Add test for DecagonLayer.

src/decagon_pytorch/convolve.py

@@ -195,9 +195,12 @@ class SparseDropoutGraphConvActivation(torch.nn.Module):
         activation: Callable[[torch.Tensor], torch.Tensor]=torch.nn.functional.relu,
         **kwargs) -> None:
         super().__init__(**kwargs)
-        self.sparse_graph_conv = SparseGraphConv(input_dim, output_dim, adjacency_matrix)
+        self.input_dim = input_dim
+        self.output_dim = output_dim
+        self.adjacency_matrix = adjacency_matrix
         self.keep_prob = keep_prob
         self.activation = activation
+        self.sparse_graph_conv = SparseGraphConv(input_dim, output_dim, adjacency_matrix)
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
         x = dropout_sparse(x, self.keep_prob)

tests/decagon_pytorch/test_layer.py

@@ -4,6 +4,7 @@ from decagon_pytorch.layer import InputLayer, \
 from decagon_pytorch.data import Data
 import torch
 import pytest
+from decagon_pytorch.convolve import SparseDropoutGraphConvActivation
 
 
 def _some_data():
@@ -121,4 +122,32 @@ def test_decagon_layer_02():
 
 
 def test_decagon_layer_03():
-    pass
+    d = _some_data_with_interactions()
+    in_layer = OneHotInputLayer(d)
+    d_layer = DecagonLayer(d, in_layer, output_dim=32)
+    assert d_layer.data == d
+    assert d_layer.previous_layer == in_layer
+    assert d_layer.input_dim == [ 1000, 100 ]
+    assert not d_layer.is_sparse
+    assert d_layer.keep_prob == 1.
+    assert d_layer.rel_activation(0.5) == 0.5
+    x = torch.tensor([-1, 0, 0.5, 1])
+    assert (d_layer.layer_activation(x) == torch.nn.functional.relu(x)).all()
+    assert len(d_layer.next_layer_repr) == 2
+    assert len(d_layer.next_layer_repr[0]) == 2
+    assert len(d_layer.next_layer_repr[1]) == 4
+    assert all(map(lambda a: isinstance(a[0], SparseDropoutGraphConvActivation),
+        d_layer.next_layer_repr[0]))
+    assert all(map(lambda a: isinstance(a[0], SparseDropoutGraphConvActivation),
+        d_layer.next_layer_repr[1]))
+    assert all(map(lambda a: a[0].output_dim == 32,
+        d_layer.next_layer_repr[0]))
+    assert all(map(lambda a: a[0].output_dim == 32,
+        d_layer.next_layer_repr[1]))
+
+
+def test_decagon_layer_04():
+    d = _some_data_with_interactions()
+    in_layer = OneHotInputLayer(d)
+    d_layer = DecagonLayer(d, in_layer, output_dim=32)
+    _ = d_layer()