move ensemble to applications

Author: refraction-ray

CHANGELOG.md

@@ -2,6 +2,14 @@
 
 ## Unreleased
 
+### Added
+
+- Add translation of r gate from qiskit
+
+### Changed
+
+- move ensemble module to applications/ai (breaking changes)
+
 ## 0.11.0
 
 ### Added

docs/source/api/applications/ai.rst

@@ -1,4 +1,4 @@
 tensorcircuit.applications.ai
 ================================================================================
 .. toctree::
-    
+    ai/ensemble.rst

docs/source/api/templates/ensemble.rst docs/source/api/applications/ai/ensemble.rst

@@ -1,6 +1,6 @@
-tensorcircuit.templates.ensemble
+tensorcircuit.applications.ai.ensemble
 ================================================================================
-.. automodule:: tensorcircuit.templates.ensemble
+.. automodule:: tensorcircuit.applications.ai.ensemble
     :members:
     :undoc-members:
     :show-inheritance:

docs/source/api/templates.rst

@@ -6,6 +6,5 @@ tensorcircuit.templates
     templates/chems.rst
     templates/conversions.rst
     templates/dataset.rst
-    templates/ensemble.rst
     templates/graphs.rst
     templates/measurements.rst

tensorcircuit/templates/ensemble.py tensorcircuit/applications/ai/ensemble.py

@@ -55,8 +55,9 @@ def compile(self, **kwargs: kwargus) -> None:
         self.permit_train = True
         for i in range(self.count):
             if not self.model_trained[i]:
-                dic_kwargs = kwargs.copy()
-                self.models[i].compile(**dic_kwargs)
+                dict_kwargs = kwargs.copy()
+                # TODO(@refraction-ray): still not compatible with new optimizer
+                self.models[i].compile(**dict_kwargs)
 
     def __get_confidence(self, model_index: int, input: NDArray) -> NDArray:
         """

tests/test_ensemble.py

@@ -8,7 +8,7 @@
 
 sys.path.insert(0, modulepath)
 
-from tensorcircuit.templates.ensemble import bagging
+from tensorcircuit.applications.ai.ensemble import bagging
 
 
 def test_ensemble_bagging():
@@ -52,7 +52,7 @@ def model():
     obj_bagging.append(model(), False)
     obj_bagging.compile(
         loss=tf.keras.losses.BinaryCrossentropy(),
-        optimizer=tf.keras.optimizers.Adam(lr),
+        optimizer=tf.keras.optimizers.legacy.Adam(lr),
         metrics=[tf.keras.metrics.AUC(), "acc"],
     )
     obj_bagging.train(