add github action test

.github/workflows/tkat_ci.yml

@@ -0,0 +1,29 @@
+name: Run tests on multiple backends
+
+on:
+  push:
+    branches: [ main, beta ]
+  pull_request:
+    branches: [ main ]
+
+
+jobs:
+  test:
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: ["3.9", "3.10", "3.11"]
+
+    steps:
+    - uses: actions/checkout@v2
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v2
+      with:
+        python-version: ${{ matrix.python-version }}
+    - name: Install dependencies
+      run: |
+        pip install poetry
+        poetry install
+    - name: Run tests
+      run: |
+        poetry run python run_tests.py

README.md

@@ -2,7 +2,7 @@
 
 ![TKAT representation](images/model_representation.jpeg)
 
-This folder includes the original code implemented for the [paper](https://arxiv.org/abs/2406.02486) of the same name.
+This folder includes the original code implemented for the [paper](https://arxiv.org/abs/2406.02486) of the same name. The model is made in keras3 and is supporting all backend (jax, tensorflow, pytorch).
 
 It is inspired on the Temporal Fusion Transformer by [google-research](https://github.com/google-research/google-research/tree/master/tft) and the [Temporal Kolmogorov Arnold Network](https://github.com/remigenet/TKAN). 
 

pyproject.toml

@@ -4,14 +4,31 @@ build-backend = "poetry.core.masonry.api"
 
 [tool.poetry]
 name = "tkat" 
-version = "0.1.1" 
+version = "0.2.0" 
 description = "Temporal KAN Transformer"
 authors = [ "Rémi Genet", "Hugo Inzirillo"]  
 readme = "README.md"  
 packages = [{include = "tkat"}]  
 
 [tool.poetry.dependencies]
-python = ">=3.10,<4.0" 
-numpy = ">=1.2,<2"
-tensorflow = ">=2.8,<3" 
-tkan = ">=0.3.0,<0.4.0"
+python = ">=3.9,<3.12"
+keras = ">=3.0.0,<4.0"
+keras_efficient_kan = "^0.1.4"
+tkan = "^0.4.1"
+
+[tool.poetry.group.dev.dependencies]
+pytest = "^7.4.0"
+pytest-xdist = "^3.3.0"
+tensorflow = "^2.15.0"
+torch = "^2.0.0"
+jax = "^0.4.13"
+jaxlib = "^0.4.13"
+
+[tool.pytest.ini_options]
+addopts = "-v"
+testpaths = ["tests"]
+filterwarnings = [
+    "ignore:Can't initialize NVML:UserWarning",
+    "ignore:jax.xla_computation is deprecated:DeprecationWarning",
+    "ignore::DeprecationWarning:jax._src.dtypes"
+]

run_tests.py

@@ -0,0 +1,26 @@
+import os
+import subprocess
+
+def run_test(backend):
+    env = os.environ.copy()
+    env['KERAS_BACKEND'] = backend
+    result = subprocess.run(['pytest', f'tests/test_{backend}.py'], env=env, capture_output=True, text=True)
+    print(f"\n--- {backend.upper()} Backend Test Results ---")
+    print(result.stdout)
+    if result.stderr:
+        print("Errors:")
+        print(result.stderr)
+    return result.returncode
+
+if __name__ == "__main__":
+    backends = ['tensorflow', 'torch', 'jax']
+    exit_codes = []
+
+    for backend in backends:
+        exit_codes.append(run_test(backend))
+
+    if any(exit_codes):
+        exit(1)
+    else:
+        print("\nAll tests passed successfully!")
+        exit(0)

tests/test_jax.py

@@ -0,0 +1,152 @@
+import os
+BACKEND = 'jax'
+os.environ['KERAS_BACKEND'] = BACKEND
+
+import pytest
+import keras
+from keras import ops
+from keras import backend
+from keras import random
+from tkat import TKAT  # Assuming you've defined TKAT in a separate file
+
+def generate_random_tensor(shape):
+    return random.normal(shape=shape, dtype=backend.floatx())
+
+def test_tkat_basic():
+    assert keras.backend.backend() == BACKEND
+    batch_size, sequence_length, n_ahead = 32, 10, 5
+    num_unknow_features, num_know_features = 3, 2
+    num_embedding, num_hidden, num_heads = 8, 16, 4
+
+    tkat_model = TKAT(
+        sequence_length=sequence_length,
+        num_unknow_features=num_unknow_features,
+        num_know_features=num_know_features,
+        num_embedding=num_embedding,
+        num_hidden=num_hidden,
+        num_heads=num_heads,
+        n_ahead=n_ahead
+    )
+
+    input_shape = (batch_size, sequence_length + n_ahead, num_unknow_features + num_know_features)
+    input_data = generate_random_tensor(input_shape)
+    output = tkat_model(input_data)
+
+    expected_output_shape = (batch_size, n_ahead)
+    assert output.shape == expected_output_shape, f"Expected shape {expected_output_shape}, but got {output.shape}"
+
+def test_tkat_variable_selection():
+    assert keras.backend.backend() == BACKEND
+    batch_size, sequence_length, n_ahead = 16, 8, 3
+    num_unknow_features, num_know_features = 4, 3
+    num_embedding, num_hidden, num_heads = 4, 8, 2
+
+    tkat_model = TKAT(
+        sequence_length=sequence_length,
+        num_unknow_features=num_unknow_features,
+        num_know_features=num_know_features,
+        num_embedding=num_embedding,
+        num_hidden=num_hidden,
+        num_heads=num_heads,
+        n_ahead=n_ahead
+    )
+
+    input_shape = (batch_size, sequence_length + n_ahead, num_unknow_features + num_know_features)
+    input_data = generate_random_tensor(input_shape)
+
+    # Get the embedding layer output
+    embedding_layer = tkat_model.get_layer('embedding_layer')  # Assuming you've named your EmbeddingLayer
+    embedded_input = embedding_layer(input_data)
+
+    # Access the variable selection networks
+    vsn_past = tkat_model.get_layer('vsn_past_features')
+    vsn_future = tkat_model.get_layer('vsn_future_features')
+
+    # Test VSN outputs
+    past_features = embedded_input[:, :sequence_length, :, :]
+    future_features = embedded_input[:, sequence_length:, :, -num_know_features:]
+
+    past_output = vsn_past(past_features)
+    future_output = vsn_future(future_features)
+
+    assert past_output.shape == (batch_size, sequence_length, num_hidden)
+    assert future_output.shape == (batch_size, n_ahead, num_hidden)
+
+
+
+def test_tkat_attention():
+    assert keras.backend.backend() == BACKEND
+    batch_size, sequence_length, n_ahead = 8, 6, 2
+    num_unknow_features, num_know_features = 4, 3
+    num_embedding, num_hidden, num_heads = 4, 8, 2
+
+    tkat_model = TKAT(
+        sequence_length=sequence_length,
+        num_unknow_features=num_unknow_features,
+        num_know_features=num_know_features,
+        num_embedding=num_embedding,
+        num_hidden=num_hidden,
+        num_heads=num_heads,
+        n_ahead=n_ahead
+    )
+
+    input_shape = (batch_size, sequence_length + n_ahead, num_unknow_features + num_know_features)
+    input_data = generate_random_tensor(input_shape)
+
+    # Get the attention layer
+    attention_layer = next(layer for layer in tkat_model.layers if isinstance(layer, keras.layers.MultiHeadAttention))
+
+    # Test attention output
+    output = tkat_model(input_data)
+    assert output.shape == (batch_size, n_ahead)
+
+def test_tkat_training():
+    assert keras.backend.backend() == BACKEND
+    batch_size, sequence_length, n_ahead = 64, 12, 4
+    num_unknow_features, num_know_features = 4, 3
+    num_embedding, num_hidden, num_heads = 8, 16, 4
+
+    tkat_model = TKAT(
+        sequence_length=sequence_length,
+        num_unknow_features=num_unknow_features,
+        num_know_features=num_know_features,
+        num_embedding=num_embedding,
+        num_hidden=num_hidden,
+        num_heads=num_heads,
+        n_ahead=n_ahead
+    )
+
+    input_shape = (batch_size, sequence_length + n_ahead, num_unknow_features + num_know_features)
+    input_data = generate_random_tensor(input_shape)
+    target_data = generate_random_tensor((batch_size, n_ahead))
+
+    tkat_model.compile(optimizer='adam', loss='mse')
+    history = tkat_model.fit(input_data, target_data, epochs=2, batch_size=16, verbose=0)
+
+    assert len(history.history['loss']) == 2
+    assert history.history['loss'][1] < history.history['loss'][0]
+
+def test_tkat_prediction():
+    assert keras.backend.backend() == BACKEND
+    batch_size, sequence_length, n_ahead = 32, 10, 5
+    num_unknow_features, num_know_features = 3, 2
+    num_embedding, num_hidden, num_heads = 8, 16, 4
+
+    tkat_model = TKAT(
+        sequence_length=sequence_length,
+        num_unknow_features=num_unknow_features,
+        num_know_features=num_know_features,
+        num_embedding=num_embedding,
+        num_hidden=num_hidden,
+        num_heads=num_heads,
+        n_ahead=n_ahead
+    )
+
+    input_shape = (batch_size, sequence_length + n_ahead, num_unknow_features + num_know_features)
+    input_data = generate_random_tensor(input_shape)
+
+    predictions = tkat_model.predict(input_data)
+    assert predictions.shape == (batch_size, n_ahead)
+
+if __name__ == "__main__":
+    pytest.main([__file__])