DecisionTree Level1.py

class DecisionTree():
    def __init__(self, x, y, idxs=None, min_leaf=5, depth = 10):
        if idxs is None: idxs=np.arange(len(y)) #bagging with all the rows 
        self.x, self.y, self.idxs, self.min_leaf, self.depth = x, y, idxs, min_leaf, depth
        self.n, self.c = len(idxs), x.shape[1]
        self.val = np.mean(y[idxs])
        self.score = float('inf')
        self.find_varsplit()
        
    # This just does one decision; we'll make it recursive later
    def find_varsplit(self):
        for i in range(self.c): self.find_better_split(i)
            
    # We'll write this later!
    def find_better_split(self, var_idx): pass
    
    @property
    def split_name(self): return self.x.columns[self.var_idx]
    
    @property
    def split_col(self): return self.x.values[self.idxs,self.var_idx]

    @property
    def is_leaf(self): return self.score == float('inf')
    
    def __repr__(self):
        s = f'n: {self.n}; val:{self.val}'
        if not self.is_leaf:
            s += f'; score:{self.score}; split:{self.split}; var:{self.split_name}'
        return s