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Introduced COHORT_LABEL field in iteration rules #103
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Karthikeyannhs
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eli-258-introduce_cohort_label_field_in_rules
May 27, 2025
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7f199f3
Considered cohort_label in rules
Karthikeyannhs caa27f3
Test get value from all attribute_levels
Karthikeyannhs 6b1e61d
change param name
Karthikeyannhs e6b4bb3
test_status_on_target_based_on_last_successful_date fixed field value.
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| Original file line number | Diff line number | Diff line change |
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@@ -19,7 +19,9 @@ def future_date(days_ahead: int = 365) -> date: | |
| class IterationCohortFactory(ModelFactory[rules.IterationCohort]): ... | ||
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| class IterationRuleFactory(ModelFactory[rules.IterationRule]): ... | ||
| class IterationRuleFactory(ModelFactory[rules.IterationRule]): | ||
| attribute_target = None | ||
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Contributor
Author
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. no random values will be ret for target_attribute and cohort_label. |
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| cohort_label = None | ||
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| class IterationFactory(ModelFactory[rules.Iteration]): | ||
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,50 @@ | ||
| from collections.abc import Collection, Mapping | ||
| from typing import Any | ||
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| import pytest | ||
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| from eligibility_signposting_api.model import rules | ||
| from eligibility_signposting_api.services.calculators.rule_calculator import RuleCalculator | ||
| from tests.fixtures.builders.model import rule as rule_builder | ||
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| Row = Collection[Mapping[str, Any]] | ||
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| @pytest.mark.parametrize( | ||
| ("person_data", "rule", "expected"), | ||
| [ | ||
| # PERSON attribute level | ||
| ( | ||
| [{"ATTRIBUTE_TYPE": "PERSON", "POSTCODE": "SW19"}], | ||
| rule_builder.IterationRuleFactory.build( | ||
| attribute_level=rules.RuleAttributeLevel.PERSON, attribute_name="POSTCODE" | ||
| ), | ||
| "SW19", | ||
| ), | ||
| # TARGET attribute level | ||
| ( | ||
| [{"ATTRIBUTE_TYPE": "RSV", "LAST_SUCCESSFUL_DATE": "20240101"}], | ||
| rule_builder.IterationRuleFactory.build( | ||
| attribute_level=rules.RuleAttributeLevel.TARGET, | ||
| attribute_name="LAST_SUCCESSFUL_DATE", | ||
| attribute_target="RSV", | ||
| ), | ||
| "20240101", | ||
| ), | ||
| # COHORT attribute level | ||
| ( | ||
| [{"ATTRIBUTE_TYPE": "COHORTS", "COHORT_LABEL": ""}], | ||
| rule_builder.IterationRuleFactory.build( | ||
| attribute_level=rules.RuleAttributeLevel.COHORT, attribute_name="COHORT_LABEL" | ||
| ), | ||
| "", | ||
| ), | ||
| ], | ||
| ) | ||
| def test_get_attribute_value_for_all_attribute_levels(person_data: Row, rule: rules.IterationRule, expected: str): | ||
| # Given | ||
| calc = RuleCalculator(person_data=person_data, rule=rule) | ||
| # When | ||
| actual = calc.get_attribute_value() | ||
| # Then | ||
| assert actual == expected |
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This looks like it will work for the case of multiple rules, with the same priority, that are executed in an AND