plt.xscale('log')
plt.show()
#ELASTICNET
from sklearn.linear_model import ElasticNet
l1_ratio_space = np.logspace(-2, 0.5, 5)
#l1_ratio_space = [0,1, 0.5, 1, 2, 3,]
R2_test = []
R2_train = []
l1_ratio_serie = []
eln = ElasticNet(normalize = True, alpha = 0.001)
for l1_ratio in l1_ratio_space:
eln.l1_ratio = l1_ratio
eln.fit(X_train_std, y_train_std)
y_train_pred = eln.predict(X_train_std)
y_test_pred = eln.predict(X_test_std)
print('l1_ratio = %.4f'%l1_ratio)
print('\tIntercept:\t%.3f' % eln.intercept_)
# print('%.3f' % eln.intercept_)
for i in range (13):
print('\tSlope #%.0f:\t%.3f' %(i+1, eln.coef_[i]))
# print('%.3f' %(eln.coef_[i]))
print('\tMSE train: %.3f, test: %.3f \tR^2 train: %.3f, test: %.3f' %
(mean_squared_error(y_train_std, y_train_pred),
mean_squared_error(y_test_std, y_test_pred),
r2_score(y_train_std, y_train_pred),
######################################### # ELASTIC NET MODEL from sklearn.linear_model import ElasticNet regr = ElasticNet(random_state=0) regr.alpha = 1.9 regr.l1_ratio = 0.65 regr.fit(X_train,y_train) # Calling the score method, which compares the predicted values to the actual values y_score = regr.score(X_test, y_test) # The score is directly comparable to R-Square print(y_score) ######### # Theil sen model
plt.ylabel('Residuals')
plt.legend(loc='upper left')
plt.hlines(y=0, xmin=-10, xmax=50, color='black', lw=2)
plt.xlim([-10, 50])
plt.show()
# Setup the array of alphas and lists to store scores
l1_space = np.logspace(-4, 0, 50)
MSE4_test_scores = []
MSE4_train_scores = []
R24_test_scores = []
R24_train_scores = []
for l1_ratio in l1_space:
y_train_pred4 = se.predict(X_train)
y_test_pred4 = se.predict(X_test)
se.l1_ratio = l1_ratio
se.fit(X, y)
MSE4_test_scores.append(mean_squared_error(y_test, y_test_pred4))
MSE4_train_scores.append(mean_squared_error(y_train, y_train_pred4))
R24_test_scores.append(r2_score(y_test, y_test_pred4))
R24_train_scores.append(r2_score(y_train, y_train_pred4))
plt.plot(alpha_space, MSE4_test_scores)
plt.xlabel('l1_ratio')
plt.ylabel('MSE4_test_scores')
plt.show()
plt.plot(alpha_space, MSE4_train_scores)
plt.xlabel('l1_ratio')
plt.ylabel('MSE4_train_scores')
plt.show()
plt.show()
#MSE
print(
'ElasticNet regression MSE train: %.3f, test: %.3f' % (mean_squared_error(
y_train, y_train_pred4), mean_squared_error(y_test, y_test_pred4)))
#R2
print('ElasticNet regression R^2 train: %.3f, test: %.3f' %
(r2_score(y_train, y_train_pred4), r2_score(y_test, y_test_pred4)))
#Compare with different l1_ratio
l1_ratio_space = np.logspace(-4, 0, 50)
MSE_test_scores = []
MSE_train_scores = []
R2_test_scores = []
R2_train_scores = []
for l1_ratio in l1_ratio_space:
elastic.l1_ratio = l1_ratio
elastic.fit(X, y)
y_train_pred3 = elastic.predict(X_train).reshape(361, 1)
y_test_pred3 = elastic.predict(X_test).reshape(91, 1)
MSE_train_scores.append(mean_squared_error(y_train, y_train_pred3))
MSE_test_scores.append(mean_squared_error(y_test, y_test_pred3))
R2_train_scores.append(r2_score(y_train, y_train_pred3))
R2_test_scores.append(r2_score(y_test, y_test_pred3))
plt.plot(l1_ratio_space, MSE_train_scores)
plt.xlabel('l1_ratio_space')
plt.ylabel('MSE_train_scores')
plt.show()
plt.plot(l1_ratio_space, MSE_test_scores)
plt.xlabel('l1_ratio_space')
plt.ylabel('MSE_test_scores')
plt.show()
# Display the plot
display_plot(lasso_scores, lasso_scores_std, 'lasso')
print('Elastic Net regression')
# Elastic Net regression:
alpha_space = np.logspace(-2, 0, 5)
elanet_scores = []
elanet_scores_std = []
# Create a ridge regressor: lasso
elanet = ElasticNet(alpha=1.0)
for alpha in alpha_space:
# Specify the alpha value to use: ridge.alpha
elanet.l1_ratio = alpha
elanet.fit(X_train, y_train)
y_train_pred = elanet.predict(X_train)
y_test_pred = elanet.predict(X_test)
# plt.plot(range(len(df.columns)-1), ridge.coef_)
# plt.xticks(range(len(df.columns)-1), df.columns.values, rotation=60)
# plt.margins(0.02)
# plt.show()
print('Slope:', elanet.coef_)
print('Intercept: %.3f' % elanet.intercept_)
print('MSE train: %.3f, test: %.3f' % (mean_squared_error(
y_train, y_train_pred), mean_squared_error(y_test, y_test_pred)))
print('R^2 train: %.3f, test: %.3f' %
(r2_score(y_train, y_train_pred), r2_score(y_test, y_test_pred)))
ary = np.array(range(100000))
plt.legend(loc='upper left')
plt.hlines(y=0, xmin=-10, xmax=50, color='black', lw=2)
plt.xlim([-10, 50])
plt.show()
print('\n')
#Elastic Net
print('Elastic Net :')
elanet = ElasticNet(alpha=1.0)
l1_ratio_space = [0.1, 0.5, 1, 2, 2.5]
for l1_ratio in l1_ratio_space:
print('l1_ratio = ', l1_ratio)
elanet.l1_ratio = l1_ratio
elanet.fit(X_train, y_train)
y_train_pred = elanet.predict(X_train)
y_test_pred = elanet.predict(X_test)
print('MSE train: %.3f, test: %.3f' % (mean_squared_error(
y_train, y_train_pred), mean_squared_error(y_test, y_test_pred)))
print('R^2 train: %.3f, test: %.3f' %
(r2_score(y_train, y_train_pred), r2_score(y_test, y_test_pred)))
print('Coefficient: %.3f' % elanet.coef_[0])
print('Intercept: %.3f' % elanet.intercept_)
print('\n')
plt.scatter(y_train_pred,
y_train_pred - y_train,
c='steelblue',
plt.savefig('lasso alpha=' + str(alpha) + '10_09.png', dpi=300)
plt.show()
print('Elastic Net regression')
# Elastic Net regression:
ratio_space = np.logspace(-2, 0, 4)
elanet_scores = []
elanet_scores_std = []
# Create a ridge regressor: lasso
elanet = ElasticNet(alpha=1.0)
for ratio in ratio_space:
# Specify the alpha value to use: ridge.alpha
elanet.l1_ratio = ratio
elanet.fit(X_train, y_train)
y_train_pred = elanet.predict(X_train)
y_test_pred = elanet.predict(X_test)
print('Slope:', elanet.coef_)
print('intercept:', elanet.intercept_)
print('MSE train: %.3f, test: %.3f' % (mean_squared_error(
y_train, y_train_pred), mean_squared_error(y_test, y_test_pred)))
print('R^2 train: %.3f, test: %.3f' %
(r2_score(y_train, y_train_pred), r2_score(y_test, y_test_pred)))
ary = np.array(range(100000))
plt.title('ratio=' + str(ratio))
plt.scatter(y_train_pred,
