def main(): if len(sys.argv) < 4: print_usage() return data = data_io.load_from_files(sys.argv[1]) model_fname = sys.argv[2] fields = sys.argv[3:] for field in fields: score_dataset(data, model_fname, field)
def main(): if len(sys.argv) < 3: print_usage() return data = data_io.load_from_files(sys.argv[1]) fields = sys.argv[3:] models_fname = sys.argv[2] try: with open('models.p') as model_file: models = pickle.load(model_file) except: models = {} print 'before training.........' for field in fields: models[field] = train_model(data, field) print 'field: ', field with open(models_fname, 'w') as model_file: pickle.dump(models, model_file)
############## #dir_name = '/home/evgeniy/ReceiptBank/rb-engine/text-training-old/' # match_file = 'data.csv' dir_train = '/home/evgeniy/ReceiptBank/rb-engine/text-training-small/' dir_test = '/home/evgeniy/ReceiptBank/rb-engine/text-training-small/' #dir_train = '/home/evgeniy/ReceiptBank/rb-engine/text-training/' #dir_test = '/home/evgeniy/ReceiptBank/rb-engine/text-test/' match_file = 'data.csv' ############## print 'Before training...........' data = data_io.load_from_files(dir_train) # model = train_model(data, 'total_amount') amount_name = 'total_amount' model = train_model(data, amount_name) print 'After training...........' data = data_io.load_from_files(dir_test) res = model.score(data, data[amount_name]) print "Score: %.4f" % res
def main(): if len(sys.argv) > 1: data = data_io.load_from_files(sys.argv[1]) score_dataset(data) else: print("Usage: {} <dataset path>".format(sys.argv[0]))
# # train_indexes = [it[0] for it in X_train ] # test_indexes = [it[0] for it in X_test ] # # extract_directory(dir_name, # '/home/evgeniy/ReceiptBank/rb-engine/text-training-old/', # train_indexes, # data00 # ) # # extract_directory(dir_name, # '/home/evgeniy/ReceiptBank/rb-engine/text-test-old/', # test_indexes, # data00 # ) # ============================================================================== data = data_io.load_from_files(dir_name) fields = ["total_amount"] print "before training..........." models = {} for field in fields: models[field] = train_model(data, field) with open("models_est45.p", "w") as model_file: pickle.dump(models, model_file) ############