[ENH] Test coverage for TimeCNNNetwork Improved

aeon/networks/tests/test_time_cnn.py

@@ -0,0 +1,274 @@
+"""Tests for the TimeCNNNetwork Model."""
+
+import pytest
+
+from aeon.networks import TimeCNNNetwork
+from aeon.utils.validation._dependencies import _check_soft_dependencies
+
+
+@pytest.mark.skipif(
+    not _check_soft_dependencies(["tensorflow"], severity="none"),
+    reason="Tensorflow soft dependency unavailable.",
+)
+def test_time_cnn_input_shape_padding():
+    """Test of CNN network with input_shape < 60."""
+    input_shape = (40, 2)
+    network = TimeCNNNetwork()
+    input_layer, output_layer = network.build_network(input_shape=input_shape)
+
+    assert hasattr(input_layer, "shape")
+    assert hasattr(output_layer, "shape")
+
+
+@pytest.mark.skipif(
+    not _check_soft_dependencies(["tensorflow"], severity="none"),
+    reason="Tensorflow soft dependency unavailable.",
+)
+@pytest.mark.parametrize(
+    "activation, n_layers, should_raise",
+    [
+        ("relu", 2, False),
+        ("sigmoid", 2, False),
+        ("tanh", 2, False),
+        (["relu", "sigmoid", "tanh"], 2, True),
+        (["relu"], 2, True),
+    ],
+)
+def test_time_cnn_activation(activation, n_layers, should_raise):
+    """Test activation configuration handling."""
+    input_shape = (100, 5)
+    if should_raise:
+        with pytest.raises(ValueError):
+            network = TimeCNNNetwork(activation=activation, n_layers=n_layers)
+            network.build_network(input_shape=input_shape)
+    else:
+        network = TimeCNNNetwork(activation=activation, n_layers=n_layers)
+        input_layer, output_layer = network.build_network(input_shape=input_shape)
+
+        assert hasattr(input_layer, "shape")
+        assert hasattr(output_layer, "shape")
+
+
+@pytest.mark.skipif(
+    not _check_soft_dependencies(["tensorflow"], severity="none"),
+    reason="Tensorflow soft dependency unavailable.",
+)
+@pytest.mark.parametrize(
+    "kernel_size, n_layers, should_raise",
+    [
+        (7, 2, False),
+        ([5, 3], 2, False),
+        ([5, 3, 2], 2, True),
+        ([5], 2, True),
+    ],
+)
+def test_time_cnn_kernel_size(kernel_size, n_layers, should_raise):
+    """Test kernel size configuration with different layer counts."""
+    input_shape = (100, 5)
+    if should_raise:
+        with pytest.raises(ValueError):
+            network = TimeCNNNetwork(n_layers=n_layers, kernel_size=kernel_size)
+            network.build_network(input_shape=input_shape)
+    else:
+        network = TimeCNNNetwork(n_layers=n_layers, kernel_size=kernel_size)
+        input_layer, output_layer = network.build_network(input_shape=input_shape)
+
+        assert hasattr(input_layer, "shape")
+        assert hasattr(output_layer, "shape")
+
+
+@pytest.mark.skipif(
+    not _check_soft_dependencies(["tensorflow"], severity="none"),
+    reason="Tensorflow soft dependency unavailable.",
+)
+@pytest.mark.parametrize(
+    "n_layers,n_filters,should_raise",
+    [
+        (2, [8, 16], False),
+        (1, [12, 10, 4], True),
+        (2, 8, False),
+        (3, [8], True),
+    ],
+)
+def test_time_cnn_n_filters(n_layers, n_filters, should_raise):
+    """Test filter configuration handling."""
+    input_shape = (100, 5)
+    if should_raise:
+        with pytest.raises(ValueError):
+            network = TimeCNNNetwork(n_layers=n_layers, n_filters=n_filters)
+            network.build_network(input_shape=input_shape)
+    else:
+        network = TimeCNNNetwork(n_layers=n_layers, n_filters=n_filters)
+        input_layer, output_layer = network.build_network(input_shape=input_shape)
+
+        assert hasattr(input_layer, "shape")
+        assert hasattr(output_layer, "shape")
+
+
+@pytest.mark.skipif(
+    not _check_soft_dependencies(["tensorflow"], severity="none"),
+    reason="Tensorflow soft dependency unavailable.",
+)
+@pytest.mark.parametrize(
+    "avg_pool_size, n_layers, should_raise",
+    [
+        (3, 2, False),
+        ([2, 3], 2, False),
+        ([2, 3, 4], 2, True),
+        ([2], 2, True),
+    ],
+)
+def test_time_cnn_avg_pool_size(avg_pool_size, n_layers, should_raise):
+    """Test average pool size configuration."""
+    input_shape = (100, 5)
+    if should_raise:
+        with pytest.raises(ValueError):
+            network = TimeCNNNetwork(avg_pool_size=avg_pool_size, n_layers=n_layers)
+            network.build_network(input_shape=input_shape)
+    else:
+        network = TimeCNNNetwork(avg_pool_size=avg_pool_size, n_layers=n_layers)
+        input_layer, output_layer = network.build_network(input_shape=input_shape)
+
+        assert hasattr(input_layer, "shape")
+        assert hasattr(output_layer, "shape")
+
+
+@pytest.mark.skipif(
+    not _check_soft_dependencies(["tensorflow"], severity="none"),
+    reason="Tensorflow soft dependency unavailable.",
+)
+@pytest.mark.parametrize(
+    "strides_pooling, n_layers, should_raise",
+    [
+        (None, 2, False),
+        (2, 2, False),
+        ([2, 3], 2, False),
+        ([2, 3, 4], 2, True),
+        ([2], 2, True),
+    ],
+)
+def test_time_cnn_strides_pooling(strides_pooling, n_layers, should_raise):
+    """Test strides pooling configuration."""
+    input_shape = (100, 5)
+    if should_raise:
+        with pytest.raises(ValueError):
+            network = TimeCNNNetwork(strides_pooling=strides_pooling, n_layers=n_layers)
+            network.build_network(input_shape=input_shape)
+    else:
+        network = TimeCNNNetwork(strides_pooling=strides_pooling, n_layers=n_layers)
+        input_layer, output_layer = network.build_network(input_shape=input_shape)
+
+        assert hasattr(input_layer, "shape")
+        assert hasattr(output_layer, "shape")
+
+
+@pytest.mark.skipif(
+    not _check_soft_dependencies(["tensorflow"], severity="none"),
+    reason="Tensorflow soft dependency unavailable.",
+)
+@pytest.mark.parametrize(
+    "padding, n_layers, should_raise",
+    [
+        ("valid", 2, False),
+        ("same", 2, False),
+        (["same", "valid"], 2, False),
+        (["same", "valid", "same"], 2, True),
+        (["same"], 2, True),
+    ],
+)
+def test_time_cnn_padding(padding, n_layers, should_raise):
+    """Test padding override behavior for different inputs."""
+    input_shape = (100, 5)
+    if should_raise:
+        with pytest.raises(ValueError):
+            network = TimeCNNNetwork(padding=padding, n_layers=n_layers)
+            network.build_network(input_shape=input_shape)
+    else:
+        network = TimeCNNNetwork(padding=padding, n_layers=n_layers)
+        input_layer, output_layer = network.build_network(input_shape=input_shape)
+        assert hasattr(input_layer, "shape")
+        assert hasattr(output_layer, "shape")
+
+
+@pytest.mark.skipif(
+    not _check_soft_dependencies(["tensorflow"], severity="none"),
+    reason="Tensorflow soft dependency unavailable.",
+)
+@pytest.mark.parametrize(
+    "dilation, n_layers, should_raise",
+    [
+        (2, 2, False),
+        ([1, 2], 2, False),
+        ([1, 2, 3], 2, True),
+        ([1], 2, True),
+    ],
+)
+def test_time_cnn_dilation_rate(dilation, n_layers, should_raise):
+    """Test dilation rate configuration."""
+    input_shape = (100, 5)
+    if should_raise:
+        with pytest.raises(ValueError):
+            network = TimeCNNNetwork(dilation_rate=dilation, n_layers=n_layers)
+            network.build_network(input_shape=input_shape)
+    else:
+        network = TimeCNNNetwork(dilation_rate=dilation, n_layers=n_layers)
+        input_layer, output_layer = network.build_network(input_shape=input_shape)
+
+        assert hasattr(input_layer, "shape")
+        assert hasattr(output_layer, "shape")
+
+
+@pytest.mark.skipif(
+    not _check_soft_dependencies(["tensorflow"], severity="none"),
+    reason="Tensorflow soft dependency unavailable.",
+)
+@pytest.mark.parametrize(
+    "strides, n_layers, should_raise",
+    [
+        (1, 2, False),
+        ([1, 2], 2, False),
+        ([1, 2, 3], 2, True),
+        ([1], 2, True),
+    ],
+)
+def test_time_cnn_strides(strides, n_layers, should_raise):
+    """Test strides configuration."""
+    input_shape = (100, 5)
+    if should_raise:
+        with pytest.raises(ValueError):
+            network = TimeCNNNetwork(strides=strides, n_layers=n_layers)
+            network.build_network(input_shape=input_shape)
+    else:
+        network = TimeCNNNetwork(strides=strides, n_layers=n_layers)
+        input_layer, output_layer = network.build_network(input_shape=input_shape)
+
+        assert hasattr(input_layer, "shape")
+        assert hasattr(output_layer, "shape")
+
+
+@pytest.mark.skipif(
+    not _check_soft_dependencies(["tensorflow"], severity="none"),
+    reason="Tensorflow soft dependency unavailable.",
+)
+@pytest.mark.parametrize(
+    "use_bias, n_layers, should_raise",
+    [
+        (True, 2, False),
+        ([True, False], 2, False),
+        ([True, False, True], 2, True),
+        ([True], 2, True),
+    ],
+)
+def test_time_cnn_use_bias(use_bias, n_layers, should_raise):
+    """Test bias usage configuration."""
+    input_shape = (100, 5)
+    if should_raise:
+        with pytest.raises(ValueError):
+            network = TimeCNNNetwork(use_bias=use_bias, n_layers=n_layers)
+            network.build_network(input_shape=input_shape)
+    else:
+        network = TimeCNNNetwork(use_bias=use_bias, n_layers=n_layers)
+        input_layer, output_layer = network.build_network(input_shape=input_shape)
+
+        assert hasattr(input_layer, "shape")
+        assert hasattr(output_layer, "shape")