def setUpClass(cls):
cls.sas = saspy.SASsession()
util = Utilities(cls.sas)
procNeeded=['reg', 'mixed', 'hpsplit', 'hplogistic', 'hpreg', 'glm', 'logistic', 'tpspline',
'hplogistic', 'hpreg', 'phreg', 'ttest', 'factor']
if not util.procFound(procNeeded):
cls.skipTest("Not all of these procedures were found: %s" % str(procNeeded))
def setUp(self):
# Use the first entry in the configuration list
self.sas = saspy.SASsession() #cfgname=saspy.SAScfg.SAS_config_names[0])
self.assertIsInstance(self.sas, saspy.SASsession, msg="sas = saspy.SASsession(...) failed")
self.util = Utilities()
procNeeded=['arima']
if not self.util.procFound(procNeeded):
self.skipTest("Not all of these procedures were found: %s" % str(procNeeded))
def setUpClass(cls):
cls.sas = saspy.SASsession()
#cls.assertIsInstance(cls.sas, saspy.SASsession, msg="sas = saspy.SASsession(...) failed")
util = Utilities()
procNeeded = ['arima']
if not util.procFound(procNeeded):
cls.skipTest("Not all of these procedures were found: %s" %
str(procNeeded))
def setUpClass(cls):
cls.sas = saspy.SASsession()
util = Utilities(cls.sas)
procNeeded = [
'reg', 'mixed', 'hpsplit', 'hplogistic', 'hpreg', 'glm',
'logistic', 'tpspline', 'hplogistic', 'hpreg', 'phreg', 'ttest',
'factor'
]
if not util.procFound(procNeeded):
cls.skipTest("Not all of these procedures were found: %s" %
str(procNeeded))
def setUpClass(cls):
cls.sas = saspy.SASsession()
#cls.assertIsInstance(cls.sas, saspy.SASsession, msg="sas = saspy.SASsession(...) failed")
util = Utilities(cls.sas)
procNeeded = [
'reg', 'mixed', 'hpsplit', 'hplogistic', 'hpreg', 'glm',
'logistic', 'tpspline', 'hplogistic', 'hpreg', 'phreg', 'ttest',
'factor'
]
if not util.procFound(procNeeded):
clsself.skipTest("Not all of these procedures were found: %s" %
str(procNeeded))
def setUp(self):
# Use the first entry in the configuration list
self.sas = saspy.SASsession(cfgname=saspy.SAScfg.SAS_config_names[0])
self.assertIsInstance(self.sas,
saspy.SASsession,
msg="sas = saspy.SASsession(...) failed")
self.util = Utilities(self.sas)
procNeeded = [
'forest', 'hp4score', 'cluster', 'neural', 'treeboost', 'hpbnet'
]
if not self.util.procFound(procNeeded):
self.skipTest("Not all of these procedures were found: %s" %
str(procNeeded))
def setUpClass(cls):
cls.sas = saspy.SASsession()
util = Utilities(cls.sas)
procNeeded = ['factmac', 'fastknn', 'forest', 'gradboost', 'nnet', 'svdd', 'svmachine']
if not util.procFound(procNeeded):
cls.skipTest("Not all of these procedures were found: %s" % str(procNeeded))
cls.sas.submit("""
options cashost="localhost" casport=5570;
cas mysession;
libname mycas cas;
data mycas.class;
set sashelp.class;
run;
""")
def setUp(self):
# Use the first entry in the configuration list
self.sas = saspy.SASsession(
) #cfgname=saspy.SAScfg.SAS_config_names[0])
self.assertIsInstance(self.sas,
saspy.SASsession,
msg="sas = saspy.SASsession(...) failed")
self.util = Utilities(self.sas)
procNeeded = [
'reg', 'mixed', 'hpsplit', 'hplogistic', 'hpreg', 'glm',
'logistic', 'tpspline', 'hplogistic', 'hpreg', 'phreg', 'ttest',
'factor'
]
if not self.util.procFound(procNeeded):
self.skipTest("Not all of these procedures were found: %s" %
str(procNeeded))
class TestSASutil(unittest.TestCase):
def setUp(self):
# Use the first entry in the configuration list
self.sas = saspy.SASsession(cfgname=saspy.SAScfg.SAS_config_names[0])
self.assertIsInstance(self.sas,
saspy.SASsession,
msg="sas = saspy.SASsession(...) failed")
self.util = Utilities(self.sas)
procNeeded = ['hpimpute', 'hpbin', 'hpsample']
if not self.util.procFound(procNeeded):
self.skipTest("Not all of these procedures were found: %s" %
str(procNeeded))
def tearDown(self):
if self.sas:
self.sas._endsas()
class TestSASml(unittest.TestCase):
def setUp(self):
# Use the first entry in the configuration list
self.sas = saspy.SASsession(cfgname=saspy.SAScfg.SAS_config_names[0])
self.assertIsInstance(self.sas,
saspy.SASsession,
msg="sas = saspy.SASsession(...) failed")
self.util = Utilities(self.sas)
procNeeded = [
'forest', 'hp4score', 'cluster', 'neural', 'treeboost', 'hpbnet'
]
if not self.util.procFound(procNeeded):
self.skipTest("Not all of these procedures were found: %s" %
str(procNeeded))
def tearDown(self):
if self.sas:
self.sas._endsas()
def testForestSmoke1(self):
ml = self.sas.sasml()
dt = self.sas.sasdata("class", "sashelp")
out1 = ml.forest(data=dt,
target='height',
input={
'interval': 'weight',
"nominal": 'sex'
})
a = [
'BASELINE', 'DATAACCESSINFO', 'FITSTATISTICS', 'LOG', 'MODELINFO',
'NOBS', 'PERFORMANCEINFO', 'VARIABLEIMPORTANCE'
]
self.assertEqual(
sorted(a),
sorted(out1.__dir__()),
msg=
u"Simple Forest model failed to return correct objects expected:{0:s} returned:{1:s}"
.format(str(a), str(out1)))
class TestSASets(unittest.TestCase):
def setUp(self):
# Use the first entry in the configuration list
self.sas = saspy.SASsession() #cfgname=saspy.SAScfg.SAS_config_names[0])
self.assertIsInstance(self.sas, saspy.SASsession, msg="sas = saspy.SASsession(...) failed")
self.util = Utilities()
procNeeded=['arima']
if not self.util.procFound(procNeeded):
self.skipTest("Not all of these procedures were found: %s" % str(procNeeded))
def tearDown(self):
if self.sas:
self.sas._endsas()
def test_smokeTimeseries(self):
# Basic model returns objects
ets = self.sas.sasets()
self.sas.submit("""
data work.seriesG;
set sashelp.air;
logair = log( air );
run;
""")
air = self.sas.sasdata('seriesG', 'work')
b = ets.timeseries(data=air, id='date interval=month', var='logair')
a = ['ACFNORMPLOT', 'ACFPLOT', 'CYCLECOMPONENTPLOT', 'CYCLEPLOT', 'DATASET', 'IACFNORMPLOT', 'IACFPLOT',
'IRREGULARCOMPONENTPLOT', 'LOG', 'PACFNORMPLOT', 'PACFPLOT', 'PERCENTCHANGEADJUSTEDPLOT', 'PERIODOGRAM',
'SEASONALCOMPONENTPLOT', 'SEASONALIRREGULARCOMPONENTPLOT', 'SEASONALLYADJUSTEDPLOT', 'SERIESHISTOGRAM',
'SERIESPLOT', 'SPECTRALDENSITYPLOT', 'SSARESULTSPLOT', 'SSARESULTSVECTORPLOT', 'SSASINGULARVALUESPLOT',
'TRENDCOMPONENTPLOT', 'TRENDCYCLECOMPONENTPLOT', 'TRENDCYCLESEASONALPLOT', 'VARIABLE',
'WHITENOISELOGPROBABILITYPLOT', 'WHITENOISEPROBABILITYPLOT']
self.assertEqual(sorted(a), sorted(b.__dir__()),
msg=u"model failed to return correct objects expected:{0:s} returned:{1:s}".format(
str(a), str(b)))
def test_smokeArima(self):
# Basic model returns objects
ets = self.sas.sasets()
air = self.sas.sasdata('air', 'sashelp')
b = ets.arima(data=air, identify='var=air(1,12)')
a = ['CHISQAUTO', 'DESCSTATS', 'LOG', 'SERIESCORRPANEL']
self.assertEqual(sorted(a), sorted(b.__dir__()),
msg=u"model failed to return correct objects expected:{0:s} returned:{1:s}".format(
str(a), str(b)))
def test_smokeUCM(self):
# Basic model returns objects
ets = self.sas.sasets()
self.sas.submit("""
data work.seriesG;
set sashelp.air;
logair = log( air );
run;
""")
air = self.sas.sasdata('seriesG', 'work')
b = ets.ucm(data=air, id = 'date interval=month', model='logair',
irregular='',
level='',
slope='',
season='length=12 type=trig print=smooth',
estimate='',
forecast='lead=24 print=decomp')
a = ['ANNUALSEASONPLOT', 'COMPONENTSIGNIFICANCE', 'CONVERGENCESTATUS', 'CUSUMPLOT', 'CUSUMSQPLOT',
'DATASET', 'ERRORPLOT', 'ERRORWHITENOISELOGPROBPLOT', 'ESTIMATIONSPAN',
'FILTEREDALLEXCEPTIRREGPLOT', 'FILTEREDALLEXCEPTIRREGVARPLOT', 'FILTEREDSEASONPLOT', 'FITSTATISTICS',
'FITSUMMARY', 'FORECASTS', 'FORECASTSONLYPLOT', 'FORECASTSPAN', 'FORECASTSPLOT', 'INITIALPARAMETERS',
'LOG', 'MODELPLOT', 'OUTLIERSUMMARY', 'PANELRESIDUALPLOT', 'PARAMETERESTIMATES', 'RESIDUALLOESSPLOT',
'SEASONDESCRIPTION', 'SMOOTHEDALLEXCEPTIRREG', 'SMOOTHEDALLEXCEPTIRREGPLOT', 'SMOOTHEDALLEXCEPTIRREGVARPLOT',
'SMOOTHEDSEASON', 'SMOOTHEDSEASONPLOT']
self.assertEqual(sorted(a), sorted(b.__dir__()),
msg=u" model failed to return correct objects expected:{0:s} returned:{1:s}".format(
str(a), str(b)))
def test_UCM2(self):
# Basic model returns objects
ets = self.sas.sasets()
self.sas.submit("""
data work.seriesG;
set sashelp.air;
logair = log( air );
run;
""")
air = self.sas.sasdata('seriesG', 'work')
b = ets.ucm(data=air, id = 'date interval=month', model='logair',
irregular=True,
level=True,
slope=True,
season='length=12 type=trig print=smooth',
estimate=True,
forecast='lead=24 print=decomp')
a = ['ANNUALSEASONPLOT', 'COMPONENTSIGNIFICANCE', 'CONVERGENCESTATUS', 'CUSUMPLOT', 'CUSUMSQPLOT',
'DATASET', 'ERRORPLOT', 'ERRORWHITENOISELOGPROBPLOT', 'ESTIMATIONSPAN',
'FILTEREDALLEXCEPTIRREGPLOT', 'FILTEREDALLEXCEPTIRREGVARPLOT', 'FILTEREDSEASONPLOT', 'FITSTATISTICS',
'FITSUMMARY', 'FORECASTS', 'FORECASTSONLYPLOT', 'FORECASTSPAN', 'FORECASTSPLOT', 'INITIALPARAMETERS',
'LOG', 'MODELPLOT', 'OUTLIERSUMMARY', 'PANELRESIDUALPLOT', 'PARAMETERESTIMATES', 'RESIDUALLOESSPLOT',
'SEASONDESCRIPTION', 'SMOOTHEDALLEXCEPTIRREG', 'SMOOTHEDALLEXCEPTIRREGPLOT', 'SMOOTHEDALLEXCEPTIRREGVARPLOT',
'SMOOTHEDSEASON', 'SMOOTHEDSEASONPLOT']
self.assertEqual(sorted(a), sorted(b.__dir__()),
msg=u"model failed to return correct objects expected:{0:s} returned:{1:s}".format(
str(a), str(b)))
def test_smokeESM(self):
# Basic model returns objects
ets = self.sas.sasets()
self.sas.submit("""
data work.seriesG;
set sashelp.air;
logair = log( air );
run;
""")
air = self.sas.sasdata('seriesG', 'work')
b = ets.esm(data=air, id='date interval=daily', forecast='_numeric_')
a = ['DATASET', 'ERRORACFNORMPLOT', 'ERRORACFPLOT', 'ERRORHISTOGRAM', 'ERRORIACFNORMPLOT', 'ERRORIACFPLOT',
'ERRORPACFNORMPLOT', 'ERRORPACFPLOT', 'ERRORPERIODOGRAM', 'ERRORPLOT', 'ERRORSPECTRALDENSITYPLOT',
'ERRORWHITENOISELOGPROBPLOT', 'ERRORWHITENOISEPROBPLOT', 'FORECASTSONLYPLOT', 'FORECASTSPLOT', 'LEVELSTATEPLOT',
'LOG', 'MODELFORECASTSPLOT', 'MODELPLOT', 'VARIABLE']
self.assertEqual(sorted(a), sorted(b.__dir__()),
msg=u"model failed to return correct objects expected:{0:s} returned:{1:s}".format(
str(a), str(b)))
def test_smokeTimeID(self):
# Basic model returns objects
ets = self.sas.sasets()
self.sas.submit("""
data work.workdays;
format day weekdate.;
input day : date. @@;
datalines;
01AUG09 06AUG09 11AUG09 14AUG09 19AUG09 22AUG09
27AUG09 01SEP09 04SEP09 09SEP09 12SEP09 17SEP09
;
run;
""")
air = self.sas.sasdata('workdays', 'work')
b = ets.timeid(data=air, id='day')
a = ['DECOMPOSITIONPLOT', 'INTERVALCOUNTSCOMPONENTPLOT', 'LOG', 'OFFSETCOMPONENTPLOT',
'SPANCOMPONENTPLOT', 'VALUESPLOT']
self.assertEqual(sorted(a), sorted(b.__dir__()),
msg=u"model failed to return correct objects expected:{0:s} returned:{1:s}".format(
str(a), str(b)))
def test_smokeTimedata(self):
# Basic model returns objects
ets = self.sas.sasets()
self.sas.submit("""
proc fcmp outlib=work.timefnc.funcs;
subroutine mylog(actual[*], transform[*]);
outargs transform;
actlen = DIM(actual);
do t = 1 to actlen;
transform[t] = log(actual[t]);
end;
endsub;
function mymean(actual[*]);
actlen = DIM(actual);
sum = 0;
do t = 1 to actlen;
sum = sum + actual[t];
end;
return( sum / actlen );
endsub;
run;
quit;
options cmplib = work.timefnc;
""")
air = self.sas.sasdata('air', 'sashelp')
b = ets.timedata(data=air,
procopts='out=work.air print=(scalars arrays)',
id='date interval=qtr acc=t format=yymmdd.',
var='air',
outarrays=' logair myair',
outscalars='mystats',
prog_stmts="""
call mylog(air,logair);
do t = 1 to dim(air);
myair[t] = air[t] - logair[t];
end;
mystats= mymean(air);
""")
a = ['ARRAYPLOT', 'ARRAYS', 'LOG', 'SCALARS']
self.assertEqual(sorted(a), sorted(b.__dir__()),
msg=u"model failed to return correct objects expected:{0:s} returned:{1:s}".format(
str(a), str(b)))
def test_missingVar(self):
ets = self.sas.sasets()
d = self.sas.sasdata('air', 'sashelp')
b = ets.arima(data=d, by='novar', identify='var=air(1,12)')
a = ['ERROR_LOG']
self.assertEqual(a, b.__dir__(),
msg=u"arima model failed to return correct objects expected:{0:s} returned:{1:s}".format(
str(a), str(b)))
def test_extraStmt(self):
# Extra Statements are ignored
ets = self.sas.sasets()
d = self.sas.sasdata('air', 'sashelp')
b = ets.arima(data=d, by='novar', identify='var=air(1,12)', crosscorr='date', decomp='air')
a = ets.arima(data=d, by='novar', identify='var=air(1,12)')
self.assertEqual(a.__dir__(), b.__dir__(), msg=u"Extra Statements not being ignored expected:{0:s} returned:{1:s}".format(
str(a), str(b)))
def test_outputDset(self):
ets = self.sas.sasets()
air = self.sas.sasdata('air', 'sashelp')
outAir = self.sas.sasdata('air')
ets.arima(data=air, identify='var=air(1,12)', out=outAir)
self.assertIsInstance(outAir, saspy.SASdata, msg="out= dataset not created properly")
def test_overridePlot(self):
# Test that user can override plot options
ets = self.sas.sasets()
air = self.sas.sasdata('air', 'sashelp')
outAir = self.sas.sasdata('air')
b = ets.arima(data=air, identify='var=air(1,12)', out=outAir, procopts='plots=none')
a = ['CHISQAUTO', 'DESCSTATS', 'LOG']
self.assertEqual(sorted(a), sorted(b.__dir__()), msg=u"plots overridden and disabled expected:{0:s} returned:{1:s}".format(
str(a), str(b)))
class TestSASstat(unittest.TestCase):
def setUp(self):
# Use the first entry in the configuration list
self.sas = saspy.SASsession(
) #cfgname=saspy.SAScfg.SAS_config_names[0])
self.assertIsInstance(self.sas,
saspy.SASsession,
msg="sas = saspy.SASsession(...) failed")
self.util = Utilities(self.sas)
procNeeded = [
'reg', 'mixed', 'hpsplit', 'hplogistic', 'hpreg', 'glm',
'logistic', 'tpspline', 'hplogistic', 'hpreg', 'phreg', 'ttest',
'factor'
]
if not self.util.procFound(procNeeded):
self.skipTest("Not all of these procedures were found: %s" %
str(procNeeded))
def tearDown(self):
if self.sas:
self.sas._endsas()
def defineData(self):
self.sas.submit("""
data Myeloma;
input Time VStatus LogBUN HGB Platelet Age LogWBC Frac
LogPBM Protein SCalc;
label Time='Survival Time'
VStatus='0=Alive 1=Dead';
datalines;
1.25 1 2.2175 9.4 1 67 3.6628 1 1.9542 12 10
1.25 1 1.9395 12.0 1 38 3.9868 1 1.9542 20 18
2.00 1 1.5185 9.8 1 81 3.8751 1 2.0000 2 15
2.00 1 1.7482 11.3 0 75 3.8062 1 1.2553 0 12
2.00 1 1.3010 5.1 0 57 3.7243 1 2.0000 3 9
3.00 1 1.5441 6.7 1 46 4.4757 0 1.9345 12 10
5.00 1 2.2355 10.1 1 50 4.9542 1 1.6628 4 9
5.00 1 1.6812 6.5 1 74 3.7324 0 1.7324 5 9
6.00 1 1.3617 9.0 1 77 3.5441 0 1.4624 1 8
6.00 1 2.1139 10.2 0 70 3.5441 1 1.3617 1 8
6.00 1 1.1139 9.7 1 60 3.5185 1 1.3979 0 10
6.00 1 1.4150 10.4 1 67 3.9294 1 1.6902 0 8
7.00 1 1.9777 9.5 1 48 3.3617 1 1.5682 5 10
7.00 1 1.0414 5.1 0 61 3.7324 1 2.0000 1 10
7.00 1 1.1761 11.4 1 53 3.7243 1 1.5185 1 13
9.00 1 1.7243 8.2 1 55 3.7993 1 1.7404 0 12
11.00 1 1.1139 14.0 1 61 3.8808 1 1.2788 0 10
11.00 1 1.2304 12.0 1 43 3.7709 1 1.1761 1 9
11.00 1 1.3010 13.2 1 65 3.7993 1 1.8195 1 10
11.00 1 1.5682 7.5 1 70 3.8865 0 1.6721 0 12
11.00 1 1.0792 9.6 1 51 3.5051 1 1.9031 0 9
13.00 1 0.7782 5.5 0 60 3.5798 1 1.3979 2 10
14.00 1 1.3979 14.6 1 66 3.7243 1 1.2553 2 10
15.00 1 1.6021 10.6 1 70 3.6902 1 1.4314 0 11
16.00 1 1.3424 9.0 1 48 3.9345 1 2.0000 0 10
16.00 1 1.3222 8.8 1 62 3.6990 1 0.6990 17 10
17.00 1 1.2304 10.0 1 53 3.8808 1 1.4472 4 9
17.00 1 1.5911 11.2 1 68 3.4314 0 1.6128 1 10
18.00 1 1.4472 7.5 1 65 3.5682 0 0.9031 7 8
19.00 1 1.0792 14.4 1 51 3.9191 1 2.0000 6 15
19.00 1 1.2553 7.5 0 60 3.7924 1 1.9294 5 9
24.00 1 1.3010 14.6 1 56 4.0899 1 0.4771 0 9
25.00 1 1.0000 12.4 1 67 3.8195 1 1.6435 0 10
26.00 1 1.2304 11.2 1 49 3.6021 1 2.0000 27 11
32.00 1 1.3222 10.6 1 46 3.6990 1 1.6335 1 9
35.00 1 1.1139 7.0 0 48 3.6532 1 1.1761 4 10
37.00 1 1.6021 11.0 1 63 3.9542 0 1.2041 7 9
41.00 1 1.0000 10.2 1 69 3.4771 1 1.4771 6 10
41.00 1 1.1461 5.0 1 70 3.5185 1 1.3424 0 9
51.00 1 1.5682 7.7 0 74 3.4150 1 1.0414 4 13
52.00 1 1.0000 10.1 1 60 3.8573 1 1.6532 4 10
54.00 1 1.2553 9.0 1 49 3.7243 1 1.6990 2 10
58.00 1 1.2041 12.1 1 42 3.6990 1 1.5798 22 10
66.00 1 1.4472 6.6 1 59 3.7853 1 1.8195 0 9
67.00 1 1.3222 12.8 1 52 3.6435 1 1.0414 1 10
88.00 1 1.1761 10.6 1 47 3.5563 0 1.7559 21 9
89.00 1 1.3222 14.0 1 63 3.6532 1 1.6232 1 9
92.00 1 1.4314 11.0 1 58 4.0755 1 1.4150 4 11
4.00 0 1.9542 10.2 1 59 4.0453 0 0.7782 12 10
4.00 0 1.9243 10.0 1 49 3.9590 0 1.6232 0 13
7.00 0 1.1139 12.4 1 48 3.7993 1 1.8573 0 10
7.00 0 1.5315 10.2 1 81 3.5911 0 1.8808 0 11
8.00 0 1.0792 9.9 1 57 3.8325 1 1.6532 0 8
12.00 0 1.1461 11.6 1 46 3.6435 0 1.1461 0 7
11.00 0 1.6128 14.0 1 60 3.7324 1 1.8451 3 9
12.00 0 1.3979 8.8 1 66 3.8388 1 1.3617 0 9
13.00 0 1.6628 4.9 0 71 3.6435 0 1.7924 0 9
16.00 0 1.1461 13.0 1 55 3.8573 0 0.9031 0 9
19.00 0 1.3222 13.0 1 59 3.7709 1 2.0000 1 10
19.00 0 1.3222 10.8 1 69 3.8808 1 1.5185 0 10
28.00 0 1.2304 7.3 1 82 3.7482 1 1.6721 0 9
41.00 0 1.7559 12.8 1 72 3.7243 1 1.4472 1 9
53.00 0 1.1139 12.0 1 66 3.6128 1 2.0000 1 11
57.00 0 1.2553 12.5 1 66 3.9685 0 1.9542 0 11
77.00 0 1.0792 14.0 1 60 3.6812 0 0.9542 0 12
;;
run;
data SocioEconomics;
input Population School Employment Services HouseValue;
datalines;
5700 12.8 2500 270 25000
1000 10.9 600 10 10000
3400 8.8 1000 10 9000
3800 13.6 1700 140 25000
4000 12.8 1600 140 25000
8200 8.3 2600 60 12000
1200 11.4 400 10 16000
9100 11.5 3300 60 14000
9900 12.5 3400 180 18000
9600 13.7 3600 390 25000
9600 9.6 3300 80 12000
9400 11.4 4000 100 13000
;;
run;
data time;
input time @@;
datalines;
43 90 84 87 116 95 86 99 93 92
121 71 66 98 79 102 60 112 105 98
;;
run;
data pressure;
input SBPbefore SBPafter @@;
datalines;
120 128 124 131 130 131 118 127
140 132 128 125 140 141 135 137
126 118 130 132 126 129 127 135
;;
run;
""")
def test_smokeReg(self):
# Basic model returns objects
stat = self.sas.sasstat()
tr = self.sas.sasdata("class", "sashelp")
# REG
b = stat.reg(data=tr, model='weight=height')
a = [
'ANOVA', 'COOKSDPLOT', 'DFBETASPANEL', 'DFFITSPLOT',
'DIAGNOSTICSPANEL', 'FITPLOT', 'FITSTATISTICS', 'LOG', 'NOBS',
'OBSERVEDBYPREDICTED', 'PARAMETERESTIMATES', 'QQPLOT',
'RESIDUALBOXPLOT', 'RESIDUALBYPREDICTED', 'RESIDUALHISTOGRAM',
'RESIDUALPLOT', 'RFPLOT', 'RSTUDENTBYLEVERAGE',
'RSTUDENTBYPREDICTED'
]
self.assertEqual(
sorted(a),
sorted(b.__dir__()),
msg=
u"Simple Regession (reg) model failed to return correct objects expected:{0:s} returned:{1:s}"
.format(str(a), str(b)))
def regResult1(self):
stat = self.sas.sasstat()
tr = self.sas.sasdata("class", "sashelp")
b = stat.reg(data=tr, model='weight=height')
self.assertIsInstance(b, saspy.SASresults, msg="correct return type")
def regResult2(self):
stat = self.sas.sasstat()
tr = self.sas.sasdata("class", "sashelp")
tr.set_results('PANDAS')
b = stat.reg(data=tr, model='weight=height')
self.assertIsInstance(b.ANOVA,
pandas.core.frame.DataFrame,
msg="correct return type")
def regResult3(self):
stat = self.sas.sasstat()
tr = self.sas.sasdata("class", "sashelp")
tr.set_results('PANDAS')
b = stat.reg(data=tr, model='weight=height')
self.assertIsInstance(b.LOG,
IPython.core.display.HTML,
msg="correct return type")
def regResult4(self):
stat = self.sas.sasstat()
tr = self.sas.sasdata("class", "sashelp")
tr.set_results('PANDAS')
b = stat.reg(data=tr, model='weight=height')
self.assertIsInstance(b.RESIDUALHISTOGRAM,
IPython.core.display.HTML,
msg="correct return type")
def test_smokeMixed(self):
# Basic model returns objects
stat = self.sas.sasstat()
tr = self.sas.sasdata("class", "sashelp")
b = stat.mixed(data=tr, model='weight=height')
a = [
'COVPARMS', 'DIMENSIONS', 'FITSTATISTICS', 'LOG', 'MODELINFO',
'NOBS', 'PEARSONPANEL', 'RESIDUALPANEL', 'STUDENTPANEL', 'TESTS3'
]
self.assertEqual(
sorted(a),
sorted(b.__dir__()),
msg=
u" model failed to return correct objects expected:{0:s} returned:{1:s}"
.format(str(a), str(b)))
def test_smokeGLM(self):
# Basic model returns objects
stat = self.sas.sasstat()
tr = self.sas.sasdata("class", "sashelp")
b = stat.glm(data=tr, model='weight=height')
a = [
'DIAGNOSTICSPANEL', 'FITPLOT', 'FITSTATISTICS', 'LOG',
'MODELANOVA', 'NOBS', 'OVERALLANOVA', 'PARAMETERESTIMATES',
'RESIDUALPLOTS'
]
self.assertEqual(
sorted(a),
sorted(b.__dir__()),
msg=
u" model failed to return correct objects expected:{0:s} returned:{1:s}"
.format(str(a), str(b)))
def test_smokeLogistic(self):
# Basic model returns objects
stat = self.sas.sasstat()
tr = self.sas.sasdata("class", "sashelp")
b = stat.logistic(data=tr, model='sex=height weight')
a = [
'ASSOCIATION', 'CONVERGENCESTATUS', 'DFBETASPLOT', 'DPCPLOT',
'EFFECTPLOT', 'FITSTATISTICS', 'GLOBALTESTS', 'INFLUENCEPLOTS',
'LEVERAGEPLOTS', 'LOG', 'MODELINFO', 'NOBS', 'ODDSRATIOS',
'ORPLOT', 'PARAMETERESTIMATES', 'PHATPLOTS', 'RESPONSEPROFILE',
'ROCCURVE'
]
self.assertEqual(
sorted(a),
sorted(b.__dir__()),
msg=
u" model failed to return correct objects expected:{0:s} returned:{1:s}"
.format(str(a), str(b)))
def test_smokeTpspline(self):
# Basic model returns objects
stat = self.sas.sasstat()
self.sas.submit("""
data work.melanoma;
input year incidences @@;
datalines;
1936 0.9 1937 0.8 1938 0.8 1939 1.3
1940 1.4 1941 1.2 1942 1.7 1943 1.8
1944 1.6 1945 1.5 1946 1.5 1947 2.0
1948 2.5 1949 2.7 1950 2.9 1951 2.5
1952 3.1 1953 2.4 1954 2.2 1955 2.9
1956 2.5 1957 2.6 1958 3.2 1959 3.8
1960 4.2 1961 3.9 1962 3.7 1963 3.3
1964 3.7 1965 3.9 1966 4.1 1967 3.8
1968 4.7 1969 4.4 1970 4.8 1971 4.8
1972 4.8
;;
run;
""")
tr = self.sas.sasdata("melanoma", "work")
b = stat.tpspline(data=tr,
model='incidences = (year) /alpha = 0.1',
output='out = result pred uclm lclm')
a = [
'CRITERIONPLOT', 'DATASUMMARY', 'DIAGNOSTICSPANEL', 'FITPLOT',
'FITSTATISTICS', 'FITSUMMARY', 'LOG', 'OBSERVEDBYPREDICTED',
'QQPLOT', 'RESIDPANEL', 'RESIDUALBYPREDICTED', 'RESIDUALHISTOGRAM',
'RFPLOT'
]
self.assertEqual(
sorted(a),
sorted(b.__dir__()),
msg=
u" model failed to return correct objects expected:{0:s} returned:{1:s}"
.format(str(a), str(b)))
def test_tpspline2(self):
# Basic model returns objects
stat = self.sas.sasstat()
self.sas.submit("""
data work.melanoma;
input year incidences @@;
datalines;
1936 0.9 1937 0.8 1938 0.8 1939 1.3
1940 1.4 1941 1.2 1942 1.7 1943 1.8
1944 1.6 1945 1.5 1946 1.5 1947 2.0
1948 2.5 1949 2.7 1950 2.9 1951 2.5
1952 3.1 1953 2.4 1954 2.2 1955 2.9
1956 2.5 1957 2.6 1958 3.2 1959 3.8
1960 4.2 1961 3.9 1962 3.7 1963 3.3
1964 3.7 1965 3.9 1966 4.1 1967 3.8
1968 4.7 1969 4.4 1970 4.8 1971 4.8
1972 4.8
;;
run;
""")
tr = self.sas.sasdata("melanoma", "work")
ds = self.sas.sasdata("result", "work")
b = stat.tpspline(data=tr,
model='incidences = (year) /alpha = 0.1',
score=ds)
a = [
'CRITERIONPLOT', 'DATASUMMARY', 'DIAGNOSTICSPANEL', 'FITPLOT',
'FITSTATISTICS', 'FITSUMMARY', 'LOG', 'OBSERVEDBYPREDICTED',
'QQPLOT', 'RESIDPANEL', 'RESIDUALBYPREDICTED', 'RESIDUALHISTOGRAM',
'RFPLOT', 'SCOREPLOT'
]
self.assertEqual(
sorted(a),
sorted(b.__dir__()),
msg=
u" model failed to return correct objects expected:{0:s} returned:{1:s}"
.format(str(a), str(b)))
def test_smokeHPLogistic(self):
# Basic model returns objects
stat = self.sas.sasstat()
tr = self.sas.sasdata("class", "sashelp")
b = stat.hplogistic(data=tr, model='sex=height weight')
a = [
'CONVERGENCESTATUS', 'DATAACCESSINFO', 'DIMENSIONS',
'FITSTATISTICS', 'GLOBALTESTS', 'ITERHISTORY', 'LOG', 'MODELINFO',
'NOBS', 'PARAMETERESTIMATES', 'PERFORMANCEINFO', 'RESPONSEPROFILE'
]
self.assertEqual(
sorted(a),
sorted(b.__dir__()),
msg=
u" model failed to return correct objects expected:{0:s} returned:{1:s}"
.format(str(a), str(b)))
def test_smokeHPReg(self):
# Basic model returns objects
stat = self.sas.sasstat()
tr = self.sas.sasdata("class", "sashelp")
# REG
b = stat.hpreg(data=tr, model='weight=height')
a = [
'ANOVA', 'DATAACCESSINFO', 'DIMENSIONS', 'FITSTATISTICS', 'LOG',
'MODELINFO', 'NOBS', 'PARAMETERESTIMATES', 'PERFORMANCEINFO'
]
self.assertEqual(
sorted(a),
sorted(b.__dir__()),
msg=
u"Simple Regession (reg) model failed to return correct objects expected:{0:s} returned:{1:s}"
.format(str(a), str(b)))
def test_selectionDict(self):
# Basic model returns objects
stat = self.sas.sasstat()
tr = self.sas.sasdata("class", "sashelp")
selDict = {'method': 'stepwise'}
b = stat.hpreg(data=tr, model='weight=height', selection=selDict)
a = [
'ANOVA', 'DATAACCESSINFO', 'DIMENSIONS', 'FITSTATISTICS', 'LOG',
'MODELINFO', 'NOBS', 'PARAMETERESTIMATES', 'PERFORMANCEINFO',
'SELECTEDEFFECTS', 'SELECTIONINFO', 'SELECTIONREASON',
'SELECTIONSUMMARY', 'STOPREASON'
]
self.assertEqual(
sorted(a),
sorted(b.__dir__()),
msg=
u"Simple Regession (HPREG) model failed to return correct objects expected:{0:s} returned:{1:s}"
.format(str(a), str(b)))
def test_selectionDict2(self):
# Basic model returns objects
stat = self.sas.sasstat()
tr = self.sas.sasdata("class", "sashelp")
# DETAILS=NONE | SUMMARY | ALL
selDict = {'method': 'forward', 'details': 'ALL', 'maxeffects': '0'}
b = stat.hpreg(data=tr, model='weight=height', selection=selDict)
a = [
'ANOVA', 'DATAACCESSINFO', 'DIMENSIONS', 'ENTRYCANDIDATES',
'FITSTATISTICS', 'LOG', 'MODELINFO', 'NOBS', 'PARAMETERESTIMATES',
'PERFORMANCEINFO', 'SELECTEDEFFECTS', 'SELECTIONINFO',
'SELECTIONREASON', 'SELECTIONSUMMARY', 'STOPREASON'
]
self.assertEqual(
sorted(a),
sorted(b.__dir__()),
msg=
u"Simple Regession (HPREG) model failed to return correct objects expected:{0:s} returned:{1:s}"
.format(str(a), str(b)))
def test_selectionDict3(self):
# Basic model returns objects
stat = self.sas.sasstat()
tr = self.sas.sasdata("class", "sashelp")
# DETAILS=NONE | SUMMARY | ALL
selDict = {
'stop': 'aic',
'method': 'backward',
'select': 'aic',
'choose': 'aic',
'maxeffects': '3'
}
b = stat.hpreg(data=tr, model='weight=height', selection=selDict)
a = [
'ANOVA', 'DATAACCESSINFO', 'DIMENSIONS', 'FITSTATISTICS', 'LOG',
'MODELINFO', 'NOBS', 'PARAMETERESTIMATES', 'PERFORMANCEINFO',
'SELECTEDEFFECTS', 'SELECTIONINFO', 'SELECTIONREASON',
'SELECTIONSUMMARY', 'STOPREASON'
]
self.assertEqual(
sorted(a),
sorted(b.__dir__()),
msg=
u"Simple Regession (HPREG) model failed to return correct objects expected:{0:s} returned:{1:s}"
.format(str(a), str(b)))
def test_selectionDictError(self):
# Basic model returns objects
stat = self.sas.sasstat()
tr = self.sas.sasdata("class", "sashelp")
# DETAILS=NONE | SUMMARY | ALL
selDict = {'method': 'stepwise', 'sl': '0.05'}
b = stat.hpreg(data=tr, model='weight=height', selection=selDict)
a = ['ERROR_LOG']
self.assertEqual(
sorted(a),
sorted(b.__dir__()),
msg=
u"Simple Regession (HPREG) model failed to return correct objects expected:{0:s} returned:{1:s}"
.format(str(a), str(b)))
def test_missingVar(self):
stat = self.sas.sasstat()
tr = self.sas.sasdata("class", "sashelp")
b = stat.mixed(data=tr, weight='novar', model='weight=height')
a = ['ERROR_LOG']
self.assertEqual(
a,
b.__dir__(),
msg=
u"Simple Regession (mixed) model failed to return correct objects expected:{0:s} returned:{1:s}"
.format(str(a), str(b)))
def test_extraStmt(self):
# Extra Statements are ignored
stat = self.sas.sasstat()
d = self.sas.sasdata('cars', 'sashelp')
b = stat.hpsplit(data=d,
target='MSRP / level=interval',
architecture='MLP',
hidden=100,
input='enginesize--length',
train='',
procopts='maxdepth=3')
a = stat.hpsplit(data=d,
target='MSRP / level=interval',
input='enginesize--length',
procopts='maxdepth=3')
self.assertEqual(
a.__dir__(),
b.__dir__(),
msg=
u"Extra Statements not being ignored expected:{0:s} returned:{1:s}"
.format(str(a), str(b)))
def test_multiTarget(self):
# multiple target variables
stat = self.sas.sasstat()
nnin = self.sas.sasdata('cars', 'sashelp')
# Need to change this assert; the exception isn't raised - I think the code changed
x = stat.hpsplit(data=nnin,
target='MSRP origin',
input='enginesize--length')
a = ['ERROR_LOG']
self.assertEqual(
a,
x.__dir__(),
msg=u"Multiple target variables didn't fail in stat.hpsplit")
def test_outputDset(self):
stat = self.sas.sasstat()
tsave = self.sas.sasdata('tsave')
tr = self.sas.sasdata("class", "sashelp")
stat.mixed(data=tr, weight='novar', model='weight=height', out=tsave)
self.assertIsInstance(tsave,
saspy.SASdata,
msg="out= dataset not created properly")
def test_target_input_syntax1(self):
stat = self.sas.sasstat()
c = self.sas.sasdata("class", "sashelp")
t1 = 'weight'
t2 = {'interval': 'weight'}
t3 = ['weight']
i1 = {'interval': ['height'], 'nominal': ['sex']}
i2 = {'interval': ['height']}
i3 = ['height']
m = stat.glm(data=c, cls='sex', model='weight = height sex')
ti1 = stat.glm(data=c, target=t1, input=i1)
self.assertEqual(m.__dir__(), ti1.__dir__())
ti2 = stat.glm(data=c, target=t2, input=i1)
self.assertEqual(m.__dir__(), ti2.__dir__())
ti3 = stat.glm(data=c, target=t3, input=i1)
self.assertEqual(m.__dir__(), ti3.__dir__())
m2 = stat.glm(data=c, model='weight = height')
ti4 = stat.glm(data=c, target=t2, input=i2)
self.assertEqual(m2.__dir__(), ti4.__dir__())
ti5 = stat.glm(data=c, target=t1, input=i3)
self.assertEqual(m2.__dir__(), ti5.__dir__())
def phregResult1(self):
stat = self.sas.sasstat()
self.defineData()
tr = self.sas.sasdata("melanoma", "work")
b = stat.reg(data=tr,
model="""Time*VStatus(0)=LogBUN HGB Platelet Age LogWBC
Frac LogPBM Protein SCalc / selection=stepwise slentry=0.25 slstay=0.15 details"""
)
self.assertIsInstance(b, saspy.SASresults, msg="correct return type")
def factorResult1(self):
stat = self.sas.sasstat()
self.defineData()
tr = self.sas.sasdata("SocioEconomics", "work")
b = stat.reg(data=tr, procopts='simple corr')
self.assertIsInstance(b, saspy.SASresults, msg="correct return type")
def factorResult2(self):
stat = self.sas.sasstat()
self.defineData()
tr = self.sas.sasdata("SocioEconomics", "work")
b = stat.reg(
data=tr,
procopts=
'priors=smc msa residual rotate=promax reorder outstat=fact_all',
var=['population', 'school'])
self.assertIsInstance(b, saspy.SASresults, msg="correct return type")
def ttestResult1(self):
stat = self.sas.sasstat()
self.defineData()
tr = self.sas.sasdata("time", "work")
b = stat.reg(data=tr, var='time', procopts='h0=80 alpha=0.1')
self.assertIsInstance(b, saspy.SASresults, msg="correct return type")
def ttestResult2(self):
stat = self.sas.sasstat()
self.defineData()
tr = self.sas.sasdata("pressure", "work")
b = stat.reg(data=tr, paired="SBPbefore*SBPafter")
self.assertIsInstance(b, saspy.SASresults, msg="correct return type")
def setUpClass(cls):
cls.sas = saspy.SASsession()
util = Utilities(cls.sas)
procNeeded = ['hpforest', 'hp4score', 'hpclus', 'hpneural', 'treeboost', 'hpbnet', 'hpcluster']
if not util.procFound(procNeeded):
cls.skipTest("Not all of these procedures were found: %s" % str(procNeeded))
def setUpClass(cls):
cls.sas = saspy.SASsession()
util = Utilities(cls.sas)
procNeeded = ['hpimpute', 'hpbin', 'hpsample', 'univariate']
if not util.procFound(procNeeded):
cls.skipTest("Not all of these procedures were found: %s" % str(procNeeded))
def setUpClass(cls):
cls.sas = saspy.SASsession()
util = Utilities()
procNeeded = ['arima', 'timeseries', 'ucm', 'esm', 'timeid', 'timedata']
if not util.procFound(procNeeded):
cls.skipTest("Not all of these procedures were found: %s" % str(procNeeded))