def test_feature_select(self):
features, values = generate_data_part_2()
model = LinearRegression(nb_features=1)
model.train(features, values)
mse = mean_squared_error(values, model.predict(features))
print(f'[part 1.3.1]\tmse: {mse:.5f}')
plt.scatter([x[0] for x in features], values, label='origin');
plt.plot([x[0] for x in features], model.predict(features), label='pre dicted');
plt.legend()
plt.show()
def test_lr_integration(self):
features, values = generate_data_part_1()
model = LinearRegression(nb_features=1)
model.train(features, values)
mse = mean_squared_error(values, model.predict(features))
self.assertAlmostEqual(0.00175, mse, places=5)
plt.scatter([x[0] for x in features], values, label='origin');
plt.plot([x[0] for x in features], model.predict(features), label='predicted');
plt.title("Holy shit")
plt.legend()
plt.show()
def test_add_polynomial_feature(self):
features, values = generate_data_part_2()
plt.scatter([x[0] for x in features], values, label='origin');
for k in [2, 4, 10]:
# TODO: confirm polynomial feature and k = Xk
features_extended = polynomial_features(features, k)
model = LinearRegression(nb_features=k)
model.train(features_extended, values)
mse = mean_squared_error(values, model.predict(features_extended))
print(f'[part 1.3.2]\tk: {k:d}\tmse: {mse:.5f}')
plt.plot([x[0] for x in features], model.predict(features_extended), label=f'k={k}');
plt.legend()
plt.show()
def test_data_procession(self):
features, values = generate_data_part_3()
train_features, train_values = features[:100], values[:100]
valid_features, valid_values = features[100:120], values[100:120]
test_features, test_values = features[120:], values[120:]
assert len(train_features) == len(train_values) == 100
assert len(valid_features) == len(valid_values) == 20
assert len(test_features) == len(test_values) == 30
best_mse, best_k = 1e10, -1
for k in [1, 3, 10]:
train_features_extended = polynomial_features(train_features, k)
model = LinearRegression(nb_features=k)
model.train(train_features_extended, train_values)
train_mse = mean_squared_error(train_values, model.predict(train_features_extended))
valid_features_extended = polynomial_features(valid_features, k)
valid_mse = mean_squared_error(valid_values, model.predict(valid_features_extended))
print(f'[part 1.4.1]\tk: {k:d}\t'
f'train mse: {train_mse:.5f}\tvalid mse: {valid_mse:.5f}')
if valid_mse < best_mse:
best_mse, best_k = valid_mse, k
def test_linear_regression2():
from hw1_lr import LinearRegression
result = []
x = np.array([[1], [-1]])
y = x * 3 + 2
x = x.tolist()
y = y.flatten().tolist()
model = LinearRegression(nb_features=1)
model.train(x, y)
result.append('[TEST LinearRegression]' +
weights_to_string(model.get_weights()))
result.append('[TEST LinearRegression]' +
weights_to_string(model.predict(x)))
return result
def test_linear_regression():
from hw1_lr import LinearRegression
result = []
x = np.linspace(-2, 2, num=5).reshape(-1, 1)
y = x * x * x
x = x.tolist()
y = y.flatten().tolist()
model = LinearRegression(nb_features=1)
model.train(x, y)
result.append('[TEST LinearRegression]' +
weights_to_string(model.get_weights()))
result.append('[TEST LinearRegression]' +
weights_to_string(model.predict(x)))
return result
class TestLinearRegression(TestCase):
def setUp(self):
self.linear_regression = LinearRegression(1)
def test_train(self):
x = [[63], [64], [66], [69], [69], [71], [71], [72], [73], [75]]
y = [127, 121, 142, 157, 162, 156, 169, 165, 181, 208]
self.linear_regression.train(x, y)
weights = self.linear_regression.get_weights()
assert_array_almost_equal([-266.53, 6.13], weights, decimal=2)
def test_predict(self):
x = [[63], [64], [66], [69], [69], [71], [71], [72], [73], [75]]
y = [127, 121, 142, 157, 162, 156, 169, 165, 181, 208]
self.linear_regression.train(x, y)
assert_array_almost_equal([120.13, 126.27, 138.55, 156.96, 156.96, 169.23, 169.23,
175.37, 181.51, 193.78],
self.linear_regression.predict([[63], [64], [66], [69], [69], [71], [71], [72], [73], [75]]), decimal=2)
def test_add_one_ahead(self):
a = numpy.array([[1, 1], [2, 2], [3, 3]])
b = numpy.insert(a, 0, 1, axis=1)
assert_array_equal(numpy.array([[1, 1, 1], [1, 2, 2], [1, 3, 3]]), b)
assert_array_equal(numpy.array([[1, 1], [2, 2], [3, 3]]), a)
def test_lr_integration(self):
features, values = generate_data_part_1()
model = LinearRegression(nb_features=1)
model.train(features, values)
mse = mean_squared_error(values, model.predict(features))
self.assertAlmostEqual(0.00175, mse, places=5)
plt.scatter([x[0] for x in features], values, label='origin');
plt.plot([x[0] for x in features], model.predict(features), label='predicted');
plt.title("Holy shit")
plt.legend()
plt.show()
def test_lr_integration_l2(self):
features, values = generate_data_part_1()
model = LinearRegressionWithL2Loss(nb_features=1, alpha=0.0)
model.train(features, values)
mse = mean_squared_error(values, model.predict(features))
# self.assertAlmostEqual(0.00175, mse, places=5)
plt.scatter([x[0] for x in features], values, label='origin');
plt.plot([x[0] for x in features], model.predict(features), label='predicted');
plt.legend()
def test_feature_select(self):
features, values = generate_data_part_2()
model = LinearRegression(nb_features=1)
model.train(features, values)
mse = mean_squared_error(values, model.predict(features))
print(f'[part 1.3.1]\tmse: {mse:.5f}')
plt.scatter([x[0] for x in features], values, label='origin');
plt.plot([x[0] for x in features], model.predict(features), label='pre dicted');
plt.legend()
plt.show()
def test_add_polynomial_feature(self):
features, values = generate_data_part_2()
plt.scatter([x[0] for x in features], values, label='origin');
for k in [2, 4, 10]:
# TODO: confirm polynomial feature and k = Xk
features_extended = polynomial_features(features, k)
model = LinearRegression(nb_features=k)
model.train(features_extended, values)
mse = mean_squared_error(values, model.predict(features_extended))
print(f'[part 1.3.2]\tk: {k:d}\tmse: {mse:.5f}')
plt.plot([x[0] for x in features], model.predict(features_extended), label=f'k={k}');
plt.legend()
plt.show()
def test_data_procession(self):
features, values = generate_data_part_3()
train_features, train_values = features[:100], values[:100]
valid_features, valid_values = features[100:120], values[100:120]
test_features, test_values = features[120:], values[120:]
assert len(train_features) == len(train_values) == 100
assert len(valid_features) == len(valid_values) == 20
assert len(test_features) == len(test_values) == 30
best_mse, best_k = 1e10, -1
for k in [1, 3, 10]:
train_features_extended = polynomial_features(train_features, k)
model = LinearRegression(nb_features=k)
model.train(train_features_extended, train_values)
train_mse = mean_squared_error(train_values, model.predict(train_features_extended))
valid_features_extended = polynomial_features(valid_features, k)
valid_mse = mean_squared_error(valid_values, model.predict(valid_features_extended))
print(f'[part 1.4.1]\tk: {k:d}\t'
f'train mse: {train_mse:.5f}\tvalid mse: {valid_mse:.5f}')
if valid_mse < best_mse:
best_mse, best_k = valid_mse, k
from hw1_lr import LinearRegression, LinearRegressionWithL2Loss
from utils import mean_squared_error
import matplotlib.pyplot as plt
from data import generate_data_part_1
features, values = generate_data_part_1()
features = data[0]
values = data[1]
model = LinearRegression(nb_features=1)
model.train(features, values)
mse = mean_squared_error(values, model.predict(features))
print('[part 1.2]\tmse: {mse:.5f}'.format(mse=mse))
plt.scatter([x[0] for x in features], values, label='origin');
plt.plot([x[0] for x in features], model.predict(features), label='predicted');
plt.legend()
from data import generate_data_part_2
features, values = generate_data_part_2()
features = data[2]
values = data[3]
model = LinearRegression(nb_features=1)
model.train(features, values)
mse = mean_squared_error(values, model.predict(features))