#
#  Copyright 2019 The FATE Authors. All Rights Reserved.
#
#  Licensed under the Apache License, Version 2.0 (the "License");
#  you may not use this file except in compliance with the License.
#  You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
#  Unless required by applicable law or agreed to in writing, software
#  distributed under the License is distributed on an "AS IS" BASIS,
#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#  See the License for the specific language governing permissions and
#  limitations under the License.
#

import argparse

from pipeline.backend.pipeline import PipeLine
from pipeline.component import DataTransform
from pipeline.component import HeteroSecureBoost
from pipeline.component import Intersection
from pipeline.component import Reader
from pipeline.interface import Data

from pipeline.utils.tools import load_job_config


def main(config="../../config.yaml", namespace=""):
    # obtain config
    if isinstance(config, str):
        config = load_job_config(config)
    parties = config.parties
    guest = parties.guest[0]
    host = parties.host[0]

    # data sets
    guest_train_data = {"name": "vehicle_scale_hetero_guest", "namespace": f"experiment{namespace}"}
    host_train_data = {"name": "vehicle_scale_hetero_host", "namespace": f"experiment{namespace}"}

    # init pipeline
    pipeline = PipeLine().set_initiator(role="guest", party_id=guest).set_roles(guest=guest, host=host,)

    # set data reader and data-io

    reader_0 = Reader(name="reader_0")
    reader_0.get_party_instance(role="guest", party_id=guest).component_param(table=guest_train_data)
    reader_0.get_party_instance(role="host", party_id=host).component_param(table=host_train_data)
    data_transform_0 = DataTransform(name="data_transform_0")
    data_transform_0.get_party_instance(
        role="guest", party_id=guest).component_param(
        with_label=True, output_format="dense")
    data_transform_0.get_party_instance(role="host", party_id=host).component_param(with_label=False)

    # data intersect component
    intersect_0 = Intersection(name="intersection_0")

    # secure boost component
    hetero_secure_boost_0 = HeteroSecureBoost(name="hetero_secure_boost_0",
                                              num_trees=3,
                                              task_type="classification",
                                              objective_param={"objective": "cross_entropy"},
                                              encrypt_param={"method": "Paillier"},
                                              tree_param={"max_depth": 3},
                                              validation_freqs=1,
                                              cv_param={
                                                  "need_cv": True,
                                                  "n_splits": 5,
                                                  "shuffle": False,
                                                  "random_seed": 103
                                              }
                                              )

    pipeline.add_component(reader_0)
    pipeline.add_component(data_transform_0, data=Data(data=reader_0.output.data))
    pipeline.add_component(intersect_0, data=Data(data=data_transform_0.output.data))
    pipeline.add_component(hetero_secure_boost_0, data=Data(train_data=intersect_0.output.data))

    pipeline.compile()
    pipeline.fit()

    print("fitting hetero secureboost done, result:")
    print(pipeline.get_component("hetero_secure_boost_0").get_summary())


if __name__ == "__main__":
    parser = argparse.ArgumentParser("PIPELINE DEMO")
    parser.add_argument("-config", type=str,
                        help="config file")
    args = parser.parse_args()
    if args.config is not None:
        main(args.config)
    else:
        main()