def build_leaf(self, sample_indice):
mx = self.x[sample_indice].mean(0)
sx = self.x[sample_indice].std(0) + self.EPSILON
nx = (self.x[sample_indice] - mx) / sx
best_estimator = LassoCV(alphas=self.reg_lambda, cv=5, random_state=self.random_state)
best_estimator.fit(nx, self.y[sample_indice])
best_estimator.coef_ = best_estimator.coef_ / sx
best_estimator.intercept_ = best_estimator.intercept_ - np.dot(mx, best_estimator.coef_.T)
xmin = np.min(np.dot(self.x[sample_indice], best_estimator.coef_) + best_estimator.intercept_)
xmax = np.max(np.dot(self.x[sample_indice], best_estimator.coef_) + best_estimator.intercept_)
predict_func = lambda x: np.clip(best_estimator.predict(x), xmin, xmax)
best_impurity = self.get_loss(self.y[sample_indice], best_estimator.predict(self.x[sample_indice]))
return predict_func, best_estimator, best_impurity