test.py

"""
    File name: test.py
    Author: Patrick Cummings
    Date created: 10/13/2019
    Date last modified: 10/18/2019
    Python Version: 3.7

    Used to test instance of LinearModel() class.
"""

import pathlib
import pickle
import pprint

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:
    pickle.dump(test_predictions, fp, pickle.HIGHEST_PROTOCOL)

# Output predictions to text file
with open(pred_file_path, 'w') as f:
    for prediction in test_predictions:
        f.write('%s\n' % prediction)

# import inspect
# pp.pprint(inspect.getmembers(LinearModel, lambda X:not(inspect.isroutine(X))))
# pp.pprint(model.__dict__.keys())

# print(learned_model)
# print(dict(zip(names, learned_model['weights'])))
# pp.pprint(val_predictions[:10])
# pp.pprint(test_predictions[:10])