add linear weighting

tests/test_weighting/test_linear_weighting.py

@@ -0,0 +1,95 @@
+import math
+import pytest
+from pina import Trainer
+from pina.solver import PINN
+from pina.model import FeedForward
+from pina.loss import LinearWeighting
+from pina.problem.zoo import Poisson2DSquareProblem
+
+
+# Initialize problem and model
+problem = Poisson2DSquareProblem()
+problem.discretise_domain(10)
+model = FeedForward(len(problem.input_variables), len(problem.output_variables))
+
+# Weights for testing
+init_weight_1 = {cond: 3 for cond in problem.conditions.keys()}
+init_weight_2 = {cond: 4 for cond in problem.conditions.keys()}
+final_weight_1 = {cond: 1 for cond in problem.conditions.keys()}
+final_weight_2 = {cond: 5 for cond in problem.conditions.keys()}
+
+
+@pytest.mark.parametrize("initial_weights", [init_weight_1, init_weight_2])
+@pytest.mark.parametrize("final_weights", [final_weight_1, final_weight_2])
+@pytest.mark.parametrize("target_epoch", [5, 10])
+def test_constructor(initial_weights, final_weights, target_epoch):
+    LinearWeighting(
+        initial_weights=initial_weights,
+        final_weights=final_weights,
+        target_epoch=target_epoch,
+    )
+
+    # Should fail if initial_weights is not a dictionary
+    with pytest.raises(ValueError):
+        LinearWeighting(
+            initial_weights=[1, 1, 1],
+            final_weights=final_weights,
+            target_epoch=target_epoch,
+        )
+
+    # Should fail if final_weights is not a dictionary
+    with pytest.raises(ValueError):
+        LinearWeighting(
+            initial_weights=initial_weights,
+            final_weights=[1, 1, 1],
+            target_epoch=target_epoch,
+        )
+
+    # Should fail if target_epoch is not an integer
+    with pytest.raises(AssertionError):
+        LinearWeighting(
+            initial_weights=initial_weights,
+            final_weights=final_weights,
+            target_epoch=1.5,
+        )
+
+    # Should fail if target_epoch is not positive
+    with pytest.raises(AssertionError):
+        LinearWeighting(
+            initial_weights=initial_weights,
+            final_weights=final_weights,
+            target_epoch=0,
+        )
+
+    # Should fail if dictionary keys do not match
+    with pytest.raises(ValueError):
+        LinearWeighting(
+            initial_weights={list(initial_weights.keys())[0]: 1},
+            final_weights=final_weights,
+            target_epoch=target_epoch,
+        )
+
+
+@pytest.mark.parametrize("initial_weights", [init_weight_1, init_weight_2])
+@pytest.mark.parametrize("final_weights", [final_weight_1, final_weight_2])
+@pytest.mark.parametrize("target_epoch", [5, 10])
+def test_train_aggregation(initial_weights, final_weights, target_epoch):
+    weighting = LinearWeighting(
+        initial_weights=initial_weights,
+        final_weights=final_weights,
+        target_epoch=target_epoch,
+    )
+    solver = PINN(problem=problem, model=model, weighting=weighting)
+    trainer = Trainer(solver=solver, max_epochs=target_epoch, accelerator="cpu")
+    trainer.train()
+
+    # Check that weights are updated correctly
+    assert all(
+        math.isclose(
+            weighting.last_saved_weights()[cond],
+            final_weights[cond],
+            rel_tol=1e-5,
+            abs_tol=1e-8,
+        )
+        for cond in final_weights.keys()
+    )