import logging
import socket
import sys
import time
import spu
from sklearn.metrics import mean_squared_error, roc_auc_score
import secretflow as sf
from secretflow.data import FedNdarray, PartitionWay
from secretflow.device.driver import reveal, wait
from secretflow.ml.boost.sgb_v import Sgb
from secretflow.utils.simulation.datasets import create_df
from secretflow.data.vertical import read_csv as v_read_csv
# init log
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.info("test")
_parties = {
    # you may change the addresses
    # 将alice、bob、carol的ip替换为实际ip
    'alice': {'address': '192.168.0.1:23041'},
    'bob': {'address': '192.168.0.2:23042'},
    'carol': {'address': '192.168.0.3:23043'},
}
def setup_sf(party, alice_ip, bob_ip, carol_ip):
    cluster_conf = {
        'parties': _parties,
        'self_party': party,
    }
    # init cluster
    _system_config = {'lineage_pinning_enabled': False}
    sf.init(
        address='local',
        num_cpus=8,
        log_to_driver=True,
        cluster_config=cluster_conf,
        exit_on_failure_cross_silo_sending=True,
        _system_config=_system_config,
        _memory=5 * 1024 * 1024 * 1024,
        cross_silo_messages_max_size_in_bytes = 2 * 1024 * 1024 * 1024 -1,
        object_store_memory=5 * 1024 * 1024 * 1024,
    )
    # SPU settings
    cluster_def = {
        'nodes': [
            {'party': 'alice', 'id': 'local:0', 'address': alice_ip},
            {'party': 'bob', 'id': 'local:1', 'address': bob_ip},
            {'party': 'carol', 'id': 'local:1', 'address': carol_ip},
        ],
        'runtime_config': {
            # SEMI2K support 2/3 PC, ABY3 only support 3PC, CHEETAH only support 2PC.
            # pls pay attention to size of nodes above. nodes size need match to PC setting.
            'protocol': spu.spu_pb2.ABY3,
            'field': spu.spu_pb2.FM64,
        },
    }
    # HEU settings
    heu_config = {
        'sk_keeper': {'party': 'alice'},
        'evaluators': [{'party': 'bob'},{'party': 'carol'}],
        'mode': 'PHEU',  # 这里修改同态加密相关配置
        'he_parameters': {
            'schema': 'paillier',
            'key_pair': {
                'generate': {
                    'bit_size': 2048,
                },
            },
        },
        'encoding': {
            'cleartext_type': 'DT_I32',
            'encoder': "IntegerEncoder",
            'encoder_args': {"scale": 1},
        },
    }
    return cluster_def, heu_config
class SGB_benchmark:
    def __init__(self, cluster_def, heu_config):
        self.alice = sf.PYU('alice')
        self.bob = sf.PYU('bob')
        self.carol = sf.PYU('carol')
        self.heu = sf.HEU(heu_config, cluster_def['runtime_config']['field'])
    def run_sgb(self, test_name, v_data, label_data, y, logistic, subsample, colsample):
        sgb = Sgb(self.heu)
        start = time.time()
        params = {
            'num_boost_round': 5,
            'max_depth': 5,
            'sketch_eps': 0.08,
            'objective': 'logistic' if logistic else 'linear',
        'reg_lambda': 0.3,
        'subsample': subsample,
        'colsample_by_tree': colsample,
        }
        model = sgb.train(params, v_data, label_data)
    #    reveal(model.weights[-1])
        print(f"{test_name} train time: {time.time() - start}")
        start = time.time()
        yhat = model.predict(v_data)
        yhat = reveal(yhat)
        print(f"{test_name} predict time: {time.time() - start}")
        if logistic:
        print(f"{test_name} auc: {roc_auc_score(y, yhat)}")
else:
        print(f"{test_name} mse: {mean_squared_error(y, yhat)}")
        fed_yhat = model.predict(v_data, self.alice)
        assert len(fed_yhat.partitions) == 1 and self.alice in fed_yhat.partitions
        yhat = reveal(fed_yhat.partitions[self.alice])
        assert yhat.shape[0] == y.shape[0], f"{yhat.shape} == {y.shape}"
        if logistic:
        print(f"{test_name} auc: {roc_auc_score(y, yhat)}")
else:
        print(f"{test_name} mse: {mean_squared_error(y, yhat)}")
        def test_on_linear(self, sample_num, total_num):
        """
        sample_num: int. this number * 10000 = sample number in dataset.
        """
        io_start = time.perf_counter()
        common_path = "/root/sf-benchmark/data/{}w_{}d_3pc/independent_linear.".format(
        sample_num, total_num
        )
        vdf = v_read_csv(
        {self.alice: common_path + "1.csv", self.bob: common_path + "2.csv", self.carol: common_path + "3.csv"},
        keys='id',
        drop_keys='id',
        )
        # split y out of dataset,
        # <<< !!! >>> change 'y' if label column name is not y in dataset.
        label_data = vdf["y"]
        # v_data remains all features.
        v_data = vdf.drop(columns="y")
        # <<< !!! >>> change bob if y not belong to bob.
        y = reveal(label_data.partitions[self.alice].data)
        wait([p.data for p in v_data.partitions.values()])
        io_end = time.perf_counter()
        print("io takes time", io_end - io_start)
        self.run_sgb("independent_linear", v_data, label_data, y, True, 1, 1)
        def run_test(party):
        cluster_def, heu_config = setup_sf(party, _parties['alice'], _parties['bob'], _parties['carol'])
        test_suite = SGB_benchmark(cluster_def, heu_config)
        test_suite.test_on_linear(100, 100)
        sf.shutdown()
        if __name__ == '__main__':
        import argparse
        parser = argparse.ArgumentParser(prog='sgb benchmark remote')
        parser.add_argument('party')
        args = parser.parse_args()
        run_test(args.party)