import pandas as pd
from sklearn.tree import DecisionTreeClassifier, plot_tree
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
import matplotlib.pyplot as plt
headers = ['adult','active_time','ill','season','guests','hunger','wet_food','dry_food']
# Wczytanie danych
data = pd.read_csv('dane.csv', header=0) 
X = data[headers]
Y = data['decision']
X = pd.get_dummies(data=X, columns=['season'])
clf = DecisionTreeClassifier(max_depth=6)
X1, X2, Y1, Y2 = train_test_split(X, Y, train_size=0.8)
clf = clf.fit(X1, Y1)
pred = clf.predict(X2)
accuracy = accuracy_score(Y2, pred)
print("Dokładność:", accuracy)



#zapisanie drzewa do pliku
plt.figure(figsize=(50,30))  
plot_tree(clf, filled=True, feature_names=X.columns.tolist(), class_names=['nie karmi', 'karmi mokrą karmą', 'karmi suchą karmą']) 
plt.savefig('tree.png')
# dane do decyzji
def feed_decision(adult,active_time,ill,season,guests,hunger,dry_food,wet_food):

    X_new = pd.DataFrame({
        'adult': [adult],
        'active_time': [active_time],
        'ill': [ill],
        'season': [season],
        'guests':[guests],
        'hunger': [hunger],      
        'wet_food': [wet_food],    
        'dry_food': [dry_food]      
})
    X_new = pd.get_dummies(X_new)
    missing_columns = set(X.columns) - set(X_new)
    for col in missing_columns:
        X_new[col] = False
        X_new = X_new.reindex(columns=X.columns, fill_value=0)
    print("Atrybuty zwierzęcia:", adult,active_time,ill,season,guests,hunger,wet_food,dry_food)
    return (clf.predict(X_new))