def main():
print "-- Welcome to movie-recommend! --"
# for output readability
np.set_printoptions(formatter={'float_kind': '{:25f}'.format})
# baseline predictor by default
mode = BASELINE
# read command-line argument, if provided
if len(sys.argv) > 1:
if sys.argv[1] == IMPROVED or sys.argv[1] == BASELINE:
mode = sys.argv[1]
print "\tYou chose", mode, "predictor!"
else:
print "\t", sys.argv[
1], "is not a valid argument. Default:", mode, "predictor!"
else:
print "\tYou did not provide any arguments. Default:", mode, "predictor!"
# read and parse text files
parser = Parser(mode)
print "\tParser initialized:"
print "\t\t", len(parser.test_set), "test points and", np.count_nonzero(
parser.training_matrix), "training points"
# initialize predictor and calculate rmse
predictor = Predictor(mode, parser.training_matrix, parser.test_set)
print "\trmse on test data (baseline):", predictor.rmse_test
if predictor.mode == BASELINE:
print "\trmse on training data (baseline):", predictor.rmse_training
else:
print "\trmse on test data (improved):", predictor.rmse_test_improved
# execute histogram plotting and get error distribution
error_dist = predictor.calculate_absolute_errors(
parser.test_set, predictor.improved_matrix
) if predictor.mode == IMPROVED else predictor.calculate_absolute_errors(
parser.test_set, predictor.baseline_matrix)
print "\tHistogram saved to file. Error distribution:", error_dist
