Exemplo n.º 1
0
    def __init__(self):
        super(self.__class__, self).__init__() #initialize DGP

        nobs = self.nobs
        y_true, x, exog = self.y_true, self.x, self.exog

        np.random.seed(8765993)
        sigma_noise = 0.1
        y = y_true + sigma_noise * np.random.randn(nobs)

        m = AdditiveModel(x)
        m.fit(y)
        res_gam = m.results #TODO: currently attached to class

        res_ols = OLS(y, exog).fit()

        #Note: there still are some naming inconsistencies
        self.res1 = res1 = Dummy() #for gam model
        #res2 = Dummy() #for benchmark
        self.res2 = res2 = res_ols  #reuse existing ols results, will add additional

        res1.y_pred = res_gam.predict(x)
        res2.y_pred = res_ols.model.predict(res_ols.params, exog)
        res1.y_predshort = res_gam.predict(x[:10])

        slopes = [i for ss in m.smoothers for i in ss.params[1:]]

        const = res_gam.alpha + sum([ss.params[1] for ss in m.smoothers])
        #print const, slopes
        res1.params = np.array([const] + slopes)
Exemplo n.º 2
0
    def __init__(self):
        super(self.__class__, self).__init__()  #initialize DGP

        nobs = self.nobs
        y_true, x, exog = self.y_true, self.x, self.exog

        np.random.seed(8765993)
        sigma_noise = 0.1
        y = y_true + sigma_noise * np.random.randn(nobs)

        m = AdditiveModel(x)
        m.fit(y)
        res_gam = m.results  #TODO: currently attached to class

        res_ols = OLS(y, exog).fit()

        #Note: there still are some naming inconsistencies
        self.res1 = res1 = Dummy()  #for gam model
        #res2 = Dummy() #for benchmark
        self.res2 = res2 = res_ols  #reuse existing ols results, will add additional

        res1.y_pred = res_gam.predict(x)
        res2.y_pred = res_ols.model.predict(res_ols.params, exog)
        res1.y_predshort = res_gam.predict(x[:10])

        slopes = [i for ss in m.smoothers for i in ss.params[1:]]

        const = res_gam.alpha + sum([ss.params[1] for ss in m.smoothers])
        #print const, slopes
        res1.params = np.array([const] + slopes)
Exemplo n.º 3
0
x1.sort()
x2 = R.standard_normal(500)
x2.sort()
y = R.standard_normal((500, ))

f1 = lambda x1: (x1 + x1**2 - 3 - 1.5 * x1**3 + np.exp(-x1))
f2 = lambda x2: (x2 + x2**2 - np.exp(x2))
z = standardize(f1(x1)) + standardize(f2(x2))
z = standardize(z) * 0.1

y += z
d = np.array([x1, x2]).T

if example == 1:
    print "normal"
    m = AdditiveModel(d)
    m.fit(y)
    x = np.linspace(-2, 2, 50)

    print m

import scipy.stats, time

if example == 2:
    print "binomial"
    f = family.Binomial()
    b = np.asarray([scipy.stats.bernoulli.rvs(p) for p in f.link.inverse(y)])
    b.shape = y.shape
    m = GAM(b, d, family=f)
    toc = time.time()
    m.fit(b)
Exemplo n.º 4
0
x2 = R.standard_normal(nobs)
x2.sort()
y = R.standard_normal((nobs,))

f1 = lambda x1: (x1 + x1**2 - 3 - 1 * x1**3 + 0.1 * np.exp(-x1/4.))
f2 = lambda x2: (x2 + x2**2 - 0.1 * np.exp(x2/4.))
z = standardize(f1(x1)) + standardize(f2(x2))
z = standardize(z) * 2 # 0.1

y += z
d = np.array([x1,x2]).T


if example == 1:
    print "normal"
    m = AdditiveModel(d)
    m.fit(y)
    x = np.linspace(-2,2,50)

    print m

    y_pred = m.results.predict(d)
    plt.figure()
    plt.plot(y, '.')
    plt.plot(z, 'b-', label='true')
    plt.plot(y_pred, 'r-', label='AdditiveModel')
    plt.legend()
    plt.title('gam.AdditiveModel')

import scipy.stats, time
Exemplo n.º 5
0
x1 = np.linspace(lb, ub, nobs)
x2 = np.sin(2*x1)
x = np.column_stack((x1/x1.max()*2, x2))
exog = (x[:,:,None]**np.arange(order+1)[None, None, :]).reshape(nobs, -1)
idx = range((order+1)*2)
del idx[order+1]
exog_reduced = exog[:,idx]  #remove duplicate constant
y_true = exog.sum(1) / 2.
z = y_true #alias check
d = x
y = y_true + sigma_noise * np.random.randn(nobs)

example = 1

if example == 1:
    m = AdditiveModel(d)
    m.fit(y)

    y_pred = m.results.predict(d)


for ss in m.smoothers:
    print ss.params

res_ols = OLS(y, exog_reduced).fit()
print res_ols.params

#assert_almost_equal(y_pred, res_ols.fittedvalues, 3)

if example > 0:
    import matplotlib.pyplot as plt