dt_L1.py

class DecisionTree():
    def __init__(self, x, y, n_features, f_idxs,idxs,depth=10, min_leaf=5):
        self.x, self.y, self.idxs, self.min_leaf, self.f_idxs = x, y, idxs, min_leaf, f_idxs
        self.depth = depth
        self.n_features = n_features
        self.n, self.c = len(idxs), x.shape[1]
        self.val = np.mean(y[idxs])
        self.score = float('inf')
        self.find_varsplit()
        
    def find_varsplit(self):
        #Will make it recursive later
        for i in self.f_idxs: self.find_better_split(i)
       

    def find_better_split(self, var_idx):
        #Lets write it later
        pass
    

        for i in range(0,self.n-self.min_leaf-1):
            xi,yi = sort_x[i],sort_y[i]
            lhs_cnt += 1; rhs_cnt -= 1
            lhs_sum += yi; rhs_sum -= yi
            lhs_sum2 += yi**2; rhs_sum2 -= yi**2
            if i<self.min_leaf or xi==sort_x[i+1]:
                continue

            lhs_std = std_agg(lhs_cnt, lhs_sum, lhs_sum2)
            rhs_std = std_agg(rhs_cnt, rhs_sum, rhs_sum2)
            curr_score = lhs_std*lhs_cnt + rhs_std*rhs_cnt
            if curr_score<self.score: 
                self.var_idx,self.score,self.split = var_idx,curr_score,xi

    @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') or self.depth <= 0 
    

    def predict(self, x):
        return np.array([self.predict_row(xi) for xi in x])

    def predict_row(self, xi):
        if self.is_leaf: return self.val
        t = self.lhs if xi[self.var_idx]<=self.split else self.rhs
        return t.predict_row(xi)