# encoding: utf-8
# Copyright 1999-2017 Alibaba Group Holding Ltd.
# 
# 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.

from __future__ import print_function

import logging
import sys

from odps.df import DataFrame
from odps.config import options
from odps.ml.utils import TEMP_TABLE_PREFIX
from odps.ml.algolib import *
from odps.ml.algolib.loader import load_classifiers
from odps.ml.tests.base import MLTestBase, tn

logger = logging.getLogger(__name__)

IONOSPHERE_TABLE = tn('pyodps_test_ml_ionosphere')

MODEL_NAME = tn('pyodps_test_out_model')


class TestAlgoBuild(MLTestBase):
    def setUp(self):
        super(TestAlgoBuild, self).setUp()
        self.create_ionosphere(IONOSPHERE_TABLE)
        self.register_algorithm()

    def tearDown(self):
        super(TestAlgoBuild, self).tearDown()

    def register_algorithm(self):
        algo_def = XflowAlgorithmDef('MyNaiveBayes', project='algo_public', xflow_name='NaiveBayes')

        algo_def.add_port(PortDef.build_data_input()).add_port(PortDef.build_model_output())

        algo_def.add_param(ParamDef.build_input_table()).add_param(ParamDef.build_input_partitions())
        algo_def.add_param(ParamDef.build_model_name())
        algo_def.add_param(ParamDef.build_feature_col_names())
        algo_def.add_param(ParamDef.build_label_col_name())

        load_classifiers(algo_def, sys.modules[__name__])

    def test_custom_algo(self):
        options.ml.dry_run = True

        df = DataFrame(self.odps.get_table(IONOSPHERE_TABLE))
        splited = df.split(0.6)

        labeled_data = splited[0].label_field("class")
        naive_bayes = MyNaiveBayes()
        model = naive_bayes.train(labeled_data)._add_case(self.gen_check_params_case(
                {'labelColName': 'class', 'featureColNames': ','.join('a%02d' % i for i in range(1, 35)),
                 'modelName': MODEL_NAME, 'inputTableName': TEMP_TABLE_PREFIX + '_split'}))
        model.persist(MODEL_NAME)

        predicted = model.predict(splited[1])
        predicted.persist(MODEL_NAME)
