def score_samples(self, X):
        # The returned density is normalized to the number of points.
        # For it to be a probability, we must scale it.  For this reason
        # we'll also scale atol.
        X = check_array(X, order='C', dtype=DTYPE)
        N = self.tree_.data.shape[0]
        atol_N = self.atol * N
        log_density = self.tree_.kernel_density(
            X, h=self.bandwidth, kernel=self.kernel, atol=atol_N,
            rtol=self.rtol, breadth_first=self.breadth_first, return_log=True)
        log_density -= np.log(N)
        return log_density
test is_valid_vendor.
dataset/ETHPy150Open scikit-learn/scikit-learn/sklearn/neighbors/kde.py/KernelDensity.score_samples--crossover with--dataset/ETHPy150Open openstack/compass-core/compass/tests/hdsdiscovery/test_hdsdiscovery.py/HDManagerTest.test_is_valid_vendor