def test_cost(self):
"""
Tests the cost function.
"""
X, y, betas, pred_y = self.solve_iris_regression()
exp_mse = mean_squared_error(y, pred_y)
# use cost function predict method
cost_func = cost_functions.LinearCostFunction(X, y)
cost = cost_func.cost(betas)
self.assertAlmostEqual(cost, exp_mse)
def test_error(self):
"""
Tests the mean-squared error implementation
"""
X, y, _, pred_y = self.solve_iris_regression()
# use cost function predict method
cost = cost_functions.LinearCostFunction(X, y)
obs_mse = cost._mse(y, pred_y)
exp_mse = mean_squared_error(y, pred_y)
self.assertAlmostEqual(obs_mse, exp_mse)
def test_predict(self):
"""
Tests the predict method using known model
params
"""
X, y, betas, exp_y = self.solve_iris_regression()
# use cost function predict method
cost = cost_functions.LinearCostFunction(X, y)
obs_y = cost.predict(X, betas)
mse = mean_squared_error(obs_y, exp_y)
self.assertEqual(obs_y.shape, y.shape)
self.assertAlmostEqual(mse, 0.0)
def test_optim(self):
"""
Test optimizing the cost function.
"""
X, y, betas, normal_pred_y = self.solve_iris_regression()
exp_mse = mean_squared_error(y, normal_pred_y)
cost_func = cost_functions.LinearCostFunction(X, y)
optimizer = optim.Optimizer(0.01, 10000, 1e-4, 1e-4)
initial_params = np.zeros(betas.shape)
optim_params, n_iters = optimizer.optimize(cost_func, initial_params)
print(n_iters)
print(betas)
print(optim_params)
gd_pred_y = cost_func._predict(X, optim_params)
obs_mse = mean_squared_error(y, gd_pred_y)
self.assertAlmostEqual(exp_mse, obs_mse, 2)