from models.perceptron import Perceptron
model = Perceptron(train='pa2_train_clean.csv',
validation='pa2_valid_clean.csv',
test='pa2_test_no_label_clean.csv',
label='label',
mod_type='average',
max_iter=15)
learned_model = model.train_model()
# Save output for learned model to .json file.
output_folder = Path('model_output')
output_path = Path(__file__).parent.resolve().joinpath(output_folder)
training_file = output_path.joinpath(Path('ap_training.json'))
# Create output directory if doesn't exist.
if not Path(output_path).exists():
Path(output_path).mkdir()
with open(training_file, 'w') as f:
json.dump(learned_model, f, indent=4)
# Best validation accuracy with 15 iterations, calculate and save predictions.
test_predictions = model.predict_test(learned_model['avg_weights'][-1])
prediction_file = output_path.joinpath(Path('aplabel.csv'))
with open(prediction_file, 'w') as fp:
writer = csv.writer(fp)
for i in test_predictions:
writer.writerows([[i]])
from models.perceptron import Perceptron
model = Perceptron(train='pa2_train_clean.csv',
validation='pa2_valid_clean.csv',
test='pa2_test_no_label_clean.csv',
label='label',
mod_type='online',
max_iter=15)
learned_model = model.train_model()
# Save output for learned model to .json file.
output_folder = Path('model_output')
output_path = Path(__file__).parent.resolve().joinpath(output_folder)
training_file = output_path.joinpath(Path('op_training.json'))
# Create output directory if doesn't exist.
if not Path(output_path).exists():
Path(output_path).mkdir()
with open(training_file, 'w') as f:
json.dump(learned_model, f, indent=4)
# Best validation accuracy with 14 iterations, calculate and save predictions.
test_predictions = model.predict_test(learned_model['weights'][13])
prediction_file = output_path.joinpath(Path('oplabel.csv'))
with open(prediction_file, 'w') as fp:
writer = csv.writer(fp)
for i in test_predictions:
writer.writerows([[i]])