[ENH] Test coverage for AEFCN Network

aeon/networks/tests/test_ae_fcn.py

@@ -0,0 +1,96 @@
+"""Tests for the AEFCN Model."""
+
+import pytest
+
+from aeon.networks import AEFCNNetwork
+from aeon.utils.validation._dependencies import _check_soft_dependencies
+
+
+@pytest.mark.skipif(
+    not _check_soft_dependencies(["tensorflow"], severity="none"),
+    reason="skip test if required soft dependency not available",
+)
+def test_default_initialization():
+    """Test if the network initializes with proper attributes."""
+    model = AEFCNNetwork()
+    assert model.latent_space_dim == 128
+    assert model.n_layers == 3
+    assert model.n_filters is None
+    assert model.kernel_size is None
+    assert model.dilation_rate == 1
+    assert model.strides == 1
+    assert model.padding == "same"
+    assert model.activation == "relu"
+    assert model.use_bias is True
+    assert model.temporal_latent_space is False
+
+
+@pytest.mark.skipif(
+    not _check_soft_dependencies(["tensorflow"], severity="none"),
+    reason="skip test if required soft dependency not available",
+)
+def test_custom_initialization():
+    """Test whether custom kwargs are correctly set."""
+    model = AEFCNNetwork(
+        latent_space_dim=64,
+        temporal_latent_space=True,
+        n_layers=4,
+        n_filters=[32, 64, 128, 256],
+        kernel_size=[9, 7, 5, 3],
+        activation="sigmoid",
+        dilation_rate=[1, 2, 4, 8],
+    )
+    model.build_network((100, 5))
+    assert model.latent_space_dim == 64
+    assert model.n_layers == 4
+    assert model._n_filters == [32, 64, 128, 256]
+    assert model._kernel_size == [9, 7, 5, 3]
+    assert model.dilation_rate == [1, 2, 4, 8]
+    assert model.activation == "sigmoid"
+    assert model.temporal_latent_space is True
+
+
+@pytest.mark.skipif(
+    not _check_soft_dependencies(["tensorflow"], severity="none"),
+    reason="skip test if required soft dependency not available",
+)
+def test_edge_case_initialization():
+    """Tests edge cases with minimal values."""
+    model = AEFCNNetwork(
+        latent_space_dim=0, n_layers=0, kernel_size=0, dilation_rate=[]
+    )
+    assert model.latent_space_dim == 0
+    assert model.kernel_size == 0
+    assert model.n_layers == 0
+    assert model.dilation_rate == []
+
+
+@pytest.mark.skipif(
+    not _check_soft_dependencies(["tensorflow"], severity="none"),
+    reason="skip test if required soft dependency not available",
+)
+def test_invalid_initialization():
+    """Test if the network raises valid exceptions or not."""
+    with pytest.raises(ValueError):
+        AEFCNNetwork(n_filters=[32, 64], n_layers=3).build_network((100, 10))
+
+    with pytest.raises(ValueError):
+        AEFCNNetwork(dilation_rate=[1, 2], n_layers=3).build_network((100, 10))
+
+
+@pytest.mark.skipif(
+    not _check_soft_dependencies(["tensorflow"], severity="none"),
+    reason="skip test if required soft dependency not available",
+)
+def test_build_network():
+    """Test call to the build_network method."""
+    import tensorflow as tf
+
+    model = AEFCNNetwork()
+    input_shape = (100, 10)
+    encoder, decoder = model.build_network(input_shape)
+
+    assert isinstance(encoder, tf.keras.Model)
+    assert isinstance(decoder, tf.keras.Model)
+    assert encoder.input_shape == (None, 100, 10)
+    assert decoder.input_shape is not None