def segmentation(self):
"""
Build a segmentation (sequence of estimators)
:return: Segmentation element
"""
# there is no notion of weighted sum, so we should take weighted average and multiply result by total weight
# in output transformation
segmentation = pmml.Segmentation(multipleModelMethod="weightedAverage")
# build the context for the nested regression models by replacing output categorical feature
# with the continuous numeric feature
regression_context = copy(self.context)
regression_context.schemas[Schema.OUTPUT] = [
RealNumericFeature(
name=self.context.schemas[Schema.OUTPUT][0].name,
namespace=Schema.NUMERIC.value)
]
# first, transform initial estimator
init_segment = pmml.Segment(weight=1)
init_segment.append(pmml.True_())
init_segment.append(
find_converter(self.estimator.init_)(self.estimator.init_,
regression_context).model())
segmentation.append(init_segment)
for est in self.estimator.estimators_[:, 0]:
s = pmml.Segment(weight=self.estimator.learning_rate)
s.append(pmml.True_())
s.append(
DecisionTreeConverter(est, regression_context,
ModelMode.REGRESSION)._model())
segmentation.append(s)
return segmentation
def setUp(self):
self.model = DecisionTreeRegressor()
self.init_data()
self.converter = DecisionTreeConverter(
estimator=self.model,
context=self.ctx,
mode=DecisionTreeConverter.MODE_REGRESSION)
def setUp(self):
self.model = DecisionTreeClassifier()
self.init_data()
self.converter = DecisionTreeConverter(
estimator=self.model,
context=self.ctx,
mode=DecisionTreeConverter.MODE_CLASSIFICATION)
def segmentation(self):
"""
Build a segmentation (sequence of estimators)
:return: Segmentation element
"""
# there is no notion of weighted sum, so we should take weighted average and multiply result by total weight
# in output transformation
segmentation = pmml.Segmentation(multipleModelMethod="weightedAverage")
# first, transform initial estimator
init_segment = pmml.Segment(weight=1)
init_segment.append(pmml.True_())
init_segment.append(
find_converter(self.estimator.init_)(self.estimator.init_,
self.context).model())
segmentation.append(init_segment)
for est in self.estimator.estimators_[:, 0]:
s = pmml.Segment(weight=self.estimator.learning_rate)
s.append(pmml.True_())
s.append(
DecisionTreeConverter(est, self.context,
self.MODE_REGRESSION)._model())
segmentation.append(s)
return segmentation
def segmentation(self):
"""
Build a segmentation (sequence of estimators)
:return: Segmentation element
"""
segmentation = pmml.Segmentation(multipleModelMethod="majorityVote")
for index, est in enumerate(self.estimator.estimators_):
s = pmml.Segment(id=index)
s.append(pmml.True_())
s.append(
DecisionTreeConverter(est, self.context,
self.MODE_CLASSIFICATION)._model())
segmentation.append(s)
return segmentation
def test_transform_with_derived_field(self):
self.est = DecisionTreeClassifier(max_depth=2)
self.est.fit([
[0, 0, 0],
[0, 1, 0],
[1, 0, 0],
[1, 1, 1],
], [0, 1, 1, 1])
mapping = pmml.MapValues(dataType="double", outputColumn="output")
mapping.append(pmml.FieldColumnPair(column="x1", field="x1"))
mapping.append(pmml.FieldColumnPair(column="x2", field="x2"))
it = pmml.InlineTable()
it.append(pmml_row(x1=0, x2='zero', output=0))
it.append(pmml_row(x1=0, x2='one', output=0))
it.append(pmml_row(x1=1, x2='zero', output=0))
it.append(pmml_row(x1=1, x2='one', output=1))
mapping.append(it)
self.ctx = TransformationContext({
Schema.INPUT: [
IntegerNumericFeature('x1'),
StringCategoricalFeature('x2', ['zero', 'one'])
],
Schema.DERIVED: [
DerivedFeature(feature=RealNumericFeature(name='x3'),
transformation=mapping)
],
Schema.MODEL: [
IntegerNumericFeature('x1'),
StringCategoricalFeature('x2', ['zero', 'one']),
RealNumericFeature(name='x3')
],
Schema.OUTPUT:
[IntegerCategoricalFeature('output', ['neg', 'pos'])]
})
self.converter = DecisionTreeConverter(estimator=self.est,
context=self.ctx,
mode=ModelMode.CLASSIFICATION)
self.converter.pmml().toxml()
def setUp(self):
np.random.seed(1)
self.est = DecisionTreeRegressor(max_depth=2)
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: [IntegerNumericFeature('output')]
})
self.converter = DecisionTreeConverter(estimator=self.est,
context=self.ctx,
mode=ModelMode.REGRESSION)
def setUp(self):
np.random.seed(1)
self.est = DecisionTreeRegressor(max_depth=2)
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'])
],
model=[
IntegerNumericFeature('x1'),
StringCategoricalFeature('x2', ['zero', 'one'])
],
derived=[],
output=[IntegerNumericFeature('output')])
self.converter = DecisionTreeConverter(
estimator=self.est,
context=self.ctx,
mode=DecisionTreeConverter.MODE_REGRESSION)
def setUp(self):
self.model = DecisionTreeClassifier(max_depth=2)
self.init_data()
self.converter = DecisionTreeConverter(estimator=self.model,
context=self.ctx,
mode=ModelMode.CLASSIFICATION)
