def test_sklearn2pmml_multiclass_ovr(self):
data = load_iris(as_frame=True)
X = data.data
y = data.target
y.name = "Class"
ref = LogisticRegression(
multi_class='ovr'
)
ref.fit(X, y)
# Export to PMML
pipeline = PMMLPipeline([
("classifier", ref)
])
pipeline.fit(X, y)
sklearn2pmml(pipeline, "lmc-sklearn2pmml.pmml", with_repr=True)
try:
# Import PMML
model = PMMLLogisticRegression(pmml='lmc-sklearn2pmml.pmml')
# Verify classification
assert np.allclose(
ref.predict_proba(X),
model.predict_proba(X)
)
finally:
remove("lmc-sklearn2pmml.pmml")
def setUp(self):
df = pd.read_csv(path.join(BASE_DIR, '../models/categorical-test.csv'))
Xte = df.iloc[:, 1:]
Xte = pd.get_dummies(Xte, prefix_sep='')
del Xte['age(20,30]']
yte = df.iloc[:, 0]
self.test = (Xte, yte)
pmml = path.join(BASE_DIR, '../models/linear-model-lmc.pmml')
self.clf = PMMLLogisticRegression(pmml)
self.ref = LogisticRegression()
self.ref.fit(Xte, yte)
def test_nonlinear_model(self):
with self.assertRaises(Exception) as cm:
PMMLLogisticRegression(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>
<DataField name="a" optype="continuous" dataType="double"/>
</DataDictionary>
<RegressionModel>
<MiningSchema>
<MiningField name="Class" usageType="target"/>
</MiningSchema>
<RegressionTable>
<NumericPredictor name="a" exponent="1" coefficient="1"/>
<NumericPredictor name="a" exponent="1" coefficient="1"/>
</RegressionTable>
</RegressionModel>
</PMML>
"""))
assert str(cm.exception) == 'PMML model is not linear.'
def test_sklearn2pmml(self):
# Export to PMML
pipeline = PMMLPipeline([
("classifier", self.ref)
])
pipeline.fit(self.test[0], self.test[1])
sklearn2pmml(pipeline, "lmc-sklearn2pmml.pmml", with_repr = True)
try:
# Import PMML
model = PMMLLogisticRegression(pmml='lmc-sklearn2pmml.pmml')
# Verify classification
Xenc, _ = self.test
assert np.allclose(
self.ref.predict_proba(Xenc),
model.predict_proba(Xenc)
)
finally:
remove("lmc-sklearn2pmml.pmml")
def test_invalid_model(self):
with self.assertRaises(Exception) as cm:
PMMLLogisticRegression(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 RegressionModel or Segmentation.'
def test_non_modelchain_segmentation(self):
with self.assertRaises(Exception) as cm:
PMMLLogisticRegression(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>
<DataField name="a" optype="continuous" dataType="double"/>
</DataDictionary>
<MiningSchema>
<MiningField name="Class" usageType="target"/>
</MiningSchema>
<MiningModel>
<Segmentation multipleModelMethod="notModelChain" />
</MiningModel>
</PMML>
"""))
assert str(cm.exception) == 'PMML model for multi-class logistic regression should use modelChain method.'
class TestLogisticRegressionIntegration(TestCase):
def setUp(self):
df = pd.read_csv(path.join(BASE_DIR, '../models/categorical-test.csv'))
Xte = df.iloc[:, 1:]
Xte = pd.get_dummies(Xte, prefix_sep='')
del Xte['age(20,30]']
yte = df.iloc[:, 0]
self.test = (Xte, yte)
pmml = path.join(BASE_DIR, '../models/linear-model-lmc.pmml')
self.clf = PMMLLogisticRegression(pmml)
self.ref = LogisticRegression()
self.ref.fit(Xte, yte)
def test_predict_proba(self):
Xte, _ = self.test
ref = np.array([
[0.3836644757747519, 0.6163355242252481],
[0.5572948024306759, 0.4427051975693241],
[0.2208655069363070, 0.7791344930636930],
[0.1226755439085095, 0.8773244560914905],
[0.1116580306897623, 0.8883419693102377],
[0.2319071635514390, 0.7680928364485610],
[0.3884229315951135, 0.6115770684048865],
[0.2465287129542991, 0.7534712870457009],
[0.6593253655911793, 0.3406746344088207],
[0.2374749130836621, 0.7625250869163379],
[0.1540677632287771, 0.8459322367712229],
[0.3435398902933879, 0.6564601097066121],
[0.1625519980431368, 0.8374480019568632],
[0.1469729880397515, 0.8530270119602485],
[0.2418801127109025, 0.7581198872890975],
[0.3625854866670420, 0.6374145133329580],
[0.7838396580528175, 0.2161603419471824],
[0.1327352123858896, 0.8672647876141104],
[0.4539315153105434, 0.5460684846894566],
[0.2653170373440615, 0.7346829626559385],
[0.2214510855011292, 0.7785489144988708],
[0.3815617982317231, 0.6184382017682769],
[0.5460774966173132, 0.4539225033826867],
[0.4194973709123712, 0.5805026290876288],
[0.5259283752311108, 0.4740716247688892],
[0.3775730286932922, 0.6224269713067078],
[0.3428809879226986, 0.6571190120773014],
[0.7310789777058304, 0.2689210222941696],
[0.7808228627650035, 0.2191771372349965],
[0.6285876142112172, 0.3714123857887828],
[0.4444194848409649, 0.5555805151590351],
[0.9253101654677492, 0.0746898345322508],
[0.7108575012260019, 0.2891424987739981],
[0.6923236892085397, 0.3076763107914603],
[0.8658398719063449, 0.1341601280936550],
[0.6859457061731435, 0.3140542938268565],
[0.7190807857278905, 0.2809192142721096],
[0.7983745824241288, 0.2016254175758712],
[0.4768552271854714, 0.5231447728145286],
[0.5321014224575110, 0.4678985775424890],
[0.4293961594534983, 0.5706038405465017],
[0.6791961042739789, 0.3208038957260211],
[0.8898092885722800, 0.1101907114277199],
[0.6579005184496933, 0.3420994815503067],
[0.7652153232481362, 0.2347846767518638],
[0.5160770109846871, 0.4839229890153129],
[0.8067983092623874, 0.1932016907376126],
[0.7877539634640341, 0.2122460365359659],
[0.6347873007218796, 0.3652126992781204],
[0.8190797854627907, 0.1809202145372093],
[0.5519351414476166, 0.4480648585523834],
[0.4482439620440842, 0.5517560379559158],
])
assert np.allclose(ref, self.clf.predict_proba(Xte))
def test_score(self):
Xte, yte = self.test
ref = 0.8076923076923077
assert np.allclose(ref, self.clf.score(Xte, yte))
def test_sklearn2pmml(self):
# Export to PMML
pipeline = PMMLPipeline([
("classifier", self.ref)
])
pipeline.fit(self.test[0], self.test[1])
sklearn2pmml(pipeline, "lmc-sklearn2pmml.pmml", with_repr = True)
try:
# Import PMML
model = PMMLLogisticRegression(pmml='lmc-sklearn2pmml.pmml')
# Verify classification
Xenc, _ = self.test
assert np.allclose(
self.ref.predict_proba(Xenc),
model.predict_proba(Xenc)
)
finally:
remove("lmc-sklearn2pmml.pmml")
def test_sklearn2pmml_multiclass_multinomial(self):
data = load_iris(as_frame=True)
X = data.data
y = data.target
y.name = "Class"
ref = LogisticRegression()
ref.fit(X, y)
# Export to PMML
pipeline = PMMLPipeline([
("classifier", ref)
])
pipeline.fit(X, y)
sklearn2pmml(pipeline, "lmc-sklearn2pmml.pmml", with_repr=True)
try:
# Import PMML
model = PMMLLogisticRegression(pmml='lmc-sklearn2pmml.pmml')
# Verify classification
assert np.allclose(
ref.predict_proba(X),
model.predict_proba(X)
)
finally:
remove("lmc-sklearn2pmml.pmml")
def test_sklearn2pmml_multiclass_ovr(self):
data = load_iris(as_frame=True)
X = data.data
y = data.target
y.name = "Class"
ref = LogisticRegression(
multi_class='ovr'
)
ref.fit(X, y)
# Export to PMML
pipeline = PMMLPipeline([
("classifier", ref)
])
pipeline.fit(X, y)
sklearn2pmml(pipeline, "lmc-sklearn2pmml.pmml", with_repr=True)
try:
# Import PMML
model = PMMLLogisticRegression(pmml='lmc-sklearn2pmml.pmml')
# Verify classification
assert np.allclose(
ref.predict_proba(X),
model.predict_proba(X)
)
finally:
remove("lmc-sklearn2pmml.pmml")
def test_fit_exception(self):
with self.assertRaises(Exception) as cm:
self.clf.fit(np.array([[]]), np.array([]))
assert str(cm.exception) == 'Not supported.'
def test_more_tags(self):
assert self.clf._more_tags() == LogisticRegression()._more_tags()