Fix failing tests & GitHub warnings

docs/advanced/model_optimization.rst

@@ -109,9 +109,11 @@ Finally, optimizing Vivado DSPs is possible, given a hls4ml config:
     # Note the change from ParameterEstimator to VivadoDSPEstimator
     optimized_model = optimize_keras_for_hls4ml(
         baseline_model, model_attributes, VivadoDSPEstimator, scheduler,
-        X_train, y_train, X_val, y_val, batch_size, epochs, optimizer, loss_fn, metric, increasing, rtol
+        X_train, y_train, X_val, y_val, batch_size, epochs,
+        optimizer, loss_fn, metric, increasing, rtol
     )
 
+
 There are two more Vivado "optimizers" - VivadoFFEstimator, aimed at reducing register utilisation and VivadoMultiObjectiveEstimator, aimed at optimising BRAM and DSP utilisation.
 Note, to ensure DSPs are optimized, "unrolled" Dense multiplication must be used before synthesing HLS, by modifying the config:
 .. code-block:: Python

setup.cfg

@@ -41,9 +41,9 @@ pytest_randomly.random_seeder =
 
 [options.extras_require]
 optimization =
-    keras-surgeon@https://github.com/fastmachinelearning/keras-surgeon
-    ortools
-    packaging
+    keras-surgeon@git+https://github.com/fastmachinelearning/keras-surgeon.git
+    ortools==9.4.1874
+    profiling
 profiling =
     matplotlib
     pandas

test/pytest/generate_ci_yaml.py

@@ -32,5 +32,26 @@ def uses_example_model(test_filename):
     else:
         yml.update(new_yml)
 
+# hls4ml Optimization API
+tests = glob.glob('test_optimization/test_*.py')
+for test in tests:
+    name = test.replace('test_optimization/', '').replace('test_', '').replace('.py', '')
+    new_yml = yaml.safe_load(template.format(name, f'test_optimization/test_{name}.py', int(uses_example_model(test))))
+    if yml is None:
+        yml = new_yml
+    else:
+        yml.update(new_yml)
+
+tests = glob.glob('test_optimization/test_keras/test_*.py')
+for test in tests:
+    name = test.replace('test_optimization/test_keras/', '').replace('test_', '').replace('.py', '')
+    new_yml = yaml.safe_load(
+        template.format(name, f'test_optimization/test_keras/test_{name}.py', int(uses_example_model(test)))
+    )
+    if yml is None:
+        yml = new_yml
+    else:
+        yml.update(new_yml)
+
 yamlfile = open('pytests.yml', 'w')
 yaml.safe_dump(yml, yamlfile)