# Training - Part 2, adjusting regularization parameter to investigate effect on the model.
lambdas = sorted([10**x for x in range(-3, 3)])
rate = 1e-05
for lam in lambdas:
model = LinearModel(train='data/PA1_train.pkl',
validation='data/PA1_dev.pkl',
test='data/PA1_test.pkl',
target='price',
rate=rate,
lam=lam,
eps=2.5,
normalize=True)
learned_model = model.train_model(max_iter=10000)
if learned_model['exploding'] is False and learned_model[
'convergence'] is True:
print('Training complete.')
# Save output for learned model to .json file.
filename = 'rate_' + str('{:.0E}'.format(rate)) + '_lam_' + str(
'{:.0E}'.format(lam))
my_path = Path('..', 'model_output', 'part_2', filename + '_train.json')
train_path = Path(__file__).parent.resolve().joinpath(my_path)
# Make output directory if doesn't exist.
output_dir = train_path.parent.resolve()
if not Path(output_dir).exists():
Path(output_dir).mkdir()
from models.linear_model import LinearModel
pp = pprint.PrettyPrinter()
model = LinearModel(train='data/PA1_train.pkl',
validation='data/PA1_dev.pkl',
test='data/PA1_test.pkl',
target='price',
rate=1e-05,
lam=0.001,
eps=0.5,
normalize=True)
names = model.weight_labels
learned_model = model.train_model(50000)
val_predictions = model.predict_validation(
learned_model['weights'])['predictions']
test_predictions = model.predict_test(
(learned_model['weights']))['predictions']
prediction_output = pathlib.Path('model_output/predictions.pkl')
prediction_file = pathlib.Path('model_output/predictions.txt')
pred_output_path = pathlib.Path(__file__).parent.resolve().joinpath(
prediction_output)
pred_file_path = pathlib.Path(__file__).parent.resolve().joinpath(
prediction_file)
# Save predictions
with open(pred_output_path, 'wb') as fp:
