feat: graph space simplification

neps/__init__.py

@@ -3,15 +3,12 @@
 from neps.plot.tensorboard_eval import tblogger
 from neps.search_spaces import (
     Architecture,
-    ArchitectureParameter,
     Categorical,
     CategoricalParameter,
     Constant,
     ConstantParameter,
     Float,
     FloatParameter,
-    Function,
-    FunctionParameter,
     GraphGrammar,
     Integer,
     IntegerParameter,
@@ -25,7 +22,6 @@
     "Categorical",
     "Constant",
     "Function",
-    "ArchitectureParameter",
     "CategoricalParameter",
     "ConstantParameter",
     "FloatParameter",

neps/search_spaces/__init__.py

@@ -1,8 +1,5 @@
 from neps.search_spaces.architecture.api import (
     Architecture,
-    ArchitectureParameter,
-    Function,
-    FunctionParameter,
 )
 from neps.search_spaces.architecture.graph_grammar import (
     CoreGraphGrammar,
@@ -26,7 +23,6 @@
 
 __all__ = [
     "Architecture",
-    "ArchitectureParameter",
     "Categorical",
     "CategoricalParameter",
     "Constant",

neps/search_spaces/architecture/api.py

@@ -51,155 +51,98 @@ def _build(graph, set_recursive_attribute):
             graph.edges[e].update(set_recursive_attribute(op_name, predecessor_values))
 
 
-def Architecture(**kwargs):
-    """Factory function."""
-    if "structure" not in kwargs:
-        raise ValueError("Factory function requires structure")
-    if not isinstance(kwargs["structure"], list) or len(kwargs["structure"]) == 1:
-        base = GraphGrammar
-
-    class _FunctionParameter(base):
-        def __init__(
-            self,
-            structure: Grammar
-            | list[Grammar]
-            | ConstrainedGrammar
-            | list[ConstrainedGrammar]
-            | str
-            | list[str]
-            | dict
-            | list[dict],
-            primitives: dict,
-            # TODO: Follow this rabbit hole for `constraint_kwargs`,
-            # it can all be deleted my friend
-            constraint_kwargs: dict | None = None,
-            name: str = "ArchitectureParameter",
-            set_recursive_attribute: Callable | None = None,
-            **kwargs,
-        ):
-            local_vars = locals()
-            self.input_kwargs = {
-                args: local_vars[args]
-                for args in inspect.getfullargspec(self.__init__).args  # type: ignore[misc]
-                if args != "self"
-            }
-            self.input_kwargs.update(**kwargs)
-
-            if isinstance(structure, list):
-                structures = [
-                    _dict_structure_to_str(
-                        st,
-                        primitives,
-                        repetitive_mapping=kwargs.get(
-                            "terminal_to_sublanguage_map", None
-                        ),
-                    )
-                    if isinstance(st, dict)
-                    else st
-                    for st in structure
-                ]
-                _structures = []
-                for st in structures:
-                    if isinstance(st, str):
-                        if constraint_kwargs is None:
-                            _st = Grammar.fromstring(st)
-                        else:
-                            _st = ConstrainedGrammar.fromstring(st)
-                            _st.set_constraints(**constraint_kwargs)
-                    _structures.append(_st)  # type: ignore[has-type]
-                structures = _structures
-
-                super().__init__(
-                    grammars=structures,
-                    terminal_to_op_names=primitives,
-                    edge_attr=False,
-                    **kwargs,
+class Architecture(GraphGrammar):
+    def __init__(
+        self,
+        structure: Grammar | ConstrainedGrammar | str | dict
+        | list[Grammar | ConstrainedGrammar | str | dict],
+        primitives: dict,
+        # TODO: Follow this rabbit hole for `constraint_kwargs`,
+        # it can all be deleted my friend
+        constraint_kwargs: dict | None = None,
+        name: str = "Architecture",
+        set_recursive_attribute: Callable | None = None,
+        **kwargs,
+    ):
+        local_vars = locals()
+        self.input_kwargs = {
+            args: local_vars[args]
+            for args in inspect.getfullargspec(self.__init__).args  # type: ignore[misc]
+            if args != "self"
+        }
+        self.input_kwargs.update(**kwargs)
+
+        if isinstance(structure, list):
+            structures = [
+                _dict_structure_to_str(
+                    st,
+                    primitives,
+                    repetitive_mapping=kwargs.get(
+                        "terminal_to_sublanguage_map", None
+                    ),
                 )
-            else:
-                if isinstance(structure, dict):
-                    structure = _dict_structure_to_str(structure, primitives)
-
-                if isinstance(structure, str):
+                if isinstance(st, dict)
+                else st
+                for st in structure
+            ]
+            _structures = []
+            for st in structures:
+                if isinstance(st, str):
                     if constraint_kwargs is None:
-                        structure = Grammar.fromstring(structure)
+                        _st = Grammar.fromstring(st)
                     else:
-                        structure = ConstrainedGrammar.fromstring(structure)
-                        structure.set_constraints(**constraint_kwargs)  # type: ignore[union-attr]
-
-                super().__init__(
-                    grammar=structure,  # type: ignore[arg-type]
-                    terminal_to_op_names=primitives,
-                    edge_attr=False,
-                    **kwargs,
-                )
-
-            self._set_recursive_attribute = set_recursive_attribute
-            self.name: str = name
-
-        def to_pytorch(self) -> nn.Module:
-            self.clear_graph()
-            if len(self.nodes()) == 0:
-                composed_function = self.compose_functions()
-                # part below is required since PyTorch has no standard functional API
-                self.graph_to_self(composed_function)
-                self.prune_graph()
-
-                if self._set_recursive_attribute:
-                    m = _build(self, self._set_recursive_attribute)
-
-                if m is not None:
-                    return m
-
-                self.compile()
-                self.update_op_names()
-            return super().to_pytorch()  # create PyTorch model
-
-        def create_new_instance_from_id(self, identifier: str):
-            g = Architecture(**self.input_kwargs)  # type: ignore[arg-type]
-            g.load_from(identifier)
-            return g
-
-    return _FunctionParameter(**kwargs)
-
-
-def ArchitectureParameter(**kwargs):
-    """Deprecated: Use `Architecture` instead of `ArchitectureParameter`.
-
-    This function remains for backward compatibility and will raise a deprecation
-    warning if used.
-    """
-    import warnings
-
-    warnings.warn(
-        (
-            "Usage of 'neps.ArchitectureParameter' is deprecated and will be removed in"
-            " future releases. Please use 'neps.Architecture' instead."
-        ),
-        DeprecationWarning,
-        stacklevel=2,
-    )
-
-    return Architecture(**kwargs)
-
-
-Function = Architecture
-
-
-def FunctionParameter(**kwargs):
-    """Deprecated: Use `Function` instead of `FunctionParameter`.
-
-    This function remains for backward compatibility and will raise a deprecation
-    warning if used.
-    """
-    import warnings
-
-    warnings.warn(
-        (
-            "Usage of 'neps.FunctionParameter' is deprecated and will be removed in"
-            " future releases. Please use 'neps.Function' instead."
-        ),
-        DeprecationWarning,
-        stacklevel=2,
-    )
+                        _st = ConstrainedGrammar.fromstring(st)
+                        _st.set_constraints(**constraint_kwargs)
+                _structures.append(_st)  # type: ignore[has-type]
+            structures = _structures
+
+            super().__init__(
+                grammars=structures,
+                terminal_to_op_names=primitives,
+                edge_attr=False,
+                **kwargs,
+            )
+        else:
+            if isinstance(structure, dict):
+                structure = _dict_structure_to_str(structure, primitives)
+
+            if isinstance(structure, str):
+                if constraint_kwargs is None:
+                    structure = Grammar.fromstring(structure)
+                else:
+                    structure = ConstrainedGrammar.fromstring(structure)
+                    structure.set_constraints(**constraint_kwargs)  # type: ignore[union-attr]
+
+            super().__init__(
+                grammar=structure,  # type: ignore[arg-type]
+                terminal_to_op_names=primitives,
+                edge_attr=False,
+                **kwargs,
+            )
+
+        self._set_recursive_attribute = set_recursive_attribute
+        self.name: str = name
+
+    def to_pytorch(self) -> nn.Module:
+        self.clear_graph()
+        if len(self.nodes()) == 0:
+            composed_function = self.compose_functions()
+            # part below is required since PyTorch has no standard functional API
+            self.graph_to_self(composed_function)
+            self.prune_graph()
+
+            if self._set_recursive_attribute:
+                m = _build(self, self._set_recursive_attribute)
+
+            if m is not None:
+                return m
+
+            self.compile()
+            self.update_op_names()
+        return super().to_pytorch()  # create PyTorch model
+
+    def create_new_instance_from_id(self, identifier: str):
+        g = Architecture(**self.input_kwargs)  # type: ignore[arg-type]
+        g.load_from(identifier)
+        return g
 
-    return Function(**kwargs)

neps_examples/experimental/expert_priors_for_architecture_and_hyperparameters.py

@@ -106,7 +106,7 @@ def run_pipeline(some_architecture, some_float, some_integer, some_cat):
 
 
 pipeline_space = dict(
-    some_architecture=neps.Function(
+    some_architecture=neps.Architecture(
         set_recursive_attribute=set_recursive_attribute,
         structure=structure,
         primitives=primitives,

neps_examples/experimental/hierarchical_architecture.py

@@ -86,7 +86,7 @@ def run_pipeline(architecture: neps.Function):
 
 
 pipeline_space = dict(
-    architecture=neps.Function(
+    architecture=neps.Architecture(
         set_recursive_attribute=set_recursive_attribute,
         structure=structure,
         primitives=primitives,