def score_one_iterm_online(model_path,feature_string):
#feature_string = "1,1,1,-1,1,1";
logger = utils.setLog(settings.logging_file_scoring, logtype='Exec')
logger = logging.getLogger('model-learner.test')
logger.info('Start testing: %s', datetime.datetime.now().time().isoformat())
# transform string to numpy array
np_data = numpy.fromstring(feature_string, dtype=int, sep=",")
np_data = np_data.reshape(1,-1)
#print np_data.shape
output,clf = mexec.applyModel(model_path, np_data, settings.RESULTS_OUTPUT_DIR, settings.MODELS_OUTPUT_DIR + 'test_data.pkl')
#print np_data
print "returnValue:",score_normalization(300,900,output[0][0])
logger.info('Finish testing: %s', datetime.datetime.now().time().isoformat())
def score_csv():
#Setup the logger
print '%s' %settings.logging_file_scoring
logger = utils.setLog(settings.logging_file_scoring, logtype='Exec')
logger = logging.getLogger('model-learner.test')
logger.info('Start testing: %s', datetime.datetime.now().time().isoformat())
logger.info('==> Load Data.')
data = data_load_csv.csv_score_file(settings.INPUT_DIR, settings.score_file_name)
logger.info('==> Preprocessing data.')
logger.info('==> Apply Data.')
output,clf = mexec.applyModel(settings.MODELS_OUTPUT_DIR +'model_'+ settings.model_pickle_file, data, settings.RESULTS_OUTPUT_DIR, settings.MODELS_OUTPUT_DIR + 'test_data.pkl')
logger.info('Finish testing: %s', datetime.datetime.now().time().isoformat())
#print output[0]
print score_normalization(300,900,output[0][0])
