from pygam import ExpectileGAM
from pygam.datasets import mcycle
X, y = mcycle(return_X_y=True)
# lets fit the mean model first by CV
gam50 = ExpectileGAM(expectile=0.5).gridsearch(X, y)
# and copy the smoothing to the other models
lam = gam50.lam
# now fit a few more models
gam95 = ExpectileGAM(expectile=0.95, lam=lam).fit(X, y)
gam75 = ExpectileGAM(expectile=0.75, lam=lam).fit(X, y)
gam25 = ExpectileGAM(expectile=0.25, lam=lam).fit(X, y)
gam05 = ExpectileGAM(expectile=0.05, lam=lam).fit(X, y)from matplotlib import pyplot as plt
XX = gam50.generate_X_grid(term=0, n=500)
plt.scatter(X, y, c='k', alpha=0.2)
plt.plot(XX, gam95.predict(XX), label='0.95')
plt.plot(XX, gam75.predict(XX), label='0.75')
plt.plot(XX, gam50.predict(XX), label='0.50')
plt.plot(XX, gam25.predict(XX), label='0.25')
plt.plot(XX, gam05.predict(XX), label='0.05')
plt.legend()
pygam.pygam.ExpectileGAM