def test_spatial(self):
X = np.array(self.db.by_col("INC"))
X = np.reshape(X, (49, 1))
yd = np.array(self.db.by_col("CRIME"))
yd = np.reshape(yd, (49, 1))
q = np.array(self.db.by_col("DISCBD"))
q = np.reshape(q, (49, 1))
w = pysal.lib.weights.Queen.from_shapefile(
pysal.lib.examples.get_path('columbus.shp'))
reg = GM_Lag(self.y, X, yd, q, spat_diag=True, w=w)
betas = np.array([[5.46344924e+01], [4.13301682e-01],
[-5.92637442e-01], [-7.40490883e-03]])
np.testing.assert_allclose(reg.betas, betas, RTOL)
vm = np.array([[
4.45202654e+02, -1.50290275e+01, -6.36557072e+00, -5.71403440e-03
], [-1.50290275e+01, 5.93124683e-01, 2.19169508e-01, -6.70675916e-03],
[
-6.36557072e+00, 2.19169508e-01, 1.06577542e-01,
-2.96533875e-03
],
[
-5.71403440e-03, -6.70675916e-03, -2.96533875e-03,
1.15655425e-03
]])
np.testing.assert_allclose(reg.vm, vm, RTOL)
ak_test = np.array([2.52597326, 0.11198567])
np.testing.assert_allclose(reg.ak_test, ak_test, RTOL)
def test_names(self):
X = np.array(self.db.by_col("INC"))
X = np.reshape(X, (49, 1))
X = SP.csr_matrix(X)
yd = np.array(self.db.by_col("CRIME"))
yd = np.reshape(yd, (49, 1))
q = np.array(self.db.by_col("DISCBD"))
q = np.reshape(q, (49, 1))
w = pysal.lib.weights.Queen.from_shapefile(
pysal.lib.examples.get_path('columbus.shp'))
gwk = pysal.lib.weights.Kernel.from_shapefile(
pysal.lib.examples.get_path('columbus.shp'),
k=5,
function='triangular',
fixed=False)
name_x = ['inc']
name_y = 'crime'
name_yend = ['crime']
name_q = ['discbd']
name_w = 'queen'
name_gwk = 'k=5'
name_ds = 'columbus'
reg = GM_Lag(self.y,
X,
yd,
q,
spat_diag=True,
w=w,
robust='hac',
gwk=gwk,
name_x=name_x,
name_y=name_y,
name_q=name_q,
name_w=name_w,
name_yend=name_yend,
name_gwk=name_gwk,
name_ds=name_ds)
betas = np.array([[5.46344924e+01], [4.13301682e-01],
[-5.92637442e-01], [-7.40490883e-03]])
np.testing.assert_allclose(reg.betas, betas, RTOL)
vm = np.array([
[5.70817052e+02, -1.83655385e+01, -8.36602575e+00, 2.37538877e-02],
[-1.85224661e+01, 6.53311383e-01, 2.84209566e-01, -6.47694160e-03],
[-8.31105622e+00, 2.78772694e-01, 1.38144928e-01, -3.98175246e-03],
[2.66662466e-02, -6.23783104e-03, -4.11092891e-03, 1.10936528e-03]
])
np.testing.assert_allclose(reg.vm, vm, RTOL)
self.assertListEqual(reg.name_x, ['CONSTANT'] + name_x)
name_yend.append('W_crime')
self.assertListEqual(reg.name_yend, name_yend)
name_q.extend(['W_inc', 'W_discbd'])
self.assertListEqual(reg.name_q, name_q)
self.assertEqual(reg.name_y, name_y)
self.assertEqual(reg.name_w, name_w)
self.assertEqual(reg.name_gwk, name_gwk)
self.assertEqual(reg.name_ds, name_ds)
def test_lag_q(self):
X = np.array(self.db.by_col("INC"))
X = np.reshape(X, (49, 1))
yd = np.array(self.db.by_col("CRIME"))
yd = np.reshape(yd, (49, 1))
q = np.array(self.db.by_col("DISCBD"))
q = np.reshape(q, (49, 1))
reg = GM_Lag(self.y, X, w=self.w, yend=yd, q=q, w_lags=2, lag_q=False)
tbetas = np.array([[108.83261383], [-0.48041099], [-1.18950006],
[-0.56140186]])
np.testing.assert_allclose(tbetas, reg.betas)
dbetas = D.se_betas(reg)
se_betas = np.array([58.33203837, 1.09100446, 0.62315167, 0.68088777])
np.testing.assert_allclose(dbetas, se_betas)
def test_init_discbd(self):
X = np.array(self.db.by_col("INC"))
X = np.reshape(X, (49, 1))
yd = np.array(self.db.by_col("CRIME"))
yd = np.reshape(yd, (49, 1))
q = np.array(self.db.by_col("DISCBD"))
q = np.reshape(q, (49, 1))
reg = GM_Lag(self.y, X, w=self.w, yend=yd, q=q, w_lags=2)
tbetas = np.array([[100.79359082], [-0.50215501], [-1.14881711],
[-0.38235022]])
np.testing.assert_allclose(tbetas, reg.betas)
dbetas = D.se_betas(reg)
se_betas = np.array([53.0829123, 1.02511494, 0.57589064, 0.59891744])
np.testing.assert_allclose(dbetas, se_betas)
def test_init_white_(self):
X = []
X.append(self.db.by_col("INC"))
X.append(self.db.by_col("CRIME"))
self.X = np.array(X).T
base_gm_lag = GM_Lag(self.y,
self.X,
w=self.w,
w_lags=2,
robust='white')
tbetas = np.array([[4.53017056e+01], [6.20888617e-01],
[-4.80723451e-01], [2.83622122e-02]])
np.testing.assert_allclose(base_gm_lag.betas, tbetas)
dbetas = D.se_betas(base_gm_lag)
se_betas = np.array([20.47077481, 0.50613931, 0.20138425, 0.38028295])
np.testing.assert_allclose(dbetas, se_betas)
def test_n_k(self):
X = []
X.append(self.db.by_col("INC"))
X.append(self.db.by_col("CRIME"))
self.X = np.array(X).T
reg = GM_Lag(self.y, self.X, w=self.w, w_lags=2, sig2n_k=True)
betas = np.array([[4.53017056e+01], [6.20888617e-01],
[-4.80723451e-01], [2.83622122e-02]])
np.testing.assert_allclose(reg.betas, betas, RTOL)
vm = np.array([
[
3.49389596e+02, -5.36394351e+00, -2.81960968e+00,
-4.35694515e+00
],
[-5.36394351e+00, 2.99965892e-01, 6.44054000e-02, -3.13108972e-02],
[-2.81960968e+00, 6.44054000e-02, 3.61800155e-02, 1.61095854e-02],
[-4.35694515e+00, -3.13108972e-02, 1.61095854e-02, 1.09698285e-01]
])
np.testing.assert_allclose(reg.vm, vm, RTOL)
def test_init_hac_(self):
X = []
X.append(self.db.by_col("INC"))
X.append(self.db.by_col("CRIME"))
self.X = np.array(X).T
gwk = pysal.lib.weights.Kernel.from_shapefile(
pysal.lib.examples.get_path('columbus.shp'),
k=15,
function='triangular',
fixed=False)
base_gm_lag = GM_Lag(self.y,
self.X,
w=self.w,
w_lags=2,
robust='hac',
gwk=gwk)
tbetas = np.array([[4.53017056e+01], [6.20888617e-01],
[-4.80723451e-01], [2.83622122e-02]])
np.testing.assert_allclose(base_gm_lag.betas, tbetas)
dbetas = D.se_betas(base_gm_lag)
se_betas = np.array([19.08513569, 0.51769543, 0.18244862, 0.35460553])
np.testing.assert_allclose(dbetas, se_betas)
def test___init__(self):
X = []
X.append(self.db.by_col("INC"))
X.append(self.db.by_col("CRIME"))
self.X = np.array(X).T
reg = GM_Lag(self.y, self.X, w=self.w, w_lags=2)
betas = np.array([[4.53017056e+01], [6.20888617e-01],
[-4.80723451e-01], [2.83622122e-02]])
np.testing.assert_allclose(reg.betas, betas, RTOL)
e_5 = np.array([[29.28976367], [-6.07439501], [-15.30080685],
[-0.41773375], [-5.67197968]])
np.testing.assert_allclose(reg.e_pred[0:5], e_5, RTOL)
h_0 = np.array([
1., 19.531, 15.72598, 18.594, 24.7142675, 13.72216667, 27.82929567
])
np.testing.assert_allclose(reg.h[0], h_0)
hth = np.array([
49., 704.371999, 1721.312371, 724.7435916, 1707.35412945,
711.31248483, 1729.63201243
])
np.testing.assert_allclose(reg.hth[0], hth, RTOL)
hthi = np.array([
7.33701328e+00, 2.27764882e-02, 2.18153588e-02, -5.11035447e-02,
1.22515181e-03, -2.38079378e-01, -1.20149133e-01
])
np.testing.assert_allclose(reg.hthi[0], hthi, RTOL)
self.assertEqual(reg.k, 4)
self.assertEqual(reg.kstar, 1)
np.testing.assert_allclose(reg.mean_y, 38.436224469387746, RTOL)
self.assertEqual(reg.n, 49)
pfora1a2 = np.array(
[80.5588479, -1.06625281, -0.61703759, -1.10071931])
np.testing.assert_allclose(reg.pr2, 0.3551928222612527, RTOL)
np.testing.assert_allclose(reg.pr2_e, 0.34763857386174174, RTOL)
np.testing.assert_allclose(reg.pfora1a2[0], pfora1a2, RTOL)
predy_5 = np.array([[50.87411532], [50.76969931], [41.77223722],
[33.44262382], [28.77418036]])
np.testing.assert_allclose(reg.predy[0:5], predy_5, RTOL)
predy_e_5 = np.array([[51.17723933], [50.64139601], [41.65080685],
[33.61773475], [28.89697968]])
np.testing.assert_allclose(reg.predy_e[0:5], predy_e_5, RTOL)
q_5 = np.array([18.594, 24.7142675, 13.72216667, 27.82929567])
np.testing.assert_allclose(reg.q[0], q_5)
self.assertEqual(reg.robust, 'unadjusted')
np.testing.assert_allclose(reg.sig2n_k, 234.54258763039289, RTOL)
np.testing.assert_allclose(reg.sig2n, 215.39625394627919, RTOL)
np.testing.assert_allclose(reg.sig2, 215.39625394627919, RTOL)
np.testing.assert_allclose(reg.std_y, 18.466069465206047, RTOL)
u_5 = np.array([[29.59288768], [-6.20269831], [-15.42223722],
[-0.24262282], [-5.54918036]])
np.testing.assert_allclose(reg.u[0:5], u_5, RTOL)
np.testing.assert_allclose(reg.utu, 10554.41644336768, RTOL)
varb = np.array([
[
1.48966377e+00, -2.28698061e-02, -1.20217386e-02,
-1.85763498e-02
],
[-2.28698061e-02, 1.27893998e-03, 2.74600023e-04, -1.33497705e-04],
[-1.20217386e-02, 2.74600023e-04, 1.54257766e-04, 6.86851184e-05],
[-1.85763498e-02, -1.33497705e-04, 6.86851184e-05, 4.67711582e-04]
])
np.testing.assert_allclose(reg.varb, varb, RTOL)
vm = np.array([
[
3.20867996e+02, -4.92607057e+00, -2.58943746e+00,
-4.00127615e+00
],
[-4.92607057e+00, 2.75478880e-01, 5.91478163e-02, -2.87549056e-02],
[-2.58943746e+00, 5.91478163e-02, 3.32265449e-02, 1.47945172e-02],
[-4.00127615e+00, -2.87549056e-02, 1.47945172e-02, 1.00743323e-01]
])
np.testing.assert_allclose(reg.vm, vm, RTOL)
x_0 = np.array([1., 19.531, 15.72598])
np.testing.assert_allclose(reg.x[0], x_0, RTOL)
y_5 = np.array([[80.467003], [44.567001], [26.35], [33.200001],
[23.225]])
np.testing.assert_allclose(reg.y[0:5], y_5, RTOL)
yend_5 = np.array([[35.4585005], [46.67233467], [45.36475125],
[32.81675025], [30.81785714]])
np.testing.assert_allclose(reg.yend[0:5], yend_5, RTOL)
z_0 = np.array([1., 19.531, 15.72598, 35.4585005])
np.testing.assert_allclose(reg.z[0], z_0, RTOL)
zthhthi = np.array([[
1.00000000e+00, -2.22044605e-16, -2.22044605e-16, 2.22044605e-16,
4.44089210e-16, 0.00000000e+00, -8.88178420e-16
],
[
0.00000000e+00, 1.00000000e+00,
-3.55271368e-15, 3.55271368e-15,
-7.10542736e-15, 7.10542736e-14, 0.00000000e+00
],
[
1.81898940e-12, 2.84217094e-14, 1.00000000e+00,
0.00000000e+00, -2.84217094e-14,
5.68434189e-14, 5.68434189e-14
],
[
-8.31133940e+00, -3.76104678e-01,
-2.07028208e-01, 1.32618931e+00,
-8.04284562e-01, 1.30527047e+00, 1.39136816e+00
]])
# np.testing.assert_allclose(reg.zthhthi, zthhthi RTOL) #another issue with rtol
np.testing.assert_array_almost_equal(reg.zthhthi, zthhthi, 7)
q = np.array(q).T
reg = TSLS(y, X, yd, q)
# create regression object for spatial test
db = pysal.lib.io.open(pysal.lib.examples.get_path("columbus.dbf"),'r')
y = np.array(db.by_col("HOVAL"))
y = np.reshape(y, (49,1))
X = np.array(db.by_col("INC"))
X = np.reshape(X, (49,1))
yd = np.array(db.by_col("CRIME"))
yd = np.reshape(yd, (49,1))
q = np.array(db.by_col("DISCBD"))
q = np.reshape(q, (49,1))
w = pysal.lib.weights.Rook.from_shapefile(pysal.lib.examples.get_path("columbus.shp"))
w.transform = 'r'
regsp = GM_Lag(y, X, w=w, yend=yd, q=q, w_lags=2)
class TestTStat(unittest.TestCase):
def test_t_stat(self):
obs = diagnostics_tsls.t_stat(reg)
exp = [(5.8452644704588588, 4.9369075950019865e-07),
(0.36760156683572748, 0.71485634049075841),
(-1.9946891307832111, 0.052021795864651159)]
np.testing.assert_allclose(obs, exp, RTOL)
class TestPr2Aspatial(unittest.TestCase):
def test_pr2_aspatial(self):
obs = diagnostics_tsls.pr2_aspatial(reg)
exp = 0.2793613712817381
np.testing.assert_allclose(obs,exp, RTOL)
def test_all_regi_sig2(self):
#Artficial:
n = 256
x1 = np.random.uniform(-10, 10, (n, 1))
x2 = np.random.uniform(1, 5, (n, 1))
q = x2 + np.random.normal(0, 1, (n, 1))
x = np.hstack((x1, x2))
y = np.dot(np.hstack((np.ones(
(n, 1)), x)), np.array([[1], [0.5], [2]])) + np.random.normal(
0, 1, (n, 1))
latt = int(np.sqrt(n))
w = pysal.lib.weights.util.lat2W(latt, latt)
w.transform = 'r'
regi = [0] * (n // 2) + [1] * (n // 2)
model = GM_Lag_Regimes(y,
x1,
regi,
q=q,
yend=x2,
w=w,
regime_lag_sep=True,
regime_err_sep=True)
w1 = pysal.lib.weights.util.lat2W(latt // 2, latt)
w1.transform = 'r'
model1 = GM_Lag(y[0:(n // 2)].reshape((n // 2), 1),
x1[0:(n // 2)],
yend=x2[0:(n // 2)],
q=q[0:(n // 2)],
w=w1)
model2 = GM_Lag(y[(n // 2):n].reshape((n // 2), 1),
x1[(n // 2):n],
yend=x2[(n // 2):n],
q=q[(n // 2):n],
w=w1)
tbetas = np.vstack((model1.betas, model2.betas))
np.testing.assert_allclose(model.betas, tbetas)
vm = np.hstack((model1.vm.diagonal(), model2.vm.diagonal()))
np.testing.assert_allclose(model.vm.diagonal(), vm, RTOL)
#Columbus:
X = np.array(self.db.by_col("INC"))
X = np.reshape(X, (49, 1))
yd = np.array(self.db.by_col("HOVAL"))
yd = np.reshape(yd, (49, 1))
q = np.array(self.db.by_col("DISCBD"))
q = np.reshape(q, (49, 1))
reg = GM_Lag_Regimes(self.y,
X,
self.regimes,
yend=yd,
q=q,
w=self.w,
regime_lag_sep=True,
regime_err_sep=True)
tbetas = np.array([[42.35827477], [-0.09472413], [-0.68794223],
[0.54482537], [32.24228762], [-0.12304063],
[-0.46840307], [0.67108156]])
np.testing.assert_allclose(tbetas, reg.betas)
vm = np.array([
200.92894859, 4.56244927, -4.85603079, -2.9755413, 0., 0., 0., 0.
])
np.testing.assert_allclose(reg.vm[0], vm, RTOL)
e_3 = np.array([[-1.32209547], [-13.15611199], [-11.62357696]])
np.testing.assert_allclose(reg.e_pred[0:3], e_3, RTOL)
u_3 = np.array([[6.99250069], [-7.5665856], [-7.04753328]])
np.testing.assert_allclose(reg.u[0:3], u_3, RTOL)
predy_3 = np.array([[8.73347931], [26.3683396], [37.67431428]])
np.testing.assert_allclose(reg.predy[0:3], predy_3, RTOL)
predy_e_3 = np.array([[17.04807547], [31.95786599], [42.25035796]])
np.testing.assert_allclose(reg.predy_e[0:3], predy_e_3, RTOL)
chow_regi = np.array([[1.51825373e-01, 6.96797034e-01],
[3.20105698e-04, 9.85725412e-01],
[8.58836996e-02, 7.69476896e-01],
[1.01357290e-01, 7.50206873e-01]])
np.testing.assert_allclose(reg.chow.regi, chow_regi, RTOL)
np.testing.assert_allclose(reg.chow.joint[0], 0.38417230022512161,
RTOL)