def plot_best_fit(data, title):
X = reg.CreateDesignMatrix_X(x,y,n=5)
beta = reg.OLS_fit(X,data)
z_tilde = reg.OLS_predict(beta, X)
z_tilde = z_tilde.reshape(z.shape)
plot_surface(z_tilde, title)
def OLS_analysis():
print('Analysis for OLS')
# matrices for table construction
MSEs = np.zeros((maxdegree, 4))
R2s = np.zeros((maxdegree, 3))
# first column of tables: polynomial degrees
degrees = [i for i in range(1,maxdegree+1)]
MSEs[:,0] = degrees
R2s[:,0] = degrees
# for bias-variance tradeoff
error_test = np.zeros(maxdegree)
error_train = np.zeros(maxdegree)
bias = np.zeros(maxdegree)
variance = np.zeros(maxdegree)
# find error as function of model complexity
for degree in degrees:
X = reg.CreateDesignMatrix_X(x,y,n=degree)
# fitting without resampling
beta = reg.OLS_fit(X,z_flat)
z_tilde = reg.OLS_predict(beta, X)
# MSE
MSE = mean_squared_error(z_flat,z_tilde)
MSEs[degree-1][1] = MSE
# R²
R_squared = r2_score(z_flat,z_tilde)
R2s[degree-1][1] = R_squared
#confidence interval of betas
uppers, lowers = reg.CI_beta(X, beta)
# fitting with resampling of data
X_train, X_test, z_train, z_test = train_test_split(X, z_flat, test_size = 0.33)
beta_train = reg.OLS_fit(X_train, z_train)
z_tilde = reg.OLS_predict(beta_train, X_test)
# MSE
MSE = mean_squared_error(z_test,z_tilde)
MSEs[degree-1][2] = MSE
# R²
R_squared = r2_score(z_test,z_tilde)
R2s[degree-1][2] = R_squared
# fitting with k-fold cross validation
MSE_kf = reg.k_fold_cross_validation(
X,z_flat, 5, reg.OLS_fit, reg.OLS_predict)[0]
MSEs[degree-1][3] = MSE_kf
# train vs test and bias/variance calculations using bootstrap
e, e2, b, v = reg.bootstrap(X, z_flat, 100, fit_type=reg.OLS_fit, predict_type= reg.OLS_predict)
error_test[degree-1] = e
error_train[degree-1] = e2
bias[degree-1] = b
variance[degree-1] = v
# train vs test using CV
#e, e2 = reg.k_fold_cross_validation(X, z_flat, k=5, fit_type=reg.OLS_fit, predict_type= reg.OLS_predict, tradeoff = False)
#error_test[degree-1] = e
#error_train[degree-1] = e2
# plot confidence interval for betas only for last degree
if degree == maxdegree:
sns.set();
plt.fill_between(np.arange(len(beta)), uppers, lowers, label='95% confidence interval', color=(.5,.5,.5,.2))
plt.plot(beta, label='beta', color="r")
plt.title('95 percent Confidence Interval for Beta from OLS when Degree=5')
plt.ylabel('beta_i')
plt.xlabel('i')
plt.legend()
plt.show()
# making LaTex table for MSEs
df = pd.DataFrame(MSEs, columns=['Degree', 'No resampling', 'train_test_split', 'k-fold CV'])
df['Degree'] = df['Degree'].astype(int)
tab = df.to_latex(index=False, float_format="%.5f")
print(f"\n\n{tab}\n\n")
# making LaTex table for R2s
df = pd.DataFrame(R2s, columns=['Degree', 'No resampling', 'train_test_split'])
df['Degree'] = df['Degree'].astype(int)
tab = df.to_latex(index=False, float_format="%.5f")
print(f"\n\n{tab}\n\n")
# plot test vs train MSE using bootstrap or CV
sns.set();
plt.plot(degrees, error_test, label='Test MSE')
plt.plot(degrees, error_train, label='Train MSE')
plt.title('Train vs Test MSE for OLS using the 5-fold CV')
plt.xlabel('Polynomial degree')
plt.ylabel('Error')
plt.legend()
plt.show()
# plot bias-variance tradeoff using bootstrap
sns.set();
plt.plot(degrees, error_test, label='MSE')
plt.plot(degrees, bias, label='bias')
plt.plot(degrees, variance, label='Variance')
plt.title('Bias-Variance Tradeoff for OLS using the Bootstrap Method')
plt.xlabel('Polynomial degree')
plt.ylabel('Error')
plt.legend()
plt.show()
def plot_best_fit():
X = reg.CreateDesignMatrix_X(x, y, n=5)
beta = reg.OLS_fit(X, z_flat)
z_tilde = reg.OLS_predict(beta, X)
z_tilde = z_tilde.reshape(terrain_downsized.shape)
plot_surface(z_tilde, 'Surface plot of OLS fifth order polynomial')
def Lasso_analysis():
print('Analysis for Lasso')
lambdas = [10**i for i in range(-5,2)] # lambda values to test
degrees = [i for i in range(1,maxdegree+1)] # complexities to test
MSEs = np.zeros((maxdegree,4)) # collection of MSEs for a given lambda
MSEs[:,0] = degrees # first column in table is complexity
R2s = np.zeros((maxdegree,3)) # collection of R"s for a given lambda
R2s[:,0] = degrees # first column in table is complexity
MSEs_kfold = np.zeros((len(lambdas),maxdegree)) # for plotting heatmap
# for bias-variance tradeoff
error_test = np.zeros(maxdegree)
error_train = np.zeros(maxdegree)
bias = np.zeros(maxdegree)
variance = np.zeros(maxdegree)
# find error as function of complexity and lambda
for degree in range(1,maxdegree+1):
X = reg.CreateDesignMatrix_X(x,y,n=degree)
lam_index = 0
for lam in lambdas:
MSE_kfold = reg.k_fold_cross_validation(
X,z_flat, 5, reg.Lasso_fit, reg.Lasso_predict, _lambda=lam)[0]
MSEs_kfold[lam_index][degree-1] = MSE_kfold
lam_index += 1
# fitting without resampling
model = reg.Lasso_fit(X,z_flat,_lambda=1)
z_tilde = reg.Lasso_predict(model, X)
# MSE
MSE = mean_squared_error(z_flat,z_tilde)
MSEs[degree-1][1] = MSE
# R²
R_squared = r2_score(z_flat,z_tilde)
print(R_squared)
R2s[degree-1][1] = R_squared
# fitting with resampling
X_train, X_test, z_train, z_test = train_test_split(X, z_flat, test_size = 0.33)
model_train = reg.Lasso_fit(X_train, z_train, _lambda=1)
z_tilde = reg.Lasso_predict(model_train, X_test)
# MSE
MSE = mean_squared_error(z_test,z_tilde)
MSEs[degree-1][2] = MSE
# R²
R_squared = r2_score(z_test,z_tilde)
R2s[degree-1][2] = R_squared
# kfold into table
MSEs[degree-1][3] = MSEs_kfold[6][degree-1]
# train vs test and bias/variance calculations using bootstrap for chosen lambda
e, e2, b, v = reg.bootstrap(X, z_flat, 100, fit_type=reg.Lasso_fit, predict_type= reg.Lasso_predict, _lambda=1)
error_test[degree-1] = e
error_train[degree-1] = e2
bias[degree-1] = b
variance[degree-1] = v
# train vs test using CV
e, e2 = reg.k_fold_cross_validation(X, z_flat, k=5, fit_type=reg.Lasso_fit, predict_type= reg.Lasso_predict, tradeoff = False, _lambda=1)
error_test[degree-1] = e
error_train[degree-1] = e2
# making LaTex table for MSEs (use the one corresponding to best lambda) to compare with OLS
df = pd.DataFrame(MSEs, columns=['Degree', 'No resampling', 'train_test_split', 'k-fold CV'])
df['Degree'] = df['Degree'].astype(int)
tab = df.to_latex(index=False, float_format="%.5f")
print(f"\n\n{tab}\n\n")
# making LaTex table for R2s (use the one corresponding to best lambda) to compare with OLS
df = pd.DataFrame(R2s, columns=['Degree', 'No resampling', 'train_test_split'])
df['Degree'] = df['Degree'].astype(int)
tab = df.to_latex(index=False, float_format="%.5f")
print(f"\n\n{tab}\n\n")
# plotting heatmap with kfold CV to choose best combination of degree and lambda
fig, ax = plt.subplots()
sns.heatmap(MSEs_kfold,xticklabels=degrees, yticklabels=np.log10(lambdas), annot=True, fmt='.2f')
plt.xlabel('Polynomial degree')
plt.ylabel('log(lambda)')
plt.title('MSE as function of lambda and complexity with Lasso regression')
plt.tight_layout()
plt.show()
# plot test vs train MSE using bootstrap or CV for chosen lambda
sns.set();
plt.plot(degrees, error_test, label='Test MSE')
plt.plot(degrees, error_train, label='Train MSE')
plt.title('Train vs Test MSE for Lasso using the 5-fold CV Method')
plt.xlabel('Polynomial degree')
plt.ylabel('Error')
plt.legend()
plt.show()
# plot bias-variance tradeoff using bootstrap for chosen lambda
sns.set();
plt.plot(degrees, error_test, label='MSE')
plt.plot(degrees, bias, label='bias')
plt.plot(degrees, variance, label='Variance')
plt.title('Bias-Variance Tradeoff for Lasso using the Bootstrap Method')
plt.xlabel('Polynomial degree')
plt.ylabel('Error')
plt.legend()
plt.show()
# find minimum MSE from kFold
print(MSEs_kfold.min())
import numpy as np
import regression as reg
import sklearn.linear_model as skl
from sklearn.metrics import mean_squared_error, r2_score, make_scorer
from sklearn.model_selection import KFold, cross_validate
# make data
x = np.arange(0, 1, 0.05)
y = np.arange(0, 1, 0.05)
x, y = np.meshgrid(x, y)
z = reg.franke_function(x, y)
z_flat = np.ravel(z)
X = reg.CreateDesignMatrix_X(x, y, n=5)
def test_MSE_wo_resamp_OLS():
''' Function testing the OLS code without resampling by comparing to scikit-learn. '''
beta = reg.OLS_fit(X, z_flat)
z_tilde = reg.OLS_predict(beta, X)
MSE_own = mean_squared_error(z_flat, z_tilde)
model = skl.LinearRegression()
model.fit(X, z_flat)
z_tilde_skl = model.predict(X)
MSE_skl = mean_squared_error(z_flat, z_tilde_skl)
tol = 1E-14
success = abs(MSE_own - MSE_skl) < tol
msg = 'Error: The MSE for own OLS code did not match the MSE from scikit-learn.'