def tearDown(self):
session.stop()
try:
session.cleanup("*", self.job_id, True)
except EnvironmentError:
pass
try:
session.cleanup("*", self.job_id, False)
except EnvironmentError:
pass
def tearDown(self):
session.stop()
def tearDown(self):
# for table in self.table_list:
# table.destroy()
session.stop()
def tearDown(self):
# self.table.destroy()
session.stop()
def tearDownClass(self):
session.stop()
fore_gradient = self.en_wx.join(self.data_inst, lambda wx, d: 0.25 * wx - 0.5 * d.label)
sparse_data = self._make_sparse_data()
gradient_computer = hetero_linear_model_gradient.HeteroGradientBase()
for fit_intercept in [True, False]:
dense_result = gradient_computer.compute_gradient(self.data_inst, fore_gradient, fit_intercept)
dense_result = [self.paillier_encrypt.decrypt(iterator) for iterator in dense_result]
if fit_intercept:
self.assertListEqual(dense_result, self.gradient_fit_intercept)
else:
self.assertListEqual(dense_result, self.gradient)
sparse_result = gradient_computer.compute_gradient(sparse_data, fore_gradient, fit_intercept)
sparse_result = [self.paillier_encrypt.decrypt(iterator) for iterator in sparse_result]
self.assertListEqual(dense_result, sparse_result)
def _make_sparse_data(self):
def trans_sparse(instance):
dense_features = instance.features
indices = [i for i in range(len(dense_features))]
sparse_features = SparseVector(indices=indices, data=dense_features, shape=len(dense_features))
return Instance(inst_id=None,
features=sparse_features,
label=instance.label)
return self.data_inst.mapValues(trans_sparse)
if __name__ == "__main__":
session.init("1111")
unittest.main()
session.stop()
