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Add tests/rule_induction/trxf/pmml_export/test_nyoka_serialize_integration.py
Signed-off-by: Ke LI <[email protected]>
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‎aix360/algorithms/rule_induction/trxf/pmml_export/models/__init__.py‎

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@@ -9,6 +9,7 @@
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from .predicate import CompoundPredicate
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from .predicate import Operator
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from .predicate import SimplePredicate
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from .predicate import TruePredicate
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from .rule import SimpleRule
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from .rule import DEFAULT_WEIGHT
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from .rule import DEFAULT_CONFIDENCE

‎aix360/algorithms/rule_induction/trxf/pmml_export/models/attribute.py‎

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@@ -21,4 +21,4 @@ class ComplexPartialScore:
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@dataclass(frozen=True)
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class Attribute:
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score: typing.Union[str, ComplexPartialScore]
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predicate: typing.Union[predicate.SimplePredicate, predicate.CompoundPredicate]
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predicate: typing.Union[predicate.SimplePredicate, predicate.CompoundPredicate, predicate.TruePredicate]

‎aix360/algorithms/rule_induction/trxf/pmml_export/models/predicate.py‎

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@@ -16,13 +16,19 @@ class Operator(enum.Enum):
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lessOrEqual = 3
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greaterThan = 4
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greaterOrEqual = 5
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isMissing = 6
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@dataclass(frozen=True)
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class SimplePredicate:
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operator: Operator = field()
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value: str = field()
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field: str = field()
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value: typing.Optional[str] = None
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@dataclass(frozen=True)
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class TruePredicate:
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pass
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# Use functional api to add aliases for `or` and `and`

‎aix360/algorithms/rule_induction/trxf/pmml_export/serializer/nyoka_serializer.py‎

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@@ -150,4 +150,6 @@ def _nyoka_pmml_attributes(self, attribute: models.Attribute) -> nyoka_pmml.Attr
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booleanOperator=attribute.predicate.booleanOperator.name,
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SimplePredicate=[
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nyoka_pmml.SimplePredicate(field=sp.field, operator=sp.operator.name, value=sp.value)
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for sp in attribute.predicate.simplePredicates]))
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for sp in attribute.predicate.simplePredicates]),
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True_=None if (attribute.predicate is None or not isinstance(
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attribute.predicate, models.TruePredicate)) else nyoka_pmml.True_())

‎tests/rule_induction/trxf/pmml_export/test_nyoka_serialize_integration.py‎

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import datetime
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import io
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from unittest import TestCase
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try:
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import pypmml
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import pandas
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except ImportError:
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pypmml = None
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pandas = None
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import aix360.algorithms.rule_induction.trxf.pmml_export.models as models
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import aix360.algorithms.rule_induction.trxf.pmml_export.serializer as serializer
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class TestNyokaSerializer(TestCase):
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nyokaSerializer = serializer.NyokaSerializer(datetime.datetime.now())
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def setUp(self):
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if pypmml is None or pandas is None:
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self.skipTest('Install pypmml and pandas for integration tests')
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def test_serialize_then_predict(self):
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# arrange
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score_card = models.Scorecard(
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models.DataDictionary(
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[
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models.DataField(
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name='department', dataType=models.DataType.string, optype=models.OpType.categorical),
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models.DataField(
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name='age', dataType=models.DataType.integer, optype=models.OpType.continuous),
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models.DataField(
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name='income', dataType=models.DataType.double, optype=models.OpType.continuous),
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models.DataField(
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name='overallScore', dataType=models.DataType.double, optype=models.OpType.continuous)
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]
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),
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miningSchema=models.MiningSchema(
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[
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models.MiningField(name='department', usageType=models.MiningFieldUsageType.active),
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models.MiningField(name='age', usageType=models.MiningFieldUsageType.active),
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models.MiningField(name='income', usageType=models.MiningFieldUsageType.active),
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models.MiningField(name='overallScore', usageType=models.MiningFieldUsageType.target),
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]
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),
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output=models.Output([
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models.OutputField(
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name='Final Score',
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feature='predictedValue',
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dataType=models.DataType.double,
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optype=models.OpType.continuous)
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]),
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characteristics=models.Characteristics(
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[
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models.Characteristic(name='departmentScore', attributes=[
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models.Attribute(
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score='-9',
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predicate=models.SimplePredicate(
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field='department', operator=models.Operator.isMissing)),
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models.Attribute(
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score='19',
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predicate=models.SimplePredicate(
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field='department', operator=models.Operator.equal, value='marketing')),
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models.Attribute(
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score='3',
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predicate=models.SimplePredicate(
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field='department', operator=models.Operator.equal, value='engineering')),
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models.Attribute(
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score='6',
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predicate=models.SimplePredicate(
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field='department', operator=models.Operator.equal, value='business')),
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models.Attribute(
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score='0',
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predicate=models.TruePredicate()),
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]),
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models.Characteristic(
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name='ageScore', attributes=[
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models.Attribute(
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score='-1',
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predicate=models.SimplePredicate(
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field='age', operator=models.Operator.isMissing)),
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models.Attribute(
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score='-3',
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predicate=models.SimplePredicate(
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field='age', operator=models.Operator.lessOrEqual, value='18')),
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models.Attribute(
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score='0',
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predicate=models.CompoundPredicate(
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booleanOperator=models.BooleanOperator.and_,
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simplePredicates=[
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models.SimplePredicate(
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field='age', operator=models.Operator.greaterThan, value='18'),
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models.SimplePredicate(
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field='age', operator=models.Operator.lessOrEqual, value='29')])),
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models.Attribute(
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score='12',
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predicate=models.CompoundPredicate(
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booleanOperator=models.BooleanOperator.and_,
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simplePredicates=[
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models.SimplePredicate(
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field='age', operator=models.Operator.greaterThan, value='29'),
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models.SimplePredicate(
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field='age', operator=models.Operator.lessOrEqual, value='39')])),
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models.Attribute(
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score='18',
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predicate=models.SimplePredicate(
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field='age', operator=models.Operator.greaterThan, value='39'))]),
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models.Characteristic(name='incomeScore', attributes=[
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models.Attribute(
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score='3',
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predicate=models.SimplePredicate(
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field='income', operator=models.Operator.isMissing)),
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models.Attribute(
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predicate=models.SimplePredicate(
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field='income', operator=models.Operator.equal, value='1000'),
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score=models.ComplexPartialScore(feature_name='income', multiplier='0.03', constant='11')),
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models.Attribute(
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score='5',
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predicate=models.CompoundPredicate(
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booleanOperator=models.BooleanOperator.and_,
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simplePredicates=[
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models.SimplePredicate(
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field='income', operator=models.Operator.greaterThan, value='1000'),
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models.SimplePredicate(
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field='income', operator=models.Operator.lessOrEqual, value='2500')])),
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models.Attribute(
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predicate=models.SimplePredicate(
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field='income', operator=models.Operator.greaterThan, value='1500'),
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score=models.ComplexPartialScore(
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feature_name='income', multiplier='0.01', constant='18'))])]))
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# when
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serialized = self.nyokaSerializer.serialize(score_card)
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pmml_model = pypmml.Model.load(io.StringIO(serialized))
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# assert
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self.assertIsNotNone(pmml_model)
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self.assertEqual(len(pmml_model.dataDictionary.fields), 4)

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