 0
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X_new_encoded = X_new_encoded.reindex(columns=X.columns, fill_value=0)
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print("Atrybuty zwierzęcia:", adult,active_time,ill,season,guests,hunger,wet_food,dry_food)
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return (clf.predict(X_new_encoded))
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