96 lines
3.1 KiB
Python
96 lines
3.1 KiB
Python
import math
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import pytest
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from pina import Trainer
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from pina.solver import PINN
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from pina.model import FeedForward
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from pina.loss import LinearWeighting
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from pina.problem.zoo import Poisson2DSquareProblem
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# Initialize problem and model
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problem = Poisson2DSquareProblem()
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problem.discretise_domain(10)
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model = FeedForward(len(problem.input_variables), len(problem.output_variables))
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# Weights for testing
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init_weight_1 = {cond: 3 for cond in problem.conditions.keys()}
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init_weight_2 = {cond: 4 for cond in problem.conditions.keys()}
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final_weight_1 = {cond: 1 for cond in problem.conditions.keys()}
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final_weight_2 = {cond: 5 for cond in problem.conditions.keys()}
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@pytest.mark.parametrize("initial_weights", [init_weight_1, init_weight_2])
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@pytest.mark.parametrize("final_weights", [final_weight_1, final_weight_2])
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@pytest.mark.parametrize("target_epoch", [5, 10])
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def test_constructor(initial_weights, final_weights, target_epoch):
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LinearWeighting(
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initial_weights=initial_weights,
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final_weights=final_weights,
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target_epoch=target_epoch,
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)
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# Should fail if initial_weights is not a dictionary
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with pytest.raises(ValueError):
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LinearWeighting(
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initial_weights=[1, 1, 1],
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final_weights=final_weights,
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target_epoch=target_epoch,
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)
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# Should fail if final_weights is not a dictionary
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with pytest.raises(ValueError):
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LinearWeighting(
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initial_weights=initial_weights,
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final_weights=[1, 1, 1],
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target_epoch=target_epoch,
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)
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# Should fail if target_epoch is not an integer
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with pytest.raises(AssertionError):
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LinearWeighting(
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initial_weights=initial_weights,
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final_weights=final_weights,
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target_epoch=1.5,
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)
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# Should fail if target_epoch is not positive
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with pytest.raises(AssertionError):
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LinearWeighting(
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initial_weights=initial_weights,
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final_weights=final_weights,
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target_epoch=0,
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)
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# Should fail if dictionary keys do not match
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with pytest.raises(ValueError):
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LinearWeighting(
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initial_weights={list(initial_weights.keys())[0]: 1},
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final_weights=final_weights,
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target_epoch=target_epoch,
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)
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@pytest.mark.parametrize("initial_weights", [init_weight_1, init_weight_2])
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@pytest.mark.parametrize("final_weights", [final_weight_1, final_weight_2])
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@pytest.mark.parametrize("target_epoch", [5, 10])
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def test_train_aggregation(initial_weights, final_weights, target_epoch):
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weighting = LinearWeighting(
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initial_weights=initial_weights,
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final_weights=final_weights,
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target_epoch=target_epoch,
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)
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solver = PINN(problem=problem, model=model, weighting=weighting)
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trainer = Trainer(solver=solver, max_epochs=target_epoch, accelerator="cpu")
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trainer.train()
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# Check that weights are updated correctly
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assert all(
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math.isclose(
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weighting.last_saved_weights()[cond],
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final_weights[cond],
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rel_tol=1e-5,
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abs_tol=1e-8,
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)
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for cond in final_weights.keys()
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)
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