#
#  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 HomoSecureBoost
from pipeline.component import Reader
from pipeline.interface.data import Data
from pipeline.component import Evaluation
from pipeline.interface.model import Model
from pipeline.utils.tools import JobConfig
from pipeline.utils.tools import load_job_config

from federatedml.evaluation.metrics import regression_metric, classification_metric
from fate_test.utils import extract_data, parse_summary_result


def main(config="../../config.yaml", param='./xgb_config_binary.yaml', namespace=""):
    # obtain config
    if isinstance(config, str):
        config = load_job_config(config)

    if isinstance(param, str):
        param = JobConfig.load_from_file(param)

    parties = config.parties
    guest = parties.guest[0]
    host = parties.host[0]
    arbiter = parties.arbiter[0]

    guest_train_data = {"name": param['data_guest_train'], "namespace": f"experiment{namespace}"}
    guest_validate_data = {"name": param['data_guest_val'], "namespace": f"experiment{namespace}"}

    host_train_data = {"name": param['data_host_train'], "namespace": f"experiment{namespace}"}
    host_validate_data = {"name": param['data_host_val'], "namespace": f"experiment{namespace}"}

    pipeline = PipeLine().set_initiator(role='guest', party_id=guest).set_roles(guest=guest, host=host, arbiter=arbiter)

    data_transform_0, data_transform_1 = DataTransform(name="data_transform_0"), DataTransform(name='data_transform_1')
    reader_0, reader_1 = Reader(name="reader_0"), Reader(name='reader_1')

    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.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=True, output_format="dense")

    reader_1.get_party_instance(role='guest', party_id=guest).component_param(table=guest_validate_data)
    reader_1.get_party_instance(role='host', party_id=host).component_param(table=host_validate_data)
    data_transform_1.get_party_instance(role='guest', party_id=guest).\
        component_param(with_label=True, output_format="dense")
    data_transform_1.get_party_instance(role='host', party_id=host).\
        component_param(with_label=True, output_format="dense")

    homo_secureboost_0 = HomoSecureBoost(name="homo_secureboost_0",
                                         num_trees=param['tree_num'],
                                         task_type=param['task_type'],
                                         objective_param={"objective": param['loss_func']},
                                         tree_param={"max_depth": param['tree_depth']},
                                         validation_freqs=1,
                                         subsample_feature_rate=1,
                                         learning_rate=param['learning_rate'],
                                         bin_num=50
                                         )
    homo_secureboost_1 = HomoSecureBoost(name="homo_secureboost_1")

    evaluation_0 = Evaluation(name='evaluation_0', eval_type=param['eval_type'])

    pipeline.add_component(reader_0)
    pipeline.add_component(data_transform_0, data=Data(data=reader_0.output.data))
    pipeline.add_component(reader_1)
    pipeline.add_component(data_transform_1,
                           data=Data(data=reader_1.output.data), model=Model(data_transform_0.output.model))
    pipeline.add_component(homo_secureboost_0, data=Data(train_data=data_transform_0.output.data,
                                                         validate_data=data_transform_1.output.data))
    pipeline.add_component(homo_secureboost_1, data=Data(test_data=data_transform_1.output.data),
                           model=Model(homo_secureboost_0.output.model))
    pipeline.add_component(evaluation_0, data=Data(homo_secureboost_0.output.data))

    pipeline.compile()
    pipeline.fit()

    sbt_0_data = pipeline.get_component("homo_secureboost_0").get_output_data()
    sbt_1_data = pipeline.get_component("homo_secureboost_1").get_output_data()
    sbt_0_score = extract_data(sbt_0_data, "predict_result")
    sbt_0_label = extract_data(sbt_0_data, "label")
    sbt_1_score = extract_data(sbt_1_data, "predict_result")
    sbt_1_label = extract_data(sbt_1_data, "label")
    sbt_0_score_label = extract_data(sbt_0_data, "predict_result", keep_id=True)
    sbt_1_score_label = extract_data(sbt_1_data, "predict_result", keep_id=True)
    metric_summary = parse_summary_result(pipeline.get_component("evaluation_0").get_summary())
    if param['eval_type'] == "regression":
        desc_sbt_0 = regression_metric.Describe().compute(sbt_0_score)
        desc_sbt_1 = regression_metric.Describe().compute(sbt_1_score)
        metric_summary["script_metrics"] = {"sbt_train": desc_sbt_0,
                                            "sbt_validate": desc_sbt_1}
    elif param['eval_type'] == "binary":
        metric_sbt = {
            "score_diversity_ratio": classification_metric.Distribution.compute(sbt_0_score_label, sbt_1_score_label),
            "ks_2samp": classification_metric.KSTest.compute(sbt_0_score, sbt_1_score),
            "mAP_D_value": classification_metric.AveragePrecisionScore().compute(sbt_0_score, sbt_1_score, sbt_0_label,
                                                                                 sbt_1_label)}
        metric_summary["distribution_metrics"] = {"homo_sbt": metric_sbt}
    elif param['eval_type'] == "multi":
        metric_sbt = {
            "score_diversity_ratio": classification_metric.Distribution.compute(sbt_0_score_label, sbt_1_score_label)}
        metric_summary["distribution_metrics"] = {"homo_sbt": metric_sbt}

    data_summary = {"train": {"guest": guest_train_data["name"], "host": host_train_data["name"]},
                    "test": {"guest": guest_validate_data["name"], "host": host_validate_data["name"]}
                    }

    return data_summary, metric_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()