class TestGradientBoostingClassifierConverter(TestCase):
def setUp(self):
np.random.seed(1)
self.est = GradientBoostingClassifier(max_depth=2, n_estimators=10)
self.est.fit([[0, 0], [0, 1], [1, 0], [1, 1]], [0, 1, 1, 1])
self.ctx = TransformationContext(
{
Schema.INPUT: [IntegerNumericFeature("x1"), StringCategoricalFeature("x2", ["zero", "one"])],
Schema.MODEL: [IntegerNumericFeature("x1"), StringCategoricalFeature("x2", ["zero", "one"])],
Schema.DERIVED: [],
Schema.OUTPUT: [IntegerCategoricalFeature("output", [0, 1])],
}
)
self.converter = GradientBoostingConverter(estimator=self.est, context=self.ctx)
def test_transform(self):
p = self.converter.pmml()
mm = p.MiningModel[0]
assert mm.MiningSchema is not None, "Missing mining schema"
assert len(mm.MiningSchema.MiningField) == 2, "Wrong number of mining fields"
assert mm.Segmentation is not None, "Missing segmentation root"
def test_transform_with_verification(self):
p = self.converter.pmml(
[
{"x1": 0, "x2": "zero", "output": self.est.predict_proba([[0, 0]])[0, 1]},
{"x1": 0, "x2": "one", "output": self.est.predict_proba([[0, 1]])[0, 1]},
{"x1": 1, "x2": "zero", "output": self.est.predict_proba([[1, 0]])[0, 1]},
{"x1": 1, "x2": "one", "output": self.est.predict_proba([[1, 1]])[0, 1]},
]
)
mm = p.MiningModel[0]
assert mm.MiningSchema is not None, "Missing mining schema"
assert len(mm.MiningSchema.MiningField) == 2, "Wrong number of mining fields"
assert mm.Segmentation is not None, "Missing segmentation root"
def setUp(self):
np.random.seed(1)
self.est = GradientBoostingClassifier(max_depth=2, n_estimators=10)
self.est.fit([
[0, 0],
[0, 1],
[1, 0],
[1, 1],
], [0, 1, 1, 1])
self.ctx = TransformationContext({
Schema.INPUT: [
IntegerNumericFeature('x1'),
StringCategoricalFeature('x2', ['zero', 'one'])
],
Schema.MODEL: [
IntegerNumericFeature('x1'),
StringCategoricalFeature('x2', ['zero', 'one'])
],
Schema.DERIVED: [],
Schema.OUTPUT: [
IntegerCategoricalFeature('output', [0, 1])
]
})
self.converter = GradientBoostingConverter(
estimator=self.est,
context=self.ctx
)
class TestGradientBoostingClassifierConverter(TestCase):
def setUp(self):
np.random.seed(1)
self.est = GradientBoostingClassifier(max_depth=2, n_estimators=10)
self.est.fit([
[0, 0],
[0, 1],
[1, 0],
[1, 1],
], [0, 1, 1, 1])
self.ctx = TransformationContext({
Schema.INPUT: [
IntegerNumericFeature('x1'),
StringCategoricalFeature('x2', ['zero', 'one'])
],
Schema.MODEL: [
IntegerNumericFeature('x1'),
StringCategoricalFeature('x2', ['zero', 'one'])
],
Schema.DERIVED: [],
Schema.OUTPUT: [
IntegerCategoricalFeature('output', [0, 1])
]
})
self.converter = GradientBoostingConverter(
estimator=self.est,
context=self.ctx
)
def test_transform(self):
p = self.converter.pmml()
mm = p.MiningModel[0]
assert mm.MiningSchema is not None, 'Missing mining schema'
assert len(mm.MiningSchema.MiningField) == 2, 'Wrong number of mining fields'
assert mm.Segmentation is not None, 'Missing segmentation root'
def test_transform_with_verification(self):
p = self.converter.pmml([
{'x1': 0, 'x2': 'zero', 'output#1': self.est.predict_proba([[0, 0]])[0, 1], 'output#0': self.est.predict_proba([[0, 0]])[0, 0], 'output': self.est.predict([[0, 0]])},
{'x1': 0, 'x2': 'one', 'output#1': self.est.predict_proba([[0, 1]])[0, 1], 'output#0': self.est.predict_proba([[0, 1]])[0, 0], 'output': self.est.predict([[0, 1]])},
{'x1': 1, 'x2': 'zero', 'output#1': self.est.predict_proba([[1, 0]])[0, 1], 'output#0': self.est.predict_proba([[1, 0]])[0, 0], 'output': self.est.predict([[1, 0]])},
{'x1': 1, 'x2': 'one', 'output#1': self.est.predict_proba([[1, 1]])[0, 1], 'output#0': self.est.predict_proba([[1, 1]])[0, 0], 'output': self.est.predict([[1, 1]])},
])
mm = p.MiningModel[0]
assert mm.MiningSchema is not None, 'Missing mining schema'
assert len(mm.MiningSchema.MiningField) == 2, 'Wrong number of mining fields'
assert mm.Segmentation is not None, 'Missing segmentation root'
def setUp(self):
np.random.seed(1)
self.est = GradientBoostingClassifier(max_depth=2, n_estimators=10)
self.est.fit([
[0, 0],
[0, 1],
[1, 0],
[1, 1],
], [0, 1, 1, 1])
self.ctx = TransformationContext(
input=[IntegerNumericFeature('x1'), StringCategoricalFeature('x2', ['zero', 'one'])],
derived=[],
model=[IntegerNumericFeature('x1'), StringCategoricalFeature('x2', ['zero', 'one'])],
output=[RealNumericFeature('output')]
)
self.converter = GradientBoostingConverter(
estimator=self.est,
context=self.ctx
)
class TestGradientBoostingClassifierConverter(TestCase):
def setUp(self):
np.random.seed(1)
self.est = GradientBoostingClassifier(max_depth=2, n_estimators=10)
self.est.fit([
[0, 0],
[0, 1],
[1, 0],
[1, 1],
], [0, 1, 1, 1])
self.ctx = TransformationContext(
input=[IntegerNumericFeature('x1'), StringCategoricalFeature('x2', ['zero', 'one'])],
derived=[],
model=[IntegerNumericFeature('x1'), StringCategoricalFeature('x2', ['zero', 'one'])],
output=[RealNumericFeature('output')]
)
self.converter = GradientBoostingConverter(
estimator=self.est,
context=self.ctx
)
def test_transform(self):
p = self.converter.pmml()
mm = p.MiningModel[0]
assert mm.MiningSchema is not None, 'Missing mining schema'
assert len(mm.MiningSchema.MiningField) == 3, 'Wrong number of mining fields'
assert mm.Segmentation is not None, 'Missing segmentation root'
def test_transform_with_verification(self):
p = self.converter.pmml([
{'x1': 0, 'x2': 'zero', 'output': self.est.predict_proba([[0, 0]])[0, 1]},
{'x1': 0, 'x2': 'one', 'output': self.est.predict_proba([[0, 1]])[0, 1]},
{'x1': 1, 'x2': 'zero', 'output': self.est.predict_proba([[1, 0]])[0, 1]},
{'x1': 1, 'x2': 'one', 'output': self.est.predict_proba([[1, 1]])[0, 1]},
])
mm = p.MiningModel[0]
assert mm.MiningSchema is not None, 'Missing mining schema'
assert len(mm.MiningSchema.MiningField) == 3, 'Wrong number of mining fields'
assert mm.Segmentation is not None, 'Missing segmentation root'
def setUp(self):
np.random.seed(1)
self.est = GradientBoostingClassifier(max_depth=2, n_estimators=10)
self.est.fit([
[0, 0],
[0, 1],
[1, 0],
[1, 1],
], [0, 1, 1, 1])
self.ctx = TransformationContext({
Schema.INPUT: [
IntegerNumericFeature('x1'),
StringCategoricalFeature('x2', ['zero', 'one'])
],
Schema.MODEL: [
IntegerNumericFeature('x1'),
StringCategoricalFeature('x2', ['zero', 'one'])
],
Schema.DERIVED: [],
Schema.OUTPUT: [IntegerCategoricalFeature('output', [0, 1])]
})
self.converter = GradientBoostingConverter(estimator=self.est,
context=self.ctx)
def setUp(self):
self.model = GradientBoostingClassifier(n_estimators=2, max_depth=2)
self.init_data_one_label()
self.converter = GradientBoostingConverter(estimator=self.model,
context=self.ctx)