34 lines
1.5 KiB
Python
34 lines
1.5 KiB
Python
from pulp import lpSum
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from random import randint, sample
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def build_constraints(solver_run, problem, items):
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total_form_items = solver_run.total_form_items
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constraints = solver_run.constraints
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for constraint in constraints:
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attribute = constraint.reference_attribute
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min = constraint.minimum
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max = constraint.maximum
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if attribute.type == 'metadata':
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con = dict(zip([item.id for item in solver_run.items],
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[item.attribute_exists(attribute)
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for item in solver_run.items]))
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problem += lpSum([con[item.id]
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* items[item.id]
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for item in solver_run.items]) >= round(total_form_items * (min / 100)), f'{attribute.id} - {attribute.value} - min'
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problem += lpSum([con[item.id]
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* items[item.id]
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for item in solver_run.items]) <= round(total_form_items * (max / 100)), f'{attribute.id} - {attribute.value} - max'
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elif attribute.type == 'bundle':
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# currently only using passage bundles, but will eventually make more dynamic for many bundle types
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bundles = solver_run.bundles('passage')
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total_bundles = randint(constraint.minimum, constraint.maximum)
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selected_bundles = sample(bundles, total_bundles)
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for bundle in selected_bundles:
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problem += lpSum([items[item.id] for item in solver_run.items if item.passage == bundle['id']]) == bundle['count'], f'Bundle constrain for {bundle["type"]} ({bundle["id"]})'
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return problem
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