import pandas as pd
#NIE AKTULAIZOWAĆ scikit / sklearn
from sklearn import tree
from sklearn.tree import DecisionTreeClassifier
from sklearn.tree.export import export_text
from sklearn.tree import export_graphviz



def decision(plant,feed,growth,dryness):

    # czytanie pliku csv 
    df = pd.read_csv("data.csv")
    #print(df)
    
    #zmiana słów na cyfry, ponieważ drzewo decyzyjne opiera się na cyfrach
    z = {'CABBAGE': 2, 'PUMPKIN':4, 'CARROT':3, 'BEETROOT': 1}
    df['PLANT'] = df['PLANT'].map(z)

    d = {'NO': 0, 'YES': 1}
    df['DEC'] = df['DEC'].map(d)

    #print(df)
    
    features_rest = ['PLANT','FEED','GROWTH','DRYNESS'] #dane, na których opiera się decyzja
    features_dec = ['DEC'] #kolumna z decyją

    X = df[features_rest]
    y = df[features_dec]

    #wyświetlkanie kolumn
    #print(X)
    #print(y)

    #tworzenie drzewa
    dtree = DecisionTreeClassifier()
    #przypisanie danych
    dtree = dtree.fit(X, y)
    #eksport drzewa do tekstu
    r = export_text(dtree, feature_names=features_rest)
    #print("\nDrzewo decyzyjne\n")
    #print(r)

    a = dtree.predict([[plant,feed,growth,dryness]])
    #return a
    #print("\n[1] means FEED THE PLANT")
    #print("[0] means NOT FEED THE PLANT\n")
    print ("Decision for: ",plant,", feed: ", feed,", growth: ", growth,", dryness:", dryness," is ", a,"")
    
"""    
plant = 1
feed = 35
growth = 20
#vermins = 0
dryness = 12
decision(plant,feed,growth,dryness)
"""