def test_fit_exception(self):
with self.assertRaises(Exception) as cm:
pmml = path.join(BASE_DIR, '../models/gb-xgboost-iris.pmml')
clf = PMMLGradientBoostingClassifier(pmml)
clf.fit(np.array([[]]), np.array([]))
assert str(cm.exception) == 'Not supported.'
def setUp(self):
df = pd.read_csv(path.join(BASE_DIR, '../models/categorical-test.csv'))
cats = np.unique(df['age'])
df['age'] = pd.Categorical(df['age'], categories=cats)
Xte = df.iloc[:, 1:]
yte = df.iloc[:, 0]
self.test = (Xte, yte)
pmml = path.join(BASE_DIR, '../models/gb-gbm-cat-pima.pmml')
self.clf = PMMLGradientBoostingClassifier(pmml)
def test_sklearn2pmml_binary(self):
# Export to PMML
pipeline = PMMLPipeline([("classifier", self.ref)])
Xte, _, yte, _ = self.test
pipeline.fit(Xte, yte)
sklearn2pmml(pipeline, "gb-sklearn2pmml.pmml", with_repr=True)
try:
# Import PMML
model = PMMLGradientBoostingClassifier(pmml='gb-sklearn2pmml.pmml')
# Verify classification
assert np.array_equal(self.ref.score(Xte, yte),
model.score(Xte, yte))
assert np.allclose(self.ref.predict_proba(Xte),
model.predict_proba(Xte))
finally:
remove("gb-sklearn2pmml.pmml")
def test_invalid_model(self):
with self.assertRaises(Exception) as cm:
PMMLGradientBoostingClassifier(pmml=StringIO("""
<PMML xmlns="http://www.dmg.org/PMML-4_3" version="4.3">
<DataDictionary>
<DataField name="Class" optype="categorical" dataType="string">
<Value value="setosa"/>
<Value value="versicolor"/>
<Value value="virginica"/>
</DataField>
</DataDictionary>
<MiningSchema>
<MiningField name="Class" usageType="target"/>
</MiningSchema>
</PMML>
"""))
assert str(cm.exception) == 'PMML model does not contain MiningModel.'
def test_non_voting_ensemble(self):
with self.assertRaises(Exception) as cm:
PMMLGradientBoostingClassifier(pmml=StringIO("""
<PMML xmlns="http://www.dmg.org/PMML-4_3" version="4.3">
<DataDictionary>
<DataField name="Class" optype="categorical" dataType="string">
<Value value="setosa"/>
<Value value="versicolor"/>
<Value value="virginica"/>
</DataField>
</DataDictionary>
<MiningModel>
<MiningSchema>
<MiningField name="Class" usageType="target"/>
</MiningSchema>
<Segmentation multipleModelMethod="mean" />
</MiningModel>
</PMML>
"""))
assert str(
cm.exception) == 'PMML model ensemble should use modelChain.'
def test_more_tags(self):
clf = PMMLGradientBoostingClassifier(
path.join(BASE_DIR, '../models/gb-xgboost-iris.pmml'))
assert clf._more_tags() == GradientBoostingClassifier()._more_tags()
class TestCategoricalPimaGradientBoostingIntegration(TestCase):
def setUp(self):
df = pd.read_csv(path.join(BASE_DIR, '../models/categorical-test.csv'))
cats = np.unique(df['age'])
df['age'] = pd.Categorical(df['age'], categories=cats)
Xte = df.iloc[:, 1:]
yte = df.iloc[:, 0]
self.test = (Xte, yte)
pmml = path.join(BASE_DIR, '../models/gb-gbm-cat-pima.pmml')
self.clf = PMMLGradientBoostingClassifier(pmml)
def test_predict_proba(self):
Xte, yte = self.test
ref = np.array([
[0.0450486288734726, 0.9549513711265273],
[0.5885160147778145, 0.4114839852221857],
[0.0009551290028415, 0.9990448709971584],
[0.0002422991756940, 0.9997577008243060],
[0.0007366887385038, 0.9992633112614963],
[0.0004013672347579, 0.9995986327652422],
[0.0690449928421312, 0.9309550071578688],
[0.0016227716534959, 0.9983772283465041],
[0.1293482327095098, 0.8706517672904902],
[0.0000087074095761, 0.9999912925904240],
[0.0000791716298258, 0.9999208283701742],
[0.0024433990519477, 0.9975566009480523],
[0.0201993339302258, 0.9798006660697742],
[0.0134746789867266, 0.9865253210132735],
[0.2373888296249749, 0.7626111703750250],
[0.0397951119541024, 0.9602048880458975],
[0.6048657697720786, 0.3951342302279213],
[0.0783995167388520, 0.9216004832611480],
[0.0770031400315019, 0.9229968599684981],
[0.0039225633807996, 0.9960774366192003],
[0.0870982756252495, 0.9129017243747506],
[0.0518153619780974, 0.9481846380219027],
[0.0529373167466456, 0.9470626832533544],
[0.1465303709628746, 0.8534696290371255],
[0.2948688765091694, 0.7051311234908306],
[0.0093530273605733, 0.9906469726394268],
[0.8000454863890519, 0.1999545136109481],
[0.9840595476042330, 0.0159404523957671],
[0.9994934828677855, 0.0005065171322145],
[0.9332373260900556, 0.0667626739099444],
[0.8301575628390390, 0.1698424371609611],
[0.9994014211291834, 0.0005985788708167],
[0.9964913762897571, 0.0035086237102429],
[0.9877457479606355, 0.0122542520393643],
[0.9866554945920915, 0.0133445054079084],
[0.9933345920713418, 0.0066654079286583],
[0.9987899464870794, 0.0012100535129206],
[0.9815251490646326, 0.0184748509353674],
[0.8197681630417475, 0.1802318369582524],
[0.6525706661194860, 0.3474293338805139],
[0.8825708419171311, 0.1174291580828689],
[0.9840595476042330, 0.0159404523957671],
[0.8106506748060572, 0.1893493251939427],
[0.9990627397651777, 0.0009372602348224],
[0.8763278695225744, 0.1236721304774257],
[0.7824982271907117, 0.2175017728092884],
[0.9992088489858751, 0.0007911510141249],
[0.9994453225672126, 0.0005546774327873],
[0.5301562607268657, 0.4698437392731343],
[0.9877457479606355, 0.0122542520393643],
[0.9698651982991124, 0.0301348017008876],
[0.7011874523008232, 0.2988125476991768],
])
assert np.allclose(ref, self.clf.predict_proba(Xte))
def test_score(self):
Xte, yte = self.test
ref = 0.9615384615384616
assert ref == self.clf.score(Xte, yte)
def test_R_xgboost(self):
pmml = path.join(BASE_DIR, '../models/gb-xgboost-iris.pmml')
clf = PMMLGradientBoostingClassifier(pmml)
# Verify classification
Xte, yte, _, _ = self.test
ref = 0.9933333333333333
assert ref == clf.score(Xte, yte)
ref = [
[0.9760594257941966, 0.0128724629749537, 0.0110681112308498],
[0.9757426417447441, 0.0128682851648328, 0.0113890730904230],
[0.9760594257941966, 0.0128724629749537, 0.0110681112308498],
[0.9760594257941966, 0.0128724629749537, 0.0110681112308498],
[0.9760594257941966, 0.0128724629749537, 0.0110681112308498],
[0.9760594257941966, 0.0128724629749537, 0.0110681112308498],
[0.9760594257941966, 0.0128724629749537, 0.0110681112308498],
[0.9760594257941966, 0.0128724629749537, 0.0110681112308498],
[0.9757426417447441, 0.0128682851648328, 0.0113890730904230],
[0.9760594257941966, 0.0128724629749537, 0.0110681112308498],
[0.9760594257941966, 0.0128724629749537, 0.0110681112308498],
[0.9760594257941966, 0.0128724629749537, 0.0110681112308498],
[0.9757426417447441, 0.0128682851648328, 0.0113890730904230],
[0.9757426417447441, 0.0128682851648328, 0.0113890730904230],
[0.9760594257941966, 0.0128724629749537, 0.0110681112308498],
[0.9760594257941966, 0.0128724629749537, 0.0110681112308498],
[0.9760594257941966, 0.0128724629749537, 0.0110681112308498],
[0.9760594257941966, 0.0128724629749537, 0.0110681112308498],
[0.9760594257941966, 0.0128724629749537, 0.0110681112308498],
[0.9760594257941966, 0.0128724629749537, 0.0110681112308498],
[0.9760594257941966, 0.0128724629749537, 0.0110681112308498],
[0.9760594257941966, 0.0128724629749537, 0.0110681112308498],
[0.9760594257941966, 0.0128724629749537, 0.0110681112308498],
[0.9760594257941966, 0.0128724629749537, 0.0110681112308498],
[0.9760594257941966, 0.0128724629749537, 0.0110681112308498],
[0.9757426417447441, 0.0128682851648328, 0.0113890730904230],
[0.9760594257941966, 0.0128724629749537, 0.0110681112308498],
[0.9760594257941966, 0.0128724629749537, 0.0110681112308498],
[0.9760594257941966, 0.0128724629749537, 0.0110681112308498],
[0.9760594257941966, 0.0128724629749537, 0.0110681112308498],
[0.9760594257941966, 0.0128724629749537, 0.0110681112308498],
[0.9760594257941966, 0.0128724629749537, 0.0110681112308498],
[0.9760594257941966, 0.0128724629749537, 0.0110681112308498],
[0.9760594257941966, 0.0128724629749537, 0.0110681112308498],
[0.9760594257941966, 0.0128724629749537, 0.0110681112308498],
[0.9760594257941966, 0.0128724629749537, 0.0110681112308498],
[0.9760594257941966, 0.0128724629749537, 0.0110681112308498],
[0.9760594257941966, 0.0128724629749537, 0.0110681112308498],
[0.9757426417447441, 0.0128682851648328, 0.0113890730904230],
[0.9760594257941966, 0.0128724629749537, 0.0110681112308498],
[0.9760594257941966, 0.0128724629749537, 0.0110681112308498],
[0.9757426417447441, 0.0128682851648328, 0.0113890730904230],
[0.9760594257941966, 0.0128724629749537, 0.0110681112308498],
[0.9760594257941966, 0.0128724629749537, 0.0110681112308498],
[0.9760594257941966, 0.0128724629749537, 0.0110681112308498],
[0.9757426417447441, 0.0128682851648328, 0.0113890730904230],
[0.9760594257941966, 0.0128724629749537, 0.0110681112308498],
[0.9760594257941966, 0.0128724629749537, 0.0110681112308498],
[0.9760594257941966, 0.0128724629749537, 0.0110681112308498],
[0.9760594257941966, 0.0128724629749537, 0.0110681112308498],
[0.0106166153088319, 0.9785400205188246, 0.0108433641723436],
[0.0106166153088319, 0.9785400205188246, 0.0108433641723436],
[0.0101155963333861, 0.9323607906747136, 0.0575236129919005],
[0.0119572590868193, 0.9754718678170027, 0.0125708730961781],
[0.0106132395880432, 0.9782288782391356, 0.0111578821728212],
[0.0106132395880432, 0.9782288782391356, 0.0111578821728212],
[0.0159323759619904, 0.9288613767350993, 0.0552062473029103],
[0.0124389433616677, 0.9744837805406320, 0.0130772760977003],
[0.0106132395880432, 0.9782288782391356, 0.0111578821728212],
[0.0106132395880432, 0.9782288782391356, 0.0111578821728212],
[0.0140073718554280, 0.9712664360693611, 0.0147261920752109],
[0.0106132395880432, 0.9782288782391356, 0.0111578821728212],
[0.0141667403550577, 0.9709395207122262, 0.0148937389327162],
[0.0106132395880432, 0.9782288782391356, 0.0111578821728212],
[0.0106132395880432, 0.9782288782391356, 0.0111578821728212],
[0.0106166153088319, 0.9785400205188246, 0.0108433641723436],
[0.0106132395880432, 0.9782288782391356, 0.0111578821728212],
[0.0106132395880432, 0.9782288782391356, 0.0111578821728212],
[0.0141667403550577, 0.9709395207122262, 0.0148937389327162],
[0.0106132395880432, 0.9782288782391356, 0.0111578821728212],
[0.0345623929310359, 0.4340977426739484, 0.5313398643950156],
[0.0106132395880432, 0.9782288782391356, 0.0111578821728212],
[0.0100985567108226, 0.9307902380901225, 0.0591112051990549],
[0.0106132395880432, 0.9782288782391356, 0.0111578821728212],
[0.0106132395880432, 0.9782288782391356, 0.0111578821728212],
[0.0106132395880432, 0.9782288782391356, 0.0111578821728212],
[0.0104458453096068, 0.9628000437291715, 0.0267541109612217],
[0.0367540158081575, 0.7002838055528258, 0.2629621786390166],
[0.0106132395880432, 0.9782288782391356, 0.0111578821728212],
[0.0106132395880432, 0.9782288782391356, 0.0111578821728212],
[0.0106132395880432, 0.9782288782391356, 0.0111578821728212],
[0.0106132395880432, 0.9782288782391356, 0.0111578821728212],
[0.0106132395880432, 0.9782288782391356, 0.0111578821728212],
[0.0498135826560765, 0.4929875804501168, 0.4571988368938067],
[0.0106132395880432, 0.9782288782391356, 0.0111578821728212],
[0.0159323759619904, 0.9288613767350993, 0.0552062473029103],
[0.0106166153088319, 0.9785400205188246, 0.0108433641723436],
[0.0119572590868193, 0.9754718678170027, 0.0125708730961781],
[0.0106132395880432, 0.9782288782391356, 0.0111578821728212],
[0.0106132395880432, 0.9782288782391356, 0.0111578821728212],
[0.0106132395880432, 0.9782288782391356, 0.0111578821728212],
[0.0106132395880432, 0.9782288782391356, 0.0111578821728212],
[0.0106132395880432, 0.9782288782391356, 0.0111578821728212],
[0.0140073718554280, 0.9712664360693611, 0.0147261920752109],
[0.0106132395880432, 0.9782288782391356, 0.0111578821728212],
[0.0106132395880432, 0.9782288782391356, 0.0111578821728212],
[0.0106132395880432, 0.9782288782391356, 0.0111578821728212],
[0.0106132395880432, 0.9782288782391356, 0.0111578821728212],
[0.0106132395880432, 0.9782288782391356, 0.0111578821728212],
[0.0106132395880432, 0.9782288782391356, 0.0111578821728212],
[0.0108567094134873, 0.0144905851493065, 0.9746527054372062],
[0.0164145902620253, 0.0255525167427301, 0.9580328929952446],
[0.0108613904938508, 0.0140656636934379, 0.9750729458127113],
[0.0108777095343255, 0.0125843153938132, 0.9765379750718612],
[0.0108777095343255, 0.0125843153938132, 0.9765379750718612],
[0.0108613904938508, 0.0140656636934379, 0.9750729458127113],
[0.0435806598526578, 0.2599986718108350, 0.6964206683365073],
[0.0108613904938508, 0.0140656636934379, 0.9750729458127113],
[0.0108613904938508, 0.0140656636934379, 0.9750729458127113],
[0.0108567094134873, 0.0144905851493065, 0.9746527054372062],
[0.0142345906453859, 0.0312354373543125, 0.9545299720003015],
[0.0108777095343255, 0.0125843153938132, 0.9765379750718612],
[0.0108613904938508, 0.0140656636934379, 0.9750729458127113],
[0.0184930026637306, 0.0305865778733861, 0.9509204194628834],
[0.0164145902620253, 0.0255525167427301, 0.9580328929952446],
[0.0108567094134873, 0.0144905851493065, 0.9746527054372062],
[0.0108777095343255, 0.0125843153938132, 0.9765379750718612],
[0.0108567094134873, 0.0144905851493065, 0.9746527054372062],
[0.0108613904938508, 0.0140656636934379, 0.9750729458127113],
[0.0510645228555055, 0.2463746443944004, 0.7025608327500941],
[0.0108567094134873, 0.0144905851493065, 0.9746527054372062],
[0.0184930026637306, 0.0305865778733861, 0.9509204194628834],
[0.0108613904938508, 0.0140656636934379, 0.9750729458127113],
[0.0125007199528260, 0.0186054175395307, 0.9688938625076433],
[0.0108567094134873, 0.0144905851493065, 0.9746527054372062],
[0.0108567094134873, 0.0144905851493065, 0.9746527054372062],
[0.0299366378213490, 0.0965402806010371, 0.8735230815776139],
[0.0120440726761590, 0.0544554457865047, 0.9335004815373361],
[0.0108777095343255, 0.0125843153938132, 0.9765379750718612],
[0.0464351273705071, 0.2443049639688132, 0.7092599086606799],
[0.0108613904938508, 0.0140656636934379, 0.9750729458127113],
[0.0108567094134873, 0.0144905851493065, 0.9746527054372062],
[0.0108777095343255, 0.0125843153938132, 0.9765379750718612],
[0.0363615404352432, 0.1786334851333196, 0.7850049744314372],
[0.0252911348271332, 0.0660520596210707, 0.9086568055517961],
[0.0108613904938508, 0.0140656636934379, 0.9750729458127113],
[0.0108567094134873, 0.0144905851493065, 0.9746527054372062],
[0.0108777095343255, 0.0125843153938132, 0.9765379750718612],
[0.0250153077815250, 0.2450614165877344, 0.7299232756307406],
[0.0108613904938508, 0.0140656636934379, 0.9750729458127113],
[0.0108613904938508, 0.0140656636934379, 0.9750729458127113],
[0.0142478270786007, 0.0303346044639386, 0.9554175684574607],
[0.0164145902620253, 0.0255525167427301, 0.9580328929952446],
[0.0108567094134873, 0.0144905851493065, 0.9746527054372062],
[0.0108567094134873, 0.0144905851493065, 0.9746527054372062],
[0.0108613904938508, 0.0140656636934379, 0.9750729458127113],
[0.0125007199528260, 0.0186054175395307, 0.9688938625076433],
[0.0108777095343255, 0.0125843153938132, 0.9765379750718612],
[0.0108567094134873, 0.0144905851493065, 0.9746527054372062],
[0.0162658935674777, 0.0343798537311987, 0.9493542527013236],
]
assert np.allclose(ref, clf.predict_proba(Xte))