import logging

import sys

import cProfile

from model_tester import FeaturePipeline, test_model

from sklearn.pipeline import FeatureUnion

from sklearn.feature_extraction.text import CountVectorizer

from baseline_transformer import GetConcatenatedNotesTransformer, GetLatestNotesTransformer, GetEncountersFeaturesTransformer, GetLabsCountsDictTransformer, GetLabsLowCountsDictTransformer, GetLabsHighCountsDictTransformer, GetLabsLatestHighDictTransformer, GetLabsLatestLowDictTransformer, GetLabsHistoryDictTransformer

from extract_data import get_doc_rel_dates, get_operation_date, get_ef_values

from extract_data import get_operation_date,  is_note_doc, get_date_key

from icd_transformer import ICD9_Transformer

from doc2vec_transformer import Doc2Vec_Note_Transformer

from value_extractor_transformer import EFTransformer, LBBBTransformer, SinusRhythmTransformer, QRSTransformer

from language_processing import parse_date 

def main():

    features = FeatureUnion([

                ('Dia', icd9 ),

                ('EF', EFTransformer('all', 1, None)),

                ('EF', EFTransformer('mean', 5, None)),

                ('EF', EFTransformer('max', 5, None)),

                ('LBBB', LBBBTransformer()),

                #('SR', SinusRhythmTransformer()),

                #('Car_Doc2Vec', Doc2Vec_Note_Transformer('Car', 'doc2vec_models/car_1.model', 10, dbow_file='doc2vec_models/car_dbow.model'))

               # ('QRS', QRSTransformer('all', 1, None)),#Bugs with QRS

                ('car_ngram', FeaturePipeline([

                    ('notes_car', GetConcatenatedNotesTransformer(note_type='Car',look_back_months=12)),

                    ('ngram_car', CountVectorizer(ngram_range=(2, 2), min_df=.05))

                ]))

                #('Car', FeaturePipeline([

                #    ('notes_transformer_car', GetConcatenatedNotesTransformer('Car')),

                #    ('tfidf', car_tfidf)

                #])),

                #('Lno', FeaturePipeline([

                #    ('notes_transformer_lno', GetConcatenatedNotesTransformer('Lno')),

                #    ('tfidf', lno_tfidf)

                #])),

                #('Enc', enc),

                #('Labs_Counts',FeaturePipeline([

                #    ('labs_counts_transformer', GetLabsCountsDictTransformer()),

                #    ('dict_vectorizer', DictVectorizer())

                #])),

                #('Labs_Low_Counts',FeaturePipeline([

                #    ('labs_low_counts_transformer', GetLabsLowCountsDictTransformer()),

                #    ('dict_vectorizer', DictVectorizer())

                #])),

                #('Labs_High_Counts', FeaturePipeline([

                #    ('labs_high_counts_transformer', GetLabsHighCountsDictTransformer()),

                #    ('dict_vectorizer', DictVectorizer())

                #])),

                #('Labs_Latest_Low', FeaturePipeline([

                #    ('labs_latest_low_transformer', GetLabsLatestLowDictTransformer()),

                #    ('dict_vectorizer', DictVectorizer())

                #])),

                #('Labs_Latest_High',FeaturePipeline([

                #    ('labs_latest_high_transformer', GetLabsLatestHighDictTransformer()),

                #    ('dict_vectorizer', DictVectorizer())

                #])),

               # ('Labs_History', FeaturePipeline([

               #     ('labs_history_transformer', GetLabsHistoryDictTransformer([1])),

               #     ('dict_vectorizer', DictVectorizer())

               # ])),

            ])

    if len(sys.argv) > 1 and unicode(sys.argv[1]).isnumeric():

        data_size = min(906, int(sys.argv[1]))

    else:

        data_size = 25

    if len(sys.argv) > 2 and unicode(sys.argv[2]).isnumeric():

        num_cv_splits = int(sys.argv[2])

    else:

        num_cv_splits = 2

    method = 'lr'

    #method = 'svm'

    show_progress = True

    test_model(features, data_size, num_cv_splits, method, show_progress)

if __name__ == '__main__':

    # Configure logging

    logger = logging.getLogger("DaemonLog")

    logger.setLevel(logging.INFO)

    out = logging.StreamHandler(sys.stdout)

    out.setLevel(logging.INFO)

    formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')

    out.setFormatter(formatter)

    logger.addHandler(out)

    main()