lasso_cv_mse, ridge_cv_mse, ols_cv_mse_deg = stat_tools.k_fold_cv_all(X_scaled,z,n_lambdas,lambdas,k_folds)
best_lasso_lambda[degree] = lambdas[np.argmin(lasso_cv_mse)]
best_ridge_lambda[degree] = lambdas[np.argmin(ridge_cv_mse)]
best_lasso_mse[degree] = np.min(lasso_cv_mse)
best_ridge_mse[degree] = np.min(ridge_cv_mse)
lasso_lamb_deg_mse[degree] = lasso_cv_mse
ridge_lamb_deg_mse[degree] = ridge_cv_mse
ols_cv_mse[degree] = ols_cv_mse_deg
# All regression bootstraps at once
lamb_ridge = best_ridge_lambda[degree]
lamb_lasso = best_lasso_lambda[degree]
ridge_mse, ridge_bias, ridge_variance, lasso_mse, lasso_bias, lasso_variance, ols_mse, ols_bias, ols_variance = \
stat_tools.bootstrap_all(X_train_scaled, X_test_scaled, z_train, z_test, n_bootstraps, lamb_lasso, lamb_ridge)
ridge_best_lambda_boot_mse[degree], ridge_best_lambda_boot_bias[degree], \
ridge_best_lambda_boot_variance[degree] = ridge_mse, ridge_bias, ridge_variance
lasso_best_lambda_boot_mse[degree], lasso_best_lambda_boot_bias[degree], \
lasso_best_lambda_boot_variance[degree] = lasso_mse, lasso_bias, lasso_variance
ols_boot_mse[degree], ols_boot_bias[degree], \
ols_boot_variance[degree] = ols_mse, ols_bias, ols_variance
# Bootstrapping for a selection of lambdas for ridge and lasso
# subset_lambda_index = 0
# for lamb in subset_lambdas:
#
# ridge_mse, ridge_bias, ridge_variance, lasso_mse, lasso_bias, lasso_variance = \
def terrain_analysis():
# Setting up the terrain data:
terrain_data = imread('../datafiles/SRTM_data_Norway_1.tif')
x_terrain = np.arange(terrain_data.shape[1])
y_terrain = np.arange(terrain_data.shape[0])
X_coord, Y_coord = np.meshgrid(x_terrain, y_terrain)
z_terrain = terrain_data.flatten() # the response values
x_terrain_flat = X_coord.flatten() # the first degree feature variables
y_terrain_flat = Y_coord.flatten() # the first degree feature variables
max_degree = 20
n_lambdas = 30
n_bootstraps = 50
k_folds = 5
lambdas = np.logspace(-3, 0, n_lambdas)
subset_lambdas = lambdas[::5]
#### Should select a subset in some manner of the terrain points
#### Should probably also make the feature variables be float that range from [0,1]
x = x_terrain_flat[::20]
y = y_terrain_flat[::20]
z = z_terrain[::20]
x_train, x_test, y_train, y_test, z_train, z_test = train_test_split(
x, y, z, test_size=0.2)
# Quantities of interest:
mse_ols_test = np.zeros(max_degree)
mse_ols_train = np.zeros(max_degree)
ols_cv_mse = np.zeros(max_degree)
ols_boot_mse = np.zeros(max_degree)
ols_boot_bias = np.zeros(max_degree)
ols_boot_variance = np.zeros(max_degree)
best_ridge_lambda = np.zeros(max_degree)
best_ridge_mse = np.zeros(max_degree)
ridge_best_lambda_boot_mse = np.zeros(max_degree)
ridge_best_lambda_boot_bias = np.zeros(max_degree)
ridge_best_lambda_boot_variance = np.zeros(max_degree)
best_lasso_lambda = np.zeros(max_degree)
best_lasso_mse = np.zeros(max_degree)
lasso_best_lambda_boot_mse = np.zeros(max_degree)
lasso_best_lambda_boot_bias = np.zeros(max_degree)
lasso_best_lambda_boot_variance = np.zeros(max_degree)
ridge_lamb_deg_mse = np.zeros((max_degree, n_lambdas))
lasso_lamb_deg_mse = np.zeros((max_degree, n_lambdas))
ridge_subset_lambda_boot_mse = np.zeros((max_degree, len(subset_lambdas)))
ridge_subset_lambda_boot_bias = np.zeros((max_degree, len(subset_lambdas)))
ridge_subset_lambda_boot_variance = np.zeros(
(max_degree, len(subset_lambdas)))
lasso_subset_lambda_boot_mse = np.zeros((max_degree, len(subset_lambdas)))
lasso_subset_lambda_boot_bias = np.zeros((max_degree, len(subset_lambdas)))
lasso_subset_lambda_boot_variance = np.zeros(
(max_degree, len(subset_lambdas)))
# Actual computations
for degree in range(max_degree):
X = linear_regression.design_matrix_2D(x, y, degree)
X_train = linear_regression.design_matrix_2D(x_train, y_train, degree)
X_test = linear_regression.design_matrix_2D(x_test, y_test, degree)
# Scaling and feeding to CV.
scaler = StandardScaler()
scaler.fit(X)
X_scaled = scaler.transform(X)
X_scaled[:, 0] = 1 # Probably should not have this.
# Scaling and feeding to bootstrap and OLS
scaler_boot = StandardScaler()
scaler_boot.fit(X_train)
X_train_scaled = scaler_boot.transform(X_train)
X_test_scaled = scaler_boot.transform(X_test)
X_train_scaled[:, 0] = 1 # Probably actually not
X_test_scaled[:,
0] = 1 # Have a bad feeling about how this might affect ridge/lasso.
# OLS, get MSE for test and train set.
betas = linear_regression.OLS_SVD_2D(X_train_scaled, z_train)
z_test_model = X_test_scaled @ betas
z_train_model = X_train_scaled @ betas
mse_ols_train[degree] = stat_tools.MSE(z_train, z_train_model)
mse_ols_test[degree] = stat_tools.MSE(z_test, z_test_model)
# CV, find best lambdas and get mse vs lambda for given degree.
lasso_cv_mse, ridge_cv_mse, ols_cv_mse = stat_tools.k_fold_cv_all(
X_scaled, z, n_lambdas, lambdas, k_folds)
best_lasso_lambda[degree] = lambdas[np.argmin(lasso_cv_mse)]
best_ridge_lambda[degree] = lambdas[np.argmin(ridge_cv_mse)]
best_lasso_mse[degree] = np.min(lasso_cv_mse)
best_ridge_mse[degree] = np.min(ridge_cv_mse)
lasso_lamb_deg_mse[degree] = lasso_cv_mse
ridge_lamb_deg_mse[degree] = ridge_cv_mse
# All regression bootstraps at once
lamb_ridge = best_ridge_lambda[degree]
lamb_lasso = best_lasso_lambda[degree]
ridge_mse, ridge_bias, ridge_variance, lasso_mse, lasso_bias, lasso_variance, ols_mse, ols_bias, ols_variance = \
stat_tools.bootstrap_all(X_train_scaled, X_test_scaled, z_train, z_test, n_bootstraps, lamb_lasso, lamb_ridge)
ridge_best_lambda_boot_mse[degree], ridge_best_lambda_boot_bias[degree], \
ridge_best_lambda_boot_variance[degree] = ridge_mse, ridge_bias, ridge_variance
lasso_best_lambda_boot_mse[degree], lasso_best_lambda_boot_bias[degree], \
lasso_best_lambda_boot_variance[degree] = lasso_mse, lasso_bias, lasso_variance
ols_boot_mse[degree], ols_boot_bias[degree], \
ols_boot_variance[degree] = ols_mse, ols_bias, ols_variance
# Bootstrapping for a selection of lambdas for ridge and lasso
subset_lambda_index = 0
for lamb in subset_lambdas:
ridge_mse, ridge_bias, ridge_variance, lasso_mse, lasso_bias, lasso_variance = \
stat_tools.bootstrap_ridge_lasso(X_train_scaled, X_test_scaled, z_train, z_test, n_bootstraps, lamb_lasso, lamb_ridge)
ridge_subset_lambda_boot_mse[degree, subset_lambda_index ], ridge_subset_lambda_boot_bias[degree, subset_lambda_index ], \
ridge_subset_lambda_boot_variance[degree, subset_lambda_index ] = ridge_mse, ridge_bias, ridge_variance
lasso_subset_lambda_boot_mse[degree, subset_lambda_index ], lasso_subset_lambda_boot_bias[degree, subset_lambda_index ], \
lasso_subset_lambda_boot_variance[degree, subset_lambda_index ] = lasso_mse, lasso_bias, lasso_variance
subset_lambda_index += 1
################ All necessary computations should have been done above. Below follows
################ the plotting part.
return
def terrain_analysis_plots(spacing=100,
max_degree=20,
n_lambdas=30,
k_folds=5,
n_bootstraps=50,
do_boot=False,
do_subset=False):
# Setting up the terrain data:
# Note structure! X-coordinates are on the rows of terrain_data
# Point_selection.flatten() moves most rapidly over the x-coordinates
# Meshgrids flattened also move most rapidly over the x-coordinates. Thus
# this should make z(x,y).reshape(length_y,length_x) be consistent with terrain_data
terrain_data = imread('../datafiles/SRTM_data_Norway_1.tif')
point_selection = terrain_data[:1801:spacing, :1801:
spacing] # Make square and downsample
x_terrain_selection = np.linspace(0, 1, point_selection.shape[1])
y_terrain_selection = np.linspace(0, 1, point_selection.shape[0])
X_coord_selection, Y_coord_selection = np.meshgrid(x_terrain_selection,
y_terrain_selection)
z_terrain_selection = point_selection.flatten() # the response values
x_terrain_selection_flat = X_coord_selection.flatten(
) # the first degree feature variables
y_terrain_selection_flat = Y_coord_selection.flatten(
) # the first degree feature variables
lambdas = np.logspace(-6, 0, n_lambdas)
subset_lambdas = lambdas[::12]
x = x_terrain_selection_flat
y = y_terrain_selection_flat
z = z_terrain_selection
x_train, x_test, y_train, y_test, z_train, z_test = train_test_split(
x, y, z, test_size=0.2)
# Centering
z_intercept = np.mean(z)
z = z - z_intercept
z_train_intercept = np.mean(z_train)
z_train = z_train - z_train_intercept
z_test = z_test - z_train_intercept
##### Setup of problem is completede above.
# Quantities of interest:
mse_ols_test = np.zeros(max_degree)
mse_ols_train = np.zeros(max_degree)
ols_cv_mse = np.zeros(max_degree)
ols_boot_mse = np.zeros(max_degree)
ols_boot_bias = np.zeros(max_degree)
ols_boot_variance = np.zeros(max_degree)
best_ridge_lambda = np.zeros(max_degree)
best_ridge_mse = np.zeros(max_degree)
ridge_best_lambda_boot_mse = np.zeros(max_degree)
ridge_best_lambda_boot_bias = np.zeros(max_degree)
ridge_best_lambda_boot_variance = np.zeros(max_degree)
best_lasso_lambda = np.zeros(max_degree)
best_lasso_mse = np.zeros(max_degree)
lasso_best_lambda_boot_mse = np.zeros(max_degree)
lasso_best_lambda_boot_bias = np.zeros(max_degree)
lasso_best_lambda_boot_variance = np.zeros(max_degree)
ridge_lamb_deg_mse = np.zeros((max_degree, n_lambdas))
lasso_lamb_deg_mse = np.zeros((max_degree, n_lambdas))
ridge_subset_lambda_boot_mse = np.zeros((max_degree, len(subset_lambdas)))
ridge_subset_lambda_boot_bias = np.zeros((max_degree, len(subset_lambdas)))
ridge_subset_lambda_boot_variance = np.zeros(
(max_degree, len(subset_lambdas)))
lasso_subset_lambda_boot_mse = np.zeros((max_degree, len(subset_lambdas)))
lasso_subset_lambda_boot_bias = np.zeros((max_degree, len(subset_lambdas)))
lasso_subset_lambda_boot_variance = np.zeros(
(max_degree, len(subset_lambdas)))
# Actual computations
for degree in range(max_degree):
X = linear_regression.design_matrix_2D(x, y, degree)
X_train = linear_regression.design_matrix_2D(x_train, y_train, degree)
X_test = linear_regression.design_matrix_2D(x_test, y_test, degree)
# Scaling and feeding to CV.
scaler = StandardScaler()
scaler.fit(X)
X_scaled = scaler.transform(X)
# X_scaled[:,0] = 1 # Probably should not have this.
# Scaling and feeding to bootstrap and OLS
scaler_boot = StandardScaler()
scaler_boot.fit(X_train)
X_train_scaled = scaler_boot.transform(X_train)
X_test_scaled = scaler_boot.transform(X_test)
# X_train_scaled[:,0] = 1 # Probably actually not
# X_test_scaled[:,0] = 1 # Have a bad feeling about how this might affect ridge/lasso.
# OLS, get MSE for test and train set.
betas = linear_regression.OLS_SVD_2D(X_train_scaled, z_train)
z_test_model = X_test_scaled @ betas
z_train_model = X_train_scaled @ betas
mse_ols_train[degree] = stat_tools.MSE(z_train, z_train_model)
mse_ols_test[degree] = stat_tools.MSE(z_test, z_test_model)
# CV, find best lambdas and get mse vs lambda for given degree.
lasso_cv_mse, ridge_cv_mse, ols_cv_mse_deg = stat_tools.k_fold_cv_all(
X_scaled, z, n_lambdas, lambdas, k_folds)
best_lasso_lambda[degree] = lambdas[np.argmin(lasso_cv_mse)]
best_ridge_lambda[degree] = lambdas[np.argmin(ridge_cv_mse)]
best_lasso_mse[degree] = np.min(lasso_cv_mse)
best_ridge_mse[degree] = np.min(ridge_cv_mse)
lasso_lamb_deg_mse[degree] = lasso_cv_mse
ridge_lamb_deg_mse[degree] = ridge_cv_mse
ols_cv_mse[degree] = ols_cv_mse_deg
if do_boot:
# All regression bootstraps at once
lamb_ridge = best_ridge_lambda[degree]
lamb_lasso = best_lasso_lambda[degree]
ridge_mse, ridge_bias, ridge_variance, lasso_mse, lasso_bias, lasso_variance, ols_mse, ols_bias, ols_variance = \
stat_tools.bootstrap_all(X_train_scaled, X_test_scaled, z_train, z_test, n_bootstraps, lamb_lasso, lamb_ridge)
ridge_best_lambda_boot_mse[degree], ridge_best_lambda_boot_bias[degree], \
ridge_best_lambda_boot_variance[degree] = ridge_mse, ridge_bias, ridge_variance
lasso_best_lambda_boot_mse[degree], lasso_best_lambda_boot_bias[degree], \
lasso_best_lambda_boot_variance[degree] = lasso_mse, lasso_bias, lasso_variance
ols_boot_mse[degree], ols_boot_bias[degree], \
ols_boot_variance[degree] = ols_mse, ols_bias, ols_variance
if do_subset:
# Bootstrapping for a selection of lambdas for ridge and lasso
subset_lambda_index = 0
for lamb in subset_lambdas:
ridge_mse, ridge_bias, ridge_variance, lasso_mse, lasso_bias, lasso_variance = \
stat_tools.bootstrap_ridge_lasso(X_train_scaled, X_test_scaled, z_train, z_test, n_bootstraps, lamb_lasso, lamb_ridge)
ridge_subset_lambda_boot_mse[degree, subset_lambda_index ], ridge_subset_lambda_boot_bias[degree, subset_lambda_index ], \
ridge_subset_lambda_boot_variance[degree, subset_lambda_index ] = ridge_mse, ridge_bias, ridge_variance
lasso_subset_lambda_boot_mse[degree, subset_lambda_index ], lasso_subset_lambda_boot_bias[degree, subset_lambda_index ], \
lasso_subset_lambda_boot_variance[degree, subset_lambda_index ] = lasso_mse, lasso_bias, lasso_variance
subset_lambda_index += 1
# Plots go here.
plt.figure()
plt.semilogy(ols_cv_mse, label='ols')
plt.semilogy(best_ridge_mse, label='ridge')
plt.semilogy(best_lasso_mse, label='lasso')
plt.title(
'CV MSE for OLS, Ridge and Lasso, with the best lambdas for each degree'
)
plt.legend()
plt.show()
# For a couple of degrees, plot cv mse vs lambda for ridge, will break program if max_degrees < 8
plt.figure()
plt.plot(np.log10(lambdas),
ridge_lamb_deg_mse[max_degree - 1],
label='degree = {}'.format(max_degree - 1))
plt.plot(np.log10(lambdas),
ridge_lamb_deg_mse[max_degree - 2],
label='degree = {}'.format(max_degree - 2))
plt.plot(np.log10(lambdas),
ridge_lamb_deg_mse[max_degree - 3],
label='degree = {}'.format(max_degree - 3))
plt.plot(np.log10(lambdas),
ridge_lamb_deg_mse[max_degree - 5],
label='degree = {}'.format(max_degree - 5))
plt.plot(np.log10(lambdas),
ridge_lamb_deg_mse[max_degree - 7],
label='degree = {}'.format(max_degree - 7))
plt.legend()
plt.show()
# For a copule of degrees, plot cv mse vs lambda for lasso, will break program if max_degree < 8.
plt.figure()
plt.plot(np.log10(lambdas),
lasso_lamb_deg_mse[max_degree - 1],
label='degree = {}'.format(max_degree - 1))
plt.plot(np.log10(lambdas),
lasso_lamb_deg_mse[max_degree - 2],
label='degree = {}'.format(max_degree - 2))
plt.plot(np.log10(lambdas),
lasso_lamb_deg_mse[max_degree - 3],
label='degree = {}'.format(max_degree - 3))
plt.plot(np.log10(lambdas),
lasso_lamb_deg_mse[max_degree - 5],
label='degree = {}'.format(max_degree - 5))
plt.plot(np.log10(lambdas),
lasso_lamb_deg_mse[max_degree - 7],
label='degree = {}'.format(max_degree - 7))
plt.legend()
plt.show()
print('best ridge lambdas:')
print(best_ridge_lambda)
print('best lasso lambda')
print(best_lasso_lambda)
return
def franke_analysis():
n = 1000
noise_scale = 0.2
x = np.random.uniform(0, 1, n)
y = np.random.uniform(0, 1, n)
z = FrankeFunction(x, y)
# Adding standard normal noise:
z = z + noise_scale * np.random.normal(0, 1, len(z))
max_degree = 20
n_lambdas = 30
n_bootstraps = 50
k_folds = 5
lambdas = np.logspace(-3, 0, n_lambdas)
subset_lambdas = lambdas[::5]
x_train, x_test, y_train, y_test, z_train, z_test = train_test_split(
x, y, z, test_size=0.2)
# Quantities of interest:
mse_ols_test = np.zeros(max_degree)
mse_ols_train = np.zeros(max_degree)
ols_cv_mse = np.zeros(max_degree)
ols_boot_mse = np.zeros(max_degree)
ols_boot_bias = np.zeros(max_degree)
ols_boot_variance = np.zeros(max_degree)
best_ridge_lambda = np.zeros(max_degree)
best_ridge_mse = np.zeros(max_degree)
ridge_best_lambda_boot_mse = np.zeros(max_degree)
ridge_best_lambda_boot_bias = np.zeros(max_degree)
ridge_best_lambda_boot_variance = np.zeros(max_degree)
best_lasso_lambda = np.zeros(max_degree)
best_lasso_mse = np.zeros(max_degree)
lasso_best_lambda_boot_mse = np.zeros(max_degree)
lasso_best_lambda_boot_bias = np.zeros(max_degree)
lasso_best_lambda_boot_variance = np.zeros(max_degree)
ridge_lamb_deg_mse = np.zeros((max_degree, n_lambdas))
lasso_lamb_deg_mse = np.zeros((max_degree, n_lambdas))
ridge_subset_lambda_boot_mse = np.zeros((max_degree, len(subset_lambdas)))
ridge_subset_lambda_boot_bias = np.zeros((max_degree, len(subset_lambdas)))
ridge_subset_lambda_boot_variance = np.zeros(
(max_degree, len(subset_lambdas)))
lasso_subset_lambda_boot_mse = np.zeros((max_degree, len(subset_lambdas)))
lasso_subset_lambda_boot_bias = np.zeros((max_degree, len(subset_lambdas)))
lasso_subset_lambda_boot_variance = np.zeros(
(max_degree, len(subset_lambdas)))
# Actual computations
for degree in range(max_degree):
X = linear_regression.design_matrix_2D(x, y, degree)
X_train = linear_regression.design_matrix_2D(x_train, y_train, degree)
X_test = linear_regression.design_matrix_2D(x_test, y_test, degree)
# Scaling and feeding to CV.
scaler = StandardScaler()
scaler.fit(X)
X_scaled = scaler.transform(X)
X_scaled[:,
0] = 1 # Maybe not for ridge+lasso. Don't want to penalize constants...
# Scaling and feeding to bootstrap and OLS
scaler_boot = StandardScaler()
scaler_boot.fit(X_train)
X_train_scaled = scaler_boot.transform(X_train)
X_test_scaled = scaler_boot.transform(X_test)
X_train_scaled[:, 0] = 1 #maybe not for ridge+lasso
X_test_scaled[:, 0] = 1 #maybe not for ridge+lasso
# OLS, get MSE for test and train set.
betas = linear_regression.OLS_SVD_2D(X_train_scaled, z_train)
z_test_model = X_test_scaled @ betas
z_train_model = X_train_scaled @ betas
mse_ols_train[degree] = stat_tools.MSE(z_train, z_train_model)
mse_ols_test[degree] = stat_tools.MSE(z_test, z_test_model)
# CV, find best lambdas and get mse vs lambda for given degree. Also, gets
# ols_CV_MSE
lasso_cv_mse, ridge_cv_mse, ols_cv_mse = stat_tools.k_fold_cv_all(
X_scaled, z, n_lambdas, lambdas, k_folds)
best_lasso_lambda[degree] = lambdas[np.argmin(lasso_cv_mse)]
best_ridge_lambda[degree] = lambdas[np.argmin(ridge_cv_mse)]
best_lasso_mse[degree] = np.min(lasso_cv_mse)
best_ridge_mse[degree] = np.min(ridge_cv_mse)
lasso_lamb_deg_mse[degree] = lasso_cv_mse
ridge_lamb_deg_mse[degree] = ridge_cv_mse
# All regressions bootstraps at once
lamb_ridge = best_ridge_lambda[degree]
lamb_lasso = best_lasso_lambda[degree]
ridge_mse, ridge_bias, ridge_variance, lasso_mse, lasso_bias, lasso_variance, ols_mse, ols_bias, ols_variance = \
stat_tools.bootstrap_all(X_train_scaled, X_test_scaled, z_train, z_test, n_bootstraps, lamb_lasso, lamb_ridge)
ridge_best_lambda_boot_mse[degree], ridge_best_lambda_boot_bias[degree], \
ridge_best_lambda_boot_variance[degree] = ridge_mse, ridge_bias, ridge_variance
lasso_best_lambda_boot_mse[degree], lasso_best_lambda_boot_bias[degree], \
lasso_best_lambda_boot_variance[degree] = lasso_mse, lasso_bias, lasso_variance
ols_boot_mse[degree], ols_boot_bias[degree], \
ols_boot_variance[degree] = ols_mse, ols_bias, ols_variance
# Bootstrapping for a selection of lambdas for ridge and lasso
subset_lambda_index = 0
for lamb in subset_lambdas:
ridge_mse, ridge_bias, ridge_variance, lasso_mse, lasso_bias, lasso_variance = \
stat_tools.bootstrap_ridge_lasso(X_train_scaled, X_test_scaled, z_train, z_test, n_bootstraps, lamb_lasso, lamb_ridge)
ridge_subset_lambda_boot_mse[degree, subset_lambda_index ], ridge_subset_lambda_boot_bias[degree, subset_lambda_index ], \
ridge_subset_lambda_boot_variance[degree, subset_lambda_index ] = ridge_mse, ridge_bias, ridge_variance
lasso_subset_lambda_boot_mse[degree, subset_lambda_index ], lasso_subset_lambda_boot_bias[degree, subset_lambda_index ], \
lasso_subset_lambda_boot_variance[degree, subset_lambda_index ] = lasso_mse, lasso_bias, lasso_variance
subset_lambda_index += 1
def franke_analysis_plots(n=1000,noise_scale=0.2,max_degree=20,n_bootstraps=100,k_folds=5,n_lambdas=30, do_boot=True, do_subset=True):
# Note that max_degrees is the number of degrees, i.e. including 0.
# n = 500
# noise_scale = 0.2
x = np.random.uniform(0, 1, n)
y = np.random.uniform(0, 1, n)
z = FrankeFunction(x, y)
# Adding standard normal noise:
z = z + noise_scale*np.random.normal(0,1,len(z))
# max_degree = 15
# n_lambdas = 30
# n_bootstraps = 100
# k_folds = 5
lambdas = np.logspace(-6,0,n_lambdas)
subset_lambdas = lambdas[::12]
x_train, x_test, y_train, y_test, z_train, z_test = train_test_split(x, y, z, test_size = 0.2)
# Centering the response
z_intercept = np.mean(z)
z = z - z_intercept
# Centering the response
z_train_intercept = np.mean(z_train)
z_train = z_train - z_train_intercept
z_test = z_test - z_train_intercept
########### Setup of problem is completed above.
# Quantities of interest:
mse_ols_test = np.zeros(max_degree)
mse_ols_train = np.zeros(max_degree)
ols_cv_mse = np.zeros(max_degree)
ols_boot_mse = np.zeros(max_degree)
ols_boot_bias = np.zeros(max_degree)
ols_boot_variance = np.zeros(max_degree)
best_ridge_lambda = np.zeros(max_degree)
best_ridge_mse = np.zeros(max_degree)
ridge_best_lambda_boot_mse = np.zeros(max_degree)
ridge_best_lambda_boot_bias = np.zeros(max_degree)
ridge_best_lambda_boot_variance = np.zeros(max_degree)
best_lasso_lambda = np.zeros(max_degree)
best_lasso_mse = np.zeros(max_degree)
lasso_best_lambda_boot_mse = np.zeros(max_degree)
lasso_best_lambda_boot_bias = np.zeros(max_degree)
lasso_best_lambda_boot_variance = np.zeros(max_degree)
ridge_lamb_deg_mse = np.zeros((max_degree, n_lambdas))
lasso_lamb_deg_mse = np.zeros((max_degree, n_lambdas))
ridge_subset_lambda_boot_mse = np.zeros((max_degree, len(subset_lambdas)))
ridge_subset_lambda_boot_bias = np.zeros((max_degree, len(subset_lambdas)))
ridge_subset_lambda_boot_variance = np.zeros((max_degree, len(subset_lambdas)))
lasso_subset_lambda_boot_mse = np.zeros((max_degree, len(subset_lambdas)))
lasso_subset_lambda_boot_bias = np.zeros((max_degree, len(subset_lambdas)))
lasso_subset_lambda_boot_variance = np.zeros((max_degree, len(subset_lambdas)))
# Actual computations
for degree in range(max_degree):
X = linear_regression.design_matrix_2D(x,y,degree)
X_train = linear_regression.design_matrix_2D(x_train, y_train, degree)
X_test = linear_regression.design_matrix_2D(x_test, y_test, degree)
# Scaling and feeding to CV.
scaler = StandardScaler()
scaler.fit(X)
X_scaled = scaler.transform(X)
# X_scaled[:,0] = 1 # Maybe not for ridge+lasso. Don't want to penalize constants...
# Scaling and feeding to bootstrap and OLS
scaler_boot = StandardScaler()
scaler_boot.fit(X_train)
X_train_scaled = scaler_boot.transform(X_train)
X_test_scaled = scaler_boot.transform(X_test)
# X_train_scaled[:,0] = 1 #maybe not for ridge+lasso
# X_test_scaled[:,0] = 1 #maybe not for ridge+lasso
# OLS, get MSE for test and train set.
betas = linear_regression.OLS_SVD_2D(X_train_scaled, z_train)
z_test_model = X_test_scaled @ betas
z_train_model = X_train_scaled @ betas
mse_ols_train[degree] = stat_tools.MSE(z_train, z_train_model)
mse_ols_test[degree] = stat_tools.MSE(z_test, z_test_model)
# CV, find best lambdas and get mse vs lambda for given degree. Also, gets
# ols_CV_MSE
lasso_cv_mse, ridge_cv_mse, ols_cv_mse_deg = stat_tools.k_fold_cv_all(X_scaled,z,n_lambdas,lambdas,k_folds)
best_lasso_lambda[degree] = lambdas[np.argmin(lasso_cv_mse)]
best_ridge_lambda[degree] = lambdas[np.argmin(ridge_cv_mse)]
best_lasso_mse[degree] = np.min(lasso_cv_mse)
best_ridge_mse[degree] = np.min(ridge_cv_mse)
lasso_lamb_deg_mse[degree] = lasso_cv_mse
ridge_lamb_deg_mse[degree] = ridge_cv_mse
ols_cv_mse[degree] = ols_cv_mse_deg
if do_boot:
# All regression bootstraps at once
lamb_ridge = best_ridge_lambda[degree]
lamb_lasso = best_lasso_lambda[degree]
ridge_mse, ridge_bias, ridge_variance, lasso_mse, lasso_bias, lasso_variance, ols_mse, ols_bias, ols_variance = \
stat_tools.bootstrap_all(X_train_scaled, X_test_scaled, z_train, z_test, n_bootstraps, lamb_lasso, lamb_ridge)
ridge_best_lambda_boot_mse[degree], ridge_best_lambda_boot_bias[degree], \
ridge_best_lambda_boot_variance[degree] = ridge_mse, ridge_bias, ridge_variance
lasso_best_lambda_boot_mse[degree], lasso_best_lambda_boot_bias[degree], \
lasso_best_lambda_boot_variance[degree] = lasso_mse, lasso_bias, lasso_variance
ols_boot_mse[degree], ols_boot_bias[degree], \
ols_boot_variance[degree] = ols_mse, ols_bias, ols_variance
if do_subset:
# Bootstrapping for a selection of lambdas for ridge and lasso
subset_lambda_index = 0
for lamb in subset_lambdas:
ridge_mse, ridge_bias, ridge_variance, lasso_mse, lasso_bias, lasso_variance = \
stat_tools.bootstrap_ridge_lasso(X_train_scaled, X_test_scaled, z_train, z_test, n_bootstraps, lamb_lasso, lamb_ridge)
ridge_subset_lambda_boot_mse[degree, subset_lambda_index ], ridge_subset_lambda_boot_bias[degree, subset_lambda_index ], \
ridge_subset_lambda_boot_variance[degree, subset_lambda_index ] = ridge_mse, ridge_bias, ridge_variance
lasso_subset_lambda_boot_mse[degree, subset_lambda_index ], lasso_subset_lambda_boot_bias[degree, subset_lambda_index ], \
lasso_subset_lambda_boot_variance[degree, subset_lambda_index ] = lasso_mse, lasso_bias, lasso_variance
subset_lambda_index += 1
# Plots go here.
# CV MSE for OLS:
plt.figure()
plt.semilogy(ols_cv_mse)
plt.title('OLS CV MSE')
plt.show()
# Bootstrap for OLS:
plt.figure()
plt.semilogy(ols_boot_mse,label='mse')
plt.semilogy(ols_boot_bias,label='bias')
plt.semilogy(ols_boot_variance, label='variance')
plt.title('OLS bias-variance-MSE by bootstrap')
plt.legend()
plt.show()
# CV for Ridge, best+low+middle+high lambdas
plt.figure()
plt.semilogy(best_ridge_mse, label='best for each degree')
plt.semilogy(ridge_lamb_deg_mse[:,0], label='lambda={}'.format(lambdas[0]))
plt.semilogy(ridge_lamb_deg_mse[:,12], label='lambda={}'.format(lambdas[12]))
plt.semilogy(ridge_lamb_deg_mse[:,24], label='lambda={}'.format(lambdas[24]))
plt.title('Ridge CV MSE for best lambda at each degree, plus for given lambdas across all degrees')
plt.legend()
plt.show()
# Bootstrap for the best ridge lambdas:
plt.figure()
plt.semilogy(ridge_best_lambda_boot_mse, label='mse')
plt.semilogy(ridge_best_lambda_boot_bias, label ='bias')
plt.semilogy(ridge_best_lambda_boot_variance, label='variance')
plt.title('Best ridge lambdas for each degree bootstrap')
plt.legend()
plt.show()
# Bootstrap only bias and variance for low+middle+high ridge lambdas
plt.figure()
plt.semilogy(ridge_subset_lambda_boot_bias[:,0], label = 'bias, lambda = {}'.format(subset_lambdas[0]))
plt.semilogy(ridge_subset_lambda_boot_variance[:,0], label = 'variance, lambda = {}'.format(subset_lambdas[0]))
plt.semilogy(ridge_subset_lambda_boot_bias[:,1],label = 'bias, lambda = {}'.format(subset_lambdas[1]))
plt.semilogy(ridge_subset_lambda_boot_variance[:,1],label = 'variance, lambda = {}'.format(subset_lambdas[1]))
plt.semilogy(ridge_subset_lambda_boot_bias[:,2],label = 'bias, lambda = {}'.format(subset_lambdas[2]))
plt.semilogy(ridge_subset_lambda_boot_variance[:,2],label = 'variance, lambda = {}'.format(subset_lambdas[2]))
plt.title('Bias+variance for low, middle, high ridge lambdas')
plt.legend()
plt.show()
# CV for lasso, best+low+middle+high lambdas
plt.figure()
plt.semilogy(best_lasso_mse,label='best lambda for each degree')
plt.semilogy(lasso_lamb_deg_mse[:,0],label='lambda={}'.format(lambdas[0]))
plt.semilogy(lasso_lamb_deg_mse[:,12],label='lambda={}'.format(lambdas[12]))
plt.semilogy(lasso_lamb_deg_mse[:,24],label='lambda={}'.format(lambdas[24]))
plt.title('Lasso CV MSE for best lambda at each degree, plus for given lambdas across all degrees')
plt.legend()
plt.show()
# Bootstrap for the best lasso lambdas:
plt.figure()
plt.semilogy(lasso_best_lambda_boot_mse, label='mse')
plt.semilogy(lasso_best_lambda_boot_bias, label='bias')
plt.semilogy(lasso_best_lambda_boot_variance, label='variance')
plt.title('Best lasso lambdas for each degree bootstrap')
plt.legend()
plt.show()
# Bootstrap only bias and variance for low+middle+high lasso lambdas
plt.figure()
plt.semilogy(lasso_subset_lambda_boot_bias[:,0],label = 'bias, lambda = {}'.format(subset_lambdas[0]))
plt.semilogy(lasso_subset_lambda_boot_variance[:,0],label = 'variance, lambda = {}'.format(subset_lambdas[0]))
plt.semilogy(lasso_subset_lambda_boot_bias[:,1],label = 'bias, lambda = {}'.format(subset_lambdas[1]))
plt.semilogy(lasso_subset_lambda_boot_variance[:,1],label = 'variance, lambda = {}'.format(subset_lambdas[1]))
plt.semilogy(lasso_subset_lambda_boot_bias[:,2],label = 'bias, lambda = {}'.format(subset_lambdas[2]))
plt.semilogy(lasso_subset_lambda_boot_variance[:,2],label = 'variance, lambda = {}'.format(subset_lambdas[2]))
plt.title('Bias+variance for low, middle, high lasso lambdas')
plt.legend()
plt.show()
# For a couple of degrees, plot cv mse vs lambda for ridge, will break program if max_degrees < 8
plt.figure()
plt.plot(np.log10(lambdas), ridge_lamb_deg_mse[max_degree-1], label = 'degree = {}'.format(max_degree-1))
plt.plot(np.log10(lambdas), ridge_lamb_deg_mse[max_degree-2], label = 'degree = {}'.format(max_degree-2))
plt.plot(np.log10(lambdas), ridge_lamb_deg_mse[max_degree-3], label = 'degree = {}'.format(max_degree-3))
plt.plot(np.log10(lambdas), ridge_lamb_deg_mse[max_degree-5], label = 'degree = {}'.format(max_degree-5))
plt.plot(np.log10(lambdas), ridge_lamb_deg_mse[max_degree-7], label = 'degree = {}'.format(max_degree-7))
plt.legend()
plt.show()
# For a copule of degrees, plot cv mse vs lambda for lasso, will break program if max_degree < 8.
plt.figure()
plt.plot(np.log10(lambdas), lasso_lamb_deg_mse[max_degree-1], label = 'degree = {}'.format(max_degree-1))
plt.plot(np.log10(lambdas), lasso_lamb_deg_mse[max_degree-2], label = 'degree = {}'.format(max_degree-2))
plt.plot(np.log10(lambdas), lasso_lamb_deg_mse[max_degree-3], label = 'degree = {}'.format(max_degree-3))
plt.plot(np.log10(lambdas), lasso_lamb_deg_mse[max_degree-5], label = 'degree = {}'.format(max_degree-5))
plt.plot(np.log10(lambdas), lasso_lamb_deg_mse[max_degree-7], label = 'degree = {}'.format(max_degree-7))
plt.legend()
plt.show()
print('best ridge lambda:')
print(best_ridge_lambda)
print('best lasso lambda:')
print(best_lasso_lambda)
return