from sklearn import tree as skltree
import pandas,os
import matplotlib.pyplot as plt

atributes=['plant_water_level','growth','disease','fertility','tractor_water_level','temperature','rain','season','current_time'] #Columns in CSV file has to be in the same order
class Drzewo:
    def __init__(self):
        self.tree=self.treeLearn()

    def treeLearn(self):
        csvdata=pandas.read_csv('Data/dataTree.csv')
        #csvdata = pandas.read_csv('Data/dataTree2.csv')
        x=csvdata[atributes]
        decision=csvdata['action']
        self.tree=skltree.DecisionTreeClassifier()
        self.tree=self.tree.fit(x.values,decision)

    def plotTree(self):
        plt.figure(figsize=(20,30))
        skltree.plot_tree(self.tree,filled=True,feature_names=atributes)
        plt.title("Drzewo decyzyjne wytrenowane na przygotowanych danych: ")
        plt.savefig('tree.png')
        #plt.show()
    def makeDecision(self,values):
        action=self.tree.predict([values]) #0- nie podlewac, 1-podlewac
        if(action==[0]):
            return "Nie"
        if(action==[1]):
            return "Tak"