/usr/share/pyshared/pandas/stats/tests/test_var.py is in python-pandas 0.7.0-1.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 | from numpy.testing import run_module_suite, assert_equal, TestCase
from pandas.util.testing import assert_almost_equal
import nose
import unittest
raise nose.SkipTest('skipping this for now')
import scikits.statsmodels.tsa.var as sm_var
import scikits.statsmodels as sm
import pandas.stats.var as _pvar
reload(_pvar)
from pandas.stats.var import VAR
try:
import rpy2.robjects as robj
from rpy2.robjects import r
from rpy2.robjects.packages import importr
import pandas.rpy.common as rpy
vars = importr('vars')
urca = importr('urca')
except ImportError:
pass
DECIMAL_6 = 6
DECIMAL_5 = 5
DECIMAL_4 = 4
DECIMAL_3 = 3
DECIMAL_2 = 2
class CheckVAR(object):
def test_params(self):
assert_almost_equal(self.res1.params, self.res2.params, DECIMAL_3)
def test_neqs(self):
assert_equal(self.res1.neqs, self.res2.neqs)
def test_nobs(self):
assert_equal(self.res1.avobs, self.res2.nobs)
def test_df_eq(self):
assert_equal(self.res1.df_eq, self.res2.df_eq)
def test_rmse(self):
results = self.res1.results
for i in range(len(results)):
assert_almost_equal(results[i].mse_resid**.5,
eval('self.res2.rmse_'+str(i+1)), DECIMAL_6)
def test_rsquared(self):
results = self.res1.results
for i in range(len(results)):
assert_almost_equal(results[i].rsquared,
eval('self.res2.rsquared_'+str(i+1)), DECIMAL_3)
def test_llf(self):
results = self.res1.results
assert_almost_equal(self.res1.llf, self.res2.llf, DECIMAL_2)
for i in range(len(results)):
assert_almost_equal(results[i].llf,
eval('self.res2.llf_'+str(i+1)), DECIMAL_2)
def test_aic(self):
assert_almost_equal(self.res1.aic, self.res2.aic)
def test_bic(self):
assert_almost_equal(self.res1.bic, self.res2.bic)
def test_hqic(self):
assert_almost_equal(self.res1.hqic, self.res2.hqic)
def test_fpe(self):
assert_almost_equal(self.res1.fpe, self.res2.fpe)
def test_detsig(self):
assert_almost_equal(self.res1.detomega, self.res2.detsig)
def test_bse(self):
assert_almost_equal(self.res1.bse, self.res2.bse, DECIMAL_4)
class Foo(object):
def __init__(self):
data = sm.datasets.macrodata.load()
data = data.data[['realinv','realgdp','realcons']].view((float,3))
data = diff(log(data),axis=0)
self.res1 = VAR2(endog=data).fit(maxlag=2)
from results import results_var
self.res2 = results_var.MacrodataResults()
class RVAR(object):
"""
Estimates VAR model using R vars package and rpy
"""
def __init__(self, data, p=1, type='both'):
self.rdata = data
self.p = p
self.type = type
self.pydata = rpy.convert_robj(data)
self._estimate = None
self.estimate()
@property
def aic(self):
pass
@property
def bic(self):
pass
@property
def beta(self):
return rpy.convert_robj(r.coef(self._estimate))
def summary(self, equation=None):
print r.summary(self._estimate, equation=equation)
def output(self):
print self._estimate
def estimate(self):
self._estimate = r.VAR(self.rdata, p=self.p, type=self.type)
def plot(self, names=None):
r.plot(model._estimate, names=names)
def serial_test(self, lags_pt=16, type='PT.asymptotic'):
f = r['serial.test']
test = f(self._estimate, **{'lags.pt' : lags_pt,
'type' : type})
return test
def data_summary(self):
print r.summary(self.rdata)
class TestVAR(TestCase):
def setUp(self):
try:
import rpy2
except ImportError:
raise nose.SkipTest("No rpy2")
self.rdata = rpy.load_data('Canada', package='vars', convert=False)
self.data = rpy.load_data('Canada', package='vars', convert=True)
self.res = VAR(self.data)
self.ref = RVAR(self.rdata)
def test_foo(self):
pass
if __name__ == '__main__':
# canada = rpy.load_data('Canada', package='vars', convert=False)
# model = RVAR(canada, p=1)
# summary(Canada)
# plot(Canada, nc=2, xlab="")ppp
# adf1 <- summary(ur.df(Canada[, "prod"], type = "trend", lags = 2))
# adf1
# adf2 <- summary(ur.df(diff(Canada[, "prod"]), type = "drift", lags = 1))
# adf2
# VARselect(Canada, lag.max = 8, type = "both")
# Canada <- Canada[, c("prod", "e", "U", "rw")]
# p1ct <- VAR(Canada, p = 1, type = "both")
# p1ct
# coefs <- coef(p1ct)
# class(coefs)
# run_module_suite()
unittest.main()
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