def testLinreg():
train_x, train_y, dev_x, dev_y, test_x, test_y, x_max, y_max = util.loadLinRegData(
)
LR = LinearRegression()
LR.fit_stochastic(train_x, train_y, eta=.01, eps=10**-12, max_iters=10**8)
preds = LR.predict(train_x)
train_linreg_rmse = util.findRMSE(preds, train_y) * y_max[0]
preds = LR.predict(test_x)
test_linreg_rmse = util.findRMSE(preds, test_y) * y_max[0]
print('Linreg RMSE: \t', test_linreg_rmse)
return train_linreg_rmse, test_linreg_rmse
for max_iter in max_iters:
print('Fitting regression with eta', eta_val, 'eps', eps_val,
'max iter', max_iter)
cur_result = []
cur_result.append(eps_val)
cur_result.append(eta_val)
cur_result.append(max_iter)
LR = LinearRegression()
LR.fit_stochastic(train_x,
train_y,
eta=eta_val,
eps=eps_val,
max_iters=max_iter)
preds = LR.predict(train_x)
rmse = util.findRMSE(preds, train_y)
cur_result.append(rmse * y_max[0])
preds = LR.predict(dev_x)
rmse = util.findRMSE(preds, dev_y)
cur_result.append(rmse * y_max[0])
results.append(cur_result)
results.sort(key=lambda x: x[4])
for result in results:
print('eps:\t', result[0], 'eta:\t', result[1], 'max iter:\t', result[2],
'Train RMSE:\t', result[3], 'Test RMSE:\t', result[4])
np.savetxt('Output/linreg_dev_rmse.csv', results, delimiter=',')
