def mcycle_data_linear():
X, y = mcycle()
gam = LinearGAM()
gam.gridsearch(X, y)
XX = gam.generate_X_grid(term=0)
plt.figure()
plt.scatter(X, y, facecolor='gray', edgecolors='none')
plt.plot(XX, gam.predict(XX), 'r--')
plt.plot(XX, gam.prediction_intervals(XX, width=.95), color='b', ls='--')
plt.title('95% prediction interval')
plt.savefig('imgs/pygam_mcycle_data_linear.png', dpi=300)
m = X.min()
M = X.max()
XX = np.linspace(m - 10, M + 10, 500)
Xl = np.linspace(m - 10, m, 50)
Xr = np.linspace(M, M + 10, 50)
plt.figure()
plt.plot(XX, gam.predict(XX), 'k')
plt.plot(Xl, gam.confidence_intervals(Xl), color='b', ls='--')
plt.plot(Xr, gam.confidence_intervals(Xr), color='b', ls='--')
plt.plot(X, gam.confidence_intervals(X), color='r', ls='--')
plt.savefig('imgs/pygam_mcycle_data_extrapolation.png', dpi=300)
def expectiles():
"""
a bunch of expectiles
"""
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)
XX = gam50.generate_X_grid(term=0, n=500)
fig = plt.figure()
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()
fig.tight_layout()
plt.savefig('imgs/pygam_expectiles.png', dpi=300)
def single_data_linear():
X, y = mcycle()
gam = LinearGAM()
gam.gridsearch(X, y)
# single pred linear
plt.figure()
plt.scatter(X, y, facecolor='gray', edgecolors='none')
plt.plot(X, gam.predict(X), color='r')
plt.title('Best Lambda: {0:.2f}'.format(gam.lam))
plt.savefig('imgs/pygam_single_pred_linear.png', dpi=300)
def mcycle_X_y():
# y is real
# recommend LinearGAM
return mcycle(return_X_y=True)
