import sys

import cProfile

from model_tester import FeaturePipeline, test_model

from sklearn.pipeline import FeatureUnion

from sklearn.feature_extraction import DictVectorizer

from sklearn.feature_extraction.text import TfidfTransformer

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 value_extractor_transformer import EFTransformer, LBBBTransformer, SinusRhythmTransformer, QRSTransformer, NYHATransformer, NICMTransformer

from language_processing import parse_date 

def main():

    transformer_list = []

    regex_features = True

    icd9_features = False

    labs_features = False

    text_features = False

    if regex_features:

        transformer_list += [ 

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

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

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

                    ('LBBB', LBBBTransformer(30*3)),

                    ('SR', SinusRhythmTransformer(30*3)),

                    ('NYHA', NYHATransformer(30*3)),

                    ('NICM', NICMTransformer(30*3)),

                    ('QRS', QRSTransformer('all', 1, None)),

                    ('QRS', QRSTransformer('mean', 5, None)),

                ]

    if icd9_features:

        transformer_list += [

                    ('Dia', ICD9_Transformer())

                ]

    if text_features:

        transformer_list += [

                    ('Car', FeaturePipeline([

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

                        ('tfidf', TfidfTransformer())

                    ])),

                    ('Lno', FeaturePipeline([

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

                       ('tfidf', TfidfTransformer)

                    ]))

                ]

    if labs_features:

        transformer_list += [

                    ('Enc', GetEncountersFeaturesTransformer(5)),

                    ('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())

                    ]))

                ]

    features = FeatureUnion(transformer_list)

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

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

    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 = 5

    print "Data size: " + str(data_size)

    print "CV splits: " + str(num_cv_splits)

    if len(sys.argv) > 3:

        method = sys.argv[3]

    else:

        method = 'adaboost'

    #method = 'lr'

    #method = 'svm'

    method = 'adaboost'

    #method = 'cdm'

    model_args = dict()

    if method in ['lr', 'svm']:

        if len(sys.argv) > 4 and unicode(sys.argv[4]).isnumeric():

            model_args['regularization'] = float(sys.argv[4])

        else:

            model_args['regularization'] = 0.

    if method == 'adaboost':

        if len(sys.argv) > 4 and unicode(sys.argv[4]).isnumeric():

            model_args['n_estimators'] = int(sys.argv[4])

        else:

            model_args['n_estimators'] = 50

    show_progress = True

    print 'Method:', method

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

if __name__ == '__main__':

    main()