##############################################################################
# Weighted Lasso with sparse-ho.
# We use the vanilla lassoCV coefficients as a starting point
alpha0 = np.log(model_cv.alpha_) * np.ones(X_train.shape[1])
# Weighted Lasso: Sparse-ho: 1 param per feature
estimator = Lasso(fit_intercept=False, max_iter=10, warm_start=True)
model = WeightedLasso(X_train, y_train, estimator=estimator)
criterion = HeldOutMSE(X_val, y_val, model, X_test=X_test, y_test=y_test)
algo = ImplicitForward()
monitor = Monitor()
grad_search(algo, criterion, alpha0, monitor, n_outer=20, tol=1e-6)
##############################################################################
##############################################################################
# MSE on validation set
mse_sho_val = mean_squared_error(y_val, estimator.predict(X_val))
# MSE on test set, ie unseen data
mse_sho_test = mean_squared_error(y_test, estimator.predict(X_test))
print("Sparse-ho: Mean-squared error on validation data %f" % mse_sho_val)
print("Sparse-ho: Mean-squared error on test (unseen) data %f" % mse_sho_test)
labels = ['WeightedLasso val', 'WeightedLasso test', 'Lasso CV']
df = pd.DataFrame(np.array([mse_sho_val, mse_sho_test, mse_cv]).reshape(
(1, -1)),
columns=labels)
df.plot.bar(rot=0)
plt.xlabel("Estimator")
plt.ylabel("Mean square error")
algo = ImplicitForward()
monitor = Monitor()
grad_search(algo,
criterion,
model,
X,
y,
log_alpha0,
monitor,
n_outer=20,
tol=1e-6)
##############################################################################
##############################################################################
# MSE on validation set
mse_sho_val = mean_squared_error(y[idx_val], estimator.predict(X[idx_val, :]))
# MSE on test set, ie unseen data
mse_sho_test = mean_squared_error(y_test, estimator.predict(X_test))
print("Sparse-ho: Mean-squared error on validation data %f" % mse_sho_val)
print("Sparse-ho: Mean-squared error on test (unseen) data %f" % mse_sho_test)
labels = ['WeightedLasso val', 'WeightedLasso test', 'Lasso CV']
df = pd.DataFrame(np.array([mse_sho_val, mse_sho_test, mse_cv]).reshape(
(1, -1)),
columns=labels)
df.plot.bar(rot=0)
plt.xlabel("Estimator")
plt.ylabel("Mean square error")