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()
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()
