--- a
+++ b/michael_tester.py
@@ -0,0 +1,124 @@
+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()