def setUp(self):
self.model = DecisionTreeRegressor()
self.init_data()
self.converter = DecisionTreeConverter(
estimator=self.model,
context=self.ctx,
mode=DecisionTreeConverter.MODE_REGRESSION)
class TestDecisionTreeRegressorConverter(TestCase):
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 test_transform(self):
p = self.converter.pmml()
tm = p.TreeModel[0]
assert tm.MiningSchema is not None, 'Missing mining schema'
assert len(tm.MiningSchema.MiningField) == 3, 'Wrong number of mining fields'
assert tm.Node is not None, 'Missing root node'
assert tm.Node.recordCount == 4
assert tm.Node.True_ is not None, 'Root condition should always be True'
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 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 = 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
class TestDecisionTreeRegressorConverter(TestCase):
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 test_transform(self):
p = self.converter.pmml()
tm = p.TreeModel[0]
assert tm.MiningSchema is not None, 'Missing mining schema'
assert len(
tm.MiningSchema.MiningField) == 2, 'Wrong number of mining fields'
assert tm.Node is not None, 'Missing root node'
assert tm.Node.recordCount == 4
assert tm.Node.True_ is not None, 'Root condition should always be True'
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 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()
mapping_df = pd.DataFrame([
dict(x1=0, x2='zero', output=0),
dict(x1=0, x2='one', output=0),
dict(x1=1, x2='zero', output=0),
dict(x1=1, x2='one', output=1),
])
for idx, line in mapping_df.iterrows():
it.append(pmml_row(**dict(line)))
mapping.append(it)
mapping_df.set_index(keys=['x1', 'x2'])
mapping_f = np.vectorize(lambda x1, x2: mapping_df.ix[x1, x2].output.values[0])
self.ctx = TransformationContext({
Schema.INPUT: [
IntegerNumericFeature('x1'),
StringCategoricalFeature('x2', ['zero', 'one'])
],
Schema.DERIVED: [
DerivedFeature(
feature=RealNumericFeature(name='x3'),
transformation=mapping,
function=mapping_f
)
],
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(
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):
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 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(
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
)
class TestDecisionTreeClassifierConverter(TestCase):
def setUp(self):
np.random.seed(1)
self.est = DecisionTreeClassifier(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.CLASSIFICATION)
def test_transform(self):
p = self.converter.pmml()
tm = p.TreeModel[0]
assert tm.MiningSchema is not None, 'Missing mining schema'
assert len(
tm.MiningSchema.MiningField) == 2, 'Wrong number of mining fields'
assert tm.Node is not None, 'Missing root node'
assert tm.Node.recordCount == 4
assert tm.Node.True_ is not None, 'Root condition should always be True'
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):
self.model = DecisionTreeClassifier(max_depth=2)
self.init_data()
self.converter = DecisionTreeConverter(estimator=self.model,
context=self.ctx,
mode=ModelMode.CLASSIFICATION)
class TestDecisionTreeClassifierConverter(TestCase):
def setUp(self):
np.random.seed(1)
self.est = DecisionTreeClassifier(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=[IntegerCategoricalFeature('output', ['neg', 'pos'])]
)
self.converter = DecisionTreeConverter(
estimator=self.est,
context=self.ctx,
mode=DecisionTreeConverter.MODE_CLASSIFICATION
)
def test_transform(self):
p = self.converter.pmml()
tm = p.TreeModel[0]
assert tm.MiningSchema is not None, 'Missing mining schema'
assert len(tm.MiningSchema.MiningField) == 3, 'Wrong number of mining fields'
assert tm.Node is not None, 'Missing root node'
assert tm.Node.recordCount == 4
assert tm.Node.True_ is not None, 'Root condition should always be True'
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(
input=[
IntegerNumericFeature('x1'),
StringCategoricalFeature('x2', ['zero', 'one']),
],
derived=[
DerivedFeature(
feature=RealNumericFeature(name='x3'),
transformation=mapping
)
],
model=[
IntegerNumericFeature('x1'),
StringCategoricalFeature('x2', ['zero', 'one']),
RealNumericFeature(name='x3')
],
output=[IntegerCategoricalFeature('output', ['neg', 'pos'])]
)
self.converter = DecisionTreeConverter(
estimator=self.est,
context=self.ctx,
mode=DecisionTreeConverter.MODE_CLASSIFICATION
)
self.converter.pmml().toxml()
class TestDecisionTreeClassifierConverter(TestCase):
def setUp(self):
np.random.seed(1)
self.est = DecisionTreeClassifier(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.CLASSIFICATION
)
def test_transform(self):
p = self.converter.pmml()
tm = p.TreeModel[0]
assert tm.MiningSchema is not None, 'Missing mining schema'
assert len(tm.MiningSchema.MiningField) == 2, 'Wrong number of mining fields'
assert tm.Node is not None, 'Missing root node'
assert tm.Node.recordCount == 4
assert tm.Node.True_ is not None, 'Root condition should always be True'
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()
mapping_df = pd.DataFrame([
dict(x1=0, x2='zero', output=0),
dict(x1=0, x2='one', output=0),
dict(x1=1, x2='zero', output=0),
dict(x1=1, x2='one', output=1),
])
for idx, line in mapping_df.iterrows():
it.append(pmml_row(**dict(line)))
mapping.append(it)
mapping_df.set_index(keys=['x1', 'x2'])
mapping_f = np.vectorize(lambda x1, x2: mapping_df.ix[x1, x2].output.values[0])
self.ctx = TransformationContext({
Schema.INPUT: [
IntegerNumericFeature('x1'),
StringCategoricalFeature('x2', ['zero', 'one'])
],
Schema.DERIVED: [
DerivedFeature(
feature=RealNumericFeature(name='x3'),
transformation=mapping,
function=mapping_f
)
],
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()
