bump to version 0.1.1 with automatic publish on pypi and fix some tests

.github/workflows/python-publish.yaml

@@ -0,0 +1,51 @@
+# This workflow will upload a Python Package using Twine when a release is created
+# For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python#publishing-to-package-registries
+
+# This workflow uses actions that are not certified by GitHub.
+# They are provided by a third-party and are governed by
+# separate terms of service, privacy policy, and support
+# documentation.
+
+name: Upload Python Package
+
+on:
+  release:
+    types: [published]
+
+permissions:
+  contents: read
+
+jobs:
+  deploy:
+
+    runs-on: ubuntu-latest
+
+    steps:
+    - uses: actions/checkout@v3
+    - name: Set up Python
+      uses: actions/setup-python@v3
+      with:
+        python-version: '3.10'
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        pip install build
+    - name: Build package
+      run: python -m build
+    - name: Install built package
+      run: |
+        pip install tensorflow
+        find dist/ -name leap_net\*.whl -type f -exec pip install {}[test] \;
+# If you install TensorFlow, critically, you should reinstall Keras 3 afterwards. 
+# This is a temporary step while TensorFlow is pinned to Keras 2, and will no 
+# longer be necessary after TensorFlow 2.16. The cause is that tensorflow==2.15 
+# will overwrite your Keras installation with keras==2.15.
+    - name: test package
+      run: |
+        cd leap_net/test
+        python -m unittest discover
+    - name: Publish package
+      uses: pypa/gh-action-pypi-publish@27b31702a0e7fc50959f5ad993c78deac1bdfc29
+      with:
+        user: __token__
+        password: ${{ secrets.PYPI_TOKEN }}

CHANGELOG.rst

@@ -1,6 +1,13 @@
 Change Log
 ===========
 
+[0.1.1] - 2024-02-21
+--------------------
+- [FIXED] Broken tests
+- [ADDED] test of both keras v3 and tf_keras implementation (when appropriate)
+  (*eg* not for python 3.8 where keras v3 is not available)
+- [ADDED] automatic upload on pypi on new version
+
 [0.1.0] - 2024-01-15
 ----------------------
 - [BREAKING] refactoring of the code to use keras >= 3.0 (compatible with 

leap_net/__init__.py

@@ -6,7 +6,7 @@
 # SPDX-License-Identifier: MPL-2.0
 # This file is part of leap_net, leap_net a keras implementation of the LEAP Net model.
 
-__version__ = "0.1.0"
+__version__ = "0.1.1"
 __all__ = []
 
 try:

leap_net/test/test_LeapNet_keras.py

@@ -0,0 +1,149 @@
+# Copyright (c) 2019-2020, RTE (https://www.rte-france.com)
+# See AUTHORS.txt
+# This Source Code Form is subject to the terms of the Mozilla Public License, version 2.0.
+# If a copy of the Mozilla Public License, version 2.0 was not distributed with this file,
+# you can obtain one at http://mozilla.org/MPL/2.0/.
+# SPDX-License-Identifier: MPL-2.0
+# This file is part of leap_net, leap_net a keras implementation of the LEAP Net model.
+
+import logging
+import os
+import numpy as np
+import unittest
+logging.disable(logging.WARNING)
+os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
+
+try:
+    import keras
+    from keras.layers import Input
+    from keras.models import Model
+    from leap_net.keras import Ltau
+except ImportError:
+    pass
+
+import pdb
+
+import sys
+
+
+class Test(unittest.TestCase):
+    def setUp(self):
+        if sys.version_info.major == 3 and sys.version_info.minor == 8:
+            self.skipTest("Keras v3 not available on python 3.8")
+        self.tol = 1e-5  # use to compare results that should be strictly equal, up to numerical error
+        self.tol_learn = 1e-2  # use to compare results from a test set
+
+        # to have "reproducible" results
+        np.random.seed(1)
+        keras.utils.set_random_seed(1)
+
+    def test_ok_tau0(self):
+        dim_x = 10
+        n_elem = 5
+        dim_tau = 1
+
+        x = Input(shape=(dim_x,), name="x")
+        tau = Input(shape=(dim_tau,), name="tau")
+
+        res_model = Ltau()((x, tau))
+        model = Model(inputs=[x, tau], outputs=[res_model])
+
+        X_train = np.random.normal(size=(n_elem, dim_x)).astype(np.float32)
+        TAU_train = np.zeros(shape=(n_elem, dim_tau), dtype=np.float32)
+        res = model.predict([X_train, TAU_train])
+        assert np.all(res == X_train)
+
+    def test_ok_tau1(self):
+        dim_x = 10
+        n_elem = 100
+        dim_tau = 1
+        X_train = np.random.normal(size=(n_elem, dim_x)).astype(np.float32)
+        TAU_train = np.ones(shape=(n_elem, dim_tau), dtype=np.float32)
+
+        # the keras model
+        x = Input(shape=(dim_x,), name="x")
+        tau = Input(shape=(dim_tau,), name="tau")
+        res_model = Ltau(initializer='ones', use_bias=False)((x, tau))
+        model = Model(inputs=[x, tau], outputs=[res_model])
+
+        # make predictions
+        res = model.predict([X_train, TAU_train])
+
+        # LEAP Net implementation in numpy in case tau is not 0
+        res_th = np.matmul(X_train, np.ones((dim_x, dim_tau), dtype=np.float32))
+        res_th = np.multiply(res_th, TAU_train)
+        res_th = np.matmul(res_th, np.ones((dim_tau, dim_x), dtype=np.float32))
+        res_th += X_train
+        assert np.mean(np.abs(res - res_th)) <= self.tol, "problem with l1"
+        assert np.max(np.abs(res - res_th)) <= self.tol, "problem with linf"
+
+    def test_ok_tau_rand(self):
+        dim_x = 10
+        n_elem = 100
+        dim_tau = 20
+
+        X_train = np.random.normal(size=(n_elem, dim_x)).astype(np.float32)
+        TAU_train = np.random.normal(size=(n_elem, dim_tau)).astype(np.float32)
+
+        # the keras model
+        x = Input(shape=(dim_x,), name="x")
+        tau = Input(shape=(dim_tau,), name="tau")
+        res_model = Ltau(initializer='ones', use_bias=False)((x, tau))
+        model = Model(inputs=[x, tau], outputs=[res_model])
+
+        # make predictions
+        res = model.predict([X_train, TAU_train])
+
+        # LEAP Net implementation in numpy in case tau is not 0
+        res_th = np.matmul(X_train, np.ones((dim_x, dim_tau), dtype=np.float32))
+        res_th = np.multiply(res_th, TAU_train)
+        res_th = np.matmul(res_th, np.ones((dim_tau, dim_x), dtype=np.float32))
+        res_th += X_train
+        assert np.mean(np.abs(res - res_th)) <= self.tol, "problem with l1"
+        assert np.max(np.abs(res - res_th)) <= self.tol, "problem with linf"
+
+    def test_can_learn(self):
+        dim_x = 30
+        n_elem = 32*32
+        dim_tau = 5
+
+        X_train = np.random.normal(size=(n_elem, dim_x)).astype(np.float32)
+        TAU_train = np.random.normal(size=(n_elem, dim_tau)).astype(np.float32)
+
+        e = np.random.normal(size=(dim_x, dim_tau)).astype(np.float32)
+        d = np.random.normal(size=(dim_tau, dim_x)).astype(np.float32)
+
+        Y_train = np.matmul(X_train, e)
+        Y_train = np.multiply(Y_train, TAU_train)
+        Y_train = np.matmul(Y_train, d)
+        Y_train += X_train
+
+        # the keras model
+        x = Input(shape=(dim_x,), name="x")
+        tau = Input(shape=(dim_tau,), name="tau")
+        res_model = Ltau()((x, tau))
+        model = Model(inputs=[x, tau], outputs=[res_model])
+
+        adam_ = keras.optimizers.Adam(learning_rate=1e-3)
+        model.compile(optimizer=adam_, loss='mse')
+        ## train it
+        model.fit(x=[X_train, TAU_train], y=[Y_train], epochs=200, batch_size=32, verbose=False)
+
+        # test it has learn something relevant
+        X_test = np.random.normal(size=(n_elem, dim_x)).astype(np.float32)
+        TAU_test = np.random.normal(size=(n_elem, dim_tau)).astype(np.float32)
+        Y_test = np.matmul(X_test, e)
+        Y_test = np.multiply(Y_test, TAU_test)
+        Y_test = np.matmul(Y_test, d)
+        Y_test += X_test
+        res = model.predict([X_test, TAU_test])
+        assert np.mean(np.abs(res - Y_test)) <= self.tol_learn, "problem with l1"
+        assert np.max(np.abs(res - Y_test)) <= self.tol_learn, "problem with linf"
+
+# TODO test saving / loading
+# TODO test name and graph visualizing
+# TODO test resnet too
+
+
+if __name__ == "__main__":
+    unittest.main()

leap_net/test/test_LeapNet.py → leap_net/test/test_LeapNet_tf_keras.py

@@ -17,7 +17,7 @@
 from tensorflow.keras.layers import Input
 from tensorflow.keras.models import Model
 
-from leap_net import Ltau
+from leap_net.tf_keras import Ltau
 import pdb
 
 
@@ -117,7 +117,7 @@ def test_can_learn(self):
         res_model = Ltau()((x, tau))
         model = Model(inputs=[x, tau], outputs=[res_model])
 
-        adam_ = tf.optimizers.Adam(lr=1e-3)
+        adam_ = tf.optimizers.Adam(learning_rate=1e-3)
         model.compile(optimizer=adam_, loss='mse')
         ## train it
         model.fit(x=[X_train, TAU_train], y=[Y_train], epochs=200, batch_size=32, verbose=False)

setup.py

@@ -6,6 +6,7 @@
 # SPDX-License-Identifier: MPL-2.0
 # This file is part of leap_net, leap_net a keras implementation of the LEAP Net model.
 
+import sys
 import setuptools
 from setuptools import setup
 
@@ -21,17 +22,31 @@
             "grid2op",
             "pandas",
             "tqdm",
-            'sklearn',
+            'scikit-learn',
             "tensorflow"
         ]
     }
 }
 
+if sys.version_info.major == 3 and sys.version_info.minor == 8:
+    # no keras v3 in python 3.8
+    pkgs["required"] = [el for el in pkgs["required"] if not "keras" in el]
+    pkgs["required"].append("tensorflow")
+
+
+pkgs["extras"]["test"] = [el for el in pkgs["extras"]["recommended"] if not "tensorflow" in el]
+# from here https://keras.io/getting_started/
+# If you install TensorFlow, critically, you should reinstall Keras 3 afterwards. 
+# This is a temporary step while TensorFlow is pinned to Keras 2, 
+# and will no longer be necessary after TensorFlow 2.16. The cause is that tensorflow==2.15 
+# will overwrite your Keras installation with keras==2.15.
+# This is why in the tests I just skip tensorflow for now. Will be fixed later
+
 with open("README.md", "r", encoding="utf-8") as fh:
     long_description = fh.read()
 
 setup(name='leap_net',
-      version='0.1.0',
+      version='0.1.1',
       description='An implementation in keras 3.0 (and tensorflow keras) of the LeapNet model',
       long_description=long_description,
       long_description_content_type="text/markdown",