def test_ziopmodel(self):
self.assertAlmostEqual(zmiopc.iopmod('ziop',
data,
X,
Y,
Z,
pstart=pstartziop,
method='bfgs',
weights=1,
offsetx=0,
offsetz=0).coefs.iloc[4, 0],
-0.29,
places=1)
self.assertAlmostEqual(zmiopc.iopmod('ziop',
data,
X,
Y,
Z,
pstart=pstartziop,
method='bfgs',
weights=1,
offsetx=0,
offsetz=0).coefs.iloc[5, 0],
0.04,
places=1)
def test_miopmodel(self):
self.assertAlmostEqual(zmiopc.iopmod('miop', DAT, X, Y,
Z).coefs.iloc[2, 0],
0.43,
places=1)
self.assertAlmostEqual(zmiopc.iopmod('miop', DAT, X, Y,
Z).coefs.iloc[14, 0],
0.90,
places=1)
def test_vuongopziopc(self):
self.assertAlmostEqual(zmiopc.vuong_opiop(
zmiopc.opmod(data,
X,
Y,
pstart=pstartop,
method='bfgs',
weights=1,
offsetx=0),
zmiopc.iopmod('ziop',
data,
X,
Y,
Z,
pstart=pstartziop,
method='bfgs',
weights=1,
offsetx=0,
offsetz=0)),
-4.909,
places=1)
self.assertAlmostEqual(zmiopc.vuong_opiopc(
zmiopc.opmod(data,
X,
Y,
pstart=pstartop,
method='bfgs',
weights=1,
offsetx=0),
zmiopc.iopcmod('ziopc',
data,
X,
Y,
Z,
pstart=pstartziopc,
method='bfgs',
weights=1,
offsetx=0,
offsetz=0)),
-5.424,
places=1)
pstartziopc = [-1.31, .32, 2.5, -.21,
.2, -0.2, -0.4, 0.2, .9, -.4, .1]
start_time = time.time()
ziopc_JCR = zmiopc.iopcmod('ziopc',
data, X, Y, Z, pstart=pstartziopc, method='bfgs',
weights=1,
offsetx=0, offsetz=0)
model_time = time.time() - start_time
print("%s seconds" % model_time)
start_time = time.time()
ziop_JCR = zmiopc.iopmod('ziop',
data, X, Y, Z, pstart=pstartziop,
method='bfgs', weights=1,
offsetx=0,
offsetz=0)
model_time = time.time() - start_time
print("%s seconds" % model_time)
# OP Model
pstartop = [-1, 0.3, -0.2, -0.5, 0.2, .9, -.4]
start_time = time.time()
JCR_OP = zmiopc.opmod(data, X, Y,
pstart=pstartop, method='bfgs', weights=1,
offsetx=0)
model_time = time.time() - start_time
print("%s seconds" % model_time)
DAT = pd.read_stata(
os.getcwd() + "/data/EUKnowledge.dta", convert_categoricals=False)
Y = ["EU_support_ET"]
X = ["polit_trust", "Xenophobia", "discuss_politics", "Professional",
"Executive", "Manual", "Farmer", "Unemployed", "rural", "female", "age",
"student", "income", "Educ_high", "Educ_high_mid", "Educ_low_mid"]
Z = ["discuss_politics", "rural", "female", "age",
"student", "EUbid_Know", "EU_Know_obj", "TV", "Educ_high",
"Educ_high_mid", "Educ_low_mid"]
start_time = time.time()
miop_model_paper = zmiopc.iopmod('miop', DAT, X, Y, Z)
model_time = time.time() - start_time
print("%s seconds" % model_time)
start_time = time.time()
miopc_model_paper = zmiopc.iopcmod('miopc', DAT, X, Y, Z)
model_time = time.time() - start_time
print("%s seconds" % model_time)
# This is the specification for the Documentation Example:
X2 = ['Xenophobia', 'discuss_politics']
Z2 = ['discuss_politics', 'EU_Know_obj']
start_time = time.time()
miop_model_short = zmiopc.iopmod('miop', DAT, X2, Y, Z2)
def test_miop_coefs():
miop_EU = zmiopc.iopmod('miop', dataeu, X2, Y2, Z2)
assert len(miop_EU.coefs) == len(X2 + Z2) + len(yvar)
def test_iop_fitted():
ziop_JCR = zmiopc.iopmod('ziop', data, X, Y, Z, pstart=pstartziop)
fitttedziop = zmiopc.iopfit(ziop_JCR)
assert len(fitttedziop.responseordered) == len(fitttedziop.responsefull)
def test_ziop_coefs():
ziop_JCR = zmiopc.iopmod('ziop', data, X, Y, Z, pstart=pstartziop)
assert len(ziop_JCR.coefs) == len(pstartziop)
pstartziopsmall = np.array([-1.31, .32, 2.5, -.21, .2, -0.2, -0.4, 0.2])
pstartziopc = np.array([-1.31, .32, 2.5, -.21,
.2, -0.2, -0.4, 0.2, .9, -.4, .1])
# These are correct pstart
ziopc_JCR = zmiopc.iopcmod('ziopc',
data, X, Y, Z, pstart=pstartziopc, method='bfgs',
weights=1,
offsetx=0, offsetz=0)
ziop_JCR = zmiopc.iopmod('ziop',
data, X, Y, Z, pstart=pstartziop,
method='bfgs', weights=1,
offsetx=0,
offsetz=0)
ziopc_JCR_test = zmiopc.iopcmod('ziopc', data, X, Y, Z)
ziop_JCR = zmiopc.iopmod('ziop', data, X, Y, Z)
ziop_JCRsmall = zmiopc.iopmod('ziop', pstartziopsmall,
data, Xsmall, Y, Z, method='bfgs', weights=1,
offsetx=0, offsetz=0)
# ziopc_JCR.coefs.to_csv("ZIOPC_0131.csv")
# ziop_JCR.coefs.to_csv("ZIOP_0131.csv")
